ISS_315_Course_Package

ISS_315_Course_Package - Global Diversity &...

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Unformatted text preview: Global Diversity & Interdependence ISS 315 DR. HUSSAIN TABLE OF CONTENTS CONTENTS PAGE Introduction..................................................................................................... 3 Assignment 1 ................................................................................................. 5 Assignment 2 ................................................................................................ 28 Assignment 3 ................................................................................................ 34 Assignment 4 ................................................................................................ 41 Assignment 5 ................................................................................................ 48 Handouts on Poverty & Hunger ................................................................... 55 Handouts on Population.............................................................................. 183 Handouts on Conflicts and Wars ................................................................ 204 Handouts on Gender Inequality .................................................................. 219 ISS315 - PAGE 2 INTRODUCTION This course pack includes five assignments on the following topics: 2005 World Population Data Human Development Index Poverty Index Gender Related Development Index Gender Empowerment Index Women's Political Participation In addition to the above assignments, there are a number of articles on the following topics: Poverty and Hunger Population Global Conflicts and terrorism Gender Inequality The articles are not from one source and are not in any particular order. Reach each one of them as independent articles. The schedule for assignments and reading material will be announced during the semester. Please note this course pack is available on ANGEL. Students may elect to print off assignment pages if they wish. Good luck and have a great semester. Fayyaz Hussain, Ph.D. 5-H Berkey Hall Michigan State University East Lansing, MI 48823 (517) 353-9964 hussain3@msu.edu ISS315 - PAGE 3 HOMEWORK This section contains data for five assignments: Assignment #1: 2005 World Population Data Assignment #2: Human Development Index Assignment #3: Gender-related Development Index Assignment #4: Gender Empowerment Index Assignment #5: Women's Political Participation NOTES: These assignments are to be completed throughout the semester There is no due date for these assignments You do not need to memorize the numbers. Instead try to comprehend the global patterns Specific assignments are attached with each data set ISS315 - PAGE 4 Assignments ISS 315 PAGE 5 Assignment #1 World Population Data ISS 315 PAGE 6 Population Reference Bureau inform empower advance prb.org 2008 WORLD POPULATION Data Sheet AFRICA SUB-SAHARAN AFRICA NORTHERN AFRICA ALGERIA EGYPT LIBYA MOROCCO SUDAN TUNISIA WESTERN SAHARA WESTERN AFRICA BENIN BURKINA FASO CAPE VERDE CTE D'IVOIRE GAMBIA GHANA GUINEA GUINEABISSAU LIBERIA MALI MAURITANIA NIGER NIGERIA SENEGAL SIERRA LEONE TOGO EASTERN AFRICA BURUNDI COMOROS DJIBOUTI ERITREA ETHIOPIA KENYA MADAGASCAR MALAWI MAURITIUS MAYOTTE MOZAMBIQUE REUNION RWANDA SEYCHELLES SOMALIA TANZANIA UGANDA ZAMBIA ZIMBABWE MIDDLE AFRICA ANGOLA CAMEROON CENTRAL AFRICAN REPUBLIC CHAD CONGO CONGO, DEM. REP. OF EQUATORIAL GUINEA GABON SAO TOME AND PRINCIPE SOUTHERN AFRICA BOTSWANA LESOTHO NAMIBIA SOUTH AFRICA SWAZILAND NORTHERN AMERICA CANADA UNITED STATES LATIN AMERICA/CARIBBEAN CENTRAL AMERICA BELIZE COSTA RICA EL SALVADOR GUATEMALA HONDURAS MEXICO NICARAGUA PANAMA CARIBBEAN ANTIGUA AND BARBUDA BAHAMAS BARBADOS CUBA DOMINICA DOMINICAN REPUBLIC GRENADA GUADELOUPE HAITI JAMAICA MARTINIQUE NETHERLANDS ANTILLES PUERTO RICO ST. KITTSNEVIS SAINT LUCIA ST. VINCENT & THE GRENADINES TRINIDAD AND TOBAGO SOUTH AMERICA ARGENTINA BOLIVIA BRAZIL CHILE COLOMBIA ECUADOR FRENCH GUIANA GUYANA PARAGUAY PERU SURINAME URUGUAY VENEZUELA ASIA WESTERN ASIA ARMENIA AZERBAIJAN BAHRAIN CYPRUS GEORGIA IRAQ ISRAEL JORDAN KUWAIT LEBANON OMAN PALESTINIAN TERRITORY QATAR SAUDI ARABIA SYRIA TURKEY UNITED ARAB EMIRATES YEMEN SOUTH CENTRAL ASIA AFGHANISTAN BANGLADESH BHUTAN INDIA IRAN KAZAKHSTAN KYRGYZSTAN MALDIVES NEPAL PAKISTAN SRI LANKA TAJIKISTAN TURKMENISTAN UZBEKISTAN SOUTHEAST ASIA BRUNEI CAMBODIA EAST TIMOR INDONESIA LAOS MALAYSIA MYANMAR PHILIPPINES SINGAPORE THAILAND VIETNAM EAST ASIA CHINA CHINA, HONG KON CHINA, MACAO JAPAN KOREA, NORTH KOREA, SOUTH MONGOLIA TAIWAN EUROPE NORTHERN EUROPE CHANNEL ISLANDS DENMARK ESTONIA FINLAND ICELAND IRELAND LATVIA LITHUANIA NORWAY SWEDEN UNITED KINGDOM WESTERN EUROPE AUSTRIA BELGIUM FRANCE GERMANY LIECHTENSTEIN LUXEMBOURG MONACO NETHERLANDS SWITZERLAND EASTERN EUROPE BELARUS BULGARIA CZECH REPUBLIC HUNGARY MOLDOVA POLAND ROMANIA RUSSIA SLOVAKIA UKRAINE SOUTHERN EUROPE ALBANIA ANDORRA BOSNIAHERZEGOVINA CROATIA GREECE ITALY MACEDONIA MALTA MONTENEGRO PORTUGAL SAN MARINO SERBIA SLOVENIA SPAIN OCEANIA AUSTRALIA FED. STATES OF MICRONESIA FIJI FRENCH POLYNESIA GUAM KIRIBATI MARSHALL ISLANDS NAURU NEW CALEDONIA NEW ZEALAND PALAU PAPUA NEW GUINEA SAMOA SOLOMON ISLANDS TONGA TUVALU VANUATU Most Populous Countries, 2008 and 2050 2008 Country China India United States Indonesia Brazil Pakistan Nigeria Bangladesh Russia Japan 2050 Population (millions) 1,324.7 1,149.3 304.5 239.9 195.1 172.8 148.1 147.3 141.9 127.7 Country India China United States Indonesia Pakistan Nigeria Brazil Bangladesh Congo, Dem. Rep. Philippines Population (millions) 1,755.2 1,437.0 438.2 343.1 295.2 282.2 259.8 215.1 189.3 150.1 Largest Population Growth or Decline, 2008 to 2050 Largest percent increase Country Uganda Niger Burundi Liberia Guinea-Bissau Congo, Dem. Rep. Timor-Leste (East Timor) Mali Somalia Angola Largest percent decline Country Bulgaria Swaziland Georgia Ukraine Japan Moldova Russia Serbia Belarus Romania Bosnia-Herzegovina Percent 263 261 220 216 205 185 179 169 166 155 Percent -35 -33 -28 -28 -25 -23 -22 -21 -20 -20 -20 N ote : Excludes countries with fewer than 1 million residents. Lowest and Highest Infant Mortality Rates Lowest Country China, Hong Kong SAR Singapore Sweden Finland Japan Slovenia Norway Czech Republic Ireland Portugal Israel Highest Country Afghanistan Sierra Leone Liberia Angola Guinea-Bissau Somalia Guinea Mozambique Burundi Chad Infant deaths per 1,000 births 1.6 2.4 2.5 2.7 2.8 3.1 3.1 3.1 3.1 3.5 3.5 Infant deaths per 1,000 births 163 158 133 132 117 117 113 108 107 106 N ote : Excludes countries with fewer than 50 infant deaths annually. 2008 Population Reference Bureau 2008 World PoPulation data Sheet 2 World PoPulation HigHligHts Africa and Other Developing Regions Make Up an Increasing Share of World Population. As world population has risen from 2.5 billion in 1950 to 6.7 billion in 2008, the proportion living in the developing countries of Africa, Asia, and Latin America and the Caribbean has expanded from 68 percent to more than 80 percent. India and China, with a billion-plus each in 2008, make up about 37 percent of the total. Projections for 2050 show this shift to developing countries continuing. The share living in the more developed countries is projected to drop from about 18 percent in 2008 to less than 14 percent in 2050. Africa's population, currently growing faster than any other major region, is projected to account for 21 percent of world population by 2050, up from just 9 percent in 1950. Population (billions) 10 9 8 7 6 5 4 3 2 1 0 1950 1970 1990 2010 Other less developed countries China India Africa More developed countries 2030 2050 S ource : UN Population Division, World Population Prospects: The 2006 Revision, Medium Variant (2007). There Has Been Little Improvement in Maternal Mortality in Developing Countries. A maternal death related to pregnancy or childbirth is a rare event in more developed countries: Just 9 women died for every 100,000 births in these countries in 2005, according to new estimates from the World Health Organization, UNICEF, the UN Population Fund, and the World Bank. But the ratio of maternal deaths to births is shockingly high in Maternal deaths per 100,000 births sub-Saharan Africa and South 2005 1990 Asia. Even more worrisome, 430 there has been little improveWORLD 400 ment over the past 15 years in developing regions as a whole, 480 despite concerted efforts to Less developed countries 450 improve mothers' health. Public health experts emphasize 920 the importance of prenatal care Sub-Saharan Africa 900 and skilled medical assistance during childbirth, including 620 South Asia the availability of emergency 490 care to deal with complica95 tions. Such health care is often East Asia 50 lacking in countries with poor infrastructure and inadequate 180 health facilities. Latin America/Caribbean 130 11 9 S ource : World Health Organization et al., Maternal Mortality in 2005 (2006; www.who.int, More developed countries accessed May 1, 2008). 2008 Population Reference Bureau 2008 World PoPulation data Sheet 3 World PoPulation HigHligHts Regional Patterns of Fertility Support Continued World Population Growth. While Europeans opt to have one or two children at most, sub-Saharan Africans have more than five children, on average, and Asians have between two and three. There are clear regional patterns of low or high fertility, but there is also wide variation within some regions. South Africa's rates are well below those of its neighbors, for example, while Bolivia's fertility is above the level in other South American countries. In the Middle East, Iran stands out as having low fertility, a contrast to much higher rates in Iraq and Yemen. In Asia, China's below-2-child fertility rate dominates the region statistically, but fertility remains high in Afghanistan, Pakistan, and Laos. Total fertility rate around 2008 Fewer than 1.5 births per woman 1.5 to 2.1 births per woman 2.2 to 2.9 births per woman 3.0 to 4.9 births per woman 5.0 or more births per woman N ote : The total fertility rate measures the total number of children a woman would have given current birth rates. S ource : C. Haub and M.M. Kent, 2008 World Population Data Sheet. Notable Decline in Some Countries, Not in Others. The last quarter century has seen significant drops in fertility among developing countries. In Bangladesh, the total fertility rate dropped from 6.7 lifetime births per woman in the early 1950s to 2.7 in 2008, aided by a strong government commitment to population policies and successful community-based family planning programs. Fertility also fell dramatically in Guatemala, from 7.0 to 4.4 children per woman, over the period. Mexico saw an even more impressive decline, as that country developed economically and embraced the idea of smaller families. Ethiopia, Niger, and Uganda show much more modest declines, helping explain why Africa's population growth continues to outstrip that in other regions. Total fertility rate 1950 1955 8.2 8.1 7.1 6.2 5.3 4.4 2.7 2.7 7.1 7.0 6.9 6.7 6.7 6.7 6.5 Around 2008 2.3 N ote : The total fertility rate measures the total number of children a woman would have given current birth rates. S ourceS : UN Population Division and Population Reference Bureau. 2008 Population Reference Bureau Yemen Niger Ethiopia Guatemala Uganda Bangladesh Mexico South Africa 2008 World PoPulation data Sheet 4 World PoPulation HigHligHts The Urban Population Is Now a Majority in Many of the Largest Countries. The world will pass a milestone in 2008: One-half of the world's residents will live in urban areas. This event is impressive when we consider that less than 30 percent lived in urban areas in 1950. Less than 15 percent were urban in Nigeria and China in 1950, and just slightly more in India. But while the urban share in these countries showed impressive increases, it is also somewhat surprising how rural they still are. India, known for its megacities of Mumbai, Kolkata, and Delhi, is very much a rural country. Less than 30 percent live in urban areas. Percent of population living in urban areas 100 90 United States 80 70 60 50 40 30 20 10 0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Nigeria India China India Nigeria United Sta China S ource : UN Population Division, World Urbanization Prospects: The 2007 Revision (2008; CD-ROM). Most Urbanites Live in Towns and Villages, Not Large Cities. While about one-half of the world lives in urban areas, the vast majority of these urbanites reside in small towns and villages, not large modern cities. Just 37 percent of urban dwellers live in cities with 1 million or more residents, and just 8 percent are in megacities of 10 million or more. S ource : UN Population Division, World Urbanization Prospects: The 2007 Revision (2008; Cd-roM). 10 million or more 8% 5 million to 9.9 million 7% 1 million to 4.9 million 22% Fewer than 500,000 52% 500,000 to 0.9 million 10% Urban popluation by size of urban area, 2005 2008 Population Reference Bureau 2008 World PoPulation data Sheet 5 World PoPulation HigHligHts Mother's Education Is Key to Children's Nutritional Status. Long-term malnutrition among children can result in stunting, a failure to reach the biological potential for growth, and an especially low height for their age. Stunting has been associated with lower IQs and fewer years in school for children, and lower productivity and incomes for adults. Efforts to combat stunting focus on fetal development and the first two years of a child's life, after which the damage may be irreversible. In most countries, children with less-educated mothers are much more likely to become stunted than those with more-educated mothers. In Nigeria, nearly one-half, and in India, nearly 60 percent of the children whose mothers had no education were stunted. The children of more-educated mothers tend to fare much better, but not everywhere. In Madagascar, for example, stunting was surprisingly high even among children of more-educated mothers. Percent of children under age 3 who are stunted, by mother's education 57 No education 47 47 39 30 22 20 Secondary or higher 36 28 23 19 10 17 8 17 5 Egypt 2005 Senegal 2005 India* 20052006 Madagascar 20032004 Nigeria 2003 Cambodia 2005 Haiti 2005 Colombia 2005 * Children under age 5. N ote : Stunting occurs when a child's height at a given age is below international standards for normal development. S ource : Demographic and Health Surveys (www.measuredhs.com). 2008 Population Reference Bureau 2008 World PoPulation data Sheet 6 Demographic Data anD estimates for the countries anD regions of the WorlD Net Migration Rate per 1,000 Population -- 3 -1 -1 -0 -1 -0 -1 -1 -1 0 -3 1 -1 20 -1 1 -1 -2 -2 1 -0 -6 0 3 -3 1 -0 -0 -2 -4 -0 -0 7 0 0 2 -0 -1 0 -0 -0 5 -0 -0 1 6 3 -2 -1 -3 -1 -0 2 0 -1 2 -2 -1 0 1 -2 Population mid-2008 (millions) WORLD MorE dEVEloPEd lEss dEVEloPEd lEss dEVEloPEd (Excl. China) lEast dEVEloPEd AFRICA suB-saHaran aFriCa nortHErn aFriCa Algeria Egypt Libya Morocco Sudan Tunisia Western Sahara WEstErn aFriCa Benin Burkina Faso Cape Verde Cte d'Ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo EastErn aFriCa Burundi Comoros Djibouti Eritrea Ethiopia Kenya Madagascar Malawi Mauritius Mayotte Mozambique Reunion Rwanda Seychelles Somalia Tanzania Uganda Zambia Zimbabwe MiddlE aFriCa Angola Cameroon Central African Republic Chad Congo Congo, Dem. Rep. Equatorial Guinea Gabon Sao Tome and Principe 6,705 1,227 5,479 4,154 797 967 809 197 34.7 74.9 6.3 31.2 39.4 10.3 0.5 291 9.3 15.2 0.5 20.7 1.6 23.9 10.3 1.7 3.9 12.7 3.2 14.7 148.1 12.7 5.5 6.8 301 8.9 0.7 0.8 5.0 79.1 38.0 18.9 13.6 1.3 0.2 20.4 0.8 9.6 0.1 9.0 40.2 29.2 12.2 13.5 122 16.8 18.5 4.4 10.1 3.8 66.5 0.6 1.4 0.2 Births per 1,000 Population 21 12 23 26 36 37 40 26 22 27 24 21 33 17 28 42 42 45 30 38 38 32 42 50 50 48 35 46 43 39 48 38 41 46 36 30 40 40 40 38 48 14 39 41 19 43 18 46 38 48 43 31 43 47 36 38 44 37 44 39 27 35 Deaths per 1,000 Population 8 10 8 9 13 14 15 7 4 6 4 6 12 6 8 15 12 15 5 14 11 10 14 19 18 15 9 15 18 10 23 10 15 16 8 12 10 15 12 10 16 7 3 20 5 16 7 19 15 16 22 21 14 21 13 19 17 13 13 10 12 8 Rate of Natural Increase (%) 1.2 0.2 1.5 1.8 2.4 2.4 2.5 1.9 1.8 2.0 2.0 1.4 2.1 1.2 2.0 2.6 3.0 3.0 2.5 2.4 2.7 2.2 2.9 3.1 3.1 3.3 2.7 3.1 2.5 3.0 2.5 2.8 2.5 3.0 2.8 1.8 3.0 2.5 2.8 2.8 3.2 0.7 3.6 2.1 1.3 2.7 1.0 2.7 2.3 3.1 2.1 1.1 2.8 2.7 2.3 1.9 2.7 2.5 3.1 2.9 1.5 2.7 Projected Population (millions) mid2025 8,000 1,269 6,731 5,255 1,139 1,358 1,161 251 43.3 95.9 8.1 36.6 54.3 12.1 0.8 419 14.5 23.7 0.7 26.2 2.3 33.7 15.7 2.9 6.8 20.6 4.5 26.3 205.4 18.0 7.6 9.9 440 15.0 1.1 1.1 7.7 110.5 51.3 28.0 20.4 1.4 0.3 27.5 1.0 14.6 0.1 14.3 58.2 56.4 15.5 16.0 189 26.2 25.5 5.5 13.9 5.6 109.7 0.9 1.7 0.2 mid2050 9,352 1,294 8,058 6,621 1,664 1,932 1,698 307 50.1 117.9 9.7 42.4 73.0 13.2 0.9 616 22.5 37.5 0.9 34.7 3.4 48.8 24.5 5.3 12.5 34.2 6.4 53.2 282.2 25.3 10.9 14.1 641 28.3 1.8 1.5 11.5 147.6 65.2 41.6 30.5 1.5 0.5 37.2 1.1 21.7 0.1 23.8 82.5 106.0 19.3 19.1 306 42.7 34.9 6.5 20.5 8.8 189.3 1.4 2.1 0.3 Projected Population Change 20082050 (%) 39 5 47 59 109 100 110 56 44 57 54 36 85 27 89 112 142 147 83 68 117 104 138 205 216 169 99 261 91 99 99 108 113 220 151 75 129 87 72 120 124 17 174 83 31 126 37 166 105 263 58 42 151 155 89 47 102 130 185 132 54 85 Infant Mortality Ratea 49 6 54 59 85 82 88 45 27 33 21 43 81 19 53 96 98 89 28 100 93 71 113 117 133 96 77 81 100 61 158 91 81 107 69 67 59 77 77 75 80 15.4 -- 108 8 86 11 117 75 76 100 60 97 132 74 102 106 75 92 91 58 77 A Woman's Lifetime Risk of Dying From Maternal Causes, 1 in: 92 6,000 75 55 22 26 22 145 220 230 350 150 53 500 -- 19 20 22 120 27 32 45 19 13 12 15 22 7 18 21 8 38 28 16 52 35 44 27 39 38 18 3,300 -- 45 -- 16 -- 12 24 25 27 43 20 12 24 25 11 22 13 28 53 -- Total Fertilty Rateb 2.6 1.6 2.8 3.2 4.7 4.9 5.4 3.0 2.3 3.1 3.0 2.4 4.5 2.0 3.0 5.7 5.7 6.2 3.5 4.9 5.1 4.3 5.7 7.1 6.8 6.6 4.8 7.1 5.9 5.3 6.1 5.1 5.4 6.8 4.9 4.2 5.3 5.3 4.9 5.0 6.3 1.7 4.5 5.4 2.5 6.0 2.2 6.7 5.3 6.7 5.5 3.8 6.1 6.8 4.7 5.0 6.3 5.3 6.5 5.4 3.2 4.1 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 7 Demographic Data anD estimates for the countries anD regions of the WorlD Net Migration Rate per 1,000 Population 0 6 -4 1 0 0 0 4 7 3 -2 -5 -1 4 -1 -1 -4 -6 -7 0 -3 3 1 -1 -3 -5 -3 -10 -0 -3 -6 -0 17 -1 -6 6 -8 -3 -1 -0 0 0 0 -1 -4 5 -10 -2 -4 -7 -3 0 -0 -0 -0 -2 -0 7 10 -3 -4 Population mid-2008 (millions) soutHErn aFriCa Botswana Lesotho Namibia South Africa Swaziland AMERICAS NORTHERN AMERICA Canada United States LATIN AMERICA/CARIBBEAN CEntral aMEriCa Belize Costa Rica El Salvador Guatemala Honduras Mexico Nicaragua Panama CariBBEan Antigua and Barbuda Bahamas Barbados Cuba Dominica Dominican Republic Grenada Guadeloupe Haiti Jamaica Martinique Netherlands Antilles Puerto Rico St. Kitts-Nevis Saint Lucia St. Vincent & the Grenadines Trinidad and Tobago soutH aMEriCa Argentina Bolivia Brazil Chile Colombia Ecuador French Guiana Guyana Paraguay Peru Suriname Uruguay Venezuela ASIA ASIA (Excl. China) WEstErn asia Armenia Azerbaijan Bahrain Cyprus Georgia Iraq 55 1.8 1.8 2.1 48.3 1.1 915 338 33.3 304.5 577 150 0.3 4.5 7.2 13.7 7.3 107.7 5.7 3.4 41 0.1 0.3 0.3 11.2 0.1 9.9 0.1 0.4 9.1 2.7 0.4 0.2 4.0 0.05 0.2 0.1 1.3 387 39.7 10.0 195.1 16.8 44.4 13.8 0.2 0.8 6.2 27.9 0.5 3.3 27.9 4,052 2,728 225 3.1 8.7 0.8 1.1 4.6 29.5 Births per 1,000 Population 24 24 27 25 23 31 18 14 11 14 21 22 27 16 24 34 27 20 26 20 19 17 17 14 10 16 24 19 15 29 17 13 14 12 18 15 17 14 20 19 29 20 14 20 26 32 21 27 21 17 14 25 19 23 25 15 18 20 12 11 34 Deaths per 1,000 Population 16 14 25 15 15 31 7 8 7 8 6 5 4 4 6 6 5 5 5 4 8 7 6 8 7 9 6 7 7 11 6 7 7 8 8 7 8 8 6 8 8 6 5 6 6 4 9 6 6 7 9 4 7 7 6 9 6 3 7 10 10 Rate of Natural Increase (%) 0.8 0.9 0.2 1.0 0.8 0.0 1.2 0.6 0.3 0.6 1.5 1.7 2.3 1.3 1.8 2.8 2.2 1.6 2.1 1.6 1.1 1.0 1.1 0.6 0.3 0.7 1.8 1.2 0.8 1.8 1.1 0.7 0.7 0.5 1.0 0.8 0.9 0.6 1.4 1.1 2.1 1.3 0.9 1.4 2.0 2.8 1.2 2.1 1.5 1.1 0.5 2.1 1.2 1.5 1.9 0.5 1.2 1.7 0.6 0.1 2.4 Projected Population (millions) mid2025 59 2.2 1.7 2.3 51.5 1.0 1,080 393 37.6 355.7 687 180 0.4 5.6 9.1 20.0 9.8 123.8 6.8 4.2 46 0.1 0.4 0.3 11.2 0.1 12.1 0.1 0.5 11.7 3.0 0.4 0.2 4.1 0.1 0.2 0.1 1.4 461 46.3 13.3 228.9 19.1 53.8 17.5 0.3 0.8 8.0 34.0 0.5 3.5 34.9 4,793 3,317 290 3.3 9.7 1.0 1.1 4.2 43.3 mid2050 62 2.4 1.6 2.1 54.8 0.8 1,258 480 41.9 438.2 778 203 0.5 6.3 11.2 27.9 12.4 131.6 7.9 5.0 50 0.1 0.5 0.3 9.9 0.1 14.0 0.1 0.5 15.1 3.4 0.4 0.2 3.8 0.1 0.2 0.1 1.3 524 52.5 16.7 259.8 20.2 59.2 20.4 0.4 0.5 10.1 39.3 0.5 3.7 41.1 5,427 3,990 363 3.3 11.6 1.2 1.1 3.3 61.9 Projected Population Change 20082050 (%) 12 29 -11 3 13 -33 37 42 26 44 35 35 57 40 55 104 69 22 40 46 24 29 34 -7 -11 -11 42 -10 21 65 25 -13 -4 -5 31 30 -13 -1 36 32 67 33 20 33 48 96 -29 62 41 -8 11 47 34 46 61 7 34 53 2 -28 110 Infant Mortality Ratea 48 44 91 47 45 85 18 7 5.4 6.6 23 22 18 9.7 24 34 23 19 29 15 33 20 14 14 5.3 16 32 17 8 57 21 6 5 9.2 15 19.4 17.6 24 23 13.3 51 24 8.8 19 25 10.4 48 36 24 16 10.5 16.5 45 51 41 26 12 8 6 16 94 A Woman's Lifetime Risk of Dying From Maternal Causes, 1 in: 90 130 45 170 110 120 420 6,000 11,000 4,800 290 320 560 1,400 190 71 93 670 150 270 130 -- 2,700 4,400 1,400 -- 230 -- -- 44 240 -- -- 2,900 -- -- -- 1,400 300 530 89 370 3,200 290 170 -- 90 170 140 530 2,100 610 120 90 170 980 670 1,300 6,400 1,100 72 Total Fertilty Rateb 2.8 2.9 3.5 3.6 2.7 3.8 2.3 2.1 1.6 2.1 2.5 2.5 3.1 1.9 2.8 4.4 3.3 2.3 2.9 2.4 2.5 2.1 1.9 1.8 1.4 3.0 3.0 2.1 2.1 4.0 2.1 1.9 2.0 1.7 2.3 1.7 2.1 1.6 2.4 2.4 3.7 2.3 2.0 2.4 3.1 3.9 2.7 3.5 2.6 2.5 2.0 2.6 2.4 2.8 3.3 1.7 2.3 2.5 1.5 1.4 4.6 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 8 Demographic Data anD estimates for the countries anD regions of the WorlD Net Migration Rate per 1,000 Population 2 7 8 -0 5 0 36 -5 2 0 16 -1 -0 0 -1 2 -0 -1 1 -10 0 -1 -1 -2 -2 -3 -2 -0 3 -0 -1 -3 1 -0 -2 37 1 0 -1 -0 -0 3 41 0 0 1 0 1 3 4 6 5 -0 3 10 15 -0 -2 8 6 3 Population mid-2008 (millions) Israel Jordan Kuwait Lebanon Oman Palestinian Territory Qatar Saudi Arabia Syria Turkey United Arab Emirates Yemen soutH CEntral asia Afghanistan Bangladesh Bhutan India Iran Kazakhstan Kyrgyzstan Maldives Nepal Pakistan Sri Lanka Tajikistan Turkmenistan Uzbekistan soutHEast asia Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Vietnam East asia China China, Hong Kong SARe China, Macao SARe Japan Korea, North Korea, South Mongolia Taiwan EUROPE nortHErn EuroPE Channel Islands Denmark Estonia Finland Iceland Ireland Latvia Lithuania Norway Sweden United Kingdom 7.5 5.8 2.7 4.0 2.7 4.2 0.9 28.1 19.9 74.8 4.5 22.2 1,683 32.7 147.3 0.7 1,149.3 72.2 15.7 5.2 0.3 27.0 172.8 20.3 7.3 5.2 27.2 586 0.4 14.7 239.9 5.9 27.7 49.2 90.5 4.8 66.1 1.1 86.2 1,558 1,324.7 7.0 0.6 127.7 23.5 48.6 2.7 23.0 736 98 0.2 5.5 1.3 5.3 0.3 4.5 2.3 3.4 4.8 9.2 61.3 Births per 1,000 Population 21 28 21 19 24 37 17 29 28 19 15 41 25 47 24 30 24 20 21 24 19 29 31 19 27 24 24 20 19 26 21 34 21 19 26 11 13 42 17 12 12 10 9 9 16 10 21 9 11 12 11 12 12 11 15 16 10 10 12 12 13 Deaths per 1,000 Population 5 4 2 5 3 4 2 3 4 6 2 9 8 21 7 7 8 5 10 7 4 9 8 7 5 6 7 7 3 8 6 10 5 10 5 5 8 11 5 7 7 6 3 9 7 5 6 6 11 10 9 10 13 9 6 6 14 14 9 10 9 Rate of Natural Increase (%) 1.6 2.4 1.9 1.4 2.1 3.3 1.5 2.7 2.5 1.2 1.3 3.2 1.7 2.6 1.7 2.3 1.6 1.4 1.0 1.6 1.6 2.1 2.2 1.2 2.2 1.7 1.7 1.4 1.6 1.8 1.5 2.4 1.6 0.9 2.1 0.6 0.5 3.2 1.2 0.5 0.5 0.5 0.6 -0.0 0.9 0.5 1.5 0.3 -0.0 0.3 0.2 0.2 -0.1 0.2 0.8 0.9 -0.4 -0.4 0.4 0.2 0.3 Projected Population (millions) mid2025 9.3 7.7 3.6 4.6 3.1 6.2 1.1 35.7 26.8 87.8 6.2 35.2 2,089 50.3 180.1 0.9 1,407.7 88.0 17.1 6.5 0.4 36.5 228.9 23.2 9.5 6.5 33.3 709 0.5 20.6 291.9 8.7 34.6 55.4 120.2 5.3 70.2 1.7 100.1 1,705 1,476.0 8.0 0.6 119.3 25.8 49.1 3.3 23.1 726 108 0.2 5.6 1.2 5.6 0.4 4.9 2.1 3.1 5.6 9.9 68.8 mid2050 11.2 9.7 4.8 5.0 3.9 8.8 1.4 49.8 34.0 88.7 7.8 55.8 2,605 81.9 215.1 1.0 1,755.2 100.2 17.4 8.1 0.5 48.7 295.2 25.4 11.5 7.6 37.6 826 0.6 30.5 343.1 12.3 40.4 58.7 150.1 5.3 68.9 3.0 112.8 1,633 1,437.0 8.8 0.6 95.2 26.4 42.3 3.8 18.9 685 117 0.1 5.5 1.1 5.7 0.4 5.1 1.9 2.9 6.6 10.4 76.9 Projected Population Change 20082050 (%) 49 65 80 26 42 113 48 77 71 19 75 151 55 150 46 45 53 39 11 54 73 81 71 25 57 47 38 41 67 108 43 110 46 19 66 10 4 179 31 5 8 26 5 -25 12 -13 45 -18 -7 19 -5 0 -18 8 37 13 -16 -14 38 13 26 Infant Mortality Ratea 3.5 24 8 19 10 25 7 16 19 23 7 77 61 163 52 40 57 32 29 50 16 48 75 15 65 74 48 31 7 67 34 70 9 70 25 2.4 16 88 16 21 23 1.6 2 2.8 21 4 41 4.6 6 4 3.7 4.0 4.9 2.7 1.3 3.1 7.6 5.9 3.1 2.5 4.9 A Woman's Lifetime Risk of Dying From Maternal Causes, 1 in: 7,800 450 9,600 290 420 -- 2,700 1,400 210 880 1,000 39 61 8 51 55 70 300 360 240 200 31 74 850 160 290 1,400 130 2,900 48 97 33 560 110 140 6,200 500 35 280 1,200 1,300 -- -- 11,600 140 6,100 840 -- 9,400 7,800 -- 17,800 2,900 8,500 12,700 47,600 8,500 7,800 7,700 17,400 8,200 Total Fertilty Rateb 2.9 3.6 2.6 1.9 3.4 4.6 2.6 4.0 3.5 2.2 2.0 6.2 3.0 6.8 2.7 3.6 2.8 2.1 2.5 2.8 2.2 3.1 4.1 2.4 3.3 2.9 2.7 2.5 2.0 3.5 2.6 4.5 2.6 2.2 3.3 1.4 1.6 6.7 2.1 1.6 1.6 1.0 1.0 1.3 2.0 1.3 2.3 1.1 1.5 1.8 1.4 1.8 1.7 1.8 2.1 2.1 1.5 1.4 1.9 1.9 1.9 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 9 Demographic Data anD estimates for the countries anD regions of the WorlD Net Migration Rate per 1,000 Population 1 4 5 1 1 3 12 8 1 1 1 6 -0 8 1 -1 -1 -0 2 1 0 9 -3 26 -0 2 4 8 -- 0 5 -1 1 10 0 6 16 Population mid-2008 (millions) WEstErn EuroPE Austria Belgium France Germany Liechtenstein Luxembourg Monaco Netherlands Switzerland EastErn EuroPE Belarus Bulgaria Czech Republic Hungary Moldova Poland Romania Russia Slovakia Ukraine soutHErn EuroPE Albania Andorra Bosnia-Herzegovina Croatia Greece Italy Kosovof Macedoniag Malta Montenegro Portugal San Marino Serbia Slovenia Spain OCEANIA Australia Federated States of Micronesia Fiji French Polynesia Guam Kiribati Marshall Islands Nauru New Caledonia New Zealand Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu 188 8.4 10.7 62.0 82.2 0.04 0.5 0.03 16.4 7.6 295 9.7 7.6 10.4 10.0 4.1 38.1 21.5 141.9 5.4 46.2 155 3.2 0.1 3.8 4.4 11.2 59.9 2.2 2.0 0.4 0.6 10.6 0.03 7.4 2.0 46.5 Births per 1,000 Population 10 9 12 13 8 10 11 25 11 10 11 11 10 11 10 11 10 10 12 10 10 10 13 10 9 9 10 9 21 11 10 12 10 10 10 10 11 Deaths per 1,000 Population 9 9 10 8 10 6 8 16 8 8 14 14 15 10 13 12 10 12 15 10 16 9 6 3 9 12 9 10 7 10 8 10 10 7 14 9 9 Rate of Natural Increase (%) 0.1 0.0 0.2 0.4 -0.2 0.4 0.3 0.9 0.3 0.2 -0.3 -0.3 -0.5 0.1 -0.4 -0.1 0.0 -0.2 -0.3 0.0 -0.6 0.1 0.7 0.7 0.0 -0.3 0.1 -0.0 1.4 0.2 0.2 0.3 -0.0 0.3 -0.4 0.1 0.2 Projected Population (millions) mid2025 191 8.8 10.8 66.1 79.6 0.04 0.5 0.04 16.9 8.1 272 9.0 6.6 10.2 9.6 3.8 36.7 19.7 129.3 5.2 41.7 156 3.5 0.1 3.7 4.3 11.3 62.0 2.7 2.0 0.4 0.6 10.5 0.04 6.7 2.1 46.2 mid2050 187 9.5 11.0 70.0 71.4 0.04 0.6 0.04 16.8 8.1 231 7.7 5.0 9.4 8.9 3.2 31.4 17.1 110.1 4.7 33.4 150 3.6 0.1 3.1 3.8 10.8 61.7 3.2 1.7 0.4 0.6 9.3 0.04 5.8 1.9 43.9 Projected Population Change 20082050 (%) -0 14 2 13 -13 17 29 9 2 6 -22 -20 -35 -9 -11 -23 -18 -20 -22 -12 -28 -3 11 -4 -20 -14 -4 3 45 -15 -6 -4 -12 13 -21 -7 -6 Infant Mortality Ratea 4 3.7 3.7 3.6 3.9 2.6 4.4 -- 4.4 4.0 9 6 9.2 3.1 5.9 12 6.0 12.0 9 6.1 11 5 8 2.5 8 5.7 3.7 4.2 33 13 3.6 11.0 3.5 3.3 7.4 3.1 3.7 A Woman's Lifetime Risk of Dying From Maternal Causes, 1 in: 11,000 21,500 7,800 6,900 19,200 -- 5,000 -- 10,200 13,800 3,500 4,800 7,400 18,100 13,300 3,700 10,600 3,200 2,700 13,800 5,200 9,400 490 -- 29,000 10,500 25,900 26,600 -- 6,500 8,300 -- 6,400 -- 4,500h 14,200 16,400 Total Fertilty Rateb 1.6 1.4 1.7 2.0 1.3 1.4 1.6 -- 1.7 1.5 1.4 1.4 1.4 1.4 1.3 1.3 1.3 1.3 1.4 1.2 1.3 1.4 1.6 1.2 1.2 1.4 1.4 1.3 2.5 1.5 1.4 1.6 1.3 1.2 1.4 1.4 1.4 35 21.3 0.1 0.9 0.3 0.2 0.1 0.1 0.01 0.2 4.3 0.02 6.5 0.2 0.5 0.1 0.01 0.2 18 14 26 21 18 19 27 38 31 18 15 13 31 29 34 26 26 31 7 7 6 6 4 4 9 6 10 5 7 7 10 6 8 6 10 6 1.1 0.7 2.0 1.5 1.3 1.5 1.8 3.2 2.1 1.3 0.8 0.6 2.1 2.4 2.6 2.0 1.6 2.5 5 9 -17 -7 1 2 0 -16 -18 5 1 2 0 -11 -2 -17 -8 0 42 24.7 0.1 0.9 0.3 0.2 0.1 0.1 0.01 0.3 4.9 0.02 8.6 0.2 0.7 0.1 0.01 0.4 49 28.1 0.1 0.9 0.4 0.2 0.2 0.1 0.02 0.4 5.5 0.03 11.2 0.2 1.0 0.1 0.02 0.5 40 32 21 8 34 38 99 101 49 46 28 27 73 14 88 -27 74 109 25 4.7 40 17 6.8 10.7 52 23 42 7 5.0 20 62 20 48 12 35 27 160 13,300 -- 160 -- -- -- -- -- -- 5,900 -- 55 -- 100 -- -- -- 2.4 1.9 4.1 2.6 2.2 2.6 3.5 4.4 3.4 2.3 2.2 1.9 3.9 4.4 4.5 3.7 3.7 4.0 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 10 Demographic Data anD estimates Percent Percent of in Urban Population Life Expectancy Areas of of Ages at Birth (years) Percent 750,000+ 2005 <15 65+ Total Males Females Urban WORLD MorE dEVEloPEd lEss dEVEloPEd lEss dEVEloPEd (Excl. China) lEast dEVEloPEd AFRICA suB-saHaran aFriCa nortHErn aFriCa Algeria Egypt Libya Morocco Sudan Tunisia Western Sahara WEstErn aFriCa Benin Burkina Faso Cape Verde Cte d'Ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo EastErn aFriCa Burundi Comoros Djibouti Eritrea Ethiopia Kenya Madagascar Malawi Mauritius Mayotte Mozambique Reunion Rwanda Seychelles Somalia Tanzania Uganda Zambia Zimbabwe MiddlE aFriCa Angola Cameroon Central African Republic Chad Congo Congo, Dem. Rep. Equatorial Guinea Gabon Sao Tome and Principe 28 17 30 34 41 41 43 33 30 33 30 29 41 25 31 44 44 46 38 40 42 40 46 48 47 48 40 49 45 44 42 43 44 45 42 39 43 43 42 44 46 23 42 43 27 44 23 45 44 49 46 40 46 46 42 43 46 42 47 42 36 42 7 16 6 5 3 3 3 5 5 5 4 6 4 6 2 3 3 3 6 2 3 4 3 3 2 4 4 3 3 4 4 3 3 3 3 3 2 3 2 3 3 7 2 3 7 3 8 3 3 3 2 4 3 2 4 4 3 3 3 4 5 4 68 77 67 65 55 54 50 69 72 72 73 70 58 74 64 51 56 51 71 52 58 59 54 45 46 56 60 57 47 62 48 58 49 49 64 54 57 49 53 58 46 72 74 43 76 47 72 48 51 48 38 40 51 43 52 43 47 53 53 59 57 64 67 74 65 63 53 53 49 67 71 70 71 68 56 72 62 50 54 49 68 50 57 58 52 43 45 54 59 58 46 60 48 56 48 47 62 53 54 48 53 57 45 69 72 42 72 47 67 47 50 47 38 40 48 41 51 43 46 52 49 59 56 63 70 81 68 66 56 55 51 71 74 74 76 72 59 76 66 52 57 52 74 53 59 59 55 47 47 59 62 56 47 64 49 60 50 50 66 55 59 51 53 60 47 76 76 44 80 48 77 49 52 48 37 40 52 44 52 44 48 54 55 60 58 66 49 74 44 44 27 38 35 50 63 43 77 56 38 65 81 42 41 16 59 48 54 48 30 30 58 31 40 17 47 41 37 40 22 10 28 87 21 16 19 30 17 42 28 29 92 18 53 37 25 13 37 37 41 57 57 38 27 60 33 39 84 58 21 30 19 18 10 14 13 19 12 21 54 22 12 18 -- 15 9 8 -- 19 -- 16 16 -- 33 12 -- 6 16 21 14 21 6 -- -- -- -- 4 10 9 -- -- -- 7 -- 8 -- 17 7 5 11 12 18 27 18 -- 9 34 17 -- -- -- nutrition environment economy Density Percent of Percent of % of Pop. Motor Population Married Women Percent of Vehicles With Access Ages 1549 1549 Using Population per 1,000 to Improved GNI PPP With HIV/ Contraception* Water Pop. Underper Capita Population AIDS Sources, 2000 per Sq. (US$) All Modern nourished 2006 2005 Kilometer 2007 2001 2007 Methods Methods 20022004 0.8 0.4 1.2 1.6 3.2 4.3 5.7 0.3 0.1 -- -- 0.1 1.4 <0.1 -- 2.7 1.3 2.1 -- 6.0 0.9 2.3 1.2 1.8 1.4 1.5 0.7 0.7 3.2 0.4 1.3 3.6 6.6 3.5 <0.1 3.1 1.2 2.4 6.7c 0.1 13.3 0.3 -- 10.3 -- 4.3 -- 0.5 7.0 7.9 15.4 26.0 2.6 1.6 6.0 6.4 3.4 4.4 d 3.7 5.6 -- 0.8 0.5 1.0 1.4 3.0 4.0 5.0 0.3 0.1 -- -- 0.1 1.4 0.1 -- 2.5 1.2 1.6 -- 3.9 0.9 1.9 1.6 1.8 1.7 1.5 0.8 0.8 3.1 1.0 1.7 3.3 5.8 2.0 <0.1 3.1 1.3 2.1 7.8c 0.1 11.9 1.7 -- 12.5 -- 2.8 -- 0.5 6.2 5.4 15.2 15.3 2.5 2.1 5.1 6.3 3.5 3.5 d 3.4 5.9 -- 62 69 61 51 27 28 21 50 61 59 49 63 8 63 -- 13 17 14 53 13 10 17 9 10 11 8 9 11 12 12 5 17 26 16 26 18 8 15 39 27 42 76 -- 17 67 17 -- 15 26 24 34 60 19 6 26 28 3 44 21 -- 33 29 55 58 55 43 21 22 16 44 52 57 26 55 6 53 -- 8 6 9 46 8 9 14 6 6 10 6 8 5 8 10 4 11 20 10 19 17 5 14 32 17 39 42 -- 12 64 10 -- 1 20 18 23 58 7 5 13 7 2 13 6 -- 12 27 14 <2.5 17 18 35 26 31 8 4 4 <2.5 6 26 <2.5 -- 15 12 15 -- 13 29 11 24 39 50 29 10 32 9 20 51 24 40 66 60 24 75 46 31 38 35 5 -- 44 -- 33 9 -- 44 19 46 47 55 35 26 44 35 33 74 -- 5 10 153 536 43 52 -- -- -- 61 91 39 137 60 -- 86 -- -- -- -- -- -- -- -- -- -- -- 10 -- -- 1 17 3 -- -- -- -- -- -- 2 18 -- -- 133 -- 8 513 3 102 -- -- 6 -- 54 -- -- 14 -- -- -- -- -- -- -- 86 97 84 82 62 64 58 87 85 98 71 83 70 94 -- 58 65 72 80 81 86 80 70 57 64 60 60 42 47 77 53 59 54 71 85 92 60 42 57 47 76 100 -- 42 -- 65 87 29 55 64 58 81 52 51 70 66 48 71 46 43 87 86 $ 9,600 31,200 4,760 4,560 1,060 2,430 1,830 4,760 5,490 5,400 11,500 3,990 1,880 7,130 -- 1,480 1,310 1,120 2,940 1,590 1,140 1,330 1,120 470 290 1,040 2,010 630 1,770 1,640 660 800 940 330 1,150 2,260 400 780 1,540 920 750 11,390 -- 690 -- 860 8,670 -- 1,200 920 1,220 -- 1,550 4,400 2,120 740 1,280 2,750 290 21,230 13,080 1,630 49 27 66 57 38 32 33 23 15 75 4 70 16 63 2 47 83 56 125 64 138 100 42 48 35 10 3 12 160 64 76 119 47 318 328 37 43 72 65 32 115 622 500 25 324 365 191 14 43 121 16 34 18 13 39 7 8 11 28 22 5 164 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 11 Demographic Data anD estimates Percent Percent of in Urban Population Life Expectancy Areas of of Ages at Birth (years) Percent 750,000+ 2005 <15 65+ Total Males Females Urban soutHErn aFriCa Botswana Lesotho Namibia South Africa Swaziland AMERICAS NORTHERN AMERICA Canada United States LATIN AMERICA/CARIBBEAN CEntral aMEriCa Belize Costa Rica El Salvador Guatemala Honduras Mexico Nicaragua Panama CariBBEan Antigua and Barbuda Bahamas Barbados Cuba Dominica Dominican Republic Grenada Guadeloupe Haiti Jamaica Martinique Netherlands Antilles Puerto Rico St. Kitts-Nevis Saint Lucia St. Vincent & the Grenadines Trinidad and Tobago soutH aMEriCa Argentina Bolivia Brazil Chile Colombia Ecuador French Guiana Guyana Paraguay Peru Suriname Uruguay Venezuela ASIA ASIA (Excl. China) WEstErn asia Armenia Azerbaijan Bahrain Cyprus Georgia Iraq 33 38 39 41 32 41 26 20 17 20 30 33 39 28 34 43 38 32 38 30 28 28 28 22 18 29 33 29 24 38 30 22 23 21 28 28 29 24 29 26 38 28 25 30 33 35 32 36 32 30 24 31 27 31 34 21 24 27 18 18 42 4 3 5 3 4 4 9 13 14 13 6 5 5 6 5 4 4 6 4 6 8 7 6 12 12 10 6 6 11 4 8 12 10 13 8 7 7 7 6 10 4 6 8 5 6 4 5 5 6 7 13 5 7 6 5 11 7 3 11 15 3 49 49 36 47 50 33 75 78 80 78 73 74 73 78 71 69 72 75 71 75 71 73 72 76 77 75 72 68 79 58 72 80 75 78 70 73 72 69 73 75 65 72 78 72 75 75 65 71 71 69 76 73 69 68 70 71 72 75 78 74 58 48 50 35 48 48 33 72 76 78 75 70 72 71 76 68 66 69 73 68 73 69 71 69 73 75 72 69 66 75 56 70 76 71 74 68 71 70 67 69 71 63 69 75 69 72 72 63 69 68 66 72 70 68 66 68 68 70 73 75 70 56 50 49 36 47 52 34 78 81 83 81 76 77 74 81 74 73 74 78 74 78 74 75 75 79 79 77 75 69 82 60 75 83 79 82 72 76 74 71 76 79 67 75 81 76 78 79 68 73 73 73 79 76 71 69 72 75 75 77 80 79 60 56 57 24 35 59 24 78 79 81 79 77 70 50 59 60 47 46 76 59 64 64 31 83 38 76 73 67 31 100 43 52 98 92 94 32 28 40 12 81 91 64 83 87 72 62 76 28 57 76 74 94 88 42 40 64 64 52 100 62 53 67 28 -- -- -- 32 -- 40 47 43 47 36 34 -- 28 21 8 13 40 17 38 22 -- -- -- 19 -- 22 -- -- 20 -- -- -- 66 -- -- -- -- 38 43 31 40 40 36 31 -- -- 30 31 -- 46 32 18 17 30 37 22 -- -- 24 30 nutrition environment economy Density Percent of Percent of % of Pop. Motor Population Married Women Percent of Vehicles With Access Ages 1549 1549 Using Population per 1,000 to Improved GNI PPP With HIV/ Contraception* Water Pop. Underper Capita Population AIDS Sources, 2000 per Sq. (US$) All Modern nourished 2006 2005 Kilometer 2007 2001 2007 Methods Methods 20022004 17.6 26.5 23.9 14.6 16.9 26.3 0.5 0.6 0.3 0.6 0.5 0.4 2.1 0.2 0.8 0.8 0.9 0.3 0.2 1.0 1.1 -- 3.1 1.2 <0.1 -- 1.3 -- -- 2.2 1.4 -- -- -- -- -- -- 1.4 0.5 0.5 0.1 0.6 0.3 0.5 0.3 -- 2.5 0.4 0.4 1.3 0.4 -- 0.3 -- -- 0.1 -- -- -- -- -- 18.5 23.9 23.2 15.3 18.1 26.1 0.6 0.6 0.4 0.6 0.5 0.4 2.1 0.4 0.8 0.8 0.7 0.3 0.2 1.0 1.1 -- 3.0 1.2 0.1 -- 1.1 -- -- 2.2 1.6 -- -- -- -- -- -- 1.5 0.6 0.5 0.2 0.6 0.3 0.6 0.3 -- 2.5 0.6 0.5 2.4 0.6 -- 0.2 0.3 -- 0.1 0.2 -- -- 0.1 -- 58 44 37 44 60 51 72 73 75 73 71 68 56 80 67 43 65 71 72 -- 62 -- -- -- 73 -- 73 -- -- 32 69 -- -- 78 -- -- -- 38 73 65 58 76 61 78 73 -- 35 73 71 42 77 70 67 56 52 53 51 62 -- 47 50 58 42 35 43 60 48 66 69 73 68 64 63 49 72 61 34 56 67 70 -- 55 -- -- -- 72 -- 60 -- -- 25 66 -- -- 68 -- -- -- 33 66 -- 35 70 58 68 59 -- 34 61 48 41 75 67 61 47 34 20 14 31 -- 27 33 4 32 13 24 <2.5 22 7 <2.5 <2.5 <2.5 10 9 4 5 11 22 23 5 27 23 21 -- 8 <2.5 <2.5 8 29 7 -- 46 9 -- 13 -- 10 5 10 10 9 3 23 7 4 13 6 -- 8 15 12 8 <2.5 18 15 17 8 24 7 -- <2.5 9 -- -- 106 -- -- -- 108 -- 768 584 787 -- 185 157 201 64 107 -- 211 37 102 -- -- -- -- 2 265 113 197 -- -- -- -- -- 580 247 216 159 295 -- 181 52 -- 146 -- -- -- -- 72 55 236 160 -- 57 74 146 -- 72 339 575 71 43 92 96 78 93 93 60 94 99 100 99 91 93 91 98 84 96 84 95 79 92 84 91 97 100 91 97 95 94 98 58 93 -- -- -- 99 98 -- 94 91 96 86 91 95 93 95 84 93 77 84 92 100 -- 88 88 89 98 78 -- 100 99 77 9,140 12,420 1,890 5,120 9,560 4,930 22,260 44,790 35,310 45,850 9,080 10,340 5,100 8,340 4,840 4,120 3,160 12,580 2,080 8,340 -- 12,610 -- 10,880 -- 5,650 5,050 6,010 -- 1,050 5,050 -- -- -- 10,430 7,090 5,720 14,580 9,290 12,990 4,140 9,370 12,590 6,640 7,040 -- 2,600 4,380 7,240 6,000 11,040 11,920 $ 5,650 5,780 10,160 5,900 6,370 34,310 26,370 4,770 -- 21 3 59 3 40 65 22 16 3 32 28 60 14 88 343 126 65 55 44 45 174 195 24 650 101 97 203 308 246 328 245 368 249 446 184 317 284 261 22 14 9 23 22 39 49 2 4 15 22 3 19 31 127 122 47 103 100 1,124 115 67 67 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 12 Demographic Data anD estimates Percent Percent of in Urban Population Life Expectancy Areas of of Ages at Birth (years) Percent 750,000+ 2005 <15 65+ Total Males Females Urban Israel Jordan Kuwait Lebanon Oman Palestinian Territory Qatar Saudi Arabia Syria Turkey United Arab Emirates Yemen soutH CEntral asia Afghanistan Bangladesh Bhutan India Iran Kazakhstan Kyrgyzstan Maldives Nepal Pakistan Sri Lanka Tajikistan Turkmenistan Uzbekistan soutHEast asia Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Vietnam East asia China China, Hong Kong SARe China, Macao SARe Japan Korea, North Korea, South Mongolia Taiwan EUROPE nortHErn EuroPE Channel Islands Denmark Estonia Finland Iceland Ireland Latvia Lithuania Norway Sweden United Kingdom 28 37 24 27 30 46 23 38 37 28 19 45 33 45 34 32 32 26 27 32 32 37 39 27 38 35 35 29 30 36 29 44 32 27 35 19 22 45 26 19 19 13 13 13 25 18 29 18 16 18 16 18 15 17 21 20 14 15 19 17 18 10 3 2 8 2 3 1 2 3 6 1 3 5 2 4 5 5 5 8 6 5 4 4 6 4 4 5 6 3 4 6 4 4 6 4 9 7 3 7 9 8 13 7 22 8 10 4 10 16 16 15 16 17 17 12 11 17 16 15 18 16 80 72 78 72 74 72 75 76 73 72 78 61 65 43 63 66 65 71 66 66 73 64 63 71 67 62 67 70 75 62 70 61 74 61 69 81 72 60 73 74 73 82 79 82 71 79 64 78 75 79 78 78 73 79 81 79 72 71 80 81 79 79 71 77 69 73 72 74 74 71 69 77 60 64 43 62 66 65 69 61 62 72 63 62 67 64 58 63 68 72 59 69 59 72 58 66 78 68 59 71 72 71 79 78 79 68 76 61 75 72 76 76 76 67 76 79 77 66 65 78 79 77 82 73 79 74 75 73 76 78 75 74 81 62 65 43 64 67 66 72 72 70 73 64 64 75 69 67 70 72 77 66 72 63 76 64 72 83 75 61 75 76 75 85 82 86 73 82 67 81 79 81 81 80 78 83 83 82 77 77 83 83 81 92 83 98 87 71 72 100 81 50 62 83 30 30 20 24 31 28 67 53 35 27 17 35 15 26 47 36 45 72 15 48 27 68 31 63 100 36 22 27 50 45 100 100 79 60 82 59 78 71 77 31 72 69 63 93 60 68 67 79 84 80 60 19 70 44 -- -- -- 43 31 29 31 15 13 12 12 -- 13 26 8 16 -- 3 18 12 -- -- 8 12 -- 10 11 -- 12 10 16 100 10 -- 13 24 21 100 -- 48 22 54 33 18 19 24 -- 20 -- 21 -- 25 -- -- 18 14 29 nutrition environment economy Density Percent of Percent of % of Pop. Motor Population Married Women Percent of Vehicles With Access Ages 1549 1549 Using Population per 1,000 to Improved GNI PPP With HIV/ Contraception* Water Pop. Underper Capita Population AIDS Sources, 2000 per Sq. (US$) All Modern nourished 2006 2005 Kilometer 2007 2001 2007 Methods Methods 20022004 0.1 0.1 -- -- -- -- 0.1 0.1 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0.5 0.3 -- -- -- -- -- 0.1 0.5 0.3 0.1 0.2 <0.1 0.1 <0.1 0.1 -- -- 0.5 0.5 -- 0.1 -- -- 0.1 0.3 -- <0.1 -- 0.1 0.5 0.5 -- -- 1.5 0.8 0.1 0.2 <0.1 0.2 0.3 0.5 0.9 0.7 -- -- 0.1 0.2 1.7 1.4 -- -- 0.3 0.5 0.1 0.1 0.1 0.1 -- -- -- -- -- -- -- -- <0.1 <0.1 -- 0.1 -- -- 0.3 0.5 0.2 0.2 -- -- 0.1 0.2 0.5 1.3 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 -- 57 52 58 24 50 43 32 58 71 28 23 54 10 56 -- 56 79 66 60 39 48 30 68 38 62 65 60 -- 40 61 32 -- 37 51 62 72 10 78 86 90 -- -- 52 69 81 66 71 69 81 -- -- 70 79 -- -- 85 47 -- 75 84 -- 42 39 34 18 39 32 29 47 43 24 13 46 9 48 31 49 60 53 49 34 44 22 53 33 53 59 54 -- 27 57 29 -- 33 36 55 70 9 67 85 90 -- -- 44 58 67 61 -- 56 75 -- -- 56 78 -- -- 60 30 -- 65 79 <2.5 6 5 3 -- 16 -- 4 4 3 <2.5 38 21 -- 30 -- 20 4 6 4 10 17 24 22 56 7 25 12 4 33 6 19 3 5 18 -- 22 9 16 11 12 -- -- <2.5 33 <2.5 27 -- <2.5 <2.5 -- <2.5 <2.5 <2.5 <2.5 <2.5 3 <2.5 <2.5 <2.5 <2.5 302 107 420 386 176 -- 510 432 41 115 -- 50 20 6 -- -- 18 -- 117 39 15 5 13 41 22 -- -- 44 701 1 42 -- 21 7 34 140 154 -- 2 84 24 71 156 579 -- 312 -- 293 418 521 -- 452 435 535 724 479 376 465 544 512 537 100 98 -- 100 82 89 100 -- 89 97 100 66 87 22 80 81 89 94 96 89 83 89 90 82 67 -- 88 86 -- 65 80 60 99 80 93 -- 98 62 92 89 88 -- -- 100 100 92 72 -- 99 100 -- 100 100 100 100 -- 99 -- 100 100 100 25,930 5,160 49,970 10,050 19,740 -- -- 22,910 4,370 12,090 -- 2,200 2,940 -- 1,340 4,980 2,740 10,800 9,700 1,950 5,040 1,040 2,570 4,210 1,710 6,640 1,680 4,440 49,900 1,690 3,580 1,940 13,570 -- 3,730 48,520 7,880 3,190 2,550 8,380 5,370 44,050 -- 34,600 -- 24,750 3,160 -- 24,320 34,490 -- 36,740 19,680 35,270 34,060 37,040 16,890 17,180 53,690 35,840 34,370 338 65 150 383 9 690 84 13 108 95 54 42 156 50 1,023 14 350 44 6 26 1,040 183 217 309 51 11 61 130 66 81 126 25 84 73 302 7,013 129 73 260 132 139 6,360 21,192 338 195 488 2 639 32 54 784 127 30 16 3 64 35 51 12 20 252 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 13 Demographic Data anD estimates Percent Percent of in Urban Population Life Expectancy Areas of of Ages at Birth (years) Percent 750,000+ 2005 <15 65+ Total Males Females Urban WEstErn EuroPE Austria Belgium France Germany Liechtenstein Luxembourg Monaco Netherlands Switzerland EastErn EuroPE Belarus Bulgaria Czech Republic Hungary Moldova Poland Romania Russia Slovakia Ukraine soutHErn EuroPE Albania Andorra Bosnia-Herzegovina Croatia Greece Italy Kosovof Macedoniag Malta Montenegro Portugal San Marino Serbia Slovenia Spain OCEANIA Australia Federated States of Micronesia Fiji French Polynesia Guam Kiribati Marshall Islands Nauru New Caledonia New Zealand Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu 16 15 17 18 14 17 18 13 18 15 15 15 13 14 15 18 16 15 15 16 14 15 27 15 18 16 14 14 33 20 17 20 15 15 16 14 14 25 19 37 31 28 29 36 41 39 28 21 24 40 41 39 35 36 41 18 17 17 17 19 12 14 22 15 16 14 15 17 15 16 10 14 15 14 12 16 18 8 12 13 17 19 20 6 11 14 13 17 16 17 16 17 10 13 4 5 5 6 4 2 1 7 13 6 2 4 3 6 6 3 80 80 80 81 79 80 80 -- 80 82 69 70 73 77 73 69 75 71 67 74 68 79 75 -- 74 76 79 81 69 74 79 73 79 82 73 78 80 76 81 67 68 75 78 61 66 55 76 80 71 57 73 62 71 64 67 77 77 77 78 77 79 78 -- 78 79 64 63 69 74 69 65 71 68 60 70 62 76 72 -- 71 73 77 79 67 71 77 71 75 80 71 74 77 73 79 67 66 73 75 59 64 53 73 78 69 54 72 62 70 62 66 83 83 82 85 82 82 83 -- 82 84 75 76 76 80 77 72 80 75 73 78 74 82 79 -- 77 79 81 84 71 76 81 75 82 85 76 81 83 78 84 67 71 77 82 63 67 58 80 82 73 60 74 63 72 65 69 75 67 97 77 73 15 83 100 66 68 68 73 71 74 66 41 61 55 73 56 68 67 45 90 46 56 60 68 -- 65 94 64 55 84 56 48 77 70 87 22 51 53 93 44 68 100 58 86 77 13 22 17 24 47 21 17 27 26 27 9 -- -- -- 12 15 16 18 15 11 17 -- 8 9 21 -- 17 21 -- -- -- -- 37 19 -- -- -- -- 39 -- 14 -- 25 40 61 -- -- -- -- -- -- -- -- 29 -- -- -- -- -- -- -- nutrition environment economy Density Percent of Percent of % of Pop. Motor Population Married Women Percent of Vehicles With Access Ages 1549 1549 Using Population per 1,000 to Improved GNI PPP With HIV/ Contraception* Water Pop. Underper Capita Population AIDS Sources, 2000 per Sq. (US$) All Modern nourished 2006 2005 Kilometer 2007 2001 2007 Methods Methods 20022004 0.2 0.1 0.2 0.4 0.1 -- 0.2 -- 0.2 0.6 0.4 0.2 -- -- 0.1 <0.1 0.1 0.1 0.5 -- 0.8 0.4 -- -- -- -- 0.1 0.4 -- -- 0.1 -- 0.5 -- 0.1h -- 0.5 0.2 0.1 -- 0.1 -- -- -- -- -- -- 0.1 -- 0.3 -- -- -- -- -- 0.2 0.2 0.2 0.4 0.1 -- 0.2 -- 0.2 0.6 0.9 0.2 -- -- 0.1 0.4 0.1 0.1 1.1 <0.1 1.6 0.4 -- -- <0.1 <0.1 0.2 0.4 -- <0.1 0.1 -- 0.5 -- 0.1h <0.1 0.5 0.4 0.2 -- 0.1 -- -- -- -- -- -- 0.1 -- 1.5 -- -- -- -- -- 77 67 79 79 75 -- -- -- 79 82 64 50 41 67 77 68 49 70 67 74 67 63 75 -- 48 -- 61 60 44 14 86 -- 67 -- 41h 71 72 -- 85 -- -- -- -- -- 34 36 -- -- -- -- -- -- -- -- -- 74 65 75 76 72 -- -- -- 76 78 44 42 26 58 68 44 19 38 49 41 48 46 8 -- 16 -- 34 39 18 10 43 -- 63 -- 19h 57 67 59 75 70 40 -- -- 21 -- 25 -- 72 17 9 54 16 23 29 15 <2.5 <2.5 <2.5 <2.5 <2.5 -- <2.5 -- <2.5 <2.5 3 4 8 <2.5 <2.5 11 <2.5 <2.5 3 7 <2.5 <2.5 6 -- 9 7 <2.5 <2.5 -- 5 <2.5 -- <2.5 -- 9h 3 <2.5 <2.5 <2.5 -- 5 4 -- 7 -- -- 10 <2.5 -- -- 4 21 -- -- 11 544 546 536 490 591 -- 756 -- 495 568 232 181 376 436 329 77 378 180 209 279 137 569 85 750 -- 341 497 670 -- 138 731 -- 765 -- 163h 517 580 636 663 -- 176 -- 468 -- -- -- 457 661 -- -- 59 -- 142 -- 55 100 100 -- 100 100 -- 100 -- 100 100 97 100 99 100 100 90 -- 88 97 100 97 -- 97 100 99 99 100 -- -- 100 100 98 99 -- 99h -- 100 85 100 94 47 100 100 65 88 -- -- -- 89 40 88 70 100 93 59 34,910 38,090 35,110 33,470 33,820 -- 64,400 -- 39,500 43,080 13,210 10,740 11,180 21,820 17,430 2,930 15,590 10,980 14,400 19,330 6,810 26,230 6,580 -- 7,280 15,050 32,520 29,900 -- 8,510 20,990 10,290 20,640 37,080 10,220h 26,640 30,110 23,910 33,340 3,710 4,370 -- -- 2,190 -- -- -- 26,340 -- 1,500 3,570 1,400 3,430 -- 2,890 170 100 350 113 230 225 189 34,000 396 185 16 47 69 132 108 122 122 90 8 110 77 117 113 182 75 78 85 199 201 80 1,304 45 115 507 95 100 92 4 3 154 47 66 322 134 294 479 13 16 44 14 66 18 136 399 20 NOTES a Infant deaths per 1,000 live births. Rates shown with decimals indicate national statistics reported as completely registered, while those without are estimates from the sources cited on reverse. Rates shown in italics are based upon fewer than 50 annual infant deaths and, as a result, are subject to considerable yearly variability. b Average number of children born to a woman during her lifetime. c Data are from national surveys taken in 2003 and 2007. d For the Democratic Republic of the Congo, the estimated range is 1.2 to 1.5 for both 2001 and 2007. e Special Administrative Region. f Kosovo declared independence from Serbia on Feb. 17, 2008. Serbia has not recognized Kosovo's independence. g The former Yugoslav Republic. h Includes Kosovo. (--) Indicates data unavailable or inapplicable. * Data prior to 2002 are shown in italics. For additional notes and sources, see page 15. Data prepared by PRB demographers Carl Haub and Mary Mederios Kent. August 2008. Population Reference Bureau. All rights reserved. 2008 Population Reference Bureau See Notes on page 14. 2008 World PoPulation data Sheet 14 Acknowledgments, Notes, Sources, and Definitions Acknowledgments Birth and Death Rate The authors gratefully acknowledge the valuable assistance of PRB staff members Donna Clifton, Jennay Ghowrwal, Zuali Malsawma, and Kelvin Pollard; staff of the International Programs Center of the U.S. Census Bureau; the United Nations (UN) Population Division; the Institut national d'etudes dmographiques (INED), Paris; and the World Bank in the preparation of this year's World Population Data Sheet. This publication is funded by the William and Flora Hewlett Foundation, the David and Lucile Packard Foundation, the U.S. Agency for International Development (Cooperative Agreement GPO-A-oo-o3-oooo4-oo), and supporters. The information in this Data Sheet was not provided by and does not represent the views of the United States government or the U.S. Agency for International Development. The annual number of births and deaths per 1,000 total population. These rates are often referred to as "crude rates" since they do not take a population's age structure into account. Thus, crude death rates in more developed countries, with a relatively large proportion of highmortality older population, are often higher than those in less developed countries with lower life expectancy. Reproductive Health Surveys, Multiple Indicator Cluster Surveys, regional survey programs, national surveys, the UN Population Division World Contraceptive Use 2007, and the International Data Base of the U.S. Census Bureau. For more developed countries, data refer to some point in the 1990s and early 2000s; and for less developed countries, from 1995. Data prior to 2002 are shown in italics. Rate of Natural Increase (RNI) Percent of Population Undernourished, 20022004 Undernourishment refers to the condition of people whose dietary energy consumption is continuously below a minimum dietary energy requirement for maintaining a healthy life and carrying out light physical activity. Data are from the Statistics Division of the United Nations Food and Agriculture Organization, accessed at www.fao.org/es/ess/faostat/foodsecurity/Files/ PrevalenceUndernourishment_en.xls. The birth rate minus the death rate, implying the annual rate of population growth without regard for migration. Expressed as a percentage. Net Migration Notes The Data Sheet lists all geopolitical entities with populations of 150,000 or more and all members of the UN. These include sovereign states, dependencies, overseas departments, and some territories whose status or boundaries may be undetermined or in dispute. More developed regions, following the UN classification, comprise all of Europe and North America, plus Australia, Japan, and New Zealand. All other regions and countries are classified as less developed. The least developed countries consist of 50 countries with especially low incomes, high economic vulnerability, and poor human development indicators. The criteria and list of countries, as defined by the United Nations, can be found at www. unohrlls.org/en/ldc/. Sub-Saharan Africa: All countries of Africa except the northern African countries of Algeria, Egypt, Libya, Morocco, Tunisia, and Western Sahara. World and Regional Totals: Regional population totals are independently rounded and include small countries or areas not shown. Regional and world rates and percentages are weighted averages of countries for which data are available; regional averages are shown when data or estimates are available for at least three-quarters of the region's population. World Population Data Sheets from different years should not be used as a time series. Fluctuations in values from year to year often reflect revisions based on new data or estimates rather than actual changes in levels. Additional information on likely trends and consistent time series can be obtained from PRB, and are also available in UN and U.S. Census Bureau publications. The estimated rate of net immigration (immigration minus emigration) per 1,000 population for a recent year based upon the official national rate or derived as a residual from estimated birth, death, and population growth rates. Migration rates can vary substantially from year to year for any particular country as well as the definition of an immigrant. Motor Vehicles in Use per 1,000 Population, 20002005 Includes motorized vehicles of all types and for all purposes. Data are from the Transport Statistics Division of the UN and taken from the UN Population Division, Urban Population, Development and the Environment, 2007. Data refer to entire national populations. Projected Population 2025 and 2050 Projected populations based upon reasonable assumptions on the future course of fertility, mortality, and migration. Projections are based upon official country projections, series issued by the UN or the U.S. Census Bureau, or PRB projections. Population Using Improved Drinking Water Sources, 2006 Infant Mortality Rate Data are from the World Health Organization and UNICEF, accessed at http://mdgs.un.org/unsd/mdg/. Data in italics are for a prior year. The annual number of deaths of infants under age 1 per 1,000 live births. Rates shown with decimals indicate national statistics reported as completely registered, while those without are estimates from the sources cited above. Rates shown in italics are based upon fewer than 50 annual infant deaths and, as a result, are subject to considerable yearly variability. GNI PPP per Capita, 2007 (US$) Lifetime Risk of Maternal Death, 2005 The chance of a woman dying during her lifetime from a pregnancy-related cause. Data are from Maternal Mortality in 2005, Estimates Developed by WHO, UNICEF, UNFPA and the World Bank. Some regional averages were calculated by PRB. GNI PPP per capita is gross national income in purchasing power parity (PPP) divided by midyear population. GNI PPP refers to gross national income converted to "international" dollars using a purchasing power parity conversion factor. International dollars indicate the amount of goods and services one could buy in the United States with a given amount of money. Data are from the World Bank. Figures in italics are for 2005 or 2006. Total Fertility Rate (TFR) The average number of children a woman would have assuming that current age-specific birth rates remain constant throughout her childbearing years (usually considered to be ages 15 to 49). Population Under Age 15/Age 65+ The percentage of the total population in these ages, which are often considered the "dependent ages." Sources Life Expectancy at Birth The rates and figures are primarily compiled from the following sources: official country statistical yearbooks, bulletins, and websites; United Nations Demographic Yearbook, 2005 of the UN Statistics Division; World Population Prospects: The 2006 Revision of the UN Population Division; Recent Demographic Developments in Europe, 2005 of the Council of Europe; and the International Data Base and library resources of the International Programs Center, U.S. Census Bureau. Other sources include recent demographic surveys such as the Demographic and Health Surveys, Reproductive Health Surveys, special studies, and direct communication with demographers and statistical bureaus in the United States and abroad. Specific data sources may be obtained by contacting the authors of the 2008 World Population Data Sheet. For countries with complete registration of births and deaths, rates are those most recently reported. For more developed countries, nearly all vital rates refer to 2007 or 2006. The average number of years a newborn infant can expect to live under current mortality levels. Percent Urban Percentage of the total population living in areas termed "urban" by that country. Countries define urban in many different ways, from population centers of 100 or more dwellings to only the population living in national and provincial capitals. Percent of Population Living in Urban Agglomerations of 750,000 or More, 2005 Data are from the UN Population Division, World Urbanization Prospects, 2007, accessed online at http://esa.un.org/unup. Percent of Adult Population Ages 15 to 49 With HIV/AIDS The estimated percentage of adults living with HIV/AIDS in 2001 and 2007 by UNAIDS, 2008 Report on the Global AIDS Epidemic, accessed at www.unaids.org. Some regional averages were calculated by PRB. Definitions Mid-2008 Population Contraceptive Use Estimates are based on a recent census, official national data, or PRB, UN, and U.S. Census Bureau projections. The effects of refugee movements, large numbers of foreign workers, and population shifts due to contemporary political events are taken into account to the extent possible. 2008 Population Reference Bureau The percentage of currently married or "in-union" women of reproductive age who are currently using any form of contraception. "Modern" methods include clinic and supply methods such as the pill, IUD, condom, and sterilization. Data are from the most recently available national-level surveys, such as Demographic and Health Surveys, For a full list of PRB publications available in English, French, Spanish, Arabic, and Portuguese, go to PRB's online store at www.prb.org. To order PRB publications (discounts available for bulk orders): Online at www.prb.org. E-mail: popref@prb.org. Call toll-free: 800-877-9881. Fax: 202-328-3937. Mail: 1875 Connecticut Ave., NW, Suite 520, Washington, DC 20009. The 2008 World Population Data Sheet is also available in French and Spanish. Data prepared by PRB demographers Carl Haub and Mary Mederios Kent. Design and production: Michelle Corbett, Black Mountain Creative. August 2008. Population Reference Bureau. All rights reserved. ISSN 0085-8315 Photo Credits from cover, top to bottom: 2001 Virginia Lamprecht, Courtesy of Photoshare; 2008 Enge/iStockPhoto; 2007 Cliff Parnell/ iStockPhoto; 2007 Jennifer Budai; 2007 Vikram Raghuvanshi/iStockPhoto; DigitalStock; 2008 Cliff Parnell/iStockPhoto; 2005 Michael Corbett. From front, left to right: 2005 Valentin Casarsa/ iStockPhoto; 2000 Mohsen Allam, Courtesy of Photoshare; 2007 Miroslav Ferkuniak/iStockPhoto; 2007 Glenda Powers/iStockPhoto; 2005 Kevin Russ/iStockPhoto; 2007 Jennifer Budai; 2006 Basil A. Safi/CCP, Courtesy of Photoshare; 2005 William Walsh/iStockPhoto; 2005 Amrita Gill-Bailey/CCP, Courtesy of Photoshare; 2006 Peeter Viisimaa/iStockPhoto. 2008 World PoPulation data Sheet 15 the population reference Bureau informs people around the world about population, health, and the environment, and empowers them to use that information to advance the well-being of current and future generations. inform PrB informs people around the world and in the united states about issues related to population, health, and the environment. to do this, we transform technical data and research into accurate, easy-to-understand information. Innovative Tools. PrB's wallcharts, including the World Population Data Sheet and the Map of Persistent Child Poverty in the U.S., are searchable via our dataFinder web tool and make accurate demographic information accessible to a wide audience. Influential Reports. Health workers in the developing world use PrB's report on cervical cancer prevention, created in collaboration with the global health nonprofit PatH, to design successful screening programs. PrB and the russell sage Foundation published The American People: Census 2000, 14 reports that describe america in the year 2000. Unbiased Policy Analysis. For more than 20 years, PrB has hosted a monthly seminar series focused on the policy implications of population issues including the color line in american society and HiV/aids in india. Online Resources. PrB's website offers full text of all PrB publications, including our respected Population Bulletins and webexclusive data and analysis on world issues ranging from aging to family planning. our Center for Public information on Population research puts new population research findings into context for journalists and policymakers. empower PrB empowers people--researchers, journalists, policymakers, and educators--to use information about population, health, and the environment to encourage action. information alone can be powerful. Frequently, however, people have knowledge but lack the tools needed to communicate it effectively to decisionmakers. PrB builds coalitions and conducts trainings in the united states and throughout the developing world to share techniques to inform policy. Journalist Networks. since 1996, PrB has shared techniques for fact-based, reproductive health reporting with a network of West african editors. the Pop'Mdiafrique program, one of several PrB journalist networks, has improved news coverage and increased demand for family planning in the region. Policy Communications Training. PrB has trained more than 700 advocates, health professionals, and government workers in asia, africa, and latin america. For example, participants in a workshop in Madagascar learned how to develop a fact sheet for policymakers to explain the complex links between population, health, and the environment. Data Workshops. PrB's data workshops assist the annie E. Casey Foundation's Kids Count network in using vital data about the status of children in the united states. Workshop participants take away the knowledge needed to access data about their particular state and communicate with policymakers. advance PrB works to advance the well-being of current and future generations. toward that end, we analyze data and research, disseminate information, and empower people to use that information in order to inform policymaking. While the numbers of publications created or workshops conducted are one way to measure PrB's work, the creation of evidencebased policies, increased demand for health services, and active coalitions are better gauges of progress toward positive social change. Evidence-Based Policies. PrB provides analysis for the KIDS COUNT Data Book, an annual report card on the well-being of children and families in the united states, that has helped promote the passage of several u.s. policies, including the state Children's Health insurance Program. Increased Demand for Health Services. information broadcast by women radio journalists who attended PrB's reproductive health workshop in senegal has increased local demand for family planning and health services. Active Coalitions. PrB worked with local groups in the Philippines to establish a national coalition that helps decisionmakers understand the impact of population on the environment through events such as an international Earth day celebration near the endangered Pasig river in Manila. PrB's World Population Data Sheet is used around the world and is widely considered to be the most accurate source of information on population. if you value the Data Sheet and are among the tens of thousands of people who eagerly anticipate its publication each year, please consider making a contribution to PrB. Your donation will help ensure that PrB can maintain its commitment to keeping the Data Sheet as affordable as possible. Visit our website to donate now: www.prb.org. P O P UL AT I O N R E F E R E N C E B U R E AU 1875 Connecticut Ave., NW, Washington, DC 20009 USA tel. 202-483-1100 | fax 202-328-3937 | email: popref@prb.org | website: www.prb.org 2008 Population Reference Bureau 2008 World PoPulation data Sheet 16 ASSIGNMENT #1 1. Rate of Natural Increase (Based on difference between Birth and Death Rates) Rank TOP FIVE NATIONS RATES 1 2 3 4 5 Rank BOTTOM FIVE NATIONS RATES 1 2 3 4 5 ISS315 - PAGE 23 2. Infant Mortality Rates (Based on number of children who die before reaching age 1) Rank TOP FIVE NATIONS RATES 1 2 3 4 5 Rank BOTTOM FIVE NATIONS RATES 1 2 3 4 5 ISS315 - PAGE 24 3. Total Fertility Rates (Average number of children born to a woman) Rank TOP FIVE NATIONS RATES 1 2 3 4 5 Rank BOTTOM FIVE NATIONS RATES 1 2 3 4 5 ISS315 - PAGE 25 4. Life Expectancy at Birth Rank TOP FIVE NATIONS RATES 1 2 3 4 5 Rank BOTTOM FIVE NATIONS RATES 1 2 3 4 5 ISS315 - PAGE 26 5. GNI PPP PER CAPITA (Gross national income in purchasing power parity divided by mid-year population. It is converted in international dollars). Rank TOP FIVE NATIONS RATES 1 2 3 4 5 Rank BOTTOM FIVE NATIONS RATES 1 2 3 4 5 ISS315 - PAGE 27 Assignment #2 Human Development Index ISS315 - PAGE 28 ASSIGNMENT # 2 There are a number of indicators that are used to measure human development. The United Nations Development Program (UNDP) uses life expectancy, adult literacy rates, school enrollment, and GDP per capita for this purpose. Use the enclosed handout on these indicators and list the top five (high developed) and bottom five (least developed) nations. TOP FIVE NATIONS (Highly Developed Nations) Nation Life Expectancy Adult Literacy Rates School Enrollment GDP Per Capita HDI Rank BOTTOM FIVE NATIONS (Least Developed Nations) Nation Life Expectancy Adult Literacy Rates School Enrollment GDP Per Capita HDI Rank ISS315 - PAGE 29 TABLE 1 Monitoring human development: enlarging people's choices . . . Human development index Combined gross enrolment ratio for primary, secondary and tertiary education (%) 2005 95.4 e 99.2 113.0 g 99.2 e,h 99.9 95.3 85.7 85.9 98.4 96.5 101.0 g 93.3 98.0 102.7 g 91.9 93.0 e 95.1 84.7 i 108.4 g 90.6 76.3 88.0 e 89.6 99.0 87.3 h,k 96.0 94.3 77.6 e 89.8 77.7 88.9 h 82.9 74.9 80.9 77.7 89.3 87.2 89.7 h 59.9 e,h 82.9 86.1 78.3 91.4 92.4 90.2 88.9 e,h 73.5 h 73.0 e 70.8 82.2 e 87.6 75.6 81.5 Human development index (HDI) value HDI rank a HIGH HUMAN DEVELOPMENT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 Iceland Norway Australia Canada Ireland Sweden Switzerland Japan Netherlands France Finland United States Spain Denmark Austria United Kingdom Belgium Luxembourg New Zealand Italy Hong Kong, China (SAR) Germany Israel Greece Singapore Korea (Republic of) Slovenia Cyprus Portugal Brunei Darussalam Barbados Czech Republic Kuwait Malta Qatar Hungary Poland Argentina United Arab Emirates Chile Bahrain Slovakia Lithuania Estonia Latvia Uruguay Croatia Costa Rica Bahamas Seychelles Cuba Mexico Bulgaria 0.968 0.968 0.962 0.961 0.959 0.956 0.955 0.953 0.953 0.952 0.952 0.951 0.949 0.949 0.948 0.946 0.946 0.944 0.943 0.941 0.937 0.935 0.932 0.926 0.922 0.921 0.917 0.903 0.897 0.894 0.892 0.891 0.891 0.878 0.875 0.874 0.870 0.869 0.868 0.867 0.866 0.863 0.862 0.860 0.855 0.852 0.850 0.846 0.845 0.843 0.838 0.829 0.824 2005 Life expectancy at birth (years) 2005 81.5 79.8 80.9 80.3 78.4 80.5 81.3 82.3 79.2 80.2 78.9 77.9 80.5 77.9 79.4 79.0 78.8 78.4 79.8 80.3 81.9 79.1 80.3 78.9 79.4 77.9 77.4 79.0 77.7 76.7 76.6 75.9 77.3 79.1 75.0 72.9 75.2 74.8 78.3 78.3 75.2 74.2 72.5 71.2 72.0 75.9 75.3 78.5 72.3 72.7 h,k 77.7 75.6 72.7 Adult literacy rate (% aged 15 and above) 1995-2005 b .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d .. d 98.4 .. j .. d 97.1 k 96.0 92.5 .. d 99.7 d,l 96.8 93.8 l 92.7 .. d,j .. d 93.3 87.9 89.0 .. d,j .. d,j 97.2 88.7 l 95.7 86.5 .. d 99.6 d 99.8 d 99.7 d 96.8 98.1 94.9 .. j 91.8 99.8 d 91.6 98.2 GDP per capita (PPP US$) 2005 36,510 41,420 f 31,794 33,375 38,505 32,525 35,633 31,267 32,684 30,386 32,153 41,890 f 27,169 33,973 33,700 33,238 32,119 60,228 f 24,996 28,529 34,833 29,461 25,864 23,381 29,663 22,029 22,273 22,699 h 20,410 28,161 h,m 17,297 h,m 20,538 26,321 n 19,189 27,664 h,m 17,887 13,847 14,280 25,514 n 12,027 21,482 15,871 14,494 15,478 13,646 9,962 13,042 10,180 n 18,380 h 16,106 6,000 o 10,751 9,032 Life expectancy index Education index GDP index GDP per capita (PPP US$) rank minus HDI rank c 30 0.941 0.913 0.931 0.921 0.890 0.925 0.938 0.954 0.904 0.919 0.898 0.881 0.925 0.881 0.907 0.900 0.897 0.891 0.913 0.922 0.949 0.902 0.921 0.898 0.907 0.882 0.874 0.900 0.879 0.862 0.861 0.849 0.871 0.901 0.834 0.799 0.836 0.831 0.889 0.889 0.837 0.821 0.792 0.770 0.784 0.848 0.839 0.891 0.789 0.795 0.879 0.843 0.795 0.978 0.991 0.993 0.991 0.993 0.978 0.946 0.946 0.988 0.982 0.993 0.971 0.987 0.993 0.966 0.970 0.977 0.942 0.993 0.958 0.885 0.953 0.946 0.970 0.908 0.980 0.974 0.904 0.925 0.877 0.956 0.936 0.871 0.856 0.852 0.958 0.951 0.947 0.791 0.914 0.864 0.921 0.965 0.968 0.961 0.942 0.899 0.876 0.875 0.886 0.952 0.863 0.926 0.985 1.000 0.962 0.970 0.994 0.965 0.981 0.959 0.966 0.954 0.964 1.000 0.935 0.973 0.971 0.969 0.963 1.000 0.922 0.944 0.977 0.949 0.927 0.910 0.950 0.900 0.902 0.905 0.888 0.941 0.860 0.889 0.930 0.877 0.938 0.866 0.823 0.828 0.925 0.799 0.896 0.846 0.831 0.842 0.821 0.768 0.813 0.772 0.870 0.848 0.683 0.781 0.752 4 1 13 6 -1 7 -1 9 3 8 3 -10 11 -6 -6 -5 -2 -17 9 1 -14 -2 3 5 -6 6 4 2 6 -8 8 2 -8 2 -12 2 11 9 -12 15 -8 -1 3 0 4 16 4 13 -12 -10 43 7 11 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 229 Human development indicators TABLE 1 Human development index Combined gross enrolment ratio for primary, secondary and tertiary education (%) 2005 73.1 e 80.1 e 94.1 e,h .. r 67.1 64.9 e 76.8 76.0 79.5 74.3 h 88.7 75.3 e 69.0 h,s 88.9 e 68.6 h 70.1 87.5 h 81.0 e 74.8 93.8 75.5 e,h 75.1 86.5 73.7 e 71.2 e 74.1 e,h 81.8 e 69.1 e 73.1 e 70.8 68.7 e 77.1 e 78.1 85.8 e 84.6 .. r 81.1 76.3 74.8 e 68.9 72.8 e 69.1 e,h 76.3 85.0 67.1 62.7 e,h 65.8 e 77.9 e 66.4 70.4 73.7 e 63.9 82.4 e Human development index (HDI) value HDI rank a 54 Saint Kitts and Nevis 55 Tonga 56 Libyan Arab Jamahiriya 57 Antigua and Barbuda 58 Oman 59 Trinidad and Tobago 60 Romania 61 Saudi Arabia 62 Panama 63 Malaysia 64 Belarus 65 Mauritius 66 Bosnia and Herzegovina 67 Russian Federation 68 Albania 69 Macedonia (TFYR) 70 Brazil MEDIUM HUMAN DEVELOPMENT 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 Dominica Saint Lucia Kazakhstan Venezuela (Bolivarian Republic of) Colombia Ukraine Samoa Thailand Dominican Republic Belize China Grenada Armenia Turkey Suriname Jordan Peru Lebanon Ecuador Philippines Tunisia Fiji Saint Vincent and the Grenadines Iran (Islamic Republic of) Paraguay Georgia Guyana Azerbaijan Sri Lanka Maldives Jamaica Cape Verde El Salvador Algeria Viet Nam Occupied Palestinian Territories 2005 0.821 0.819 0.818 0.815 0.814 0.814 0.813 0.812 0.812 0.811 0.804 0.804 0.803 0.802 0.801 0.801 0.800 0.798 0.795 0.794 0.792 0.791 0.788 0.785 0.781 0.779 0.778 0.777 0.777 0.775 0.775 0.774 0.773 0.773 0.772 0.772 0.771 0.766 0.762 0.761 0.759 0.755 0.754 0.750 0.746 0.743 0.741 0.736 0.736 0.735 0.733 0.733 0.731 Life expectancy at birth (years) 2005 70.0 h,p 72.8 73.4 73.9 h,p 75.0 69.2 71.9 72.2 75.1 73.7 68.7 72.4 74.5 65.0 76.2 73.8 71.7 75.6 h,q 73.1 65.9 73.2 72.3 67.7 70.8 69.6 71.5 75.9 72.5 68.2 71.7 71.4 69.6 71.9 70.7 71.5 74.7 71.0 73.5 68.3 71.1 70.2 71.3 70.7 65.2 67.1 71.6 67.0 72.2 71.0 71.3 71.7 73.7 72.9 Adult literacy rate (% aged 15 and above) 1995-2005 b 97.8 k 98.9 84.2 l 85.8 q 81.4 98.4 l 97.3 82.9 91.9 88.7 99.6 d 84.3 96.7 99.4 d 98.7 96.1 88.6 88.0 q 94.8 q 99.5 d 93.0 92.8 99.4 d 98.6 l 92.6 87.0 75.1 q 90.9 96.0 q 99.4 d 87.4 89.6 91.1 87.9 .. j 91.0 92.6 74.3 .. j 88.1 q 82.4 93.5 l 100.0 d,v .. j 98.8 90.7 w 96.3 79.9 81.2 l 80.6 l 69.9 90.3 92.4 GDP per capita (PPP US$) 2005 13,307 h 8,177 n 10,335 h,m 12,500 h 15,602 h 14,603 9,060 15,711 n 7,605 10,882 7,918 12,715 7,032 h,t 10,845 5,316 7,200 8,402 6,393 h 6,707 h 7,857 6,632 7,304 n 6,848 6,170 8,677 8,217 n 7,109 6,757 u 7,843 h 4,945 8,407 7,722 5,530 6,039 5,584 4,341 5,137 8,371 6,049 6,568 7,968 4,642 n 3,365 4,508 n 5,016 4,595 5,261 h,m 4,291 5,803 n 5,255 n 7,062 n 3,071 .. x Life expectancy index 0.750 0.797 0.806 0.815 0.833 0.737 0.782 0.787 0.836 0.811 0.728 0.790 0.825 0.667 0.853 0.814 0.779 0.844 0.802 0.682 0.804 0.788 0.711 0.763 0.743 0.776 0.849 0.792 0.720 0.779 0.773 0.743 0.782 0.761 0.775 0.828 0.767 0.808 0.722 0.768 0.754 0.771 0.761 0.670 0.702 0.776 0.701 0.787 0.766 0.772 0.778 0.812 0.799 Education index 0.896 0.926 0.875 0.824 0.766 0.872 0.905 0.806 0.878 0.839 0.956 0.813 0.874 0.956 0.887 0.875 0.883 0.857 0.881 0.973 0.872 0.869 0.948 0.903 0.855 0.827 0.773 0.837 0.884 0.896 0.812 0.854 0.868 0.872 0.871 0.858 0.888 0.750 0.879 0.817 0.792 0.853 0.914 0.943 0.882 0.814 0.862 0.792 0.763 0.772 0.711 0.815 0.891 GDP index 0.816 0.735 0.774 0.806 0.843 0.832 0.752 0.844 0.723 0.783 0.730 0.809 0.710 0.782 0.663 0.714 0.740 0.694 0.702 0.728 0.700 0.716 0.705 0.688 0.745 0.736 0.712 0.703 0.728 0.651 0.740 0.725 0.670 0.684 0.671 0.629 0.657 0.739 0.685 0.698 0.731 0.641 0.587 0.636 0.653 0.639 0.661 0.627 0.678 0.661 0.711 0.572 0.505 GDP per capita (PPP US$) rank minus HDI rank c -4 15 4 -4 -15 -14 3 -19 15 -6 8 -13 17 -9 30 11 -3 19 15 1 14 4 9 14 -13 -10 1 5 -7 20 -18 -9 11 6 8 21 11 -23 0 -4 -23 10 24 12 4 7 -1 11 -7 -3 -22 18 33 Human development indicators 31 230 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Human development index (HDI) value HDI rank a 107 Indonesia 108 Syrian Arab Republic 109 Turkmenistan 110 Nicaragua 111 Moldova 112 Egypt 113 Uzbekistan 114 Mongolia 115 Honduras 116 Kyrgyzstan 117 Bolivia 118 Guatemala 119 Gabon 120 Vanuatu 121 South Africa 122 Tajikistan 123 Sao Tome and Principe 124 Botswana 125 Namibia 126 Morocco 127 Equatorial Guinea 128 India 129 Solomon Islands 130 Lao People's Democratic Republic 131 Cambodia 132 Myanmar 133 Bhutan 134 Comoros 135 Ghana 136 Pakistan 137 Mauritania 138 Lesotho 139 Congo 140 Bangladesh 141 Swaziland 142 Nepal 143 Madagascar 144 Cameroon 145 Papua New Guinea 146 Haiti 147 Sudan 148 Kenya 149 Djibouti 150 Timor-Leste 151 Zimbabwe 152 Togo 153 Yemen 154 Uganda 155 Gambia LOW HUMAN DEVELOPMENT 156 157 158 159 Senegal Eritrea Nigeria Tanzania (United Republic of) 2005 0.728 0.724 0.713 0.710 0.708 0.708 0.702 0.700 0.700 0.696 0.695 0.689 0.677 0.674 0.674 0.673 0.654 0.654 0.650 0.646 0.642 0.619 0.602 0.601 0.598 0.583 0.579 0.561 0.553 0.551 0.550 0.549 0.548 0.547 0.547 0.534 0.533 0.532 0.530 0.529 0.526 0.521 0.516 0.514 0.513 0.512 0.508 0.505 0.502 0.499 0.483 0.470 0.467 Life expectancy at birth (years) 2005 69.7 73.6 62.6 71.9 68.4 70.7 66.8 65.9 69.4 65.6 64.7 69.7 56.2 69.3 50.8 66.3 64.9 48.1 51.6 70.4 50.4 63.7 63.0 63.2 58.0 60.8 64.7 64.1 59.1 64.6 63.2 42.6 54.0 63.1 40.9 62.6 58.4 49.8 56.9 59.5 57.4 52.1 53.9 59.7 40.9 57.8 61.5 49.7 58.8 62.3 56.6 46.5 51.0 Adult literacy rate (% aged 15 and above) 1995-2005 b 90.4 80.8 98.8 76.7 99.1 d,l 71.4 .. d,j 97.8 80.0 98.7 86.7 69.1 84.0 l 74.0 82.4 99.5 d 84.9 81.2 85.0 52.3 87.0 61.0 76.6 k 68.7 73.6 89.9 47.0 v .. j 57.9 49.9 51.2 82.2 84.7 l 47.5 79.6 48.6 70.7 67.9 57.3 .. j 60.9 aa 73.6 .. j 50.1 ab 89.4 l 53.2 54.1 l 66.8 .. j 39.3 .. j 69.1 l 69.4 Combined gross enrolment ratio for primary, secondary and tertiary education (%) 2005 68.2 e 64.8 e .. r 70.6 e 69.7 e 76.9 e 73.8 e,h 77.4 71.2 e 77.7 86.0 e,h 67.3 e 72.4 e,h 63.4 e 77.0 h 70.8 65.2 69.5 e 64.7 e 58.5 e 58.1 e,h 63.8 e 47.6 61.5 60.0 e 49.5 e .. r 46.4 e 50.7 e 40.0 e 45.6 66.0 e 51.4 e 56.0 h 59.8 e 58.1 e 59.7 e 62.3 e 40.7 e,h .. r 37.3 e 60.6 e 25.3 72.0 e 52.4 e,h 55.0 e 55.2 63.0 e 50.1 e,h 39.6 e 35.3 e 56.2 e 50.4 e GDP per capita (PPP US$) 2005 3,843 3,808 3,838 h 3,674 n 2,100 4,337 2,063 2,107 3,430 n 1,927 2,819 4,568 n 6,954 3,225 n 11,110 n 1,356 2,178 12,387 7,586 n 4,555 7,874 h,n 3,452 n 2,031 n 2,039 2,727 n 1,027 h,y .. h,z 1,993 n 2,480 n 2,370 2,234 n 3,335 n 1,262 2,053 4,824 1,550 923 2,299 2,563 n 1,663 n 2,083 n 1,240 2,178 n .. h,ac 2,038 1,506 n 930 1,454 n 1,921 n 1,792 1,109 n 1,128 744 Life expectancy index 0.745 0.811 0.627 0.782 0.724 0.761 0.696 0.682 0.739 0.676 0.662 0.746 0.521 0.738 0.430 0.689 0.665 0.385 0.444 0.757 0.423 0.645 0.633 0.637 0.550 0.596 0.662 0.651 0.568 0.659 0.637 0.293 0.484 0.635 0.265 0.626 0.557 0.414 0.532 0.575 0.540 0.451 0.482 0.578 0.265 0.547 0.608 0.412 0.563 0.622 0.527 0.359 0.434 Education index 0.830 0.755 0.903 0.747 0.892 0.732 0.906 0.910 0.771 0.917 0.865 0.685 0.801 0.705 0.806 0.896 0.783 0.773 0.783 0.544 0.773 0.620 0.669 0.663 0.691 0.764 0.485 0.533 0.555 0.466 0.493 0.768 0.736 0.503 0.730 0.518 0.670 0.660 0.518 0.542 0.531 0.693 0.553 0.574 0.770 0.538 0.545 0.655 0.450 0.394 0.521 0.648 0.631 GDP index 0.609 0.607 0.609 0.601 0.508 0.629 0.505 0.509 0.590 0.494 0.557 0.638 0.708 0.580 0.786 0.435 0.514 0.804 0.723 0.637 0.729 0.591 0.503 0.503 0.552 0.389 0.589 0.499 0.536 0.528 0.519 0.585 0.423 0.504 0.647 0.458 0.371 0.523 0.541 0.469 0.507 0.420 0.514 0.390 0.503 0.453 0.372 0.447 0.493 0.482 0.402 0.404 0.335 1 6 7 5 6 25 -1 25 21 3 29 7 -11 -35 2 -65 32 10 -70 -47 -18 -54 -11 14 11 -6 35 -14 10 -8 -8 -5 -17 16 0 -37 8 27 -13 -19 2 -10 9 -15 16 -9 -1 16 -2 -9 -9 6 4 15 GDP per capita (PPP US$) rank minus HDI rank c Human development indicators 32 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 231 TABLE 1 Human development index Combined gross enrolment ratio for primary, secondary and tertiary education (%) 2005 45.1 e 50.9 e 25.6 e,h 50.7 e 63.1 e 60.5 e 39.6 e,h 37.9 e 33.7 e,h 42.1 e 37.5 e 29.8 e,h 52.9 36.7 22.7 36.7 e,h 29.3 44.6 h 64.1 48.0 65.5 69.4 81.2 60.3 50.6 83.5 88.6 93.5 88.4 65.3 45.8 92.3 73.3 56.3 67.8 Human development index (HDI) value HDI rank a 160 Guinea 161 Rwanda 162 Angola 163 Benin 164 Malawi 165 Zambia 166 Cte d'Ivoire 167 Burundi 168 Congo (Democratic Republic of the) 169 Ethiopia 170 Chad 171 Central African Republic 172 Mozambique 173 Mali 174 Niger 175 Guinea-Bissau 176 Burkina Faso 177 Sierra Leone Developing countries Least developed countries Arab States East Asia and the Pacific Latin America and the Caribbean South Asia Sub-Saharan Africa Central and Eastern Europe and the CIS OECD High-income OECD High human development Medium human development Low human development High income Middle income Low income World NOTES a. The HDI rank is determined using HDI values to the sixth decimal point. b. Data refer to national literacy estimates from censuses or surveys conducted between 1995 and 2005, unless otherwise specified. Due to differences in methodology and timeliness of underlying data, comparisons across countries and over time should be made with caution. For more details, see http://www.uis.unesco.org/. c. A positive figure indicates that the HDI rank is higher than the GDP per capita (PPP US$) rank, a negative the opposite. d. For purposes of calculating the HDI, a value of 99.0% was applied. e. National or UNESCO Institute for Statistics estimate. f. For purposes of calculating the HDI, a value of 40,000 (PPP US$) was applied. g. For purposes of calculating the HDI, a value of 100% was applied. h. Data refer to a year other than that specified. i. Statec 2006. Data refer to nationals enrolled both in the country and abroad and thus differ from the standard definition. Life expectancy at birth (years) 2005 54.8 45.2 41.7 55.4 46.3 40.5 47.4 48.5 45.8 51.8 50.4 43.7 42.8 53.1 55.8 45.8 51.4 41.8 66.1 54.5 67.5 71.7 72.8 63.8 49.6 68.6 78.3 79.4 76.2 67.5 48.5 79.2 70.9 60.0 68.1 Adult literacy rate (% aged 15 and above) 1995-2005 b 29.5 64.9 67.4 34.7 64.1 68.0 48.7 59.3 67.2 35.9 25.7 48.6 38.7 24.0 28.7 .. j 23.6 34.8 76.7 53.9 70.3 90.7 90.3 59.5 60.3 99.0 .. .. .. 78.0 54.4 .. 89.9 60.2 78.6 GDP per capita (PPP US$) 2005 2,316 1,206 n 2,335 n 1,141 667 1,023 1,648 699 n 714 n 1,055 n 1,427 n 1,224 n 1,242 n 1,033 781 n 827 n 1,213 n 806 5,282 1,499 6,716 6,604 8,417 3,416 1,998 9,527 29,197 33,831 23,986 4,876 1,112 33,082 7,416 2,531 9,543 2005 0.456 0.452 0.446 0.437 0.437 0.434 0.432 0.413 0.411 0.406 0.388 0.384 0.384 0.380 0.374 0.374 0.370 0.336 0.691 0.488 0.699 0.771 0.803 0.611 0.493 0.808 0.916 0.947 0.897 0.698 0.436 0.936 0.776 0.570 0.743 Life expectancy index 0.497 0.337 0.279 0.506 0.355 0.259 0.373 0.391 0.346 0.446 0.423 0.311 0.296 0.469 0.513 0.347 0.440 0.280 0.685 0.492 0.708 0.779 0.797 0.646 0.410 0.726 0.888 0.906 0.854 0.709 0.391 0.903 0.764 0.583 0.718 Education index 0.347 0.602 0.535 0.400 0.638 0.655 0.457 0.522 0.560 0.380 0.296 0.423 0.435 0.282 0.267 0.421 0.255 0.381 0.725 0.519 0.687 0.836 0.873 0.598 0.571 0.938 0.912 0.961 0.922 0.738 0.516 0.937 0.843 0.589 0.750 GDP index 0.524 0.416 0.526 0.406 0.317 0.388 0.468 0.325 0.328 0.393 0.444 0.418 0.421 0.390 0.343 0.353 0.417 0.348 0.662 0.452 0.702 0.699 0.740 0.589 0.500 0.761 0.947 0.972 0.915 0.649 0.402 0.968 0.719 0.539 0.761 GDP per capita (PPP US$) rank minus HDI rank c -30 -1 -33 -2 13 3 -17 9 7 -5 -17 -13 -16 -8 -1 -4 -17 -5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. j. In the absence of recent data, estimates from UNESCO Institute for Statistics 2003, based on outdated census or survey information, were used and should be interpreted with caution: Bahamas 95.8, Barbados 99.7, Comoros 56.8, Djibouti 70.3, Eritrea 60.5, Fiji 94.4, Gambia 42.5, Guinea-Bissau 44.8, Guyana 99.0, Haiti 54.8, Hong Kong, China (SAR) 94.6, Hungary 99.4, Lebanon 88.3, Poland 99.8 and Uzbekistan 99.4. k. Data are from national sources. l. UNESCO Institute for Statistics estimates based on its Global age-specific literacy projections model, April 2007. m. Heston, Summers and Aten 2006. Data differ from the standard definition. n. World Bank estimate based on regression. o. Efforts to produce a more accurate estimate are ongoing (see Readers guide and notes to tables for details). A preliminary estimate of 6,000 (PPP US$) was used. p. Data are from the Secretariat of the Organization of Eastern Caribbean States, based on national sources. q. Data are from the Secretariat of the Caribbean Community, based on national sources. r. Because the combined gross enrolment ratio was unavailable, the following HDRO estimates were used: Antigua and Barbuda 76, Bhutan 52, Ecuador 75, Haiti 53 and Turkmenistan 73. s. UNDP 2007. t. World Bank 2006. u. World Bank estimate based on a bilateral comparison between China and the United States (Ruoen and Kai 1995). v. UNICEF 2004. w. Data refer to 18 of the 25 states of the country only. x. In the absence of an estimate of GDP per capita (PPP US$), the HDRO estimate of 2,056 (PPP US$) was used, derived from the value of GDP in US$ and the weighted average ratio of PPP US$ to US$ in the Arab States. y. Heston, Summers and Aten 2001. Data differ from the standard definition. z. In the absence of an estimate of GDP per capita (PPP US$), the HDRO estimate of 3,413 (PPP US$) was used, derived from the value of GDP per capita in PPP US$ estimated by Heston, Summers and Aten 2006 adjusted to reflect the latest population estimates from UN 2007e. aa. Data refer to North Sudan only. ab. UNDP 2006. ac. For the purposes of calculating the HDI, a national estimate of 1,033 (PPP US$) was used. SOURCES Column 1: calculated on the basis of data in columns 68; see Technical note 1 for details. Column 2: UN 2007e, unless otherwise specified. Column 3: UNESCO Institute for Statistics 2007a, unless otherwise specified. Column 4: UNESCO Institute for Statistics 2007c, unless otherwise specified. Column 5: World Bank 2007b, unless otherwise specified; aggregates calculated for the HDRO by the World Bank. Column 6: calculated on the basis of data in column 2. Column 7: calculated on the basis of data in columns 3 and 4. Column 8: calculated on the basis of data in column 5. Column 9: calculated on the basis of data in columns 1 and 5. Human development indicators 33 232 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 Assignment #3 Gender-related Development Index ISS315 - PAGE 34 ASSIGNMENT # 3 There are a number of indicators one can use to measure gender inequality. In the attached data set, Life Expectancy, Adult Literacy Rates, School Enrollment radios, and Estimated Income are used by UNDP to measure gender inequality. Use this data set and answer the following questions: Gender-related Development Index TOP FIVE NATIONS LIFE EXPECTANCY ADULT LITERACY RATES SCHOOL ENROLLMENT ESTIMATED INCOME Men Women Men Women Men Women Men Women ISS315 - PAGE 35 BOTTOM FIVE NATIONS LIFE EXPECTANCY ADULT LITERACY RATES SCHOOL ENROLLMENT ESTIMATED INCOME Men Women Men Women Men Women Men Women ISS315 - PAGE 36 TABLE 28 . . . and achieving equality for all women and men Gender-related development index Gender-related development index (GDI) HDI rank HIGH HUMAN DEVELOPMENT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 Iceland Norway Australia Canada Ireland Sweden Switzerland Japan Netherlands France Finland United States Spain Denmark Austria United Kingdom Belgium Luxembourg New Zealand Italy Hong Kong, China (SAR) Germany Israel Greece Singapore Korea (Republic of) Slovenia Cyprus Portugal Brunei Darussalam Barbados Czech Republic Kuwait Malta Qatar Hungary Poland Argentina United Arab Emirates Chile Bahrain Slovakia Lithuania Estonia Latvia Uruguay Croatia Costa Rica Bahamas Seychelles Cuba Mexico Bulgaria 1 3 2 4 15 5 9 13 6 7 8 16 12 11 19 10 14 23 18 17 22 20 21 24 .. 26 25 27 28 31 30 29 32 33 37 34 35 36 43 40 42 39 38 41 44 45 46 47 48 .. 49 51 50 0.962 0.957 0.960 0.956 0.940 0.955 0.946 0.942 0.951 0.950 0.947 0.937 0.944 0.944 0.934 0.944 0.940 0.924 0.935 0.936 0.926 0.931 0.927 0.922 .. 0.910 0.914 0.899 0.895 0.886 0.887 0.887 0.884 0.873 0.863 0.872 0.867 0.865 0.855 0.859 0.857 0.860 0.861 0.858 0.853 0.849 0.848 0.842 0.841 .. 0.839 0.820 0.823 Rank Value Life expectancy at birth (years) 2005 Female 83.1 82.2 83.3 82.6 80.9 82.7 83.7 85.7 81.4 83.7 82.0 80.4 83.8 80.1 82.2 81.2 81.8 81.4 81.8 83.2 84.9 81.8 82.3 80.9 81.4 81.5 81.1 81.5 80.9 79.3 79.3 79.1 79.6 81.1 75.8 77.0 79.4 78.6 81.0 81.3 77.0 78.2 78.0 76.8 77.3 79.4 78.8 80.9 75.0 .. 79.8 78.0 76.4 Male 79.9 77.3 78.5 77.9 76.0 78.3 78.5 78.7 76.9 76.6 75.6 75.2 77.2 75.5 76.5 76.7 75.8 75.4 77.7 77.2 79.1 76.2 78.1 76.7 77.5 74.3 73.6 76.6 74.5 74.6 73.6 72.7 75.7 76.8 74.6 68.8 71.0 71.1 76.8 75.3 73.9 70.3 66.9 65.5 66.5 72.2 71.8 76.2 69.6 .. 75.8 73.1 69.2 Adult literacy rate a (% aged 15 and older) 1995 2005 Female .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e 98.0 97.3 j .. e 97.7 j 94.2 88.6 .. e 99.6 f,k 95.1 92.0 k 90.2 99.7 f,j .. e 91.0 89.2 88.6 .. e .. e 97.2 87.8 k 95.6 83.6 .. e 99.6 f 99.8 f 99.7 f 97.3 97.1 f 95.1 95.0 j 92.3 99.8 f 90.2 97.7 Male .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e .. e 98.8 97.3 j .. e 97.7 j 97.8 96.6 .. e 99.7 f,k 98.6 95.8 k 95.2 99.7 f,j .. e 94.4 86.4 89.1 .. e .. e 97.2 89.0 k 95.8 88.6 .. e 99.6 f 99.8 f 99.8 f 96.2 99.3 f 94.7 95.0 j 91.4 99.8 f 93.2 98.7 Combined gross enrolment ratio for primary, secondary and tertiary education b (%) 2005 Female 101 f 103 f 114 f 101 f,g 102 f 100 f 83 85 98 99 105 f 98 101 f 107 f 93 96 97 85 i 115 f 93 73 87 92 101 f .. 89 f 99 78 93 79 94 g 84 79 81 85 93 91 94 g 68 g 82 90 80 97 99 97 95 g 75 g 74 71 84 92 76 81 Male 90 f 95 f 112 f 98 f,g 98 f 91 f 88 87 99 94 98 f 89 95 f 99 f 91 90 94 84 i 102 f 88 79 88 87 97 f .. 102 f 90 77 87 76 84 g 82 71 81 71 86 84 86 g 54 g 84 82 77 87 86 83 83 g 72 g 72 71 81 83 75 82 Estimated earned income c (PPP US$) 2005 Female 28,637 f 30,749 f 26,311 25,448 f,h 21,076 f 29,044 25,056 f 17,802 f 25,625 23,945 26,795 25,005 f,h 18,335 h 28,766 18,397 f 26,242 f 22,182 f 20,446 f 20,666 18,501 h 22,433 f 21,823 20,497 h 16,738 20,044 12,531 17,022 h 16,805 l 15,294 15,658 h,m 12,868 h,m 13,992 12,623 h 12,834 9,211 h,m 14,058 10,414 h 10,063 h 8,329 h 6,871 h 10,496 11,777 h 12,000 12,112 h 10,951 7,203 h 10,587 6,983 14,656 h,l .. h 4,268 h,m 6,039 7,176 Male 40,000 f 40,000 f 37,414 40,000 f,h 40,000 f 36,059 40,000 f 40,000 f 39,845 37,169 37,739 40,000 f,h 36,324 h 39,288 40,000 f 40,000 f 40,000 f 40,000 f 29,479 39,163 h 40,000 f 37,461 31,345 h 30,184 39,150 31,476 27,779 h 27,808 l 25,881 37,506 h,m 20,309 h,m 27,440 36,403 h 25,623 37,774 h,m 22,098 17,493 h 18,686 h 33,555 h 17,293 h 29,796 20,218 h 17,349 19,430 h 16,842 12,890 h 15,687 13,271 20,803 h,l .. h 9,489 h,m 15,680 11,010 HDI rank minus GDI rank d Human development indicators 37 0 -1 1 0 -10 1 -2 -5 3 3 3 -4 1 3 -4 6 3 -5 1 3 -1 2 2 0 .. -1 1 0 0 -2 0 2 0 0 -3 1 1 1 -5 -1 -2 2 4 2 0 0 0 0 0 .. 0 -1 1 326 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Gender-related development index (GDI) HDI rank 54 Saint Kitts and Nevis 55 Tonga 56 Libyan Arab Jamahiriya 57 Antigua and Barbuda 58 Oman 59 Trinidad and Tobago 60 Romania 61 Saudi Arabia 62 Panama 63 Malaysia 64 Belarus 65 Mauritius 66 Bosnia and Herzegovina 67 Russian Federation 68 Albania 69 Macedonia (TFYR) 70 Brazil MEDIUM HUMAN DEVELOPMENT 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 Dominica Saint Lucia Kazakhstan Venezuela (Bolivarian Republic of) Colombia Ukraine Samoa Thailand Dominican Republic Belize China Grenada Armenia Turkey Suriname Jordan Peru Lebanon Ecuador Philippines Tunisia Fiji Saint Vincent and the Grenadines Iran (Islamic Republic of) Paraguay Georgia Guyana Azerbaijan Sri Lanka Maldives Jamaica Cape Verde El Salvador Algeria Viet Nam Occupied Palestinian Territories Rank .. 53 62 .. 67 56 54 70 55 58 57 63 .. 59 61 64 60 .. .. 65 68 66 69 72 71 74 52 73 .. 75 79 78 80 76 81 .. 77 83 82 .. 84 86 .. 88 87 89 85 90 93 92 95 91 .. Value .. 0.814 0.797 .. 0.788 0.808 0.812 0.783 0.810 0.802 0.803 0.796 .. 0.801 0.797 0.795 0.798 .. .. 0.792 0.787 0.789 0.785 0.776 0.779 0.773 0.814 0.776 .. 0.772 0.763 0.767 0.760 0.769 0.759 .. 0.768 0.750 0.757 .. 0.750 0.744 .. 0.742 0.743 0.735 0.744 0.732 0.723 0.726 0.720 0.732 .. Life expectancy at birth (years) 2005 Female .. 73.8 76.3 .. 76.7 71.2 75.6 74.6 77.8 76.1 74.9 75.8 77.1 72.1 79.5 76.3 75.5 .. 75.0 71.5 76.3 76.0 73.6 74.2 74.5 74.8 79.1 74.3 n 69.8 74.9 73.9 73.0 73.8 73.3 73.7 77.7 73.3 75.6 70.6 73.2 71.8 73.4 74.5 68.1 70.8 75.6 67.6 74.9 73.8 74.3 73.0 75.7 74.4 Male .. 71.8 71.1 .. 73.6 67.2 68.4 70.3 72.7 71.4 62.7 69.1 71.8 58.6 73.1 71.4 68.1 .. 71.3 60.5 70.4 68.7 62.0 67.8 65.0 68.6 73.1 71.0 n 66.5 68.2 69.0 66.4 70.3 68.2 69.4 71.8 68.9 71.5 66.1 69.0 68.7 69.2 66.7 62.4 63.5 67.9 66.6 69.6 67.5 68.2 70.4 71.9 71.3 Adult literacy rate a (% aged 15 and older) 1995 2005 Female .. 99.0 74.8 k .. 73.5 97.8 k 96.3 76.3 91.2 85.4 99.4 f 80.5 94.4 f 99.2 f 98.3 f 94.1 88.8 .. .. 99.3 f 92.7 92.9 99.2 f 98.3 k 90.5 87.2 94.6 j 86.5 .. 99.2 f 79.6 87.2 87.0 82.5 93.6 j 89.7 93.6 65.3 95.9 j .. 76.8 92.7 k .. 99.2 f,j 98.2 f 89.1 o 96.4 85.9 o 75.5 k 79.2 k 60.1 86.9 88.0 Male .. 98.8 92.8 k .. 86.9 98.9 k 98.4 87.5 92.5 92.0 99.8 f 88.2 99.0 f 99.7 f 99.2 f 98.2 88.4 .. .. 99.8 f 93.3 92.8 99.7 f 98.9 k 94.9 86.8 94.6 j 95.1 .. 99.7 f 95.3 92.0 95.2 93.7 93.6 j 92.3 91.6 83.4 95.9 j .. 88.0 94.3 k .. 99.2 f,j 99.5 f 92.3 o 96.2 74.1 o 87.8 k 82.1 k 79.6 93.9 96.7 Combined gross enrolment ratio for primary, secondary and tertiary education b (%) 2005 Female 74 81 97 g .. 67 66 79 76 83 77 g 91 75 .. 93 68 g 71 89 g 84 78 97 76 g 77 87 76 72 78 g 81 69 74 74 64 82 79 87 86 .. 83 79 76 70 73 70 g 77 87 66 64 g 66 82 66 70 74 62 84 Male 72 79 91 g .. 67 64 75 76 76 72 g 87 76 .. 85 69 g 69 86 g 78 72 91 73 g 74 86 72 71 70 g 83 70 72 68 73 72 77 85 83 .. 79 74 74 68 73 69 g 75 84 68 63 g 65 74 67 70 73 66 81 28 HDI rank minus GDI rank d .. -1 -9 .. -13 -1 2 -13 3 1 3 -2 .. 3 2 0 5 .. .. 1 -1 2 0 -2 0 -2 21 1 .. 0 -3 -1 -2 3 -1 .. 4 -1 1 .. 0 -1 .. -2 0 -1 4 0 -2 0 -2 3 .. Estimated earned income c (PPP US$) 2005 Female .. h,l 5,243 h 4,054 h,m .. h,l 4,516 h,l 9,307 h 7,443 4,031 h 5,537 5,751 6,236 7,407 h 2,864 h,m 8,476 h 3,728 h 4,676 h 6,204 .. h,l 4,501 h,l 6,141 4,560 h 5,680 4,970 3,338 h 6,695 4,907 h 4,022 h 5,220 h .. h,l 3,893 h 4,385 4,426 h 2,566 4,269 h 2,701 h 3,102 h 3,883 3,748 h 3,928 h 4,449 h 4,475 h 2,358 1,731 2,665 h 3,960 h 2,647 3,992 h,m 3,107 h 3,087 h 3,043 3,546 h 2,540 h .. Male .. h,l 10,981 h 13,460 h,m .. h,l 23,880 h,l 20,053 h 10,761 25,678 h 9,636 15,861 9,835 18,098 h 4,341 h,m 13,581 h 6,930 h 9,734 h 10,664 .. h,l 8,805 h,l 9,723 8,683 h 8,966 9,067 8,797 h 10,732 11,465 h 10,117 h 8,213 h .. h,l 6,150 h 12,368 11,029 h 8,270 7,791 h 8,585 h 5,572 h 6,375 12,924 h 8,103 h 8,722 h 11,363 h 6,892 5,188 6,467 h 6,137 h 6,479 7,946 h,m 5,503 h 8,756 h 7,543 10,515 h 3,604 h .. Human development indicators 38 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 327 TABLE 28 Gender-related development index Gender-related development index (GDI) HDI rank 107 Indonesia 108 Syrian Arab Republic 109 Turkmenistan 110 Nicaragua 111 Moldova 112 Egypt 113 Uzbekistan 114 Mongolia 115 Honduras 116 Kyrgyzstan 117 Bolivia 118 Guatemala 119 Gabon 120 Vanuatu 121 South Africa 122 Tajikistan 123 Sao Tome and Principe 124 Botswana 125 Namibia 126 Morocco 127 Equatorial Guinea 128 India 129 Solomon Islands 130 Lao People's Democratic Republic 131 Cambodia 132 Myanmar 133 Bhutan 134 Comoros 135 Ghana 136 Pakistan 137 Mauritania 138 Lesotho 139 Congo 140 Bangladesh 141 Swaziland 142 Nepal 143 Madagascar 144 Cameroon 145 Papua New Guinea 146 Haiti 147 Sudan 148 Kenya 149 Djibouti 150 Timor-Leste 151 Zimbabwe 152 Togo 153 Yemen 154 Uganda 155 Gambia LOW HUMAN DEVELOPMENT 156 157 158 159 Senegal Eritrea Nigeria Tanzania (United Republic of) Rank 94 96 .. 99 97 .. 98 100 101 102 103 104 105 .. 107 106 110 109 108 112 111 113 .. 115 114 .. .. 116 117 125 118 119 120 121 123 128 122 126 124 .. 131 127 129 .. 130 134 136 132 133 135 137 139 138 Value 0.721 0.710 .. 0.696 0.704 .. 0.699 0.695 0.694 0.692 0.691 0.675 0.670 .. 0.667 0.669 0.637 0.639 0.645 0.621 0.631 0.600 .. 0.593 0.594 .. .. 0.554 0.549 0.525 0.543 0.541 0.540 0.539 0.529 0.520 0.530 0.524 0.529 .. 0.502 0.521 0.507 .. 0.505 0.494 0.472 0.501 0.496 0.492 0.469 0.456 0.464 Life expectancy at birth (years) 2005 Female 71.6 75.5 67.0 75.0 72.0 73.0 70.0 69.2 73.1 69.6 66.9 73.2 56.9 71.3 52.0 69.0 66.7 48.4 52.2 72.7 51.6 65.3 63.8 64.5 60.6 64.2 66.5 66.3 59.5 64.8 65.0 42.9 55.2 64.0 41.4 62.9 60.1 50.2 60.1 61.3 58.9 53.1 55.2 60.5 40.2 59.6 63.1 50.2 59.9 64.4 59.0 47.1 52.0 Male 67.8 71.8 58.5 69.0 64.7 68.5 63.6 62.8 65.8 61.7 62.6 66.2 55.6 67.5 49.5 63.8 63.0 47.6 50.9 68.3 49.1 62.3 62.2 61.9 55.2 57.6 63.1 62.0 58.7 64.3 61.5 42.1 52.8 62.3 40.4 62.1 56.7 49.4 54.3 57.7 56.0 51.1 52.6 58.9 41.4 56.0 60.0 49.1 57.7 60.4 54.0 46.0 50.0 Adult literacy rate a (% aged 15 and older) 1995 2005 Female 86.8 73.6 98.3 f 76.6 98.6 f,k 59.4 99.6 f,j 97.5 80.2 98.1 f 80.7 63.3 79.7 k .. 80.9 99.2 f 77.9 81.8 83.5 39.6 80.5 47.8 o .. 60.9 64.1 86.4 .. 63.9 j 49.8 35.4 43.4 90.3 79.0 k 40.8 78.3 34.9 65.3 59.8 50.9 56.5 j 51.8 o 70.2 79.9 j .. 86.2 k 38.5 34.7 k 57.7 49.9 j 29.2 71.5 j 60.1 k 62.2 Male 94.0 87.8 99.3 f 76.8 99.6 f,k 83.0 99.6 f,j 98.0 79.8 99.3 f 93.1 75.4 88.5 k .. 84.1 99.7 f 92.2 80.4 86.8 65.7 93.4 73.4 o .. 77.0 84.7 93.9 .. 63.9 j 66.4 64.1 59.5 73.7 90.5 k 53.9 80.9 62.7 76.5 77.0 63.4 56.5 j 71.1 o 77.7 79.9 j .. 92.7 k 68.7 73.1 k 76.8 49.9 j 51.1 71.5 j 78.2 k 77.5 Combined gross enrolment ratio for primary, secondary and tertiary education b (%) 2005 Female 67 63 .. 72 73 .. 72 g 83 74 80 84 g 64 68 g 61 77 g 64 65 70 66 55 52 g 60 46 56 56 51 .. 42 48 34 45 67 48 56 g 58 54 58 57 38 g .. 35 59 22 71 51 g 46 43 62 49 g 37 29 51 49 Male 70 67 .. 70 67 .. 75 g 72 68 76 90 g 70 72 g 66 77 g 77 65 69 63 62 64 g 68 50 67 64 48 .. 50 53 45 47 65 54 56 g 62 62 61 68 43 g .. 39 62 29 73 54 g 64 67 64 51 g 42 41 61 52 Estimated earned income c (PPP US$) 2005 Female 2,410 h 1,907 h 6,108 h,m 1,773 h 1,634 h 1,635 1,547 h 1,413 h 2,160 h 1,414 h 2,059 h 2,267 h 5,049 h 2,601 h 6,927 h 992 h 1,022 h 5,913 5,527 h 1,846 h 4,635 h,l 1,620 h 1,345 h 1,385 h 2,332 h .. 2,141 h,m 1,337 h 2,056 h 1,059 h 1,489 h 2,340 h 841 h 1,282 h 2,187 1,038 h 758 h 1,519 h 2,140 h 1,146 h 832 h 1,126 1,422 h .. h 1,499 h 907 h 424 h 1,199 h 1,327 h 1,256 h 689 652 h 627 h Male 5,280 h 5,684 h 9,596 h,m 5,577 h 2,608 h 7,024 2,585 h 2,799 h 4,680 h 2,455 h 3,584 h 6,990 h 8,876 h 3,830 h 15,446 h 1,725 h 3,357 h 19,094 9,679 h 7,297 h 10,814 h,l 5,194 h 2,672 h 2,692 h 3,149 h .. 4,463 h,m 2,643 h 2,893 h 3,607 h 2,996 h 4,480 h 1,691 h 2,792 h 7,659 2,072 h 1,090 h 3,086 h 2,960 h 2,195 h 3,317 h 1,354 2,935 h .. h 2,585 h 2,119 h 1,422 h 1,708 h 2,525 h 2,346 h 1,544 1,592 h 863 h HDI rank minus GDI rank d 1 0 .. -2 1 .. 1 0 0 0 0 0 0 .. -1 1 -2 0 2 -1 1 0 .. -1 1 .. .. 0 0 -7 1 1 1 1 0 -4 3 0 3 .. -3 2 1 .. 1 -2 -3 2 2 1 0 -1 1 Human development indicators 39 328 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Gender-related development index (GDI) HDI rank 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 Guinea Rwanda Angola Benin Malawi Zambia Cte d'Ivoire Burundi Congo (Democratic Republic of the) Ethiopia Chad Central African Republic Mozambique Mali Niger Guinea-Bissau Burkina Faso Sierra Leone Rank 141 140 142 145 143 144 146 147 148 149 152 153 150 151 155 156 154 157 Value 0.446 0.450 0.439 0.422 0.432 0.425 0.413 0.409 0.398 0.393 0.370 0.368 0.373 0.371 0.355 0.355 0.364 0.320 Life expectancy at birth (years) 2005 Female 56.4 46.7 43.3 56.5 46.7 40.6 48.3 49.8 47.1 53.1 51.8 45.0 43.6 55.3 54.9 47.5 52.9 43.4 Male 53.2 43.6 40.1 54.1 46.0 40.3 46.5 47.1 44.4 50.5 49.0 42.3 42.0 50.8 56.7 44.2 49.8 40.2 Adult literacy rate a (% aged 15 and older) 1995 2005 Female 18.1 59.8 54.2 23.3 54.0 59.8 38.6 52.2 54.1 22.8 12.8 33.5 25.0 15.9 15.1 60.0 16.6 24.2 Male 42.6 71.4 82.9 47.9 74.9 76.3 60.8 67.3 80.9 50.0 40.8 64.8 54.8 32.7 42.9 60.0 j 31.4 46.7 Combined gross enrolment ratio for primary, secondary and tertiary education b (%) 2005 Female 38 51 24 g 42 62 58 32 g 34 28 g 36 28 23 g 48 31 19 29 g 25 38 g Male 52 51 28 g 59 64 63 47 g 42 39 g 48 47 36 g 58 42 26 45 g 33 52 g 28 HDI rank minus GDI rank d -1 1 0 -2 1 1 0 0 0 0 -2 -2 2 2 -1 -1 2 0 Estimated earned income c (PPP US$) 2005 Female 1,876 h 1,031 h 1,787 h 732 h 565 h 725 h 795 h 611 h 488 h 796 h 1,126 h 933 h 1,115 h 833 h 561 h 558 h 966 h 507 h Male 2,734 h 1,392 h 2,898 h 1,543 h 771 h 1,319 h 2,472 h 791 h 944 h 1,316 h 1,735 h 1,530 h 1,378 h 1,234 h 991 h 1,103 h 1,458 h 1,114 h NOTES a. Data refer to national literacy estimates from censuses or surveys conducted between 1995 and 2005, unless otherwise specified. Due to differences in methodology and timeliness of underlying data, comparisons across countries and over time should be made with caution. For more details, see http://www.uis.unesco.org/. b. Data for some countries may refer to national or UNESCO Institute for Statistics estimates. For details, see http://www.uis.unesco.org/. c. Because of the lack of gender-disaggregated income data, female and male earned income are crudely estimated on the basis of data on the ratio of the female nonagricultural wage to the male nonagricultural wage, the female and male shares of the economically active population, the total female and male population and GDP per capita in PPP US$ (see Technical note 1). The wage ratios used in this calculation are based on data for the most recent year available between 1996 and 2005. d. The HDI ranks used in this calculation are recalculated for the 157 countries with a GDI value. A positive figure indicates that the GDI rank is higher than the HDI rank, a negative the opposite. e. For the purposes of calculating the GDI, a value of 99.0 % was applied. f. For the purpose of calculating the GDI, the female and male values appearing in this table were scaled downward to reflect the maximum values for adult literacy (99%), gross enrolment ratios (100%), and GDP per capita ($40,000). For more details, see Technical note 1. g. Data refer to an earlier year than that specified. h. No wage data are available. For the purposes of calculating the estimated female and male earned income, a value of 0.75 was used for the ratio of the female nonagricultural wage to the male nonagricultural wage. i. Statec. 2006. j. In the absence of recent data, estimates from UNESCO Institute for Statistics 2003, based on k. l. m. n. o. outdated census or survey information were used, and should be interpreted with caution. UNESCO Institute for Statistics estimates based on its Global age-specific literacy projections model. Data from earlier years were adjusted to reflect their values in 2005 prices. Heston, Alan, Robert Summers and Bettina Aten. 2006. Data may differ from the standard definition. For statistical purposes, the data for China do not include Hong Kong and Macao, SARs of China. Data refer to years or periods other than those specified in the column heading, differ from the standard definition or refer to only part of a country. SOURCES Column 1: determined on the basis of the GDI values in column 2. Column 2: calculated on the basis of data in columns 310; see Technical note 1 for details. Columns 3 and 4: UN 2007e. Columns 5 and 6: UNESCO Institute for Statistics 2007a. Columns 7 and 8: UNESCO Institute for Statistics 2007c. Columns 9 and 10: calculated on the basis of data on GDP per capita (PPP US$) and population data from World Bank 2007b unless otherwise specified; data on wages from ILO 2007b; data on the economically active population from ILO 2005. Column 11: calculated on the basis of recalculated HDI ranks and GDI ranks in column 1. GDI ranks for 157 countries and areas 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Iceland Australia Norway Canada Sweden Netherlands France Finland Switzerland United Kingdom Denmark Spain Japan Belgium Ireland United States Italy New Zealand Austria Germany Israel Hong Kong, China (SAR) Luxembourg Greece Slovenia Korea (Republic of) Cyprus 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Portugal Czech Republic Barbados Brunei Darussalam Kuwait Malta Hungary Poland Argentina Qatar Lithuania Slovakia Chile Estonia Bahrain United Arab Emirates Latvia Uruguay Croatia Costa Rica Bahamas Cuba Bulgaria Mexico Belize Tonga Romania 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Panama Trinidad and Tobago Belarus Malaysia Russian Federation Brazil Albania Libyan Arab Jamahiriya Mauritius Macedonia (TFYR) Kazakhstan Colombia Oman Venezuela (Bolivarian Republic of) Ukraine Saudi Arabia Thailand Samoa China Dominican Republic Armenia Peru Philippines Suriname Turkey Jordan 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 Lebanon Fiji Tunisia Iran (Islamic Republic of) Maldives Paraguay Azerbaijan Guyana Sri Lanka Jamaica Viet Nam El Salvador Cape Verde Indonesia Algeria Syrian Arab Republic Moldova Uzbekistan Nicaragua Mongolia Honduras Kyrgyzstan Bolivia Guatemala Gabon Tajikistan South Africa 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 Namibia Botswana Sao Tome and Principe Equatorial Guinea Morocco India Cambodia Lao People's Democratic Republic Comoros Ghana Mauritania Lesotho Congo Bangladesh Madagascar Swaziland Papua New Guinea Pakistan Cameroon Kenya Nepal Djibouti Zimbabwe Sudan Uganda Gambia 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 Togo Senegal Yemen Eritrea Tanzania (United Republic of) Nigeria Rwanda Guinea Angola Malawi Zambia Benin Cte d'Ivoire Burundi Congo (Democratic Republic of the) Ethiopia Mozambique Mali Chad Central African Republic Burkina Faso Niger Guinea-Bissau Sierra Leone Human development indicators 40 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 329 Assignment #4 Gender Empowerment Index ISS315 - PAGE 41 ASSIGNMENT # 4 There are a number of indicators one can use to measure gender empowerment. The United Nations Development Program (UNDP) uses the number of seats occupied by women in parliament, number of female administrators and managers, number of female professionals and technical workers, and ratio of estimated female to male income as some of the indicators for this purpose. Use the attached data set, on these indicators and list the top five and bottom file nations. Gender-related Development Index TOP FIVE NATIONS Nation Seat in Parliament Held by Women Female Legislator, Senior Officials, and Managers Female Professional and Technical Workers Ratio of Female to Male Income HDI Rank 1 2 3 4 5 ISS315 - PAGE 42 BOTTOM FIVE NATIONS Nation Seat in Parliament Held by Women Female Legislator, Senior Officials, and Managers Female Professional and Technical Workers Ratio of Female to Male Income HDI Rank 1 2 3 4 5 ISS315 - PAGE 43 TABLE 29 . . . and achieving equality for all women and men Gender empowerment measure Gender empowerment measure (GEM) HDI rank HIGH HUMAN DEVELOPMENT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 Iceland Norway Australia Canada Ireland Sweden Switzerland Japan Netherlands France Finland United States Spain Denmark Austria United Kingdom Belgium Luxembourg New Zealand Italy Hong Kong, China (SAR) Germany Israel Greece Singapore Korea (Republic of) Slovenia Cyprus Portugal Brunei Darussalam Barbados Czech Republic Kuwait Malta Qatar Hungary Poland Argentina United Arab Emirates Chile Bahrain Slovakia Lithuania Estonia Latvia Uruguay Croatia Costa Rica Bahamas Seychelles Cuba Mexico Bulgaria 5 1 8 10 19 2 27 54 6 18 3 15 12 4 13 14 7 .. 11 21 .. 9 28 37 16 64 41 48 22 .. 30 34 .. 63 84 50 39 17 29 60 .. 33 25 31 38 59 40 24 20 .. 26 46 42 0.862 0.910 0.847 0.820 0.699 0.906 0.660 0.557 0.859 0.718 0.887 0.762 0.794 0.875 0.788 0.783 0.850 .. 0.811 0.693 .. 0.831 0.660 0.622 0.761 0.510 0.611 0.580 0.692 .. 0.649 0.627 .. 0.514 0.374 0.569 0.614 0.728 0.652 0.519 .. 0.630 0.669 0.637 0.619 0.525 0.612 0.680 0.696 .. 0.661 0.589 0.606 Rank Value MDG Seats in parliament held by women a (% of total) 31.7 37.9 28.3 24.3 14.2 47.3 24.8 11.1 36.0 13.9 42.0 16.3 30.5 36.9 31.0 19.3 35.7 23.3 32.2 16.1 .. 30.6 14.2 13.0 24.5 13.4 10.8 14.3 21.3 .. e 17.6 15.3 3.1 f 9.2 0.0 10.4 19.1 36.8 22.5 12.7 13.8 19.3 24.8 21.8 19.0 10.8 21.7 38.6 22.2 23.5 36.0 21.5 22.1 Female legislators, senior officials and managers b (% of total) 27 30 37 36 31 30 8 10 d 26 37 30 42 32 25 27 34 32 .. 36 32 27 37 26 26 26 8 33 15 34 26 43 30 .. 20 8 35 33 33 8 25 d .. 31 43 37 42 40 24 25 46 .. 34 d 29 34 Female professional and technical workers b (% of total) 56 50 56 56 52 51 22 46 d 50 47 55 56 48 53 49 47 49 .. 53 46 40 50 54 49 44 39 57 45 50 44 52 52 .. 38 24 62 61 53 25 52 d .. 58 67 70 65 54 50 40 60 .. 62 d 42 60 Ratio of estimated female to male earned income c 0.72 0.77 0.70 0.64 0.53 0.81 0.63 0.45 0.64 0.64 0.71 0.63 0.50 0.73 0.46 0.66 0.55 0.51 0.70 0.47 0.56 0.58 0.65 0.55 0.51 0.40 0.61 0.60 0.59 0.42 0.63 0.51 0.35 0.50 0.24 0.64 0.60 0.54 0.25 0.40 0.35 0.58 0.69 0.62 0.65 0.56 0.67 0.53 0.70 .. 0.45 0.39 0.65 Human development indicators 44 330 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Gender empowerment measure (GEM) HDI rank 54 Saint Kitts and Nevis 55 Tonga 56 Libyan Arab Jamahiriya 57 Antigua and Barbuda 58 Oman 59 Trinidad and Tobago 60 Romania 61 Saudi Arabia 62 Panama 63 Malaysia 64 Belarus 65 Mauritius 66 Bosnia and Herzegovina 67 Russian Federation 68 Albania 69 Macedonia (TFYR) 70 Brazil MEDIUM HUMAN DEVELOPMENT 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 Dominica Saint Lucia Kazakhstan Venezuela (Bolivarian Republic of) Colombia Ukraine Samoa Thailand Dominican Republic Belize China Grenada Armenia Turkey Suriname Jordan Peru Lebanon Ecuador Philippines Tunisia Fiji Saint Vincent and the Grenadines Iran (Islamic Republic of) Paraguay Georgia Guyana Azerbaijan Sri Lanka Maldives Jamaica Cape Verde El Salvador Algeria Viet Nam Occupied Palestinian Territories Rank .. .. .. .. 80 23 68 92 49 65 .. 51 .. 71 .. 35 70 .. 66 74 56 69 75 .. 73 53 62 57 .. .. 90 .. .. 32 .. 43 45 .. .. .. 87 78 79 .. .. 85 76 .. .. 58 .. 52 .. Value .. .. .. .. 0.391 0.685 0.497 0.254 0.574 0.504 .. 0.562 .. 0.489 .. 0.625 0.490 .. 0.502 0.469 0.542 0.496 0.462 .. 0.472 0.559 0.517 0.534 .. .. 0.298 .. .. 0.636 .. 0.600 0.590 .. .. .. 0.347 0.428 0.414 .. .. 0.369 0.437 .. .. 0.529 .. 0.561 .. MDG Seats in parliament held by women a (% of total) 0.0 3.3 7.7 13.9 7.8 25.4 10.7 0.0 16.7 13.1 29.8 17.1 14.0 8.0 7.1 28.3 9.3 12.9 10.3 g 8.6 18.6 9.7 8.7 6.1 8.7 17.1 11.9 20.3 28.6 9.2 4.4 25.5 7.9 29.2 4.7 25.0 22.1 19.3 .. h 18.2 4.1 9.6 9.4 29.0 11.3 4.9 12.0 13.6 15.3 16.7 6.2 25.8 .. Female legislators, senior officials and managers b (% of total) .. .. .. 45 9 43 29 31 43 23 .. 25 .. 39 .. 29 34 48 55 38 27 d 38 d 38 .. 29 32 41 17 .. .. 7 .. .. 34 .. 35 58 .. .. .. 16 23 26 .. .. 21 15 .. .. 33 .. 22 11 Female professional and technical workers b (% of total) .. .. .. 55 33 53 57 6 51 40 .. 43 .. 65 .. 52 52 55 53 67 61 d 50 d 64 .. 54 51 50 52 .. .. 32 .. .. 46 .. 48 61 .. .. .. 34 54 d 62 .. .. 46 40 .. .. 45 32 51 35 29 .. 0.48 0.30 .. 0.19 0.46 0.69 0.16 0.57 0.36 0.63 0.41 .. 0.62 0.54 0.48 0.58 .. 0.51 0.63 0.53 0.63 0.55 0.38 0.62 0.43 0.40 0.64 .. 0.63 0.35 0.40 0.31 0.55 0.31 0.56 0.61 0.29 0.48 0.51 0.39 0.34 0.33 0.41 0.65 0.41 0.50 0.56 0.35 0.40 0.34 0.70 .. Ratio of estimated female to male earned income c Human development indicators 45 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 331 TABLE 29 Gender empowerment measure Gender empowerment measure (GEM) HDI rank 107 Indonesia 108 Syrian Arab Republic 109 Turkmenistan 110 Nicaragua 111 Moldova 112 Egypt 113 Uzbekistan 114 Mongolia 115 Honduras 116 Kyrgyzstan 117 Bolivia 118 Guatemala 119 Gabon 120 Vanuatu 121 South Africa 122 Tajikistan 123 Sao Tome and Principe 124 Botswana 125 Namibia 126 Morocco 127 Equatorial Guinea 128 India 129 Solomon Islands 130 Lao People's Democratic Republic 131 Cambodia 132 Myanmar 133 Bhutan 134 Comoros 135 Ghana 136 Pakistan 137 Mauritania 138 Lesotho 139 Congo 140 Bangladesh 141 Swaziland 142 Nepal 143 Madagascar 144 Cameroon 145 Papua New Guinea 146 Haiti 147 Sudan 148 Kenya 149 Djibouti 150 Timor-Leste 151 Zimbabwe 152 Togo 153 Yemen 154 Uganda 155 Gambia LOW HUMAN DEVELOPMENT 156 157 158 159 Senegal Eritrea Nigeria Tanzania (United Republic of) Rank .. .. .. .. 55 91 .. 77 47 89 67 .. .. .. .. .. .. 61 36 88 .. .. .. .. 83 .. .. .. .. 82 .. .. .. 81 .. 86 .. .. .. .. .. .. .. .. .. .. 93 .. .. .. .. .. 44 Value .. .. .. .. 0.547 0.263 .. 0.429 0.589 0.302 0.500 .. .. .. .. .. .. 0.518 0.623 0.325 .. .. .. .. 0.377 .. .. .. .. 0.377 .. .. .. 0.379 .. 0.351 .. .. .. .. .. .. .. .. .. .. 0.129 .. .. .. .. .. 0.597 MDG Seats in parliament held by women a (% of total) 11.3 12.0 16.0 18.5 21.8 3.8 16.4 6.6 23.4 0.0 14.6 8.2 13.7 3.8 32.8 i 19.6 7.3 11.1 26.9 6.4 18.0 9.0 0.0 25.2 11.4 .. j 2.7 3.0 10.9 20.4 17.6 25.0 10.1 15.1 k 16.8 17.3 l 8.4 8.9 0.9 6.3 16.4 7.3 10.8 25.3 m 22.2 8.6 0.7 29.8 9.4 19.2 22.0 .. 30.4 Female legislators, senior officials and managers b (% of total) .. .. .. .. 39 9 .. 50 41 d 25 36 .. .. .. .. .. .. 33 30 12 .. .. .. .. 14 .. .. .. .. 2 .. .. .. 23 .. 8 .. .. .. .. .. .. .. .. .. .. 4 .. .. .. .. .. 49 Female professional and technical workers b (% of total) .. 40 d .. .. 66 30 .. 54 52 d 57 40 .. .. .. .. .. .. 51 55 35 .. .. .. .. 33 .. .. .. .. 26 .. .. .. 12 .. 19 .. .. .. .. .. .. .. .. .. .. 15 .. .. .. .. .. 32 Ratio of estimated female to male earned income c 0.46 0.34 0.64 0.32 0.63 0.23 0.60 0.50 0.46 0.58 0.57 0.32 0.57 0.68 0.45 0.57 0.30 0.31 0.57 0.25 0.43 0.31 0.50 0.51 0.74 .. .. 0.51 0.71 0.29 0.50 0.52 0.50 0.46 0.29 0.50 0.70 0.49 0.72 0.52 0.25 0.83 0.48 .. 0.58 0.43 0.30 0.70 0.53 0.54 0.45 0.41 0.73 Human development indicators 46 332 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Gender empowerment measure (GEM) HDI rank 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 Guinea Rwanda Angola Benin Malawi Zambia Cte d'Ivoire Burundi Congo (Democratic Republic of the) Ethiopia Chad Central African Republic Mozambique Mali Niger Guinea-Bissau Burkina Faso Sierra Leone Rank .. .. .. .. .. .. .. .. .. 72 .. .. .. .. .. .. .. .. Value .. .. .. .. .. .. .. .. .. 0.477 .. .. .. .. .. .. .. .. MDG Seats in parliament held by women a (% of total) 19.3 45.3 15.0 8.4 13.6 14.6 8.5 31.7 7.7 21.4 6.5 10.5 34.8 10.2 12.4 14.0 11.7 14.5 Female legislators, senior officials and managers b (% of total) .. .. .. .. .. .. .. .. .. 20 .. .. .. .. .. .. .. .. Female professional and technical workers b (% of total) .. .. .. .. .. .. .. .. .. 30 .. .. .. .. .. .. .. .. NOTES a. Data are as of 31 May 2007, unless otherwise specified. Where there are lower and upper houses, data refer to the weighted average of women's shares of seats in both houses. b. Data refer to the most recent year available between 1994 and 2005. Estimates for countries that have implemented the International Standard Classification of Occupations (ISCO-88) are not strictly comparable with those for countries using the previous classification (ISCO-1968). c. Calculated on the basis of data in columns 9 and 10 in Table 27. Estimates are based on data for the most recent year available between 1996 and 2005. Following the methodology implemented in the calculation of the GDI, the income component of the GEM has been scaled downward for countries whose income exceeds the maximum goalpost GDP per capita value of 40,000 (PPP US$). For more details, see Technical note 1. d. Data follow the ISCO-1968 classification. e. Brunei Darussalam does not currently have a parliament. f. No woman candidate was elected in the 2006 elections. One woman was appointed to the 16-member cabinet sworn in July 2006. A new cabinet sworn in March 2007 included two women. As cabinet ministers also sit in parliament, there are two women out of a total of 65 members. g. No woman candidate was elected in the 2006 elections. However one woman was appointed Speaker of the House and therefore became a member of the House. h. Parliament has been dissolved or suspended for an indefinite period. i. The figures on the distribution of seats do not include the 36 special rotating delegates appointed on an ad hoc basis. All percentages given are therefore calculated on the basis of the 54 permanent seats. j. The parliament elected in 1990 has never been convened nor authorized to sit, and many of its members were detained or forced into exile. k. In 2004, the number of seats in parliament was raised from 300 to 345, with the additional 45 seats reserved for women. These reserved seats were filled in September and October 2005, being allocated to political parties in proportion to their share of the national vote received in the 2001 election. I. A transitional assembly was established in January 2007. Elections for the constituent assembly will be held in 2007. m. The purpose of the elections held on 30 August 2001 was to elect the members of the constituent assembly of Timor-Leste. This body became the national parliament on 20 May 2002, the date on which the country became independent, without any new elections. 29 0.69 0.74 0.62 0.47 0.73 0.55 0.32 0.77 0.52 0.60 0.65 0.61 0.81 0.68 0.57 0.51 0.66 0.45 Ratio of estimated female to male earned income c SOURCES Column 1: determined on the basis of GEM values in column 2. Column 2: calculated on the basis of data in columns 36; see Technical note 1 for details. Column 3: calculated on the basis of data on parliamentary seats from IPU 2007c. Columns 4 and 5: calculated on the basis of occupational data from ILO 2007b. Column 6: calculated on the basis of data in columns 9 and 10 of Table 28. GEM ranks for 93 countries 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Norway Sweden Finland Denmark Iceland Netherlands Belgium Australia Germany Canada New Zealand Spain Austria United Kingdom United States Singapore Argentina 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 France Ireland Bahamas Italy Portugal Trinidad and Tobago Costa Rica Lithuania Cuba Switzerland Israel United Arab Emirates Barbados Estonia Peru 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Slovakia Czech Republic Macedonia (TFYR) Namibia Greece Latvia Poland Croatia Slovenia Bulgaria Ecuador Tanzania (United Republic of) Philippines Mexico Honduras Cyprus 49 50 51 52 53 54 55 56 Panama Hungary Mauritius Viet Nam Dominican Republic Japan Moldova Venezuela (Bolivarian Republic of) China El Salvador Uruguay Chile Botswana Belize Malta 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Korea (Republic of) Malaysia Saint Lucia Bolivia Romania Colombia Brazil Russian Federation Ethiopia Thailand Kazakhstan Ukraine Maldives Mongolia Paraguay Georgia Oman 81 82 83 84 85 86 87 88 89 90 91 92 93 Bangladesh Pakistan Cambodia Qatar Sri Lanka Nepal Iran (Islamic Republic of) Morocco Kyrgyzstan Turkey Egypt Saudi Arabia Yemen Human development indicators 57 58 59 60 61 62 63 47 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 333 Assignment #5 Women's Political Participation ISS315 - PAGE 48 ASSIGNMENT # 5 There are a number of indicators one can use to measure the level of political participation of women. United Nations Development Program (NDP) uses Y ear women received right to vote, year first woman was elected to parliament, Percent of women in the cabinet, and number of seats occupied by women in the parliament. Use the attached data set, on these indicators and list the top five and bottom five nations. TOP FIVE NATIONS Year First Woman Elected or Appointed in the Parliament Percent in the Lower House Right To Vote Percent in the Cabinet Nation Year Nation Year Nation Year Nation Year ISS315 - PAGE 49 BOTTOM FIVE NATIONS Year First Woman Elected or Appointed in the Parliament Percent in the Lower House Right To Vote Percent in the Cabinet Nation Year Nation Year Nation Year Nation Year ISS315 - PAGE 50 TABLE 33 . . . and achieving equality for all women and men Women's political participation Year women received right a HDI rank HIGH HUMAN DEVELOPMENT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 Iceland Norway Australia Canada Ireland Sweden Switzerland Japan Netherlands France Finland United States Spain Denmark Austria United Kingdom Belgium Luxembourg New Zealand Italy Hong Kong, China (SAR) Germany Israel Greece Singapore Korea (Republic of) Slovenia Cyprus Portugal Brunei Darussalam Barbados Czech Republic Kuwait Malta Qatar Hungary Poland Argentina United Arab Emirates Chile Bahrain Slovakia Lithuania Estonia Latvia Uruguay Croatia Costa Rica Bahamas Seychelles Cuba Mexico Bulgaria 1915, 1920 1913 1902, 1962 1917, 1960 1918, 1928 1919, 1921 1971 1945, 1947 1919 1944 1906 1920, 1965 1931 1915 1918 1918, 1928 1919, 1948 1919 1893 1945 .. 1918 1948 1952 1947 1948 1946 1960 1931, 1976 -- 1950 1920 2005 1947 2003 h 1918, 1945 1918 1947 -- 1949 1973, 2002 1920 1919 1918 1918 1932 1945 1949 1961, 1964 1948 1934 1947 1937, 1945 1915, 1920 1907, 1913 1902, 1962 1920, 1960 1918, 1928 1919, 1921 1971 1945, 1947 1917 1944 1906 1788 d 1931 1915 1918 1918, 1928 1921 1919 1919 1945 .. 1918 1948 1952 1947 1948 1946 1960 1931, 1976 -- 1950 1920 2005 1947 .. 1918, 1945 1918 1947 -- 1949 1973, 2002 1920 1919 1918 1918 1932 1945 1949 1961, 1964 1948 1934 1953 1945 To vote To stand for election Year first woman elected (E) or appointed (A) to parliament 1922 E 1911 A 1943 E 1921 E 1918 E 1921 E 1971 E 1946 E 1918 E 1945 E 1907 E 1917 E 1931 E 1918 E 1919 E 1918 E 1921 A 1919 E 1933 E 1946 E .. 1919 E 1949 E 1952 E 1963 E 1948 E 1992 E e 1963 E 1934 E -- 1966 A 1992 E e 2005 A 1966 E .. 1920 E 1919 E 1951 E -- 1951 E 2002 A 1992 E e 1920 A 1919 E .. 1942 E 1992 E e 1953 E 1977 A 1976 E+A 1940 E 1952 A 1945 E Women in government at ministerial level (% of total) b 2005 27.3 44.4 20.0 23.1 21.4 52.4 14.3 12.5 36.0 17.6 47.1 14.3 50.0 33.3 35.3 28.6 21.4 14.3 23.1 8.3 .. 46.2 16.7 5.6 0.0 5.6 6.3 0.0 16.7 9.1 29.4 11.1 0.0 15.4 7.7 11.8 5.9 8.3 5.6 16.7 8.7 0.0 15.4 15.4 23.5 0.0 33.3 25.0 26.7 12.5 16.2 9.4 23.8 MDG Seats in parliament held by women (% of total) c Lower or single house 1990 20.6 35.8 6.1 13.3 7.8 38.4 14.0 1.4 21.3 6.9 31.5 6.6 14.6 30.7 11.5 6.3 8.5 13.3 14.4 12.9 .. .. 6.7 6.7 4.9 2.0 .. 1.8 7.6 .. f 3.7 .. .. 2.9 .. 20.7 13.5 6.3 0.0 .. .. .. .. .. .. 6.1 .. 10.5 4.1 16.0 33.9 12.0 21.0 2007 31.7 37.9 24.7 20.8 13.3 47.3 25.0 9.4 36.7 12.2 42.0 16.3 36.0 36.9 32.2 19.7 34.7 23.3 32.2 17.3 .. 31.6 14.2 13.0 24.5 13.4 12.2 14.3 21.3 .. f 13.3 15.5 3.1 g 9.2 0.0 10.4 20.4 35.0 22.5 15.0 2.5 19.3 24.8 21.8 19.0 11.1 21.7 38.6 12.2 23.5 36.0 22.6 22.1 Upper house or senate 2007 -- -- 35.5 35.0 16.7 -- 23.9 14.5 34.7 16.9 -- 16.0 23.2 -- 27.4 18.9 38.0 -- -- 13.7 .. 21.7 -- -- -- -- 7.5 -- -- .. f 23.8 14.8 -- -- -- -- 13.0 43.1 -- 5.3 25.0 -- -- -- -- 9.7 -- -- 53.8 -- -- 17.2 -- Human development indicators 51 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 343 TABLE 33 Women's political participation Year women received right a HDI rank 54 Saint Kitts and Nevis 55 Tonga 56 Libyan Arab Jamahiriya 57 Antigua and Barbuda 58 Oman 59 Trinidad and Tobago 60 Romania 61 Saudi Arabia 62 Panama 63 Malaysia 64 Belarus 65 Mauritius 66 Bosnia and Herzegovina 67 Russian Federation 68 Albania 69 Macedonia (TFYR) 70 Brazil MEDIUM HUMAN DEVELOPMENT 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 Dominica Saint Lucia Kazakhstan Venezuela (Bolivarian Republic of) Colombia Ukraine Samoa Thailand Dominican Republic Belize China Grenada Armenia Turkey Suriname Jordan Peru Lebanon Ecuador Philippines Tunisia Fiji Saint Vincent and the Grenadines Iran (Islamic Republic of) Paraguay Georgia Guyana Azerbaijan Sri Lanka Maldives Jamaica Cape Verde El Salvador Algeria Viet Nam Occupied Palestinian Territories To vote 1951 1960 1964 1951 1994, 2003 1946 1929, 1946 -- 1941, 1946 1957 1918 1956 1946 1918 1920 1946 1932 1951 1951 1924, 1993 1946 1954 1919 1948, 1990 1932 1942 1954 1949 1951 1918 1930, 1934 1948 1974 1955 1952 1929 1937 1959 1963 1951 1963 1961 1918, 1921 1953 1918 1931 1932 1944 1975 1939 1962 1946 .. To stand for election 1951 1960 1964 1951 1994, 2003 1946 1929, 1946 -- 1941, 1946 1957 1919 1956 1946 1918 1920 1946 1932 1951 1951 1924, 1993 1946 1954 1919 1948, 1990 1932 1942 1954 1949 1951 1918 1930, 1934 1948 1974 1955 1952 1929 1937 1959 1963 1951 1963 1961 1918, 1921 1945 1918 1931 1932 1944 1975 1961 1962 1946 .. Year first woman elected (E) or appointed (A) to parliament 1984 E 1993 E .. 1984 A .. 1962 E+A 1946 E -- 1946 E 1959 E 1990 E e 1976 E 1990 E e 1993 E e 1945 E 1990 E e 1933 E 1980 E 1979 A 1990 E e 1948 E 1954 A 1990 E e 1976 A 1948 A 1942 E 1984 E+A 1954 E 1976 E+A 1990 E e 1935 A 1975 E 1989 A 1956 E 1991 A 1956 E 1941 E 1959 E 1970 A 1979 E 1963 E+A 1963 E 1992 E e 1968 E 1990 E e 1947 E 1979 E 1944 E 1975 E 1961 E 1962 A 1976 E .. Women in government at ministerial level (% of total) b 2005 0.0 .. .. 15.4 10.0 18.2 12.5 0.0 14.3 9.1 10.0 8.0 11.1 0.0 5.3 16.7 11.4 0.0 8.3 17.6 13.6 35.7 5.6 7.7 7.7 14.3 6.3 6.3 40.0 0.0 4.3 11.8 10.7 11.8 6.9 14.3 25.0 7.1 9.1 20.0 6.7 30.8 22.2 22.2 15.0 10.3 11.8 17.6 18.8 35.3 10.5 11.5 .. MDG Seats in parliament held by women (% of total) c Lower or single house 1990 6.7 0.0 .. 0.0 .. 16.7 34.4 .. 7.5 5.1 .. 7.1 .. .. 28.8 .. 5.3 10.0 0.0 .. 10.0 4.5 .. 0.0 2.8 7.5 0.0 21.3 .. 35.6 1.3 7.8 0.0 5.6 0.0 4.5 9.1 4.3 .. j 9.5 1.5 5.6 .. 36.9 .. 4.9 6.3 5.0 12.0 11.7 2.4 17.7 .. 2007 0.0 3.3 7.7 10.5 2.4 19.4 11.2 0.0 16.7 9.1 29.1 17.1 14.3 9.8 7.1 28.3 8.8 12.9 5.6 i 10.4 18.6 8.4 8.7 6.1 8.7 19.7 6.7 20.3 26.7 9.2 4.4 25.5 5.5 29.2 4.7 25.0 22.5 22.8 .. j 18.2 4.1 10.0 9.4 29.0 11.3 4.9 12.0 11.7 15.3 16.7 7.2 25.8 .. Upper house or senate 2007 -- -- -- 17.6 15.5 32.3 9.5 -- -- 25.7 31.0 -- 13.3 3.4 -- -- 12.3 -- 18.2 5.1 -- 11.8 -- -- -- 3.1 25.0 -- 30.8 -- -- -- 12.7 -- -- -- 18.2 13.4 .. j -- -- 8.9 -- -- -- -- -- 19.0 -- -- 3.1 -- .. Human development indicators 52 344 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 TABLE Year women received right a HDI rank 107 Indonesia 108 Syrian Arab Republic 109 Turkmenistan 110 Nicaragua 111 Moldova 112 Egypt 113 Uzbekistan 114 Mongolia 115 Honduras 116 Kyrgyzstan 117 Bolivia 118 Guatemala 119 Gabon 120 Vanuatu 121 South Africa 122 Tajikistan 123 Sao Tome and Principe 124 Botswana 125 Namibia 126 Morocco 127 Equatorial Guinea 128 India 129 Solomon Islands 130 Lao People's Democratic Republic 131 Cambodia 132 Myanmar 133 Bhutan 134 Comoros 135 Ghana 136 Pakistan 137 Mauritania 138 Lesotho 139 Congo 140 Bangladesh 141 Swaziland 142 Nepal 143 Madagascar 144 Cameroon 145 Papua New Guinea 146 Haiti 147 Sudan 148 Kenya 149 Djibouti 150 Timor-Leste 151 Zimbabwe 152 Togo 153 Yemen 154 Uganda 155 Gambia LOW HUMAN DEVELOPMENT 156 157 158 159 Senegal Eritrea Nigeria Tanzania (United Republic of) To vote 1945, 2003 1949, 1953 1927 1955 1924, 1993 1956 1938 1924 1955 1918 1938, 1952 1946 1956 1975, 1980 1930, 1994 1924 1975 1965 1989 1963 1963 1935, 1950 1974 1958 1955 1935 1953 1956 1954 1935, 1947 1961 1965 1947, 1961 1935, 1972 1968 1951 1959 1946 1964 1957 1964 1919, 1963 1946 .. 1919, 1957 1945 1967, 1970 1962 1960 1945 1955 p 1958 1959 To stand for election 1945 1953 1927 1955 1924, 1993 1956 1938 1924 1955 1918 1938, 1952 1946, 1965 1956 1975, 1980 1930, 1994 1924 1975 1965 1989 1963 1963 1935, 1950 1974 1958 1955 1946 1953 1956 1954 1935, 1947 1961 1965 1963 1935, 1972 1968 1951 1959 1946 1963 1957 1964 1919, 1963 1986 .. 1919, 1978 1945 1967, 1970 1962 1960 1945 1955 p 1958 1959 Year first woman elected (E) or appointed (A) to parliament 1950 A 1973 E 1990 E e 1972 E 1990 E 1957 E 1990 E e 1951 E 1957 E 1990 E e 1966 E 1956 E 1961 E 1987 E 1933 E 1990 E e 1975 E 1979 E 1989 E 1993 E 1968 E 1952 E 1993 E 1958 E 1958 E 1947 E 1975 E 1993 E 1960 A 1973 E e 1975 E 1965 A 1963 E 1973 E 1972 E+A 1952 A 1965 E 1960 E 1977 E 1961 E 1964 E 1969 E+A 2003 E .. 1980 E+A 1961 E 1990 E e 1962 A 1982 E 1963 E 1994 E .. .. Women in government at ministerial level (% of total) b 2005 10.8 6.3 9.5 14.3 11.1 5.9 3.6 5.9 14.3 12.5 6.7 25.0 11.8 8.3 41.4 3.1 14.3 26.7 19.0 5.9 4.5 3.4 0.0 0.0 7.1 .. 0.0 .. 11.8 5.6 9.1 27.8 14.7 8.3 13.3 7.4 5.9 11.1 .. 25.0 2.6 10.3 5.3 22.2 14.7 20.0 2.9 23.4 20.0 20.6 17.6 10.0 15.4 Lower or single house 1990 12.4 9.2 26.0 14.8 .. 3.9 .. 24.9 10.2 .. 9.2 7.0 13.3 4.3 2.8 .. 11.8 5.0 6.9 0.0 13.3 5.0 0.0 6.3 .. .. l 2.0 0.0 .. 10.1 .. .. 14.3 10.3 3.6 6.1 6.5 14.4 0.0 .. .. 1.1 0.0 .. 11.0 5.2 4.1 12.2 7.8 12.5 .. .. .. 2007 11.3 12.0 16.0 18.5 21.8 2.0 17.5 6.6 23.4 0.0 16.9 8.2 12.5 3.8 32.8 k 17.5 7.3 11.1 26.9 10.8 18.0 8.3 0.0 25.2 9.8 .. l 2.7 3.0 10.9 21.3 17.9 23.5 8.5 15.1 m 10.8 17.3 n 6.9 8.9 0.9 4.1 17.8 7.3 10.8 25.3 o 16.7 8.6 0.3 29.8 9.4 19.2 22.0 6.4 q 30.4 33 Upper house or senate 2007 -- -- -- -- -- 6.8 15.0 -- -- -- 3.7 -- 15.4 -- 33.3 k 23.5 -- -- 26.9 1.1 -- 10.7 -- -- 14.8 .. l -- -- -- 17.0 17.0 30.3 13.3 -- 30.0 -- 11.1 -- -- 13.3 4.0 -- -- -- 34.8 -- 1.8 -- -- -- -- 7.3 -- MDG Seats in parliament held by women (% of total) c Human development indicators 53 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 345 TABLE 33 Women's political participation Year women received right a HDI rank 160 Guinea 161 Rwanda 162 Angola 163 Benin 164 Malawi 165 Zambia 166 Cte d'Ivoire 167 Burundi 168 Congo (Democratic Republic of the) 169 Ethiopia 170 Chad 171 Central African Republic 172 Mozambique 173 Mali 174 Niger 175 Guinea-Bissau 176 Burkina Faso 177 Sierra Leone OTHERS Afghanistan Andorra Iraq Kiribati Korea (Democratic People's Rep) Liberia Liechtenstein Marshall Islands Micronesia (Federated States of) Monaco Montenegro Nauru Palau San Marino Serbia Somalia Tuvalu NOTES a. Data refer to the year in which the right to vote or stand for national election on a universal and equal basis was recognized. Where two years are shown, the first refers to the first partial recognition of the right to vote or stand for election. In some countries, women were granted the right to vote or stand at local elections before obtaining these rights for national elections. Data on local election rights are not included in this table. b. Data are as of 1 January 2005. The total includes deputy prime ministers and ministers. Prime ministers who hold ministerial portfolios and vice-presidents and heads of ministerial level departments or agencies who exercise a ministerial function in the government structure are also included. c. Data are as of 31 May 2007 unless otherwise specified. The percentage was calculated using as a reference the number of total seats filled in parliament at that time. To vote 1958 1961 1975 1956 1961 1962 1952 1961 1967 1955 1958 1986 1975 1956 1948 1977 1958 1961 1963 1970 1980 1967 1946 1946 1984 1979 1979 1962 1946 r 1968 1979 1959 1946 r 1956 1967 To stand for election 1958 1961 1975 1956 1961 1962 1952 1961 1970 1955 1958 1986 1975 1956 1948 1977 1958 1961 1963 1973 1980 1967 1946 1946 1984 1979 1979 1962 1946 r 1968 1979 1973 1946 r 1956 1967 Year first woman elected (E) or appointed (A) to parliament 1963 E 1981 E 1980 E 1979 E 1964 E 1964 E+A 1965 E 1982 E 1970 E 1957 E 1962 E 1987 E 1977 E 1959 E 1989 E 1972 A 1978 E .. 1965 E 1993 E 1980 E 1990 E 1948 E .. 1986 E 1991 E .. 1963 E .. 1986 E .. 1974 E .. 1979 E 1989 E Women in government at ministerial level (% of total) b 2005 15.4 35.7 5.7 19.0 14.3 25.0 17.1 10.7 12.5 5.9 11.5 10.0 13.0 18.5 23.1 37.5 14.8 13.0 10.0 33.3 18.8 0.0 .. 13.6 20.0 0.0 .. 0.0 .. 0.0 12.5 12.5 .. .. 0.0 MDG Seats in parliament held by women (% of total) c Lower or single house 1990 .. 17.1 14.5 2.9 9.8 6.6 5.7 .. 5.4 .. .. 3.8 15.7 .. 5.4 20.0 .. .. 3.7 .. 10.8 0.0 21.1 .. 4.0 .. .. 11.1 .. 5.6 .. 11.7 .. 4.0 7.7 2007 19.3 48.8 15.0 8.4 13.6 14.6 8.5 30.5 8.4 21.9 6.5 10.5 34.8 10.2 12.4 14.0 11.7 14.5 27.3 28.6 25.5 7.1 20.1 12.5 24.0 3.0 0.0 20.8 8.6 0.0 0.0 11.7 20.4 8.2 0.0 Upper house or senate 2007 -- 34.6 -- -- -- -- -- 34.7 4.6 18.8 -- -- -- -- -- -- -- -- 22.5 -- -- -- -- 16.7 -- -- -- -- -- -- 0.0 -- -- -- -- Human development indicators d. No information is available on the year all women received the right to stand for election. However, the constitution does not mention gender with regard to this right. e. Refers to the year women were elected to the current parliamentary system. f. Brunei Darussalam does not currently have a parliament. g. No woman candidate was elected in the 2006 elections. One woman was appointed to the 16-member cabinet sworn in July 2006. A new cabinet sworn in March 2007 included two women. As cabinet ministers also sit in parliament, there are two women out of a total of 65 members. h. According to the new constitution approved in 2003, women are granted suffrage. To date no legislative elections have been held. i. No woman was elected in the 2006 elections. However one woman was appointed Speaker of the House and therefore became a member of the House. j. Parliament has been dissolved or suspended for an indefinite period. k. The figures on the distribution of seats do not include the 36 special rotating delegates appointed on an ad hoc basis, and all percentages given are therefore calculated on the basis of the 54 permanent seats. l. The parliament elected in 1990 has never been convened nor authorized to sit, and many of its members were detained or forced into exile. m. In 2004, the number of seats in parliament was raised from 300 to 345, with the addition of 45 reserved seats for women. These reserved seats were filled in September and October 2005, being allocated to political parties in proportion to their share of the national vote received in the 2001 election. n. A transitional legislative parliament was established in January 2007. Elections for the Constituent Assembly will be held in 2007. o. The purpose of the elections held on 30 August 2001 was to elect the members of the Constituent Assembly of Timor-Leste. This body became the National Parliament on 20 May 2002, the date on which the country became independent, without any new elections. p. In November 1955, Eritrea was part of Ethiopia. The Constitution of sovereign Eritrea adopted on 23 May 1997 stipulates that "All Eritrean citizens, of eighteen years of age or more, shall have the right to vote." q. Data are as of 31 May 2006. r. Serbia and Montenegro separated into two independent states in June 2006. Women received the right to vote and to stand for elections in 1946, when Serbia and Montenegro were part of the former Yugoslavia. SOURCES Columns 13: IPU 2007b. Column 4: IPU 2007a. Column 5: UN 2007c, based on data from IPU. Columns 6 and 7: IPU 2007c. 54 H U M A N D E V E L O P M E N T R E P O R T 2 0 0 7/ 2 0 0 8 346 HANDOUTS ON POVERTY AND HUNGER This section contains the following handouts: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 14. 15. 16. 16. 17. POET Model and Hunger Diagram Kids Count 2005 (U.S.) Poverty in the U.S. 2005 (U.S. Census) Poverty in America: Beyond Welfare Reforms Who was poor in 2003? The High Cost of Being Poor Who is Hungry in the U.S. The Private Food Assistance Network Single Parent Families and the food safety net U.S. Elites Celebrate Patriarchy, Racism, and Class Privilege The Level, Trend, and Composition of Poverty 12 Myths About Hunger Grameen Bank Child Poverty In Rural Areas Darfur, Today's Worst Humanitarian Crisis Determinants of Relative Poverty in Advanced Capitalist Democracies ISS315 - PAGE 55 POET MODEL AND HUNGER ISS315 - PAGE 56 States Listed by Overall Rank Based on 10 Key Indicators NE VA WA KS ID CA OR MD NY SD RI WY HI MI CO PA IL OH IN DE NV MO MT FL AK TX OK GA NC AZ KY TN AR SC NM WV AL LA MS NH VT MN NJ ND MA ME IA UT WI CT Overall Rank based on 10 key indicators Teen death rate (deaths per 100,000 teens ages 1519): 2002 RATE RANK RATE RANK RATE RANK RATE RANK RATE RANK RATE RANK RATE Percent low-birthweight babies: 2002 Infant mortality rate (deaths per 1,000 live births): 2002 Child death rate (deaths per 100,000 children ages 114): 2002 Teen birth rate (births per 1,000 females ages 1519): 2002 Percent of teens who are high school dropouts (ages 1619): 2003 Percent of teens not attending school and not working (ages 1619): 2003 Percent of children in poverty: 2003 Percent of children living in families where no parent has full-time, year-round employment: 2003 Percent of children in single-parent households: 2003 RANK RATE RANK RATE RANK RATE RANK United States Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri ISS315 - PAGE 57 Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming 48 36 41 44 17 26 11 31 N.R. 35 39 24 16 28 30 8 15 42 49 7 19 6 25 3 50 33 34 12 32 1 4 46 20 40 5 29 38 18 27 22 45 21 43 37 9 2 13 14 47 10 23 7.8 9.9 5.8 6.8 8.6 6.4 8.9 7.8 9.9 11.6 8.4 8.9 8.3 6.1 8.2 7.6 6.6 7.0 8.6 10.4 6.3 9.0 7.5 8.0 6.3 11.2 8.0 6.8 7.2 7.5 6.3 8.0 8.0 7.9 9.0 6.3 8.3 8.0 5.8 8.2 7.9 10.0 7.2 9.2 7.7 6.4 6.4 7.9 5.9 9.0 6.6 8.4 46 1 14 38 9 40 23 46 N.R. 36 40 34 4 32 21 12 16 38 49 5 42 19 27 5 50 27 14 17 19 5 27 27 24 42 5 34 27 1 32 24 48 17 45 22 9 9 24 3 42 12 36 7.0 9.1 5.5 6.4 8.3 5.5 6.1 6.5 8.7 11.3 7.5 8.9 7.3 6.1 7.4 7.7 5.3 7.1 7.2 10.3 4.4 7.5 4.9 8.1 5.4 10.3 8.5 7.5 7.0 6.0 5.0 5.7 6.3 6.0 8.2 6.3 7.9 8.1 5.8 7.6 7.0 9.3 6.5 9.4 6.4 5.6 4.4 7.4 5.8 9.1 6.9 6.7 45 7 19 41 7 15 21 43 N.R. 32 44 29 15 30 36 5 27 28 49 1 32 3 38 6 49 42 32 25 13 4 10 17 13 40 17 37 38 11 35 25 47 21 48 19 9 1 30 11 45 24 23 21 29 29 24 30 18 21 13 27 23 22 23 17 23 20 22 21 25 25 35 20 20 15 22 23 37 25 23 23 19 12 17 24 17 23 20 19 24 21 21 14 27 31 25 23 23 15 20 19 24 20 34 44 44 34 46 9 19 2 42 N.R. 23 26 6 26 13 23 19 38 38 49 13 13 4 23 26 50 38 26 26 10 1 6 34 6 26 13 10 34 19 19 3 42 47 38 26 26 4 13 10 34 13 48 68 100 76 86 94 58 74 48 65 168 68 70 42 74 65 73 57 70 85 100 58 73 42 63 57 100 83 100 72 77 34 47 94 49 75 69 59 80 62 67 52 93 94 94 74 65 60 64 58 103 62 77 46 34 40 42 10 30 5 19 N.R. 23 25 2 30 19 28 8 25 39 46 10 28 2 17 8 46 38 46 27 35 1 4 42 6 33 24 13 37 15 22 7 41 42 42 30 19 14 18 10 50 15 35 43 55 40 61 60 41 47 26 46 69 44 56 38 39 42 45 32 43 51 58 25 35 23 35 27 65 44 36 37 54 20 27 62 29 52 27 40 58 37 32 36 53 38 54 64 37 24 38 33 46 32 40 42 25 47 46 28 36 5 34 N.R. 31 43 21 24 29 33 10 30 37 44 4 14 2 14 6 50 31 16 18 40 1 6 48 9 38 6 25 44 18 10 16 39 21 40 49 18 3 21 13 34 10 25 8 10 10 12 6 7 7 8 7 6 8 11 5 7 8 11 7 5 9 12 7 6 5 6 7 11 8 10 7 10 7 4 10 7 11 4 7 7 8 8 7 7 7 8 9 6 5 5 6 10 4 5 39 39 49 10 15 15 30 15 N.R. 30 45 4 15 30 45 15 4 37 49 15 10 4 10 15 45 30 39 15 39 15 1 39 15 45 1 15 15 30 30 15 15 15 30 37 10 4 4 10 39 1 4 9 11 13 11 9 8 9 7 6 10 8 11 13 8 8 8 7 8 12 14 5 8 8 7 4 12 8 10 7 11 6 5 10 9 10 6 8 11 9 7 9 8 8 11 10 8 4 6 10 11 4 6 39 48 39 29 16 29 11 6 N.R. 16 39 48 16 16 16 11 16 46 50 4 16 16 11 1 46 16 34 11 39 6 4 34 29 34 6 16 39 29 11 29 16 16 39 34 16 1 6 34 39 1 6 33 35 40 36 37 35 31 28 29 54 33 31 33 35 32 30 26 27 39 40 31 27 31 34 26 41 29 32 23 30 27 27 39 33 36 25 32 33 35 31 33 36 24 33 33 26 27 27 35 37 30 28 36 48 41 44 36 20 13 15 N.R. 28 20 28 36 25 17 4 7 46 48 20 7 20 35 4 50 15 25 1 17 7 7 46 28 41 3 25 28 36 20 28 41 2 28 28 4 7 7 36 44 17 13 18 24 14 21 24 19 13 11 12 36 19 19 15 18 16 14 12 14 24 30 13 10 12 16 9 29 16 18 13 15 8 12 26 19 19 14 18 22 18 16 17 19 14 20 23 12 12 12 14 25 14 12 44 16 41 44 34 13 4 5 N.R. 34 34 23 30 25 16 5 16 44 50 13 3 5 25 2 49 25 30 13 23 1 5 48 34 34 16 30 42 30 25 29 34 16 40 43 5 5 5 16 47 16 5 30 35 31 34 33 29 26 28 32 62 36 33 30 20 29 28 23 26 29 41 27 32 28 30 23 42 29 27 20 30 25 27 36 34 33 23 32 29 28 31 32 37 22 33 28 17 28 28 28 30 26 25 45 33 43 39 24 10 16 35 N.R. 46 39 29 2 24 16 5 10 24 49 13 35 16 29 5 50 24 13 2 29 8 13 46 43 39 5 35 24 16 33 35 48 4 39 16 1 16 16 16 29 10 8 Children Living in Vulnerable Households Percent of children in households where the household head did not finish high school: 2003 United States Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Data compiled by Kelvin M. Pollard, Population Reference Bureau. N.R.=Not Ranked. Percent of children in households where the household head has limited English proficiency: 2003 Percent of children in households where the household head has a work disability: 2003 Percent of children in low-income households where no adult worked in the past 12 months: 2003 Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming ISS315 - PAGE 58 17 19 10 22 17 26 15 10 14 27 17 17 11 11 15 15 8 10 15 20 7 12 10 11 7 22 13 7 10 23 7 11 25 16 18 5 12 15 13 12 18 15 8 15 26 10 6 12 11 15 11 9 12 2 5 18 3 30 12 8 6 11 13 6 12 8 13 4 3 4 2 2 1 5 11 4 5 1 2 1 5 19 4 15 14 17 6 1 2 5 8 4 16 2 2 2 22 6 1 6 8 1 4 2 5 8 7 5 9 5 3 4 5 6 6 5 6 4 4 4 3 3 9 7 8 4 5 6 3 10 5 4 5 5 4 4 5 5 7 3 5 5 4 5 7 5 3 6 4 3 5 5 4 9 5 3 5 7 4 5 6 4 3 3 3 17 4 5 3 3 4 3 3 3 8 9 3 3 6 4 2 8 4 3 3 3 2 3 5 7 7 3 5 7 3 5 8 5 4 6 4 2 2 4 3 8 3 2 Poverty Thresholds 2004 Poverty Thresholds for 2004 by Size of Family and Number of Related Children Under 18 Years Related children under 18 years Size of family unit None One Two Three Four Five Six Seven Eight or more ISS315 - PAGE 59 9,827 9,060 12,649 11,418 14,776 19,484 23,497 27,025 31,096 34,778 41,836 15,205 19,803 23,838 27,133 31,290 35,086 42,039 15,219 19,157 23,108 26,573 30,621 34,454 41,480 13,020 12,971 19,223 22,543 26,037 30,154 33,901 41,010 22,199 25,241 29,285 33,115 40,240 One person (unrelated individual).... Under 65 years....................... 65 years and over.................... Two persons............................ Householder under 65 years........... Householder 65 years and over...... Three persons.......................... Four persons........................... Five persons........................... Six persons............................ Seven persons.......................... Eight persons.......................... Nine persons or more................... Source: U.S. Census Bureau. 24,768 28,271 32,119 39,179 27,159 31,082 38,220 30,818 37,983 36,520 Table 8. Percentage of People in Poverty by State Using 2- and 3-Year Averages: 2001 to 2003 (People as of March of the following year) 3-year average 2001-2003 States Percentage United States . . . . . . . . . . . . . Alabama. . . . . . . . . . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . . . . . . . . . Arizona. . . . . . . . . . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . . . . . . . . . California . . . . . . . . . . . . . . . . . . . . Colorado . . . . . . . . . . . . . . . . . . . . Connecticut . . . . . . . . . . . . . . . . . . Delaware . . . . . . . . . . . . . . . . . . . . District of Columbia . . . . . . . . . . . . Florida . . . . . . . . . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . . . . . . . . . Illinois. . . . . . . . . . . . . . . . . . . . . . . Indiana . . . . . . . . . . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . . . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . . . . . . . . . Louisiana . . . . . . . . . . . . . . . . . . . . Maine . . . . . . . . . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . . . . . . . . . Massachusetts . . . . . . . . . . . . . . . . Michigan. . . . . . . . . . . . . . . . . . . . . Minnesota . . . . . . . . . . . . . . . . . . . Mississippi . . . . . . . . . . . . . . . . . . . Missouri . . . . . . . . . . . . . . . . . . . . . Montana . . . . . . . . . . . . . . . . . . . . . Nebraska . . . . . . . . . . . . . . . . . . . . Nevada . . . . . . . . . . . . . . . . . . . . . New Hampshire . . . . . . . . . . . . . . . New Jersey . . . . . . . . . . . . . . . . . . New Mexico . . . . . . . . . . . . . . . . . . New York . . . . . . . . . . . . . . . . . . . . North Carolina . . . . . . . . . . . . . . . . North Dakota . . . . . . . . . . . . . . . . . Ohio . . . . . . . . . . . . . . . . . . . . . . . . Oklahoma. . . . . . . . . . . . . . . . . . . . Oregon . . . . . . . . . . . . . . . . . . . . . . Pennsylvania . . . . . . . . . . . . . . . . . Rhode Island . . . . . . . . . . . . . . . . . South Carolina . . . . . . . . . . . . . . . . South Dakota . . . . . . . . . . . . . . . . . Tennessee . . . . . . . . . . . . . . . . . . . Texas . . . . . . . . . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . . . . . . . . . Virginia . . . . . . . . . . . . . . . . . . . . . . Washington . . . . . . . . . . . . . . . . . . West Virginia . . . . . . . . . . . . . . . . . Wisconsin. . . . . . . . . . . . . . . . . . . . Wyoming . . . . . . . . . . . . . . . . . . . . 12.1 15.1 9.0 13.9 18.5 12.9 9.4 7.9 7.7 17.3 12.7 12.0 10.7 11.0 11.8 9.2 8.5 10.3 13.7 16.9 11.8 7.7 9.7 10.8 7.1 17.9 10.1 14.0 9.9 9.0 6.0 8.2 18.0 14.2 14.2 11.7 10.4 14.0 11.7 9.9 10.7 14.0 10.9 14.3 15.8 9.8 9.4 9.3 11.4 16.9 8.8 9.1 90-percent confidence interval2 () 0.2 1.4 1.1 1.4 1.6 0.6 1.0 0.9 1.1 1.7 0.8 1.3 1.2 1.3 0.8 1.0 1.0 1.1 1.3 1.6 1.1 0.9 1.0 0.9 0.9 1.7 1.1 1.5 1.2 1.0 0.8 0.8 1.8 0.7 1.1 1.2 0.8 1.4 1.2 0.7 1.1 1.3 1.1 1.4 0.8 1.2 1.1 1.1 1.2 1.4 1.0 1.1 Percentage 11.9 15.2 8.7 14.1 18.8 12.8 9.2 7.8 7.9 17.6 12.6 12.1 11.4 11.4 11.5 8.8 8.3 10.1 13.4 16.9 11.9 7.3 9.5 10.5 6.9 18.9 9.8 13.4 10.0 8.0 6.1 8.0 17.9 14.1 13.4 12.7 10.1 14.6 11.3 9.5 10.3 14.7 10.0 14.5 15.3 10.2 9.8 8.9 10.8 16.6 8.2 8.8 90-percent confidence interval2 () 0.2 1.7 1.2 1.7 1.9 0.7 1.2 1.1 1.3 1.9 0.9 1.5 1.5 1.6 1.0 1.1 1.2 1.3 1.5 1.8 1.3 1.1 1.1 1.0 1.0 2.0 1.3 1.8 1.4 1.2 1.0 0.9 2.1 0.8 1.3 1.5 1.0 1.6 1.4 0.9 1.2 1.6 1.3 1.7 1.0 1.4 1.3 1.2 1.4 1.6 1.1 1.3 Percentage 12.3 14.7 9.2 13.5 18.8 13.1 9.7 8.2 8.2 16.9 12.6 11.5 10.3 10.8 12.7 9.5 9.1 10.4 14.3 17.2 12.5 8.0 10.1 11.5 6.9 17.2 10.3 14.3 10.2 9.9 5.8 8.3 18.0 14.2 15.0 10.6 10.3 13.5 11.7 10.0 11.3 13.5 12.1 14.4 16.3 9.5 9.2 10.0 11.8 17.1 9.2 9.4 90-percent confidence interval2 () 0.2 1.6 1.3 1.7 1.9 0.7 1.2 1.1 1.3 1.9 0.9 1.4 1.4 1.5 1.0 1.1 1.2 1.3 1.6 1.8 1.3 1.1 1.2 1.0 1.0 1.9 1.3 1.8 1.4 1.3 1.0 0.9 2.1 0.8 1.3 1.4 1.0 1.6 1.4 0.9 1.3 1.5 1.4 1.7 1.0 1.3 1.2 1.3 1.4 1.6 1.1 1.3 Percentage *0.4 0.5 0.6 0.6 0.3 0.5 0.4 0.3 0.7 0.5 1.1 0.6 *1.2 0.7 0.8 0.3 0.9 0.4 0.6 0.7 0.7 *1.0 *-1.6 0.5 0.9 0.2 *1.9 0.3 0.3 0.1 0.1 *1.6 *-2.1 0.2 1.1 0.3 0.5 1.0 1.2 *2.1 *1.0 0.7 0.6 *1.0 0.9 0.5 0.9 0.6 90-percent confidence interval2 () 0.2 1.4 1.1 1.4 1.6 0.6 1.0 0.9 1.0 1.6 0.7 1.2 1.2 1.2 0.8 0.9 1.0 1.1 1.3 1.5 1.0 0.9 0.9 0.8 0.9 1.6 1.1 1.5 1.1 1.0 0.8 0.8 1.7 0.7 1.1 1.2 0.8 1.3 1.2 0.7 1.0 1.3 1.1 1.4 0.8 1.1 1.0 1.0 1.2 1.3 0.9 1.1 2-year average 2001-2002 2002-2003 Change in percentage points (2002-2003 average less 2001-2002 average)1 -Represents zero or rounds to zero. *Statistically different from zero at the 90-percent confidence level. Details may not sum to totals because of rounding. A 90-percent confidence interval is a measure of an estimate's variability. The larger the confidence interval in relation to the size of the estimate, the less reliable the estimate. For more information see ``Standard errors and their use'' at <www.census.gov/hhes/www/p60-226sa.pdf>. 2 1 Source: U.S. Census Bureau, Current Population Survey, 2002 to 2004 Annual Social and Economic Supplements. 1 U.S. Census Bureau ISS315 - PAGE 60 Table B-1. Poverty Status of People by Family Relationship, Race, and Hispanic Origin: 1959 to 2003 (Numbers in thousands. People as of March of the following year) All people All families Race and Hispanic Origin and Year Below poverty level Below poverty level Total Number ALL RACES 2003 . . . . . . . . . . . . . . . . . . . . . . 2002 . . . . . . . . . . . . . . . . . . . . . . 2001 . . . . . . . . . . . . . . . . . . . . . . 20001 . . . . . . . . . . . . . . . . . . . . . 19992 . . . . . . . . . . . . . . . . . . . . . 1998 . . . . . . . . . . . . . . . . . . . . . . 1997 . . . . . . . . . . . . . . . . . . . . . . 1996 . . . . . . . . . . . . . . . . . . . . . . 1995 . . . . . . . . . . . . . . . . . . . . . . 1994 . . . . . . . . . . . . . . . . . . . . . . 1993 . . . . . . . . . . . . . . . . . . . . . . 19923 . . . . . . . . . . . . . . . . . . . . . 19914 . . . . . . . . . . . . . . . . . . . . . 1990 . . . . . . . . . . . . . . . . . . . . . . 1989 . . . . . . . . . . . . . . . . . . . . . . 19885 . . . . . . . . . . . . . . . . . . . . . 19875 . . . . . . . . . . . . . . . . . . . . . 1986 . . . . . . . . . . . . . . . . . . . . . . 1985 . . . . . . . . . . . . . . . . . . . . . . 1984 . . . . . . . . . . . . . . . . . . . . . . 1983 . . . . . . . . . . . . . . . . . . . . . . 1982 . . . . . . . . . . . . . . . . . . . . . . 1981 . . . . . . . . . . . . . . . . . . . . . . 1980 . . . . . . . . . . . . . . . . . . . . . . 1979 . . . . . . . . . . . . . . . . . . . . . . 1978 . . . . . . . . . . . . . . . . . . . . . . 1977 . . . . . . . . . . . . . . . . . . . . . . 1976 . . . . . . . . . . . . . . . . . . . . . . 1975 . . . . . . . . . . . . . . . . . . . . . . 1974 . . . . . . . . . . . . . . . . . . . . . . 1973 . . . . . . . . . . . . . . . . . . . . . . 1972 . . . . . . . . . . . . . . . . . . . . . . 1971 . . . . . . . . . . . . . . . . . . . . . . 1970 . . . . . . . . . . . . . . . . . . . . . . 1969 . . . . . . . . . . . . . . . . . . . . . . 1968 . . . . . . . . . . . . . . . . . . . . . . 1967 . . . . . . . . . . . . . . . . . . . . . . 1966 . . . . . . . . . . . . . . . . . . . . . . 1965 . . . . . . . . . . . . . . . . . . . . . . 1964 . . . . . . . . . . . . . . . . . . . . . . 1963 . . . . . . . . . . . . . . . . . . . . . . 1962 . . . . . . . . . . . . . . . . . . . . . . 1961 . . . . . . . . . . . . . . . . . . . . . . 1960 . . . . . . . . . . . . . . . . . . . . . . 1959 . . . . . . . . . . . . . . . . . . . . . . See footnotes at end of table. People in families Families with female householder, no husband present Below poverty level Total Number Percent Unrelated individuals Below poverty level Percent Total Number Percent Total Number Percent 287,699 285,317 281,475 278,944 276,208 271,059 268,480 266,218 263,733 261,616 259,278 256,549 251,192 248,644 245,992 243,530 240,982 238,554 236,594 233,816 231,700 229,412 227,157 225,027 222,903 215,656 213,867 212,303 210,864 209,362 207,621 206,004 204,554 202,183 199,517 197,628 195,672 193,388 191,413 189,710 187,258 184,276 181,277 179,503 176,557 35,861 34,570 32,907 31,581 32,791 34,476 35,574 36,529 36,425 38,059 39,265 38,014 35,708 33,585 31,528 31,745 32,221 32,370 33,064 33,700 35,303 34,398 31,822 29,272 26,072 24,497 24,720 24,975 25,877 23,370 22,973 24,460 25,559 25,420 24,147 25,389 27,769 28,510 33,185 36,055 36,436 38,625 39,628 39,851 39,490 12.5 12.1 11.7 11.3 11.9 12.7 13.3 13.7 13.8 14.5 15.1 14.8 14.2 13.5 12.8 13.0 13.4 13.6 14.0 14.4 15.2 15.0 14.0 13.0 11.7 11.4 11.6 11.8 12.3 11.2 11.1 11.9 12.5 12.6 12.1 12.8 14.2 14.7 17.3 19.0 19.5 21.0 21.9 22.2 22.4 238,903 236,921 233,911 231,909 230,789 227,229 225,369 223,955 222,792 221,430 219,489 217,936 212,723 210,967 209,515 208,056 206,877 205,459 203,963 202,288 201,338 200,385 198,541 196,963 195,860 191,071 190,757 190,844 190,630 190,436 189,361 189,193 188,242 186,692 184,891 183,825 182,558 181,117 179,281 177,653 176,076 173,263 170,131 168,615 165,858 25,684 24,534 23,215 22,347 23,830 25,370 26,217 27,376 27,501 28,985 29,927 28,961 27,143 25,232 24,066 24,048 24,725 24,754 25,729 26,458 27,933 27,349 24,850 22,601 19,964 19,062 19,505 19,632 20,789 18,817 18,299 19,577 20,405 20,330 19,175 20,695 22,771 23,809 28,358 30,912 31,498 33,623 34,509 34,925 34,562 10.8 10.4 9.9 9.6 10.3 11.2 11.6 12.2 12.3 13.1 13.6 13.3 12.8 12.0 11.5 11.6 12.0 12.0 12.6 13.1 13.9 13.6 12.5 11.5 10.2 10.0 10.2 10.3 10.9 9.9 9.7 10.3 10.8 10.9 10.4 11.3 12.5 13.1 15.8 17.4 17.9 19.4 20.3 20.7 20.8 41,311 40,529 39,261 38,375 38,580 39,000 38,412 38,584 38,908 37,253 37,861 36,446 34,795 33,795 32,525 32,164 31,893 31,152 30,878 30,844 30,049 28,834 28,587 27,565 26,927 26,032 25,404 24,204 23,580 23,165 21,823 21,264 20,153 19,673 17,995 18,048 17,788 17,240 16,371 (NA) (NA) (NA) (NA) (NA) (NA) 12,413 11,657 11,223 10,926 11,764 12,907 13,494 13,796 14,205 14,380 14,636 14,205 13,824 12,578 11,668 11,972 12,148 11,944 11,600 11,831 12,072 11,701 11,051 10,120 9,400 9,269 9,205 9,029 8,846 8,462 8,178 8,114 7,797 7,503 6,879 6,990 6,898 6,861 7,524 7,297 7,646 7,781 7,252 7,247 7,014 30.0 28.8 28.6 28.5 30.5 33.1 35.1 35.8 36.5 38.6 38.7 39.0 39.7 37.2 35.9 37.2 38.1 38.3 37.6 38.4 40.2 40.6 38.7 36.7 34.9 35.6 36.2 37.3 37.5 36.5 37.5 38.2 38.7 38.1 38.2 38.7 38.8 39.8 46.0 44.4 47.7 50.3 48.1 48.9 49.4 47,594 47,156 46,392 45,624 43,977 42,539 41,672 40,727 39,484 38,538 38,038 36,842 36,845 36,056 35,185 34,340 32,992 31,679 31,351 30,268 29,158 27,908 27,714 27,133 26,170 24,585 23,110 21,459 20,234 18,926 18,260 16,811 16,311 15,491 14,626 13,803 13,114 12,271 12,132 12,057 11,182 11,013 11,146 10,888 10,699 9,713 9,618 9,226 8,653 8,400 8,478 8,687 8,452 8,247 8,287 8,388 8,075 7,773 7,446 6,760 7,070 6,857 6,846 6,725 6,609 6,740 6,458 6,490 6,227 5,743 5,435 5,216 5,344 5,088 4,553 4,674 4,883 5,154 5,090 4,972 4,694 4,998 4,701 4,827 5,143 4,938 5,002 5,119 4,926 4,928 20.4 20.4 19.9 19.0 19.1 19.9 20.8 20.8 20.9 21.5 22.1 21.9 21.1 20.7 19.2 20.6 20.8 21.6 21.5 21.8 23.1 23.1 23.4 22.9 21.9 22.1 22.6 24.9 25.1 24.1 25.6 29.0 31.6 32.9 34.0 34.0 38.1 38.3 39.8 42.7 44.2 45.4 45.9 45.2 46.1 40 Income, Poverty, and Health Insurance Coverage in the United States: 2003 U.S. Census Bureau ISS315 - PAGE 61 Table B-2. Number of Poor and Poverty Rate by State: 2000 (Numbers in thousands) Expanded sample State Number United States . . . Alabama . . . . . . . . . . . . Alaska . . . . . . . . . . . . . . Arizona . . . . . . . . . . . . . Arkansas . . . . . . . . . . . . California . . . . . . . . . . . . Colorado . . . . . . . . . . . . Connecticut. . . . . . . . . . Delaware . . . . . . . . . . . . District of Columbia. . . Florida . . . . . . . . . . . . . . Georgia . . . . . . . . . . . . . Hawaii . . . . . . . . . . . . . . Idaho . . . . . . . . . . . . . . . Illinois. . . . . . . . . . . . . . . Indiana. . . . . . . . . . . . . . Iowa . . . . . . . . . . . . . . . . Kansas. . . . . . . . . . . . . . Kentucky . . . . . . . . . . . . Louisiana. . . . . . . . . . . . Maine . . . . . . . . . . . . . . . Maryland . . . . . . . . . . . . Massachusetts . . . . . . . Michigan . . . . . . . . . . . . Minnesota . . . . . . . . . . . Mississippi. . . . . . . . . . . Missouri . . . . . . . . . . . . . Montana . . . . . . . . . . . . Nebraska. . . . . . . . . . . . Nevada . . . . . . . . . . . . . New Hampshire . . . . . . New Jersey. . . . . . . . . . New Mexico . . . . . . . . . New York. . . . . . . . . . . . North Carolina . . . . . . . North Dakota . . . . . . . . Ohio . . . . . . . . . . . . . . . . Oklahoma . . . . . . . . . . . Oregon. . . . . . . . . . . . . . Pennsylvania . . . . . . . . Rhode Island . . . . . . . . South Carolina . . . . . . . South Dakota . . . . . . . . Tennessee. . . . . . . . . . . Texas . . . . . . . . . . . . . . . Utah . . . . . . . . . . . . . . . . Vermont . . . . . . . . . . . . . Virginia. . . . . . . . . . . . . . Washington . . . . . . . . . . West Virginia . . . . . . . . Wisconsin . . . . . . . . . . . Wyoming . . . . . . . . . . . . 31,089 582 47 582 428 4,260 405 250 64 78 1,696 966 105 160 1,288 506 234 213 503 755 127 377 598 968 269 421 502 127 142 169 55 596 307 2,530 962 63 1,119 498 361 1,013 99 435 77 752 3,166 167 60 572 628 265 490 52 90percent C.I. () Percent 628 86 9 101 62 313 63 44 12 12 167 151 19 26 141 81 41 38 76 104 19 70 88 117 56 65 83 20 26 30 13 88 48 191 122 10 131 71 59 120 15 70 12 117 259 33 10 103 102 35 76 9 11.3 13.3 7.5 11.7 16.5 12.4 9.6 7.6 8.3 15.2 11.0 12.1 8.9 12.4 10.6 8.5 8.3 8.1 12.6 17.3 10.1 7.2 9.7 9.8 5.6 15.0 9.2 14.2 8.7 8.7 4.6 7.3 17.4 13.8 12.4 10.2 10.0 14.8 10.7 8.6 10.1 11.0 10.6 13.5 15.3 7.6 10.1 8.2 10.8 14.7 9.3 10.9 90percent C.I. () Number 0.2 1.8 1.4 1.9 2.2 0.9 1.4 1.3 1.6 2.2 1.0 1.8 1.6 1.9 1.1 1.3 1.4 1.4 1.8 2.2 1.4 1.3 1.4 1.1 1.1 2.1 1.5 2.1 1.5 1.5 1.0 1.0 2.5 1.0 1.5 1.6 1.1 1.9 1.7 1.0 1.5 1.7 1.6 2.0 1.2 1.4 1.6 1.4 1.7 1.8 1.4 1.7 31,054 642 53 590 467 4,441 343 219 72 75 1,604 869 115 161 1,406 504 206 251 471 730 106 387 629 993 285 358 440 136 148 170 64 666 299 2,460 911 61 1,157 504 382 1,062 85 400 67 820 3,013 212 71 534 593 248 518 54 Original sample 90percent C.I. () Percent 879 135 15 127 88 398 95 78 21 18 208 185 34 35 198 140 64 68 110 139 33 122 120 161 94 80 130 28 42 46 26 124 58 247 155 17 182 102 96 169 27 106 18 175 318 51 19 149 156 52 134 13 11.3 14.4 8.2 12.0 17.8 12.8 8.1 6.6 9.1 14.9 10.6 11.2 9.9 12.9 11.5 8.7 7.2 9.6 11.9 17.3 8.4 7.6 10.1 10.0 6.0 12.9 8.0 15.7 9.0 8.5 5.2 8.0 16.8 13.4 12.1 10.1 10.0 15.4 11.2 8.9 9.1 10.6 9.6 14.7 14.7 9.6 11.3 7.7 10.1 14.0 9.6 11.0 Difference (expanded sample minus original sample)1 90percent C.I. () Number 0.3 2.8 2.2 2.4 3.0 1.1 2.2 2.3 2.6 3.4 1.3 2.2 2.8 2.6 1.5 2.3 2.2 2.5 2.6 3.0 2.5 2.3 1.8 1.5 1.9 2.7 2.3 2.9 2.4 2.2 2.1 1.4 3.0 1.3 1.9 2.6 1.5 2.9 2.7 1.4 2.7 2.7 2.4 2.9 1.4 2.2 2.8 2.1 2.5 2.7 2.4 2.6 35 59 6 8 39 181 62 31 8 3 93 97 10 1 *118 3 28 38 32 25 21 10 31 25 16 *62 62 10 6 1 8 *70 8 71 51 1 38 6 21 50 13 34 10 68 *153 *45 11 38 34 17 28 1 90percent C.I. () Percent 533 92 11 57 46 193 69 61 14 13 97 103 26 18 103 103 44 49 70 77 26 92 59 85 68 44 93 14 28 33 21 66 26 124 80 11 94 61 69 88 21 76 12 106 147 31 15 97 115 33 97 9 1.2 0.7 0.3 1.3 0.4 1.5 1.0 0.8 0.3 0.4 0.9 1.0 0.4 *0.9 0.1 1.1 1.6 0.7 1.7 0.4 0.4 0.2 0.4 *2.1 1.2 1.5 0.3 0.2 0.6 0.7 0.6 0.4 0.2 0.1 0.6 0.5 0.3 1.0 0.4 1.0 1.2 *0.7 *2.0 1.2 0.5 0.6 0.7 0.3 0.1 90percent C.I. () 0.2 1.9 1.6 1.1 1.6 0.5 1.6 1.8 1.8 2.3 0.6 1.3 2.1 1.3 0.8 1.7 1.5 1.8 1.7 1.7 1.9 1.7 0.9 0.8 1.4 1.5 1.6 1.5 1.6 1.6 1.6 0.8 1.3 0.6 1.0 1.8 0.8 1.7 1.9 0.7 2.2 1.9 1.7 1.8 0.7 1.3 2.2 1.3 1.9 1.7 1.7 1.7 -Represents zero. *Statistically significant at the 90-percent confidence level. For explanation of confidence intervals (C.I.), see ``Standard errors and their use'' at www.census.gov/hhes/poverty/poverty01/pov01src.pdf. 1 As a result of rounding, some differences may appear to be higher or lower than the differences between the reported rates. Source: U.S. Census Bureau, Current Population Survey, 2001 Annual Demographic Supplement. 34 Poverty in the United States: 2001 U.S. Census Bureau ISS315 - PAGE 62 States in Rank Order, by Percent of Persons in Poverty, 2000 Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 State District of Columbia Mississippi Louisiana New Mexico West Virginia Alabama Arkansas Kentucky Texas Oklahoma Montana New York California South Carolina Arizona Tennessee South Dakota Georgia Florida North Carolina Rhode Island North Dakota Idaho Missouri Oregon Wyoming Pennsylvania Maine Hawaii Illinois Washington Ohio Michigan Nevada Kansas Nebraska Virginia Indiana Vermont Utah Alaska Massachusetts Colorado Delaware Iowa Wisconsin New Jersey Maryland Minnesota Connecticut New Hampshire UNITED STATES Source: U.S. Census Bureau, Census 2000, Demographic Profiles: 100 Percent and Sample Data Percent of persons in poverty 20.2 19.9 19.6 18.4 17.9 16.1 15.8 15.8 15.4 14.7 14.6 14.6 14.2 14.1 13.9 13.5 13.2 13 12.5 12.3 11.9 11.9 11.8 11.7 11.6 11.4 11 10.9 10.7 10.7 10.6 10.6 10.5 10.5 9.9 9.7 9.6 9.5 9.4 9.4 9.4 9.3 9.3 9.2 9.1 8.7 8.5 8.5 7.9 7.9 6.5 12.4 ISS315 - PAGE 63 Population Vol. 57, No. 2 June 2002 BULLETIN A publication of the Population Reference Bureau Poverty in America: Beyond Welfare Reform Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Box 1. Key Provisions of the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) . . . . . . . . . . . . . . . . . . . . . . . 4 America's Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Figure 1. Number and Percent of Americans in Poverty, 19592000. . . . . . . . 5 Box 2. Measurement of Poverty and Income Inequality . . . . . . . . . . . . . . . . 6 Figure 2. U.S. Poverty Rates by Age, 19592000. . . . . . . . . . . . . . . . . . . . . . . . 8 Figure 3. Poverty Rates by Race or Ethnicity, 19652000 . . . . . . . . . . . . . . . . . 9 Declining Poverty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Figure 4. Poverty Rate by Education and Race or Ethnicity, 2000 . . . . . . . . . 10 Figure 5. Income Sources for Poor Female-Headed Families With Children, 19872000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Poverty Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Box 3. Does Unwed Childbearing Cause Poverty?. . . . . . . . . . . . . . . . . . . . . 13 Widening Income Gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Figure 6. Total Income Going to the Richest and Poorest Fifths of U.S. Households, 1973 and 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Living Conditions of the Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Why Are People Poor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Table 1. Attitudes About Poverty and the Poor, 2001. . . . . . . . . . . . . . . . . . . 19 Box 4. Is Marriage an Economic Panacea? . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Geography of Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Table 2. U.S. States Ranked by Poverty Rate, 1999. . . . . . . . . . . . . . . . . . . . . 21 Figure 7. Poverty Rates in U.S. Counties, 1998. . . . . . . . . . . . . . . . . . . . . . . . 25 Consequences of Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Figure 8. Percent of Americans Reporting `Fair' or `Poor' Health by Annual Family Income, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Table 3. Victims of Violent Crime by Income Level, 2000 . . . . . . . . . . . . . . . 28 Continued on page 2 1 ISS315 - PAGE 64 Poverty in America: Beyond Welfare Reform by Daniel T. Lichter and Martha L. Crowley T hroughout its history, the United States has struggled with the paradox of poverty amidst affluence. Why do so many people struggle economically in a nation blessed, by almost any international or historical standard, with abundant opportunities? Are the poor themselves to blame? Or are they victims of unequal educational opportunities, racism and sexism, or an economic system that favors the rich over the poor? As a rich society, how can we help poor families without fostering economic dependency, unwed childbearing, or other unintended consequences that may perpetuate rather than end poverty? How do we redress persistent racial or ethnic inequality without affecting the opportunities of others? How do we help poor children without rewarding decisions of parents that may have led to their children's disadvantaged circumstances? The paradoxes of American poverty are not new. What is new is the intensity of public policy attention directed at America's poor population. More attention is being paid now than at any time since the War on Poverty of the 1960s. One major reason for the increased attention is America's latest overhaul of the welfare system. The 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) ended "welfare as we know it." One major target of reform was the Aid to Families with Depen- Photo removed for copyright reasons The 1996 welfare reform bill aimed to get poor single mothers off welfare and into jobs. dent Children (AFDC) program, which provided cash payments to very low-income families with children. The legislation sought to end AFDC and other government assistance by promoting self-sufficiency and personal responsibility through "work first" programs (see Box 1, page 4). PRWORA set strict time limits on cash assistance, imposed work requirements, and encouraged marriage and two-parent families as a context for having and raising children. Welfare reform legislation has also challenged us to re-examine the circumstances of America's least advantaged residents. The reforms did not set out to reduce poverty. 3 ISS315 - PAGE 65 Box 1 Key Provisions of the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) Establishes Temporary Assistance for Needy Families (TANF). TANF replaces former entitlement programs (such as Aid to Families with Dependent Children) with federal block grants; devolves responsibility for welfare programs from federal to state government; and uses time limits and work requirements to emphasize the move from welfare to work. Tightens eligibility standards for Supplemental Security Income (SSI) child disability benefits. Requires states to enforce strong child support programs for collecting child support payments from absent fathers. Restricts eligibility for welfare and other public benefits for recent immigrants. PRWORA denies illegal aliens most benefits, except emergency medical services. It also allows states to provide federal cash assistance to legal aliens already in the country and to use state funds for cash assistance to aliens not eligible for federal funds. Provides resources for foster care data systems and a federal child welfare custody study. Establishes a block grant to allow states to provide child care for working parents. Alters eligibility criteria and benefits for child nutrition programs. Tightens national standards for food stamps and other benefits. It reduces benefit levels and denies most benefits to childless ablebodied adults unless the person is working or in training. Limits eligibility for TANF receipt. It sets a five-year time limit for TANF and bars people convicted of drug-related crimes from TANF for life. Source: Adapted from Leslie A. Whitener, Bruce A. Weber, and Greg J. Duncan, "Reforming Welfare: Implications for Rural America," Rural America 16, no. 3 (2001): 2-10. 4 Welfare reform has been a big success, at least as measured by the reduction in welfare caseloads. The number of families receiving welfare declined by more than 50 percent between 1994 and 2000, and the percentage of families receiving cash assistance is lower than it has been since 1960. In 2000, only 2.1 percent of the U.S. population received cash assistance (through the Temporary Assistance for Needy Families [TANF] program).1 Such success, which was helped by a booming economy, silenced many early critics of welfare reform. Happily, welfare caseload declines have occurred alongside reductions in poverty, even among female-headed families with children. Most of the early predictions that poverty and hardship would increase among the most vulnerable segments of the population have not occurred, at least not yet. The welfare reform bill is up for reauthorization in late 2002, and many poverty and welfare rights advocates argue that PRWORA's emphasis on reducing caseloads should be balanced by placing a higher priority on reducing poverty. Indeed, welfare reform comes with an obligation to refocus our attention on those left behind, those remaining at the economic margins of American society. Who are they? Why are they still poor? Why does it matter? What can we do about it? This Population Bulletin evaluates whether America's poor are different today: Are they better or worse off than in the past? And it examines whether persistent stereotypes and negative images of poor people match the current reality. Has ISS315 - PAGE 66 welfare reform led America's poor to adopt a new or different set of values and standards of behavior? Or does poverty, especially during childhood, transmit socioeconomic disadvantages that carry over from one generation to the next? Figure 1 Number and Percent of Americans in Poverty, 19592000 40 Millions in poverty 30 America's Poor In 2000, 11.3 percent of the U.S. population was officially poor, according to the poverty income guidelines provided by the U.S. Office of Management and Budget. This is the lowest poverty rate since the late 1970s (see Figure 1). Moreover, only 4.4 percent of the U.S. population was deeply impoverished, defined as having a family income below one-half of the official poverty threshold. There is little evidence that the poor have been getting poorer since PRWORA was signed into law. In fact, the average income (in 2000 dollars) of families in the bottom 20 percent of the U.S. income distribution rose from $12,625 in 1990 to $14,232 in 2000. Rising real incomes, even among the poor, reinforced the national euphoria over the expanding economy, while validating claims that welfare reform was a success. Some poverty analysts are less sanguine. Indeed, optimistic readings of the statistical evidence are sometimes belied by the sheer size of America's poor population: 31.1 million people in 2000. In contrast, just 5.7 million people received welfare income under TANF in 2000.2 The welfare poor, those low-income people who receive government cash assistance, represent a fraction of America's poor. Advocates for the poor claim that the income thresholds used by the federal government to measure poverty are too low to cover housing, food, and clothing. In 2000, a single mother with two children needed only $13,874 to avoid being counted as poor; a twoparent, two-child family needed just $17,463. In contrast, the 2000 median income for U.S. families was $50,891. Many poverty experts argue that family incomes at or just above the of- 20 Percent in poverty 10 0 1959 1965 1970 1975 1980 1985 1990 1995 2000 Source: U.S. Census Bureau, "Poverty Status of People by Family Relationship, Race, and Hispanic Origin: 1959 to 2000" (www.census.gov/hhes/poverty/histpov/ hstpov2.html, accessed March 29, 2002). ficial poverty income threshold cannot realistically provide for basic necessities, especially in New York, San Francisco, Washington, D.C., and other large cities where housing is very expensive. Indeed, the substantial geographic differences in living costs are an argument against having a single national income standard that defines poverty. A recent report by the National Academy of Sciences highlighted other limitations of the official definition, including its failure to account for in-kind income that families may receive, such as food stamps. The report also cites research criticizing the current measure for inadequately adjusting for economies of scale in large families; failing to adjust for income that is diverted to pay child support or taxes (and that is therefore not available for purchasing basic necessities); and not considering income-sharing among nonfamily members (see Box 2, page 6). Changing Demographics When the 1996 welfare reform bill was first signed into law, a muchpublicized Urban Institute study estimated that the new legislation would result in an additional 1 million poor 5 ISS315 - PAGE 67 Poverty rates have declined even among the groups that have historically been the most vulnerable. children.3 Other critics worried that welfare reform, especially the strict time limits on welfare receipt, would hurt single mothers with children, particularly among minorities. Fortunately, evidence supporting these early grim forecasts has not materialized. Instead, poverty rates have declined across population groups, even among the groups that have historically been the most vulnerable: the elderly, children, women, and minorities. Child poverty rates dropped from 22.7 percent in 1993 to 16.2 percent in 2000 (see Figure 2, page 8). Still, roughly 11.6 million children and 3.4 million people age 65 or older are poor, making up nearly one-half of America's poor population. Children and the elderly are sometimes considered the deserving poor--deserving of government assistance--because they are not usuBox 2 ally held responsible for their impoverished circumstances. America's racial minorities continue to have disproportionately high poverty rates (see Figure 3, page 9), but the racial composition of America's poor population has not changed appreciably over the past decade. In 1990, 50 percent of the poor were non-Hispanic white. In 2000, the figure was slightly lower, 47 percent. Significantly, America's growing racial diversity has occurred simultaneously with declining poverty rates across most racial and ethnic minority groups. Between 1990 and 2000, the poverty rate declined from 28.1 percent to 21.2 percent among Hispanics, and from 12.2 percent to 10.8 percent among Asians and Pacific Islanders. The declines have been especially steep among African Americans, with rates dropping from 31.9 percent to Measurement of Poverty and Income Inequality The official U.S. poverty measure compares families' pre-tax cash income to poverty thresholds adopted by the Social Security Administration (SSA) in 1965. Developed by the President's Council of Economic Advisers in 1963 and refined by the SSA later that year, the thresholds are based on the minimum cost of a nutritionally balanced meal plan, as designed by the Department of Agriculture and adjusted for variations in family size and needs. Based on a 1955 survey that indicated that food costs are about one-third of the average family's post-tax cash expenditures, the costs of these meal plans are multiplied by a factor of three to compute official poverty thresholds tailored to varying family types. The official measure has been largely unaltered since its initial adoption. In 1995, the Panel on Poverty and Family Assistance, under the aegis of the National Academy of Sciences (NAS), released a report evaluating the official measure of poverty in light of changing social circumstances. The panel recommended creating a new poverty measure that would better reflect changing work patterns of families with children, changing composition of families and households, geographic variation in prices, changes in living costs, increases in medical care costs and benefits, taxation, in-kind benefits, and increasing consumption and rising living standards. Many analysts feel that failure to consider the last two issues obscures both the potential effects of government assistance and possible changes in relative poverty (in contrast to absolute poverty). The panel proposed measuring family resources as the value of cash and inkind income received from all sources, including government benefits, minus work expenses (including child care), medical costs, child support payments, and taxes. The panel also suggested calculating poverty thresholds with a budget for basic goods, such as food, clothing, shelter, utilities, and other necessities; updating the budget annually to reflect changes in consumption; and adjusting thresholds by family type and geographic location. While these recommendations do improve the conceptualization behind poverty measurements, slight changes in their actual implementation yield different estimates of the size of the poverty population in the United States. 6 ISS315 - PAGE 68 22.1 percent between 1990 and 2000. Still, poverty rates among blacks and Hispanics are roughly twice the national average. Critics of welfare reform also worried that the reforms would accelerate the "feminization of poverty." Persistent wage inequality between men and women, coupled with the increase in single-mother families, has reinforced the perception that America's poor are increasingly comprised of females. In 2000, the poverty rate among females (12.5 percent) was roughly 25 percent higher than that of males (9.9 percent). But this sex differential has remained largely unchanged over the past three decades. Moreover, in 2000, 57 percent of the poor population was female, just as it was in the mid-1960s.4 The gender gap persists despite rising female employment rates, increasing real wages, and declines in the wage gap between men and women. Single Mothers and Children PRWORA targeted female-headed families--single mothers living with children. The reason is clear: Roughly one-half of America's 6 million poor families are headed by women, even though female-headed families represent only about a quarter of all families with children. In 2000, the poverty rate for female-headed families was 32.5 percent, roughly six times the rate for married-couple families with children (4.7 percent).5 Poverty rates declined much faster for families headed by single women than for other families after PRWORA was enacted. After peaking at 47 percent in the early 1990s, the poverty A variety of poverty measures based on these recommendations and applied to March 2000 Current Population Survey data, yielded poverty rates ranging between 11.3 percent and 15.0 percent of the total U.S. population. The official poverty measure estimated from that data was 11.8 percent. While the NAS panel acknowledged the limitations of the official poverty measure and made some welcome recommendations, change has been slow. The official poverty measure has been institutionalized; it is entrenched in federal funding formulas. Recent shifts in family and household composition pose perhaps the greatest challenge to the continuing use of existing instruments of poverty measurement. Increasing rates of single parenthood, childlessness, and cohabitation, as well as the aging of society and the presence of grandparents in the home, have increased diversity among households. The family may no longer be the most appropriate unit for analyzing income generation and expenditures. Nor is it appropriate to simply examine households, because how income is spent, and the resulting benefits to the house- hold's members, may vary by the nature of relations among members. For example, a cohabiting single parent and a single parent living with a grandparent may have identical household incomes, but they may spend their incomes in systematically different ways, with varying results for their children. One possible solution involves incorporating some indicator of household-family combinations, taking into account the total number of residents of a household as well as the relations existing between them that might affect patterns of spending and sharing. References Suzanne M. Bianchi and Lynne M. Casper, "American Families," Population Bulletin 55, no. 4 (2000): 3-43; Constance F. Citro and Robert T. Michael, eds., Measuring Poverty: A New Approach (Washington, DC: National Academy Press, 1995); Bradley R. Schiller, The Economics of Poverty and Discrimination (Upper Saddle River, NJ: Prentice Hall, 2001); and Kathleen Short, Experimental Poverty Measures: 1999 (Washington, DC: U.S. Census Bureau, 2001). 7 ISS315 - PAGE 69 Figure 2 U.S. Poverty Rates by Age, 19592000 Percent in poverty 35 30 65 and older 25 Under 18 20 15 10 5 0 1959 18-64 1965 1970 1975 1980 1985 1990 1995 2000 Source: U.S. Census Bureau, "Poverty Status of People by Age, Race, and Hispanic Origin: 1959 to 2000" (www.census.gov/hhes/poverty/histpov/hstpov3.html, accessed March 29, 2002). rate for female-headed families declined to nearly 33 percent in 2000, although it remained well above the poverty rates for married-couple and male-headed families. Working but Poor One major goal of welfare reform is to promote work and economic selfsufficiency among the poor, especially among single mothers. Welfare reform has pushed a large share of welfare-dependent mothers into the labor force. While employment does not necessarily shield people from poverty, people who work are less likely to be poor. In 2000, the poverty rate among full-time, full-year workers ages 16 to 64 was only 2.4 percent, compared with 12.7 percent among part-time workers, and 25.7 percent among those who did not work at all. The work-poverty link is especially strong among single mothers with children. In 2000, 12 percent of single mothers who worked full-time were poor, while 49 percent of those who worked part-time were poor, and 74 percent of those who did not work at all were poor. Poor single mothers, who are the main target of welfare reform, have shown unprecedented increases in work effort. Between 1994 and 2000, for example, the percentage of unmarried mothers who were in the labor force rose nearly 12 percentage points, to 79 percent. Nearly 70 percent of married mothers were in the labor force in 2000.6 The percentage of poor single mothers with at least some earnings from work increased from 55 percent to nearly 72 percent between 1995 and 1999.7 Although a growing share of single women with children are working, many remain poor because they earn poverty-level wages. A recent study by policy analyst Wendell Primus indicates that the economic situation may have worsened for as many as 700,000 American families since 1996.8 The group includes people who were forced off the welfare rolls and who no longer receive cash assistance; people with serious health problems, such as drug dependency, depression, or disabilities, that make sustained employment difficult; people who have trouble keeping a stable job because of transportation or child-care problems; and people trapped in economically depressed communities or regions, such as rural Appalachia or cities in the Rust Belt. The unemployed welfare recipients left behind by welfare reform may be particularly disadvantaged compared with those who found stable jobs. Education and National Origin Getting a good job often requires a good education, something that many poor people lack. Education, therefore, is commonly considered to be the best solution to poverty; indeed, the poor left behind in the new economy may be the least educated and least prepared to assume steady employment. The poverty rate among high school dropouts was 22.2 percent in 2000, compared with 3.2 percent among people with at least a 8 ISS315 - PAGE 70 bachelor's degree. Education had a more protective effect against poverty for whites than it did for blacks or Hispanics (see Figure 4, page 10). Figure 3 U.S. Poverty Rates by Race or Ethnicity, 19652000 Percent in poverty 40 Black 30 Hispanic 20 Asian & Pacific Islander 10 White non-Hispanic Immigrants The poverty status of America's new immigrants is an important welfare policy issue for at least two reasons. First, high rates of poverty among immigrants may indicate a lack of economic and social assimilation into mainstream American society. Second, poor immigrants may receive welfare and other services financed by American taxpayers. To address these concerns, PRWORA and other welfare legislation restricted benefits available to recent immigrants. Immigration, mostly from Latin America and Asia, has accounted for more than one-third of U.S. population growth in recent decades. The foreign-born population in the United States grew from 19.8 million to 28.4 million between 1990 and 2000, according to the U.S. Census Bureau. Recent immigrants are heavily concentrated in such cities as Los Angeles, New York, and Houston, and in states such as California, New York, Florida, and border states in the Southwest. Immigrants often live in higher-poverty neighborhoods within these cities and states. Foreign-born Americans tend to have higher poverty rates than the general population. In 2000, the poverty rate was nearly 17 percent for the foreign-born, about 50 percent higher than for all Americans. The rate was slightly higher (22 percent) among immigrants from Latin America. The good news is that poverty among immigrants declines significantly the longer they live in the United States, suggesting that they eventually become economically assimilated. In 2000, the poverty rate for immigrants who had entered the United States before 1970 was 8.3 percent. Rates for those arriving in the 1970s, 1980s, and 1990s were 11.5 percent, 15.2 percent, and 23.5 percent, respectively.9 Newly arrived immigrants tend to be younger and to 0 1965 1970 1975 1980 1985 1990 1995 2000 Note: Hispanics may be of any race. Source: U.S. Census Bureau, "Poverty Status of People by Family Relationship, Race, and Hispanic Origin: 1959 to 2000" (www.census.gov/hhes/poverty/histpov/ hstpov2.html, accessed March 29, 2002). have less education and work experience than those who have been in the country longer, which partly accounts for their lower incomes. Declining Poverty A cursory examination of data from the past 30 years seems to indicate that poverty rates are tied directly to economic recessions and booms. High poverty rates from 1979 to 1983 and from 1989 to 1993 reflect economic recessions during those years.10 But economic cycles do not explain poverty trends for all groups. And the factors responsible for recent declines in poverty may be different from the factors that drove poverty trends in the past. Macroeconomic Change Critics of U.S. policies often claim that the United States has pursued a rather laissez-faire welfare policy of trickledown economics. Simply put, current policies assume that a rising economic tide lifts all boats. But economists disagree about whether economic growth and low unemployment rates really benefit everyone. 9 ISS315 - PAGE 71 Figure 4 Poverty Rates by Education and Race or Ethnicity, 2000 Percent in poverty 33 Black Hispanic White non-Hispanic 25 18 18 13 10 7 7 5 5 3 7 Less than high school High school graduate Some college Bachelor's degree or more Source: U.S. Census Bureau, "Years of School Completed by People 25 Years and Over, by Age, Race, Household Relationship, and Poverty Status: 2000" (http://ferret.bls.census.gov/ macro/032001/pov/new07_000.htm, accessed March 29, 2002). The conventional wisdom, which holds that the United States, as a nation, can grow its way out of poverty, may be too simplistic. Critics point out that the benefits of economic growth are unevenly distributed: Job growth in one sector (information and technology, for example) often accompanies job losses and higher unemployment in another sector (such as manufacturing). Many poor people, especially children, the elderly, and single mothers, are only loosely attached to the labor force, and do not directly benefit from a full-employment economy. To attack poverty, some economists say, policies must encompass investments in human capital and income transfers to the poor, while assisting the hard-to-reach poor, such as the homeless, in less traditional ways.11 may mean that the poor have replaced one source of family income (public assistance) with another (earnings from work). In other words, the "welfare poor" have become the "working poor." Indeed, falling welfare caseloads have not translated easily into reductions in poverty and inequality. While the number of welfare cases fell 48 percent between 1994 and 1999, the number of poor female-headed families with children fell by only 18 percent, from 3.8 million to 3.1 million.12 Since the early 1990s, the incomes of poor female-headed families have increased only modestly, but the sources of that income have shifted notably. Poor female-headed families got more than half of their income from public assistance in the early 1990s, and only about a third from earnings. In 2000, nearly three-fifths came from job earnings, and less than one-fifth was from public assistance (see Figure 5). Many working single mothers will eventually return to the welfare rolls, but others have gained a solid foothold in the labor force. Whether these are deadend jobs or jobs leading to long-term economic security and upward mobility is unclear.13 Government Programs Average welfare benefit levels have dropped significantly since the late 1970s. At the same time, the government is helping low-income families by reducing the marginal tax rate and by expanding the Earned Income Tax Credit (EITC). The EITC, which provided a tax credit up to $4,000 for low-income workers in 2001, has helped many low-wage workers close the poverty gap by giving them the income needed to escape poverty.14 Some policy analysts have less benign interpretations of the role of EITC. Some feel that the expansion of the EITC credit has allowed wages to remain artificially low for poor workers in an otherwise low-unemployment economy. These analysts claim that the expansion of the EITC has subsidized employers, allowing them to offer lower wages while still attracting Declining Welfare Dependency Many scholars attribute recent declines in poverty to the work mandates and supports contained in most recent state welfare reforms, but other researchers disagree. Welfare reform 10 ISS315 - PAGE 72 employees. Workers may be no better off economically with the EITC, but employers and their stockholders can cut their labor costs. The EITC is a less useful poverty-reducing strategy for disadvantaged people who are only loosely tied to the workforce. Young adults with little work experience, for example, may not qualify for the EITC, and since children and the elderly usually do not earn income, they would not benefit directly. Social Security, which is indexed to inflation, has been a major factor in the decline of poverty among the elderly over the past three decades. Yet even with these federal safeguards, a sizeable share of the nation's oldest residents--those age 85 or older--are poor. An even larger share have incomes just above poverty. Older women living alone are especially vulnerable; they have poverty rates above 20 percent. Increases in the minimum wage during the 1990s also benefited lowwage workers. Federal legislation raised the minimum wage from $4.25 to $4.75 on Oct. 1, 1996, and to $5.15 on Sept. 1, 1997. But the minimum wage still has less buying power in 2002 than it did in 1970, when it equaled about $8.00 in 2000 dollars.15 Figure 5 Income Sources for Poor Female-Headed Families With Children, 19872000 Percent of income 100 Other Child support and alimony 80 Earnings 60 Social Security* 40 20 Welfare/public assistance 0 1987 1990 1992 1994 1996 1998 2000 *Includes SSI (Social Security Insurance) for disabled workers. Source: U.S. Census Bureau, March Supplements of the Current Population Surveys. Increasing Family Stability Any explanation for changing poverty rates over the past quarter-century must take into account the rise in single-parent families. Because of increases in divorce and unmarried childbearing, a much greater share of women and children are living in families at risk of poverty today than a generation ago. Roughly one-third of all births today occur to unmarried mothers, a pattern that resulted from long-term declines in marriage rates and continuing low fertility within marriage. According to the National Center for Health Statistics, just onetenth of births in 1970 were out of wedlock. Each year, about 1 million children are born to unmarried mothers, and another 1 million or so experience the divorce of their parents.16 Changes in family structure in recent decades have played an important role in poverty trends. Economists Maria Cancian and Deborah Reed found that the overall poverty rate between 1969 and 1998 would have increased by 3.6 percentage points because of changing family structure alone.17 For children the effects have been even larger: One recent study found that half of the increase in child poverty in the 1980s occurred because more children were living in female-headed families and fewer were in married-couple families.18 But these trends may be shifting: Growth in the share of children living in single-parent families ended after 1996, coinciding with the passage of PRWORA. Lifetime divorce rates also stopped rising or even fell slightly, while nonmarital fertility rates have stabilized at about 45 births per 1,000 unmarried women per year. Between 1994 and 2000, out-of-wedlock childbearing declined 24 percent among women ages 15 to 17 and 10 percent among women ages 18 to 19. After 1996, the incidence of unmarried childbearing and single-parent families declined disproportionately among 11 ISS315 - PAGE 73 Photo removed for copyright reasons Recent immigrants line up in search of temporary jobs. Lack of job security and low wages keep many working adults in or near poverty. disadvantaged groups, who were welfare reform's main targets. Declines have been most rapid among African Americans. Moreover, an Urban Institute study reported larger declines in the percentage of single-mother families among lower-income and poorly educated groups than among other groups between 1997 and 1999.19 Poverty Dynamics Current welfare policy debates often reinforce the idea that poor people are a relatively stable population with certain characteristics that set them apart from the rest of American society. In truth, America's poor population is highly dynamic; low-income people frequently move in and out of poverty. Less than half of the poor experience long-term poverty. Chronic vs. Episodic Poverty Evaluating the success of welfare reform using poverty rates can be misleading. Indeed, the official poverty rates released each year by the Census Bureau underestimate the average person's lifetime incidence of poverty. A 12 large share of Americans adults will experience poverty at some point; many will slip into poverty a number of times.20 But most people are poor for a relatively short time. The federal Survey of Income and Program Participation indicated that the median length of a poverty spell was 4.5 months in 1995, and that only about 10 percent of people were still poor after 28 months. The focus on single poverty spells can downplay how frequently some Americans move in and out of poverty and, as a result, spend much of their lives in poverty. One recent study showed that about one-half of those who escape poverty sink back into poverty within four years.21 The effects of persistent or chronic poverty are clearly revealed in the economic circumstances of America's children, about one-third of whom will experience poverty at some point during childhood.22 Few if any systematic studies have evaluated whether cycling into and out of poverty has changed since PRWORA was enacted. Policymakers tend to be most concerned with the long-term or chronically poor, who disproportionately tax the welfare system and other social support services. For these individuals, poverty is chronic and may be caused by limited job opportunities, education, and job skills, as well as discrimination. This population is different from those who move in and out of poverty, and may be less likely to benefit from welfare reform's emphasis on work. The chronically poor, sometimes called the underclass, exemplify the persistent and often negative stereotypes about the poor. They are considered out of step with mainstream American values, exhibiting high rates of workforce idleness, having children out of wedlock, or engaging in illegal activities, such as drug trafficking. Such poverty is often intractable and difficult to eradicate. Although the underclass looms large in the public mind, recent estimates suggest that less than half of the poor population (and just 5 percent of the total U.S. population) is comprised of the longterm or chronically poor.23 ISS315 - PAGE 74 Box 3 Does Unwed Childbearing Cause Poverty? The conventional wisdom is that unwed childbearing, especially by teenagers, fundamentally alters lives and can sentence single mothers to a life of economic hardship. Teen childbearing is assumed to cut short educational attainment and other human capital investments, and to increase the risk of later poverty and welfare dependency. Indeed, the statistical evidence shows a strong correlation between teen childbearing and later economic deprivation. For these reasons, teen pregnancy and childbearing have long occupied the attention of policymakers and have been viewed as a serious social problem. But some scholars have recently questioned that conventional wisdom. Some experts now believe that unwed mothers are more likely than other women to become poor, regardless of whether or not they become mothers as teenagers. In other words, it is not early childbearing that causes poverty, but other social and economic factors that are often not measured or controlled for in observational studies based on survey data. Young women who have a nonmarital birth often were socially or economically disadvantaged in the years preceding the birth. For them, unmarried childbearing may be largely irrelevant to their likelihood of becoming poor as adults. To deal with the issues raised by a lack of statistical controls, Arline Geronimus and Sanders Korenman compared sets of sisters, one of whom became an unwed mother while the other did not. The assumption is that sisters share many factors that might constitute risk factors for later poverty, such as growing up poor, having the same parents and same parenting, attending ineffective schools, and living in the same disadvantaged neighborhoods. The studies produced a striking result. Despite their different childbearing histories, the sisters were very similar on most adult outcomes, including education and poverty. The results imply that previous studies may have overestimated the negative effect of unmarried childbearing on later adult poverty. Other studies have yielded different conclusions from the same data using alternative approaches. For example, some studies have compared single mothers who have singleton and twin births, hypothesizing that the additional child in a pair of twins would have negative long-term effects on the mother. In fact, one such study showed that the second child does have negative effects on education status and income. Studies that compared unwed mothers with women who miscarried and who were presumably drawn from the same population found that unwed childbearing affects the likelihood of subsequent marriage, which in turn is strongly related to later economic well-being. Clearly, whether unwed childbearing is a cause or a consequence of poverty remains open to debate. References E. Michael Foster et al., "The Economic Impact of Nonmarital Childbearing: How Are Older, Single Mothers Faring?" Journal of Marriage and the Family 60, no. 1 (1998): 163-74; Arline Geronimus and Sanders Korenman, "The Socioeconomic Consequences of Teen Childbearing Reconsidered," Quarterly Journal of Economics 107 (Nov. 1992): 1187-214; Sandra L. Hofferth et al., "The Effects of Early Childbearing on Schooling Over Time," Family Planning Perspectives 33, no. 6 (2001): 259-67; Stephen G. Bronars and Jeff Grogger, "The Economic Consequences of Unwed Motherhood: Using Twin Births as a Natural Experiment," American Economic Review 84, no. 5 (1994): 1141-56; and Daniel T. Lichter and Deborah Roempke Graefe, "Finding a Mate? The Marital and Cohabitation Histories of Unwed Mothers," in Out of Wedlock, ed. L. Wu and B. Wolfe (New York: Russell Sage Foundation, 2001): 317-43. 13 ISS315 - PAGE 75 There is little evidence of increasing upward mobility of the poor. There is little evidence of increasing upward mobility of the poor, even for the temporary poor. More than half of young adults who were in the lowest income quintile (the bottom 20 percent of the income distribution) in the late 1960s were still in the lowest quintile 20 years later. Low-income minorities fared worse; more than 70 percent remained in the lowest income quintile. The story is much the same for the elderly poor. Sociologists Leif Jensen and Diane McLaughlin estimated that 40 percent of the elderly poor escape poverty quickly, after a year or so,24 but many remain just above the poverty income threshold. People who move out of poverty often enter the ranks of the "near-poor" and face continuing hardship. Entrances and Exits Slipping into and out of poverty often reflects temporary adjustments to divorce, job loss, or acute health problems that are resolved within a few months. Yet different population groups, such as children, women, working-age adults, and the elderly, experience poverty differently. Many studies have focused on entrances into poverty. For an elderly woman, the death of a husband can cause a significant drop in income and push her below the poverty threshold. Among women of reproductive age, divorce is associated directly with becoming poor, while getting married or remarried is associated with escaping poverty.25 Having children out of wedlock is also linked to poverty, although there is considerable disagreement about whether unmarried childbearing causes poverty (see Box 3, page 13). Children generally enter poverty according to their parents' status, particularly when their parents divorce, suffer from a debilitating illness, or lose their jobs. Other studies focus on exits from poverty. The mother's marital status is key to determining whether children remain poor. About 70 percent of poor children living with a single mother in the early 1980s were no longer poor in the early 1990s if they had moved into a two-parent family. Only 28 percent of those whose mothers remained unmarried escaped poverty.26 Transitions into and out of poverty are clearly affected by economic conditions. During the recession of 1975, for example, the one-year exit rate from poverty dropped from 61 percent to 51 percent, and reentry into poverty was higher than average.27 Whether leaving welfare is associated with leaving poverty or whether the welfare poor simply become the working poor has clear implications for welfare reform. Researchers Daniel Meyer and Maria Cancian, for example, found that 55 percent of poor women were still poor one year after exiting welfare and that 40 percent were poor five years later.28 Women who stayed out of poverty were significantly more likely to be working, married, or both. Women with two or more children, minority women, and high school dropouts were most likely to experience poverty following an exit from welfare. Widening Income Gap The official poverty rate is based on whether family income exceeds some absolute income threshold required to meet basic needs, such as food and housing. But the incomes of the poor today may be falling further behind the national average, and the income gap between rich and poor may have widened. Indeed, the growing income inequality may lead to cultural and geographic isolation for some groups, as the affluent and even the middle-class distance themselves from economically disadvantaged Americans, creating a kind of cultural balkanization. Are the Poor Falling Further Behind? Perhaps the most common measure of relative poverty is the share of individuals living in families with income 14 ISS315 - PAGE 76 below one-half the median income of all families. (Median income, which has risen over the past several decades, is used here as an income standard for economic well-being.) In 1997, 16.9 percent of the U.S. population had incomes below one-half the median income, a figure roughly 25 percent higher than the official poverty rate of 13.3.29 The advantage of the relative poverty measure is that it recognizes income disparities between poor and middle-class Americans. The disparities reflect the fall in the ratio of the minimum wage rate to the average wage rate over the recent past, as well as the fall in the dollar value of AFDC/ TANF cash assistance relative to average family income. Relative poverty measures, such as one-half the median family income, are useful in demonstrating whether the average poor family's income measures up to that of the average family. Yet, unlike absolute measures of poverty that are based on fixed income cutoffs, relative poverty measures are routinely criticized because they are not set against an income standard based on real need. As it is currently defined, relative poverty can never be eradicated. Paradoxically, escaping relative poverty is more difficult when real income is rising, because the poverty income threshold increases at the same time. Conversely, relative poverty may understate growing economic hardship during periods when the average income of all families is falling. Nevertheless, as long as average family incomes trend upward, the relative measure is valuable because it reveals how much the income of poor people lags behind the average for all Americans. Figure 6 Total Income Going to the Richest and Poorest Fifths of U.S. Households, 1973 and 2000 Percent of total U.S. household income 49.6 43.6 4.2 1973 3.6 2000 Annual household income level Bottom fifth Top fifth of incomes of incomes Source: U.S. Census Bureau, "Percent Distribution of Households, by Selected Characteristics Within Income Quintile and Top 5 Percent in 2000" (http://ferret.bls.census.gov/macro/032001/ hhinc/new06_000.htm, accesssed April 4, 2002). Growing Inequality While average family incomes have increased nominally since the 1970s, the median family income was lower in 2000 than in 1973, in inflationadjusted dollars. The share of all household income received by the top 20 percent increased at the same time that the share received by the bottom 20 percent declined slightly (see Figure 6). Concerns about fairness have been heightened by the recent retrenchment in federal cash assistance policies and by tax cuts for the middle class and the wealthy.30 Indeed, income inequality in the United States is the highest in the industrialized world. Some analysts insist that income inequality is a necessary by-product of America's freeenterprise system. America's market economy rewards individual risktaking and entrepreneurial activity that, in the long run, benefits all Americans either directly (for example, by creating more job opportunities) or indirectly (by raising living standards). But this argument does not speak to wealth disparities, which are much larger than income or earnings disparities. In 1995, the richest 20 percent of American households owned 84 percent of the nation's wealth. 15 ISS315 - PAGE 77 While growing inequality is welldocumented, its causes are less clearcut. One argument is that much of the rise in income inequality has been fueled by transformations in the economy as the discrepancy in earnings has increased between people in unstable, low-wage jobs in the service sector and high-salaried workers in the high-tech information sector. But income inequality has been observed broadly across U.S. population groups.31 Earnings inequality has increased among white men employed full-time, within as well as among people with different education levels, and within and between married-couple families and femaleheaded families. Likewise, inequality has increased both within and between the industrial and occupational sectors. This evidence has made it difficult to argue convincingly that growing inequality simply reflects America's growing demographic or economic diversity. Food Insecurity, Housing Food in America is abundant and comparatively cheap by historical and international standards, yet America's poor often face food insecurity, which is defined by the government as inadequate access to food or as the physical sensation of hunger. A national study conducted by the U.S. Department of Agriculture in 1995 found that almost 13 percent of households with annual incomes under $10,000 experienced food insecurity, compared with nearly 7 percent of those with annual incomes between $10,000 and $20,000, and 3 percent of those with annual incomes between $20,000 and $30,000.33 Poor families also have limited access to high-quality food, in part because grocery stores in low-income neighborhoods charge higher prices and carry lower-quality produce than grocers in more prosperous neighborhoods.34 Many of the poor struggle to pay housing costs, which consume a large percentage of total household expenses in all households, but especially among low-income people. In the 1990s, housing prices increased faster than average family income. The poor are three times more likely than the nonpoor to be unable to pay the full cost of housing and utilities, and many require housing assistance from the government or from private charities.35 Compared with other families, the poor also face many more housing maintenance problems, including leaking roofs; broken plumbing; broken windows; exposed wiring; infestations of rodents or roaches; or holes in walls, floors, or ceilings. Likewise, people in poverty are two to three times more likely than other Americans to report neighborhood problems with crime, trash, abandoned buildings, and neighborhood conditions bad enough to make them want to move. Living Conditions of the Poor If improving the living conditions of poor families and children is a goal of welfare reform, then we need to learn more about the economic challenges poor people face daily. The official poverty rate tells us little about the actual living conditions or consumption patterns of low-income people: their material assets, food insecurity, or spending patterns. In 1995, for example, 49 million Americans, or roughly 20 percent of the population, reported difficulties paying bills for food, housing, or health care; 11 percent had difficulty with more than one of these expenses.32 The poor were nine times more likely than the nonpoor to experience two or more such problems. Welfare advocates claim that that the official poverty rate may no longer accurately gauge or track trends in the living standards of the poor, and that recent poverty declines may be illusory. Health Care Many poor people cannot afford health care. In 1992, people in poverty were nearly three times more likely than other Americans to go 16 ISS315 - PAGE 78 without seeing a doctor when they felt they needed medical attention.36 Children in low-income families are more vulnerable to health risks than other children because they are more likely to lack health insurance.37 Interestingly, children of the working poor were more likely to be uninsured than children in other poor families. Welfare-to-work policies can exacerbate this discrepancy because lowincome parents who work are not eligible for Medicaid and other health benefits, yet many take jobs that do not include health insurance. Clearly, the poor have fewer dollars to spend on food, housing, and health care than wealthier Americans do, and they use a greater proportion of their incomes to meet basic needs.38 Because assistance levels are low, many people who receive welfare and other public assistance have difficulty meeting even these basic needs. Food, housing, and transportation made up 72 percent of expenditures among the poor who received assistance, and 62 percent among the poor who did not receive assistance, according to a recent study.39 $15,000, the figures are only 23 percent and 14 percent, respectively.40 The poor are persistently stereotyped as having difficulties managing their money, being unable to save, or spending unwisely. But one study showed that that the poor not on welfare spend less money on entertainment than do the nonpoor (although both groups spend roughly the same percentage of their incomes: 5 percent).41 People in poverty are more likely than other Americans to smoke, but they gamble less and consume about the same amount of alcohol (with the exception of malt liquor).42 Stretching the Dollar If the poor have difficulties managing money, it may be because they pay more for goods and services than wealthier people do, making it more difficult for them to "stretch the dollar." The poor often lack the cash to buy in bulk or to take advantage of sales. Supermarket chains offering the lowest prices do not often locate stores in poor communities or neighborhoods. Most poor people shop in smaller, locally owned stores that charge relatively high prices. Lower automobile ownership and shortages of public transportation limit access to shopping malls or retail outlets.43 The poor also have limited access to banks or other financial institutions. The poor often live paycheck-topaycheck, with little cash to put in a bank account.44 "Predatory" lending practices, such as those offered by "check-cashing stores," have filled the void left by traditional financial institutions. These lenders often target low-income groups for high-interest mortgages, payday loans, car pawns, and rent-to-own consumer goods. Such practices inevitably inflate the cost of basic necessities and sometimes lead to a loss of assets, such as a car or even a home. The poor pay more for goods and services than wealthier people do. Luxuries, Entertainment Expenditures on food, housing, and health care often leave the poor with little money for other commodities that are commonplace in American homes. A large majority of America's poor have access to a refrigerator and stove, and more than half own a microwave. But a much smaller share of poor than nonpoor people have access to a telephone, washing machine, clothes dryer, or air conditioner--amenities that most Americans take for granted. People in poverty are also less likely to own computers or to have Internet access, creating a "digital divide" that some analysts see as further marginalizing the poor population. A recent government report indicated that 59 percent of American families have a computer in the home, and 48 percent have home Internet access. For families with annual incomes under Wealth and Assets The poor and near-poor often lack a "nest egg" of cash or assets for emergencies. In 1995, the median net 17 ISS315 - PAGE 79 worth of American households was $11,773, excluding home equity, and $40,200 if home equity was included. But many poor households had a total net worth of less than $5,000, especially among racial and ethnic minorities.45 In 1995, for example, African Americans' median net worth, excluding home equity, was only $2,657. The level of wealth inequality across race is especially striking, and is much greater than for income or earnings inequality.46 Researchers Robert Haveman and Edward Wolff found that, despite the economic boom of the 1990s, the level of "asset poverty" has actually increased nationally. They estimated that about onefourth of U.S. households have insufficient net worth to tide them over for three months at a povertylevel living standard.47 Attributions of Blame In early 2001, a national poll conducted by National Public Radio (NPR), the Kaiser Family Foundation, and Harvard University's Kennedy School asked nearly 2,000 Americans 18 or older, "Which is the bigger cause of poverty today: that people are not doing enough to help themselves out of poverty, or that circumstances beyond their control cause them to be poor?" Respondents were roughly equally divided between "people not doing enough" (48 percent) and "circumstances" (45 percent), as shown in Table 1. About 50 percent of the more affluent people polled believed that the poor were not doing enough to help themselves, but so did about 39 percent of the poor. The poor were more likely to blame "circumstances" than themselves for their financial hardship. The poll also showed that about two-thirds of Americans believe that the poor have the same moral values as other Americans. But about onefifth thought the poor had lower moral values. The poor themselves share this belief: About one-fourth believe the poor have lower moral values than other Americans. Even with work-based welfare reform, a sizeable share of the American public holds unfavorable views about poor people. Why Are People Poor? PRWORA's strict time limits on cash assistance and tough sanctions imply a new social contract with the poor. The welfare poor today are expected to work, avoid legal problems, and behave responsibly, lest they lose eligibility for cash assistance. This "carrot and stick" approach has sent a strong and unmistakable message: The poor must share the blame for their own circumstances and, with help from the government, must take responsibility for bettering their lives through hard work, job training and education, and maintaining a stable family life. But many Americans question whether the poor subscribe to America's core values of hard work, economic independence, personal responsibility, and strong family values. Public opinion polls suggest that PRWORA may have helped poor people on welfare today earn more respect--or at least less disrespect-- than was the case before the welfare system was overhauled.48 Hard Work and Motivation One persistent stereotype is that the poor, especially the welfare poor, are unmotivated: They lack aspirations to "get ahead," or don't work hard enough to succeed. The NPR/Kaiser/ Kennedy School poll, in fact, showed that 52 percent of the American public believed that lack of motivation was a major cause of poverty; another 35 percent believed it was a minor cause of poverty. Differences in responses by poverty status were surprisingly small. Most Americans, including the poor, said they strongly believe that America is a land of opportunity. Their responses suggest they believe that motivation and hard 18 ISS315 - PAGE 80 Table 1 Attitudes About Poverty and the Poor, 2001 1. Are the following major causes, minor causes, or not causes of poverty? Major cause 70 58 54 54 34 46 30 52 47 Total Percent of respondents Minor cause Not a cause 24 5 32 7 32 32 41 37 40 35 36 10 12 23 11 26 9 13 Don't know 2 2 4 2 2 7 4 4 4 Drug abuse Medical bills Too many jobs being part-time or low-wage Too many single-parent families A shortage of jobs The welfare system Too many immigrants Poor people lacking motivation Poor quality of public schools Percent of respondents by poverty status Poor Near poor Nonpoor 2. Do poor people have higher, lower, or the same moral values as other Americans? Higher 8 19 7 6 Lower 21 22 23 20 The same 67 57 65 68 Don't know 5 2 5 5 3. Which is the bigger cause of poverty today: people not doing enough or circumstances beyond their control? Not doing enough 48 39 44 50 Circumstances 45 57 46 44 Don't know 7 4 10 6 4. Do most welfare recipients today really want to work? Yes 47 52 No 44 41 Don't know 9 7 42 47 11 48 43 9 Note: Less than 100 percent of the poverty threshold = poor; 100 percent to 200 percent of the poverty threshold = near poor; 200 percent or more of the poverty threshold = nonpoor. Source: National Public Radio, Kaiser Family Foundation, and Harvard University Kennedy School Poll on Poverty in America (www.npr.org/programs/specials/poll/poverty/, accessed April 5, 2002). work can pull people out of poverty, regardless of their background. Other studies of the poor typically reveal that values among the poor are remarkably similar to those of the rest of society. A study in Milwaukee showed that most teens, including teenage mothers, regarded education as being valuable for its own sake, as a source of personal pride and as an example for their children, as well as a route to upward economic mobility.49 But people in poverty often fail to translate educational values into concrete goals, in part because they do not know about or have access to local educational resources, or because those resources are limited or difficult to reach. Surveys also indicate that the poor prefer work to receiving help from the government or from family members. The NPR/Kaiser/Kennedy School poll, in fact, showed that 52 percent of poor people believed that "most welfare recipients today really want to work." Work provides purpose in life, a place to go, a sense of control, and income. For many low-income people, however, jobs are often unavailable; if available, they often pay poorly or do not provide health insurance. To make ends meet, many people in poverty rely on public or familial assistance. According to 19 ISS315 - PAGE 81 researchers Kathryn Edin and Laura Lein, the poor often require "something special" in order to find and keep a job, such as low rent, free child care from a relative, help with bills, a reliable car, good public transportation, or a generous benefactor.50 Poor women tend to dislike or disapprove of welfare; they "hate it," "don't want it," "hope [to] never have to be on it," and "want to get off it."51 Some studies have shown that the poor believed they are entitled to cash assistance if they experience economic need, but that very few approved of welfare receipt per se.52 Welfare mothers often feel degraded, and resent the public view that they are lazy or avoid work, even as they maintain a home and raise their children. Most women value their ability to combine work, welfare, and family support, and to use welfare while improving their job prospects. But many poor people distrust the government policies and programs that were ostensibly designed to help them.53 late adolescence and young adulthood. Researchers have investigated whether a shortage of employed males discourages marriage among low-income young women. Lowincome women appear to have many of the same aspirations for marriage, children, and a stable family life as middle-class Americans. But these women may be reluctant to take on the financial or emotional risk of marriage if their potential partners cannot or do not work. In-depth interviews with 130 black, white, and Puerto Rican mothers in Philadelphia in the mid-1990s revealed that poor mothers often aspired to marry, but chose not to after weighing the potential benefits and risks of marrying the men available to them.55 A recent study by Ellen Scott and colleagues suggests that single mothers almost always place their children first, before a relationship with a man, and that they often regard marriage as a potential threat to their children's well-being or as an indulgence that they cannot afford.56 Family Values Nearly 90 percent of Americans believe that the presence of too many single-parent families contributes to poverty. Indeed, recent welfare reform was motivated in large part by a negative image of poor women as unemployed, unmarried mothers who lived on government handouts. Some observers feel that moral lassitude has contributed to high rates of out-ofwedlock childbearing and to poverty. Others maintain that the availability of cash welfare assistance, if not an active motivation to have children out of wedlock, has nevertheless reduced the economic disincentives of bearing and rearing a child outside of marriage.54 The "breakdown of the family" has been viewed as a major cause of poverty and welfare dependence. It is not surprising that initiatives to promote marriage are being discussed as a way to keep caseloads and poverty low (see Box 4, page 22). Marital and childbearing decisions are shaped by economic conditions in Geography of Poverty While opinion polls show that the public often attributes poverty to moral and personal deficiencies, many other observers believe that, to quote Janet Kodras, "the changing map of American poverty does not represent an ebb and flow of lassitude among the nation's population; rather, it reflects the geographic contours of recent transformations in the American political economy."57 Millions of the world's poor live in countries plagued by endemic poverty and economic underdevelopment; few Americans would hold those people responsible for their own and their country's poverty. But the constraints imposed by limited local or community opportunities affect the options of many poor Americans as well. These constraints are shaped by larger economic and political forces, 20 ISS315 - PAGE 82 not by character flaws of individual poor people. The Census 2000 Supplementary Survey indicates that poverty rates vary enormously among states, from a low of 6 percent in New Hampshire to a high just above 20 percent in Louisiana (see Table 2). In 1989, poverty rates ranged from 6 percent in New Hampshire to 25 percent in Mississippi. Despite warnings that welfare reform would lead to a "race to the bottom," and speculation that the economic boom was largely an urban and bicoastal phenomenon, the gap in poverty rates among states did not widen during the 1990s. If anything, states have converged economically. Table 2 U.S. States Ranked by Poverty Rate, 1999 Percent of population State below poverty Louisiana 20.3 West Virginia 19.3 Mississippi 18.2 New Mexico 18.0 District of Columbia 17.7 Arkansas 17.4 Kentucky 16.5 Alabama 16.0 Arizona 15.6 Texas 15.3 South Carolina 14.8 Montana 14.4 Oklahoma 14.4 Tennessee 14.1 California 13.9 New York 13.5 Florida 13.4 North Carolina 13.2 Georgia 13.1 Oregon 13.0 UNITED STATES 12.5 North Dakota 12.3 Washington 11.9 Wyoming 11.9 Idaho 11.6 Missouri 11.5 South Dakota 11.5 Illinois 11.4 Rhode Island 11.3 Ohio 11.1 Vermont 11.0 Iowa 10.7 Pennsylvania 10.6 Indiana 10.5 Michigan 10.4 Maine 10.3 Nebraska 10.3 Nevada 10.1 Massachusetts 9.9 Delaware 9.6 Virginia 9.6 Kansas 9.4 Maryland 9.3 Wisconsin 9.3 Utah 9.0 Alaska 8.9 Colorado 8.8 Hawaii 8.8 New Jersey 8.2 Connecticut 7.9 Minnesota 7.2 New Hampshire 6.0 Note: These poverty rates are from the 2000 Census and differ from rates based on the Current Population Survey. Neighborhoods In most large American cities, the poor do not live near middle-class and affluent Americans. There tend to be particularly clear disparities in economic and social conditions between cities and suburbs. Although nearly as many poor people lived in the suburbs as in central cities (11 million compared with 13 million) in 2000, the rate of poverty was roughly half as high in suburbs (7.8 percent compared with 16.1 percent). Historically, America's poorest groups, such as immigrants, female-headed families, and racial and ethnic minorities, have been concentrated in cities rather than in suburbs. Whites and privileged groups have high rates of out-migration from cities to suburbs, often leaving areas with heavy concentrations of minorities. The suburbs, rather than inner cities, are more likely to attract people moving out of rural areas.58 Have America's poor become increasingly segregated from the affluent in urban neighborhoods? This is an important question, because physical separation can foster cultural and economic separation from mainstream society. In his compelling analysis, Paul Jargowsky showed that both the percentage of neighborhoods that were poor (with poverty rates above 40 percent) and the percentage of poor people who Source: U.S. Census Bureau, Census 2000 Supplementary Survey (www.census.gov/c2ss/www/ Products/Rank/RankPL040.htm, accessed April 4, 2002). 21 ISS315 - PAGE 83 Box 4 Is Marriage an Economic Panacea? Marriage is high on the public policy agenda. One of goals of the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) was to "encourage the formation and maintenance of two-parent families." A 2001 report by the Heritage Foundation proposed that 10 percent of state funds for the Temporary Assistance for Needy Families (TANF) program be set aside for activities promoting marriage. And in early 2002, President George W. Bush proposed spending $300 million annually over five years for marriage promotion initiatives. Some states, including Florida, Oklahoma, and Arizona, already have been attempting to encourage marriage and discourage divorce by adding marriage preparation courses to the high school curriculum, creating tax incentives to encourage marriage (or at least not creating disincentives), and providing divorce counseling programs for the poor. A recent poll commissioned by the David and Lucile Packard Foundation indicated about 60 percent of Americans believed that encouraging unmarried parents to marry is "very important" or "somewhat important" for government programs. The marriage movement has been helped by mounting evidence that marriage confers economic and social advantages, and that children tend do best when raised by their two biological parents. Married women and their children have much lower rates of poverty than single mothers and their children. Moreover, new evidence suggests that many poor couples want to marry. Whether low-income women would benefit from marriage is a contentious issue. Critics believe that there is a very limited pool of financially stable men for low-income women to marry. Activities promoting marriage may only serve to further stigmatize single mothers, who often say they would marry if they found a suitable spouse. Some researchers are concerned that some low-income women may feel compelled to stay in abusive relationships because of welfare requirements, exposing their children to violence and themselves to psychological and physical abuse. Exposure to family violence can have strong negative effects on children's development. Other observers feel that marriage and intimate relationships are largely private matters and, as such, should remain outside the purview of government influence. The economic benefits of marriage depend on whether single mothers are able to marry, stay married, and marry well (that is, to marry a man with an adequate income). The evidence of marriage's benefits for single mothers is not clear. But a few facts stand out: Unwed mothers have low rates of marriage; marriages begun with an out-ofwedlock birth are very unstable; and, for single mothers who marry and stay married, the husband's income is often insufficient to escape poverty. To some observers, the best way to promote marriage, reduce poverty, and reduce welfare dependency is to eliminate unmarried childbearing in the first place. References Patrick F. Fagan, "Encouraging Marriage and Discouraging Divorce," Backgrounder, no. 1421 (Washington, DC: Heritage Foundation, 2001); Daniel T. Lichter, "Marriage as Public Policy," Policy Brief (Washington, DC: Progressive Policy Institute, 2001); Lake Snell Perry and Associates, Inc., "Public Views on Welfare Reform and Children in the Current Economy," accessed online at www.futureofchildren.org/usr_doc/ lsp_welfare_survey.PDF, on March 29, 2002; Sara McLanahan et al., "Fragile Families, Welfare Reform, and Marriage," Welfare Reform Policy Brief no. 10 (Washington, DC: Brookings Institution, 2001); Isabel Sawhill, "Is Lack of Marriage the Real Problem?" The American Prospect, Spring Supplement (2002): 8-9; and Ellen K. Scott et al., "My Children Come First: WelfareReliant Women's Post-TANF Views of WorkFamily Trade-Offs and Marriage," in For Better and For Worse: Welfare Reform and the Well-Being of Children and Families, ed. G.J. Duncan and P.L. Chase-Lansdale (New York: Russell Sage Foundation, 2002): 132-55. 22 ISS315 - PAGE 84 lived in poor neighborhoods increased during the 1970s and 1980s.59 Such increases reflect a number of factors, including the flight of middle-class blacks from central-city neighborhoods to the suburbs, housing discrimination against disadvantaged groups, and a growing mismatch between where low-income workers live and where good jobs are located. The question of whether poverty has become more or less concentrated in the 1990s will be answered when detailed results from the 2000 Census become available in 2003 or 2004. The existing data indicate that welfare caseloads have declined more slowly in cities than in the rest of the nation since PRWORA, and that welfare recipients are increasingly concentrated in 10 large urban counties.60 The geographic isolation of poor people has raised new concerns about the emergence of an American underclass. Concentrated neighborhood poverty--marked by idleness, family disorganization, crime, and other social pathologies that may reflect a rejection of mainstream American values--has stimulated research on the adverse effects of growing up in economically disadvantaged neighborhoods.61 Children's development suffers when they are exposed only to the values and behaviors of other impoverished peers, when they attend underfunded and understaffed neighborhood schools, when neighborhood adult supervision is limited, and when neighborhoods lack adequate police and other community resources that can safeguard residents. Children growing up in poor neighborhoods tend to have lower educational achievement, poorer health, and more developmental problems than other children. Neighborhood effects on child development and well-being seem strongest in early childhood and in late adolescence, although family background and income have a greater effect in early childhood.62 The loss of family income caused by divorce, for example, may mean that children move Photo removed for copyright reasons Children who grow up in poverty face more problems in school than other children. into poor neighborhoods, reinforcing the negative effects of family instability.63 Poor children, on average, have higher school achievement if they live in middle-class neighborhoods than if they live in poor neighborhoods. Rural Pockets of Poverty Historically, poverty has been more prevalent in rural communities than in urban or metropolitan areas; this is still the case today. In 2000, the official poverty rate in nonmetropolitan areas was 13.4 percent, compared with 10.8 percent in metropolitan areas. Rural residents have higher unemployment and earn lower wages than urban residents, on average. Part of the problem is that residents in rural areas tend to have below-average educational levels and limited job skills. But many rural areas also lack jobs that pay a living wage or that pay enough to cover the child-care or transportation costs of working. The rural poor are less likely than the urban poor to receive welfare income or food stamps, however, and the rural poor who do receive welfare get less cash assistance than they would in urban areas.64 Much of rural poverty is invisible, occurring in isolated rural pockets. Poverty rates are exceptionally high in rural counties in Appalachia, the Mis- 23 ISS315 - PAGE 85 Much of rural poverty is invisible, occurring in isolated rural pockets. sissippi Delta, American Indian reservations in the Southwest and Great Plains, the lower Rio Grande Valley in Texas, and the central valley of California (see Figure 7). Rural poverty is distinctive: It is often extreme (in excess of 40 percent) and has often persisted for decades, especially in the rural South. Except for rural Appalachia, which is predominately white, rural pockets of poverty also are disproportionately comprised of minorities: mostly communities of African Americans, Mexican Americans, and American Indians.65 Many Americans assume that disadvantaged minorities are concentrated exclusively in urban ghettos, but some of the most impoverished minorities live in isolated, economically depressed rural areas. In many rural places, the problem of low family income is compounded by physical isolation, inadequate infrastructure, and limited institutional resources and social support services. Many impoverished rural areas lack safe drinking water, public transportation, good schools with qualified teachers, and quality child care. Residents in such areas may be exposed to environmental toxins or face longstanding traditions of race discrimination and economic oppression. In the mid-1960s, Michael Harrington's influential book, The Other America, portrayed the economic circumstances of the rural Appalachian poor. The book caught the attention of President John F. Kennedy, and helped launch President Lyndon Johnson's War on Poverty. While these government programs improved the lives of many rural families in the ensuing decades, circumstances have changed little for many impoverished residents living in isolated areas. healthy cognitive and psychosocial development; it breeds a variety of antisocial behaviors, including violence; and it is associated with poorer physical health and shorter life expectancy. Growing up Poor Poverty often begets more poverty. Although most people believe that America is "a land of opportunity," common aphorisms sometimes suggest otherwise. "Like father, like son," "the apple doesn't fall far from the tree," "a chip off the old block"--each implies that risk characteristics (such as low intelligence, inadequate education, and dysfunctional or disorganized families) are passed down from generation to generation. One recent study suggests that between 16 percent and 25 percent of adult poverty results from the transmission of poverty from parents to children.66 Growing up in poverty is associated with negative outcomes in adolescence that provide a weak foundation for successful adult roles. Poor children are more likely to perform badly in their classes and on tests of cognitive ability.67 They are more likely to repeat grades or drop out of school; they are less likely to be highly engaged in school or to participate in extracurricular activities; and they experience significantly more serious emotional and behavioral problems, particularly during adolescence. Poor children are more likely to be depressed, have low self-esteem, and exhibit antisocial behaviors. Povertyrelated problems tend to be magnified if poverty occurs early in childhood rather than in adolescence. The negative effects of material deprivation on health and development appear to be cumulative; they keep those born into poverty in the ranks of the poor, even into adulthood.68 Small wonder, then, that disadvantaged children often become disadvantaged adults. Sociologists Paul Amato and Alan Booth's Generation at Risk, for example, showed that financial stress, measured in terms of parental income, welfare use, and change over the child's life Consequences of Poverty 24 Poverty is sometimes viewed as a public health issue. It has adverse consequences for health and psychological well-being: It undermines children's ISS315 - PAGE 86 Figure 7 Poverty Rates in U.S. Counties, 1998 National average poverty rate, 1998: 12.7% Below the national average (0.0%-12.6%) One to two times the national average (12.7%-25.4%) More than twice the national average (25.5%-43.8%) Source: U.S. Census Bureau, 1998 Small Area Income and Poverty Estimates. course, has long-term negative effects on children's later socioeconomic attainment (including schooling) and marital stability. While poverty has deleterious consequences for children and adolescents, the literature reveals surprisingly little consensus about how much poverty affects different adolescent outcomes, the significance of poverty compared with such other factors as parenting styles or residence, and how poverty produces negative outcomes. Many studies have sought to find out why poverty or low incomes matter during childhood. Most poverty researchers emphasize the effects of the lack of material resources, such as nutritious food; insufficient investments in child development, such as learning-rich environments; and ineffective parenting, especially in poor single-parent families.69 Poverty reduces the likelihood that there will be educational resources in the home, including books, magazines, and toys. The stresses and constraints of supporting a family on a small income may also affect parents' abilities to adequately nurture their children, provide appropriate role models, and supervise and instruct their children. Income becomes less important as children move into elementary school. Cognitive stimulation in the home turns out to be the single most important factor in a child's intellectual development; parenting style, physical environment of the home, and poor health at birth are relatively less important.70 In Growing Up With a Single Parent, Sara McLanahan and Gary Sandefur also claimed that the harmful effects of growing up in a single-parent 25 ISS315 - PAGE 87 Poor children have higher Poverty and Health rates of asthma, The links between health and race, diabetes, and ethnicity, and class have attracted increasing attention over the past other health decade. The National Institutes of problems. Health, the Centers for Disease Con- home result in large part from poverty. Income explained roughly 50 percent of the effects of single motherhood on adolescent problem behaviors. Still, scholars disagree about whether poverty causes these negative outcomes, and if so, how. In What Money Can't Buy, for example, Susan Mayer shows that low family income during childhood is only modestly associated with a variety of negative outcomes in late adolescence and early adulthood, including teen pregnancy and male unemployment. 26 trol and Prevention (CDC), and private philanthropies, such as the Robert Wood Johnson Foundation, have launched ambitious programs focused on socioeconomic disparities in health. Renewed concerns about social justice in public health are rooted in evidence linking socioeconomic status and health. According to demographers Samuel Preston and Paul Taubman, "Mortality rates and the prevalence of ill health are higher among groups of low social standing in all contemporary Western countries." Preston and Taubman claim that the gaps generally widened between the 1970s and 1990s.71 Low-income mothers are more likely to have low birth-weight babies, who are at greater risk than other babies for a variety of cognitive and emotional problems.72 In addition, poor children are more likely than other children to be exposed to toxic substances and other environmental health risks and to have less healthy diets. These greater health and environmental risks help explain the higher rates of asthma, diabetes, learning disabilities, and speech or hearing problems that limit the school attendance of poor children and interfere with their academic performance and physical activities. The percentage of poor children with such chronic health conditions increased between the mid-1980s and mid-1990s, and the gap between poor children and other children widened.73 More than 12 percent of poor children ages 1 to 5 have elevated levels of lead in their blood, compared with about 2 percent of high-income children. Among whites, poor adolescents are twice as likely to be obese as affluent adolescents. Many of these problems go untreated. Nearly one-quarter of children from poor or near-poor families lack health insurance coverage, compared with 4 percent of high-income children. Poor and near-poor children also are less likely to be fully vaccinated against childhood diseases or to have seen a personal physician in the past year. Not surprisingly, low-income children are four times as likely to be in "fair" or "poor" health as higher-income children.74 Infant mortality rates are also substantially higher among children of high school dropouts than among college-educated mothers. Poor mothers often receive inadequate prenatal care, and are more likely to suffer health conditions that affect the fetus, including hypertension and diabetes, vitamin deficiencies, drug or alcohol dependencies, and HIV infection.75 More generally, the CDC shows that there are large socioeconomic status differentials in health among adults (see Figure 8). More than 25 percent of poor adults ages 18 and over indicate that their health is "fair" or "poor," compared with less than 5 percent of adults with annual incomes above $50,000. A similar health disparity by income status exists within racial and ethnic groups. The poor are also three to four times more likely than wealthier Americans to report limitations in their activities because of health, and to suffer a greater number of acute and chronic health conditions. The poor have a lower life expectancy, regardless of race or ethnicity. Among white men age 65, for ISS315 - PAGE 88 example, additional life expectancy is about 14 years for those with annual incomes below $10,000, compared with 17 years among those with incomes over $25,000. Part of the reason for the difference is that poor people are more likely to suffer from heart disease, lung cancer, diabetes, and various degenerative diseases. In 1995, the risk of dying of heart disease was 2.5 times greater for men with incomes below $10,000 than it was for men with incomes over $25,000. People in lower socioeconomic groups are also more likely to die violent deaths. Homicide and suicide rates are substantially higher among the least educated in every racial and ethnic group. Current debates focus on whether existing socioeconomic differentials in health and longevity reflect the causal effects of low income on access to health care, or instead reflect unhealthy behavioral patterns, including smoking, lack of exercise, and poor diets, that are born of cultural patterns, low education, inadequate health knowledge, or feelings of hopelessness. But one recent study suggests that disparities in health and longevity cannot be attributed to the bad habits or lifestyles of the poor. The study found that males over age 30 with annual incomes below $10,000 were nearly three times more likely to die than those earning $10,000 or more, even considering differences in rates of smoking, drinking, obesity, and physical inactivity.76 Figure 8 Americans Reporting `Fair' or `Poor' Health by Annual Family Income, 1995 Percent of adults age 18 or older 21 13 8 6 4 Less than $15,000 $15,000$24,999 $25,000$34,999 $35,000$49,999 $50,000 or more Source: National Center for Health Statistics, Health, United States, 1998 With Socioeconomic and Health Chartbook (1998): 272. Concentrated urban poverty seems to exacerbate the negative mental health consequences of low income. Compared with other adults, adults who live in the most disadvantaged neighborhoods see more drug use and drinking on the street; report more crime, graffiti, and vandalism; and are more likely to feel that their neighborhoods are unsafe. The daily stresses that poor adults experience from the social disorder in their neighborhoods adversely affect psychological functioning and physical health.79 Poor health lowers productivity and earnings, undermines positive social interaction and social support, and depletes adults' capacities as caretakers. Mental Health Economic hardship is an emotional strain on adults who are struggling to provide for their families. Feelings of depression, such as hopelessness, sadness, and worry, prey disproportionately on poor adults, particularly those under age 60.77 Indeed, lowincome parents are at least twice as likely as other parents to exhibit poor mental health and highly aggravated behavior.78 Psychosocial problems generally coexist with physical symptoms, including chronic fatigue and insomnia. Poverty and Crime The association between poverty and crime arouses passionate debate among social scientists and public opinion leaders. The empirical evidence is unequivocal: A higher percentage of the poor than the nonpoor are arrested, convicted for violent crimes, and incarcerated. Violent and property crime rates tend to be higher in poor neighborhoods and economically depressed urban areas than in other areas.80 Poor people are 27 ISS315 - PAGE 89 Table 3 Victims of Violent Crime by Income Level, 2000 Victims per 1,000 people age 12 or older Rape/sexual assault Robbery Assault 4.3 8.1 45.1 1.6 6.9 35.9 3.2 4.8 27.2 1.2 3.1 33.7 1.6 3.5 25.3 1.5 2.2 29.7 0.8 1.8 20.3 Annual income Less than $7,500 $7,500-$14,999 $15,000-$24,999 $25,000-$34,999 $35,000-$49,999 $50,000-$74,999 $75,000 or more bearing. According to this view, poverty is a consequence of bad decisionmaking early in life. Spending time in jail, especially in early adulthood, may cut short education and job preparation, elevating the likelihood of chronic poverty. Welfare Reform Poverty and welfare receipt are inextricably linked. Government programs may help low-income women and children meet their basic daily needs (through cash assistance programs such as TANF or food stamps). But there is a continuing fear that welfare itself has negative effects on lowincome women and their children, and that welfare creates economic dependency and perpetuates the cycle of poverty. The welfare reform bill was designed largely to encourage economic self-sufficiency. However, some analysts are concerned that the work requirements imposed by TANF, along with time limits, may hurt women and children in unforeseen ways. Source: U.S. Bureau of Justice Statistics, "Criminal Victimization 2000: Changes 19992000 With Trends 1993-2000" (2001): Table 14. 28 also more likely than other Americans to be the victims of crime (see Table 3).81 But the interpretation of this evidence is not straightforward. One common view is that poverty and inequality sow the seeds of crime and deviant social behavior. Poor children are more likely than other children to be raised by single mothers, have minimal supervision, become involved with delinquent peers, and be socialized into deviant subcultures, such as gangs and organized crime. According to another view, disadvantaged persons, even if they aspire to middle-class values and goals, may turn to illegal activities when they find that legitimate routes to a better material life are blocked by their low educational attainment or by discrimination.82 A related view holds that the poor are disproportionately targeted for arrest, and that they are more likely to be convicted and jailed than nonpoor people because they have weaker legal representation, among other disadvantages. Critics claim that white-collar crime by wealthier Americans is rarely targeted in the same way. Some analysts suggest that delinquent or criminal behaviors lead directly to poverty. Underage drinking and drug use, for example, may lead indirectly to other criminal behaviors, including gang activity and violent and property crimes, that lead ultimately to dropping out of school, unemployment, or unmarried child- Single Mothers Time limits on welfare eligibility may encourage some women to enter or stay in unhealthy or abusive relationships. An Urban Institute study reported that the rate of cohabitation among welfare recipients doubled between 1997 and 1999.83 Women who cohabit suffer a much higher rate of physical violence than similar women who are married.84 Welfare dependency has been associated with substance use and mental health problems. Sociologist Rukmalie Jayakody and her colleagues estimated that 21 percent of welfare recipients, compared with 13 percent of single mothers not receiving welfare, had used an illegal substance in the past year; marijuana use was the most common.85 But it is not clear whether substance abuse is a cause or consequence of poverty and welfare dependence. Higher rates of drug abuse among welfare mothers ISS315 - PAGE 90 may also be a response to their higher levels of depression. Mental health problems are more common among single mothers on welfare than other single mothers. The 1995 National Household Survey of Drug Abuse (NHSDA) reported that about 20 percent of women on welfare had experienced psychiatric disorders, such as major depression, anxiety disorders, panic attacks, and agoraphobia, within the past year, compared with 13 percent of other single mothers.86 Psychiatric problems are associated with a higher probability of going on and staying on welfare, according to a longitudinal survey of welfare recipients.87 Mental health problems also increase women's risk of being barred from welfare for failing to comply with TANF work requirements and other regulations. There is little evidence so far as to whether women's mental health has generally deteriorated or improved as a result of leaving welfare.88 Children's Well-Being How does welfare mothers' greater risk of substance abuse and depression affect their ability to care for their children? The optimistic view is that work-based welfare will, on balance, enhance children's cognitive and emotional development. By working, advocates argue, mothers can enhance their own mental health, selfesteem, and sense of personal power. A working mother provides a positive role model for her children. Through regular employment, she may instill values in her children that emphasize work over welfare. Steady employment also "routinizes" daily life and gives children's lives needed structure. In turn, the children grow up to become better parents, more effective in supervising their children and meting out appropriate discipline. In a recent evaluation of 10 welfare demonstration projects, Martha Zaslow and colleagues reported that welfare had minimal effects on children.89 In a related evaluation of many of the same demonstration pro- grams, however, Greg Duncan and Lindsay Chase-Lansdale found that impacts varied by children's age. Cash assistance had generally positive effects on school achievement among elementary-school age children, but negative effects on adolescents.90 Evaluation of the New Hope Project in Milwaukee revealed that cash assistance had significant positive effects on children's educational progress and aspirations and on teachers' assessments of students' compliance and self-control, competence and sensitivity, and autonomy.91 Much of the positive effect reflected higherquality child-care arrangements and after-school programs, rather than maternal psychological benefits (such as higher self-esteem) or improved parenting practices. The opposing view is that working for low pay creates additional stress on mothers, reduces the time-- especially quality time--spent with children, and diverts income to work-related expenses such as transportation and child care. Indeed, the evaluation by Zaslow and colleagues suggested that unfavorable child outcomes tended to occur when the economic circumstances of welfare families did not improve or got worse.92 Such studies buttress the arguments that reducing poverty and improving the well-being of children should be explicit goals of PRWORA's reauthorization. Initially, critics worried that welfare reform's emphasis on getting welfare mothers into the labor force would lead to more cases of child abuse and neglect, fosterage, and abandonment. They feared that reform would undermine effective parenting and supervision. Some single mothers might be forced to turn over parenting responsibilities to grandparents or other relatives to hold a job. If mothers were unable to find work and were forced off welfare, the loss of income might also adversely affect children's psychosocial development: Reports of child abuse and neglect are higher in low-income and welfare-dependent families than in other families, and Mental health problems are more common among single mothers on welfare. 29 ISS315 - PAGE 91 the effects such abuses have on children's development are welldocumented.93 Data from the National Survey of America's Families provided no evidence of increased child abuse or neglect following welfare reform, although the data may reflect the improved economy and declining poverty rates rather than effects of welfare reforms.94 Poverty reduction was not among the stated purposes of the 1996 welfare reform bill. Reauthorization of PRWORA Poverty reduction was not among the stated purposes of the 1996 welfare reform bill. The main goals were to reduce welfare dependence and encourage greater self-sufficiency, as well as to promote two-parent families as a context for having and raising children. Caseloads have declined in large part because roughly two-thirds of recent welfare leavers have become employed.95 The growing consensus among public officials and policy analysts is that we should redouble our efforts to promote employment. Some welfare advocates argue that reduction of poverty should be a specific component of the next phase of welfare reform. The American public seems to agree: A recent national poll indicated that 71 percent of Americans believed that reducing poverty is "very important" for government programs such as welfare.96 The Center for Law and Social Policy urged "that one key message should be the need to broaden the focus of state efforts from caseloads to efforts to reduce family poverty."97 The Progressive Policy Institute also recommended making support for low-wage workers "the central organizing principle of America's 21st-century social policy," and proposed a poverty-reduction bonus to reward states that reduce poverty rates among working families.98 There is a growing consensus across the political spectrum that the government should expand supports for low-wage workers. Proposals 30 include expanding the EITC, increasing funds for child care, providing health care and housing vouchers, and reinstating cash benefits for legal immigrants. These proposals are consistent with the common view that working families who play by the rules should not be poor. Other proposals would allow working parents to keep some of their welfare benefits so that they do not simply go from being welfare poor to working poor. Some states have already expanded the use of earned income disregards, which allow working mothers to have a certain percentage of their earnings ignored when they apply for assistance. More generous earning disregards might create additional incentives for lowwage mothers to work. Another incentive would be to stop or even reverse the clock on the five-year time limit for cash assistance if welfare mothers work. The 1996 welfare reform bill included provisions to establish paternity, as well as responsible fatherhood initiatives designed to increase fathers' involvement with and financial commitment to their children. Many states have actively sought delinquent child support payments from fathers. But rather than passing the money on to families directly, many states use those funds as reimbursement for welfare payments to the family. Child advocates argue that there is little incentive for responsible fathers to pay child support if their money will not benefit their children directly, and suggest greater use of "pass-through" policies that would allow families to keep part of the child support. California and New York, for example, pass through part of the support they collect monthly (typically $50) to the mothers. The idea is that fathers may be more likely to pay child support, as well as maintain a closer connection to their children, if their payments directly benefit their children. Other analysts suggest that the government could reduce poverty by allowing TANF programs to cover ISS315 - PAGE 92 more low-income, two-parent families. Most TANF monies now target single mothers and their children. Recent findings from the Fragile Families Study suggest that 50 percent of unmarried mothers are living with the father of their children, and another 30 percent are intimately involved with the father. The current system may discourage marriage, since marriage threatens mothers' eligibility for TANF. Much of the debate over reauthorization involves how best to serve the needs of low-income families, and how to allow low-income women to marry and have children without penalty from the welfare system. Uncertain Future It is unclear whether recent declines in poverty rates will stop or even reverse as the U.S. economy ends its boom years. And it is difficult to tell whether those at the bottom of the economic ladder, including former welfare recipients, will be hurt most by a changing economy. It is clear, however, that poverty--and what public policies can do about it--will continue to arouse the passions of both liberals and conservatives. Most Americans, who hope to create a just society, are unusually generous, if charitable contributions and volunteerism are the measure. But many people also remain ambivalent toward the poor, debating whether the poor truly deserve public assistance, and whether the poor themselves bear most of the responsibility for their current circumstances in this "land of opportunity." The 1996 welfare reform bill helped refocus national attention on the plight of America's poor. The good news is that welfare caseloads have plummeted since the mid-1990s, without an increase in poverty. Welfare reform has not been the unmitigated disaster first feared by its critics. Poverty rates have declined even among America's historically disadvantaged groups. But poor people still live with food insecurity, inadequate housing, and poor medical care, all of which are reflected in poorer physical and mental health. There is some indication that public attitudes and stereotypes about the poor, although still decidedly negative, may be softening. And there may be greater public commitment to helping poor people today than at any other time in recent memory. Indeed, the debate over reauthorization of the welfare bill has stimulated new proposals--from the political left and right--about how to keep welfare dependence low while reinforcing the downward trend in poverty rates and improving the economic well-being of single mothers and their children. Yet, despite innumerable studies of poverty and its causes, poverty remains a distinctive part of the American economic and political landscape. The gap between rich and poor has widened. The root causes of poverty are complex and manifold; eradicating poverty and its effects will require many different solutions. There is no panacea. In many cases, the welfare poor have simply become the working poor, without experiencing a significant improvement in economic wellbeing. Helping those who work and behave responsibly will continue to be a policy concern for the foreseeable future, especially during an economic downturn. The political fault lines are perhaps less clear-cut than in the past. Simple nostrums, such as "economic growth is the best solution to poverty," are viewed with more skepticism in light of ambiguous evidence of their accuracy over the past several decades. Moreover, few Americans want to return to the days of AFDC, when poor single mothers often remained on welfare indefinitely, with no real future for themselves or their children. The story of welfare reform and poverty is still being written. 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Ann Huff Stevens, "Climbing Out of Poverty, Falling Back In: Measuring the Persistence of Poverty Over Multiple Spells," Journal of Human Resources 34, no. 3 (1999): 557-88. Mark R. Rank and Thomas A. Hirschl, "The Economic Risk of Childhood in America: Estimating the Probability of Poverty Across the Formative Years," Journal of Marriage and the Family 61, no. 4 (1999): 1058-67. Mary Corcoran, "Mobility, Persistence, and the Intergenerational Determinant of Children's Success," Focus 21, no. 2 (2000): 16-20. Leif Jensen and Diane K. McLaughlin, "The Escape From Poverty Among Rural and Urban Dwellers," Gerontologist 37, no. 4 (1997): 462-68. Donna R. Morrison and Amy Ritualo, "Routes to Children's Economic Recovery After Divorce: Are Cohabitation and Remarriage Equivalent?" American Sociological Review 65, no. 4 (2000): 560-80; and Suzanne M. Bianchi et al., "The Gender Gap in the Economic Well-Being of Nonresident Fathers and Custodial Mothers," Demography 36, no. 2 (1999): 195-203. Peter Gottschalk and Sheldon Danziger, "Income Mobility and Exits From Poverty of American Children, 1970-1992," in The Dynamics of Child Poverty in Industrialised Countries, ed. B. Badbury et al. (Cambridge, England: Cambridge University Press, 2001). 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 32 ISS315 - PAGE 94 27. Stevens, "Climbing Out of Poverty, Falling Back In." 28. Daniel R. Meyer and Maria Cancian, "Economic Well-Being Following an Exit From Aid to Families With Dependent Children," Journal of Marriage and the Family 60, no. 3 (May 1998): 479-92. 29. Luxembourg Income Study, "Relative Poverty Rates for the Total Population, Children, and the Elderly," accessed online at http://lisweb.ceps.lu/keyfigures/povertytable.htm, on April 10, 2002; and U.S. Census Bureau, "Poverty Status of People by Family Relationship, Race, and Hispanic Origin: 1959 to 2000," accessed online at www.census.gov/hhes/poverty/histpov/hstpov2.html, on April 10, 2002. 30. Robert D. Plotnick et al., "Inequality, Poverty, and the FISC in Twentieth-Century America," Journal of Post Keynesian Economics 21, no. 1 (1998): 51-75. 31. Martina Morris and Bruce Western, "Inequality in Earnings at the Close of the Twentieth Century," Annual Review of Sociology 25 (1999): 623-57. 32. Kurt J. Bauman, "Extended Measures of Well-Being: Meeting Basic Needs" (U.S. Department of Commerce, Economics and Statistics Administration, Household Economic Studies, June 1999). 33. William L. Hamilton et al., Household Food Security in the United States in 1995: Summary Report of the Food Security Measurement Project (Washington, DC: U.S. Department of Agriculture, September 1997). See also Marion Nestle, "Hunger in America: A Matter of Policy," Social Research 66, no. 1 (1999): 257-82. 34. Bauman, "Extended Measures of Well-Being: Meeting Basic Needs"; and Ronald Paul Hill and Debra Lynn Stephens, "Impoverished Consumers and Consumer Behavior: The Case of AFDC Mothers," Journal of Macromarketing 17, no. 2 (1997): 32-48. 35. Kathleen Short and Martina Shea, "Beyond Poverty, Extended Measures of Well-Being: 1992" (U.S. Department of Commerce, Economics and Statistics Administration, Household Economic Studies, November 1995). 36. Short and Shea, "Beyond Poverty, Extended Measures of Well-Being: 1992." 37. 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Donley, The Low-Income Consumer: Adjusting the Balance of Exchange (Thousand Oaks, CA: Sage, 1996). 43. Chanjin Chung and Samuel L. Myers, Jr., "Do the Poor Pay More for Food? An Analysis of Grocery Store Availability and Food Price Disparities," Journal of Consumer Affairs 33, no. 2 (1999): 276-91; Phil R. Kaufman, "Rural Poor Have Less Access to Supermarkets, Large Grocery Stores," Rural Development 13, no. 3 (1999): 19-25; Linda F. Allwitt and Thomas D. Donley, "Retail Stores in Poor Urban Neighborhoods," Journal of Consumer Affairs 31, no. 1 (1997): 139-64; and Alan R. Andreason, "Revisiting the Disadvantaged: Old Lessons and New Problems," Journal of Public Policy and Marketing 12 (Fall 1993): 270-75. 44. Anne Kim, "Taking the Poor Into Account: What Banks Can Do to Better Serve Low-Income Markets," Policy Report (Washington, DC: Progressive Policy Institute, Aug. 2, 2001). 45. U.S. Census Bureau, "Asset Ownership of Households, 1995," accessed online at www.census.gov/hhes/ www/wealth/1995/highlights.html, on April 12, 2002. 46. Melvin L. Oliver and Thomas M. Shapiro, Black Wealth/White Wealth (New York: Routledge Press, 1997). 47. Robert Haveman and Edward N. Wolff, "Who Are the Asset Poor? Levels, Trends, and Composition, 1983-1998," Working Paper 1227-01 (Madison, WI: University of Wisconsin-Madison, Institute for Research on Poverty, 2000). Liquid assets include cash deposits, bonds and securities, stocks and mutual funds, and the surrender value of life insurance. 48. National Public Radio, Kaiser Family Foundation, and Harvard University Kennedy School, "Poverty in America," accessed online at http://npr.org/programs/specials/poll/poverty/, on April 8, 2002. 49. Bonnie Thornton Dill, "A Better Life for Me and My Children: Low-Income Single Mothers' Struggle for Self-Sufficiency in the Rural South," Journal of Comparative Family Studies 29, no. 2 (1998): 419-28; and Roberta Rehner Iversen and Naomi B. Farber, "Transmission of Family Values, Work, and Welfare Among Poor Urban Black Women," Work and Occupations 23, no. 4 (1996): 437-60. 50. Kathryn Edin and Laura Lein, Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work (New York: Russell Sage Foundation, 1997). 51. Iversen and Farber, "Transmission of Family Values, Work, and Welfare Among Poor Urban Black Women." 52. Robin L. Jarrett, "Welfare Stigma Among Low-Income African American Single Mothers," Family Relations 45 (1996): 368-74. 53. Dill, "A Better Life for Me and My Children: Low-Income Single Mothers' Struggle for Self-Sufficiency in the Rural South"; Kathryn Edin, "The Myths of Dependence and Self-Sufficiency: Women, Welfare, and Low-Wage Work," Focus 17, no. 2 (1995): 1-9; and Jarrett, "Welfare Stigma Among Low-Income African American Single Mothers." 54. Charles Murray, "Family Formation," in The New World of Welfare, ed. R. Blank and R. Haskins (Washing- 33 ISS315 - PAGE 95 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 34 ton, DC: Brookings Institution, 2001): 137-68; and Patrick Fagin, "Encouraging Marriage and Discouraging Divorce" (Washington, DC: Heritage Foundation, March 26, 2001). Kathryn Edin, "What Do Low-Income Single Mothers Say about Marriage?" Social Problems 47, no. 1 (2000): 112-33. Ellen K. Scott et al., "My Children Come First: Welfare-Reliant Women's Post-TANF Views of Work-Family Trade-Offs and Marriage," in For Better and For Worse: Welfare Reform and the Well-Being of Children and Families, ed. G.J. Duncan and P.L. Chase-Lansdale (New York: Russell Sage Foundation, 2002): 132-53. Janet E. Kodras, "The Changing Map of American Poverty in an Era of Economic Restructuring and Political Realignment," Economic Geography 73 (January 1997): 67-93. Kyle Crowder, "The Racial Context of White Mobility: An Individual-Level Assessment of the White Flight Hypothesis," Social Science Research 29, no. 2 (2000): 223-57. Paul A. Jargowsky, Poverty and Place: Ghettos, Barrios, and the American City (New York: Russell Sage Foundation, 1997); see also Mario Luis Small and Katherine Newman, "Urban Poverty After The Truly Disadvantaged: The Rediscovery of the Family, the Neighborhood, and Culture," Annual Review of Sociology 27 (2001): 23-45 Katherine Allen and Maria Kirby, "Unfinished Business: Why Cities Matter to Welfare Reform" (Washington, DC: Brookings Institution, 2000). Between 1994 and 1999, the share of the nation's caseloads in 10 counties increased from 24 percent to 33 percent (Los Angeles, New York City, Cook [Chicago], Philadelphia, San Bernadino, Wayne [Detroit], San Diego, Sacramento, Fresno, and Cuyahoga [Cleveland]). Jeanne Brooks-Gunn et al., eds., Neighborhood Poverty: Context and Consequences for Children (New York: Russell Sage Foundation, 1997). Brooks-Gunn et al., Neighborhood Poverty: Context and Consequences. Scott J. South et al., "Children's Residential Mobility and Neighborhood Environment Following Parental Divorce and Remarriage," Social Forces 77, no. 2 (1998): 667-93. Robert Gibbs, "Nonmetro Labor Markets in the Era of Welfare Reform," Rural America 16, no. 3 (2001): 11-21; and Daniel T. Lichter and Leif Jensen, "Poverty and Welfare Among Rural Female-Headed Families: Before and After PRWORA," Rural America 16, no. 3 (2001): 28-35. Daniel T. Lichter et al., "Rural Children and Youth at Risk," in Challenges for Rural America in the Twenty-First Century, ed. D.L. Brown and L.E. Swanson (University Park, PA: Pennsylvania State University Press, 2002). Mary Corcoran, "Mobility, Persistence, and the Intergenerational Determinant of Children's Success." Greg J. Duncan and Jeanne Brooks-Gunn, eds., Consequences of Growing Up Poor (New York: Russell Sage Foundation, 1997); and Jennifer Ehrle and Kristin Moore, "Children's Environment and Behavior: Behavior and Emotional Problems in Children," Snapshots of America's Families: 1997 Results, accessed online at http://newfederalism.urban.org/nsaf/children_c6.html, on April 10, 2002. Alex Marsh et al., "Housing Deprivation and Health: A Longitudinal Analysis," Housing Studies 15, no. 3 (2000): 411-28; Richard A. Miech et al., "Low Socioeconomic Status and Mental Disorders: A Longitudinal Study of Selection and Causation During Young Adulthood," American Journal of Sociology 104, no. 4 (1999): 1096-131; and Guang Guo, "The Timing of the Influences of Cumulative Poverty on Children's Cognitive Ability and Achievement," Social Forces 77 (1998): 257-87. Pamela Klebanov et al., "Does Neighborhood and Family Affect Mothers' Parenting, Mental Health, and Social Support?" Journal of Marriage and the Family 56, no. 2 (1994): 441-55; and Sanders Korenman et al., "Long-Term Poverty and Child Development in the United States: Results From the NLSY," Children and Youth Services Review 17, no. 1-2 (1995): 127-55. Greg J. Duncan and Jeanne Brooks-Gunn, "Income Effects Across the Life Span: Integration and Interpretation," in Consequences of Growing Up Poor, ed. G. J. Duncan and J. Brooks-Gunn (New York: Russell Sage Foundation, 1977): 596-610; and Guang Guo and Kathleen Mullan Harris, "The Mechanisms Mediating the Effects of Poverty on Children's Intellectual Development," Demography 37, no. 4 (2000): 431-47. Samuel H. Preston and Paul Taubman, "Socioeconomic Differences in Adult Mortality and Health Status," in Demography of Aging, ed. L. Martin and S. Preston (Washington, DC: National Academy Press, 1994): 279-318; see also James S. House, "Relating Social Inequalities in Health and Income," Journal of Heath Politics, Policy, and Law 26, no. 3 (2001): 523-32; and Diane K. McLaughlin and C. Shannon Stokes, "Income Inequality and Mortality in U.S. Counties: Does Minority Racial Concentration Matter?" American Journal of Public Health 92, no. 1 (2002): 99-104. Jason D. Boardman et al., "Low Birth Weight, Social Factors, and Developmental Outcomes Among Children in the United States," Demography 39, no. 2 (2002); and Sanders Koreman and Jane E. Miller, "Long-Term Poverty and Child Development in the United States: Results From the NLSY," in Consequences of Growing Up Poor for Young Children, ed. G.J. Duncan and J. Brooks-Gunn (New York: Russell Sage Publications, 1997): 70-99. National Center for Health Statistics, Health, United States, 1998 With Socioeconomic Status and Health Chartbook (Washington, DC: U.S. Government Printing Office, 1998): 46-84. Stephen Zuckerman and Stephen Norton, "Health: Health Status of Nonelderly Adults and Children," Snapshots of America's Families: 1997 Results, accessed online at http://newfederalism.urban.org/nsaf/ snapshots_index.html, on April 9, 2002. Karen Seccombe, "Families in Poverty in the 1990s: Trends, Causes, Consequences, and Lessons Learned," Journal of Marriage and Family 62, no. 6 (2000): 1094-113. Paula M. Lantz et al., "Socioeconomic Factors, Health Behaviors, and Mortality: Results from a National Representative Prospective Study of U.S. Adults," Journal of the American Medical Association 279, no. 21 (1998): 1703-08. ISS315 - PAGE 96 77. Catharine E. Ross and Marieke Van Willigen, "Education and the Subjective Quality of Life," Journal of Health and Social Behavior 38, no. 3 (1997): 275-97; and John Mirowsky and Catherine E. Ross, "Age and the Effect of Economic Hardship on Depression," Journal of Health and Social Behavior 42, no. 2 (2001): 132-50. 78. Jenifer Ehrle et al., "Adults' Environment and Behavior," Snapshots of America's Families: 1997 Results, accessed online at http://newfederalism.urban.org/nsaf/snapshots_index.html, on April 9, 2002. 79. Catherine E. Ross, "Neighborhood Disadvantage and Adult Depression," Journal of Health and Social Behavior 41, no. 2 (2000): 177-87; and Catharine E. Ross and John Mirowsky, "Neighborhood Disadvantage, Disorder, and Health," Journal of Health and Social Behavior 42, no. 3 (2001): 258-76. 80. Jeffrey Reiman, The Rich Get Richer and the Poor Get Prison: Ideology, Class, and Criminal Justice (Boston: Allyn and Bacon, 1997); and C. Hsieh and M.D. Pugh, "Poverty, Income Inequality, and Violent Crime: A Meta-Analysis of Recent Aggregate Data Studies," Criminal Justice Review 18 (1993): 182-202. 81. Saul D. Levitt, "The Changing Relationship Between Income and Crime Victimization," Economic Policy Review 5, no. 3 (1999): 87-98. 82. See A. Tesser, ed., Advanced Social Psychology (Boston: McGraw-Hill, 1995). 83. Shelia R. Zedlewski and Donald W. Alderson, "Before and After Reform: How Have Families on Welfare Changed?" Assessing the New Federalism Policy Brief B-32 (Washington, DC: Urban Institute, 2001). 84. Eleanor Lyon, "Poverty, Welfare and Battered Women: What Does the Research Tell Us?" accessed online at www.vaw.umn.edu/Vawnet/welfare.htm, on April 9, 2002; and Patricia Tjaden and Nancy Thoennes, Extent, Nature, and Consequences of Intimate Partner Violence: Findings From the National Violence Against Women Survey (Washington, DC: U.S. Department of Justice, Office of Justice Programs, 2000). 85. Rukmalie Jayakody et al., "Welfare Reform, Substance Use, and Mental Health," Journal of Health Politics, Policy and Law 25, no. 4 (2000): 623-51; and Rukmalie Jayakody and Dawn Stauffer, "Mental Health Problems Among Single Mothers: Implications for Work and Welfare Reform," Journal of Social Issues 56, no. 4 (2000): 617-34. 86. Jayakody et al., "Welfare Reform, Substance Use, and Mental Health." 87. Harold A. Pollack et al., "Drug Testing Welfare Recipients: False Positives, False Negatives, Unanticipated Opportunities," Poverty Research and Training Center Working Paper (Ann Arbor, MI: University of Michigan, 2001); and Sandra K. Danziger, "Why Some Women Fail to Achieve Economic Security: Low Job Skills and Mental Health Problems Are Key Barriers," The Forum 4, no. 2 (2001): 1-3. 88. Jayakody and Stauffer, "Mental Health Problems Among Single Mothers: Implications for Work and Welfare Reform." 89. Martha J. Zaslow et al., "Experimental Studies of Welfare Reform and Children," Future of Children 12 (2002): 79-95. 90. Greg J. Duncan and Lindsay Chase-Landale, "Welfare Reform and Child Well-Being," in The New World of Welfare, ed. R. Blank and R. Haskins (Washington, DC: Brookings Institution, 2001): 391-417. 91. Rashmita S. Mistry et al., "Lessons from New Hope: The Impact on Children's Well-Being of a Work-Based Anti-Poverty Program for Parents," in For Better and For Worse: Welfare Reform and the Well-Being of Children and Families, ed. G.J. Duncan and L. Chase-Landale (New York: Russell Sage Foundation, 2001): 179-200. 92. Zaslow et al., "Experimental Studies of Welfare Reform and Children." 93. David J. Fein and Wang S. Lee, The ABC Evaluation: Impacts of Welfare Reform on Child Maltreatment (Cambridge, MA: Abt Associates, 2000); and Greg J. Duncan and Jeanne Brooks-Gunn, "Family Poverty, Welfare Reform, and Child Development," Child Development 71, no. 1 (2000): 188-96. 94. Rob Geen et al., "Welfare Reform's Effect on Child Welfare Caseloads," Assessing the New Federalism Discussion Paper 01-04 (Washington, DC: Urban Institute, 2001); and Christina Paxson and Jane Waldfogel, "Welfare Reform, Family Resources, and Child Maltreatment," in The Incentives of Government Programs and the Well-Being of Families, ed. B. Meyer and G.J. Duncan (Evanston, IL: Joint Center for Research on Poverty, 2001). 95. Robert A. Moffitt, "From Welfare to Work: What the Evidence Shows," Welfare and Beyond Policy Brief 13 (Washington, DC: Brookings Institution, 2002). 96. Future of Children, Public Views on Welfare Reform and Children in the Current Economy (Los Altos, CA: Future of Children, 2002). 97. Center for Law and Social Policy, "Comments on Reauthorization of the Temporary Assistance for Needy Families (TANF) Block Grant," submitted to the U.S. Department of Health and Human Services, Nov. 31, 2001. 98. Will Marshall and Anne Kim, Finishing the Welfare Revolution: A Blueprint for TANF Renewal (Washington, DC: Progressive Policy Institute, 2002). 35 ISS315 - PAGE 97 Who was poor in 2003? National poverty data are calculated using the official Census definition of poverty, which has remained fairly standard since it was introduced in the 1960s and is useful for measuring progress against poverty. Under this definition, poverty is determined by comparing pretax cash income with the poverty threshold, which adjusts for family size and composition. 1 In 2003, according to the official measure, 12.5 percent of the total U.S. population lived in poverty (Table 1). Is Poverty Different for Different Groups in the Population? The poverty rate represents an average over the entire population, and does not really tell us who, in particular, is well off, who is worse off. For that, it is necessary to examine poverty levels for particular groups. Most notably, blacks and Hispanics have poverty rates that greatly exceed the average. The poverty rate for all blacks and Hispanics remained near 30 percent during the 1980s and mid-1990s. Thereafter it began to fall. In 2000, the rate for blacks dropped to 22.1 percent and for Hispanics to 21.2 percent--the lowest rate for both groups since the United States began measuring poverty. The Current Population Survey, from which the poverty statistics are drawn, implemented a new question in 2003 to collect information on race, allowing individuals to report one or more races. There is no way of knowing how people who reported more than one race would have reported their race under the old question. Table 1 shows that those who defined themselves as black only or as black and some other race had the highest poverty rates--around 24 percent. Among those of Hispanic origin, who can be of any race, the poverty rate was 22.5 percent. Poverty rates for Asians were 11.8 percent. Among children under age 18, 17.6 percent, nearly 13 million children, lived in poverty. (See Table 1 and also the FAQ, How Many Children Are Poor?) The poverty rate for those over 65, which in 1959 exceeded the overall poverty rate, fell below it beginning in 1982. In 2003 this was the only major population group for which poverty continued to decline--the rate was 10.2 percent. The poverty rate for whites who were not Hispanic was below the overall poverty rate from 1959 through 2003. In 2003 it was 8.2% In 2003, the poverty rate for families was 10 percent, comprising 7.6 million families. Of all family groups, poverty is highest among those headed by single women. In 2003, 28 percent of all female-headed families (3.9 million families) were poor, compared to 5.4 percent of married-couple families (3.1 million families). Poverty levels also differ depending on where people live (See Table 1). The metropolitan poverty rate differs greatly between suburbs and the central city. In 1979, the average central city poverty rate was 15.7 percent; at its highest point, in 1993, it was 21.5; by 2003 it was 17.5 percent, almost twice the rate for the suburbs (9.1 percent). Poverty in rural areas is not negligible either; in 2003, 14.2 percent of people living outside metropolitan areas (that is, in the countryside and small country towns) were poor. The poverty rate also varies by region and within regions. In 2003 it was greatest in the South, at 14.1 percent, and lowest in the Midwest, at 10.7 percent. Over the years 20012003, the poverty rate in the state of Maryland was 7.7 percent--yet in the adjacent District of Columbia, it stood at 17.3 percent. Has Poverty Changed over Time?2 In the late 1950s, the overall poverty rate for individuals in the United States was 22 percent, representing 39.5 million poor persons. Between 1959 and 1969, the poverty rate declined dramatically and steadily to 12.1 percent. As a result of a sluggish economy, the rate increased slightly to 12.5 percent by 1971. In 1972 and 1973, however, it began to decrease again. In 1973, the poverty rate was 11.1 percent. At that time roughly 23 million people were poor. In 1975 the poverty rate increased to 12.3 percent. It then oscillated around 11.5 percent for the next few years. After 1978, however, the rate rose steadily, reaching 15.2 percent in 1983. Thereafter it remained mostly higher than 13 percent. In 1993 it reached a new high of 15.1 percent, and then began to fall slowly. In 2000, 31 million people were poor (11.3 percent of the population). In 2001 the number of poor and the poverty rate both rose as economic difficulties moved into recession, and the rate has continued to rise; in 2003, 35.8 million people (12.5 percent of the population) were poor by the official measure of poverty. Poverty Using Different Measures of Income The existing official measure of poverty has been widely criticized. Under the procedures by which the official ISS315 - PAGE 98 poverty rate is calculated, only cash income is counted in determining whether a family is poor; cash welfare programs count, but benefits from noncash programs, such as food stamps, medical care, social services, education and training, and housing are not included. Taxes paid, such as social security payroll taxes, and tax credits, such as the Earned Income Credit, are also excluded from poverty calculations. Because government spending on means-tested noncash benefits and tax credits has increased more rapidly than spending on means-tested cash benefits over the years, ignoring noncash benefits is an increasingly serious omission if we want a broad picture of the impact of government programs on poverty. In 1995 a panel of the National Academy of Sciences published an influential report on revising the poverty measure (Measuring Poverty: A New Approach, edited by Constance F. Citro and Robert T. Michael). The Census Bureau has calculated alternative poverty rates using various experimental adjustments to the official poverty rate. It has, for example, expanded the definition of income to take into account some noncash income, including government benefits. The experimental poverty measures are the subject of an issue of the IRP newsletter Focus (volume 19, no. 2, Spring 1998, "Revising the Poverty Measure", pdf, 64 pp.), were discussed in an April 1999 IRP conference, and were the topic of a June 2004 workshop hosted by the Committee on National Statistics of the NAS. Papers presented at the workshop reviewed the effects of possible changes in the measure, drawing on the decade of research that has followed the publication of Measuring Poverty. The Census Bureau's poverty report for 2002 estimated the effects of government programs on poverty using experimental measures. For example, it compared the official measure of poverty with measures based on recommendations of the 1995 NAS panel. The panel suggested, among other changes, adjusting the poverty measure to account for geographic differences in housing costs, counting noncash benefits as income, and subtracting from income some work-related, health, and child care expenses. Using alternative definitions of poverty based on the NAS study, the poverty rate for 2002 was in general higher than under the official measure, depending on the particular definition of medical costs and on whether geographic differences were taken into account (see the 2002 Poverty Report, Table 7, "Alternative Poverty Estimates"). Not all groups are affected uniformly, however, when the poverty definition changes. There is considerable disagreement on the best way to incorporate medical care in a measure of poverty, even though medical costs have great implications for poverty rates. But costs differ greatly depending upon personal health, preferences, and age, and family costs may be very different from year to year, making it hard to determine what exactly should be counted. Subtracting out-of-pocket costs from income is one imperfect approach, but if someone's expenses are low because they are denied care, then they would usually be considered worse off, not better off. If the value of Medicaid or Medicare benefits is included, should not the value of private insurance also be included? And although poor persons are clearly better off with medical coverage, such benefits, unlike cash, cannot be used by recipients to meet other needs of daily living. Including the value of housing is equally controversial. How should the respective value of rented and owned housing be measured? Including the equity value of housing would alter the distribution of poverty according to age, because of the large numbers of elderly who are homeowners. State Poverty Rates Table 2 presents poverty rates by state for 20012003 and earlier years, based on 3-year averages (state poverty rates in a single year are not very reliable, owing to small sample sizes). Two states had poverty rates 18 percent or over: Arkansas and New Mexico. The poverty rate was lowest in New Hampshire--6 percent. Connecticut, Delaware, Maryland, and Minnesota were the only other states that had poverty rates below 8 percent from 2001 to 2003. Note: This discussion has been adapted from the U.S. Bureau of the Census, Poverty in the United States: 2002, Series P60-222, and Income, Poverty, and Health Insurance Coverage in the United States: 2003, Series P60-226; it also uses information from the Green Book, 2000, Appendix H, which presents statistics on poverty in the United States. The 2004 Green Book has now been published by the U.S. House of Representatives Committee on Ways and Means. Source: http://www.irp.wisc.edu/faqs/faq3.htm ISS315 - PAGE 99 Table 2. Poverty Rate Estimates by Selected Demographic Characteristics and by Region, Based on Alternative Measures of Income: 2003 U.S. Census Bureau (Poverty rate estimates and their confidence intervals in percentage points. Estimates are based on poverty thresholds adjusted for inflation using the CPI-U) Characteristic MI-Tx (Money income plus realized capital gains 90-percent (losses), less confidence income and interval1 payroll taxes) (+/-) 12.0 9.9 5.6 27.8 13.4 16.0 10.7 10.2 10.1 8.0 23.4 11.1 21.0 10.5 10.3 13.5 12.3 0.5 0.4 0.4 0.5 0.2 0.2 0.8 1.1 0.8 0.5 0.3 0 .4 13.0 9. 3 8. 7 8.6 7.0 19.4 9.8 17.4 8.7 8.6 11.7 10.4 1.1 11.1 0.8 23.2 0.8 1.0 0.4 0.3 0 .4 0.2 0.2 0.8 1.0 0.7 0.4 0.4 0.4 0.5 0.2 4.7 0.2 0.2 0.2 10.2 8.2 0.2 0.2 9.7 7.8 4.3 22.3 10.9 12.3 8.9 8.1 8.2 6.6 18.5 9.6 16.3 8.3 8 .2 11.3 9.7 MI (Money income; 90-percent used in official confidence interval1 measure of (+/-) poverty) 12.5 10.8 6.2 30.0 0 .9 1.1 0.5 0.3 0.4 0.2 0.2 0.9 1.2 0.8 0.5 0.5 0.5 0.6 14.2 17.6 10.8 10.2 10.5 8.2 24.4 11.8 22.5 11.3 10.7 14.1 12.6 0.2 0.2 0.2 MI-Tx+NC-MM (Money income plus realized capital gains MI-Tx+NC+HE (losses), less (Money income income and plus payroll taxes, MI-Tx+NC capital gains plus value of (Money income and (losses), less income and plus capital employergains (losses), payroll taxes, provided health less income and plus value of benefits and all payroll taxes, 90-percent all noncash noncash trans- 90-percent transfers, plus fers except confidence plus value of all confidence 1 1 noncash interval interval imputed return Medicare (+/-) transfers) (+/-) to home equity) and Medicaid) 0.2 0 .2 0.2 0.8 1.0 0.4 0 .3 0.3 0 .2 0.2 0.8 1.0 0.7 0.4 0.4 0.4 0.5 9.0 7.4 4.0 21.4 10.5 12.0 8.5 5.7 7.6 6.0 17.5 9.1 15.8 7.8 7 .8 10.4 9.0 90-percent confidence interval1 (+/-) 0.2 0.2 0 .2 0.8 1.0 0.4 0.2 0.3 0.2 0.2 0 .8 1.0 0.7 0.4 0.4 0.4 0.5 ISS315 - PAGE 100 All people . . . . . . . . . . . . . . . . . . . People in families . . . . . . . . . . . . People in married-couple families . . . . . . . . . . . . . . . . . . . People in families with a female householder, no husband present . . . . . . . . . . . . People in families with a male householder, no wife present . . . . . . . . . . . . . . . . . . . Age Under 18 years . . . . . . . . . . . . . . . . 18 to 64 years . . . . . . . . . . . . . . . . 65 years and over . . . . . . . . . . . . . Race2 and Hispanic Origin White alone3 . . . . . . . . . . . . . . . . . . Non-Hispanic White alone . . . . Black alone4 . . . . . . . . . . . . . . . . . . Asian alone5 . . . . . . . . . . . . . . . . . . Hispanic (of any race) . . . . . . . . . Region Northeast . . . . . . . . . . . . . . . . . . . . Midwest . . . . . . . . . . . . . . . . . . . . . . South . . . . . . . . . . . . . . . . . . . . . . . . West . . . . . . . . . . . . . . . . . . . . . . . . . 7 1 A 90-percent confidence interval is a measure of an estimate's variability. The larger the confidence interval in relation to the size of the estimate, the less reliable the estimate. For more information, see ``Standard Errors and Their Use'' in Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2003 at <www.census.gov/hhes/www/p60-226sa.pdf>. 2 Data for American Indians and Alaska Natives, and Native Hawaiians and Other Pacific Islanders are not shown separately. 3 The 2003 and 2004 CPS asked respondents to choose one or more races. White alone refers to people who reported White and did not report any other race category. The use of this single-race population does not imply that it is the preferred method of presenting or analyzing data. The Census Bureau uses a variety of approaches. Information on people who reported more than one race, such as White and American Indian and Alaska Native or Asian and Black or African American, is available from Census 2000 through American FactFinder. About 2.6 percent of people reported more than one race in Census 2000. 4 Black alone refers to people who reported Black and did not report any other race category. 5 Asian alone refers to people who reported Asian and did not report any other race category. Source: U.S. Census Bureau, Current Population Survey, 2004 Annual Social and Economic Supplement. Table 2 Poverty among Families by the Official Poverty Measure, 2001 Characteristic All Families Married-Couple Families White, not Hispanic Black Hispanic origina Asian/Pacific Islander Female-Headed Families White, not Hispanic Black Hispanic origina Asian/Pacific Islander a No. (in 000) 6,813 2,760 1,477 328 799 156 3,470 1,305 1,351 711 61 Poverty Rate (%) 9.2 4.9 3,3 7.8 13.8 6.6 26.4 19.0 35.2 37.0 14.6 Persons of Hispanic origin may be of any race. Source: U.S. Census, Poverty in the United States: 2001, P60-219, Table 1. ISS315 - PAGE 101 Table 3 Poverty among Individuals, under Two Different Definitions, 2001 Characteristic % Who Are Poor, on the Basis of: Official Poverty Measure All Age Under 18 65 Years and Over Race and Hispanic Origin White, not Hispanic Black Hispanic origina Region Northeast Midwest South West People in Families In married-couple families Female householder, no husband present 5.7 28.6 6.6 27.0 10.7 9.4 13.5 12.1 12.9 8.7 12.5 15.1 7.8 22.7 21.4 8.4 20.8 24.4 16.3 10.1 14.6 10.8 11.7 NAS-Based Measure 12.3 Source, U.S. Census, Poverty in the United States: 2001, P60-219, Table 8. Note: This measure is based on the measure suggested by a 1995 panel of the National Academy of Sciences (NAS), which, among other changes, adjusts for geographic differences in housing costs, counts noncash benefits as income, and subtracts from income some work-related, health, and child care expenses. a Persons of Hispanic origin may be of any race. ISS315 - PAGE 102 THE HIGH COST OF BEING POOR KIDS COUNT, a project of the Annie E. Casey Foundation, is a national and state-by state effort to track the status of children in the United States. By providing policymakers and citizens with benchmarks of child well-being, KIDS COUNT seeks to enrich local, state, and national discussions concerning ways to secure better futures for all children. At the national level, the principal activity of the initiative is the publication of the annual KIDS COUNT Data Book, which uses the best available data to measure the educational, social, economic, and physical wellbeing of children. Another Perspective on Helping Low-Income Families Get By and Get Ahead Since 1990, the Annie E. Casey Foundation's KIDS COUNT Data Book has been a steady reminder of the risks that our nation's poorest kids face. Each year, it confirms the fundamental ink between poverty and a range of negative outcomes--illness, academic failure, early pregnancy--that can diminish a child's chances of adult achievement and success. Over the past decade, social policy reforms have helped almost 2.5 million parents transition from welfare to work. At the same time, far too many low-income parents still encounter numerous obstacles in their path out of poverty. Despite their best efforts to succeed in the workplace, many find it nearly impossible to build the savings and assets needed to achieve genuine economic security. One oftenignored, but particularly critical, factor is undercutting our national efforts to help working families get by and get ahead: the very high cost of being poor in America. Many low-income families, especially those living in high-poverty communities, end up paying far too much for life's necessities: food, shelter, transportation, credit, and financial services. Compounding this is the fact that many low-income families still see their income excessively "taxed" as a result of reduced financial subsidies due to i8ncreased job earnings. Combined, these factors make it tough for low-income parents to believe that their efforts will ever translate into economic security. How the Poor Pay More All working Americans face some built-in costs associated with "going to work"--transportation, child care, payroll taxes, and work clothes. Although many workers are able to absorb such costs, they provide a real disincentive to scores of low-wage workers. Simply getting to work can be much more expensive for low-income workers. Buying and owning a car can be particularly expensive for low-income workers in poor communities--not only because they have less money to pay for a reliable car, but also because they are likely to incur excessive fees, high-interest financing, and high insurance premiums. The cost of child care--a necessity for most low-income families--also can be tough to absorb on modest earnings. In addition to the high cost of participating in the workforce, low-income workers frequently end up paying a lot more for family health care than higher paid workers whose employers subsidize their coverage. Higher prices for transportation, child care, and health care are not the only ways the working poor end up paying more and getting less. Many of these workers also confront an "earning tax": the loss of needs-based assistance--such as Temporary Assistance for Needy Families (TANF), child-care help, and ISS315 - PAGE 103 Medicaid--after they reach a certain level of income. Thus, many who previously have benefited from these support programs actually wind up losing income by working. Because many low-income families live in economically isolated neighborhoods, shipping near home means paying more for food, clothing, furniture, or any of the myriad items that all families need. Housing can also carry very high comparative costs for poor families, particularly for those who must rent. There is no housing market in the country where a family earning today's full-time minimum wage can afford a modest two-bedroom rental without exceeding the accepted standard of allocating 30 percent of one's income toward housing. Furthermore, low-income communities are often abandoned by the mainstream financial institutions that commonly provide savings and asset-building mechanisms. Isolated from banks and credit unions, these communities are saturated with subprime and predatory financial outlets, check-cashing services, payday lenders, and other fringe industries. Although these services are convenient, the high fees and questionable business practices of these outlets tend to strip, rather than build, consumer wealth. Leveling the Playing Field We believe that it's important to tackle this affordability problem on several fronts. Our four-part platform, proposed here, hopefully will serve as a model for stimulating innovative thinking and action. Encourage Quality Retailers to Locate in Low-Income Communities If low-income consumers living in economically isolated neighborhoods are to make the most of scarce resources, then they need greater access to the affordable retail goods that most American families enjoy. One way to achieve this is to help mainstream businesses see the market potential in low-income neighborhoods. Provide Consumers with Financial Education and Access to Basic Financial Services Many low-income families are induced to accept fees that are far too excessive, credit terms that are unnecessarily burdensome, and payment terms that are unreasonable--particularly when they're packaged in marketing schemes that make them sound too good to refuse. It's important to supply low0income consumers with essential information to help them make sound financial decisions; greater access to fair financial services, and opportunities to build credit so that they can move beyond the grasp of predators and begin building assets. Promote Regulatory Reforms We also advocate regulatory reforms to combat predatory practices that strip wealth and prevent asset development, especially in high-poverty communities. Insufficient regulation at the federal level has allowed some predatory industries to proliferate. Fortunately, a number of states and cities around the country already have passed their own ordinances to curb exploitive practices within their jurisdictions. Reinforce Policies That Protect Earnings and Benefits Leveling the consumer playing field also entails helping low-income families bolster and stretch their income and earnings. One way to do this is through refundable tax credits for workers whose earnings are so low that they currently have little or no income tax liability. The Earned Income Tax Credit (EITC) has lifted almost 2.5 million children out of poverty since 1998. Given this success, it makes sense to protect and expand the EITC and other important tax credits. Also helpful are subsidies aimed at the core costs that take the biggest bite out of paychecks: food, housing, and child care. ISS315 - PAGE 104 Conclusion If we are truly to deliver on the fundamental promise that hard work, self-sacrifice, and prudent investment are the building blocks of economic security, then we must promote approaches that demonstrate a new national seriousness about leveling the cost of living for low-income families. No single aspect of the platform advanced here is strong enough by itself to help America's most vulnerable working families become economically self-sufficient. Taken together, however we believe that they offer a more powerful, realistic, and rational approach to addressing this critical national goal. Douglas W. Nelson, President The Annie E. Casey Foundation www.kidscount.org ISS315 - PAGE 105 ISS315 - PAGE 106 ISS315 - PAGE 107 Combatting Myths Recently, arguments have been made that nutrient intake among poor and non-poor Americans is similar. Such arguments have been used to make the point that hunger is not a serious problem in America. The following excerpted research debunks this myth and clearly establishes the strong link between inadequate nutrient intake and hunger among low-income populations in the United States. Differences In Nutrient Adequacy Among Poor And Non-Poor Children Food Expenditure And Consumption Patterns Of Food Stamp Households Could There Be Hunger In America? "Differences In Nutrient Adequacy Among Poor And Non-Poor Children" (Summary of Findings) John T. Cook, Ph.D. and Katie S. Martin Tufts University School of Nutrition -- Center on Hunger, Poverty & Nutrition Policy, March 1995 Millions of poor children have substandard intakes of important major nutrients. Analysis of government data reveals major differences in the intakes of poor and non-poor children for ten out of sixteen nutrients (food energy calories, folate, iron, magnesium, thiamin, vitamin A, vitamin B6, vitamin C, vitamin E, and zinc). Moreover, these differences in intakes appear for nutrients considered crucial to sound health and normal developments. For several major nutrients analyzed, between one to four million poor children have substandard intakes on each (food energy, folate, vitamin B6, vitamin C, vitamin E, iron, and zinc). The proportion of poor children with substandard intakes of food energy is more than two and a half times as great as for non-poor children. The proportion of poor children with substandard intakes of some nutrients (e.g., vitamin A and magnesium) is nearly six times as large as for non-poor children. The proportion of poor children with substandard intakes of zinc is over 50%, for iron it is over 40%, and for vitamin E it is over 33%. Reliance on protein as an indicator of the adequacy of nutrient intakes is inconsistent with current nutrition science which emphasizes the importance of a much wider range of nutrients in human health. Though some other nutrients are also found in protein-rich foods, protein intake alone is provides insufficient information about the adequacy of intakes of other important nutrients. Moreover, since protein is not a public health concern among the U.S. population, comparisons of intakes of poor and non-poor populations are less likely to yield useful information about their relative overall dietary adequacy. Significantly larger proportions of poor children have intakes below 70% of RDAs for ten of the sixteen nutrients. Moreover, these differences in intakes appear for nutrients considered crucial to sound health and normal development. Food energy, stored in the liver as glycogen, is required for all life processes, including maintenance of body temperature, muscle movement, and growth and repair of bones and tissues. Children with food energy intakes below 70% of the recommended daily allowance consume fewer calories than are consumed on average by about 2/3 of the people of comparable age and gender. The small size of children's livers, relative to total body mass, necessitates that they eat more frequently than adults to ensure adequate supplies of food energy for normal activity, learning and functioning. A child's liver can store only about four hours' worth of glycogen hence the need to eat fairly often. Inadequate food energy intake can cause problems with attention, concentration, learning, and other ISS315 - PAGE 108 important daily activities. For children who have not eaten breakfast, the educational value of a morning spent in the classroom may be lost. Repeated episodes of inadequate food energy intake can lead to cumulative deficits in learning, lower academic achievement, higher rates of school failure, and even cognitive impairment. Examining government data, we found substantially higher proportions of poor children than nonpoor children with intakes below 70% of the RDAs for 14 of 16 nutrients measured. Tests of the significance of differences between these proportions showed that for 10 of 16 nutrients, the differences between the proportions were statistically significant. Poor children are at risk of nutrient deficiencies which can lead to serious health problems, including impaired cognitive development, growth failure, physical weakness, anemia and stunting. Several of these problems can lead to irreparable damage to young children. Millions of poor children suffer from chronic undernutrition, the under-consumption of essential nutrients and food energy. This undernutrition is apparent from the large proportions of poor children receiving less than 70% of recommended dietary intakes for the ten nutrients highlighted in this analysis. According to current consensus within the scientific community, household food insecurity and hunger manifest in a progressive manner. As food insecurity worsens, it may eventually lead to hunger among members of the household, first among the adults, then among children. If hunger persists, among the children in a family, or occurs with sufficient frequency, reductions in food intake lead to observable inadequacies in nutrient intakes of the kind identified in this analysis. The results of this analysis provide evidence consistent with widespread hunger among low-income American children. "Food Expenditure And Consumption Patterns Of Food Stamp Households" Testimony before the U.S. House of Representatives Committee on Agriculture Subcommittee on Department Operations and Nutrition Thomas M. Fraker, Ph.D. Mathematica Policy Research, Inc., November 16, 1993 Food stamp recipients consume less snack foods than do nonrecipients. Research by the federal government shows that recipients consume 20 to 50 percent less of cakes, salty snacks, candy, and soft drinks than do all nonrecipients. Food stamp households allocate a greater share of their total expenditures to food than do lowincome households that do not receive food stamps. The evidence from USDA data sets indicates that food stamp recipients use large amounts of food relative to their food stamp benefits and devote the majority of their food purchases to foods that are relatively high in nutritional value. "Could There Be Hunger In America?" U.S. Department of Agriculture Center for Nutrition Policy & Promotion, September 1998 According to a national survey taken in 1995, a year marked by good economic news, hunger existed among persons in 4.2 million households, that is 4.1% of all U.S. households. These households had one or more persons that reported experiencing reduced food intake because of a lack of financial resources. Nearly 20% of households (817,000 of the 4.2 million) had one or more members who experienced severe hunger. In some of these households, children experienced reduced food intake (332,000) or, where no children were present, adults experienced a prolonged lack of food. Not all households were equally likely to be hungry. A slightly larger proportion of households with children were classified as experiencing hunger. The lower a household's income, the higher the chance of experiencing hunger. The count of hungry people was obtained through a scientific survey conducted by the U.S. Bureau of the Census and sponsored by the USDA's Food and Nutrition Service (FNS). FNS administers the nation's food assistance programs with annual expenditures of almost $40 billion. The agency's interest in measuring hunger arises from its legal mandate to serve those that meet the requirements for assistance programs. These include the Food Stamp Program, the Special Supplemental Nutrition Program for Women, Infants and Children (WIC), and the National School Meals programs. ISS315 - PAGE 109 Current Hunger & Poverty Statistics The Number of Americans Living in Poverty is Rising: Between 2000 and 2001, poverty rose to 11.7% of the population, or 32.9 million people, up from 11.3% and 31.6 million. 1 The 2001 median household income in the US was $42, 228, representing a 2.2 percent decline in real income from its 2000 level of $43,162. 2 The Number of Americans Unemployed is Rising: Average unemployment rates in the past year have risen: in 2001, the rate was 4.8%, but jumped to 5.7% in 2002. 3 The Number of Americans Food Insecure and Hungry is Rising: In 2001, the number of Americans who were food insecure, or hungry or at risk of hunger, was 33.6 million, a rise over 2000, when 33.2 million Americans were food insecure. The number of individuals who are suffering from hunger rose from 8.5 million in 2000 to 9 million in 2001. 4 The number of food insecure households with children has also risen since 2000 by 10,000 to 6.18 million. 5 The Number of People Seeking Emergency Food Assistance is Rising: America's Second Harvest's Hunger in America 2001 report found that 23.3 million people sought and received emergency hunger relief from our network of charities in 2001. The study also found that between 1997 and 2001, demand for emergency food assistance through the America's Second Harvest network has risen 9% since 1997. 6 23 million people receiving emergency food assistance is equivalent to the combined populations of the 10 largest U.S. cities: New York, Los Angeles, Chicago, Houston, Philadelphia, San Diego, Phoenix, San Antonio, Dallas, and Detroit. 7 A survey of 55 Catholic Charities agencies found that in anticipation of the busy winter holidays, 85 percent are expecting an increase in people seeking emergency financial assistance, 66 percent are expecting greater need for food, and 71 percent are anticipating a shortage this holiday season in financial donations. 8 A survey of America's Second Harvest affiliates in late 2001 and early 2002 found that 86% had seen an increase in requests for food assistance during the past year. 9 In U.S cities across the nation, hunger and demand for emergency food assistance is rising: o o o o New York City's soup kitchens and food pantries fed 45% more people in 2002 than in 2000. In the one year following September 11, 73% of the agencies fed more children - with 39% saying the number of children they fed increased "greatly." 10 In Chicago, the Greater Chicago Food Depository, which serves 600 agencies, distributed 36 million pounds of food. It is estimated that the food bank will distribute 42 million pounds this year, which translates to about 91,000 families a week. 11 The Greater Boston Food Bank is experiencing unprecedented demand: while the food bank normally distributes up to 350,000 pounds of food a week, since October the number has risen to 500,000 to 600,000 pounds of food a week. 12 In Los Angeles, the L.A. Regional Food Bank is bracing for a busy holiday season. Says Executive Director Michael Flood, "I don't want to sound dire, but looking through this holiday season the only trend we can see is increased need." 13 NOTES: 1. U.S. Census Bureau, Poverty in the United States 2001. 2. U.S. Census Bureau, Money Income in the US: 2001. 3. U.S. Bureau of Labor Statistics. 4. USDA's Economic Research Service, Household Food Security in the United States, 2001. 5. Ibid. 6. America's Second Harvest, Hunger in America 2001. 7. Ibid. 8. Catholic Charities USA, Catholic Charities Agencies Brace for Harsh Holiday Season. 9. America's Second Harvest, Local Impact Survey. 10. New York City Coalition against Hunger, Hunger among Hidden Victims. 11. Chicago Tribune, Weak Economy Increases Need, Slows Donations. 12. The Boston Herald, State Pushes Food Stamps in Face of Increasing Need. 13. Whittier Daily News, Charities Starving for Food, Finances. ISS315 - PAGE 110 CARRIE HUNGER PROFILE Carrie, a 27-year-old single mother from Olympia, Washington, knows firsthand how difficult negotiating the food stamp system can be. She was 19 years old when she had her daughter, Kylee. Carrie had finished high school, but in order to support her daughter, took the first job she could find as a waitress. She knew that her low-paying job wasn't a long-term solution. "I had to have a job that was going to provide us a living wage, and I knew that going back to school could provide that for us." A combination of financial aid and food stamps allowed Carrie to quit her job and enroll in college, but when the Welfare Reform Act was passed in 1996, Washington state began its Work First program, requiring anyone receiving public assistance to work 20 hours a week. Carrie worked the required 20 hours a week, went to school full-time (16 credit hours a quarter), and raised her child. She said the biggest obstacle to getting food stamps was the requirement that every three months, she must visit her Work First site for orientation. Unfortunately, this often meant taking time off from work, or missing classes, to get her food stamps benefit. "A lot of times when you go into the welfare office, whether you're applying for food stamps or applying for some other benefit, you get treated like you're dirt basically, I have so many horror stories." -Carrie Carrie's eligibility came up for re-certification and she had always been able to re-certify by mail. When she called her caseworker and left a message asking him to send her the paperwork, he didn't call her back. She left several more unreturned messages. She then received a notice in the mail saying that there would be no change in her benefits. Two days later she received another notice saying her food stamps benefits had been terminated. "I called him up a couple more times," Carrie says, "and he didn't call me back. Graduation was coming up. I was studying for finals. Finally I called him up in tears and left a message saying `Listen, I don't have any food for my daughter and me. I haven't had benefits in two months. Please call me back.' When he finally did, two months later, it was to tell me that he couldn't do mail correspondence - something he had been doing with me for a year." So for almost two months Carrie says she and daughter "basically lived off of rice, beans and the kindness of friends" who would occasionally lend her money for food. Unfortunately, Carrie's story is not unusual. Many hungry Americans are unable to access the food stamps for which they are eligible because of the red tape that stands in their way. Frequently, they are forced to turn to their local food bank for assistance. This year, America's Second Harvest released a state-by-state review of the food stamp application process, which brought many of the obstacles that food stamp applicants and recipients face to light. SOURCE: America's Second Harvest ISS315 - PAGE 111 Childhood Hunger As a nation, we have a special responsibility to vulnerable populations, including children. Children are in special need of proper nutrition to help them avoid the consequences undernutrition can have on their economic security as adults. In November of 2001 America's Second Harvest released its third and most comprehensive study of hunger in the United States: Hunger in America 2001. The following are some key findings of the study regarding children in our country: Over 9 million children are the recipients of food from either a pantry, kitchen or shelter within the network of America's Second Harvest. Among all members of client households, 9.1% of pantry recipients are ages 0 to 5; 7.7% are shelter clients. 75.7% of all client households with children under the age of 18 are food insecure; an estimated 2.6 million households. Among all members of client households, 18.4% of kitchen clients stated that their child/children had skipped meals within the last 12 months because there wasn't enough money for food. 22.2% of shelter clients indicated that their child/children was/were hungry at least once during the previous 12 months but couldn't afford more food. 26.3% of all client households stated that their child/children were sometimes or often not eating enough during the previous 12 months because they just couldn't afford enough food; an estimated 0.9 million households. Among all client households with at least one child under the age of 18; 63.2% utilize the school lunch program and 49.9% the school breakfast program. 52.5% of all client households with at least one child age 0 to 5 are enrolled in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC); an increase from 31% in 1997. 61.9% of agencies run feeding programs that target children only: Kids Cafes, youth after-school programs, child day care programs, and summer camps for low-income clients. 46.2% of pantry programs indicated there are "many more children in the summer," while 68.2% of kitchen programs indicated an increase during the same time of year. SOURCE: America's Second Harvest ISS315 - PAGE 112 HUNGER PROFILE: Pamela Pamela is a 45 year-old single mother with a high school diploma. She works for a janitorial service in Waterloo, Iowa for $7 an hour with no benefits. She ended a 21-year marriage with the father of her two children when he became abusive after being laid off from his job. When he found a new job, the court ordered him to include his children on his benefit lan, but his company is currently gearing up for another round of lay-offs, and Pamela expects that her ex-husband, being a new employee, will be one of the first to go. To stretch her paycheck and ensure that she can feed her family, Pamela does things like budgeting her gas and electricity usage and never places long distance phone calls. She also visits the Cedar Valley Food Bank in Waterloo. "We have a special program that focuses on the working poor," explains executive director Barbara Prather. "Families of a certain income 125 percent of the poverty line or less can come to the food bank once a week to get what we call a bakery box. The box contains a variety of USDA commodities that supplement the family's income. If they're making $7 an hour, like Pamela, the program just helps them get through to the next pay check." The program currently serves more than 35,000 underemployed individuals and families annually. "I try not to use it unless I absolutely have to," Pamela says. "I don't want to come to the food bank just because it's here." Still, she finds herself needing the assistance once or twice a month, but is determined to make it on her own. "I feel much better about myself this way. My biggest worry is the fact that my kids will soon be without health insurance." SOURCE: America's Second Harvest ISS315 - PAGE 113 HUNGER PROFILE: June and Vernon Ward June and Vernon Ward are two of the four million elderly Americans served each year by the America's Second Harvest network of food banks. For 16 years, the Wards have lived in a small rural town in Arizona, the state where June's family has resided for five generations. A few times each year, the Wards are forced to go to the Westside Food Bank in Sun City to receive emergency food boxes. June has been unemployed due to health problems since 1988 while Vernon, who worked for years in construction, lives with the long-term consequences of work-related injuries. Additionally, Vernon is on heart medication since suffering a heart attack three years ago. Although both Vernon and June are covered by Medicaid, they are faced a few times each year with unforeseen expenses that compel them to seek out the help of the Westside Food Bank. "I take care of my bills and so forth, but if other things come up I'll take away from my food. If I need glasses or if a vehicle breaks down." -June Ward Because the Wards live in a rural community, about 16 miles from the nearest sizeable town, they are dependent on their vehicles for buying food, buying gas, and hauling water. Recently the local grocery stores and gas station in their own town of Whitmann have been closed and only small, more expensive stores remain. With a tight budget the Wards at times have to make the impossible choice not to buy food. Mrs. Ward recalls being turned away from a food bank in Phoenix in the late 1960s. She remembers feeling that after living in Arizona all of her life the treatment she received by the state was "pathetic." June says that today her experience with her local food bank is quite different. "They have been very helpful and they never ask questions. They have been very good to Me." -June Ward Westside Food Bank in Sun City is an affiliate food bank of America's Second Harvest and part of the national effort to ensure that no one has to make the choice between buying food and buying other necessities such as medicine or transportation. By providing emergency food boxes, local food banks are able to meet some of the need faced by elderly Americans each day. SOURCE: America's Second Harvest ISS315 - PAGE 114 The private food assistance network Privately sponsored "emergency" programs are becoming an integral component of the food assistance network in virtually every U.S. community. In 1997, the private food assistance network had an estimated value of over $2.3 billion and provided food to about 20 million people. Although it is still very much smaller than the largest public food assistance program, Food Stamps, it has clearly become an important supplemental system.1 This article describes how it works and whom it serves, and examines some interactions with public food assistance programs. Until the 1980s, most emergency food relief programs were set up as temporary measures in times of economic hardship. Once the crisis abated, they closed their doors until the next economic downturn. This cyclical trend appears to have changed. The private food assistance network in its present form emerged from the convergence of two forces, one public, one private. The public initiative was the establishment in 1983 of the Temporary Emergency Food Assistance Program (TEFAP), a commodities distribution program in the U.S. Department of Agriculture.2 The private initiative was the formation of an entirely new kind of private food assistance network, Second Harvest. Together they provided food banks and pantries, for the first time, with a steady stream of revenue and food from public and private sources. 3 Increased supply was matched by increased demand for free food. One factor was the structural unemployment of the early 1980s, which created a group of newly poor. Substantial cutbacks and other changes in the Food Stamp program also contributed. After the program was revised in 1977, food stamp allotments were no longer intended to last a full month, and this may have increased chronic reliance on food pantries among the poorest.4 Word of mouth alone created long lines at the pantries, which were not allowed to advertise. In the first few years of TEFAP, over 2.1 billion pounds of surplus foods--primarily dairy products but also wheat flour, cornmeal, and honey--were distributed and as many as 19 million people received assistance each month. In 1987, TEFAP was expanded to include a wider range of surplus foods and in 1988, the Emergency Hunger Prevention Act authorized the purchase of food to be distributed in addition to surplus commodities. Despite some vicissitudes in funding, it has been regularly renewed by Congress, and in 1990 the word "temporary" was removed from its name. The provision of funds for purchase in essence rebalanced TEFAP's priorities from price stabilization for farmers toward food assistance for the poor. Using public funds to purchase commodities for distribution also represented a shift away from the approach embedded in the Food Stamp program, which provided assistance in the form of an earmarked income transfer. Anxiety about fraud had dogged the Food Stamp program almost from the beginning, but it was a point of emphasis during the Reagan administration. Distributing commodities through private charities was seen as a more direct way to get the food to the poor, and less subject to fraud. TEFAP pleased farmers, by providing for price stability; it appeased antihunger activists and organizations, who had strongly criticized Reagan administration cutbacks in food assistance, because it appeared to reach the needy; and it pleased the food industry, which, in addition to donating food out of goodwill, found that it could The Emergency Food Assistance Program In its origin, TEFAP, like the Food Stamp program, reflected dual policy goals reaching back to experiments with food assistance during the Great Depression: helping farmers and ending hunger among needy families. Depending upon political and economic circumstances, now one, now the other has predominated in federal policy. The initial impulse underlying TEFAP was to ameliorate food insecurity in a way more acceptable to government and to many public critics than the Food Stamp program, which was in disfavor under the Reagan administration. The willingness of the administration to implement TEFAP was grounded in the recession of the early 1980s and in the existence a large surplus of perishable commodities such as cheese. The program was originally authorized for two years; the federal government paid processing, packaging, and delivery costs and a large proportion of state agency expenses, and much of the food was to be distributed by the states through private food banks and food pantries, in line with the conservative emphasis on private initiatives. This article is based upon two recent IRP discussion papers: Beth Osborne Daponte and Shannon Lee Bade, The Evolution, Cost, and Operation of the Private Food Assistance Network, IRP DP 1211-00 (2000), and Ann Nichols-Casebolt and Patricia McGrath Morris, Making Ends Meet: Private Food Assistance and the Working Poor, IRP DP 1222-01 (2001). 12 Focus Vol. 21, No. 3, Spring 2001 ISS315 - PAGE 115 use the network to dispose of food for which it might otherwise have had to pay salvage costs. The new policy institutionalized the private food distribution network. Before TEFAP, food pantries relied mostly on unpredictable donations of food and money from individuals and businesses. TEFAP goods represented a dramatic increase in their responsibilities and provided a regular and substantial supply of nutritious food. TEFAP funding also provided reliable budgets for administration, storage, and distribution. The food assistance network at the street level: Food pantries In 1997, Second Harvest sponsored a large national survey of food banks, food pantries, and food pantry clients. 5 Almost three-quarters of food pantries were church-affiliated; 23 percent were private nonprofit agencies. Very few had a long history: about three-quarters had been formed after 1981, a third were less than 6 years old. Most pantries operated on a shoestring--60 percent had incomes of less than $5,000, another 13 percent between $5,000 and $10,000. On average, they had one or two paid staffers and relied heavily on volunteers to assist with operations and food distribution. It is hardly surprising that 18 percent of pantries viewed themselves as threatened or unstable, half because of funding difficulties, almost a quarter because of lack of volunteers. Food pantries obtained over 60 percent of their food from food banks. The remainder came from food purchases (13 percent), churches (13 percent), government (3 percent), merchants and farmers (4 percent), and other sources (5 percent). In 1997, food pantries distributed about 960.5 million pounds of food, over a quarter of it provided through Second Harvest. Pantries affiliated with Second Harvest served over 19 million persons at least once in that year. Each pantry provided food, on average, to 1,507 individuals living in 545 households. Getting private donors involved: Second Harvest The second precipitating element in the growth of the private food assistance network was the formation of a nonprofit organization that provides both a relatively easy way for potential donors of large quantities of food (e.g., the food industry) to give to the private network and an efficient and equitable means for distributing these goods nationwide. This organization is Second Harvest, which began in the 1970s as a small network of food banks based in the Southwest. It is now a network of 185 member food banks (95 percent of U.S. food banks), with some 34,000 affiliated food pantries. In 1998, Second Harvest, with a budget of over $9 million, managed the distribution of 260 million pounds of food. Second Harvest has no warehouses and never handles food directly. It acts as a liaison between the food industry and the assistance network, soliciting donations from food companies, farmers, and agricultural cooperatives-- half a truckload of cereal, two truckloads of apples--and then offering the food to food banks. By certifying that member food banks meet a clear set of standards, it gives potential donors confidence that the food and paperwork will be properly handled and that the act of donation will be trouble-free. To determine how to allocate food on any given day, Second Harvest maintains a computerized ranking of food banks according to the amount they have already received and the number of people in poverty in their area. It also considers the nearness of the food bank to a particular donor. When a donation is pledged, it is systematically offered to food banks, starting at the top of the list. The food bank has two hours to respond; if it is not interested, the next on the list is queried. Food is not transported to a central location, but goes directly from the donor to the food bank, which usually arranges its own transportation. Second Harvest has, however, developed an ancillary program, Relief Fleet, which allows trucking companies to donate transportation on empty freight runs. Who uses private food assistance? Pantry administrators tend to regard their assistance as emergency aid. But the Second Harvest interviews make it clear that for most clients, using a food pantry is chronic and long-term; 60 percent of those interviewed had been using the pantry for more than a year. Asked why they had sought aid (respondents could give more than one reason), over half of clients cited recent unemployment, 35 percent long-term unemployment. Other reasons included an illness (23 percent), high fixed expenses (22 percent), and not enough food stamps to last the month (13 percent). Only one-third cited a temporary emergency, even though the system is predicated upon emergency use. Nationally, about 22 percent of pantry users were disabled, 12 percent retired. One-third of those relying on food pantries were children under 18, and over half of the households with children were headed by single parents. The annual household income of food pantry clients was low: two-thirds had an annual income of less than $10,000. 13 ISS315 - PAGE 116 In all, about a third of food pantry clients were also receiving food from other sources, such as senior nutrition sites or school programs; 19 percent used more than one pantry, a practice generally frowned upon by the pantries. Working poor families and the food pantries A significant percentage of users of private food assistance appear to be working poor families. Nearly 60 percent of those interviewed by Second Harvest said that they were working, but needed more money. About half of these claimed to be working full time. A picture of these families emerges from a study of the users of Virginia food pantries and soup kitchens.6 Over one-third of participating households had at least one employed member. About 70 percent of those who were currently or recently employed worked at least 20 hours a week. Longer-term unemployed--those in which no household member had worked in the last six months-- constituted 30 percent of the food pantry users. Of these, almost a third cited health as a barrier to employment. Most users had characteristics that, over the long term, would make it difficult for them to earn enough to support themselves and their families. Most were women, many of them single parents. Nearly half had less than a high school education, and more than half earned less than $6.50 an hour. At some time in the last six months, about 10 percent of households in which one or even two adults were working had been homeless, around 20 percent had phone service cut off, and about 11 percent had heat or electricity cut off. Nearly 40 percent of families in which one adult was working or recently unemployed had skipped meals. The interaction of public and private food assistance Many food pantry clients surveyed by Second Harvest appeared to be slipping through or not adequately served by the public safety net, whose cornerstone programs are Food Stamps, the Supplemental Nutrition Program for Women, Infants, and Children (WIC), and the School Lunch and Breakfast programs. Among the children in the Second Harvest study, 65 percent were participating in the school breakfast and lunch programs, and 31 percent of age-eligible households were participating in WIC. In the Virginia study, around 10 percent of all clients, whether employed, short-term unemployed, or long-term unemployed, had recently had trouble with public benefits, having lost Medicaid, cash welfare benefits, or food stamps. Nearly 60 percent of pantry clients in the Second Harvest survey were not receiving food stamps--indeed, almost 40 percent had never even applied. Fewer than one-third in the Virginia study were currently receiving food stamps; about half of those who had recently stopped receiving stamps had become ineligible because they had 14 found work or their income improved, but over 18 percent had not returned for recertification or felt that the benefits were too small to "put up with the hassle." Nationally, participation in Food Stamps, the largest program, has declined substantially.7 The decline began before the 1996 passage of welfare reform legislation tightened eligibility standards and reduced the value of the benefit, but accelerated thereafter. Even as early as 1994, just over half of eligible households in which someone was working appeared to be participating. Much of the decline has occurred among families with incomes below 130 percent of the poverty line. Almost one-third of families at these income levels have been measured as "food insecure"--that is, they do not always or reliably have enough food to feed all family members and are uncertain about whether they will have household resources adequate to acquire enough food to meet their family's needs. It seems likely that many are discouraged from applying for food stamps because they lack reliable information about the rules and are daunted by the administrative complexity of the program.8 Such individuals may be using private food networks as an alternative to the challenges of Food Stamp application and recertification. Others are using these networks to supplement food stamp allotments. Among poor families in an Allegheny County, Pennsylvania, study, 23 percent used both food stamps and private food pantries. Operation of the private food assistance network There is much local variation in the way food banks and pantries operate and in how effectively they can respond to need. Some of this is due to political and economic circumstances, some to different state governmental structures. These problems have lessened as the private food network has matured and expanded, but local conditions can strain the resources of small organizations. For example, the closing of three factories in northeastern Connecticut in the 1990s created demand for services that local food pantries were unable to meet. The distribution of federal food aid generally depends on the existence of an effective, local, volunteer-based organization willing to administer the program. Poor people living in urban areas with a large number of charitable resources have a much greater chance of being helped than equally poor people living in rural areas. The variability becomes clear from a comparison of two major Eastern U.S. food banks, the Connecticut Food Bank (CFB) and the Greater Pittsburgh Community Food Bank (GPCFB), in Pennsylvania. 9 Both began during the early 1980s, and both are now affiliated with Second Harvest. In each service area, about the same number of people are below the poverty line, but the food banks are ISS315 - PAGE 117 very different in the size, scope, and intensity of their activities. In 1996, the CFB distributed 3.5 million pounds of food in six of Connecticut's eight counties to 450 member agencies--soup kitchens, day care and senior citizen centers, emergency shelters, and food pantries. It employed 18 full-time-equivalent staff members; in 1995 96, volunteer workers donated about 3,100 hours to help with operations. In 1997, the GPCFB distributed over 13 million pounds of food in 12 counties in western Pennsylvania through 350 member agencies, 265 of them food pantries. With 45 full-time-equivalent staff members, it has the fourth largest staff of all Second Harvest food banks. In 1997, volunteers donated over 40,000 hours. Differences in the two states are also important. For example, Pennsylvania provides the GPCFB with twice as much money as Connecticut provides the CFB for the purchase of food. Connecticut does not have county government, but Pennsylvania does. So, in Pennsylvania, food assistance and many other programs are administered from the state to the county level and finally to a charitable organization. In Allegheny County, the primary TEFAP contractor, for historical reasons, is the Lutheran Services Society, which subcontracts with seven food banks, including the GPCFB, to distribute food to local pantries. In Connecticut, all government food assistance programs are administered by the state and distributed directly to each municipality or to the nearest charitable organization or human service agency. The state Department of Social Services contracts directly with the CFB. The state does not monitor the pantries, soup kitchens, and other agencies that actually distribute food. These examples point to a general characteristic of the food assistance network. One source of its variability is the flexible rules of the TEFAP program itself. The federal government does not spell out exactly what administrative structures or eligibility criteria should be used. When the program was established, federal administrators felt that stringent eligibility requirements were not necessary or cost effective (the typical distribution to a household was then worth less than $15). Moreover, then and thereafter, food banks and pantries strenuously resisted such record-keeping, both because of the burden on small, largely volunteer organizations and because they felt that collecting such data would be intrusive and inconsistent with their mission. Thus monitoring of clients and agencies is minimal--to be eligible for TEFAP, food pantries generally have clients sign a form in which they self-report that their income is less than 150 percent of poverty. This simplicity and flexibility stand in sharp contrast with the reporting requirements of the Food Stamp program, which are notoriously extensive and complex, re- quiring pay stubs, cancelled rent checks, utility bills, etc. TEFAP appears likely to pose a greater risk of noncompliance and inefficiencies in its procedures. Conclusions Private food networks have now become an integral part of the nation's food assistance program. Even if the public food assistance programs were easier to access, the very existence of a substantial, reliable source of free food is likely to generate steady demand among poor families with many pressing claims on their limited resources. But beyond that, soup kitchens and food pantries are providing critical assistance to the working poor and the chronically unemployed throughout the United States. The increases in the use of "emergency" food assistance sites, the persistence of food insecurity and hunger in the United States, and the prospect that these programs may be supplementing the Food Stamp program among an increasing proportion of the poor raise serious questions of equity and social justice. Treating problems as "emergencies" may seem to be a less costly approach for government than establishing policies and programs to guarantee adequate income and services for individuals. But defining hunger as an individual, short-term problem that can be solved through expansion of voluntary emergency programs may divert attention from the underlying problems of unstable employment and inadequate income, and from the government's role in assuring a safety net for vulnerable families. n 1 In FY1997 the Food Stamp budget was $21.5 billion and nearly 23 million people were participants. U.S. Department of Agriculture, Food and Nutrition Service, Food Stamp Program Data. <http:// www.fns.usda.gov/pd/fspmain.htm>. The private network data refer to the Second Harvest network, which constitutes about 95 percent of the entire network. Food pantry users likely received a much smaller portion of their monthly food needs than is provided to participants in the Food Stamp program. 2 The legislation establishing this program was sponsored by Rep. Leon Panetta (D-CA). 3 Food banks act as middlemen, providing food to food pantries, which distribute food for consumption at home, and to soup kitchens, which serve food on site. Food banks may also provide food to senior centers and other organizations that serve meals. 4 Under the 1964 act establishing the Food Stamp program, participants had to purchase food stamps; this purchase requirement was heavily criticized on the ground that the very poorest families were simply unable to make the copayment and meet other obligations, such as rent. Counties that switched from commodities distribution to food stamps as their primary means of food assistance found that overall participation in such programs dropped. The purchase requirement was ended in 1977. 5 Hunger 1997: The Faces and Facts (Chicago: Second Harvest, 1998), reported information drawn from 79 of its member food banks and 25,319 agencies operating food programs; 27,771 clients of emergency food programs were interviewed. Two large national surveys of the 15 ISS315 - PAGE 118 Single-parent families and the food safety net Judi Bartfeld Which households are food insecure? Judi Bartfeld is Assistant Professor of Consumer Science in the School of Human Ecology, University of WisconsinMadison. Food insecurity and hunger are closely linked to poverty. The lower a household's income is relative to the poverty line, the more likely it is to be food insecure. In Wisconsin, an average of 31 percent of poor households were food insecure from 1996 to 2000, as compared to 15 percent of low-income households and only 5 percent of moderate- and higher-income households (above 1.85 times the poverty line).4 Nonetheless, the majority of food insecure households are not poor. In Wisconsin, for instance, only 35 percent of food insecure households in 19962000 were poor, and 38 percent had income above 1.85 times the poverty line. This simply reflects the fact that the substantial majority of all households have incomes above poverty. Even the relatively low risk of food insecurity among nonpoor households translates into large numbers of households. Although poverty is the strongest predictor of food insecurity, other factors are important as well--particularly family structure. Households with children, especially young children, were more likely to be food insecure than were childless households. The food insecurity rate for Wisconsin households with children, according to the CPS data for 19962000, was 12 percent, twice the rate among childless households. Nationwide during the same period, the pattern was similar, although the rates of food insecurity were somewhat lower. The consequences of food insecurity for children include a higher frequency of behavioral and health problems, lower test scores, and poorer school achievement.5 Single-mother households appear to be especially vulnerable. The CPS data show that, between 1996 and 2000, the food insecurity rate of single-mother households in Wisconsin was almost five times that of married couples with children (33 percent versus 7 percent). Although the high poverty rate of single-mother households is a contributing factor, it is not the only cause. Households headed by single mothers have a substantially higher risk of food insecurity than do married-couple households with income and other characteristics like theirs. Besides poverty and family structure, food insecurity also varies by such factors as race, geography, and home ownership. Together, the factors I have described can have a devastating cumulative impact on food security. As an "Pantries help fill the working poor's growing need for food security." Hartford Courant, December 2, 2002. 19 In the private, emergency food assistance network, food pantries are the central point of contact with families. A recent comprehensive study estimated that there were over 32,000 food pantries nationwide, distributing around 2.9 billion pounds of food each year--the equivalent, roughly, of 2,200 million meals.1 Nevertheless, the role of food pantries is poorly understood. We know virtually nothing about the factors that contribute to food pantry use among low-income families, nor about the circumstances in which food pantries complement or substitute for publicly provided food aid. To fill some gaps in our understanding of these issues, the research reported here explores the use of food pantries among one of the most vulnerable U.S. populations, lowincome single mothers. To provide context and perspective, I examine the use of the federal Food Stamp Program among the same set of families.2 I draw upon data from two sources, the Current Population Survey--Food Security Supplement (CPS-FSS) and the Wisconsin Survey of Food Pantry Clients (see box, p. 30). I begin, though, with a broad look at the extent of food insecurity in Wisconsin and nationwide, highlighting the factors that appear to put households at the greatest risk. The extent of food insecurity Food security--the assured access to enough food for a healthy and active life--is widely acknowledged as an essential component of well-being. But national and regional studies suggest that a startlingly high number of American families are considered "food insecure"; these families experience persistent anxiety about their ability to afford food, eat inadequately, or skip meals because they lack the money to buy food. How widespread is food insecurity? The most recent CPS-FSS data, from 2001, indicate that almost 11 percent of American families cannot always be sure whether or how they will obtain their next meal. To be sure, hunger, the most severe form of food insecurity, is relatively rare; only 3.3 percent of American families experienced food insecurity with hunger. Another 7.4 percent, however, were food insecure.3 Focus Vol. 22, No. 3, Summer 2003 ISS315 - PAGE 119 "Food shelves are serving more middle class families." St. Paul Pioneer Press, March 20, 2002. example, take two families in Wisconsin. The first consists of white, married homeowners, living in a rural county, with children and an income above 1.85 times the poverty line; the family has at least one worker, and no elderly or disabled members. This family has only a 3 percent likelihood of being food insecure. In stark contrast, a family in many respects similar, but headed by a black, working single mother, with an income below the poverty line, renting in the inner city, has a 73 percent likelihood of being food insecure. How the food safety net responds to family needs in volatile circumstances, and how vulnerable families access it--and which families--are clearly major policy concerns. nents of the food security network, in particular food pantries, remain high. Trends in food stamp and pantry use appear, in other words, to have moved in opposite directions during the late 1990s, food stamp use falling, pantry use rising. These trends are particularly relevant to use of food assistance among single parents. In 1998, 58 percent of households participating in the Food Stamp Program included children, and two-thirds of these households were headed by single parents. The general decline in food stamp use was most pronounced among low-income, single-mother households--those with incomes below 130 percent of the poverty line, the cutoff for food stamps. 8 From 1995 to 1999 enrollment among this group fell from 63.5 percent to 42.5 percent. Single parents were also quite prominent among pantry users. A survey by Second Harvest, the nation's largest food bank, found that 25 percent of pantry clients' households, and 50 percent of the households of clients with children, included single parents.9 The decline in food stamp use among single parents, coupled with evidence of growing use of food pantries, and the prevalence of single parents among pantry clientele, raises important questions about the relationship between public and private forms of food assistance for these families. Data from the CPS reveal that there are some notable differences between the single mothers who make use of food pantries and those on food stamps (Table 1). Just over half of the pantry users are white, whereas minorities are much more heavily represented among the food stamp recipients. Food stamp recipients are less likely to have been married and more likely to have a young child. Their Food pantry and food stamp use by lowincome single mothers Public programs constitute by far the larger portion of the national food security network, and among these the Food Stamp Program is the largest. But coincident with the welfare reforms of the 1990s there came a steep drop in Food Stamp participation, from 28 million to 17 million between March 1994 and September 2000. The reasons are still in large part unexplained, and it is not clear how much food stamp use declined because need declined in economic boom times.6 The evidence suggests, indeed, that the need for at least some forms of food assistance did not decline: estimates by providers indicate that demand for food pantry aid increased, on average, by 5 percent each year from 1997 to 2000.7 Anecdotal reporting, too, suggests that demands upon the private compo- The sources of the data on food insecurity and food program participation The research reported here makes use of two sources of information. The Current Population SurveyFood Security Supplement (CPS-FSS), administered by the Census Bureau since 1995, is the only representative, national data set that provides information on food security and the use of public and private food aid. I used several different samples from the CPS-FSS: To examine food stamp and food pantry use, I used a national sample of over 5,500 mothers with incomes below 185 percent of poverty, from the 19982000 waves of the CPS-FSS. To examine food security in Wisconsin, data from the 19962000 supplements were pooled to create a sample of just over 3,000 Wisconsin households, large enough to describe food insecurity in the state with reasonable precision. To provide national comparisons for the Wisconsin food security analysis, I included all households from the 19962000 supplements. The CPS-FSS uses an 18-item scale to classify households into one of three categories--food secure, food insecure without hunger, and food insecure with hunger--on the basis of their experiences over the previous 12 months; all persons in a household are assigned the same food security status. The Wisconsin Survey of Food Pantry Clients (WSFPC), a voluntary, self-administered questionnaire coordinated by the University of Wisconsin Extension, was implemented in 27 Wisconsin counties in October 1999. It included questions regarding demographic characteristics, employment and any barriers to employment, economic well-being, income sources, program participation, and pantry usage. For the analyses in this article, I used data from the 868 single mothers who completed the survey at participating food pantries. 20 ISS315 - PAGE 120 educational levels are lower, but their employment and labor force status are fairly similar those of food pantry users, although the percentage below poverty is higher. The starkest difference between the two groups is in the level of food security. Almost half the food stamp recipients are food secure, but only 19 percent of the pantry users are. Moreover, food pantry users are twice as likely to have experienced hunger as are food stamp recipients. The difference in food security between these two groups is almost certainly larger than could be explained by food stamp participation alone; it is the more striking because Table 1 suggests that food pantries serve single mothers Table 1 Profiles of Single-Mother Food Pantry and Food Stamp Participants, 19982000 Food Pantry Clients (% of total) Race White Black Hispanic Other Marital Status Never married Divorced Separated/Spouse absent Widowed Age 25 or younger 2635 3645 46+ Number of Children 1 2 3 or more Child under 6 Labor Force Status Employed Unemployed & looking for work Disabled out of labor force Out of labor force other Education Less than high school High school/GED More than high school Food Security Status Food secure Food insecure without hunger Food insecure with hunger Income-to-Poverty Ratio <50% 50%100% 100%130% 130%185% Food Stamp Recipients (% of total) Food Pantries Only Food Stamps and Food Pantries No Food Assistance Food Stamps Only Figure 1. Food stamp and food pantry use among low-income single mothers. Source: Current Population Survey, Food Security Supplements, 19982000. 51 30 15 4 41 33 21 5 16 41 33 10 31 35 34 50 48 15 12 24 30 34 35 19 42 39 41 38 12 9 36 42 19 3 54 25 19 3 25 41 27 7 29 33 38 58 46 13 10 30 37 39 24 46 36 18 49 36 11 4 who are more advantaged by conventional measures, with higher incomes and educational attainment. Although the profiles of food pantry and food stamp clients are somewhat different, the evidence is mixed regarding the extent of overlap between the two programs. On the one hand, the CPS data show that a sizable majority of single mothers who report food pantry use in the past year also report receiving food stamps (see Figure 1). Overall, 49 percent of low-income single mothers received assistance from at least one of the two sources, including 35 percent who received only food stamps, 4 percent who received only food from pantries, and 10 percent who received food assistance from both sources.10 On the other hand, the single mothers in the Wisconsin Survey of Food Pantry Clients appeared relatively unconnected to food stamps and other public assistance programs. Only about a quarter reported that they were receiving food stamps in the month of the survey, suggesting that at least for some families emergency food aid operates as an alternative rather than a complement to public food assistance. It is possible, of course, that some parents who do not participate simultaneously in the two programs do receive assistance from both over the course of a year the period of time examined in the CPS. And, because Wisconsin had experienced food stamp declines well above the national average in the period preceding the food pantry survey, the limited overlap between the two programs may be more severe than elsewhere. But the low participation in public programs was not limited to food stamps. Only 12 percent of the single mother pantry clients were currently participating in the state's welfare program, Wisconsin Works, although around three-quarters had at some point been welfare recipients and 40 percent had left the rolls relatively recently, after 1995.11 The Wisconsin women were, in fact, twice as 21 Source: Author's estimates from Current Population Survey, Food Security Supplements 19982000. ISS315 - PAGE 121 "High demand strains food pantries. Weak economy increases need, slows donations." Chicago Tribune, December 3, 2002. likely to be receiving Supplemental Security Income (SSI) as W-2. Perhaps most interesting, however, are the current and recent employment patterns of these mothers. The Wisconsin pantry users fell into four primary categories: employed mothers, the recently nonemployed (out of work less than 3 months), the medium-term nonemployed (out of work 312 months), and the long-term nonemployed (out of work for over a year).12 Employed mothers constituted almost 50 percent of all single-mother pantry users--a strikingly high figure given the perception that the emergency food network is intended as a temporary safety net for people in crisis. Over two-thirds worked part time, most of them more than 20 hours a week, and the vast majority earned less than $8 an hour (this is an amount often used as a proxy for a "living wage" for a family of four). Almost 40 percent, indeed, earned less than $6 an hour. The large share of the Wisconsin single mother pantry clientele who are employed is less surprising, perhaps, when one considers that, among all food-insecure Wisconsin households during 19962000, 74 percent had at least one worker, 57 percent had at least one full-time worker, and 30 percent had two or more workers. At the other end of the spectrum were the long-term nonemployed, including almost one-quarter of all single mothers using food pantries.13 Most notable was the incidence of serious health problems among this group; almost two-thirds received SSI, 53 percent for an adult and 22 percent for a child. Given the stringent disability standards that control SSI eligibility, the long-term nonemployed were almost certainly dominated by those who had very limited prospects of returning to work. Notably, women in this group were the most frequent food pantry users; 62 percent were moderate to heavy food pantry users, having visited a food pantry at least 7 times in the past six months, whereas nearly 70 percent of the recently nonemployed were new or light users. The extent of hardships and the barriers to work among the Wisconsin mothers are reported in Table 2. Healthrelated hardships--specifically, going without needed health care because there is no money--were more common among employed than among nonemployed pantry clientele, perhaps reflecting the difficulties of the working poor in finding access to either public or private health insurance. Among recently nonemployed mothers, low incomes (only 9 percent reported monthly income over $1,000) and frequent reports of hardships, especially in housing, suggest that these families were in a particularly unstable situation. Interestingly, most of the 22 barriers to work that they faced reflected a perceived lack of opportunity and work supports, rather than personal or family issues. The long-term nonemployed, with their very high rates of disability, had substantially lower rates of hardship overall, and especially in the areas of housing and health care. This suggests that they may be in a more stable situation than those who are newly out of work. Single mothers who use food pantries represent a broad cross-section of the low-income population. They include the working poor, who struggle to support themselves with limited education and skills, poor job opportunities, and difficulties in finding and paying for child care; the newly nonemployed, who often lack job opportunities and work supports, and are among the newest users of food pantries; and the long-term nonemployed, many with significant health problems, who tend to be ongoing regular users of a system developed for temporary crises. The evidence remains ambiguous regarding the extent to which food pantries serve as a complement or a substitute to food stamps as a source of support to vulnerable families. What seems clear, however, is that the role of the emergency food network as a component of the broader public-private safety net has evolved largely by default rather than by design. 1 Food kitchens, in contrast, provide about 173 million meals a year. J. Ohls, F. Saleem-Ismail, R. Cohen, B. Cox, and L. Tiehen, The Emergency Food Assistance System--Findings from the Provider Survey, Volume II: Final Report, U. S. Department of Agriculture, Economic Research Service, Food Assistance and Nutrition Research Report No. 16-2, October 2002. <http://www.ers.usda.gov/publications/fanrr162/> 2 This article is based upon J. Bartfeld and C. David, Food Insecurity in Wisconsin, 19962000, report prepared for the Wisconsin Department of Health and Family Services, Madison, WI, February 2003; J. Bartfeld, "Single Mothers and Emergency Food Assistance in the Welfare Reform Era," IRP Discussion Paper 1253-02, University of WisconsinMadison, April 2002; and J. Bartfeld, "Emergency Food Assistance as a Component of the Public-Private Safety Net for LowIncome Single-Mother Households," paper prepared for the Association for Public Policy Analysis and Management meetings, November 2002. 3 M. Nord, M. Andrews, and S. Carlson, Household Food Security in the United States, 2001, U.S. Department of Agriculture, Economic Research Service, Food Assistance and Nutrition Research Report No. 29, October 2002. 4 The Wisconsin statistics in this section are drawn from CPS-FSS data for 19962000, as reported in Bartfeld and David, Food Insecurity in Wisconsin, unless otherwise indicated. 5 Center on Hunger and Poverty, The Consequences of Hunger and Food Insecurity for Children: Evidence from Recent Scientific Studies (Waltham, MA: The Heller School for Social Policy and Management, Brandeis University, 2002). 6 P. Wilde, P. Cook, C. Gundersen, M. Nord, and L. Tiehen, The Decline in Food Stamp Program Participation in the 1990s, U.S. ISS315 - PAGE 122 Table 2 Hardships and Employment Barriers among Single Mothers Using Food Pantries in Wisconsin, 1999 (percentage of each subgroup with characteristic) Recently Nonemployed 15 38 36 43 28 79 Medium-Term Nonemployed 15 31 35 31 31 73 Long-Term Nonemployed 23 33 39 20 17 62 Characteristics % of entire sample Hardships Food Utility Housing* Health* Any* Employment Barriers Finding and keeping jobs Problem of job availability* Lack of skills, education, work experience* Essential resources Problem finding or affording child care* Lack of transportation* Homeless/housing problems* Health concerns Health/disability, long-term Health/disability, short-term Any health/disability concerns Personal and family responsibilities and prioritiesa Prefer to be home with kids Caring for disabled family member* In school# Employed 47 39 35 27 38 73 20 27 20 12 3 7 7 14 8 3 4 32 13 26 20 9 8 11 20 9 1 3 17 15 20 10 4 28 14 43 9 8 8 7 16 10 11 2 56 7 67 13 9 1 Source: J. Bartfeld, "Single Mothers and Emergency Food Assistance in the Welfare Reform Era," IRP Discussion Paper 1253-02, Madison, WI, April 2002. Note: The recently nonemployed = out of work less than 3 months, the medium-term nonemployed = out of work 312 months, and the long-term nonemployed = out of work for over a year. a Around 35 percent of all groups specified "other family circumstances" including alcohol or drug problems, criminal record, partner problems, lack of English, or lack of Green Card. Difference between employment groups: *, significant at the 1% level; #, at the 5% level. Department of Agriculture, Economic Research Service, Food Assistance and Nutrition Research Report No. 7, July 2001. See also M. Nord, "Food Stamp Participation and Food Security," Welfare Reform and Food Assistance 24, no.1 (2001): 1319. 7 8 9 uting factor may be that the CPS sample does not include homeless persons. 11 Ohls and colleagues, The Emergency Food Assistance System. Nord, "Food Stamp Participation." The survey was conducted when W-2 participation was extremely low; there were fewer than 600 open W-2 cases in the 27 counties in the survey sample. Bartfeld, "Single Mothers and Emergency Food Assistance," p. 16. 12 Pantry user statistics from Second Harvest report, Hunger in America 2001 <http://www.hungerinamerica.org/>, Table 5.2.1. Food stamp statistics from Nord and colleagues, The Decline in Food Stamp Program Participation. I use the term "nonemployed" rather than "unemployed," to cover both women not working but actively looking for work and women who are out of the labor force entirely. 13 10 Both food pantry and food stamp use appear to be underreported in the CPS-FSS when compared to client-based measures of participation. In the case of food pantry participation in particular, one contrib- The medium-term nonemployed, as one might expect, fall within the boundaries set by the recently nonemployed and the long-term nonemployed, and I do not discuss them here. 23 ISS315 - PAGE 123 U.S. Elites Celebrate Patriarchy, Racism and Class Privilege By PETER PHILLIPS San Francisco Bohemian Club members and guests from around the world recently completed two weeks of celebration, self entertainment and partying at their private 2,700 acre redwood retreat on the Russian River in Sonoma County, California. Described as the "Greatest Men's Party on Earth," the members of the Club and international elites have been gathering in their redwoods for over 100 years.Private men's clubs have existed in the U.S. for over two and half centuries. U.S. clubs were modeled after British gentlemen's clubs, which date back 400 years. Gentlemen's clubs followed the English around the world and were a sanctum of racial, sexual and class homogeneity for English aristocrats throughout the British Empire. American men's clubs have served a similar function as did their British models. In most major American cities there are one or two distinguished metropolitan men's clubs whose members dominate the social and economic life of the community. Club activities are a blending of arts, business, and socio-political discussions. Men's clubs are private places where elites can mingle in an atmosphere of gentlemanly civility away from the common everyday world. The San Francisco Bohemian Club is unique among private men's clubs in that it holds an annual 16-day summer encampment where the 2,400 members are free to invite several hundred distinguished business associates and guests from around the world. Long days of glad-handing, off-the-record political discussions, government policy reviews, and the building of business friendships serve to facilitate consensus and ease of interaction among some of the top governmental and business leaders in the world. The collective corporate stock ownership by members and guests conservatively exceeds $100 billion. The Bohemian Grove summer gathering brings together the top business elite of California along with hundreds of men from leadership positions in government, education, business, military, and the arts from throughout the United States and the world. Foremost among attendees are former Republican presidents, numerous current and past U.S. cabinet members; military generals, famous actors; members of national policy councils, and CEOs and directors of hundreds of the largest corporations in the world. It is safe to say that the Bohemian Grove is one of the few locations in the world where such a large high level gathering of elites occurs without press coverage or public scrutiny. During the summer of 2003 the men at the Bohemian Grove heard off-the-record presentations -no media is allowed from William F. Buckley Jr., William Safire, Charles Murray, George Shultz, Michael York and Charlie Rose. Additionally, there were daily lectures from world-class experts on global warming, war policy, school vouchers, mad deer disease, horse racing, stem cell research, terrorism, American-Russian relations, and marine ecosystem. Concerts, plays, and daily parties rounded out the two-week session for 2003. On June 4, 1994 a presentation at the Grove from a University of California Berkeley professor stressed that, elites are important and must set the values for society that are translated into "standards of authority," and that elites cannot allow the "unqualified masses" to carry out policy. The speech was given an enthusiastic standing ovation by the over 1,000 men present and seemed to represent the feelings of many club members. Like the British Empire's gentlemen's clubs, the American Empire elite gather annually in Sonoma County for an allmale 99%-white private party to find homogeneous comradeship and celebrate themselves through poetry, music, discussions and plays. And like the British before them they employ a cadre of servants, waiters, waitresses, grounds people, on-site medical personnel, and security officers to meet their every need, -women are prohibited from 90% of the Grove and can only work in the main dining area, the skeet range, and the parking lots. The Bohemian Club's summer encampment is the institutionalized embodiment of elite class privilege, a de facto celebration of race and gender exclusiveness, and a slap in the face to democratic process in the United States. Institutions of elite privilege like the San Francisco Bohemian Club run counter to the core American values of equality, due process and political openness. Americans deserve a public apology from the Bohemian Club for their celebration of eliteness, ongoing full disclosures of their lectures and presentations, and the transformation of the club to one of public service and gender and racial inclusiveness. Peter Phillips is a Professor of Sociology and Department Chair at Sonoma State University: Email peter.phillips@sonoma.edu. His 1994 dissertation on the San Francisco Bohemian Club is available at: http://libweb.sonoma.edu/ ISS315 - PAGE 124 The level, trend, and composition of poverty Official statistics suggest that the poor have become more numerous and their poverty more intense since the 1970s. But this conclusion depends on the way we measure changes in consumer prices, how we account for in-kind income and refundable tax credits, and whether we adopt a new measure of poverty.1 Gary Burtless and Timothy M. Smeeding Gary Burtless is a Senior Fellow at the Brookings Institution, and Timothy M. Smeeding is Maxwell Professor of Public Policy and Professor of Economics and Public Administration, Syracuse University. Both are IRP associates. the operation of a farm, business, or partnership, pensions, interest, dividends, and government transfer payments that are distributed in the form of cash, including social security and public assistance benefits. This measure of resources is not comprehensive, as it ignores, among other things, all sources of noncash income, including food stamps, housing subsidies, and government- and employer-provided health insurance, and it does not account for taxes paid. Because of these flaws in the official measure of resources, in this article we make use of three different measures of resources in determining whether individuals, families, or households are poor. We use (1) the Census Bureau's official income definition; (2) a definition that adjusts for taxes and near-cash income such as the value of food stamps, and (3) a definition that also includes an estimate of out-of-pocket medical spending. The last two measures are closely linked to recommendations for improving the official poverty measure that were made in 1995 by a National Research Council (NRC) panel on poverty statistics and that have been widely discussed.3 The percentage of people or families who are poor cannot be calculated using income alone. To determine who is poor, income must be compared with some kind of poverty standard reflecting a family's needs. Such poverty standards may be absolute or relative. Absolute standards (or thresholds) are defined in terms of a fixed level of purchasing power sufficient to buy a bundle of basic necessities. Relative standards, in contrast, are defined in terms of the typical income or consumption level in the wider society. The purchasing power of a relative standard will change over time as society-wide income or consumption levels change. The poverty thresholds annually calculated by the Census Bureau are, essentially, absolute: they provide estimates of the incomes necessary for persons or families in different circumstances to purchase a minimally adequate level of consumption.4 The most important circumstance affecting income needs is family size--larger families need higher incomes than smaller ones. In 1998, for example, the weighted-average poverty threshold for a typical single person (referred to as an "unrelated individual" by the Census Bureau) was $8,316. The poverty threshold for a family with four members was $16,660, almost exactly twice that for one person. Focus Vol. 21, No. 2, Fall 2000 Measuring poverty Measuring poverty in rich nations involves comparing some index of household well-being or economic resources with household needs. When command over economic resources falls short of needs, a household (or person or family) is classified as poor. From a practical standpoint, measures of resources must be based on regularly available data of reasonable quality, and they must document the level and trend in poverty across a wide range of subpopulations. That aside, there is clearly no single way to measure such a multidimensional concept as poverty, which reflects many forms of deprivation, both economic and social.2 Particularly in nations like the United States where there is heavy reliance on the market to provide such essential services as health care, postsecondary education, and child care, money income is a crucial resource. The official measure of American poverty is defined in terms of personal or family income. Persons and families with incomes below a set of official poverty thresholds are classified as poor; those with incomes above official poverty thresholds are classified as nonpoor. Under this conception of poverty, a poor person is one whose income places him or her below a level of minimally adequate resources. The U.S. Bureau of the Census has tracked and published statistics on the distribution of cash income under a standard income definition since shortly after World War II, even before there was a widely accepted definition of American poverty. The Bureau's principal measure of income includes before-tax cash income from all sources except gains or losses on the sale of property. This definition includes gross wages and salaries, net income from 4 ISS315 - PAGE 125 150 130 110 1973 = 100 90 70 Average incomes in: 50 Bottom fifth All families Top fifth 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 30 1945 Figure 1. Trend in real average family income, 19471998. Average income for these three groups in 1973 is normalized to 100. Thus a value of 50 for the top quintile in the 1940s indicates a real income that was half its value in 1973. Source: U.S. Census Bureau, Historical Income Tables--Families. Measuring inequality Whereas poverty tells us about the absolute well-being of those at the bottom of the income distribution, inequality tells us how those at the bottom are faring relative to the rest of the population. A common way to measure inequality is to calculate the percentage of total income received by families in different parts of the income distribution. The Census Bureau, for example, calculates the income rank of every family, ranks families from lowest to highest, and then divides families into five equal-sized groups. In 1998, the one-fifth of families with the lowest incomes received 4.2 percent of total income. Families in the highest one-fifth received 47.3 percent of all income. If incomes were distributed equally across the five groups, each fifth of the distribution would receive exactly 20 percent of aggregate income. 5 Figure 1 shows the trend in real family income after 1947, both average cash income and average cash incomes received by families in the top and bottom fifths of the annual income distribution. From World War II until the mid-1970s, the relative position of all families--those at the bottom, top, and middle of the income ladder--improved more or less in tandem. From 1970, the average incomes of high-income families have grown substan- tially, except for 198793, when all incomes shifted down. In contrast, the average incomes of the bottom group fell until 1993, and are only now climbing back to the 1970s level. The trends in poverty The prevalence of poverty in the United States, according to the three measures of income defined earlier, is tracked in Figure 2. As measured by the official U.S. government poverty rate (the thick line in Figure 2), poverty fell steeply in the decade after 1959, reached an all-time low in 1973, and then increased in the early 1980s and early 1990s. The sharp increases in poverty in 197983 and in 1989 93 were connected to the recessions that occurred in those years, but the magnitude of the increase was a surprise to most economists. More surprising still was the failure of the poverty rate to fall back to the level reached in the 1970s, even after prolonged economic expansions in the 1980s and 1990s. The recessions of the 1980s and 1990s were accompanied or soon followed by large increases in income inequality. Even when average incomes rose in the economic expansions of these decades, the share of income received by low-income households stagnated or declined. In the 20 years after 1978, poverty 5 ISS315 - PAGE 126 25 Alternative with medical Official definition Alternative, no medical Percentage of Population 20 15 10 5 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 Figure 2. The poverty rate under alternative definitions of poverty, 19591998. Option 1 = alternative, no medical; option 2 = alternative with medical. Source: U.S. Census Bureau and authors' tabulations of 19801999 March CPS files. climbed from 11.4 percent to 12.7 percent of the population, according to the official measure. Part of this apparent increase may be an artifact of the measurement methods used by the Census Bureau. In particular, poverty rates are sensitive to the price index used when the official thresholds are updated to reflect changes in the cost of living. Most economists believe that the index used--the Consumer Price Index for All Urban Consumers--overstated increases in consumer prices in many of the years after 1959. If the reformed index now used by the Bureau of Labor Statistics to measure price change had been used in the past, the poverty thresholds would have increased more slowly. The number of Americans with incomes below the poverty line would then have been smaller, and the real income trends in Figure 1 would have been more favorable after 1973. Setting aside the accuracy of the index to which it is linked, the official definition of income poverty has a number of serious deficiencies. As we noted earlier, it is based on a definition of income that ignores all noncash government transfers, even though many noncash transfers help pay for basic necessities, such as food, shelter, and medical care. The official income measure makes no allowance for income and payroll taxes, which reduce household resources available to pay for necessities. Because the impact of the tax system is ignored, income for some low-income working families with children may be seriously understated; these families are eligible for tax credits, such as the Earned 6 Income Tax Credit (EITC), that can significantly boost the family's access to economic resources. The official definition also makes no distinction between sources of income that are costly to earn, such as income from wages, and those that have little or no direct cost to the recipient, such as pensions and dividends. In calculating the income produced by a job, the official definition ignores the significant costs associated with getting to work and paying for child care when all adults in a household are employed. If we use a broader definition of income, following the recommendations of the 1995 NRC panel to include near-cash benefits and refundable tax credits, the poverty rate in 1997 drops from 13.3 percent (under the official definition) to 11.1 percent, using the official poverty thresholds. If at the same time we subtract an estimate of work-related expenses from countable income, measured income declines and the poverty rate increases again slightly.6 (This is option 1 in Figure 2.) The NRC panel's most controversial recommendation was to subtract out-of-pocket spending on medical care from household income. Because such spending is often burdensome, this procedure substantially increases the number of poor, especially the elderly and disabled. The Census Bureau estimates that subtracting medical spending from the official definition of countable income would have increased the 1997 poverty rate from 13.3 percent to 16.3 percent.7 (This is option 2 in Figure 2.) ISS315 - PAGE 127 Table 1 Sources of Net Income among People Who Are Poor under Alternative Poverty Line and Two Income Concepts, 1998 a Poor after Taxes, Transfers, Work and Medical Expenses b (2) 48 -5 16 20 -25 __________ Income Concept or Component of Net Income Market income Taxes (except EITC) Social insurance Means-tested transfers (including EITC) Out-of-pocket medical spending Total income of poor, ignoring medical spending Total income of poor, subtracting medical spending People below poverty threshold (millions) % of population in each category Source: Authors' tabulations of March 1999 CPS files. Market-Income Poor (1) 35 -3 50 22 -18 __________ 104 86 57.6 21.3% 78 53 43.6 16.1% a Each element of income is measured as a percentage of the poverty threshold (set at 100 percent) for people who are poor according to that concept. b The poverty thresholds are derived from the Census Bureau's estimates of the food, clothing, and shelter consumption patterns of the median reference family, updated to 1998 using the CPI-U and the 3-parameter equivalence scale described by Short and colleagues, Experimental Poverty Measures, 19901997. The income-to-poverty-line ratio Assuming, for the present, that the U.S. poverty thresholds accurately measure the different consumption requirements associated with different family sizes and compositions, the thresholds offer a convenient benchmark for assessing a family's income. Each family's income can be divided by its poverty threshold to determine how far its income falls short of or exceeds minimum consumption requirements (this ratio is known as the income-to-poverty-line ratio). Families with an income that is one-half the threshold must see their incomes double in order to pay for a minimum consumption basket. Families with incomes more than three times the poverty threshold can comfortably pay for their minimum consumption needs and still have money left over to buy other goods and services. The median income-to-poverty-line ratio in 1998 was 3.09. For a family containing three members, this was equivalent to an annual income of about $40,200. The poverty gap In 1998, 7.2 million families and 8.5 million unrelated individuals (in all, about 34.5 million people, or 12.7 percent of the population) had pretax cash incomes below the official poverty thresholds.8 The average family or individual in this group had an income that was only 54 percent of the poverty threshold. This family would need $5,350 in extra annual income to reach the poverty line. In the aggregate, then, about $83.8 billion--or 1 percent of Gross Domestic Product (GDP)--would be needed to eliminate cash poverty in the United States, if all of it could be accurately directed ("targeted") toward only those with incomes below the poverty line.9 The difference between poor families' actual incomes and the incomes needed for all of them to reach the poverty line is usually called the "poverty gap." Under all three definitions of poverty, the poverty gap is roughly 1 percent of GDP. The sources of income of the poor Table 1 sheds light on the income sources of the poor. We examine two groups of people who are poor under very different definitions of income. The first group consists of those who are poor if only their before-tax market incomes are counted in determining their poverty status. This group of people is often referred to as the "pretax and pretransfer poor." Market incomes include pretax wages, salaries, self-employment income, pensions (except social security), interest, dividends, and capital gains and losses. (Note that this is not the same as the official Census definition of income, because it does not include transfer income.) This group comprises 21.3 percent of the American population. The second group consists of those who are poor after taxes and some work expenses are subtracted, transfers added, and an estimate of their out-of-pocket medical expenses is also subtracted from market income. This group comprises about 16 percent of the population. 7 ISS315 - PAGE 128 Each element of income is measured as a percentage of the person's poverty threshold. The poverty thresholds that we use in Table 1 as a measure of poverty for these two groups are not the official thresholds, but are a set of alternative poverty thresholds developed in part by the Census Bureau on the basis of recommendations by the NRC panel. We use these alternative thresholds because we believe they offer a more defensible measure of deprivation than the official thresholds. Not surprisingly, the average market income of poor households who are poor because their market incomes are below the poverty line (those in column 1) is very low. This group includes the elderly, who have little or no earned income, as well as families with children and single people with very low earnings. Social insurance payments, which are primarily targeted to the elderly, and means-tested transfers, which are primarily targeted to families with children, are important income sources for these families and individuals. Market income averages barely one-third of a poverty-line income, whereas social insurance payments average 50 percent and means-tested transfers 22 percent of the poverty threshold among people with market incomes below that threshold. The population reflected in column 2 consists of those who are poor after taxes and transfers. Since social insurance payments are large enough to remove many elderly families from poverty, the composition of the populations reflected by columns 1 and 2 is quite different. The population reflected in column 2 will have far fewer elderly persons than the population reflected in column 1. It will have more working poor families, as a consequence of including the effects of medical expenses on available resources. The compositional differences in the populations show up clearly in the table. Average social insurance payments constitute a much smaller fraction of family budgets of the after-tax and -transfer poor than they do for the market-income poor, primarily because there are far fewer elderly in this group. With more working-poor families, market incomes average roughly half the poverty line. Means-tested transfers are about the same as for the market-income poor. Resources for the poor come primarily from a few sources. Regardless of how poverty is defined, the poor rely on earnings, social insurance, and means-tested transfers. The evolution, magnitude, and antipoverty effectiveness of these programs are the topic of the following article by John Karl Scholz and Kara Levine. considered here. The nation's experience since 1979 suggests, however, that a healthy economy by itself will never reduce the American poverty rate to levels prevailing in northwestern Europe. To achieve a much lower poverty rate without major overhaul of public policy, the United States would need to experience a dramatic--and unlikely--reduction in wage inequality or a sharp reversal in the family composition trends that have prevailed over the past four decades. Changes in public policy that assure good health insurance, provide better incomes to the indigent elderly and disabled, and supplement the earned incomes of working-but-poor breadwinners represent the best hope for achieving large poverty reductions in the near term. n 1 This article draws upon Gary Burtless and Timothy Smeeding, "The Level, Trend, and Composition of Poverty," presented at the IRP conference, Understanding Poverty in America: Progress and Problems, on May 2224, 2000, in Madison, WI. The revised conference papers will be jointly published by Harvard University Press and the Russell Sage Foundation in a volume tentatively titled Understanding Poverty: Progress and Prospects. 2 Of course, there are other important kinds of resources, such as social capital, noncash benefits, primary education, and access to basic health care, all of which add to human capabilities (see J. Coleman, "Social Capital in the Creation of Human Capital," American Journal of Sociology 94 [1988]: S95S120). These resources may be available more or less equally to all people in some societies, regardless of their money incomes. Other factors aside from the absence of money can reduce well-being by limiting capabilities for full participation in society, including racial discrimination, neighborhood violence, low-quality public schools, and job instability. We do not examine these limiting forces or investigate related topics, such as social exclusion. 3 Research related to measuring poverty and the revision of the official poverty measure is the subject of Focus 19, no. 2 (Spring 1998). The NRC panel also suggested using poverty thresholds based on proportions of a median family's consumption of food, clothing, and shelter, with a "little bit more" for other expenses, in place of the existing official thresholds. 4 The income thresholds in the official measure of poverty, which was established in the 1960s, are based upon a multiple of the Department of Agriculture's basic food consumption estimates (the "Economy Food Plan" developed in the 1950s). 5 There is, of course, no fixed relationship between inequality and poverty. Under the right circumstances, even a rise in inequality can be associated with an improvement in living standards at the bottom, if overall income growth is strong enough. Conversely, the share of income going to the bottom fifth of families might jump, but if average incomes in the population at large are shrinking, the real incomes of those at the bottom could still fall. 6 K. Short, T. Garner, D. Johnson, and P. Doyle, Experimental Poverty Measures, 19901997, Report P60-205 (Washington, DC: U.S. Census Bureau, 1999), pp. 910. 7 Short and others, Experimental Poverty Measures, 19901997, p. 11. 8 Another 48.9 million, or 18.0 percent, had pretax cash incomes between one and two times the poverty thresholds. Conclusion The healthy economy of the late 1990s helped push down the poverty rate under all three definitions we have 8 9 As the article in this Focus by Scholz and Levine shows, no existing transfer program achieves any such level of accuracy in targeting benefits only to the poor. ISS315 - PAGE 129 What's next? Some reflections on the poverty conference Poverty and children Jane Waldfogel, Associate Professor of Social Work, Columbia University Papers presented at the conference raise a number of issues that should receive more attention in future research on poverty and poverty policies.1 Here I consider two that have particular salience for the study of child poverty: interactions between poverty and other forms of disadvantage, and some pathways by which poverty might affect child outcomes. ciated with unemployment, whereas poverty in singleparent families may be more strongly associated with early childbearing. These differences, rather than differences in family structure per se, may account for the differential effects on outcomes. Future research on the interaction of poverty with other types of disadvantage or risk factors might address whether the effects of growing up in poverty are different for: children living in poor neighborhoods, segregated neighborhoods, or neighborhoods low in social cohesion;4 children from immigrant families, or children who do not speak English as their first language; children who attend poor-quality schools or who have learning disabilities; children who have chronic health conditions or limitations; children whose mothers work early in their childhood, or who experience poor-quality child care or multiple child care transitions. Interactions between poverty and other forms of disadvantage Children who grow up in poverty fare worse than other children on a number of outcomes, for example, educational attainment and health. So also do children raised in mother-only families.2 But are there interactions between poverty and other forms of disadvantage? Can we conjecture that children in single-parent families are especially vulnerable to the adverse effects of poverty, whereas children in two-parent families are buffered from those effects? Drawing on data from the National Survey of Families and Households, Thomas Hanson and his colleagues found that the adverse effects of low income on child outcomes did differ according to family structure. The effects of poverty were larger for children in single-parent families on five outcome measures (school performance, grade point average, and three measures of behavioral problems), but were larger for children in two-parent families on three other measures of child well-being (sociability, initiative, and quality of life).3 A different pattern of interactions appears in the children of the National Longitudinal Survey of Youth. When I examined cognitive outcomes for children up to the age of 8, I found that the significant negative effect of growing up in poverty was no greater for children in singleparent families. There were, as expected, adverse effects of poverty on behavioral problems of young children, but these effects were smaller for children in singlemother families than two-parent families. At this point, then, the evidence on our conjecture is mixed. Children in single-parent families may be more vulnerable on some outcomes, whereas children from two-parent families may be more vulnerable on others. Or the correlates of poverty may tend to be different in single-parent versus two-parent families. Poverty in twoparent families, for instance, may be more strongly assoFocus Vol. 21, No. 2, Fall 2000 If the effects of growing up in poverty are more pronounced for children experiencing other types of disadvantage or risk factors, these interactions have implications for our understanding of those effects and also for our thinking about remedial policies. If, for instance, the effects of growing up in poverty are more severe for children from particular groups, estimating these effects across all groups will lead us to underestimate the impacts for the more vulnerable children. Moreover, understanding which children are more vulnerable can help us to target policy interventions to them and to design interventions that more effectively address that vulnerability. How poverty affects child outcomes The question of how and why poverty matters for child outcomes is a hotly contested topic.5 Some insights that come from the literature on poverty and child maltreatment are relevant. Research has established that poor children, and children who live in poor communities, are more likely to be identified as abused or neglected and are more likely to be placed into foster care than nonpoor children and 61 ISS315 - PAGE 130 those living in nonpoor areas.6 At least four hypotheses have been put forward to explain this: 1. Individuals who report families to the child welfare agency are biased and are more likely to report families if they are poor. If this hypothesis is true, we should see elevated rates of reports of all types of maltreatment among poor children. 2. Poor parents are under more stress and may therefore resort to harsher parenting. If so, we should see elevated rates of physical abuse among poor children. 3. Poor parents do not have the resources to provide adequate care for their children. If so, we should see elevated rates of neglect among poor children. 4. The connection between poverty and maltreatment is due to unobserved heterogeneity, i.e., there may be underlying problems, such as substance abuse or mental illness, that cause both the poverty and the abuse or neglect. Usually these underlying problems are thought to lead to parents' failure to provide appropriate care for (rather than actively maltreating) their children. Thus this hypothesis predicts that we should see elevated rates of reported neglect among poor children. We do not yet have enough evidence to determine which hypotheses are valid. What we do have suggests that there is probably some truth to each, but that poverty in the United States is most strongly related to neglect.7 Cross-country comparisons are also suggestive. The United States, which has a higher rate of child poverty than Canada or England, also has a much higher rate of child maltreatment, and this is due primarily to a higher rate of neglect (rates of physical and sexual abuse are not notably higher).8 Thus, it is probably the case that poor parents simply do not have the resources to provide adequate care for their children or are affected by some underlying condition that explains both the poverty and the neglect. Differentiating between these two hypotheses is difficult, but Christina Paxson and I recently found evidence that in states and years where welfare benefits are higher, fewer children are reported for neglect and fewer children are placed in foster care.9 These results suggest that when it comes to neglect, money, and not just the unobserved characteristics of poor parents, may matter, since presumably the level of welfare benefits in a state and year are not determined by the unobserved characteristics of poor parents. Focusing on how poverty affects children has implications both for our understanding of poverty and for the design of policy responses. If poverty is related to child neglect, then that connection may help us understand the processes by which poverty leads to other adverse outcomes for children, and that understanding in turn might help us design interventions to help ameliorate those outcomes. n 62 1 These remarks form part of rapporteur's comments by Professor Jane Waldfogel, presented at the IRP conference, Understanding Poverty in America: Progress and Problems, on May 2224, 2000, in Madison, WI. The revised conference papers will be jointly published by Harvard University Press and the Russell Sage Foundation in a volume tentatively titled Understanding Poverty: Progress and Prospects. 2 G. Duncan and J. Brooks-Gunn (eds.), Consequences of Growing Up Poor (New York: Russell Sage Foundation, 1997); S. McLanahan and G. Sandefur, Growing Up with a Single Parent: What Hurts, What Helps (Cambridge, MA: Harvard University Press, 1994). 3 T. Hanson, S. McLanahan, and E. Thomson, "Economic Resources, Parental Practices, and Children's Well-Being," in Consequences of Growing Up Poor, ed Duncan and Brooks-Gunn, pp. 190238. 4 See J. Yinger, "Housing Discrimination and Residential Segregation as Causes of Poverty," in this Focus. 5 See M. Corcoran, "Mobility, Persistence, and the Intergenerational Determinants of Children's Success," in this Focus. See, for instance, L. Pelton, "Child Abuse and Neglect: The Myth of Classlessness," American Journal of Orthopsychiatry 48 (1978): 60817; J. Garbarino and D. Sherman, "High-Risk Neighborhoods and High-Risk Families: The Human Ecology of Child Maltreatment," Child Development 51 (1980):18898; J. Garbarino and K. Kostelny, "Child Maltreatment as a Community Problem," Child Abuse and Neglect 16 (1992): 45564; D. Lindsey, The Welfare of Children (New York: Oxford University Press, 1994); C. Coulton, J. Korbin, M. Su, and J. Chow, "Community Level Factors and Child Maltreatment Rates," Child Development 66 (1995): 126276. 7 6 See, for instance, R. Hampton and E. Newberger, "Child Abuse Incidence and Reporting by Hospitals: Significance of Severity, Class, and Race," American Journal of Public Health 75 (1985): 5660; G. Zellman, "The Impact of Case Characteristics on Child Abuse Reporting Decisions," Child Abuse and Neglect 16 (1992): 5774; P. Trickett, L. Aber, V. Carlson, and D. Cicchetti, "Relationship of Socioeconomic Status to the Etiology and Developmental Sequelae of Physical Child Abuse," Developmental Psychology 27 (1991):14858; V. McLoyd, "The Impact of Economic Hardship on Black Families and Children: Psychological Distress, Parenting, and Socioemotional Development," Child Development 61 (1990): 31146; J. Waldfogel, The Future of Child Protection: How to Break the Cycle of Abuse and Neglect (Cambridge, MA: Harvard University Press, 1998); C. Paxson and J. Waldfogel, "Parental Resources and Child Abuse and Neglect," American Economic Review Papers and Proceedings, May 1999, pp. 23944, and "Work, Welfare, and Child Maltreatment," Working Paper no. 7343, National Bureau of Economic Research, 1999. 8 9 Waldfogel, Future of Child Protection. Paxson and Waldfogel, "Work, Welfare, and Child Maltreatment." ISS315 - PAGE 131 Why so much hunger? What can we do about it? To answer these questions we must unlearn much of what we have been taught. Only by freeing ourselves from the grip of widely held myths can we grasp the roots of hunger and see what we can do to end it. Myth 7 The Free Market Can End Hunger Reality: Unfortunately, such a "market-is-good, government-isbad" formula can never help address the causes of hunger. Such a dogmatic stance misleads us that a society can opt for one or the other, when in fact every economy on earth combines the market and government in allocating resources and distributing goods. The market's marvelous efficiencies can only work to eliminate hunger, however, when purchasing power is widely dispersed. So all those who believe in the usefulness of the market and the necessity of ending hunger must concentrate on promoting not the market, but the consumers! In this task, government has a vital role to play in countering the tendency toward economic concentration, through genuine tax, credit, and land reforms to disperse buying power toward the poor. Recent trends toward privatization and de-regulation are most definitely not the answer. Myth 1 Not Enough Food to Go Around Reality: Abundance, not scarcity, best describes the world's food supply. Enough wheat, rice and other grains are produced to provide every human being with 3,500 calories a day. That doesn't even count many other commonly eaten foodsvegetables, beans, nuts, root crops, fruits, grass-fed meats, and fish. Enough food is available to provide at least 4.3 pounds of food per person a day worldwide: two and half pounds of grain, beans and nuts, about a pound of fruits and vegetables, and nearly another pound of meat, milk and eggs-enough to make most people fat! The problem is that many people are too poor to buy readily available food. Even most "hungry countries" have enough food for all their people right now. Many are net exporters of food and other agricultural products. ISS315 - PAGE 132 Myth 2 Nature's to Blame for Famine Reality: It's too easy to blame nature. Human-made forces are making people increasingly vulnerable to nature's vagaries. Food is always available for those who can afford it--starvation during hard times hits only the poorest. Millions live on the brink of disaster in south Asia, Africa and elsewhere, because they are deprived of land by a powerful few, trapped in the unremitting grip of debt, or miserably paid. Natural events rarely explain deaths; they are simply the final push over the brink. Human institutions and policies determine who eats and who starves during hard times. Likewise, in America many homeless die from the cold every winter, yet ultimate responsibility doesn't lie with the weather. The real culprits are an economy that fails to offer everyone opportunities, and a society that places economic efficiency over compassion. Myth 8 Free Trade is the Answer Reality: The trade promotion formula has proven an abject failure at alleviating hunger. In most Third World countries exports have boomed while hunger has continued unabated or actually worsened. While soybean exports boomed in Brazil-to feed Japanese and European livestock-hunger spread from one-third to two-thirds of the population. Where the majority of people have been made too poor to buy the food grown on their own country's soil, those who control productive resources will, not surprisingly, orient their production to more lucrative markets abroad. Export crop production squeezes out basic food production. Pro-trade policies like NAFTA and GATT pit working people in different countries against each other in a 'race to the bottom,' where the basis of competition is who will work for less, without adequate health coverage or minimum environmental standards. Mexico and the U.S. are a case in point: since NAFTA we have had a net loss of 250,000 jobs here, while Mexico has lost 2 million, and hunger is on the rise in both countries. Myth 3 Too Many People Reality: Birth rates are falling rapidly worldwide as remaining regions of the Third World begin the demographic transition-- when birth rates drop in response to an earlier decline in death rates. Although rapid population growth remains a serious concern in many countries, nowhere does population density explain hunger. For every Bangladesh, a densely populated and hungry country, we find a Nigeria, Brazil or Bolivia, where abundant food resources coexist with hunger. Costa Rica, with only half of Honduras' cropped acres per person, boasts a life expectancy--one indicator of nutrition --11 years longer than that of Honduras and close to that of developed countries. Rapid population growth is not the root cause of hunger. Like hunger itself, it results from underlying inequities that deprive people, especially poor women, of economic opportunity and security. Rapid population growth and hunger are endemic to societies where land ownership, jobs, education, health care, and old age security are beyond the reach of most people. Those Third World societies with dramatically successful early and rapid reductions of population growth rates-China, Sri Lanka, Colombia, Cuba and the Indian state of Kerala-prove that the lives of the poor, especially poor women, must improve before they can choose to have fewer children. Myth 9 Too Hungry to Fight for Their Rights Reality: Bombarded with images of poor people as weak and hungry, we lose sight of the obvious: for those with few resources, mere survival requires tremendous effort. If the poor were truly passive, few of them could even survive. Around the world, from the Zapatistas in Chiapas, Mexico, to the farmers' movement in India, wherever people are suffering needlessly, movements for change are underway. People will feed themselves, if allowed to do so. It's not our job to 'set things right' for others. Our responsibility is to remove the obstacles in their paths, obstacles often created by large corporations and U.S. government, World Bank and IMF policies. Myth 10 More U.S. Aid Will Help the Hungry Reality: Most U.S. aid works directly against the hungry. Foreign aid can only reinforce, not change, the status quo. Where governments answer only to elites, our aid not only fails to reach hungry people, it shores up the very forces working against them. Our aid is used to impose free trade and free market policies, to promote exports at the expense of food production, and to provide the armaments that repressive governments use to stay in power. Even emergency, or humanitarian aid, which makes up only five percent of the total, often ends up enriching American grain companies while failing to reach the hungry, and it can dangerously undercut local food production in the recipient country. It would be better to use our foreign aid budget for unconditional debt relief, as it is the foreign debt burden that forces most Third World countries to cut back on basic health, education and anti-poverty programs. Myth 4 The Environment vs. More Food? Reality: We should be alarmed that an environmental crisis is undercutting our food-production resources, but a tradeoff between our environment and the world's need for food is not inevitable. Efforts to feed the hungry are not causing the environmental crisis. Large corporations are mainly responsible for deforestation-creating and profiting from developed-country consumer demand for tropical hardwoods and exotic or out-ofseason food items. Most pesticides used in the Third World are applied to export crops, playing little role in feeding the hungry, while in the U.S. they are used to give a blemish-free cosmetic appearance to produce, with no improvement in nutritional value. Alternatives exist now and many more are possible. The success of organic farmers in the U.S. gives a glimpse of the possibilities. Cuba's recent success in overcoming a food crisis through selfreliance and sustainable, virtually pesticide-free agriculture is another good example. Indeed, environmentally sound agricultural alternatives can be more productive than environmentally destructive ones. Myth 11 We Benefit From Their Poverty Reality: The biggest threat to the well-being of the vast majority of Americans is not the advancement but the continued deprivation of the hungry. Low wages-both abroad and in inner cities at home-may mean cheaper bananas, shirts, computers and fast food for most Americans, but in other ways we pay heavily for hunger and poverty. Enforced poverty in the Third World jeopardizes U.S. jobs, wages and working conditions as ISS315 - PAGE 133 Myth 5 The Green Revolution is the Answer Reality: The production advances of the Green Revolution are no myth. Thanks to the new seeds, million of tons more grain a year are being harvested. But focusing narrowly on increasing production cannot alleviate hunger because it fails to alter the tightly concentrated distribution of economic power that determines who can buy the additional food. That's why in several of the biggest Green Revolution successes--India, Mexico, and the Philippines--grain production and in some cases, exports, have climbed, while hunger has persisted and the long-term productive capacity of the soil is degraded. Now we must fight the prospect of a 'New Green Revolution' based on biotechnology, which threatens to further accentuate inequality. corporations seek cheaper labor abroad. In a global economy, what American workers have achieved in employment, wage levels, and working conditions can be protected only when working people in every country are freed from economic desperation. Here at home, policies like welfare reform throw more people into the job market than can be absorbed-at below minimum wage levels in the case of 'workfare'-which puts downward pressure on the wages of those on higher rungs of the employment ladder. The growing numbers of 'working poor' are those who have partor full-time low wage jobs yet cannot afford adequate nutrition or housing for their families. Educating ourselves about the common interests most Americans share with the poor in the Third World and at home allows us to be compassionate without sliding into pity. In working to clear the way for the poor to free themselves from economic oppression, we free ourselves as well. Myth 6 We Need Large Farms Reality: Large landowners who control most of the best land often leave much of it idle. Unjust farming systems leave farmland in the hands of the most inefficient producers. By contrast, small farmers typically achieve at least four to five times greater output per acre, in part because they work their land more intensively and use integrated, and often more sustainable, production systems. Without secure tenure, the many millions of tenant farmers in the Third World have little incentive to invest in land improvements, to rotate crops, or to leave land fallow for the sake of long-term soil fertility. Future food production is undermined. On the other hand, redistribution of land can favor production. Comprehensive land reform has markedly increased production in countries as diverse as Japan, Zimbabwe, and Taiwan. A World Bank study of northeast Brazil estimates that redistributing farmland into smaller holdings would raise output an astonishing 80 percent. Myth 12 Curtail Freedom to End Hunger? Reality: There is no theoretical or practical reason why freedom, taken to mean civil liberties, should be incompatible with ending hunger. Surveying the globe, we see no correlation between hunger and civil liberties. However, one narrow definition of freedom-the right to unlimited accumulation of wealth-producing property and the right to use that property however one sees fit-is in fundamental conflict with ending hunger. By contrast, a definition of freedom more consistent with our nation's dominant founding vision holds that economic security for all is the guarantor of our liberty. Such an understanding of freedom is essential to ending hunger. Hunger: 12 Myths, 12 Myths About Hunger based on World 2nd Edition, by Frances Moore Lapp, Joseph Collins and Peter Rosset, with Luis Esparza (fully revised and updated, Grove/Atlantic and Food First Books, Oct. 1998) Institute for Food and Development Policy Backgrounder Summer 1998, Vol.5, No. 3 ISS315 - PAGE 134 The Grameen Bank A small experiment begun in Bangladesh has turned into a major new concept in eradicating poverty by Muhammad Yunus Over many years, Amena Begum had become resigned to a life of grinding poverty and physical abuse. Her family was among the poorest in Bangladesh-one of thousands that own virtually nothing, surviving as squatters on desolate tracts of land and earning a living as day laborers. In early 1993 Amena convinced her husband to move to the village of Kholshi, 112 kilometers (70 miles) west of Dhaka. She hoped the presence of a nearby relative would reduce the number and severity of the beatings that her husband inflicted on her. The abuse continued, however-until she joined the Grameen Bank. Oloka Ghosh, a neighbor, told Amena that Grameen was forming a new group in Kholshi and encouraged her to join. Amena doubted that anyone would want her in their group. But Oloka persisted with words of encouragement. "We're all poor-or at least we all were when we joined. I'll stick up for you because I know you'll succeed in business. Amena's group joined a Grameen Bank Center in April 1993. When she received her first loan of $60, she used it to start her own business raising chickens and ducks. When she repaid her initial loan and began preparing a proposal for a second loan of $110, her friend Oloka gave her some sage advice: "Tell your husband that Grameen does not allow borrowers who are beaten by their spouses to remain members and take loans." From that day on, Amena suffered significantly less physical abuse at the hands of her husband. Today her business continues to grow and provide for the basic needs of her family. Unlike Amena, the majority of people in Asia, Africa and Latin America have few opportunities to escape from poverty- According to the World Bank, more than 1.3 billion people live on less than a dollar a day. Poverty has not been eradicated in the 50 years since the Universal Declaration on Human Rights asserted that each individual has a right to: A standard of living adequate for the health and well-being of himself and of his family, including food, clothing, housing and medical care and necessary social services, and the right to security in the event of unemployment, sickness, disability, widowhood, old age or other lack of livelihood in circumstances beyond his control. Will poverty still be with us 50 years from now? My own experience suggests that it need not. After completing my Ph.D. at Vanderbilt University, I returned to Bangladesh in 1972 to teach economics at Chittagong University. I was excited about the possibilities for my newly independent country. But in 1974 we were hit with a terrible famine. Faced with death and starvation outside my classroom, I began to question the very economic theories I was teaching. I started feeling there was a great distance between the actual life of poor and hungry people and the abstract world of economic theory. I wanted to learn the real economics of the poor. Because Chittagong University is located in a rural area, it was easy for me to visit impoverished households in the neighboring village of Jobra. Over the course of many visits, I learned all about the lives of my struggling neighbors and much about economics that is never taught in the classroom. I was dismayed to see how the indigent in Jobra suffered because they could not come up with small amounts of working capital. Frequently ISS315 - PAGE 135 they needed less than a dollar a person but could get that money only on extremely unfair terms. In most cases, people were required to sell their goods to money-lenders at prices fixed by the latter. This daily tragedy moved me to action. With the help of my graduate students, I made a list of those who needed small amounts of money. We came up with 42 people. The total amount they needed was $27. I was shocked. It was nothing for us to talk about millions of dollars in the classroom, but we were ignoring the minuscule capital needs of 42 hardworking, skilled people next door. From my own pocket, I lent $27 to those on my list. Still, there were many others who could benefit from access to credit. I decided to approach the university's bank and try to persuade it to lend to the local poor. The branch manager said, however, that the bank could not give loans to the needy: the villagers, he argued, were not creditworthy. I could not convince him otherwise. I met with higher officials in the banking hierarchy with similar results. Finally, I offered myself as a guarantor to get the loans. In 1976 I took a loan from the local bank and distributed the money to poverty-stricken individuals in Jobra. Without exception, the villagers paid back their loans. Confronted with this evidence, the bank still refused to grant them loans directly: And so I tried my experiment in another village, and again it was successful. I kept expanding my work, from two to five, to 20, to 50, to 100 villages, all to convince the bankers that they should be lending to the poor. Although each time we expanded to a new village the loans were repaid, the bankers still would not change their view of those who had no collateral. Because I could not change the banks, I decided to create a separate bank for the impoverished. After a great deal of work and negotiation with the government, the Grameen Bank ("village bank" in Bengali) was established in 1983. From the outset, Grameen was built on principles that ran counter to the conventional wisdom of banking. We sought out the very poorest borrowers, and we required no collateral. The bank rests on the strength of its borrowers. They are required to join the bank in self-formed groups of five. The group members provide one another with peer support in the form of mutual assistance and advice. In addition, they allow for peer discipline by evaluating business viability and ensuring repayment. If one member fails to repay a loan, all members risk having their line of credit suspended or reduced. The Power of Peers Typically a new group submits loan proposals from two members, each requiring between $25 and $100. After these two borrowers successfully repay their first five weekly installments, the next two group members become eligible to apply for their own loans. Once they make five repayments, the final member of the group may apply. After 50 installments have been repaid, a borrower pays her interest, which is slightly above the commercial rate. The borrower is now eligible to apply for a larger loan. The bank does not wait for borrowers to come to the bank; it brings the bank to the people. Loan payments are made in weekly meetings consisting of six to eight groups, held in the villages where the members live. Grameen staff attend these meetings and often visit individual borrowers' homes to see, how the business - whether it be raising goats or growing vegetables or hawking utensils - is faring. ISS315 - PAGE 136 Today Grameen is established in nearly 39,000 villages in Bangladesh. It lends to approximately 2.4 million borrowers, 94 percent of whom are women. Grameen reached its first $1 billion in cumulative loans in March 1995, 18 years after it began in Jobra. It took only two more years to reach the $2-billion mark. After 20 years of work, Grameen's average loan size now stands at $180. The repayment rate hovers between 96 and 100 percent. A year after joining the bank, a borrower becomes eligible to buy shares in Grameen. At present, 94 percent of the bank is owned by its borrowers. Of the 13 members of the board of directors, nine are elected from among the borrowers; the rest are government representatives, academics, myself and others. A study carried out by Sydney R. Schuler of John Snow, Inc., a private research group, and her colleagues concluded that a Grameen loan empowers a woman by increasing her economic security and status within the family. In 1998 a study by Shahidur R. Khandker an economist with the World Bank, and others noted that participation in Grameen also has a significant positive effect on the schooling and nutrition of children - as long as women rather than men receive the loans. (Such a tendency was clear from the early days of the bank and is one reason Grameen lends primarily to women: all too often men spend the money on themselves.) In particular, a 10 percent increase in borrowing by women resulted in the arm circumference of girls - a common measure of nutritional status - expanding by 6 percent. And for every 10 percent increase in borrowing by a member the likelihood of her daughter being enrolled in school increased by almost 20 percent. Not all the benefits derive directly from credit. When joining the bank, each member is required to memorize a list of 16 resolutions. These include commonsense items about hygiene and health drinking clean water, growing and eating vegetables, digging and using a pit latrine, and so on as well as social dictums such as refusing dowry and managing family size. The women usually recite the entire list at the weekly branch meetings, but the resolutions are not otherwise enforced. Even so, Schuler's study revealed that women use contraception more consistently after joining the bank. Curiously, it appears that women who live in villages where Grameen operates, but who are not themselves members, are also more likely to adopt contraception. The population growth rate in Bangladesh has fallen dramatically in the past two decades, and it is possible that Grameen's influence has accelerated the trend. In a typical year 5 percent of Grameen borrowers - representing 125,000 families - rise above the poverty level. Khandker concluded that among these borrowers extreme poverty (defined by consumption of less than 80 percent of the minimum requirement stipulated by the Food and Agriculture Organization of the United Nations) declined by more than 70 percent within five years of their joining the bank. To be sure, making a microcredit program work well - so that it meets its social goals and also stays economically sound - is not easy. We try to ensure that the bank serves the poorest only those living at less than half the overty line are eligible for loans. Mixing poor participants with those who are better off would lead to the latter dominating the groups. In practice, however, it can be hard to include the most abjectly poor, who might be excluded by their peers when the borrowing groups are being formed. And despite our best efforts, it does sometimes happen that the money lent to a woman is appropriated by her husband. Given its size and spread, the Grameen Bank has had to evolve ways to monitor the performance of its branch managers and to guarantee honesty and transparency. A manager is not allowed to remain in the same village for long, for fear that he may develop local connections that impede his performance. Moreover, a manager is never posted near his home. Because of such ISS315 - PAGE 137 constraints and because managers are required to have university degrees - very few of them are women. As a result, Grameen has been accused of adhering to a paternalistic pattern. We are sensitive to this argument and are trying to change the situation by finding new ways to recruit women. Grameen has also often been criticized for being not a charity but a profit-making institution. Yet that status, I am convinced, is essential to its viability. Last year a disastrous flood washed away the homes, cattle and most other belongings of hundreds of thousands of Grameen borrowers. We did not forgive the loans, although we did issue new ones, and give borrowers more time to repay. Writing off loans would banish accountability, a key factor in the bank's success. Liberating Their Potential The Grameen model has now been applied in 40 countries. The first replication, begun in Malaysia in 1986, currently serves 40,000 poor families; their repayment rate has consistently stayed near 100 percent. In Bolivia, micro-credit has allowed women to make the transition from "food for work" programs to managing their own businesses. Within two years the majority of women in the program acquire enough credit history and financial skills to qualify for loans from mainstream banks. Similar success stories are coming in from programs in poor countries everywhere. These banks all target the most impoverished, lend to groups and usually lend primarily to women. The Grameen Bank in Bangladesh has been economically self-sufficient since 1995. Similar institutions in other countries are slowly making their way toward self-reliance. A few small programs are also running in the U.S., such as in innercity Chicago. Unfortunately - because labor costs are much higher in the U.S. than in developing countries - which often have a large pool of educated unemployed who can serve as managers or accountants - the operations are more expensive there. As a result, the U.S. programs have had to be heavily subsidized. In all, about 22 million poor people around the world now have access to small loans. Micro-credit Summit, an institution based in Washington, D.C., serves as a resource center for the various regional Micro-credit institutions and organizes yearly conferences. Last year the attendees pledged to provide 100 million of the world's poorest families, especially their women, with credit by the year 2005. The campaign has grown to include more than 2,000 organizations, ranging from banks to religious institutions to nongovernmental organizations to United Nations agencies. The standard scenario for economic development in a poor country calls for industrialization via investment. In this "topdown" view, creating opportunities for employment is the only way to end poverty. But for much of the developing world, increased employment exacerbates migration from the countryside to the cities and creates low-paying jobs in miserable conditions. I firmly believe that, instead, the eradication of poverty starts with people being able to control their own fates. It is not by creating jobs that we will save the poor but rather by providing them with the opportunity to realize their potential. Time and time again I have seen that the poor are poor not because they are lazy or untrained or illiterate but because they cannot keep the genuine returns on their labor. Self-employment may be the only solution for such people, whom our economies refuse to hire and our taxpayers will not support. Microcredit views each person as a potential entrepreneur and turns on the tiny economic engines of a rejected portion of society. Once a large number of these engines start working, the stage can be set for enormous socioeconomic change. Applying this philosophy, Grameen has established more than a dozen enterprises, often in partnership with other entrepreneurs. By assisting microborrowers and microsavers to take ownership of large enterprises and even infrastructure companies, we are trying to speed the process of overcoming poverty. Grameen Phone, for instance, is a cellular telephone company ISS315 - PAGE 138 that aims to serve urban and rural Bangladesh. After a pilot study in 65 villages, Grameen Phone has taken a loan to extend its activities to all villages in which the bank is active. Some 50,000 women, many of whom have never seen a telephone or even an electric; light, will become the providers of telephone service in their villages. Ultimately they will become the owners of the company itself by buying its shares. Our latest innovation, Grameen Investments, allows U.S. individuals to support companies such as Grameen Phone while receiving interest on their investment. This is a significant step toward putting commercial funds to work to end poverty. I believe it is the responsibility of any civilized society to ensure human dignity to all members and to offer each individual the best opportunity to reveal his or her creativity. Let us remember that poverty is not created by the poor but by the institutions and policies that we, the better off, have established. We can solve the problem not by means of the old concepts but by adopting radically new ones. The Author MUHAMMAD YUNUS, the founder and managing director of the Grameen Bank, was born in Bangladesh. He obtained a Ph.D. in economics from Vanderbilt University in 1970 and soon after returned to his home country to teach at Chittagong University. In 1976 he started the Grameen project, to which he has devoted all his time for the past decade. He has served on many advisory committees: for the government of Bangladesh, the United Nations, and other bodies concerned with poverty, women and health. He has received the World Food Prize, the Ramon Magsaysay Award, the Humanitarian Award, the Man for Peace Award and numerous other distinctions as well as six honorary degrees. Grameen Bank site is available at www.grameenfoundation.org on the World Wide Web. -From Scientific American, November 1999, pp. 114-119. 1999 by Dr. Muhammad Yunus. Article Citation: Yunus, Muhammad. 2001. "The Grameen Bank." Robert M. Jackson (ed.), Annual Editions: Global Issues 01/02 Seventh Edition. pp. 187-191. ISS315 - PAGE 139 P R B R E P O R T S O N A M E R I C A CHILD POVERTY numbers, that amounts to more than 2.6 million rural children. Millions more live just above the poverty line in families struggling to make ends meet. For example, in rural America 6.1 million children are living in low-income families defined as having income below 200 percent of poverty. In recent decades, rural poverty has been overshadowed by the plight of impoverished families living in disadvantaged urban neighborhoods. For example, there IN RURAL AREAS has been little attention paid to the special circumstances of the rural poor during the recent national discussions about the reauthorization of the federal welfare reform legislation. A review of more than 1,400 newspaper articles on federal welfare reform in major papers during the early part of 2002 found that not a single story dealt with welfare issues in rural areas. This lack of attention is particularly vexing since many of the barriers to moving from welfare to work, such as lack of transportation and child care services, are bigger problems in rural areas than urban areas. Though little public attention has focused on the plight of the rural poor, statistics indicate that rural poverty is very serious. The 2000 Census provides a stark picture of child poverty in rural America, showing that of the 50 counties with the highest child poverty rates, 48 are located in rural America. Rural areas have historically had higher child poverty rates than metropolitan areas, but the rural-urban gap grew significantly during the late 1990s. As illustrated in Figure 3, in 1994 the gap between child poverty in urban and rural areas was only 1 percentage point (22 percent in urban areas versus 23 percent in rural areas), but by 2001 the gap had widened to 5 percentage points (15 percent in urban areas T he child poverty rate-- the percentage of children living in families with incomes below the official poverty line (about $18,000 per year for a family of four)--is probably the most widely used indicator of child well-being. At the end of the 1990s, one of the most prosperous decades in our country's history, one of every five rural children was living in a family with income below the official poverty line. In raw THE CHILD POVERTY RATE IN RURAL AMERICA REMAINS SIGNIFICANTLY HIGHER THAN IN URBAN AMERICA. Figure 3 Percent 30 Nonmetropolitan 25 20 Metropolitan 15 1985 Year Note: Child poverty rates are based on related children under 18. Sources: Economic Research Service, U.S. Department of Agriculture, analysis of data from the U.S. Census Bureau, Current Population Survey (March supplement), 1986 through 2001 (www.ers.usda.gov/Briefing/IncomePoverty Welfare/ChildPoverty/); and Population Reference Bureau analysis of data from the U.S. Census Bureau, Current Population Survey (March supplement), 2002 through 2003. 1990 1995 2000 6 ISS315 - PAGE 140 P R B R E P O R T S O N A M E R I C A compared with 20 percent in rural areas). Figures for 2002 (the latest available) show the child poverty rate in rural America (20 percent) remains significantly higher than in urban America (16 percent). Because of their isolation, poor rural kids may actually be more disadvantaged in some ways than poor kids in urban areas. In fact, poverty rates in rural areas are highest in counties most remote from, and lowest in counties adjacent to, metropolitan areas. Clearly, the economic boom of the late 1990s benefited urban families more than rural families because economic gains were greatest in sectors of the economy that were concentrated in metropolitan areas (finance, real estate, and high technology) and the poverty gap between rural and urban children widened during this period. Metropolitan areas include both central cities and suburbs. In 1999, the child poverty rate in central cities (24 percent) was much higher than in suburbs (11 percent). Child poverty rates in central cities (24 percent) are higher than in rural areas (20 percent), but this is largely a compositional effect. African Americans and Hispanics, two groups with very high poverty rates, are more highly concentrated in central cities than in rural areas. For every racial and ethnic group, child poverty rates are actually higher in nonmetro areas than in central cities. If central cities had the same racial and ethnic composition as the nonmetro population, the child poverty rate would be 16 percent rather than 24 percent. In both urban and rural America, the risk of poverty is greater for children than for any other age group. In 2002, the rural child poverty rate was 20 percent, compared with 13 percent for the SINCE 1974, CHILD POVERTY RATES HAVE EXCEEDED THOSE FOR OLDER AMERICANS. Figure 4 Percent 40 35 30 65 years and over 25 20 15 10 5 0 1959 Year Notes: The data points represent the midpoints of the respective years. Data for people 18 to 64 and 65 and older are not available from 1960 to 1965. Source: U.S. Census Bureau, Current Population Survey, 1960-2003 Annual Social and Economic Supplements. Under 18 years 18 to 64 years 1965 1970 1975 1980 1985 1990 1995 2002 working-age population (18 to 64) and 12 percent for the elderly population (ages 65 and older). In urban America, the child poverty rate was 15 percent, compared with 10 percent for working-age adults and 9 percent for urban elderly. Yet historically, children were less likely to live in poverty than the elderly. As recently as 1973, elderly poverty rates exceeded those of children (see Figure 4). Since then, child poverty rates have exceeded those for older Americans. Why has the nation been so successful at diminishing the risk of poverty for its elderly and failed at doing so for children? The sharp reduction in poverty for people 65 and older is one of the great American social policy triumphs of the 20th century. Social Security, Medicare, and federal initiatives to encourage retirement savings and regulate the pension system, together with an expansion of private pensions, dramatically improved the financial security of older Americans. Dependence on wages makes rural children more vulnerable than the rural elderly, who get by on pensions, Social Security, and Medicare. While child poverty fell by 12 percent between 1990 and 2001, the poverty rate among older people (ages 65 and over) in rural areas fell by 24 percent. The remarkable success of these policies offers hope that Americans will find the public will to tackle the plight of America's children with the same resolve they apply to issues affecting older Americans. In many ways the rural poor are more diverse than the poor in big cities. This fact is reflected in the imagery often associated with the urban and rural poor. For most people the term "urban poverty" conjures a mental image of minority families living in disadvantaged inner city neighborhoods. In contrast, rural ISS315 - PAGE 141 7 P R B R E P O R T S O N A M E R I C A RURAL CHILD POVERTY RATES IN 1999 WERE HIGHEST IN APPALACHIA, THE GREAT PLAINS, THE RIO GRANDE VALLEY, AND THE SOUTHERN BLACK BELT Figure 5 Source: Authors' analysis of 2000 Census data. poverty has many faces, encompassing impoverished rural hamlets in the Appalachian Mountains, sharecroppers' shacks in the Mississippi Delta, desolate Indian reservations on the Great Plains, and emerging colonias along the Rio Grande (see "Rural Counties With High Poverty Rates," pages 12 and 13, and Figure 5). There is a strong racial and ethnic overlay to the distribution of high-poverty communities that blurs the face of rural poverty. If you live in Appalachia, rural poverty looks white; if you live in the Mississippi Delta, rural poverty looks black; if you live in the Rio Grande Valley, rural poverty looks Hispanic; and if you live in the Dakotas, American Indians make up the bulk of the rural poor. Child poverty rates are higher in rural areas for every racial and ethnic group except for Asian Americans (see Table 1). Also common is the physical and social isolation of poor rural families. Houses are farther apart, and rural families must travel significant distances to work, buy groceries, or access social and medical services. Nearly one in five poor kids living in rural areas does not have a phone at home, significantly higher than in central cities or suburbs. And in 2000, two-thirds (66 percent) of children living in metro areas had a computer at home compared with 61 percent of children living in rural America. Geographic isolation makes transportation an important issue for rural families. In 2000, 8 percent of rural house- holds had no vehicle available and more than 65 percent of people in rural areas lacked easy access to public transportation. Obtaining good health care is more challenging for rural children. In metropolitan counties containing a large city, there are nearly four times as many physicians per 100,000 residents as there are in rural counties with only small towns. Family doctors have traditionally provided most of the health care in rural areas, but the number of family practice doctors per capita in rural areas is not much different than in metropolitan areas, and rural areas also have far fewer specialist physicians. Of particular concern for families with children is the fact that rural areas lag far behind urban areas in the number of 8 ISS315 - PAGE 142 P R B R E P O R T S O N A M E R I C A obstetricians and pediatricians available to care for children in the first critical years of life. For example, there are six times as many pediatricians per 100,000 people in large cities as there are in small rural counties. Rural areas also have far fewer specialist physicians--only 32 specialists per 100,000 people compared with 189 per 100,000 in metro counties with large cities. The relative dearth of health care professionals in rural areas is exacerbated by the long distances rural residents often have to travel to get to a health facility. Differences in the level of health care insurance coverage do not explain the uneven distribution of physicians. Nonmetro children are as likely to have health care insurance as their metropolitan counterparts. Approximately 88 percent of each group of children has some coverage. However, urban kids are more likely to be covered by private insurance (77 percent compared with 72 percent), while rural children are more likely to be covered through public programs such as Medicaid. Health insurance through employers is generally better than that in the public sector because it is accepted more widely and provides more benefits. CHILD POVERTY RATES ARE HIGHER IN RURAL AREAS FOR EVERY RACIAL AND ETHNIC GROUP EXCEPT FOR ASIAN AMERICANS. Table 1 Race/Ethnicity All children Black Alone American Indian Alone Asian Alone Native Hawaiian and Other Pacific Islander Alone Some other race Alone Two or more races Hispanic Non-Hispanic white U.S. 17 33 32 14 23 30 20 28 9 Metro 16 32 27 14 22 30 19 27 8 Nonmetro 19 42 36 14 26 33 24 32 14 Note: Data for specific racial groups include people who selected only one race. People of Hispanic origin can be of any race. Source: Population Reference Bureau analysis of data from the 2000 Census. STATE PATTERNS T he 2000 Census provides the best state-by-state data on rural child poverty. Rural child poverty rates range from a low of 7 percent in Connecticut to a high of 31 percent in Louisiana (see Table 2, page 10). There are seven states--Alabama, Arizona, Kentucky, Louisiana, Mississippi, New Mexico, and West Virginia--where more than 25 percent of children living in rural areas are poor. Rural poverty rates generally reflect the overall economic situation in a state, but it is worth noting that several states with similar statewide economic resources have quite different levels of rural child poverty. For example, the 1999 per capita income in Iowa and Texas are almost identical ($19,674 in Iowa and $19,617 in Texas), but the child poverty rate in rural Texas (25 percent) is more than twice as high as in Iowa (11 percent). The situation is similar in Alabama and Utah. They have virtually identical per capita income figures, but the rural child poverty rate in Alabama is twice that in Utah. While a rigorous analysis of why the rural child poverty rates differ in states like Texas and Iowa is beyond the scope of this study, some factors that might help explain state differences are obvious. First, the per capita income figure does not reflect how dispersed or different incomes are in a state. A state that has a high degree of income inequality--a lot of rich families and a lot of poor families--could have the same per capita income as a state where most families have incomes close to the state average. Research has shown that states with high levels of income inequality tend to have higher child poverty rates. Sociocultural differences across states may also play a role in determining child poverty levels. For example, the 2000 Census shows that 26 percent of kids in Alabama lived in single-parent families, compared with only 14 percent in Utah. Demographic trends such as the influx of immigrants into rural Texas and the large concentration of minority children in some states also make a difference. Some states may have policies more favorable to rural areas. Between 1989 and 1999, 38 states saw rural child poverty rates fall, only 11 saw an increase, and one was unchanged. In five states--Colorado, Michigan, Min- ISS315 - PAGE 143 9 P R B R E P O R T S O N A M E R I C A MORE THAN 25 PERCENT OF CHILDREN LIVING IN RURAL ALABAMA, ARIZONA, KENTUCKY, LOUISIANA, MISSISSIPPI, NEW MEXICO, AND WEST VIRGINIA ARE POOR. Table 2 Rank by nonmetro child poverty rate, 1999 Metro State U.S. 1 Connecticut 2 New Hampshire 3 Wisconsin 4 Massachusetts 5 Iowa 6 Minnesota 7 Indiana 8 Rhode Island 9 Nevada 10 Vermont 11 Ohio 11 Utah 13 Alaska 13 Michigan 13 Nebraska 16 Kansas 17 Colorado 18 Wyoming 19 Illinois 20 Maryland 21 Pennsylvania 22 Maine 23 Delaware 24 Idaho 25 North Dakota 26 Hawaii 27 Virginia 28 New York 29 Oregon 30 Tennessee 30 Washington 32 Missouri 32 South Dakota 34 Montana 35 North Carolina 36 California 37 Florida 38 Oklahoma 39 Georgia 40 South Carolina 41 Arkansas 42 Texas 43 Kentucky 44 Alabama 45 West Virginia 46 Arizona 47 New Mexico 48 Mississippi 49 Louisiana NA District of Columbia NA New Jersey Below poverty 9,102,806 83,572 13,509 106,928 171,729 35,474 80,425 138,166 39,494 61,676 3,351 335,451 48,874 6,861 295,408 26,185 41,714 97,508 5,342 392,811 128,241 356,642 11,850 18,132 17,174 6,935 26,155 152,639 855,023 81,927 163,527 153,559 131,708 7,633 12,106 183,050 1,694,579 572,995 94,768 215,139 114,786 64,613 984,169 75,764 150,771 33,119 208,367 56,609 56,074 227,153 35,367 227,754 Percent below poverty 15.9 10.5 7.2 11.6 12.1 10.8 8.9 12.4 17.2 14.2 8.4 14.6 9.1 9.3 13.9 11.3 10.3 10.7 14.4 14.3 10.3 14.6 11.2 11.6 12.1 10.2 12.9 11.1 20.2 13.6 17.2 12.5 13.7 11.2 16.2 13.9 19.4 17.2 17.6 14.5 16.6 19.1 19.8 15.8 19.4 19.7 17.8 20.9 20.6 25.1 31.7 11.1 Nonmetro Below Percent poverty below poverty 2,644,052 2,336 10,126 43,238 5,654 43,773 41,266 49,635 1,668 8,101 13,244 73,234 22,891 15,180 57,527 28,292 42,243 24,106 12,873 64,090 13,636 65,103 28,321 5,273 34,694 15,228 14,387 56,893 60,687 39,533 83,870 49,332 88,848 26,332 30,806 128,003 62,521 55,002 77,161 150,267 72,489 81,708 205,766 127,783 87,110 62,977 49,343 68,609 150,376 92,517 N/A N/A 19.2 6.7 8.8 10.2 10.3 11.2 11.3 11.6 12.0 12.3 12.6 13.4 13.4 13.5 13.5 13.5 14.2 14.4 14.5 14.7 14.9 15.0 15.1 15.3 15.7 16.8 16.9 17.0 17.3 17.8 19.7 19.7 20.3 20.3 20.4 20.6 22.1 22.7 22.9 23.1 23.8 24.6 24.9 25.6 26.2 27.7 29.5 29.9 30.6 31.2 N/A N/A N/A = Not applicable. Source: Population Reference Bureau analysis of data from the 2000 Census. 10 ISS315 - PAGE 144 P R B R E P O R T S O N A M E R I C A nesota, Ohio, and Wisconsin--the rural child poverty rate fell by more than 25 percent during the decade. The economic rebound of the Great Lakes region may have contributed to the decline of poverty among children in rural areas of Michigan, Minnesota, Ohio, and Wisconsin. Continued sprawl of midwestern metros, bringing relatively well-to-do families into previously rural areas, may also be responsible for lowering child poverty rates there. FAMILY STRUCTURE he structure of rural families is another factor that has significant implications for child poverty. In both rural and urban areas, the poverty rate for children growing up in a married-couple family is one-fourth the rate of those growing up in single-parent families. However, in both types of families child poverty is higher in rural America. In 2002, 43 percent of rural kids in female-headed families were poor, compared with 34 percent of those in femaleheaded families in metro areas. The structure of American families has been changing over the past several decades in both rural and urban areas, and these T family changes have important implications for the incidence of child poverty. Research by demographer Daniel Lichter and colleagues provides important new information about how the changing structure of rural families has affected the incidence of rural child poverty. In attempting to account for diminishing levels of rural child poverty in the 1990s, Lichter noted that the growth in the number of single-parent households (those at the greatest risk of having poor children) slowed during the 1990s. He also found that more rural single mothers were working, and at higher average incomes than in prior years. These trends have contributed to declining levels of child poverty rates in rural areas. However, there has also been a significant drop in the proportion of rural children residing in two-parent households (the type least likely to be poor) over the past several decades. This temporal decline in the proportion of rural children residing in two-parent households closely mirrors the national decline and, as a result, the proportion of rural kids in twoparent households is now only slightly above the national average. A particular concern of Lichter and his colleagues is the impact of unwed childbearing on young rural women. Although the proportion of rural and urban women who have nonmarital births is quite similar, rural women have unwed births at an earlier age than their urban counterparts. Such early births are a major concern because they have a significant impact on the future of both the mother and her children. Early premarital childbearing cuts short the education of the mother, reduces the likelihood that the mother will experience an enduring marriage, and increases the likelihood of maternal and child poverty. Early unwed births are an important factor in the intergenerational persistence of poverty common in some areas of rural America. Fortunately, unwed teenage births declined precipitously in the 1990s, but the continuing disparity between the incidence of teenage births in rural and urban areas remains a serious concern. The importance of family formation can be illustrated by the following stark comparison.i The poverty rate for children born to a teenage mother who has never married and who did not graduate from high school is 78 percent. On the other hand, the poverty rate for children born to women over age 20 who are currently married and did graduate from high school is only 6 percent. i These figures are computed from the 2000 Census 1-Percent Public Use Microdata Sample file and include the own children of the householder by birth, marriage (a stepchild), or adoption. ISS315 - PAGE 145 11 P R B R E P O R T S O N A M E R I C A RURAL HIGH POVERTY RATES COUNTIES WITH T he six counties profiled here, located in different regions of the United States, illustrate the diversity of rural poverty. OWSLEY COUNTY, KENTUCKY 2000 Population: 4,858 Population Change 1990 to 2000: -3.5 percent Owsley is a core county of the eastern Kentucky hill country, but it was historically a small-scale farming area, not a coal-mining county. The population today is only half as large as it was in 1940. Without adequate sources of work, it has Owsley County evolved into the poorest nonHispanic white county in the KENTUCKY country, with a child poverty rate of 56 percent and a total poverty rate of 45 percent. The median household income of $15,800 in 1999 was less than half of the U.S. nonmetro median of $33,700. There is very low labor force participation--just 39 percent compared with 60 percent for all nonmetro counties nationally. More than a third (36 percent) of children in Owsley County have no working parent in the household; this is the fourth-highest rate of all the counties in the country. There is a very high incidence of disability among people ages 21 to 64--42 percent compared with 21 percent nationally), and educational attainment is low, with 34 percent of adults having completed less than one year of high school, compared with 9 percent nationally. In Owsley County, 44 percent of births occur to women without a high school education, twice the statewide rate. Blacks make up 70 percent of the East Carroll Parish population. Forty-four percent of households with children under age 18 are headed by women with no husbands present, compared with 20 percent LOUISIANA nationally. The lack of two potential breadwinners in many households is one reason East Carroll Parish has the sixthhighest child poverty rate in the country. The labor force participation rate for males (42 percent) is not just very low, but lower than that for women, a very unusual circumstance. The child poverty rate was 59 percent, compared with an overall poverty rate of 40 percent, an exceptionally high disparity between the two rates. Median household income was just $20,700. The incidence of overall poverty in the black population is nearly four times that of the white population--54 percent compared with 14 percent. STARR COUNTY, TEXAS 2000 Population: 53,597 Population Change 1990 to 2000: 32 percent Starr County is in the Lower Rio Grande Valley, bordering Mexico. The county contains many colonias where homes are often built from used or dilapidated materials and typically do not meet building codes. The population is very young, with a median age of TEXAS 26 years, compared with a U.S. nonmetro average of 37 years. This age structure stems both from large families (with Starr County many children) and continued immigration. Ninety-eight percent of the population is Hispanic, with Spanish the common household language. Half the residents report that they do not speak English very well. Formal education is low, with 46 percent of adults having finished no more than the 8th grade. The EAST CARROLL PARISH, LOUISIANA 2000 Population: 9,421 Population Change 1990 to 2000: -3.0 percent East Carroll Parish, in the northeastern corner of Louisiana, is in the heart of Mississippi River Delta plantation country. The county is still highly dependent on the production of soybeans, cotton, and rice. 12 ISS315 - PAGE 146 P R B R E P O R T S O N A M E R I C A proportion of children in two-parent families is higher than average, a condition conducive to low poverty. However, earnings are very low. Even men with full-time, year-round jobs earned an average of only $17,500 in 1999 in the county's low-wage, agriculturally dominated economy, compared with the national average of $30,900. Housing space is often cramped, with 26 percent of households having more than one person per room, compared with the U.S. average of just 3.5 percent. Among children, 59 percent were living in households with poverty-level income. The overall poverty rate is 51 percent. Seventeen percent of the children receive public assistance (TANF), giving the county a rate four times higher than the state's. LIBERTY COUNTY, MONTANA 2000 Population: 2,158 Population Change 1990 to 2000: -6.0 percent Liberty County is similar to a number of other counties in the northern Great Plains states in that it is very sparsely settled, has no urban area, and is almost fully dependent on agriculture. The population is non-Hispanic white, with Liberty County educational levels at the U.S. average, both for high school completion and college MONTANA degrees. There is a high proportion of two-parent families. But the dependence on agriculture in an area of marginal rainfall means that incomes fluctuate from one period to another based on harvest yields and on grain and cattle prices. After several years of drought, income received in the year preceding the 2000 Census was low enough to characterize 20 percent of the population as poor, with the rate for children at 29 percent. Here and in some other counties of the northern Plains, the poverty rate is somewhat elevated in times of stress by high levels in Hutterite communities. The Hutterites, a religious group practicing communal farming, have very large families with a much higher percentage of children than the general population, resulting in lower per capita incomes. ture, and the location is too remote SOUTH DAKOTA for a highly profitable casino Shannon County business similar to what some tribal governments have developed. In 2000, fully 60 percent of employed people worked in providing public services--education, health, social services, or government--whereas only 25 percent do so in the nonmetro United States. Nearly 18 percent of the labor force is unemployed. The median age of the population (20.8 years) is extraordinarily young, similar to that of the United States in 1880, because of high birth rates and belowaverage life expectancy. With an unusually high proportion of children and few earning opportunities for adults, 61 percent of all children are in families with poverty-level income. Often the poverty conditions are severe. Shannon County has the fourthhighest child poverty rate in the country. Children account for fully half of all people in poverty, a rare situation; the overall poverty rate is 52 percent. The infant mortality rate in Shannon County (20 deaths per 1,000 births) is more than twice the rate for all of South Dakota. WASHINGTON COUNTY, MAINE 2000 Population: 43,926 Population Change 1990 to 2000: 2 percent Bordering Canada and the Atlantic Ocean and known as the "Sunrise Coast," Washington County contains the easternmost point of land in the United States. The county is 94 percent white. Washington County has the highest poverty Washington rate of all nonmetro counties in the County Northeast. The child poverty rate was MAINE 22 percent, and the total poverty rate was 19 percent in 1999. Median household income is $25,869. Over many years, jobs in the county's fishing, farming, and wood industries have declined considerably. The population level is smaller now than it was 100 years ago. Tourism brings revenue to the area in the summer--25 percent of housing stock is second homes--but many families have to piece together income from different seasonal jobs, and few retirees or vacationers come so far up the Maine coast. More than half (54 percent) of the children in Washington County receive subsidized school lunches, compared with only 31 percent statewide. SHANNON COUNTY, SOUTH DAKOTA 2000 Population: 12,466 Population Change 1990 to 2000: 26 percent Shannon County is the largest area of the Pine Ridge Sioux Indian Reservation, and 95 percent of the people in the county are American Indian. Conditions are not suitable for productive agricul- Calvin Beale, senior demographer at the Economic Research Service of the U.S. Department of Agriculture, selected these counties and provided basic data on them. The authors gratefully acknowledge his assistance and expertise. ISS315 - PAGE 147 13 DARFUR TODAY'S WORST HUMANITARIAN CRISIS Oure Cassoni CHAD ABCH Iridimi Touloum Mile Kounoungo SUDAN Farchana Bredjing EL GENEINA EL FASHER West Darfur Djabal Goz Amer NYALA by Kitty McKinsey lues to the enormity of the death, destruction and sheer terror being wreaked on Sudan's western Darfur region can be read in Babiker Yahya's family `compound.' The 70-year-old patriarch and five other members of his extended family live in a tiny house built of straw, complete with a thatched gable roof. Twelve other family members live in two shelters just meters away--round knee-high structures the size of a small breakfast table in a European or North American kitchen, with `walls' of twigs and dead leaves stuck in the sand that do little more than demarcate space in the vast desert. There's no roof on either of these shelters. When the torrential seasonal rains come late at night, all 18 family members rush to a more stable mud-brick home where they huddle tightly with dozens of others and wait for the violent storm to pass. The fact that Babiker's family prefer their miserable hovels in Dorti camp for displaced people on the outskirts of the West Darfur capital of El Geneina to their intact home and farmlands just a few kilometers away speaks volumes about the horrors they have lived through. Like more than a million others displaced by 18 months of violence in Darfur, Babiker's family was chased out of their home village of Shariken by gunmen on horseback who killed indiscriminately, raped, pillaged and then torched most of the mud-brick and thatch houses in the village. Babiker, a thin man with a white beard, wearing a dirty white traditional robe and a tight white cap, says REFUGEES C Relatives grieve over the body of one-year-old Ali who died of malnutrition in a makeshift encampment in El Geneina. "THEY RIPPED MY CHILD FROM MY BACK he buried three of his neighbors who were killed in the attack. Still, he counts himself one of the luckiest men in Darfur. Face to face with one of the horsemen, he survived only because the militiaman's assault rifle malfunctioned. "He aimed his gun at me," Babiker says, telling his story with lively hand gestures. "He wanted to kill me. I saw him point his gun at me, he prepared the gun to shoot, but the gun wouldn't go off. I grabbed the opportunity to run away." Even though his own house was not burned, Babiker got the message: Shariken village is now off limits. "I am not going back," he says firmly, his arm around the shoulder of one of his 6 ISS315 - PAGE 148 AND WHEN THEY SAW HE WAS A BOY, THEY KILLED HIM IN FRONT OF ME." granddaughters. "The Arabs will not allow us. If I go back, they will kill me." A MAN- MADE CATASTROPHE Just as peace seems near in the 21-year civil war in southern Sudan, prospects for the safe return home of 500,000 south Sudanese refugees from neighboring countries have been overshadowed by the violence in Darfur, an impoverished area the size of France in the west of Africa's biggest country. The U.N. has called Darfur the world's worst humanitarian crisis, but it's a man-made one. The janjaweed militias are targeting civilians, committing appalling human rights violations. Many accounts by witnesses tell similar, horrifying stories. The janjaweed roar into the village in pick-up trucks, or on the back of camels and horses. Firing up to 600 rounds a minute from their German G3 assault rifles, they kill the men, steal the animals that are the Darfurians' wealth, and loot the homes. As the coup de grce to make sure victims will not be eager to return to the site of this terror, the attackers systematically rape the women and burn homes, leaving most villages nothing more than charred ruins. The countryside is largely empty of inhabitants. Those chased out of their homes--well over half the popREFUGEES 7 ISS315 - PAGE 149 M. LONGARI/AFP/GETTY IMAGES/DP/SDN2004 DARFUR TODAY'S WORST HUMANITARIAN CRISIS UNHCR HAS A PARTICULARLY UNENVIABLE ASSIGNMENT--TO PROTECT DISPLACED PEOPLE ulation of 1.7 million--now take refuge in miserable makeshift camps clustered around the region's main cities. As of mid-August, some 180,000 Darfurians had fled to neighboring Chad, where UNHCR has established refugee camps to care for most of them. Sudan's government says it is fighting a rebellion that began in February 2003, when two groups (the Sudan Liberation Army and the Justice and Equality Movement) took up arms to protest what they saw as economic marginalization of Darfur by Khartoum. HORROR UPON HORROR Across Darfur's devastated countryside, horror has piled on horror, beyond the grotesque imagination of any demented Hollywood scriptwriter. Hawa Ishaq, a young mother who guesses she might be about 20, was nearly nine months pregnant with her second child when the janjaweed came to her village of Kaileik in West Darfur. "They beat me until I suffered a miscarriage," she says, sorrow written all over her face. To compound her tragedy, her first child, a baby girl, died when she and her husband--both his arms broken-- reached Kas town. "They ripped my (four-year-old) child from my back and when they saw he was a boy, they killed him in front of me," says Kaltum Haroun, another Kaileik villager. "I wanted to carry my dead child with me and bury him," she says. "They wouldn't even let me pick up his body." But the worst was to come. "Then I saw my brother and my husband shot in front of me," Kaltum continues. 8 REFUGEES ISS315 - PAGE 150 IN A REGION WHERE ALMOST NO PLACE IS SAFE. "I had to leave my husband's body behind too. I couldn't even do anything." The bandits stole all her clothes and she fled, naked, walking nine hours to the relative safety of Kas town. Tragically, her one remaining child, a baby girl, died in the chaotic camp for displaced people. Despite suffering more tragedy than most people know in a lifetime, Kaltum is just 20 years old. Will she ever remarry? A look of disdain passes over her face at the stupidity of the question. "I won't get married again. They killed all the men. Where would I find a husband?" Ismail Abdel Karim, 62, disputes the idea that the janjaweed kill only men and spare women. "Whoever they find, they kill," he says fiercely. "They make no distinction between men and women." He remembers vividly the day the attack came on his village--March 10, 2004. "We were peacefully staying in our village. We don't know why they attacked us. We were in our home and they came and killed us." His grown sons managed to flee when the horsemen rode into town. "They got off their horses. I was sitting on the ground with the girls. They shot and killed my two daughters. One was married, one was young. Then they shot me." He holds up his right arm, displaying a huge scar where the bullet entered the inside of his forearm, and came out the top of his arm. When the armed men rode off again, he managed to stop the bleeding and heal his wound using a poultice made of the bark of a local tree. Still mourning the loss of his daughters, he's baffled by the violence that has engulfed his homeland. "I had nothing to do with the fighting. I was a farmer." Amina Mohammed, a 39-yearold farmer and mother of six dressed in a vibrant pink dress and headscarf, sits on the ground in Kalma camp southeast of Nyala in South Darfur, and tells of the day the janjaweed came to her village. "They killed my family in our home, they killed five men, tak, tak, tak, tak," she says, using her hands and her voice to imitate the action of an assault rifle. "They killed my five brothers. They killed Yousif. They killed Yahiya, Hussein, Bakr and Adam. Now I only have one brother left." These survivors tell their stories mostly in emotionless tones, almost as though these horrors happened to someone else. Perhaps they are still in shock. Perhaps they've suffered so much they can't register emotion any more. Or perhaps they are so intent on simply surviving until tomorrow that they don't have the luxury of dwelling on their losses. F. ZIZOLA/MAGNUM PHOTOS/DP/SDN2004 Sudanese choose the uncertainty of flight into the harsh desert towards Chad over violent attacks at home in Darfur. A HOSTILE ENVIRONMENT And, as they know only too well, life here has always been brutal. The environment UNHCR and other aid agencies are working in is hostile in every sense of the word. Scorching temperatures (up to 55 degrees) in the shadeless desert alternate with violent seasonal rain storms that flood many displaced people's shelters and make impassable the already poor roads. REFUGEES 9 ISS315 - PAGE 151 DARFUR TODAY'S WORST HUMANITARIAN CRISIS Sudan bowed to international pressure to let the world community try to ameliorate the humanitarian catastrophe. But even giving that help is difficult. UNHCR has a particularly unenviable assignment-- to protect displaced people in a region where almost no place is safe (a U.N. map shows only three types of territory in Darfur--insecure, very insecure and extremely insecure areas). Obstacles of every sort have confronted humanitarian agencies trying to help Darfur's victims. Whether the obstacles are intentional is hard to say. By mid-year, months-long wrangles over visas for humanitarian workers were finally ironed out, but the bureaucracy continued to impede the import of cars and communications equipment vital for aid work. One medical charity reported its shipment of medical supplies had sat in metal containers in Port Sudan's 50-degree heat for three months. When the shipment was finally released, 70 percent of the medicine was ruined and had to be destroyed. Despite government assurances of easier access to Darfur, local authorities in July were still demanding travel permits for every single car trip to visit displaced people. ETHNIC IDENTITIES Although ethnic identities have traditionally been rather fluid in Darfur--a generation ago `Arabs' who wanted to give up their nomadic way of life could join the Fur black African tribe simply by taking up farming--Darfur's victims unanimously believe they are being targeted because they are black. (Religion is not an issue, both sides are Muslim.) One displaced man in Kalma camp says firmly he Faki Abdel Karim, the 53-year-old father of eight children, used to be the richest man in his village near Wadi Saleh, in West Darfur. He ticks off his personal toll in the violence: "They killed six family members. They killed four of my brother's children. They stole 30 cows, 20 goats and four donkeys and a donkey cart. They took 25 sacks of wheat and two scales, one big scale and one small scale. Looting is also one of their motives." The attackers even took a bicycle especially adapted for his disability--he lost the use of both legs six years ago after an attack by bandits. Al Nour Adam, a 52-year-old farmer, also knows what it's like to lose everything. When his village of Adar was attacked, he lost horses, cows, sheep, chickens, a store of grain, and his life savings of 150,000 Sudanese dinars ($577)--a fortune in this country. Now living in Riyadh camp, he works in nearby El Geneina as a casual laborer for just 200-300 dinars a day (about $1), barely enough to give his wife and nine children two meals a day of porridge. One of the most disturbing aspects of this conflict is the apparent systematic use of rape as a war tactic. In July, an Amnesty International report said women in Darfur were being systematically raped by Arab militiamen who use sexual abuse to torture and humiliate their victims. Describing what it called "a systematic policy designed to humiliate a group of people and tear apart their social fabric," Amnesty said it had interviewed hundreds of women raped in their villages or abducted and used as sex slaves. It said girls as young as eight had been taken as sex slaves, and had arms and legs broken to AN AMNESTY INTERNATIONAL REPORT SAID WOMEN IN DARFUR WERE BEING SYSTEMATICALLY RAPED and his fellow villagers were targeted "because of the color, the black color," he says, pulling the skin on the back of his hand. "They attacked us because we are Fur, 100 percent Fur." (Darfur means "homeland of the Fur.") Another Fur woman says the men who attacked her village screamed: "We are going to kill you. We are going to use you women, and we are not going to leave anybody. Because you are black, we are going to finish you all." Kaltum Haroun, the 20-year-old woman who saw her four-year-old son and her husband killed before her eyes, says the janjaweed particularly target male children. "They say, `if he grows up he will become Tora Bora.' But we don't even know what Tora Bora is," she says helplessly. "We have never seen Tora Bora." It's a slang name the janjaweed gave to the rebel SLA, after alQaeda's mountain redoubt in Afghanistan. For their part, the victims of this violence use janjaweedand `Arab' interchangeably to identify their attackers. (Janjaweed is a local corruption of the Arabic phrase "devils on horseback carrying G3 rifles.") Whatever the original motive for their attacks, the janjaweed don't miss the opportunity for personal profit. stop them escaping. "They take the women to faraway places and they bring them back the next day," a 19-year-old married woman from Kaileik tells UNHCR. Do the attackers rape the women? She responds with a bitter laugh: "It's obvious, what else do they do with them?" And how many women were raped? She throws up her hands helplessly: "Many, many, too many to count." Babiker, the patriarch with the large family in Dorti camp, says the attacks on his village happened just over nine months ago. "They raped the women," he says. "All of them came back pregnant, and now they have borne babies." "My daughter was raped by two janjaweed men," Mariam, a 46-year-old mother of nine now living in Kas town, says as she introduces her 17-year-old daughter to a UNHCR visitor. "During the fighting, women were raped. They took them to the mountains, away from the village. When my daughter came back the next day, she was turned into a woman," Mariam explains, using a euphemism for the loss of her daughter's virginity. The girl's 15-year-old sister was also raped. 10 REFUGEES ISS315 - PAGE 152 Sadly, Mariam says her daughters will now never get married, though she defiantly adds that they have nothing to be ashamed of. Their rapes, Mariam says, have been crushing to her husband, a very religious man, but he has not rejected the girls, as sometimes happens in conservative Muslim cultures. "He can't do anything about it. What can he do? He doesn't have anyone who is strong to protect us. He has just accepted it." SUB - HUMAN CONDITIONS who circle the camps on horseback by day and often raid by night, continue to refine their fiendish tactics. At Krinding camp in El Geneina, men had to stop venturing outside for firewood and water because they got killed, and young girls and women had to stop because they got raped. When the displaced people hit on the idea of sending out elderly women, the janjaweed attacked three of them and mutilated their genitals. THE LIMITS OF RESILIENCY Acceptance is the order of the day in the wretched camps for displaced people. Resilient survivors have put up thatched straw huts--the lucky ones have plastic sheeting covers--wherever they could. All outdoors is their toilet, and when the seasonal rains come, waste washes over the whole area. Camp residents have no facilities for washing themselves, and doctors fear outbreaks of diseases like cholera. "Worse is yet to come," many aid workers warn grimly. In a region that already suffered from what one U.N. official calls "out of control malnutrition," the number of malnourished Darfurians is estimated to have grown to 20 or 25 percent of the population. An African doctor with an international aid agency calls the health of the displaced people "appalling." U.N. Secretary-General Kofi Annan says the camps' residents are living in "subhuman conditions--with inadequate food, shelter, water, medicine and other basic supplies." In July, aid agencies were just beginning to distribute food, and organize latrines, water supplies, and medical care in the spontaneous camps. UNHCR hopes to work with local non-governmental organizations to provide These people are resilient, but they've nearly exhausted their capacity to withstand the horrors around them. The villagers of Wadi Saleh survived three attacks between September and November last year before they finally decided to flee. "The first time the government and the janjaweed came, we fled to the mountains and then came back for our harvest," recalls Habib Husein, a 37-year-old farmer. "The second time they attacked, they killed people. We fled but we came back. The third time we came back to collect our things, but they came with their airplanes and their cars and horses. They killed people and attacked women. That's when we left for good." He says bitterly he doesn't trust any government promises of protection. In Riyadh camp with his four wives, 16 children and 12 other relatives, Ishaq Abdel Salam, the 52-year-old chief of Kera village, is equally pessimistic. "There is no place like home," he says, surrounded by women from his village who declare adamantly they will never go back. "If I could start living my life normally, of course it is better to be at home than in this camp," he says. But he doubts even U.N. peacekeepers could bring true security "A POLICY DESIGNED TO HUMILIATE A GROUP OF PEOPLE AND TEAR APART THEIR SOCIAL FABRIC." counseling for rape survivors, and is working with local police forces to try to stop continuing attacks on women within the camps by the janjaweed raiders. "Hundreds of thousands of people may die," U.N. Emergency Relief Coordinator Jan Egeland has warned, if steps aren't taken to end the fighting with two rebel groups and to disarm and demobilize the rampaging militias. There was still little sign of that happening in mid-July. The Sudanese government has repeatedly encouraged the displaced people to go home to their destroyed villages, but the displaced say they have no faith in security pledges from those they believe allowed them to be chased away in the first place. "I think they are right to be nervous, they are right not to have confidence. They need to see practical measures that will offer them the kind of protection that we are discussing here. And, until they get that, they cannot be confident that security is around the corner," U.N. Secretary-General Kofi Annan said at a press conference in mid-July. Even the camps are not entirely safe. The janjaweed, to Darfur. "This is a vast country," Ishaq says. "There are wadis (riverbeds), there are mountains, creeks, lots of places for people to hide. If we do not have soldiers distributed to many points, there will not be real security." "We have problems eating and finding water in this place," says Zahra Abas, an outspoken 25-year-old woman living in Riyadh camp with her six children, aged one year to 11. "We don't have food. We don't have anything but we are not going to move a single step away from here," because at least the camp offers a modicum of safety. Life here, she vows, is infinitely preferable to a return to her torched, deserted village. "We will not go back. They will kill us. I am willing to stay here in this camp for the rest of my life," she says with a defiant shake of her head. On a continent where there have been so many hopeful developments lately, where many long-standing conflicts are finally coming to an end, and millions of refugees now face the realistic prospect of going home after decades in exile, Darfur is a grim reminder of Africa's discouraging, enduring capacity to produce protracted humanitarian disasters. REFUGEES 11 ISS315 - PAGE 153 EASTERN CHAD Diary rise. Electricity is on from 7 p.m. to 5 a.m. but even with the ceiling fan it still feels like a blow dryer is being directed at you. Happy that we are finally able to go to the office again, and even happier about the fact that at least most of us were able to take a shower before the water was turned off (there is always the unlucky one who finds himself under the shower fully shampooed when the water goes off), we set off. We all live together in two houses. We also have all our meals together in the office as there are no kitchens in the houses. Surprisingly, there is almost no tension between team members although we spend almost 24 hours together. As usual, we stop on our way to the office to buy bread. As soon as the bread man sees us he starts to fill UNHCR/H. CAUX/DP/TCD2004 ANNE-KIRSTEN GARBE was a Program Officer in eastern Chad from March to early May of this year, where UNHCR is helping tens of thousands of Sudanese refugees fleeing from Darfur. Following are excerpts from her diary, a personal account of the daily challenges of a humanitarian worker in the field 11 APRIL 2004, EASTER SUNDAY, Abeche It is 8 p.m. on a Saturday evening and we have just finished working. We are tired and as usual our little team gathers in the courtyard of the office to have a few beers and eat cold leftovers from lunch. Sometimes, somebody heats up the food but not today. The generator has just been switched off as city power goes on. It is still hot and only a slight breeze outside gives us some relief. We are exhausted from the heat, the endless noise of the generator and from work. Even making a simple phone call can take hours and e-mail only seems to work when you are about to smash your Thuraya satellite phone against the wall. At least the beer comes from the freezer as does the Coke. We all smoke too much. But, right now our own health does not seem to be our main concern. That is as far as it gets with evening entertainment in Abeche sub-office, eastern Chad. Tonight is special, though. It is Easter Sunday tomorrow and we have decided that for once we will sleep in. No one is allowed to go to the office before 8 a.m.! What a luxury. No one here has had a day off since they arrived and everyday seems the same there is no differAt night, after a ence between Sundays, workdays long day visiting or Easter, for that matter. When we refugee sites along collect our colleagues the next the remote ChadSudan border, morning from the two houses UNHCR team leader where we live, it turns out that evYvan Sturm eryone was awake by 6 a.m. but downloads his e-mails dared not move. After all, it was on his laptop our long morning sleep-in. But, through his Thuraya the heat is unbearable and makes it satellite phone by lamplight. impossible to sleep long after sun- 12 REFUGEES ISS315 - PAGE 154 UNHCR/H. CAUX/DP/TCD2004 "MOST OF THE TENTS ARE SAND COLORED AND SEEM TO MERGE WITH THE SOIL. THE NEW TENTS ARE STILL WHITE, BUT THE SANDSTORMS WILL DO THEIR JOB QUICKLY." a plastic bag with bread. Daily routine no words needed. To make this morning even more special, we bought fresh eggs from the market the day before to make our first omelet. But, even this fails because egg number 11 is bad and spoils all the rest. Nevertheless, we are determined to enjoy our Easter Sunday breakfast together. We ignore the sand in the bread, we pretend that the Nido powder milk tastes like real milk and that La vache qui rit is the cheese we just love. The usual procedure every morning only one hour later. This morning breakfast, besides our evening drinks, is the only time when we relax and chat and laugh. That is before the day starts with its little catastrophes and all the things that could go wrong and do go wrong. We talk about what we would eat if we were not here but at home. We all start doing that after a while we talk about things we miss, such as the cinema. Today, we learnt that a Canadian movie won the Oscar. We talk a lot about mountains, snow, lakes and the sea, about sailing and scuba diving. This is not surprising given the weather and the fact that finding water for people and animals proves to be one of the most difficult problems we are faced with in our operation in Chad. At this point, the capacity of the wells and boreholes in the existing six camps is insufficient to cover the needs of the refugees and those still steadily arriving. At the same time, we know that in two or three months there will be so much water with the rains that we will not be able to move people into the camps. So, even if we suffer from the dry heat, how much worse must it be for the refugees in the camps and for those at the border? Caught in a sandstorm in Chad, a UNHCR staff member helps new arrivals from Sudan take their belongings to a UNHCR truck that will transport them to Touloum camp, near Iriba, eastern Chad. 1 MAY 2004, Farchana The heat is practically unbearable now. Nearly 50 degrees. It is noon and we are standing on a small hill. REFUGEES 13 ISS315 - PAGE 155 EASTERN CHAD Diary "WE ARE ALL JUST TRYING OUR BEST, AND THERE IS ONLY SO MUCH WE CAN DO FOR NOW..." A UNHCR staff member with a newly arrived Sudanese family in eastern Chad. Beyond us stretches the huge tent city of Farchana the first UNHCR camp opened for Sudanese refugees in Chad. Most of the tents are sand colored and seem to merge with the soil. To our left, some tents are still white. But they won't be like that much longer. Sandstorms will do their job quickly. The white tents are new. They were erected just the previous week to accommodate refugees arriving spontaneously from the border. The camp was planned and built for 6,000 people but by the end of April, their number had already risen to roughly 8,000 refugees, with more arriving by foot or buses every day. As we climb down we decide to walk around and take a closer look. It is still surprising how organized and friendly the mainly female refugees in the camps are, after all they have been through and all the things that are still lacking in the camps. When talking to them they say they are grateful for what we are doing for them, that we give them shelter, food and water. All they are asking for is salt, sugar for tea and some vegetables. Not much, really. We are working on that. When passing the water point we see a long line of jerry cans and women in colorful thaub (the traditional Sudanese cloth that women wrap around themselves) waiting in the sun. When asked, they tell us they have been there all morning but no water had been distributed yet. Again, no anger. But, this does show our main problem water. Although MSF is working hard to finish the water distribution system, it will not be enough to give a minimum of 15 liters per person to the refugees. We are in a REFUGEES real dilemma. What shall we do? We cannot send back refugees to the border where they might be attacked and their few belongings plundered by Sudanese Arab militias crossing the border, and where they have no food and no water. In a discussion with the members of the refugee camp committee, we try to explain all this and encounter not surprisingly understanding. Although that makes us feel better, we cannot ignore the animal corpses around the camp, the trees and bushes that have been cut for firewood and for building fences around the tents and people who are sleeping on the ground. These are problems we still have to tackle. Time is running out. The refugees keep arriving. The camp is at its maximum capacity and cannot keep taking in more refugees. Finding new camp sites before the rains start seems a "mission impossible." The same evening, during a heated discussion with our implementing partners, we are, it seems, trying to find a solution to the unsolvable water problem. In the end a plea from a very tired-looking colleague What can we do? We all are just trying our best and there is only so much we can do for now but we cannot send them back! And if that means reducing the water ration even further we will do it. Afterwards, we have dinner and go to bed. It makes us think of one of the concerns raised during today's meeting with the camp committee. They asked whether UNHCR could give them beds as they are afraid of the snakes and scorpions that come into their tents at night. 14 ISS315 - PAGE 156 UNHCR/H. CAUX/DP/TCD2004 SIERRA LEONE "We want reconciliation. We will never forget. BUT WE TRY TO FORGIVE" The first steps in a newfound peace in post-war Sierra Leone by Annette Rehrl T here is a beach on the African coast of the Atlantic Ocean that looks very tempting. The sea is calm, palm trees sway softly in the warm breeze. Children are laughing and playing on the beach. Dogs chase each other in mad joy. On the horizon appears the silhouette of a fishing boat out at sea. Leisure time, it seems. A time to feel confident. Lulled by the dreamy ambiance, an unwary swimmer starts to step into the water. All of a sudden, a powerful wave knocks him to the ground, swirls him away, then with a wild temper slings him back ashore. Every year, experienced swimmers drown on the beautiful beaches of Sierra Leone. Danger and violence are always lurking, even in peaceful surroundings. Just as daily reminders of a violent past live alongside hopes for a better future in this small country. You never know what is lying beneath the surface. You never know if the friendly shopkeeper in downtown Freetown might be a war criminal who hacked people to death during the decade-long civil war from 1991 to 2002. That's unless you go for a walk with an exchild soldier of the former Revolutionary United Front (RUF). Only then you find out that he and other small children were abducted by this seemingly friendly man. Only then you find out that back in 1998, when the boy was just seven years old, the shopkeeper forced him to witness his parents being killed, then packed stolen loot onto his small head and ordered him and other captured children to follow him. "Everyday when I walk by, he says hello to me," says an indignant Suleiman, now 13 years old. "I don't even want to look into his eyes. We both know what we went through. But when I have no choice to avoid him, I say hello too. We've been told to reconcile in our country. B. CURTIS/AP/DP/SLE2004 Abubakar, 9, whose left arm was cut off by rebel soldiers when he was five years old, stops to play during a soccer match on the beach. REFUGEES 15 ISS315 - PAGE 157 COURTESY OF A. REHRL/CP/SLE2004 SIERRA LEONE IN POST-WAR SIERRA LEONE, PEOPLE `DISAPPEAR' DURING THE NIGHT. DAYS LATER THEY ARE FOUND The war is over," he said, with a mixture of relief and resignation. "But, these people should be tried," he adds. "They harmed us so much and only showed us how to do evil." Since March 2004, some alleged war criminals are being tried by the re-established Special Court for Sierra Leone. Even so, one of its top prosecutors, David Crane, is still looking for the `Most Wanted'. "This Special Court is nothing more than a cosmetic intervention," comments OsGUINEA man Jalloh, a skeptical 35-yearold teacher. He lost his family CONAKRY when his village was attacked by KAMBIA the government forces who switched sides at night and perSIERRA LEONE formed even worse atrocities FREETOWN KOIDU than the rebels they were supTaiama Gerihun KAILAHUN posed to be fighting. Jembe Largo BO KENEMA Everything was allowed, back Gondama Tobanda then. Almost everything is alJimmi Bagbo Bandajuma lowed today. ZIMMI LEGEND "People in the villages reBOPOLU Capital member exactly who did what UNHCR LIBERIA presence during the war," Osman asserts. Refugee camp That is why sometimes in International post-war Sierra Leone, people still `disappear' during boundary the night. Days later they are found dead in the bush. Some call it local justice in times of official trials. Others just call it revenge. AT LA NT IC OC EA N A STRAND OF HOPE Further east, in the middle of the bush on the Guinean border, the small city of Kailahun has the appearance of a vibrant way station. Between the skeletons of the once colorful colonial architecture, men chat on doorsteps while small boys memorize the Koran. Pakistani UNAMSIL troops have helped rebuild the town's beautiful mosque and conference hall. "8,909 kms to Islamabad", declares a road sign in the center of town. The signs on the three main roads list almost all the NGOs and international organizations operating in the country. Every second building hosts a so-called "program". Kailahun's population is a mixture of returnees, displaced people, ex-combatants, soldiers and locals. The people here lost their property and their loved ones long ago. They lost their confidence, their pride, some even their self-respect. All lost part of themselves during the war. But they are all trying to keep their dignity intact. Everyone here is searching for a new starting point and looking for some strand of hope to hold on to. Newly arrived Sierra Leonean returnees at Kailahun's way station nurture hope too. After living for nearly 13 years in refugee camps across the river in Guinea, they have finally decided to come home. UNHCR has already resettled nearly 30,000 returnees. On a balmy tropical morning on the beach of the Moa River, 90 returnees set foot on home ground after crossing the river on rafts and canoes provided by UNHCR. A four-year-old boy stumbles out of the canoe crying for his mother somewhere in the small crowd. "Welcome home!" a voice shouts from a megaphone. A UNHCR staff member spots the lost child and leads him gently by the hand to the convoy where his mother is waiting. As she steps into the truck, the boy grabs her skirt, then looks around for the first time at his home country. 16 REFUGEES ISS315 - PAGE 158 DEAD IN THE BUSH. SOME CALL IT LOCAL JUSTICE. OTHERS JUST CALL IT REVENGE. Later another family arrives at Kailahun way station carrying two wooden chairs, four sacks of rice, seven bags and three children--all born in a refugee camp near Kissidougou, in Guinea. "Those are all your belongings?" "Yes," they say, not counting the mats, kerosene lamps, kitchen items and bowls handed out by UNHCR. "How shall we get all that to our village?" the man asks with desperation. His more pragmatic wife first feeds their middle child, then with the newborn baby slung on her back, patiently shifts all their possessions into the shade. She stacks the bags, arranges the chairs and places the domestic items on top. Her husband watches with admiration. After her careful intervention their household goods look definitely more compact but the problem of transportation to their village 200 kms away still remains. It's the first steps of a new life. A STRANGE NEW WORLD Many Sierra Leoneans are attempting their first steps in this newfound peace. Almost everybody is at least ten years "behind", but no one wants to look back. It's the future that counts, they say. In this strange new world, stranded veterans find themselves living next door to war criminals. Former child combatants come face to face with their torturers in the streets. War amputees who had their limbs brutally hacked off during the war are confronted with the perpetrators of their horrible mutilations, if they happen to live in the same area. "If the boy who cut off my arm goes to prison now, well, then maybe that's called justice," 45-year-old Siah Mansaray says angrily. He is living in Aberdeen's war amputee camp, waiting for a new home. "But even if that boy goes to jail, I will never get my arm back." The father of four lost his right arm in 1998 when RUF rebels started hacking off limbs of their fellow citizens to stop them voting for President Ahmed Kabbah. Even though the government is involved in a major construction program throughout the country to provide free housing for several thousand war amputees, the victims are complaining. "They give us a house, but how are we supposed to survive? I was a farmer. How shall I work the fields with only one arm? Shall I serve my family bricks instead of rice?" cries one amputee. Amongst the amputees, it's the young that are facing a particularly merciless future. Even well-trained Sierra Leonean youths with their limbs intact don't find jobs. "The same vehicle, the same driver, the same passengers. And all the time we're going in circles," is how a member of the government armed forces describes the current situation in his country. "Nothing has improved. Not enough food. No jobs. No light. I wonder why we fought for ten years. Peace is okay, but what do you get out of peace, if you have no future?" That is a question the government still has to answer. "Slum" is too kind a word to describe the living conditions of many Sierra Leonean soldiers and their families, especially in rural areas. Some refugee camps seem like five-star shelters in comparison to the grinding poverty these families face. And, face patiently. Trying to get along somehow. Waiting for better times. Twenty-year-old Ibrahim is an ex-child soldier who currently lives in a center for displaced and unaccompanied minors. He committed indescribable atrocities during the war and suffers now from ongoing nightREFUGEES The rebuilt mosque in Kailahun. Returnees at the way station in Kailahun. ISS315 - PAGE 159 COURTESY OF A. REHRL/CP/SLE2004 17 SIERRA LEONE The CAFF, as they are called, are playing football, holding on to each other like wounded animals in a pack. Their caretakers are skeptical, sometimes helpless. Young girls are walking around in T-shirts with slogans railing against domestic violence. A 72-year-old Imam, a member of the grievance committee who spent three months hiding in the bush, keeps repeating two phrases: "All killed" and "God is Almighty". He fled with nothing but the clothes on his back. At Tobanda camp he was given a Koran. All that is left for him now are the suras to recite and memories at night of rebels attacking his village. "Killing" is the most common word heard. "Surviving" the most appreciated one. The powerful emotions surging through the refugees required special attention. "We engaged ourselves in the camp administration because we wanted to do some service for our community," 76-year-old Tigan Mansaray of the council of elders explains. "First we had to cool down tensions. Whenever people are frustrated, aggressions show up. Most of the aggressions were due to offences," he recounts. "Refugees easily feel offended living in such circumstances. So, we tried to calm them down. And it worked. Now people have learned that they can live together as neighbors. We belong to different tribes, but here in the camp we get along well. Even with the CAFF children we get along. They adapt to caring surroundings. This is encouraging for the future of Liberia. Once we go back, we want to be living witnesses to our communities there. We want to engage in civil society." Encouraging plans. OVERCOMING THE PAST B. CURTIS/AP/DP/SLE2003 EVERYONE HERE IS SEARCHING FOR A NEW STARTING POINT AND LOOKING FOR SOME STRAND OF HOPE TO HOLD ON TO. A young boy walks past a sign for a post-conflict youth training center carrying the message "No wicked heart shall prosper." mares. The ghosts of the past keep haunting him. "I see people screaming, running away in fear of me," he says. Every morning when he awakes, he is bathed in sweat. "If I go back to my village, I'll be finished," he says. "I know my people. They know me. People in Africa don't forget." Fearing for his life, Ibrahim chose not to go through any of the numerous demobilisation, disarmament, reintegration and rehabilitation programs. Nor did he get any psychological counselling or skills training. In other words, he is neither one of the thousands of carpenters these programs have produced, nor one of its thousands of tailors. He would prefer instead to go to school and become literate. Ibrahim wants to leave Sierra Leone and get resettled in a third country, hopefully with the help of UNAMSIL which runs a witness program for former child soldier special cases. Although he's now an adult, Ibrahim might have a chance of being accepted into this program since he was a child when he committed atrocities. Ibrahim is in hiding all day long. His commanders, who abducted him 12 years ago when he was just eight years old and forced him to kill, burn and loot, and who injected cocaine into his open wounds--they have not been punished. Not yet. LIFE MOVES ON While Sierra Leoneans are coming to terms with each other, 7,500 Liberian refugees at Tobanda camp, near Kenema, are also getting a glimpse of how it feels to cope with the former enemy. They are involved in workshops and care for former CAFF children (Children Associated with Fighting Forces). The very children who might have killed their loved ones back in Liberia. They do it because life moves on. But, there is a big question mark hanging over the entire region and over Sierra Leone in particular. A question mark reflected in everybody's eyes. Ten years of killing, devastation and mutilation have not vanished, although people desperately want peace. "Why did you do this to each other?" visitors keep asking. "We don't know," they reply. "We need reconciliation. We want reconciliation. We will never forget. But we try to forgive." That resolution to overcome the past will be tested when the last UNAMSIL troops pull out in December 2004. It will then be up to this newborn society to show themselves and their neighbors that war can have a cathartic effect. That the senseless decade of killing need not be repeated. That re-education programs in refugee camps might offer an alternative for civil coexistence. That humanity can improve, if only there is a will. Annette Rehrl is a freelance journalist in Berlin and author of a book on Sierra Leone's children, published in 2004, in cooperation with UNHCR. 18 REFUGEES ISS315 - PAGE 160 LIBERIA A prayer for peace in the UNHCR/C. SHIRLEY/CS/LBR1997 `LAND OF THE FREE' Liberia is trying to overcome its seemingly unending nightmare by Fernando del Mundo ama Kamara fled to a refugee camp in Guinea in 1989 when Charles Taylor began an uprising in Liberia. Four years later, she went back to her home at Sarkannedou, a village outside Voinjama in Liberia's volatile northern county of Lofa, but armed men torched the palm and mud huts in the area, forcing her to scurry away to Guinea once more. In February, 40-year-old Kamara returned again with her farmer husband and five children, hoping D their homecoming will be permanent. "I pray that God will protect us. I pray that those who fought during the war will lay down their arms or no peace will come." Kamara needs more than divine intercession to make a new life. The nation has been devastated by 14 years of continual civil strife. Towns and villages lay in ruins or are being reclaimed by the forest. Some see in the long monsoon rains a sign that the gods weep for Liberia's seemingly unending nightmare. But many more are optimistic, voting with their feet on the future of their country after Taylor, the cause of their recent miseries, went into exile in Nigeria in AuREFUGEES Liberians who returned from Sierra Leone find their village in ruins. 19 ISS315 - PAGE 161 LIBERIA gust 2003 as rebels pushed to his presidential mansion in the capital, Monrovia, at the edge of the Atlantic. Since then, thousands of uprooted Liberians have been trickling back to their homes--from refugee camps in neighboring Guinea, Sierra Leone and Cte d'Ivoire; from facilities for internally displaced people, or IDPs. Some Liberian refugees have risked taking leaky boats from Nigeria and Ghana to reach their homeland. Following Taylor's departure, a peace accord was hammered out in Ghana, ending a war that claimed 200,000 lives and forced close to a million people from their homes. It called for the establishment of a transitional government in the nation of 2.6 million people and elections in October 2005. Rebels of the Liberians United for Reconciliation and Democracy (LURD) and the Movement for Democracy in Liberia (MODEL) signed the accord. GREATER COMMITMENT the Wind." Large parts of the country are covered by majestic, triple-canopy rain forests. DESCENT INTO HELL A 15,000-strong United Nations Mission in Liberia (UNMIL) subsequently began deploying across the country. A nation-wide program of disarmament, demobilization and reintegration was implemented. By late July, some 60,000 fighters had surrendered their weapons and cantonment sites had been opened. "The current form of involvement of the United Nations in Liberia is the first of its kind since the initial outbreak of the conflict in the country in 1989," says Moses Okello, UNHCR's representative in Liberia. "There is a The descent into hell started 133 years after Liberia's founding when Master Sergeant Samuel Doe staged a coup in 1980 and executed President Samuel Tolbert, Jr. Doe himself was assassinated a decade later in the rebellion Taylor and his National Patriotic Front of Liberia (NPFL) mounted. Even before Taylor, a former Liberian economics minister and escaped convict in the United States, finally became president of Liberia, NPFL itself had splintered and West Africa was never the same again. A victorious and vengeful Taylor turned his eye toward Sierra Leone, used as a staging area for a West African peacekeeping effort to stave off a bloodbath by NPFL troops in Monrovia following Doe's murder. Allies in the Revolutionary United Front (RUF) led by Foday Sankoh and Sam Bukarie, alias Mosquito, plunged Sierra Leone into a decade of brutality that ended two years ago with the arrival of British and U.N. peacekeepers. Diamonds, drugs and timber along the lush Sierra Leone-Liberia border funded Taylor's adventures in the politically fragile region. Liberian militias and teen soldiers crossed porous borders where guns and war booty were traded freely. Localized conflicts ensued, resulting in a tragic game of musical chairs for refugees caught in the maelstrom time and again. Once a beacon of light and previously the world's ma- "THE LIBERIANS ARE DEEPLY TRAUMATIZED. THEY HAVE TO REDISCOVER THEIR OWN RHYTHMS OF LIFE, THEIR SMALL JOYS AND CELEBRATIONS." greater commitment by the international community to see to it that the conflict is resolved and, hopefully, also to make sure that it does not backslide to where it has come from." But Okello adds: "Liberia's problems cannot be seen only within the time capsule of the 14 years through which it has undergone violence. Liberia's story begins at its very founding back in 1847, with the arrival of the American Colonization Society... Liberians need to accept their Liberian identity. There is a section of the Liberian population that tends to control the political and economic life of the country and that tends to think that it is American and look too much to America to the detriment of the rest of Liberians." Liberia, which means "Land of the Free," was established by emancipated African slaves in the Americas who promulgated a constitution patterned after the United States. Despite its diverse ethnic makeup, most people talk in Liberian English. There is a gentility in the air in some areas, particularly in the lovely port city of Harper in the east, as residents greet strangers on the streets. Harper has quaint buildings and half a dozen churches reminiscent of the antebellum period in the U.S. south. It is like a snapshot in the movie "Gone with jor cocoa producer, Cte d'Ivoire has become the most recent casualty of civil strife that has troubled many African countries. There, a failed mutiny turned into a full blown rebellion in September 2002. Liberian fighters were enlisted by the warring factions in Cte d'Ivoire, and Taylor reportedly provided bodyguards to the late Ivorian General Robert Guei. At Harper, relief agencies are attempting to piece together reports based on eyewitness accounts of a massacre around nearby Freetown, a Liberian village near the border with Cte d'Ivoire. They say helicopterborne troops landed in the area sometime in August last year and slaughtered 359 residents in villages that had provided Liberian fighters to government forces in Cte d'Ivoire. The dead were later dumped into mass graves. A full blown investigation has not been carried out because U.N. troops have not deployed in the area. The report, although unconfirmed, underscores the security anxieties of Liberians and fears of a prevailing climate of impunity. HEADING HOME Where there is a U.N. troop presence, uprooted people are heading home--from neighboring refugee camps 20 REFUGEES ISS315 - PAGE 162 hosting 350,000 Liberians or the 20 facilities inside the country sheltering another 300,000 IDPs. UNHCR planned to begin organized repatriation from the refugee camps in October. In the meantime, it has begun implementing community-agreed projects involving revival of basic services to provide people with modest incomes. With the U.N., came the relief agencies. At Sarkannedou, Peace Wind Japan has begun rebuilding schools, with funding from the U.N. refugee agency. Japanese workers have also been handing out shelter kits to 700 of the most impoverished families and rehabilitating water and sanitation facilities. Nearby Gbarnga and Voinjama in the northern counties of Lofa and Bong have attracted returnees despite the lack of infrastructure. "We are in dire need of everything," says Ester Walker, Gbarnga's mayor who held office under a tree. Thousands of Liberians have also returned from Cte d'Ivoire into the Harper area. Shops and restaurants have opened there. The Danish Refugee Council has begun rehabilitating schools and clinics. A dollar-aday project provides employment to Harper residents in road and canal clearing projects and repairs to the harbor lighthouse. The International Committee of the Red Cross distributes machetes and seeds to farmers under a program designed to help the displaced gradually return to normal life. "The Liberians are deeply traumatized. They have seen so much killing, women have been raped, homes have been looted. They have to rediscover their own rhythms of life, their small joys and celebrations," says ICRC's Marc Beuniche. IS THE LIBERIAN NIGHTMARE FINALLY OVER? Some rebel leaders have publicly apologized for their role in the conflict--men like Joshua Blaye, previously known as General "Butt Naked" who led a brigade of fighters in their birthday suits in the belief that this made them invincible against their well-armed enemies in their wacky wars in Buchanan, Kakata and Tubmanburg. He has become an evangelist and goes around in fashionable western suits. Although Taylor is gone, his sympathizers remain. MODEL and LURD leaders have little control over their men in the field. In Voinjama, ex-combatants or LURD people occasionally fire their guns during the night to scare people away so they can loot their houses--a tactic used to sow terror during the war. LURD or MODEL people are regarded in the countryside as nothing more than thugs. They wear soccer jerseys, NBA sneakers, shades and funky get-ups and race through villages on motorbikes. They harass returnees at the Guinean and Ivorian frontier crossings and extort "taxes" from villagers. Taylor is no longer regarded as a threat for so long as he is denied contact with his former associates in Liberia, says Okello, who came to Liberia first in 1991 and has had several brushes with death at the hands of Taylor's forces while looking after refugees in the field. "However, over the 14 years of the Charles Taylor phenomenon, there have been several thousand other `Charles Taylors' that have been created. They pose the threat to the future of Liberia," Okello says, unless political and economic conditions improve. And there is another imponderable: what its neighbors will do to a weakened Liberia. REFUGEES West African peacekeepers watch as displaced Liberians trickle home. 21 ISS315 - PAGE 163 S. DAS/AP/DP/LBR2003 DETERMINANTS OF RELATIVE POVERTY IN ADVANCED CAPITALIST DEMOCRACIES STEPHANIE MOLLER DAVID BRADLEY University of North Carolina, Chapel Hill University of North Carolina, Chapel Hill EVELYNE HUBER FRANCOIS NIELSEN University of North Carolina, Chapel Hill University of North Carolina, Chapel Hill Using relative poverty measures based on micro-level data from the Luxembourg Income Study, in conjunction with pooled time-series data for 14 advanced capitalist democracies between 1970 and 1997, the authors analyze separately the rate of pre-tax/transfer poverty and the reduction in poverty achieved by systems of taxes and transfers. Socioeconomic factors, including de-industrialization and unemployment, largely explain pre-tax/transfer poverty rates of the working-age population in these advanced capitalist democracies. The extent of redistribution (measured as poverty reduction via taxes and transfers) is explained directly by welfare state generosity and constitutional structure (number of veto points) and the strength of the political left, both in unions and in government. The alleviation of poverty has been a central goal of public policy in almost all industrial societies. Although governments of different political colorings disagree on the extent to which the state should intervene in the economy or redistribute income to achieve equality, all advanced industrial democracies have implemented social policies to reduce poverty (Goodin et al. 1999; Kenworthy 1999). Researchers have recently begun to examine the predictors cross-national differences in the rates of relative poverty, but their analyses have been limited to cross-sectional data (Kentworthy 1999; Kim 2000; Korpi and Palme 1998) or case-study research (Goodin et al. 1999(. Furthermore, previous research has failed to decompose the causal mechanisms that explain poverty rates across countries, in so far as it has not distinguished between the determinants of pretax/transfer poverty (i.e., the rate of poverty characterizing the distribution of incomes prior to taxes and transfers) and the determinants of the reduction in poverty resulting from the tax and transfer systems. We aim to overcome some of the limitations in previous work. First, we examine the determinants of poverty in a longitudinal framework, using unbalanced pool cross-sections and time-series data on 14 advanced capitalist democracies between 1967 and 1997 (E. Huber, Ragin, and Stephens 1997). Second, we utilize relative poverty measures based on micro-level data that allow us to measure household incomes before and after taxes and transfers. Thus we can examine separately the determinants of poverty before taxes and transfers (pre-tax/transfer poverty) and the determinants of the reduction in poverty achieved by government taxes and transfers. The principal theme of this research is that pre-tax/transfer poverty and the reduction in poverty resulting from taxes and transfers are determined by distinct (although partially overlapping) sets of factors. By distinguishing between pre-tax/transfer poverty and the reduction in poverty, we are able to integrate theories of inequality that focus on economic and labor market structures with theories of redistribution that focus on political power and state structure. With respect to pre-tax/transfer poverty, we draw on the literature on income inequality and wage dispersion. We hypothesize that pretax/transfer poverty rates are largely determined by characteristics associated with economic development (including agricultural employment, the spread of education, the size of the youth population, de-industrialization, globalization, unemployment, and changing women's roles), and institutional features of the labor market, such as union density and bargaining centralization (Alderson and Nielsen 2002; Bradley et al. forthcoming; Pontusson, Rueda, and Way 2002; Rueda and Pontusson 2000; Wallerstein 1999). ISS315 - PAGE 164 By Contrast, we view the reduction in poverty as driven by state action in redistributi8ng income. We expect the extent and nature of redistribution to be more directly affected by characteristics of the polity (Bradley et al. forthcoming; Kenworthy 1999). Hypotheses concerning determinants of poverty reduction are therefore more often related to the role of governments and are derived from the political science literature. We expect the extent to which states redistribute income and reduce poverty to depend on the cumulative impact of government policies, and that these policies in turn are shaped by the ideology and social base of the parties in power. Left or social democratic parties, as well as Christian democratic parties, are ideologically committed to poverty reduction, whereas this goal figures less prominently among the commitments of secular right and center parties. Social democratic parties are also strongly committed to a reduction in inequality, while Christian democratic and secular right and center parties are not (Goodin et al. 1999; Stephens 1979; Tilton 1990; Van Kersbergen 1995). We also hypothesize that the poverty reduction process is affected by the number of constitutional veto points in the government. Veto points are points in the political process at which legislation can be blocked. Political structures with many veto points tend to reduce poverty less effectively. These and alternative political mechanisms of poverty reduction are discussed in detail later. THEORY AND HYPOTHESIS FOR DETERMINANTS OF PRE-TAX/TRANSFER POVERTY Aspects of Development The relationship between industrial development and poverty is an old and once bitterly debated issue. In view of the horrendous social ills ushered in by early industrialization, nineteenth century observers (including Karl Marx) had good reasons to prophesy that the development of industrial capitalism would result in deepening poverty among the masses. From the vantage point of the twenty-first century we know this dismal scenario did not take place. Instead, the majority of the population of industrial societies has enjoyed a secular trend of rising standards of living. The decline in poverty resulting from economic development is attributable to trends discussed in a parallel literature on changes in inequality initiated by Kuznets (1955). In that literature, the long-term decline in inequality is associated with aspects of late industrial development, including economic development, agricultural employment, the size of the youth population, and the expansion of education. We can draw on this literature to explain cross-national variations in pr-tax/transfer poverty across advanced capitalist democracies. Gross Domestic Employment Product and Agricultural Over the past 200 years, increasing productivity of labor associated with industrial development coupled with moderating population growth has had a massive impact in raising standards of living and reducing poverty in advanced industrial societies (Noal and Lenski 1999). The poverty-reducing effect of development was still manifest during part of the post-World War II period in such countries as the United States (Danziger and Gottschalk 1995). Thus, a straightforward hypothesis for comparing industrial societies is that greater economic development, as measured by a country's gross domestic product per capita, will be associated with less poverty. Another measure of development, employment in the agricultural sector, is expected to be positively associated with poverty insofar as I; Nielsen t represents reliance on economic activities associated with lower productivity and wages. The inclusion of agricultural employment in models of pre-tax/transfer poverty is motivated by the findings of a strong positive association of this variable with income inequality in advanced industrial societies (Alderson and Nielsen 2002). If agricultural employment increases inequality by inflating the bottom of the income distribution, this effect should also be reflected in a positive association with poverty. The historical association of development with decreasing poverty may have ended in recent decades, at least in some industrial economies. Research on trends in income inequality in the United States, for example, shows that real wages at the bottom of the income distribution have not kept pace with economic growth in the last quarter of the twentieth century (Blank and Blinder 1986; Cutler and Katz 1991; Tobin 1994). If this failure of development to lift incomes at the bottom of the distribution affects other industrial economies outside the United States, the expected negative association of poverty with economic development may be attenuated or reversed. There is some evidence (admittedly tentative) that income inequality becomes positively associated with GDP at high levels of development (Alderson and Nielsen 2002). If this upswing in the overall ISS315 - PAGE 165 inequality of the income distribution is also reflected in a greater proportion of low incomes, one would expect an attenuation of the historical pattern, or even the emergence of a positive association between poverty and GDP. Youth Population. An old theme of research on income inequality is that a large youth population is associated with greater inequality because it represents both a dependent population and a labor reserve (often less skilled), and is expected to depress incomes at the bottom of the income distribution (Kuznets 1955; Lindert and Williamson 1985; Nielsen 1994). Cross-national research has often demonstrated the predicted association between size of the youth population and income inequality (Gustafsson and Johannson 1999; Smeeding 1989; Smeeding, Torrey, and Rein 1988). The same logic argues for a positive association between size of the youth population and poverty. Education. Development is associated with the expansion of educational systems. The spread of education is directly linked to the increase in labor productivity that has historically contributed to the decline in poverty in industrial societies (Becker 1993). Furthermore, by increasing the supply of skilled and professional workers, expanded education reduces the income gap between the skilled and unskilled (Lecaillon et al. 1984; Nielsen and Alderson 1995; Simpson 1990; Tinbergen 1975). Gottschalk and Joyce (1996), using a crossnational sample, find a systematic negative relationship between the size of supply shifts (educated workers) and changes in the educational premium. Thus, as the supply of educated workers increased, the education premium decreases. Country studies cited by Gottschalk and Smeeding (1997) also support the simple supply-demand model of the labor market, although the relative size of the education premium varies substantially across countries. Extending the argument from inequality to poverty, one would predict that greater educational expansion is associated with less poverty. Two caveats to this prediction are in order. First, Gottschalk and Smeeding (1997) review evidence that the United States in the 1980s experienced a large increase in returns to skill (measured either by education or experience), despite a large increase in the supply of educated and experienced workers. This combination suggests that the demand for skilled labor outstripped the supply in the United States, leading to greater inequality. Thus, supply is not the whole story. Second, one drawback of the cross-national studies just cited on the effects of education is that education is measured by formal educational attainment, usually secondary or tertiary enrollment ratios, which ignores the possibly imperfect correspondence between these measures and actual differences in skills, due to the variation in education systems across countries. Vocational Education. The advanced industrial countries also vary greatly in the development of their systems of vocational education. Vocational education is particularly important for workers who have less of the type of generalized skills emphasized in academic education tracks. The strong vocational education systems that characterize European coordinated market economies (CMEs) allow these workers to develop vocational skills that raise their productivity and pay. Vocational training is often carried out in partnership with firms, and acquired skills are often specific to a given industry or firm (Esteves-Abe, Iversen, and Soskice 2001). EstevesAbe et al. (1002) have argued that these systems of vocational education give workers an incentive to work harder in their academic courses, as success in the courses often determines their placement into vocational tracks. Improved general skills at the bottom are expected to result in lower poverty. Thus, strong systems of vocational education are expected to reduce poverty by (1) increasing specific vocational skills, and (2) increasing general academic skills among workers who would otherwise be most at risk of poverty. This prediction is consistent with the compressed wage and salary differentials typical of CMEs. The U-Turn Problematic Many social trends that may affect income inequality in industrial nations have received intense scrutiny following discovery of the "Great U-Turn," the inequality upswing that took place in the United States beginning in the early 1970s *Harrison and Bluestone 1988; Nielsen and Alderson 1997, 2001; Thurow 1987). To the extent that any social trend produces greater inequality by inflating the bottom of the income distribution, it should also produce greater poverty. Researches have identified numerous factors associated with the "Great U-Turn," including de-industrialization, globalization, an increase in long-term ISS315 - PAGE 166 unemployment, and the changing roles of women (Bluestone and Harrison 1982; Neilsen and Alder4son 1997, 2001). We now discuss how these factors may affect pre-tax/transfer poverty. DE-INDUSTRIALIZATION. Deindustrialization, the shift in employment from manufacturing to services, has affected all industrial economies during the last third of the twentieth century. (As we discuss below, the deindustrialization trend may be exacerbated by the effect of globalization.) The manufacturing sector is typically characterized by higher average wages and a more equal income distribution than in the service sector. Therefore, the transfer of jobs from manufacturing to services produces a larger share of low-wage jobs and greater poverty (e.g., see Alderson and Nielsen 2002; Bluestone and Harrison 1982; Esping-Andersen 1999; Gustafsson and Johansson 1999). Thus, we expect that a larger proportion of employment in manufacturing, indicating less de-industrialization, will be associated with lower poverty. ASPECTS OF GLOBALIZATION: LDC IMPORTS, CAPITAL MOBILITY, AND IMMIGRATION. Advanced economies have become increasingly integrated into international markets for goods, capital and labor during the last three decades. Three trends associated with globalization may have affected the incidence of poverty in developed nations: Increases in imports from nonindustrial economies ("southern imports"), capital mobility, and immigration (Alderson and Nielsen 2002). Importation of manufactured goods from less-developed nations places workers in industrial nations in direct competition with lower-paid workers in developing nations. As trade between nations increases, the wages and jobs of the least skilled workers in industrialized countries are threatened because they compete with lower-paid workers in less developed countries (Wood 1994). This competition reduces wages and increases unemployment. Thus, increased penetration by southern imports is hypothesized to increase pretax/transfer poverty. A second feature of globalization is increasing capital mobility, which means more options for the outflow of capital from developed to developing economies (i.e., "capital flight"). If firms take advantage of these options to shift manufacturing production from core countries to less developed countries that offer tax incentives and low-wage labor, then the de-industrialization process in core nations is exacerbated (Alderson 1999; Bluestone and Harrison 1982:6). The extent to which firms take advantage of these options is an empirical question, as they may prefer to locate production in developed countries for a number of reasons, such as closeness to markets, quality of infrastructure, availability of a highly skilled labor force, and so on. Nevertheless, one would expect outward capital flows to be related to higher levels of pre-tax/transfer poverty. Moreover, capital mobility per se enhances the power of capitalists relative to the government and labor, undermining the bargaining power of labor and the capacity of governments. Because of the availability of easy exit options, business may demand tax and social policy concessions from the government and wage concessions from organized labor (Alderson and Nielsen 2002; Bradley et al. forthcoming). Thus we expect capital mobility, both in the form of lack of restraints on outflows and in the form of actual outflows, to be associated with greater pretax/transfer poverty. The final component of the globalization triad is immigration-increased labor mobility across international borders. This trend is experienced by developed nations as a swelling flow of immigrants (Borjas 1994). A high rate of immigration has been associated with greater poverty (as well as greater inequality) in advanced economies because (1) immigrants have lower average skills than the resident population, and (2) the immigrant population is typically "bifurcated" into low-skill and high-skill components (Alderson and Nielsen 2002; Borjas, Freeman, and Katz 1992). The influx of low-skill migrants has been viewed as increasing poverty in part by displacing native workers and threatening their wages, although this relationship is contested (see the review by Tienda and Liang 1994). Overall, our expectation is that a greater rate of immigration will be associated with greater poverty. UNEMPLOYMENT. Unemployment has long been identified as a major economic determinant of poverty. The unemployment rate is strongly associated with pre-tax/transfer poverty because individuals experiencing unemployment suffer a loss or reduction of their income. Furthermore, real wages often decline during economic downturns, so that even workers who are able to keep their jobs may be more likely to fall below the poverty line during periods of high unemployment (Gramlich and Laren 1984; McFate, Smeeding, and Rainwater 1995). Thus we expect that high unemployment will be associated with higher levels of pre-tax/transfer poverty. CHANGING ROLES OF WOMEN: FEMALE LABOR FORCE PARTICIPATION ISS315 - PAGE 167 AND SINGLE-MOTHER FAMILIES. Researchers have also linked women's changing roles with the upswing in inequality associated with the Great U-Turn (Alderson and Nielsen 2002). Women's roles have changed dramatically because they are more likely to work and to head households. The effect of increasing labor force participation by women on the distribution of income has been much debated. Greater female labor force participation has been viewed as increasing the dispersion of incomes by amplifying the relative advantage or disadvantage of married couples (Thurow 1987), although this argument has been contested (Cancian, Danziger, and Gottschalk 1993; Nielsen and Alderson 1997). In the context of poverty, the situation may be more straightforward. As women have entered the labor force, they have enhanced their abilities to contribute to family income and to independently raise families (Casper, McLanahan, and Garfinkel 1994). If women are largely relegated to low-wage employment, their joining the labor force may not reduce overall poverty much, especially if they are attempting to maintain autonomous households (McFate 1995). Still, overall we expect that greater labor force participation by women will reduce pretax/transfer poverty. Researchers have found that single-mother families with children under 18 have remarkably high poverty rates, even if the mother is employed full-time (Casper et al. 1994; Kammerman 1984; Kilkey and Bradshaw 1999; McFate 1995; Smeeding 1989). A study of the United States, Canada, the United Kingdom, West Germany, the Netherlands, France and Sweden finds that singleparent households have substantially higher rates of poverty before taxes and transfers than coupleheaded households in all these countries (McFate, Smeeding, and Rainwater 1995). In consideration of these findings, we expect that countries with a larger proportion of households headed by singlemothers will have higher rates of pre-tax/transfer poverty). LABOR MARKET INSTITUTIONS: UNION DENSITY AND BARGAINING CENTRALIZATION Our main premise here is that the politicalinstitutional structure of industrial societies will affect poverty primarily through government efforts at redistribution. However, the strength of labor unions and the degree of coordination of wagesetting are two institutional features that are likely to affect income levels (and thus poverty) directly, prior to any government redistribution. UNION DENSITY. Insofar as unions strive to raise or maintain the salaries of workers (among other goals), one expects union strength to be negatively associated with pre-tax/transfer poverty. This straightforward hypothesis is made even more plausible by the repeated finding that greater unionization is associated with reduced income inequality among industrial societies (Alderson and Nielsen 2002; Gustafsson and Johansson 1999; Stephens 1979). BARGAINING CENTRALIZATION. Another institutional feature that should contribute to raising the lowest salaries above the poverty level is centralized bargaining. Centralized bargaining is associated with less wage dispersion and less income inequality (Alderson and Nielsen 2002; Iversen 1996; Rueda and Pontusson 2000; Wallerstein 1999). Bargaining centralization should therefore be associated with lower pretax/transfer poverty rates. POLITICES AND POLICIES A principal theme here is that socioeconomic factors and labor market institutions explain the bulk of the variation in pre-tax/transfer poverty. However, we explore arguments that some political factors may affect poverty directly. LEFT GOVERNMENT. Studies of wage dispersion have founds that left government has a negative effect on wage inequality, even when bargaining centralization and union density are controlled (Pontusson et al. 2002; Rueda and Pontusson 2000). Policies promoted by left parties, such as minimum wages and active labor market policies, are hypothesized to explain this effect. Studies suggest that left government might be associated with lower levels of poverty before taxes and transfers. WELFARE POLICIES. Based on conventional economic reasoning, critics of the welfare state contend that generous welfare state benefits, particularly unemployment benefits and other transfers, such as social assistance, available to able-bodied working-age persons, increase pretax/transfer poverty because they act as disincentives for recipients to seek work. Indeed, it is sometimes argued that, to the extent that generous welfare states reduce post-tax/transfer poverty, they simply make up for the damage done to pre-tax/transfer poverty levels. We are skeptical regarding this argument as it ignores the fact that generous welfare states are often labor mobilizing and invest hevily in skill formation, particularly ISS315 - PAGE 168 under the influence of social democratic parties. Moreover, the empirical work on the welfare state and work disincentives offers little support for the view that generous social policy discourages work effort and reduces labor supply (Atkinson and Mogensen 1993). Nevertheless, we will test the hypothesis that welfare state generosity may increase pre-tax/transfer poverty indicators of welfare policies, such as the unemployment replacement rate, that are discussed among determinants of poverty reduction in the next section. The replacement rate variable, as well as the welfare state generosity variable, are appropriate for testing the hypothesis that generosity of welfare states in general, and generous unemployment benefits specifically, increase the levels of pre-tax/transfer poverty by providing the unemployed and those out of the workforce with a negative work incentive. THEORY AND HYPOTHESES FOR POVERTY REDUCTION EFFECTED BY TAXES AND TRANSFERS For hypotheses on poverty reduction, we draw primarily on the literature on welfare state development. Some of the variables hypothesized to affect pre-tax/transfer poverty are not hypothesized to affect poverty reduction, and vice versa (see Table 1). Moreover, even when the same independent variable is hypothesized to affect both pre-tax/transfer poverty and poverty reduction, the logic of each predicted effect is generally different. We draw on the findings of E. Huber and Stephens (2001, chap. 3, esp. p. 81) on welfare state determinants and effects to argue that partisan incumbency and state structure are the key determinants of poverty reduction. ISS315 - PAGE 169 WELFARE STATE GENEROSITY DISTRIBUTIVE PROFILE AND WELFARE STATE GENEROSITY. We posit that the key determinants of poverty reduction are the size and structure of tax and transfer systems. Researchers have found that the size of the welfare state (as measured, for example, by spending on social programs) is associated with poverty reduction; countries with the largest welfare states typically have the lowest post-transfer poverty rates (Burtless, Rainwater, and Smeeding 2001; Kenworthy 1999; Kim 2000; Korpi and Palme 1998; McFate et al. 1995). Ceteris paribus, large welfare states redistribute more income. This assumption, central to our argument, is supported by cross-sectional comparative research on poverty reduction (Goodin et al. 1999; Kenworthy 1999; Kim 2000). DISTRIBUTIVE PROFILE OF THE WELFARE STATE. The redistributive impact of the welfare state is not simply a product of its size but also of the incidence of taxes and the distribution of transfers and services provided by the state. Indeed, as Aberg (1989) emphasizes, the size and distributive profile of taxes, transfers, and services are logically the sole proximate causes of welfare state redistribution. Thus, one would expect factors such as partisan government to operate through their effect on the size and/or distributive profile of the welfare state. However, to foreshadow the discussion of operationalization, the date we use (Luxembourg Income Study) do not measure the impact of public services on (in-kind) income, and the measures of the composition of transfers do not allow the researcher to measure the distributive profile of transfers very accurately. Short of actually measuring the distributive profile of transfers, one can identify certain types of transfers that ought to reduce poverty because they tend to benefit lower income groups disproportionately. These include meanstested benefits, child and family allowances (which are usually flat-rate and independent of income), maternity allowances, and generosity of unemployment compensation (measured as the unemployment replacement rate). Holding the size of total transfer expenditure constant, we hypothesize that the welfare state is more effective ISS315 - PAGE 170 at reducing poverty in a given country at a given time point when transfers are concentrated in these programs. PARTISAN INCUMBENCY CONSTITUTIONAL STRUCTURE AND by either house or the president, but may not even be under full control of the federal government. We expect that polities with more veto points will be associated with less poverty reduction. ADDITIONAL THEORIES Additional explanations of poverty reduction are also drawn from the political science literature. These include women's political mobilization, vocational education, capital market openness, and labor market institutions. WOMEN'S POLITICAL MOBILIZATION. The great changes in the roles of women brought about by industrial development have created a demand for expansion of the welfare state. As more women have entered the labor force, they have been confronted with the problem of combining paid work outside the home and unpaid care work in the family. More women have also established autonomous households. This has generated demands for greater state support for working women and single mothers, such as paid maternity and parental leave, child allowances, and free or subsidized services for child care and care for the elderly. Women's labor force participation has been shown to be associated with expansion of welfare state services (E. Huber and Stephens 2000, 2001). These socioeconomic changes have also resulted in women's political mobilization, as manifested in increasing participation at all levels of the polity. Women's political mobilization has altered the balance of political power in favor of welfare state expansion. In countries where women mobilized effectively both inside and outside of political parties, they were able to use their political influence to get some of the specific demands met that related to women's concerns (Misra 1998; O'Connor, Orloff, and Shaver 1999; Sainsbury 1999). Thus, we expect women's political mobilization to be positively associated with poverty reduction. As in the case of social democracy, we expect women's political mobilization to have a dampening effect on poverty both because it increases the size of the welfare state and because it shapes the profile of welfare state programs toward redistribution. VOCATIONAL EDUCATION. We conjectured earlier that the development of vocational education reduces pre-tax/transfer poverty. Iversen and Soskice (2001) also argue that the industry-specific and firm-specific nature of many of the skills acquired in vocational education systems results in higher support for social spending as workers with these skills are vulnerable to longer spells of unemployment and to loss of LEFT AND CHRISTIAN DEMOCRATIC CABINETS. We argue that duration of left government is a key determinant of the reduction in poverty achieved by state policy. The literature on welfare state development has paid considerable attention to the role of partisan ideologies that have dominated the political history of most industrial societies. Earlier research using extensive analysis of pooled time-series data has found that partisan government (categorized as left or social democratic, Christian democratic, secular right, and center parties) is the most consistently decisive determinant of eight different indicators of welfare state effort (E. Huber and Stephens 2001). With regard to poverty reduction, we expect social democracy to have a larger impact than Christian democracy, not primarily because of its impact on the size of the welfare state but because of its impact on the redistributive profile of taxes and benefits. We expect this because of historical differences in the social bases of the parties and the related differences in ideology. Social democratic ideology traditionally calls for greater redistribution, whereas Christian democratic ideology is more focused on providing for the needy. CONSTITUTIONAL VETO POINTS. We agree with Statist and new institutionalist theorists that a country's constitutional structure is an important determinant of welfare state development (Skocpol and Amenta 1986) and thus of the extent of redistribution through the tax and transfer system. An important aspect of the constitutional structure is the presence of "veto points," that is, points in the political process at which legislation can be blocked. A relatively large number of veto points in a country's constitutional structure depresses welfare state expansion as it enables relatively small groups to obstruct legislation (Bradley et al. forthcoming; E. Huber, Ragin, and Stephens 1993; E. Hubar and Stephens 2000, 2001). The extreme types are represented by, on one hand, the unicameral, unitary parliamentary systems of Scandinavia in which the party or coalition of parties with a single seat majority in the national legislature can pass any policy it desires, and, on the other hand, the strongly bicameral, federal, presidential system of the United States, in which legislation may not only find itself blocked ISS315 - PAGE 171 income if forced to move between jobs with different skill requirements. If so, one would expect the extent of vocational education to be positively related to higher social spending and poverty reduction. While Iversen and Soskice suggest that most of the effect of skill structure on poverty reduction will be via an increase in the size of the welfare state, it might also have a direct effect, as it has been shown hat the type of earnings-related benefits they have in mind do have a net redistribution effect (Korpi and Palme 1998; Stephens 1995). CAPITAL MARKET OPENNESS. As a factor of capital mobility, capital market openness was predicated to increase pre-tax/transfer poverty. This variable may also affect poverty reduction. In the golden age of post-war capitalism, advanced industrial countries varied greatly in the degree to which government controlled capital movements across borders. By the middle 1990s, almost all countries had completely dismantled capital controls. The absence of capital controls enhances the power of capitalists relative to the government. Because of the availability of easy exit options, business may demand tax and social policy concessions from the government (Bradley et al. forthcoming). Thus, we expect capital market openness to be associated with less reduction in poverty. LABOR MARKET INSTITUTIONS. Labor market institutions may affect poverty reduction in addition to their impact on pretax/transfer poverty. Bargaining centralization and wage coordination are frequently used as indicators of corporatism, that is, tripartite policymaking by centralized business and labor confederations and the state. Corporatism has long been associated with generous welfare states (Hicks 1999; Swang 2002). Researchers have found that greater unionization is associated with welfare spending and redistribution (Bradley et al. forthcoming; Stephens 1979). Although we agree with researchers who contend that political incumbency is a better predictor of welfare state development than are labor market institutions (E. Huber and Stephens 2001), we test the alternative hypothesis that corporatism and organizational strength of unions (both of which are highly correlated with left government) are associated with poverty reduction. MEASURES OF RESOURCES AND SIZE OF TARGET POPULATIONS. We include in models of poverty reduction several variables whose primary effect is on pretax/transfer poverty, but that may also affect poverty reduction, either because they increase the social need for redistribution or because they increase the resources available for such transfers. Because unemployment compensation is a components of all industrialized welfare systems, as unemployment and economic need increase a higher proportion of transfers will go to the unemployed, who are likely to be poor in terms of their pre-transfer incomes (Bradley et al. forthcoming; Makinen 1999). Thus, unemployment may be associated with greater reduction in poverty (Bradley et al. forthcoming). Given that single-parent families (in reality most of these are single-mother families) are among those most vulnerable to pre-tax/transfer poverty (Smeeding 1989), we expect the proportion of single-mother households to increase poverty reduction. The same logic applies to pretax/transfer poverty: a larger target population might be expected to increase poverty reduction, ceteris paribus. The "logic of industrialism" school of though argues that economic development leads to welfare state expansion by creating new vulnerabilities and needs for social protection and by providing states with the means to offer such protection (Wilensky 1975). If so, we would expect higher levels of industrialization and affluence to result in larger welfare states and greater poverty reduction through taxes and transfers. DATA DEPENDENT VARIABLES The measures of poverty are derived from the Luxembourg Income Study (LIS) database. LIS collects data from national microdata sources (i.e., survey data based on individual-level data rather than macro aggregates) and harmonizes the data sets to make income comparisons across countries and over time possible. LIS data are arranged by waves, with the first wave starting in the late 1970s and the most recent wave in the mid-to-late 1990s. There also exist historical data (pre-1979) for a handful of countries. The LIS surveys provide the best available comparable cross-national, over-time data source for income in OECD countries (OECD 1995). The poverty figures published on the LIS web site and in the many publications using the LIS data are not adequate for our purposes because they include pensioners, which distorts the pretax/transfer poverty rates and exaggerates reductions in poverty. In countries with comprehensive public pension systems, such as the Nordic countries, which give the pensioner a ISS315 - PAGE 172 replacement rate that is often three-quarters of his or her working income, pensioners make little other provision for retirement. For instance, in an analysis of LIS data, Makinen (1999) found that 93 percent of Finns over age 65 and 89 percent of Swedes in this age group are poor before transfers, and only 4 percent and 2 percent are poor, respectively, after transfers are added to their incomes. Thus, we conducted our own analysis of the LIS micro data excluding those over age 59 and under age 25. This excludes most early pensioners and students as well, so the remaining population is clearly working age. We constructed three measures of poverty: pre-tax/transfer poverty, post-tax/transfer poverty, and reduction in poverty effected by taxes and transfers (see Table 1, pp. 8-9 and Table 2, p. 15). Following the lead of virtually all comparative research on poverty, we utilize a relative poverty rate for our measure of poverty (e.g., see Casper et al. 1994; Korpi and Palme 1998; McFate et al. 1995). We measure poverty as the percent of the population in each country below 50 percent of median household income adjusted for household size (see below). The measure of redistribution is calculated as the proportional reduction in poverty effected by taxes and transfers; reduction is calculated as 100 x {1-[post-tax/transfer poverty rate)/(pre-tax/transfer poverty rate]}. Our analysis focus on pre-tax/transfer poverty and reduction in poverty due to taxes and transfers. Pre-tax/transfer poverty rates are calculated from market income: the total income from wages and salaries, self-employment income, property income, and private pension income. The post-tax transfer poverty rate is based on disposable personal income. This includes all market income, social transfers, and taxes. Figures for both market income and disposable income were bottom coded at 1 percent of mean income and top coded at 10 times median income, adjusted for household size and composition. Because we are using an income concept based on households, adjustments had to be made for household size. Equivalence scales are used to adjust the number of persons in a household to an equivalent number of adults. If one chooses not to use an equivalence scale, one ignores the economies of scale resulting from sharing household expenses and assumes that each additional equivalent adult in a household has the same "cost" as other members of the household. We choose the commonly used OECD scale that adjusts for household size and composition (see Figini 1998 and OECD 1995 for a discussion of equivalence scales). INDEPENDENT VARIABLES We include five measures associated with economic development (see Table 1). Gross domestic product per capita (GDP) is adjusted for purchasing power parities, and agricultural employment is measured as the proportion of the civilian labor force employed in agriculture. Youth is measured as the percent of the population age 15 and younger. Two human capital variables include education, measured as secondary school enrollment as a percentage of the population of secondary school age and vocational education, measured as the percentage of an age cohort in either secondary or post secondary vocational training (following Esteves-Abe et al. 2001). The vocational training measure would appear to be a good measure of general skills at the bottom of the skill distribution as well as of vocational skills, as the correlation between the Esteves-Abe measure and the OECD/HRDC (2000) measure of literacy skills of the fifth percentile is .73. The vocational education data were available for only 49 of the 61 cases. For the remaining cases, we have substituted the mean value for the country in question. The U-turn problematic is characterized by seven variables. Industrial employment is an indicator of de-industrialization. It is measured as the percent of the population aged 15 to 64 in an industry. LDC imports, capital market openness, outward direct foreign investment, and immigration represent the extent of globalization. LDC imports are measured as manufacturing imports from Standard International Trade classification groups 5, 6, 7 and 8 from non-OECD countries as a percent of GDP (following Alderson and Nielsen 2002; OECD various years [b]). Capital market openness is operationalized with the Quinn/Inclan measure of capital controls. The maximum score indicates no capital controls. Outward direct foreign investment (DFI) is measured as outward DFI divided by GDP. The immigration rate is calculated as population growth adjusted for crude birth and death rates (following Alderson and Nielsen 2002; World Bank various years). The measure of percentage of the total labor force unemployed and female labor force participation are self-explanatory. Single-mother families are measured as the percentage of all families with children under age 18 headed by a woman. Labor market institutions are measured by union density and wage coordination. For union density, we use union membership as a percentage of total wage and salary earners (Ebbinghaus and Visser 1992). The wage coordination/corporatism ISS315 - PAGE 173 measure is Kenworthy's (2001) measure in which a higher score indicates stronger wage coordination. Politics variables include left cabinet and Christian democratic cabinet, women's organization strength, and constitutional structure (veto points). We coded the partisan government variables, left-cabinet and Christian democratic cabinet, 1 for each year that these parties were in government alone starting from 1946, and as a fraction of their seats in parliament of all governing parties' seats for coalition governments. Comparable data on participation in women's organizations for the 14 countries in our data set are available from the World Values Surveys (Inglehart 1997), but only for 31 country/year data points between 1981 and 1997 and often not for the year for which the LIS data exist. However, the measures we developed from the World Values Surveys were high correlated with the proportion of women in the lower house of the national legislature, which is available in an annual time series from the end of World War II to 2000 (inter-Parliamentary Union [IPU] 1995). The notion that stronger women's movements both within and outside political parties should be reflected in larger proportions of female legislators has face validity. One weakness in this link is that electoral rules strongly influence the proportion of female legislators. In proportional representation systems, parties can more easily increase the proportion of women in their parliamentary delegation by changing the gender composition of their lists of candidates. As citizens in these systems tend to vote for parties, not candidates, more women end up being elected. In singlemember district systems, the strong incumbent advantage also works against increasing the representation of women, as the overwhelming majority of incumbents are men. Thus, women's movements of equivalent strengths will produce more women representatives in proportional representation systems. An additional problem specific to the World Values Survey Data is a wording change in questions on organizational membership in the last wave of the survey. We developed two different measures of women's organizational membership: the percentage of women in at least one nonreligious organization, and the percentage of women in at least one political or union organization. We excluded religious organizations, reasoning that these were unlikely to favor gender egalitarian social policies. To deal with the two distortions just mentioned we regressed the two measures of women's organizational membership on women in parliament (results not shown), an indicator for proportional representation and an indicator for the last World Values Study wave. The fit was very good in both equations with an R2 of .82 for the total membership variable and .76 for the union and political organization membership variable. We then calculated the predicted value of women's organization for the country/years of the LIS using the coefficients for women's parliamentary representation, the proportional representation indicator, and a constant. In the case of percent membership in any nonreligious organization, the equation was: Membership = 30.39 +1.58 (women in parliament) -16.36 (proportional representation). Because we expect that policy would reflect the long-term strength of women's movements and not any sudden increases in participation in women's organizations, we calculated the cumulative average of each of the two membership variables. This procedure makes these variables consistent with long-term measurement of the cabinet variables. Since the two measures performed almost identical in the analyses, we report only the results for membership in any non-religious organization. Our measure of constitutional structure (presence of veto points) is an additive index of federalism (none, weak, strong) presidentialism (absent, weak, strong), and the use of popular referenda as a normal element of the political process (absent, present). Thus, a high score indicates high dispersion of political power and the presence of multiple veto points in the political process. ISS315 - PAGE 174 Policies include five variables that characterize the welfare state structure. These include welfare generosity, means-tested benefits, child and family allowances, maternity allowances, and unemployment replacement rates. Our measure of welfare generosity is strongly conditioned by the nature of the LIS data. The LIS data on posttax/transfer income measure disposable cash income. No effort was made to estimate the redistributive effects of the provision of free or subsidized public goods and services, a dimension of the welfare state on which the social democratic welfare state is most distinctive. Thus, variations in the funding and delivery of social services have no obvious effect on the measures of reduction in poverty we have calculated from the LIS data. Our measure of welfare state generosity (taxes and transfers) is the sum of the standard scores for total taxes as a percentage of GDP and transfer payments as a percentage of GDP. We standardize the two measures in order to weight them equally. The resulting variable reflects the size of the welfare state but not the distributive profile of taxes and transfers. The LIS data classify the types of transfers provided to households, which provides some indication of which types of transfers benefit lowincome households disproportionately. The most obvious type consists of means-tested transfers. Family and child allowances should also have a disproportionate effect on poor households, because these transfers are almost always at a flat rate and thus, proportionate to income, larger for poor families than rich ones (Wennemo 1994). Single mothers are disproportionately represented among the poor in many countries (Smeeding 1989). The most effective programs to reduce poverty among single mother families are child benefits, family allowances, and maternity allowances (Gornick, Meyers, and Ross 1997; O'Connor 1999; Orloff 1996). The measures of welfare policies therefore include means-tested transfers, family and child allowances, and maternity allowances, each as a percentage of total social transfers. Unemployment compensation should also benefit poor households disproportionately. Although unemployment compensation is often related to earnings, the OECD unemployment replacement data show that, with very few exceptions, the replacement rate is higher for lowerincome workers than for higher-income workers. Among four variables measuring unemployment replacement rates at different income levels and duration of benefits culled from the OECD data (based on persons with average wages versus twothirds the average wage and five years of benefits, as this variable had the most explanatory power. UNITS OF ANALYSIS Fourteen of the 18 large advanced industrial countries that have been democracies since at least World War II are included in the analysis. New Zealand and Japan are excluded because there are ISS315 - PAGE 175 no LIS surveys for these countries. The one Austrian LIS survey and the one Irish LIS survey are excluded because of missing data on key variables. The average values for the dependent variables and the key independent variables are listed in Table 2 for the countries in the data set, grouped by welfare state regime. METHODS UNBALANCED PANEL CORRELATED ERRORS DATA AND We use an unbalanced panel data set with 61 observations on 14 countries, with countries providing different numbers of observations according to data availability. There are a minimum of two and a maximum of seven observations per country. The time-span between observations is irregular, varying across countries and time points. A central problem in estimating regression models from panel data is that the assumption of independence of errors across observations is unlikely to be satisfied. As a result, OLS produces incorrect standard errors for the regression coefficients (Greene 1993). There are several strategies for dealing with correlated errors in panel data. One approach (exemplified by the Parks method) assumes serially correlated errors within each unit (country) obeying a unit-specific autoregressive process (which may optionally be constrained to be the same across units). As pointed out by Beck and Katz (1995:635-40) this approach requires what Stimson (1985) calls temporally dominated time-series of cross-sections (i.e., data structures consisting of relatively few units observed over many equally spaced time points). The small number of time points and irregular spacing of observations in our data set preclude this approach. Another approach is to estimate a random effect model (REM) in which the error term contains a unit-specific component that differs across units but is constant over time for a given unit. Such an error structure would arise if unmeasured unit-specific causes, such as systematic measurement differences or other overlooked aspects of the social and cultural makeup of a country, affect the dependent variable in the same way at each point in time over the period of the data. The stable unit-specific component implies that observations for the same unit at different time points are all correlated by the same amount, . The REM strategy is feasible with our data. One attractive feature of REM is that it allows estimating the value of . But REM requires relatively strong assumptions (such as equal correlations among errors within units) and may not be optimal given the small size of the sample. Because it is not substantively essential in this situation to measure , we adopt an alternative estimation strategy that addresses the correlation problem while requiring a minimum of assumptions on the behavior of the errors. We combine OLS estimation of the regression coefficients, which provides consistent estimates of the regression coefficients, with the use of a robust-cluster estimator of the standard errors. The standard (i.e., noncluster) Huber-White or "sandwich" robust estimator of the variance matrix of parameter estimates was discovered independently by P. Huber (1967), White (1980), and others (see Long and Ervin 2000 for a detailed description). It provides correct standard errors in the presence of any pattern of heteroskedasticity (i.e., unequal variances of the error terms) but not in the presence of correlated errors (i.e., nonzero off-diagonal elements in the covariance matrix of the errors). The robust-cluster variance estimator is a variant of the Huber-White robust estimate that remains valid (i.e., provides correct coverage) in the presence of any pattern of correlations among errors within units, including serial correlation and correlation due to unit-specific components (Rogers 1993; also see Scribney 1998; StataCorp 1999:256-60). Thus, the robust-cluster standard errors are unaffected by the presence of unmeasured stable country-specific factors causing correlation among errors of observations for the same country, or for that matter by any other form of within-unit error correlation. The robust-cluster estimator of the standard errors in only impervious to correlations of errors within clusters. It requires errors to be uncorrelated between clusters. The latter assumption might be violated if unmeasured factors affect the dependent variable (the poverty rate) in all units at the same point in time. Global economic fluctuations could produce such contemporaneous effects. To evaluate the potential impact of such unmeasured period-specific factors we reestimated the models with indicator variables for the 1980s and for the 1990s; the baseline category corresponds to the 1970s and includes two observations from the late 1960s. None of the two indicators reached significance in any of the models (for either dependent variable), suggesting that period-specific effects are not present in this data set. Given the superiority of robust-cluster estimation, we utilized this method as our primary technique. However, to demonstrate the robustness of our results, we also employed OLS and REM ISS315 - PAGE 176 estimation, following explained below. the reduction criteria MODEL-BUILDING STRATEGY We constructed models for both dependent variables by successfully introducing substantively related sets of independent variables. With each model, we conducted an F-test of the joint significance of all variables with nonsignificant individual effects (at p<.10) to see if they could be safely dropped from the model. When using robust standard errors, the degrees of freedom of the F-test of joint significance are in principle equal to the number of clusters (countries). We relied instead on the more conservative F-test using degrees of freedom equal to the total number of data points, which is more likely to conclude that the variables in a subset are jointly significant and thus must be kept in the model. It turns out that the decisions to keep or drop variables would have been the same using number of countries or number of observations as degrees of freedom. All F-test permitted dropping nonsignificant variables (p<.10; results not shown). RESULTS As Table 2 shows, post-tax/transfer poverty rates are lower than pre-tax/transfer poverty rates for all countries studied. However, the extent of redistribution varies across countries. Countries with Christian democratic welfare states (excluding Germany and Switzerland) have the highest pretax/transfer poverty rates, while countries with social democratic welfare states (excluding Denmark) have the lowest. Belgium is the most successful state at reducing poverty, followed by Denmark and Finland. The least redistributive states are Switzerland and the United States. To assess the causes of poverty rates and redistribution, we estimate a series of regressions for pre-tax/transfer poverty rates and for reductions in poverty due to taxes and transfers. PRE-TAX/TRANSFER POVERTY The first set of analyses examines the determinants of pre-tax/transfer poverty rates (see Table 3). Models 1 through 5 separately test variables measuring economic development, the U-turn problematic, labor market institutions, politics, and policies. Models 6 and 7 combine the variables significant at p<.10 or better from the previous models, following the model-building strategy described in the methods section. Model 8 incorporates time-period effects. In model 1, the significant positive coefficients of GDP and vocational education, and the marginally significant negative coefficient of agricultural employment contradict our expectation that economic development and a strong system of vocational education reduce poverty. The significant positive effect of youth on poverty is the only coefficient in the expected direction. We note, however, that in the more fully specified Model 6 vocational education is the only one of these factors that retains significance, and even this factor becomes nonsignificant in the reduced model (Model 7) and in the model with period indicators (Model 8). We conclude from Model 1 that factors associated with economic development are not the expected predictors of pre-tax/transfer poverty rates. This supports the view that, in these advanced industrial countries, economic development has lost its antipoverty effectiveness. Model 2 tests the effect of variables associated with the "U-Turn problematic," which include industrial employment, factors associated with globalization (LDC imports, capital market openness, outward direct foreign investment, and immigration), unemployment, and women's changing roles (labor force participation and singlemother families). The coefficients for industrial employment and unemployment are significant and in the expected direction. Thus, a comparatively larger industrial sector (reflecting less deindustrialization) and lower unemployment are associated with less pre-tax/transfer poverty. Interestingly, coefficients for the globalization variables are nonsignificant. This suggests that the increasing interdependence of nations does not adequately explain variations in poverty rates across countries during the latter third of the twentieth century. Note that most of the relevant globalization literature argues that globalization has an indirect effect on poverty and inequality, largely through its impact on de-industrialization and unemployment. With regard to de-industrialization, recent pooled time-series analyses of the same advanced industrial societies we study here have shown that de-industrialization is driven largely by factors internal to these economies. Thus, globalization has had little if any effect on industrial employment (Iversen and Cuasck 2000; Rowthorn and Ramaswamy 1998; but see Alderson 1999). Additional analyses are consisted with theses findings (results not shown). When we dropped e-industrialization from combined Model 6 in Table 3 and entered the globalization variables, none of them had significant effects on pre- ISS315 - PAGE 177 tax/transfer poverty. Similarly, when we dropped unemployment from the model and entered the globalization variables, none of them had significant effects on the dependent variable. Measures of women's changing roles are also not significant. Women's labor force participation does not have the expected negative effect on poverty, and single-mother families do not have the expected positive effect. Model 3 tests the impact of the labor market institution variables-wage coordination and union density. We find mixed support for the hypotheses associated with these variables. Union density does not significantly affect pre-tax/transfer poverty rates, but wage coordination has a strong predictors of pre-tax/transfer poverty rates. Left cabinet is not significant in Model 4, but welfare generosity and maternity allowances are significant in Model 5. Some critics contend that the welfare state creates poverty by reducing work incentives and encouraging dependency. The significant positive coefficient of welfare generosity and the marginally significant coefficient of means-tested benefits (p<.1), but not the significant negative coefficient of maternity allowances, would seem to be consistent with the view that welfare induces poverty. However, these results are not robust, as these coefficients become nonsignificant in the more fully specified Model 6. The nonsignificance of left cabinet and the policy significant negative effect on poverty. Therefore, countries with more centralized wage bargaining have lower poverty rates, presumably because the dispersion of their wage distribution is less. In Model 4, we test the effect of left cabinet and Model 5, we test the policy variables, including welfare generosity, means-tested benefits, child and family allowances, maternity allowances, and unemployment replacement rates. We have hypothesized that these variables would not be variables in Model 6 suggests that government ideology and the size and shape of the welfare state do not explain variation in pre-tax/transfer poverty. Models 6 to 8 document the reduction process used to determine the trimmed model of pre-tax/transfer poverty. Model 6 combines the variables from previous models significant at p<.10. We perform F-tests to ensure that dropping variables does not significantly reduce the explanatory power of each model. We find that we ISS315 - PAGE 178 can confidently drop education from Model 1 [F(1,55) = .20]. We can drop the six nonsignificant variables from Model 2 [F(6,52) = .37]. We can drop union density from Model 3, and the two nonsignificant policy variables from Model 5. This combined model is further reduced in Model 7, where we drop the six variables below the p<.10 level of significance from Model 6. Finally, Model 8 adds indicators for the 1980 and 1990 time periods to the reduced model. These period indicators test for the existence of unmeasured period-specific influences that might induce correlations among errors across units (countries). The nonsignificance of the period indicators suggests that the errors are not correlated across countries. In sum, Model 8 indicates that the most powerful predictors of pre-tax/transfer poverty are industrial employment (negative effect), unemployment (positive effect), and wage coordination (negative effect). These variables together explain 67 percent of the variation in pretax/transfer poverty rates. POVERTY REDUCTION Table 4 presents analyses predicting reductions in poverty resulting from taxes and transfers. Models 1 through 4 test the impact of economic variables, labor market institutions, political variables, and policy variables, respectively. Model 5 combines the variables significant at p<.10 or better from the previous models, and Model 6 further reduces the combined model. Model 7 substitutes union density for left cabinet, and Model 8 incorporates the time-period indicators. Model 1 tests the effects of variables measuring economic resources and the size of the target population. Unemployment is not significant in Model 1. However, this effect becomes significant when the models are combined and reduced (Models 5 through 8). These findings suggest that states with higher unemployment rates have greater redistribution because unemployment generates financial need, which leads to increased welfare expenditures and, at any given level of benefits, greater reduction of poverty. The remaining economic and population variances are ISS315 - PAGE 179 not significant. Model 2 tests the effects of labor market institutions. We find, as expected, that states with greater union density have higher reductions in poverty. Because of the high correlation between left cabinet and union density, we do not include union density in our first combined and reduced models (Models 5 and 6). Wage coordination is not significant in model 2, but it becomes significant in Models 5 and 6. When we substitute union density for left cabinet in the reduced Model 7, the significance of wage coordination is reduced again. We conclude that, among labor market institutions, union density is the more significant determinant of redistribution. ISS315 - PAGE 180 Model 3 incorporates politics variables. We contend that left cabinet and veto points are the key determinants of poverty reduction. We find, in support of our argument, that states with stronger left parties have greater reductions in poverty. Furthermore, as expected, the coefficient for Christian democratic cabinet is not significant. This supports the assertion that left governments are more supportive of redistribution. We also find that constitutional structure (veto points) have a significant negative effect on poverty reduction, supporting the view that many veto points offer interest groups more opportunities to block legislation aimed at developing welfare states with a strongly redistributive profile. Although vocational education has only a marginally significant positive effect on poverty reduction in Model 3 (p < .07), the coefficient becomes significant in the combined and reduced models (Models 5 through 8), suggesting that states with more developed vocational education systems have stronger social programs that lead to poverty reduction. Model 4 introduces the policy variables to determine whether government policies predict poverty reduction. This model tests the effects of both the size (i.e., generosity) and the shape (as represented by the policy variables) of the welfare state on poverty reduction. We find, in support of our main assertion, that more generous welfare states have significantly greater reductions in poverty. Moreover, child and family allowances as a proportion of social transfers have a significant positive effect on poverty reduction, and the significance of this variable increases in the combined and reduced models. In contrast, generosity of unemployment replacement rates loses its significance in the combined model, and the proportion of transfers that are maternity allowances or means-tested never achieve statistical significance. Thus, we find that overall size of the welfare state and a strong reliance on child and family allowances are important determinants of poverty reduction. Because union density and left cabinet are highly correlated, we include them in separate models. Model 6 includes left cabinet, and Model 7 includes union density. We find, as expected, that left cabinet explains the larger proportion of the variation in poverty reduction, at 91 percent compared with 88 percent when union density is included. This suggests that for citizens to have a more substantial impact on the state, employees must not just unionize, they and their families must also attain effective political representation through a strong left party. Model 8 includes left cabinet because it has greater explanatory power. This model also incorporates the period indicators. The nonsignificant time-period variables offer no evidence of period-specific errors. This cabinet, constitutional structure, and welfare policy structure are the central determinants of poverty reduction. This model, which explains 91 percent of the variance in poverty reduction, indicates that states are more likely to effectively redistribute income to the poor when they have longer periods of left rule and more generous welfare states. Furthermore, the structure of the welfare state matters, as child and family allowances are the most effective at reducing poverty. Two additional explanations of poverty reduction are also important. Unemployment is significant because it increases poverty and thus-in the presence of decent unemployment compensation schemes, which most of these countries have-it also increases poverty reduction. Finally, vocational education is significant because workers employed in states with stronger vocational education systems often experience longer spells of unemployment. They, therefore, support the development of social programs that might reduce poverty. To test the robustness of robot-cluster estimation, we also generated OLS and REM estimates. These results provide overwhelming support for our findings (see Appendices B and C). For reduction in poverty, our main results of the strong effects of welfare generosity, left cabinet, veto points, and unemployment appear in all equations. In the case of pre-tax/transfer poverty, the results are similar in all three equations for industrial employment and unemployment, but wage coordination does not appear in the final equation in the REM estimates. CONCLUSIONS To briefly summarize our main findings, we stress the importance of the polity in reducing levels of poverty, which is largely created by economic structures and conditions. Pre-tax and transfer poverty is mainly a function of industrial employment, unemployment, and wage coordination. Countries with coordinated market economies, where a large percentage of the working-age population is employed in industry and where the unemployment rate is low, have low levels of pre-tax and transfer poverty. However, these poverty levels in all countries are reduced through the tax and transfer system, albeit to greatly varying degrees. ISS315 - PAGE 181 The more generous the welfare state, the greater is the extent of poverty reduction. In addition, long-term incumbency of left parties affects poverty reduction positively by giving the tax and transfer system a particularly redistributive profile. One of the most effective antipoverty policy instruments is child and family allowances. Unemployment raises the pre-tax/transfer poverty rate and thus leads to move poverty reduction, at the given level of welfare state generosity. Finally, vocational education systems enhance poverty reduction, and we take this to be largely an effect of the greater support of workers experiencing unstable work patterns for spending on social safety nets. These findings have important implications for theory and policy. Theoretically, we have advanced two sets of literatures. First, we have contributed to the sociological literature on poverty by explaining how the welfare state reduces poverty. By distinguishing between pretax/transfer poverty and reductions in poverty, we have been able to integrate traditional theories of poverty and inequality that focus on economic and labor market structures with theories of redistribution that focus on political power and state structure. We have demonstrated that in advanced industrial economies, pre-tax/transfer poverty rates are determined primarily by factors associated with the Great U-Turn on inequality. An influential view holds that, as advanced economies have become more global and as de-industrialization has progressed, industrial employment has declined and unemployment has risen, particularly among workers with few skills (Bluestone and Harrison 1982). Our findings support the argument on deindustrialization and unemployment. However, we do not find much support for the globalization hypothesis. Nevertheless, the contrary findings of Alderson and Nielsen's (2002) recent work on globalization and inequality argue that our rejection of the effects of migration, Third World imports, and outward direct foreign investment on poverty must remain tentative. Alderson and Nielsen found globalization effects on longitudinal variations in inequality; it is plausible that the greater temporal reach of their data and larger sample size (n = 187) may account for the differences in findings. Second, we have developed the welfare state literature by demonstrating both the extent and the conditions under which the welfare state has a significant impact on poverty. Before taxes and transfers are offered to families, the average poverty rate among the working-age population across these countries is 16 percent, ranging from 10 percent in Germany to a remarkably high 22 percent in France. After taxes and transfers are incorporated as income, the average poverty rate is sliced in half, and the range drops dramatically to a low of 3 percent in Finland to a high of 15 percent in the United States. Remarkably, the difference between the two end-points of the range remains at 12 percent, while the ordering of countries changes, indicating great variation in income redistribution. Previous studies have demonstrated a strong relationship between the welfare state and poverty reduction, but they are limited by crosssectional data and a neglect of the causal mechanisms explaining poverty rates (Kenworthy 1999; Kim 2000). We find that the welfare state itself and the balance of political power are central determinants of poverty reduction. When states spend more of their financial resources on citizen welfare, poverty is reduced. When they spend it under the influence of left-wing parties, they spend it in a more redistributive way and are particularly effective at reducing poverty. A particularly effective antipoverty instrument is child and family allowances, as opposed to means-tested benefits in general. If governments want to attack poverty directly, they must invest in these more effective programs. We find that Christian democratic cabinet is not significant for poverty reduction, but left cabinet is significant, indicating that the structure of social democratic welfare state policies account for the greater effectiveness of left cabinets in reducing poverty. This effect is substantively important: 10 years of left cabinet control of the government result in a 6.3 percent proportional reduction in poverty. Moreover, left cabinet also has an indirect effect on poverty reduction because it is one of the main determinants of welfare state generosity (E. Huber and Stephens 2001, chap. 3). Our research, then, has advanced our understanding of poverty and redistribution in industrialized democracies. We have gone beyond the limited number of previous studies examining the relationship between the welfare state and poverty by systematically testing theoretical explanations for their relationships and providing a solid quantitative account of their relationship. Our analyses of an unbalanced pooled time-series data set have shown that de-industrialization, unemployment, left government, constitutional structure, and welfare state policies determine the welfare of citizens by shaping their pre-tax/transfer income and the extent to which states redistribute income through taxes and transfers. ISS315 - PAGE 182 HANDOUTS ON POPULATION 1. 2. 3. 4. 5. 6. Historical Growth of Population Social Problems Associated with Overpopulation Population Variables Demographic Transition Theory Structural and Proximate Determinants of Fertility U.S. Population ISS315 - PAGE 183 Historical Growth of Population YEAR 8000 BC 3000 BC 1000 BC 0 BC 1350 AD 1650 AD 1750 AD 1850 AD 1900 AD 1940 AD 1990 AD 1998 AD 2003 AD EST. POP 1 MIL 10 MIL 20 MIL 30 MIL 35 MIL 60 MIL 77 MIL 1,171 MIL 1,608 MIL 2,171 MIL 5,321 MIL 5,883 MIL 6,314 MIL G.R. .005 .05 .00 .04 .06 .27 .25 .51 .63 .75 1.8 1.5 1.3 Population Reference Bureau ISS315 - PAGE 184 SOCIAL PROBLEMS ASSOCIATED WITH OVERPOPULATION 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. ILLITERACY PRESSURE ON FISHERIES RECREATION FACILITIES POLLUTION INFLATION ENVIRONMENTAL ILLNESSES HUNGER HOUSING CLIMATE CHANGE OVERGRAZING CROWDING DECLINE IN INCOME URBANIZATION DEFORESTATION POLITICAL CONFLICTS DEPLETION OF MINERALS LIMITS TO HEALTH SERVICES WATER SHORTAGES UNEMPLOYMENT ENDANGERED SPECIES ENERGY SHORTAGES INDIVIDUAL FREEDOM (Marden and McCoy, 1982) ISS315 - PAGE 185 POPULATION VARIABLES (CAUSAL MODEL) ISS315 - PAGE 186 The Demographic Transition ISS315 - PAGE 187 CAUSAL LINK BETWEEN STRUCTURAL AND PROXIMATE DETERMINANTS OF FERTILITY ISS315 - PAGE 188 Population Change The 2000 Census recorded an addition of 32.7 million U.S. residents during the 1990s. It was the greatest increase in population ever between two censuses, and the largest percentage increase since the 1960s. The 2000 Census marked the only decade in the 20th century in which every U.S. state gained population. The national count was 6.9 million higher than estimated for April 2000 based on the 1990 Census count and demographic analyses of births, deaths, and migration trends over the decade (see Box 3). Shifting South and West The Western and Southern states increased the fastest in population and the Northeastern states grew slowest -- continuing demographic trends evident since the 1950s. The South has emerged as the most populous of the four regions defined by the Census Bureau. Its share of the U.S. population expanded from 31 percent in 1950 to 36 percent in 2000. But the westward movement of the population has been the most dramatic shift over the past few decades, and this trend was still evident in the 2000 Census results. In 1950, just 13 percent of Americans lived in the West; in 2000, 22 percent lived in the West -- up slightly from 21 percent in 1990. Although the Midwest and Northeast have gained population in the past five decades, their growth has been overshadowed by the rapid gains in the West and South. The share of the U.S. population living in the Northeast fell from 26 percent in 1950 to 19 percent in 2000, while the Midwest's share declined from 29 percent to 23 percent. The population living in the Midwest, which includes such big states as Ohio, Michigan, and Illinois, just barely outnumbered those living in Western states in 2000. With its consistently faster growth, the West is likely to overtake the Midwest before the next census, just as it overtook the Northeast after the 1990 Census. Nevada, Arizona, Colorado, Utah, and Idaho -- all in the West -- were the five fastest growing states over the decade (see Appendix Table). Nevada, which had just 1.2 million people in 1990, surged 66 percent over the decade to reach nearly 2 million. Arizona grew 40 percent to 5.1 million, for a much larger numerical gain. Hawaii, Montana, and Wyoming were the only Western states with relatively slow growth. Montana's increase was just under the national growth rate of 13.2 percent, while Hawaii and Wyoming grew just 9 percent. Georgia, Florida, Texas, and North Carolina were the fastest growing Southern states, and among the top 10 gainers nationwide. The populations of all four of these Southern states increased more than 20 percent between 1990 and 2000. Population growth was much slower in the Midwestern and Northeastern states. New York grew by just 5.5 percent -- although it added nearly 1 million people -- and Pennsylvania increased by just 3.4 percent. North Dakota barely grew at all over the decade, and had the slowest rate and smallest numerical growth of any state. It added just 3,400 people to bring its population total to 642,000. Although California is not growing as rapidly as in the past, it still logged the largest numerical increase over the decade -- 4.1 million people -- to reach a population of 33.9 million. The volume of new residents was nearly as high in Texas, which added 3.9 million and pushed past New York to become the nation's second most populous state. Florida gained 3.0 million people over the decade, the third largest increase. Wyoming remained the least populous state with 493,782 residents. It gained 40,194 residents over the decade. Increase in Hispanics One of the biggest surprises of the 2000 Census was the phenomenal growth in the U.S. Hispanic population. The number of people who identified themselves as Hispanic increased from 22 million to 35 million between 1990 and 2000; the number of Hispanics edged past the number of non-Hispanic African Americans for the first time (see Table 1). The Hispanic population has grown faster than the U.S. black population because Hispanics have higher birth rates and immigration rates than blacks. Although many ISS315 - PAGE 189 blacks immigrated from Africa and the Caribbean, the flow is minor compared with the entry of Hispanic immigrants from Latin America. Figure 3 Other explanations for the rapid growth of the Hispanic U.S. Hispanic Population by Race, 2000 population revolve around the census itself. The Census Bureau made special efforts to count undocumented immigrants (many of whom are from Latin America), and it moved the question on Hispanic origin to a more userfriendly location, preceding the race question, to encourage a greater response. Many analysts thought people were confused in past censuses by being asked their race first, then whether they were Hispanic. And many Americans do not distinguish between race and ethnicity as defined by the federal government.14 Previous analyses have estimated that about 90 percent of * American Indian, Alaska Natives, Asians, and Native Hawaiians and other Pacific Islanders. Source: E.M. Grieco and R.C. Cassidy, Overview of Race and Hispanic Origin, Census Brief 2000 (March 2001): Hispanics would be considered white by current Census Tables 10-11. Bureau definitions, yet 42 percent of U.S. Hispanics said they were "some other race," in the 2000 Census (see Figure 3). Another 5 percent said they were "some other race" and white, black, or some other multiracial combination. Similarly, nearly 40 percent of Hispanics checked "other race" in the 1990 Census. The U.S. Hispanic population has been highly concentrated in the Southwest and West, and in a few metropolitan areas outside these regions, such as Miami, New York New Jersey, and Chicago. One of the big demographic stories of the decade has been the dispersion of Hispanics out of these areas to smaller cities and even rural areas in the Midwest, South, and Northeast. These areas saw the largest percentage increases in Hispanics between 1990 and 2000, as shown by the darker areas in Figure 4. Long-term residents in many Midwestern towns and Southern cities had little interaction with immigrants, or in some cases with minorities, before the arrival of Hispanic workers and families in the 1990s. This dispersion of Hispanics means that many more Americans are seeing and experiencing the country's new racial and ethnic diversity. FIGURE 4: HISPANIC POPULATION GROWTH IN U.S. COUNTIES, 1990-2000 Source: Created by the Population Reference Bureau based on data from the 2000 Census. ISS315 - PAGE 190 The 2000 Census documented anecdotal evidence that the Hispanic population is getting more diverse. U.S. Hispanics of Mexican origin -- the largest Hispanic group -- added more than 7 million people over the decade. They account for nearly 60 percent of all Hispanics. And, while Central and South Americans were the second largest group in 1990, they were superceded by an amorphous "other Spanish/Hispanic/Latino" group in the 2000 Census.15 This surprising shift may reflect confusion with the questionnaire; it may also be connected to the inability to mark more than one national origin. However, some demographers and social commentators suggest that it may signal the gradual assimilation of Americans from diverse national origins in Latin America to a "pan-Latino" identity. The term Hispanic was created as catchall statistical category for people from Spanish-speaking countries, but was not widely embraced by the people it meant to identify. Many identified with their national origin rather than as "Hispanic." This may be changing for U.S.-born Hispanics, who may feel less affinity with the country of their forebears than they do with other Spanish-speaking Americans, who increasingly include high-profile athletes, politicians, and entertainers. Hispanics whose parents are from different origins (Puerto Rican and Dominican, for example, or Salvadoran and Mexican) might also favor the more general term.16 After the country of birth and other variables from the 2000 Census are released in 2002 and 2003, demographers will have additional clues about why the Hispanic, Spanish, or Latino category surged over the past decade. Racial and Ethnic Diversity The number of Asian Americans also soared during the 1990s, continuing a trend of recent decades spurred by high levels of immigration from Asian countries. In 1990, 6.9 million non-Hispanic Americans identified themselves as Asian (including Pacific Islanders). By 2000, the number was nearly 10.5 million. Survey data and immigration records indicate that most Asian Americans (about 60 percent) are foreign born, and many settled in the United States after 1980.17 Asian Americans were also less concentrated geographically in the 1990s than they previously had been. While 36 percent live in California, large communities of Asian Americans are now found in Georgia, Pennsylvania, Minnesota, and several other states. This dispersion reflects the settlement of refugee populations that entered the United States in the 1980s and 1990s, and the arrival of new Asian immigrant groups from, for example, India and Pakistan, which do not have the thriving communities that Chinese or Filipino immigrants already had in the United States. Asian Indians were the fastest growing major Asian American group during the 1990s. Among Asian Americans who identified with one race, Asian Indians are now the third-largest U.S. Asian group, after Chinese and Filipinos. They were the fifth-largest group in 1990. Many Asian Indians are settling where they find jobs rather than where there are existing communities of the same ethnic origin. The result, again, is more diversity in the country's heartland and small cities. The African American population increased faster than the non-Hispanic white majority, but lacked the additional push from immigration to keep up with the Hispanic or Asian American growth rates. African Americans remain the predominant minority group in the South, however. Blacks made up 19 percent of the population of the South in 2000, while they made up about 12 percent of the total U.S. population. Although Hispanics outnumbered blacks nationally, Hispanics made up just 12 percent of the South's population in 2000. During the 1990s, record numbers of blacks moved to the South from other regions, which reversed a pattern that prevailed for most of the 20th century. More than 90 percent of African Americans lived in the South in 1900, but the percentage dropped to 53 percent by 1970, reflecting an African American exodus to Northern and Midwestern cities. The 1970s saw a reversal of this trend, and the flow south intensified during the 1990s. Demographers suggest that blacks have been attracted to Southern states by the region's booming economy, attractive life style, improved racial climate, and the historic African American roots.18 ISS315 - PAGE 191 The racial and ethnic diversity of the U.S. population is most evident among children. The 2000 Census found that nearly 40 percent of the population under age 18 was African American, Asian American, Hispanic, American Indian, or another minority, while 61 percent was non-Hispanic white (see Figure 5). One of the big news stories from the 2000 Census was that California is now a "minority majority" state, meaning that non-Hispanic whites make up less than one-half the state population. But minorities already make up more than one-half of the population under age 18 in five states (Arizona, California, Hawaii, New Mexico, and Texas), and in selected counties throughout the country (see Figure 6). In some cases, minority majority counties are clustered around large urban areas; in other cases, these counties identify Indian reservations or nonmetropolitan counties with large black or Hispanic populations. Figure 6 Minority Share of the Population Under Age 18 in U.S. Counties, 2000 Tracking changes among racial and ethnic groups is more difficult with the 2000 Census: This was the first to allow people to mark more than one race. The federal government added this option because of increasing rates of interracial marriage and the growing population that identifies with more than one race, especially among children. Of the 281.4 million people counted in the census, about 6.8 million (2.4 percent) identified with two or more races. About 4 percent of children were identified as multiracial, compared with 2 percent of adults. The multiracial population included 3.2 million people who reported "some other race" in combination with one or more other races. About 41 percent of these respondents were Hispanic. They often use the "some other race" designation to express their nationalities -- for example, Mexican or Salvadoran or Nicaraguan -- which for them have more meaning than the category Hispanic. Those who chose some other race along with white, black, or Asian were the most common multiracial combinations in the 2000 Census. Next to these came white and American Indian and Alaska Native (1.1 million), white and Asian (868,000), white and black (785,000), and black and American Indian and Alaska Native (182,000) (see Figure 7). The new options for answering the race question on the census form have made it difficult to measure the size of racial groups and to track trends over time, especially for groups with high rates of intermarriage. The American Indian and Alaska Native population, for example, could number as low as 2.5 million or as ISS315 - PAGE 192 high as 4.1 million, depending on how the multiracial American Indian population is classified. It is also a challenge to measure the growth or decline of racial groups since 1990. Using the single-race definition, the American Indian and Alaska Native population grew by 26 percent, but under the alternative definition, which combines single-race and multiracial American Indian groups, the population grew by 110 percent. Figure 7 Americans Who Identified With More Than One Race, 2000 Total multiracial Americans = 6,826,228 Source: U.S. Census Bureau, Census 2000 Redistricting Data (PL94-171) Summary file for States Tables PL1. ISS315 - PAGE 193 Metropolitan Growth The vast majority of Americans live in metropolitan areas -- urban counties surrounding a city (or urbanized area) with a population of at least 50,000. The 2000 Census has painted a broad-brush picture of the 276 metro areas that are the center of American society. Eighty percent of the population lives in metropolitan areas, a slight increase over the 1990 share. The metro area population increased by 14 percent between 1990 and 2000, much faster than the population in nonmetropolitan counties, which grew about 10 percent over the decade. Most of the nation's metropolitan areas saw their populations increase in the 1990s, but growth was much faster among metro areas with populations between 1 million and 5 million: They grew 19 percent during the 1990s, while larger and smaller metro areas grew by 11 percent and 12 percent respectively. Table 2 Ten Fastest Growing and Fastest Declining Metropolitan Areas, 19902000 Change 1990 2000 Rank Metropolitan Area 2000 Population (thousands) Number (thousands) Percent Fastest Growth 1 2 3 4 5 6 7 8 9 10 Las Vegas, NV/AZ Naples, FL Yuma, AZ McAllen-Edinburg-Mission, TX Austin-San Marcos, TX Fayetteville-Springdale-Rogers, AR Boise, ID Phoenix-Mesa, AZ Laredo, TX Provo-Orem, UT 1,563 251 160 569 1,250 311 432 3,252 193 369 711 99 53 186 404 100 136 1,013 60 105 83.3 65.3 49.7 48.5 47.7 47.5 46.1 45.3 44.9 39.8 Fastest Decline 267 268 269 270 271 272 273 274 275 Anniston, AL Johnstown, PA Wheeling, WV/OH Alexandria, LA Elmira, NY Pittsfield, MA Binghamton, NY Utica-Rome, NY Grand Forks, ND 112 233 153 126 91 85 252 300 97 -4 -9 -6 -5 -4 -4 -12 -17 -6 -3.3 -3.6 -3.8 -4.0 -4.3 -4.5 -4.6 -5.3 -5.5 -7.4 Metro areas in the West and South grew fastest -- by about 20 276 Steubenville-Weirton, OH/WV 132 -11 percent on average -- while metro areas in the Midwest and Northeast Source: U.S. Census Bureau. Table accessed at www.census.gov/ increased by less than 10 percent. population/cen2000/phc-t3/tab02.xls on April 17, 2001. Las Vegas, Nev., saw its population soar from 853,000 in 1990 to nearly 1.6 million in 2000 (an 83 percent increase). Such major regional hubs as Phoenix, Atlanta, and Denver grew at least 30 percent during the last decade as did emerging areas like Austin, Tex. (see Table 2). The faster growth in the South and West continues overall regional trends evident for the last 40 years that have shifted the U.S. population away from the Midwest and Northeast. In the 1960 Census, for example, the South and West accounted for 46 percent of the U.S. population; by 2000, these two regions accounted for 58 percent. Most of the 24 metropolitan areas that lost population between 1990 and 2000 included smaller, aging cities in the Northeast and Midwest -- Pittsburgh, Pa., Buffalo, N.Y., and Youngstown, Ohio, for example -- that have been losing population for decades. But some metro areas centered on small manufacturing towns in the South -- such as Anniston, Ala. -- also lost population. Counties within the same metropolitan areas grew at varying rates (see Figure 8). ISS315 - PAGE 194 Figure 8 Population Growth in Metropolitan Area Counties, 19902000 What is behind these trends? The 2000 Census data slated for release in 2002 and 2003 will allow more detailed analyses, but the existing data suggest some explanations. First, fast-growing metropolitan areas tended to be "job magnets," assisted by rapid growth in one or more economic sectors. Las Vegas, for example, has continued to emerge as a major entertainment and tourism center; tourists alone pumped $22.5 billion into the local economy in the late 1990s.19 Other fast-growing metropolitan areas -- such as Austin, Tex., Phoenix, and the Research Triangle area of Raleigh-DurhamChapel Hill, N.C. -- were high-tech boom areas.20 Conversely, many slow-growing and declining metropolitan areas suffered recent or long-term economic downturns. The loss of population in the areas surrounding Buffalo, Pittsburgh, and the eastern Ohio cities of Youngstown and Steubenville, for example, continued a trend initiated by losses of manufacturing jobs decades earlier.21 Economic factors are not the only explanation for metro area population change. Several booming metro areas have emerged as retirement Meccas, including Naples and Ocala in Florida; Yuma, Ariz.; Myrtle Beach, S.C.; Wilmington, N.C.; and Las Cruces, N.M.22 International migration also influences metro area population growth. While 2000 Census data on migration will not be released until 2002, an analysis of demographic trends from 1990 to 1998 by demographers William Frey and Ross DeVol suggests that "gateway" metros such as New York, Los Angeles, Miami, and Chicago were top destination choices for immigrants. Many immigration magnets lost native-born residents to other states and metro areas. Among the 10 metros that Frey and DeVol listed as high immigration magnets, only Dallas, Houston, and Miami grew significantly faster than the national average over the 1990s, partly because eight of those 10 (Dallas and Houston were the exceptions) had a net loss of native-born residents.23 Frey, among others, suggests that U.S.-born residents left because the growing immigrant population held down wages. ISS315 - PAGE 195 Patterns of Growth Metropolitan areas usually encompass one or more central city areas surrounded by an inner ring of suburban counties, and often an outer ring of less densely settled suburban counties. In recent decades, central cities have lost population to suburban counties as middle-class families moved out to suburbs under the assumption that the schools were better, neighborhoods safer, and property values more stable. But the 2000 Census documented a surprising population gain in most central cities, especially in newer metropolitan areas, and drew the outlines of at least three general patterns of metropolitan growth: increasing density in the central counties; sprawling, less concentrated urban growth; and a declining urban center, surrounded by slower-growing inner suburbs and faster-growing outer suburbs. Eight of the nation's 10 largest cities (Detroit and Philadelphia were the exceptions) gained population between 1990 and 2000.24 While many smaller cities in the Northeast and Midwest lost population during the 1990s, the declines generally were less steep than expected and less severe than the declines of the 1980s. Baltimore's population total was down 11.5 percent; Cleveland was down 5.4 percent; New Haven, Conn., lost 5.2 percent, and Philadelphia lost 4.3 percent of its population. Other cities in the Northeast and Midwest actually grew, notably Chicago (up 4.0 percent) and New York City (up 9.4 percent). New York City, as defined by five constituent counties, topped 8 million in 2000, just above its previous high count of 7.9 million in 1970. Demographers will know more about who moved into these metro areas after census data on place of birth and migration are released in 2002. But it appears likely that central-city population growth was fueled mainly by increasing numbers of international migrants who offset a continuing exodus of the U.S.-born population out of central cities to surrounding suburbs or to other metro areas and states. Chicago, for example, had fewer black and white non-Hispanic residents in 2000 than in 1990. However, an increase in Hispanic residents, many from abroad, more than offset the losses and allowed the city to grow. Suburbs in many metropolitan areas also gained population from international migration. More international migrants lived in the suburbs than in central cities during the 1990s, according to Census Bureau estimates. Stories about high schools in which dozens of languages are spoken or about hospitals needing emergency-room translators have been familiar fare in newspapers for decades. What changed during the 1990s is that these stories were written about inner suburbs like Wheaton, Md., outside Washington, D.C., or Marietta, Ga., outside Atlanta, rather than traditional immigrant gateways like lower Manhattan or downtown Los Angeles. Three Patterns of Metro Growth The patterns of growth across metropolitan areas are difficult to compare using only the census concepts of "central city" and "suburb." Some city boundaries, especially in the West, are large and still encompass areas of low-density settlement, and some cities like Houston and Indianapolis can annex new land as the metropolitan area grows. But maps of population growth linked with aerial photos or remote-sensing images of land use, confirm that "sprawl" is not a uniform nationwide phenomenon. Population densities and growth rates vary across metropolitan areas, and even within suburbs of metropolitan areas, as illustrated by Kansas City, Atlanta, and Los Angeles. The Kansas City metropolitan area, which includes four counties in Kansas and seven in Missouri, showed a pattern common for slow-growing metropolitan areas in the North and Midwest. The central cities grew little or lost population: Kansas City, Mo., grew 1.5 percent for the decade; Kansas City, Kan., lost almost 2 percent of its population. The inner metropolitan counties grew slowly (less than 10 percent during the decade), while the outlying counties grew rapidly (more than 20 percent). The metropolitan area as a whole grew 12 percent, just below the growth rate for the nation as a whole. Many fast-growing metropolitan areas, especially in the South, followed a more haphazard growth pattern. In this prototypical "sprawl," rapid development occurs at the outer fringes of metropolitan areas, often ISS315 - PAGE 196 leaping over low-density areas and following major highways. The Atlanta metropolitan area exemplifies this pattern as its population surged by 39 percent during the 1990s. Forsyth County, Ga., on the northeast border of the Atlanta metropolitan area, and Henry County, southeast of central Atlanta, more than doubled in population during the 1990s (see Figure 9). Major highways run through both counties. Portions of several of the Atlanta metro area's western and southern counties also saw their populations increase by 50 percent or more, while adjacent areas both closer and further from the center had relatively slow growth. Atlanta's central-city population, in portions of Fulton and DeKalb Counties, grew less than 6 percent. Even this low growth rate was higher than expected -- the city had lost population during the 1970s and 1980s. Figure 9 Population Growth in the Atlanta Metropolitan Area, 19902000 Fast-growing metropolitan areas in the West followed another pattern as their populations became more concentrated during the 1990s. Suburbs in Western metro areas gained population, but did not expand in land area as rapidly as in the South. * Atlanta central city area. Physical geography was one reason these areas became more Source: Created by the Population Reference densely settled -- many Western metropolitan areas are Bureau using data from the 2000 Census. physically limited by coastlines, desert, or mountain ranges. Their outlying areas are seeing more development than in past years, but still cannot support the densities of settlement that "exurbs" in other parts of the country can.25 Changing Neighborhoods Population shifts within metropolitan areas are altering the racial and ethnic makeup of cities and suburbs. In previous decades, metro areas have experienced "white flight": White families would move from centralcity areas to the suburbs, which concentrated minorities and urban poverty in the inner cities. Neighborhoods and schools were highly segregated; central cities were predominantly minority, suburbs were predominantly white. Immigrant groups typically settled in central cities and created unique ethnic communities. Some of this same phenomenon occurred during the 1990s. The non-Hispanic white percentage of the population in the 100 largest cities dipped from 52 percent to 44 percent, according to a recent report from the Brookings Institution.26 But some cities, including Washington, D.C., and Atlanta, saw the non-Hispanic white share of their populations increase. In Washington, this reflected a larger exodus of blacks than whites; but in the city of Atlanta, the increase captured an influx of non-Hispanic whites during the decade. Atlanta was one of a few rapidly growing cities, including Austin, Tex., and Las Vegas, Nev., that saw an increase in their non-Hispanic white population, according to census results and the Brookings Institution study. The 2000 Census data show high levels of racial segregation in residential areas of metropolitan America, and only a slight decline in segregation since 1990. White Americans tend to live in neighborhoods that are overwhelmingly white; minorities live in neighborhoods with other minorities. A recent study shows that the average white American in a metropolitan area lives in a neighborhood that is about 83 percent white and about 7 percent black, 6 percent Hispanic, and 3 percent Asian. In 1990, whites lived in neighborhoods that were 86 percent white. The 2000 Census showed the average black person lives in a neighborhood that is 33 percent white and 51 percent black. Compared with 1990, blacks were more likely to have Hispanic and Asian neighbors in 2000, but they were no more likely to have white ISS315 - PAGE 197 neighbors.27 Asian and Hispanic populations -- which include large numbers of recent immigrants -- were somewhat more isolated from other racial and ethnic groups in 2000 than they were in 1990. Older, large metro areas in the Northeast and Midwest, like Newark, N.J., and Detroit, tend to be the most segregated, while newer, rapidly growing areas in the West and South are least segregated. Metropolitan areas with large military populations, such as Norfolk, Va., and San Diego, also tend to be less segregated. One of the distinctive patterns of the 1990s was the movement of middle-class minority families from cities to suburbs, and in some areas, the emergence of immigrant communities in the suburbs rather than city neighborhoods. This has introduced ethnic and racial diversity into what once were all-white communities. But minorities who move to the suburbs do not necessarily live in integrated neighborhoods. The decade saw an increase in "minority suburbs" in such places as Atlanta and Washington, D.C. In some suburban areas, neighborhoods evolve over time from predominantly white, to mixed white/minority to predominantly minority -- similar to the progression that took place in central city areas in the last half of the 20th century. Washington, D.C., for example, was two-thirds white and about onethird black in 1950. In 2000, the city was two-thirds black and one-fourth non-Hispanic white. In suburban Prince George's County, Md., just across the district line, the population was about 85 percent white in 1970. In 2000, the county's population was 24 percent non-Hispanic white and 63 percent black, reflecting the movement of blacks from the District of Columbia into the county. Herndon, Va., a suburban community in the Washington, D.C. metro area, attracted many immigrants from Asia and South and Central America during the 1990s. Hispanics increased from nearly 10 percent to 26 percent of Herndon's population; and Asian Americans increased from about 8 percent to 14 percent of the population.28 In Atlanta's metro area, minority suburbs have emerged in newly developing areas. Middle-class black families moved from the central city to suburban areas in Clayton County. Between 1990 and 2000, the county population rose by 30 percent, and the percentage of residents who were black more than doubled, from 24 percent to 51 percent. The percentage who were Hispanic rose from 2 percent to more than 7 percent in Clayton county over the decade.29 In contrast, the explosive suburban growth in neighboring Henry County primarily involved white families, although the county also saw an increase in blacks and Hispanics. About 80 percent of Henry County's population was white in 2000, down from 88 percent in 1990. Political Implications The legal requirement to conduct a census is rooted in the American system of representative government. The law requires that the total population for each state, as determined by the census, be given to the president by December 31 of the census year and that this population be used to reallocate the number of seats held by the states in the U.S. House of Representatives. The 2000 Census reapportionment will take effect when the 108th Congress is elected in November 2002 and convenes in 2003. The new census numbers also determine the number of electoral votes each state will wield in the 2004 and 2008 presidential elections. Because the U.S. Constitution grants all states at least one representative and two senators, each state has at least three electoral votes (equal to its number of representatives plus the two senators). The number of electoral votes was fixed at 535 during the apportionment following the 1910 Census; three more were added in 1961 when the District of Columbia was granted three electoral votes (although the District has no voting representation in Congress). The number of votes held by an individual state increases or decreases depending on its relative share of the total U.S. population at each census (see Box 4). ISS315 - PAGE 198 The census figures reported to the president in December 2000 added congressional seats to eight states, and subtracted seats from 10 states (see Figure 10). Arizona, Florida, Georgia, and Texas will each gain two seats in the 108th Congress, while California, Colorado, Nevada, and North Carolina will each pick up one seat. New York and Pennsylvania will lose two seats each, while Connecticut, Illinois, Indiana, Michigan, Mississippi, Ohio, Oklahoma, and Wisconsin will lose one seat each. Figure 10 Electoral Votes by State, 2000 Source: U. S. Census Bureau. Accessed online www.census.gov/population/www/censusdata/apportionment.html on April 21, 2001. The population totals used to reapportion congressional seats include the resident population of each state plus military and U.S. government civilian employees from that state (and their dependents) who are posted overseas. Overseas populations were included in the apportionment population after the 2000, 1990, and 1970 Censuses, but were not included in other census years. If only the current state resident population totals had been used in 2000, North Carolina would not have gained an additional seat -- that seat would have gone to Utah. North Carolina's substantial overseas military population pushed up the state total just enough so it could claim another seat. Utah has contested the result, arguing that if the Census Bureau had included the 11,000 Utah residents serving temporary tours as missionaries overseas, Utah's apportionment population would have been enough so that Utah rather than North Carolina would have gained a seat.30 Even with the expected gains for the South and West, the new apportionment numbers contained some surprises. Apportionment projections based on 1999 Census Bureau estimates had indicated that Florida and Georgia would each gain only one seat, while Indiana, Michigan, and North Carolina would remain the same. Those projections also mistakenly predicted that Montana would regain the second House seat that it had lost in the 1990 apportionment. Because the states vary tremendously in population size -- from just under 500,000 in Wyoming to nearly 34 million in California -- and the total number of House seats stays at 435, this system guarantees a wide disparity among states in how many people each state delegation represents. Montana has one member in the U.S. House of Representatives, who represents 902,000 state residents, for example, while Wyoming's one member represents 494,000 people. Under the new apportionment, New York has 29 members, or one for every 654,000 New York residents. This disparity among states will be reflected in the 108th Congress when it convenes in 2003. ISS315 - PAGE 199 How will the 2000 Census affect the U.S. political scene in the next decade, and what are some of the political implications of the population changes captured by the census? In general, states like New York and Pennsylvania that have gone for Democrats in recent presidential elections lost ground, while states like Texas and Arizona that have tended to support Republican presidential candidates in recent elections gained electoral votes. This shift reflects the general population movement to Southern, Western, and Mountain states -- from abroad and from Midwestern and Northeastern states. The 2000 Census results reaffirm these trends. The increasing percentage of the U.S. population in states that were Republican-leaning in the 1990s, however, does not guarantee a long-term dominance of the Republicans or any other party. The population moving into these high-growth states may not have the same political leanings as the population already living there. The political future is especially uncertain in states that are gaining large immigrant populations. Immigrants tend not to vote: Most recent immigrants are not citizens and many are young, poor, and have little formal education -- population groups that have low rates of voter turnout.31 But over time, some become politically involved: They may naturalize, vote, and even run for office. Recent legal changes have accelerated the transition in some areas. The number of immigrants seeking U.S. citizenship surged in the late 1990s, in part because of changes in the procedures for obtaining immigrant visas and because welfare and other public services for noncitizens were limited by the Personal Responsibility and Work Opportunity Reconciliation Act of 1996.32 In New York City, this increase in the number of naturalized citizens coincided with the imposition of term limits that forced out two-thirds of the incumbent city council members, which created opportunities for political newcomers. At least a dozen foreign-born New Yorkers ran for city council in 2001.33 The U.S.born children of immigrants -- citizens by birth -- are much more likely than their parents to participate in elections, which could shift the political balance in some areas. The major political parties are well aware of the potential for political support or opposition from the growing Hispanic population. Hispanics are underrepresented in the U.S. Congress: They are 12 percent of the U.S. population, yet hold just 4 percent (19 voting seats) in Congress in 2001. African Americans, also about 12 percent of the national population, hold 36 seats. Asian and Pacific Islanders hold 6 voting seats, while non-Hispanic whites occupy 472 seats.34 Hispanics have long been prominent in the political parties in states like California, Florida, New Mexico, and Texas, however, and their influence is expected to increase elsewhere. During the 1990s, the Hispanic population doubled in many states, including Iowa, North Carolina, and Oregon, and rose sharply in fast-growing states like Georgia. Political candidates in Hispanic districts may increasingly champion issues of special concern to Hispanics, such as combating high school dropout rates and providing health care for uninsured workers.35 African Americans make up nearly the same percentage of the U.S. population as Hispanics, but they make up a larger percentage of the population that is eligible to vote. And African Americans are bucking the national decline in voter turnout. In the last two national elections, the percentage of blacks who voted has increased or held steady, while the percentage has slipped for whites.36 Blacks have not been particularly successful at winning elected office in majority white districts, but they wield considerable power in many states and metropolitan areas. In the November 2000 presidential election, the black share of the vote was greater than their share of the voting-age population in five states: Florida, Mississippi, Missouri, Tennessee, and Texas. Although Asian Americans remain a relatively small proportion of the U.S. population, they have become increasingly active politically. Even more than blacks and Hispanics, Asian Americans must gain the support of other racial and ethnic groups to win elected office, but several have achieved this.37 Political analysts have noted that coalitions among minority voters can sway an election even when the size of a specific population is relatively small. In 1998, black, Hispanic, and Asian coalitions were instrumental to the U.S. Senate race in New York and the governor's race in the California, and a coalition may be emerging among Hispanics and blacks in New York City.38 ISS315 - PAGE 200 TABLE 3: If you want data on: The growing share of minorities in the U.S. population -- and their increasing involvement in politics -- all but guarantees to raise their visibility on the political landscape. The major political parties are positioning themselves to woo more of the minority vote. Recently, one analyst calculated that if George W. Bush wins the same percentage of the minority vote in 2004 as he did in 2000, he would lose the popular vote by 3 million votes because minorities will account for a larger share of the voting-age population in 2004.39 The rate of minority participation in elections will be increasingly important for candidates' success. Race and ethnicity for the total population and the 18+ population Population Housing Age Family structure/household relationship Gender Hispanic origin Race Tenure (whether home is owned or rented) Vacancy characteristics More from the 2000 Census The flurry of news stories generated by each new release of data from the 2000 Census is unparalleled. The news value stems from the surprises and mysteries associated with the census, but it also reflects a more sophisticated approach by the news media and the easier availability of information through the Internet and other technologies (see Box 5). A wealth of additional and even more detailed information about the American people -- some of it down to the census-tract level -- will be released over the next few years (see Table 3). What will the legacy of the 2000 Census be? Kenneth Prewitt, director of the Census Bureau during the 2000 enumeration, cites the new approach to classifying race as a watershed event in 2000 (see Box 6). Legal battles that are brewing over adjustment, coverage, and redistricting may lead to precedent-setting court rulings. And the press attention and easy accessibility of census data may well expand the use of these data far beyond the traditional business, academic, and public consumers. The above population and housing characteristics for many detailed race and Hispanic categories, American Indian and Alaska Native tribes, and ancestry groups Sample Characteristics (questions asked of long-form recipients) Population Ancestry Citizenship Disability Educational attainment and school enrollment Grandparents as primary caregivers Income and poverty Labor force status Language ability Marital status Migration Occupation/industry Place of birth Place of work/journey to work Veteran status Housing Farm residence Heating fuel Plumbing and kitchen facilities Number of rooms and bedrooms Telephone service Units in structure Utilities, mortgage, taxes, insurance costs Value of home/rent Vehicles available Year structure built The above population and housing characteristics for many detailed race and Hispanic categories, American Indian and Alaska Native tribes, and ancestry groups ISS315 - PAGE 201 Figure 10 Electoral Votes by State, 2000 Source: U. S. Census Bureau. Accessed online www.census.gov/population/www/censusdata/apportionment.html on April 21, 2001. ISS315 - PAGE 202 Box 5 Return to text Media Coverage of the 2000 Census Steve Doig, Professor and Knight Chair in Journalism, Walter Cronkite School of Journalism and Telecommunication, Arizona State University, was interviewed about 2000 Census media coverage by the Population Reference Bureau's Bingham Kennedy, Jr., on April 17, 2001. PRB: How does the media coverage of the 2000 Census compare with coverage of previous ones? Doig: There's been much wider interest among the media in the 2000 Census. I was very familiar with the 1990 Census, which I covered while working at the Miami Herald. Back then, I thought there would be widespread competitive rush to get the good stories coming out of the census, but there wasn't in 1990. The only news organization besides Knight-Ridder papers like the Miami Herald that was aggressive about using the 1990 Census data was USA Today, with its interest in demographics and so on. I finally realized that part of the reason was that the computer revolution was just getting underway in news organizations. There weren't that many people who were proficient at using computers, the equipment was much harder to work with, the software required more programming skills, and things like that. So there were maybe half a dozen reporters who were actually ready to do big stories on the census back then. ...There are now hundreds of news organizations -- not just newspapers, but TV and radio stations, too -- that are doing major census stories. Several things happened to spark such widespread interest. One, computer-assisted reporting has really spread across the industry: The idea of reporters using social science methods to do much more precise stories is much more of a reality these days. Also, the equipment is much easier to work with -- back in 1990, I had to use a mainframe computer to work with census data. ...The Census Bureau has really gone out of its way to make its data more accessible this time. Not only have they put it on the Internet with American FactFinder, which makes it easy for even very small papers to work with the data, but you can also get the bulk data on CD-ROM. At the same time, we reached a critical mass of computer-assisted reporting in newsrooms around the country. Given the competitive nature of reporters, once it became apparent that the census was a story that some of us were interested in, nobody else dared ignore it. And so everyone said, "Oh my god, we have to be ready!" There was also the realization that, because of the heightened skills, that everybody would have to do it on deadline, too. So there's really been an incredible improvement in the quality of the stories that can be done with each wave of data ... . The Los Angeles Times had an eight-page special section that was out the morning after the California data came out. You have to be in the news business to appreciate what a testament that is to planning, flexibility, and commitment by the higher management that this is an important story. Not every news organization can match that kind of production, but papers of all kinds of sizes have made really wonderful use of the information. PRB: Where do you think there is room for improvement in the media's coverage of the census? Doig: It will be interesting to see how well they learn to go back to the data when other stories come along. Right now, the data is coming out, and we're treating that as an event -- pulling stories out of it and telling those stories. I think the best use of the data will be when other kinds of stories need to be told, and the reporters hopefully will be familiar enough with the demographic data that they will see it as another source, another way of adding precision to all those other stories. Another thing that journalists certainly can do better is that we can learn more about some of the good statistical tools that academic researchers use. We won't turn journalists into academics, but greater use of these statistical tools can help keep the media from drawing conclusions that aren't there and other pitfalls that journalists sometimes fall prey to when you need to tell a story right away. PRB: What kinds of stories do you think can really benefit from greater use of census data? Doig: Any of the issues that revolve around change, whether its growth, ethnic tensions, education, whatever. Journalists have known forever how to tell those stories anecdotally ... but it really adds credibility to the story to show that the specific examples are not isolated instances but part of a broader pattern. If we can show that the pattern is there using census data, it really adds credibility to the journalism. PRB: What are the main difficulties that remain for journalists in reporting census data? Doig: The Census Bureau in releasing the data isn't really thinking of journalists as their primary audience -- and no one expects them to think of journalists that way -- so it is often difficult to do a story on very short notice on data that is coming out on any given day. There are also a variety of technical problems in dealing with census data. Once you get to the sample data, for example, you have to make sure you understand things like error margins. You don't want to make a big thing out of a small thing that is actually within the margin of error. One other thing to keep in mind is that there are very few journalists today who make use of the full-range of Census Bureau products. There are a lot of information products that come out between censuses, such as the Current Population Survey or Census of Agriculture. These are every bit as rich in information as the census about the communities that we cover, but most journalists are only dimly aware that these things exist, if they know about them at all. Maybe one of the good things about all this coverage of the 2000 Census, and the interest that readers have shown in it, is that smart reporters will start turning their skills in computer-assisted reporting to making better use of these other products and more complicated data. ISS315 - PAGE 203 CONFLICTS AND WARS 1. Refugee Children in Africa 2. Defense Expenditures, Armed Forces, Refugees, and the Arms Trade 3. Armed Conflicts: 1990-1996 4. U.S. Military Spending: Fact Sheet 5. How many people did we lose in wars? ISS315 - PAGE 204 REFUGEE CHILDREN IN AFRICA TRENDS AND PATTERNS IN THE REFUGEE POPULATION IN AFRICA BELOW THE AGE OF 18 YEARS, 2000 POPULATION DATA UNIT POPULATION AND GEOGRAPHIC DATA SECTION UNITED NATIONS HIGH COMMISSIONER FOR REFUGEES GENEVA, JUNE 2001 HTTP://WWW.UNHCR.CH HQCS00@UNHCR.CH ISS315 - PAGE 205 INTRODUCTION 1. The information contained in this note is based on the annual statistical survey on asylum-seekers, refugees and other persons of concern, carried out by the United Nations High Commissioner for Refugees (UNHCR) at the end of 2000. The survey uses both Government and UNHCR sources. In Africa, where UNHCR carries out many of its assistance programmes, the Office is generally closely associated with the refugee data collection process. Refugees are persons who are recognized under the 1951 United Nations Convention relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa or in accordance with the UNHCR Statute. It should be stressed, however, that refugee registration and data collection practices vary significantly between and within countries, populations and locations. This note also covers selected groups, which are not refugees, but considered of concern to UNHCR. At the end of 2000, the total refugee population in Africa was estimated at some 3.6 million refugees. Statistical information on the demographic composition of the refugee population is available for some 2.5 million refugees or 79 per cent of African refugee population. As this note analyzes the available data on the gender and age of refugees, the total refugee population in asylum countries as reported in this note may not necessarily reflect the official refugee population in these countries. The provision and targeting of protection and (or) assistance activities to refugee individuals and households requires detailed registration, including information ong ender and age. Therefore, most of the refugee populations covered in this note are assisted by or through UNHCR on a regular basis. In accordance with the provisions of 1989 United Nations Convention on the Rights of the Child, this note defines children as persons below the age of 18 years. MAIN OBSERVATIONS 2. 3. 4. 5. 6. Refugee children under the age of 18 constitute 56 per cent of all refugees in Africa (Table 1) The percentage of refugees under the age of 19 varies little between the main geographical regions. In Western Africa, refugees appear to be slightly younger (60%) than in other regions of the continent. Conversely, Southern Africa hosts the smallest proportion of refugee children (37%), mainly as a result of the small proportion of refugee children reported for South Africa (20%). Countries of asylum where the proportion of refugee children exceeds 60 per cent include Angola (69%), Togo (64%), Guinea (63%), Burundi (62%), Rwanda (61%), the Democratic Republic of the Congo (61%) and Sudan (60%). 7. ISS315 - PAGE 206 8. In Benin, Cameroon, Mauritania, Niger, Nigeria, Tunisia and South Africa, children constitute less than 30 per cent of the refugee population. In these countries, the refugee population (for which demographic information was available tends to live in urban areas. As opposed to refugees living in camps (see Table 3), urban asylum-seekers and refugees tend to live in relatively small households with few children. Half (49.6%) of all refugee children in Africa are female. In three out of every four countries listed in Table 1, the proportion of female refugees under the age of 18 varies between 45 and 55 per cent. These findings reflect the gender composition of refugee populations of all age groups, which was found to 50.5 per cent in 40 main asylum countries located in all world regions. Refugees originating from Western Africa tend to have the highest proportion of children (60%). The proportion of children is 60 percent or more among refugees from Eritrea (60%), Ghana (64%), Mauritania (66%) and Sierra Leone (61%) (See Table 2). Some 57 per cent of the refugees living in the 30 main camps and centers are aged below 18. There is very little variation in the proportion of refugee children who live in camps. Indeed, the percentage children was more than 50 per cent in all major locations considered here, covering almost 2 million refugees. The proportion of refugee children exceeded 60 per cent in five location in Guinea, Sudan and Zambia. In 28 of the 30 locations listed in Table 3, female refugee children constituted between 45 to 55 per cent of all refugee children. 9. 10. 11. 12. ISS315 - PAGE 207 Table 1. Refugee Population aged under 18 by country of asylum, end-2000 Including selected groups which are not refugees, but considered of concern to UNHCR Excluding refugees for which no demographic information is available *Demographic breakdown available for less than 10 per cent of the population Population under the age of 18 UN region / Country of asylum Burundi Djibouti Eritrea Ethiopia Kenya Malawi Mozambique Rwanda Somalia Uganda United Rep. of Tanzania Zambia Zimbabwe Eastern Africa Angola Cameroon* Central African Rep. Chad Congo* Dem. Rep. of the Congo Gabon Middle Africa Algeria Egypt Libyan Arab Jamahiriya Morocco Sudan Tunisia Northern Africa Botswana Namibia South Africa Swaziland Southern Africa Benin Burkina Faso Cte d'Ivoire Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo Western Africa Total Africa Notes Female 8,700 6,170 480 50,700 48,600 860 130 9,620 40 58,700 139,700 35,700 460 359,860 4,300 350 14,300 3,290 850 20,600 4,330 48,020 42,300 1,100 450 .. 47,100 20 90,970 650 4,770 1,760 200 7,380 230 60 39,900 2,510 2,660 135,900 1,760 8,430 620 40 60 420 7,990 .. 4,280 204,860 711,090 Male 8,090 5,240 580 52,800 58,800 1,060 120 8,720 60 64,000 142,900 35,500 570 378,440 4,080 380 16,100 6,150 1,050 22,100 4,070 5,330 44,700 1,230 330 .. 48,400 20 94,680 700 5,030 2,020 200 7,950 250 180 31,200 2,370 2,630 131,500 1,870 8,350 590 30 60 520 5,390 .. 3,320 188,260 723,260 Total 16,790 11,410 1,060 103,500 107,400 1,920 250 18,340 100 122,700 282,600 71,200 1,030 738,300 8,380 730 30,400 9,440 1,900 42,700 8,400 101,950 87,000 2,330 780 95,500 40 185,650 1,350 9,800 3,780 400 15,330 480 240 71,100 4,880 5,290 267,400 3,630 16,780 1,210 70 120 940 13,380 7,600 393,120 1,434,350 % female 51.8 54.1 45.3 49.0 45.3 44.8 52.0 52.5 40.0 47.8 49.4 50.1 44.7 48.7 51.3 47.9 47.0 34.9 44.7 48.2 51.5 47.1 48.6 47.2 57.7 .. 49.3 50.0 49.0 48.1 48.7 46.6 50.0 48.1 47.9 25.0 56.1 51.4 50.3 50.8 48.5 50.2 51.2 57.1 50.0 44.7 59.7 .. 56.3 52.1 49.6 Total population 27,100 21,100 1,980 191,600 206,100 3,900 830 30,100 180 219,000 511,000 129,400 4,130 1,346,420 12,100 4,030 55,700 17,700 3,630 70,600 1,000 184,760 155,400 6,840 2,020 60 158,700 180 33,200 3,540 17,700 18,700 1,010 40,950 1,890 700 122,900 12,000 12,700 46,100 7,590 33,800 3,220 350 500 4,320 22,700 11,900 660,670 2,556,000 % under 18 In total population 62.0 54.1 53.5 54.0 52.1 49.2 30.1 60.9 55.6 56.0 55.3 55.0 24.9 54.8 69.3 18.1 54.6 53.3 52.3 60.5 40.0 55.2 56.0 34.1 38.6 .. 60.2 22.2 57.4 38.1 55.4 20.2 39.6 37.4 25.4 34.3 57.9 40.7 41.7 62.8 47.8 49.6 37.6 20.0 24.0 21.8 59.9 .. 63.9 59.5 56.1 Data are provisional and subject to change. Two dots (..) indicate that no data are available. Source: UNHCR, Population Statistics 2000 (Provisional), Geneva, 11 April 2001 (http://www.unhcr.ch). ISS315 - PAGE 208 Table 2. Refugee Population aged under 18 by origin, end-2000 Including selected groups which are not refugees, but considered of concern to UNHCR Excluding refugees for which no demographic information is available *Demographic breakdown available for less than 10 per cent of the population Population under the age of 18 UN region / origin Burundi Eritrea Ethiopia Kenya Rwanda Somalia Uganda Eastern Africa Angola Cameroon Central African Rep. Chad Congo Dem. Rep. of the Equatorial Guinea Middle Africa Algeria Sudan Western Sahara Northern Africa Namibia Southern Africa Ghana Guinea-Bissau Liberia Mauritania Nigeria Senegal Sierra Leone Togo Western Africa Total Africa Notes Female 100,500 44,200 4,190 20 13,400 85,600 1,430 249,340 21,500 40 10 1,320 4,880 72,600 30 100,380 20 99,200 42,300 141,520 370 370 4,100 77,800 7,900 50 2,810 110,500 200 203,360 694,970 Male 102,100 45,300 4,490 40 14,100 89,100 1,400 256,530 21,700 30 10 1,430 4,700 72,600 20 100,490 20 116,300 44,800 161,120 400 400 3,410 20 69,200 5,260 80 2,610 106,200 210 186,720 705,260 Total 202,600 89,500 8,680 60 27,500 174,700 2,830 505,870 43,200 70 20 2,750 9,580 145,200 50 200,870 40 215,500 87,100 302,640 770 770 7,240 20 147,000 13,160 130 5,420 216,700 410 390,080 1,400,230 Total population % under 18 In total population 54.1 60.3 45.5 18.3 49.8 51.3 43.4 53.5 58.0 17.2 33.9 49.3 40.8 57.7 32.3 56.5 17.9 55.0 55.8 55.2 33.6 33.6 64.0 3.4 59.2 65.7 21.4 50.1 60.5 29.4 59.9 55.9 % female 49.6 49.4 48.3 33.3 48.7 49.0 50.5 49.3 49.8 57.1 50.0 48.0 50.9 50.0 60.0 50.0 50.0 46.0 48.6 46.8 48.1 48.1 56.6 52.9 60.0 38.5 51.8 51.0 48.8 52.1 49.6 374,700 148,400 19,100 330 55,200 340,500 6,520 944,750 74,500 410 60 5,570 23,500 251,500 160 355,700 220 392,100 156,200 548,520 2,300 2,300 11,300 590 248,500 20,000 610 10,800 358,400 1,400 651,600 2,502,870 Data are provisional and subject to change. Two dots (..) indicate that no data are available. Source: UNHCR, Population Statistics 2000 (Provisional), Geneva, 11 April 2001 (http://www.unhcr.ch). ISS315 - PAGE 209 Table 3. Refugee Population aged under 18 by main refugee camp/center, end-2000* Including selected groups which are not refugees, but considered of concern to UNHCR Excluding refugees for which no demographic information is available Name of camp/center* Country of asylum Population under the age of 18 Female Male 110,800 44,700 31,700 30,000 29,300 20,300 20,600 17,100 17,200 15,900 15,600 15,800 13,300 13,000 13,200 13,200 13,000 11,200 11,800 9,760 10,300 10,200 10,600 10,300 8,890 8,670 6,520 6,780 4,810 5,130 549,660 Total 226,200 87,000 64,200 68,600 58,000 39,300 35,200 33,600 33,500 31,400 28,700 31,000 26,500 25,900 26,800 24,600 25,000 22,300 24,700 19,550 18,760 19,220 20,800 19,430 17,200 16,210 12,720 13,510 9,870 10,240 1,090,01 Total population % < 18 In total population Gueckedou Tindouf Jijiga Danane/Tabou Lukole-1 Adjumani Kakuma Mtambira Lugufu Nyarugusu Gambela Nzerekore Meheba Karago Nduta Ifo (Dadaab) Hagadera (Dadaab) Mtendeli Wad Sherife Muyovosi Mboki Dagahaley (Dadeaab) Shagrab Rhino camp Moyo Kitgum Lukole-2 Mwange Montserrado Kane Mbwa Total Notes Guinea Algeria Ethiopia Cte d'Ivoire United Rep. of Tanzania Uganda Kenya United Rep. of Tanzania United Rep. of Tanzania United Rep. of Tanzania Ethiopia Guinea Zambia United Rep. of Tanzania United Rep. of Tanzania Kenya Kenya United Rep. of Tanzania Sudan United Rep. of Tanzania Central African Rep. Kenya Sudan Uganda Uganda Uganda United Rep. of Tanzania Zambia Liberia United Rep. of Tanzania 115,400 42,300 32,500 38,600 28,700 19,000 14,600 16,500 16,300 15,500 13,100 15,200 13,200 12,900 13,600 11,400 12,000 11,100 12,900 9,790 8,460 9,020 10,200 9,130 8,310 7,540 6,200 6,730 5,060 5,110 540,350 % female 51.0 48.6 50.6 56.3 49.5 48.3 41.5 49.1 48.7 49.4 45.6 49.0 49.8 49.8 50.7 46.3 48.0 49.8 52.2 50.1 45.1 46.9 49.0 47.0 48.3 46.5 48.7 49.8 51.3 49.9 49.6 359,100 155,400 120,900 117,700 108,100 73,500 69,700 57,700 57,400 53,000 51,800 50,000 49,800 49,500 49,000 47,100 46,300 42,200 40,300 35,800 35,500 34,600 34,200 33,1400 31,200 25,700 24,400 22,000 18,500 18,300 1,911,800 63.0 56.0 53.1 58.3 53.6 53.5 50.4 58.3 58.2 59.3 55.4 62.0 53.2 52.4 54.6 52.3 54.1 52.9 61.2 54.7 53.0 55.5 60.8 58.5 55.2 63.1 52.2 61.5 53.5 56.0 57.0 Data are provisional and subject to change. Two dots (..) indicate that no data are available. Source: UNHCR, Population Statistics 2000 (Provisional), Geneva, 11 April 2001 (http://www.unhcr.ch). ISS315 - PAGE 210 Defense Expenditures, Armed Forces, Refugees, and the Arms Trade Page 1 of 4 DEFENSE NUMBERS IN REFUGEE EXPORTS EXPENDITURES ARMED FORCES POPULATION ($US millions) ($US millions) (in thousands) WORLD AFRICA Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Congo Djibouti Egypt Equatorial Guinea Ethiopia Eritrea Gabon Gambia Ghana Guinea Guinea-Bissau Ivory Coast Kenya Lesotho Liberia Libya Madagascar Malawai Mali Mauritania Mauritius Morocco Mozambique Namibia Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Zaire Zambia Zimbabwe ASIA Afghanistan Armenia Azerbaijan Bangladesh X X 1,300 1,117 33 199 104 25 102 30 74 110 26 3,582 3 140 12 154 14 30 50 9 140 136 4 14 1,400 29 10 66 33 11 1,380 84 64 32 172 112 82 14 46 2,900 313 22 69 48 535 56 46 96 236 245 75 43 481 24,908.1 2,098.3 121.7 82.0 4.4 4.5 8.7 7.2 11.7 6.5 17.0 8.8 3.8 436.0 1.3 320.0 12.3 4.8 0.9 12.2 9.7 9.2 7.1 24.2 2.0 7.8 80.0 21.0 7.3 7.3 9.6 7.2 195.5 34.8 9.0 3.3. 77.1 5.2 9.7 6.2 64.5 136.9 118.5 25.4 49.1 5.9 35.0 70.0 51.0 16.2 45.0 45.0 60.0 86.7 115.5 17,556,900 5,698,450 210,000 9,000 4,300 500 6,300 107,350 1,500 18,000 7,800 9,400 96,500 10,650 5,600 416,000 246,000 200 3,300 12,100 485,000 12,000 195,500 422,900 200 100,000 15,000 4,000 1,070,000 10,000 40,000 900 800 250 150 3,600 2,900 24,500 55,100 7,600 10,000 250,000 750,500 52,500 257,800 350 500 179,600 442,400 155,700 265,000 52,000 300,000 246,00 245,300 48,640 110 0 0 0 0 0 0 0 0 0 0 X 170 0 0 X 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 X 0 0 0 0 0 0 60 0 0 0 0 0 0 0 0 0 0 X X 0 IMPORTS ($US millions) 48,610 4,870 825 1,600 10 30 10 10 5 0 40 20 X 725 0 725 X 5 10 10 20 30 0 160 0 10 575 10 0 70 10 0 90 160 X 0 150 0 10 0 30 0 60 0 70 0 20 80 10 0 0 X X X 80 http://www.dushkin.com/connectext/wpold/ch10/table3bot.html ISS315 - PAGE 211 8/6/2004 Defense Expenditures, Armed Forces, Refugees, and the Arms Trade Page 2 of 4 Bhutan Cambodia China Georgia India Indonesia Iran Iraq Israel Japan Jordan Kazakhstan Korea,North Korea,South Kuwait Kyrgyzstan Laos Lebanon Malaysia Mongolia Myanmar Nepal Oman Pakistan Philippines Saudi Arabia Singapore Sri Lanka Syria Taiwan Tajikistan Thailand Turkey Turkmenistan United Arab Emirates Uzbekistan Vietnam Yemen NORTH AND MIDDLE AMERICA Belize Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago United States SOUTH AMERICA Argentina Bolivia Brazil Chile Colombia X 85 12,025 65 8,000 2,700 4,270 X 9,200 50,200 589 549 5,980 17,359 3,559 438 105 278 2,400 23 135 36 1,820 3,100 1,000 12,100 3,900 640 1,050 1,220 68 4,040 13,010 33 1,590 47 544 910 + 8 9,000 55 696 116 100 130 21 41 30 2,240 281 78 83 272,200 4,700 145 6,376 970 2,003 X 88.5 2,930.0 29.0 1,145.0 274.0 513.0 382.5 172.0 239.5 98.6 40.0 1,128.0 633.0 16.6 7.0 37.0 44.3 114.5 21.1 286.0 35.0 35.7 587.0 106.5 105.5 53.9 125.3 423.0 376.0 55.0 259.0 579.2 11.0 70.0 25.0 572.0 39.5 + 0.8 70.5 12.3 105.0 22.2 30.5 44.6 7.4 17.5 3.4 175.0 14.7 11.7 2.7 1,913.8 67.3 31.5 295.0 99.0 146.4 X 10,000 12,500 13,000 378,000 15,600 2,781,800 64,600 84,0002 700 1,010,850 300 240 150 13,500 350 12,000 322,900 16,700 570 11,500 89,400 100 1,577,000 5,600 27,400 100 2,540 307,500 5,000 3,500 255,500 31,700 2,750 400 15,000 19,000 52,500 + 8,700 37,700 34,350 1,100 2,440 250 4,900 100 150 1,200 47,300 5,850 850 300 103,700 300 500 200 350 400 X 0 3,100 X 0 0 0 80 140 70 40 X 470 50 0 X 0 0 0 0 0 0 0 10 0 5 10 0 0 490 X 0 0 X 0 3 70 X + X 180 0 230 0 0 0 0 0 0 0 0 0 0 14,300 30 0 380 280 0 X 240 270 X 3,200 130 2,000 4,600 1,900 1,100 320 X 1,000 600 190 X 150 10 30 0 30 0 30 340 60 3,000 310 20 1,300 420 X 525 775 X 60 50 1,500 X + X 210 0 1,700 5 60 5 0 40 0 60 525 10 0 725 20 10 250 30 60 http://www.dushkin.com/connectext/wpold/ch10/table3bot.html ISS315 - PAGE 212 8/6/2004 Defense Expenditures, Armed Forces, Refugees, and the Arms Trade Page 3 of 4 Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela EUROPE Albania Austria Belarus Belgium Bosnia-Herzegovina Bulgaria Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Macedonia Moldova Netherlands Norway Poland Portugal Romania Russia Slovakia Slovenia Spain Sweden Switzerland Ukraine United Kingdom Yugoslavia OCEANIA Australia Fiji New Zealand Papua New Guinea Solomon Islands 386 7 94 998 16 256 902 45 2,100 492 4,600 405 352 337 931 3,200 35 1,900 47,700 42,800 4,900 620 0 618 20,400 56 32 47 48 8,200 3,700 2,400 1,900 885 72,000 430 81 6,300 5,800 3,740 3,140 35,100 1,030 4,210 23 423 40 X 57.1 2.0 16.0 115.0 3.0 25.6 79.0 73.0 55.7 98.4 47.2 92.0 101.9 105.0 86.4 33.1 11.4 31.1 409.0 339.9 171.3 70.5 0.0 12.9 328.7 11.0 9.7 13.4 11.8 74.4 30.0 278.6 54.2 217.4 1,520.0 47.0 12.3 206.0 64.0 35.0 452.5 236.9 126.5 56.1 5.0 10.0 3.5 X 200 100 50 400 100 100 1,350 1,870 82,100 3,700 100 5,050 1,200 7,800 2,200 13,900 850 3,500 29,400 536,000 1,900 40,000 0 200 19,100 1,400 600 5,000 1,560 24,600 5,700 1,500 1,000 3,000 14,500 1,000 890 12,700 88,400 81,700 1,600 24,600 621,000 24,000 0 300 3,800 0 0 0 0 0 0 0 0 0 60 X X X 380 X 760 0 X 10 1,890 X 30 160 0 0 390 X X X X 525 10 675 110 150 18,400 270 X 150 210 110 X 725 200 40 0 5 0 X 40 0 30 30 0 5 60 0 1,300 X X X 400 X 190 200 X 70 140 X 575 60 0 10 270 X X X X 410 230 1000 50 20 1,000 40 X 900 150 90 X 625 40 70 0 80 30 X 1 Only countries for which data are available are listed. 2 Excludes figures from the West Bank and the Gaza Strip. Sources: International Institute for Strategic Studies, The Military Balance (London, 1996); U.S. Arms Control and Disarmament Agency, World Military Expenditures and Arms Transfers (Washington, D.C., 1993); U.S. Committee for Refugees, World Refugee Survey (Washington, D. C., 1996); http://www.dushkin.com/connectext/wpold/ch10/table3bot.html ISS315 - PAGE 213 8/6/2004 Armed Conflict, 1990-1996 COUNTRY TYPE OF WAR LOCATION OF WAR ADVERSARIES DATE WAR (in interstate BEGAN wars) 1978 1996 1992 1975 1978 Azerbaijan 1990 Armenian 1990 1973 1992 1988 1970 1996 1965 1986 1993 Yugoslavia (SerbiaMontenegro) 1991 1995 Peru 1991 1995 1992 1979 Ethiopia Eritrea 1962 1962 1974 1991 1991 1991 1992 1994 1968 1991 1982 1990 1969 1981 1987 1963 1975 COMBAT STATUS (1/1/1997) continuing continuing continuing continuing continuing suspended by agreement 1994 suspended by agreement 1994 suspended by agreement 1992 suspended by agreement 1995 continuing continuing continuing continuing continuing suspended by agreement 1994 suspended by agreement 1992 suspended by agreement 1995 continuing suspended by agreement 1995 continuing suspended by agreement 1979 suspended by agreement 1991 suspended by agreement 1991 suspended by agreement 1991 suspended by agreement 1991 break in action 1993 suspended by agreement 1996 suspended by agreement 1993 break in action 1995 continuing break in action 1991 break in action 1992 continuing continuing break in action 1993 continuing continuing continuing Afghanistan Albania Algeria Angola Armenia Azerbaijan Bangladesh BosniaHerzegovina Burundi Cambodia Cameroon Chad Colombia Congo Croatia civil war civil war civil war civil war regional civil war interstate war interstate war general southern regions general general Cabinda enclave Nagorno-Karabakh Nagorno-Karabakh regional civil war Chittagong civil and interstate war civil war civil war interstate war civil war civil war civil war general general general Bakassi border region general general general Nigeria civil & interstate Slavonia/Krajina war regional civil war Western Slavonia/Krajina regional civil war Afar interstate war border region civil war civil war war of independence against war of independence civil war interstate war general general Eritrea Eritrea general Kuwait/Iraq Djibouti Ecuador Egypt El Salvador Eritrea Ethiopia France Georgia regional civil war western region regional civil war South Ossetia regional civil war Abkhazia Ghana Guatemala Haiti India Indonesia regional civil war civil war civil war interstate war regional civil war regional civil war regional civil war regional civil war regional civil war regional civil war northern regions general general Kashmir Kashmir Andhra Pradesh Punjab Assam Irian Jaya East Timor Pakistan ISS315 - PAGE 214 Iran Iraq regional civil war Sumatra civil war general regional civil war northwestern Kurdish regions regional civil war northern regions/Kurdistan interstate war Iraq/Kuwait 1989 1978 1979 1974 Kuwait,France, 1990 Saudi Arabia, Syria, United Kingdom, United States 1991 1948 1991 1993 1990 1975 1975 1989 1995 1990 Senegal 1989 1994 1991 Polisario Front 1975 (western Sahara) 1976 1948 1948 1949 1991 1992 1992 1970 1991 1994 1996 1982 1992 1988 1980 1995 1969 1974 1992 break in action 1994 break in action 1993 break in action 1995 continuing suspended by agreement 1991 Israel Kurdistan Kuwait Laos Lebanon Liberia Libya Mali Mauritania Mexico Moldova Morocco Mozambique Myanmar (Burma) regional civil war southern Shia regions civil war general, including occupied territories regional civil war Turkish border region civil war general interstate war Kuwait/Iraq civil war general,then regional civil war civil war civil war regional civil war interstate war general southern zone, from 1990 general general northern Tuareg regions border regions continuing continuing continuing continuing suspended by agreement 1991 break in action 1990 continuing continuing continuing suspended by agreement 1995 suspended by agreement 1991 suspended by agreement 1995 suspended by agreement 1992 break in action 1991 suspended by agreement 1992 suspended by agreement 1994 continuing continuing break in action 1992 suspended by agreement 1994 continuing suspended by agreement 1990 continuing continuing continuing break in action 1992 continuing continuing continuing suspended by agreement 1995 continuing suspended by agreement 1996 break in action 1992 Iraq regional civil war Chiapas regional civil war Dniestr Repubic against war of independence civil war western Sahara general regional civil war Kachin regional civil war regional civil war civil war regional civil war Shan Karen general Arakan Nicaragua Niger Nigeria Pakistan Papua New Guinea Peru Philippines Russia regional civil war Kaya civil war general regional civil war regional civil war interstate war interstate war regional civil war regional civil war civil war interstate war northern Tuareg regions eastern region Bakassi border region Cameroon Kashmir India Karachi/Sind Bougainville general border region Ecuador civil war general regional civil war Mindanao regional civil war North Ossertia/Ingushetia ISS315 - PAGE 215 regional civil war Moscow regional civil war Chechnya Rwanda Saudi Arabia Senegal Sierra Leone Slovenia Somalia Somaliland South Africa Spain Sri Lanka Sudan Suriname Syria Tajikistan Togo Trinidad and Tobago Turkey civil war interstate war interstate war general Kuwait/Iraq border regions Iraq Mauritania 1993 1994 1990 1991 1989 1990 1991 Yugoslavia (SerbiaMontenegro) 1991 1977 1991 1984 1968 1977 1983 1955 1994 1986 Iraq 1991 1992 1991 1990 regional civil war Casamance region civil war general interstate war civil war civil war civil war regional civil war regional civil war civil war regional civil war regional civil war civil war interstate war civil war civil war civil war Slovenia general general general Basque region Tamil areas/northeast general southern regions Beja general Kuwait/Iraq general general general break in action 1993 suspended by agreement 1996 continuing suspended by agreement 1991 suspended by agreement 1991 continuing suspended by agreement 1996 suspended by agreement 1991 continuing break in action 1995 suspended by agreement 1994 break in action 1992 continuing break in action 1990 continuing break in action 1995 suspended by agreement 1992 suspended by agreement 1991 continuing break in action 1991 break in action 1990 regional civil war southeastern Kurdish region /northern Iraq 1977 continuing regional civil war western region Uganda regional civil war northern region regional civil war central region regional civil war southeastern region United Kingdomregional civil war northern Ireland interstate war United States Venezuela Yemen Yugoslavia interstate war civil war civil war interstate war interstate war Zaire civil war Kuwait/Iraq Kuwait/Iraq general general Slovenia Croatia general Slovenia Croatia Iraq Iraq 1991 1986 1994 1995 1969 1991 1991 1992 1994 1991 1991 1995 break in action 1992 continuing break in action 1995 break in action 1995 suspended by agreement 1994 suspended by agreement 1991 suspended by agreement 1991 break in action 1992 suspended by agreement suspended by agreement 1991 suspended by agreement 1992 continuing ISS315 - PAGE 216 America's Wars and Casualties American Revolution, 1775 to 1784 Participants 290,000 Deaths in Service Living Veterans 4,000 0 Last Veteran: Daniel F. Bakeman, died 4/5/1869, age 109 War of 1812, 1812 to 1815 Participants 287,000 Deaths in Service Living Veterans 2,000 0 Last Veteran: Hiram Cronk, died 5/13/05, age 105 Indian Wars, approx. 1817 to 1898 Participants 106,000 Deaths in Service Living Veterans 1,000 0 Last Veteran: Fredrak Fraske, died 6/18/73, age 101 Mexican War, 1846 to 1848 Participants 79,000 Deaths in Service Living Veterans 13,000 0 Last Veteran: Owen Thomas Edgar, died 9/3/29, age 98 Civil War, 1861 to 1865 Union Participants 2,213,000 Confederate Participants* 1,000,000 Union Deaths in Service 364,000 Confederate Deaths in Service* 133,821 Union Living Veterans 0 Confederate Living Veterans 0 Last Union Veteran: Albert Woolson, died 8/2/56, age 109 Last Confederate Veteran: Disputed.* Learn more... Spanish-American War, 1898 to 1902 Participants 392,000 Deaths in Service Living Veterans 11,000 0 Last Veteran: Nathan E. Cook, died 9/10/92, age 106 ISS315 - PAGE 217 World War I, 1917 to 1918 Participants 4,744,000 Deaths in Service 116,000 Living Veterans 4,800 Living Veterans does not include World War I veterans with military service in other eras. World War II, 1940 to 1947 Participants 16,535,000 Deaths in Service Living Veterans 406,000 6,319,000 Korean Conflict, 1950 to 1955 Participants 6,807,000 Deaths in Service Living Veterans 55,000 4,179,000 Vietnam Era, 1964 to 1975 Participants 9,200,000 Deaths in Service Living Veterans 109,000 8,166,000 Gulf War Era, 1990 to TBD Participants 3,800,000 Deaths in Service Living Veterans 9,000 2,048,000 America's Wars Total Participants Deaths in Service Living Veterans 41,790,000 1,090,200 19,300,000 Living Ex-Servicemembers 25,188,000 Source: U.S. Department of Veterans Affairs, July 1998 * Authoritative statistics for Confederate Forces are not available. An estimated 28,000 Confederate soldiers died in Union prisons. In the VA press release, the last Confederate veteran is listed as John Salling. Note: Figures on the number of living veterans reflect final 1990 Census data and include only veterans living in the United States. Details may not add to total due to rounding. ISS315 - PAGE 218 GENDER INEQUALITY 1. Global Overview: Gender Based Violence 2. Violence Against Women 3. Female Genital Mutilation 4. Dalit Women: The Triple Oppression of Dalit women in Nepal 5. Many Faces of Gender Inequality 6. U.S. Data on Income by Gender ISS315 - PAGE 219 Global Overview: Gender-based Violence Gender-based violence occurs in all societies and is largely unpoliced. Such violence occurs within the home or in the wider community and affects women and girls disproportionately. Women are vulnerable to this violence at all stages of life. They are threatened by female infanticide, incest, child prostitution, rape, partner violence, psychological abuse, sexual harassment, wartime violence, and harmful traditional practices such as forced early marriage, female genital cutting, and widow burning. The World Health Organization (WHO) estimates that at least one in every five of the world's female population has been physically or sexually abused at some time. In adopting the 1993 Declaration on the Elimination of Violence Against Women, the UN General Assembly defined the problem as "any act of gender-based violence that results in, or is likely to result in, physical, sexual, or psychological harm or suffering to women, including threats of such acts, coercion, or arbitrary deprivation of liberty, whether occurring in public or private life." Domestic violence, which typically occurs when a man beats his female partner, is the most prevalent form of gender-based violence. In the United States, more than a million and a half women are beaten by partners each year, according to a study by Murray A. Straus and Richard J. Gelles, published in 1986. In the 1995 Egypt Demographic and Health Survey, 35 percent of women reported being beaten by their husbands during marriage. Though gender-based violence is widespread, information is fragmented and anecdotal. A culture of silence surrounds cases of violence against women in most countries, making it difficult to get a true picture of its extent. Part of the difficulty is that gender-based violence mostly occurs in what is thought of as the private sphere within families, inside homes, and out of sight. This type of violence is underreported and even deliberately disguised by both the survivors and the societies in which they live. The reliability of crime and health statistics varies among countries, and refusal to recognize the problem is a barrier to its solution. Rape, a pervasive form of gender-based violence, has long symbolized a man's ability to have his way with a woman. Around the world, most rapists are known by those they attack and are often the victim's father, partner, or some other household figure. Statistics on rape suggest that 40 percent to 60 percent of those raped are 15 years old or younger. A recent U.S. National Academy of Sciences study on reproductive health noted that penal codes define rape in different ways. In many Latin American countries, rape, even by strangers, is considered a crime against morality rather than a crime against a person. Consequently, if the judicial system does not consider rape victims to have impeccable morals, the crime may not be prosecuted. In some societies, the rape of the girl or woman is thought to bring shame on her family. The family may consider marrying the girl to her rapist as the only way to recover its honor. In some cases, the girl is condemned to prostitution. Wartime rape and other forms of gender-based violence remain a constant threat in politically unsettled lands. In countries like Rwanda and what was Yugoslavia, rape has been used as an instrument of war to suppress and humiliate the enemy. According to the United Nations High Commissioner for Refugees, there are now 13 million cross-border refugees around the world and an additional 30 million displaced persons, or people living like refugees in their own countries. Women and children uprooted by war and located in makeshift refugee camps are easy targets for marauders. Female genital cutting, another form of gender-based violence, is practiced in 28 African countries and in about a dozen Middle Eastern and Asian nations. Some 130 million women and girls have been subjected to the practice, which is fundamentally about controlling women's sexuality. The effects of gender-based violence can be devastating and long lasting. Such violence is a particular danger to a woman's reproductive health and can scar a survivor psychologically, cognitively, and interpersonally. Since girls are more often subjected to sexual violence, they are at risk of becoming infected with HIV at a much younger age than are boys. A man's refusal to have protected intercourse increases his partner's risk of a sexually transmitted infection and subsequent pelvic pain, pelvic inflammatory disease, and infertility. Forced and unprotected sex also leads to unintended pregnancies, abortions, and unwanted children. Boys who witness battering in their homes are more likely to become batterers themselves, while girls are more likely to become victims of battering. Economic costs also flow from violence against women. The Inter-American Development Bank, for instance, says that such violence is a pervasive drain on Latin American economies. The cost adds up: health care, absenteeism and reduced family income, and outlays for law enforcement and the courts. The World Bank has calculated that gender-based violence is as heavy a health burden for women ages 15 to 44 as that posed by HIV, tuberculosis, infection during childbirth, cancer, and heart disease. Multilateral institutions have begun to address genderbased violence, however, and the problem was placed high on the agendas of recent UN world conferences. In 1996, the World Health Assembly passed a resolution calling for public health interventions to combat violence. The 1995 Fourth World Conference on Women in Beijing adopted a Platform for Action, which declares that "Violence against women is an obstacle to the achievement of the objective of equality, development, and peace." At the 1994 International Conference on Population and Development in Cairo, nearly 180 countries recognized the role of violence in the definition of women's reproductive health, which "includes the right of all to make decisions concerning reproduction free of discrimination, coercion, and violence..." International conventions and legislation are just beginning to be translated into action at a level that can effectively protect women the level of families, communities, and even national governments. The initiatives are a beacon for women at the grassroots, where there are efforts to pull the issue out of the closet and to clearly define gender-based violence as a problem for society. SOURCE: Conveying Concerns: Women Report on Gender-based Violence ISS315 - PAGE 220 Article 31 VIOLENCE AGAINST WOMEN It may be the biggest human rights issue in the world--and it is certainly one of the least discussed. Yet increasingly, women are finding ways to fight the mutilation, rape, beating, and murder that have been their lot. Toni Nelson A GIRL IS MUTILATED IN EGYPT the passage of urine and menstrual blood. Nagla's mutilation, performed by a local barber without anesthesia or sanitary precautions, was typical. Although the physical and psychological consequences of FGM are severe and often life-threatening, the practice persists due to beliefs that emerged from ancient tribal customs but which have now come to be associated with certain major religions. In Israel, for instance, FGM is practiced by Jewish migrants from the Ethiopian Falasha community; elsewhere in Africa, it is found among Christian and Islamic populations. But FGM has no inherent association with any of these religions. Although some Islamic scholars consider it an important part of that religion, FGM actually predates Islam, and neither the Qur'an, the primary source for Islamic law, nor the Hadith, collections of the Prophet Mohammed's lessons, explicitly require the practice. Justifications for FGM vary among the societies where it occurs (FGM is practiced in 28 African nations, as well as in scattered tribal communities in the Arabian Peninsula and various parts of South Asia). But most explanations relate in some way to male interest in controlling women's emotions and sexual behavior. One of the most common explanations is the need to lessen desire so women will preserve their virginity until marriage. The late Gad-Alhaq Ali Gad-Alhaq, Sheik of Cairo's al-Azhar Islamic University at the time of the CNN broadcast, explained it this way: the purpose of FGM is "to moderate sexual desire while saving womanly pleasures in order that women may enjoy their husbands." For Mimi Ramsey, an anti-FGM activist in the United States who was mutilated in her native Ethiopia at age six, FGM is meant to reinforce the power men have over women: "the reason for my mutilation is for a man to be able to control me, to make me a good wife." Today, migrants are bringing FGM out of its traditional societies and into Europe, North America, and Australia. Approximately 2 million girls are at risk each year. As in other countries where the practice is commonplace, Egypt's official policy on FGM has been ambiguous. Although a Ministry of Health decree in 1959 prohibited health professionals and public hospitals from performing the procedure, and national law makes it a crime to permanently mutilate anyone, clitoridectomies and other forms of FGM are not explicitly prohibited. An estimated 80 percent of Egyptian women and girls, or more than 18 million people, have undergone some form of FGM, which is often carried out by barbers in street booths on main squares of both small towns and large cities. Before the CNN broadcast, Egyptian public opinion seemed to be turning against the practice. In early 1994, activists I t is not a ritual that many people would expect--much less want--to witness. Yet in the fall of 1994, the television network CNN brought the practice of female genital mutilation (FGM) into living rooms around the world, by broadcasting the amputation of a young Egyptian girl's clitoris. Coinciding with the United Nations International Conference on Population and Development in Cairo, the broadcast was one of several recent events that have galvanized efforts to combat the various forms of violence that threaten women and girls throughout the world. The experience suffered by 10-year-old Nagla Hamza focused international attention on the plight of the more than 100 million women and girls in Africa victimized by FGM. In doing so, it helped spur conference delegates into formulating an official "Programme of Action" that condemned FGM and outlined measures to eliminate the practice. Euphemistically referred to as female circumcision, FGM encompasses a variety of practices ranging from excision, the partial or total removal of the clitoris and labia minora, to infibulation, in which all the external genitals are cut away and the area is restitched, leaving only a small opening for 1 ISS315 - PAGE 221 Article 31. VIOLENCE AGAINST WOMEN founded the Egyptian Task Force Against Female Genital Mutilation. Later that year, during the population conference, Population and Family Welfare Minister Maher Mahran vowed to delegates that "Egypt is going to work on the elimination of female genital mutilation." Plans were even laid for legislation that would outlaw FGM. But some members of Egypt's religious community saw the broadcast as a form of Western imperialism and used it to challenge both the secular government of Hosni Mubarak and the conference itself. In October 1994, Sheik Gad-Alhaq ruled that FGM is a religious obligation for Muslims. The same month, Minister of Health Dr. Ali Abdel Fattah issued a decree permitting the practice in selected government hospitals. The Minister's directive came just 10 days after a committee of experts convened by him condemned FGM and denied that it had any religious justification. Fattah affirmed his personal opposition, but insisted that the decree was necessary to "save those victimized girls from being `slaughtered' by unprofessionals." In the wake of the Minister's decision, plans for the bill outlawing FGM were postponed. Contending that Fattah had effectively legalized the procedure, national and international nongovernmental organizations sought to reverse the decision through petition drives, public education initiatives, and lawsuits. And on October 17, 1995, Fattah reversed his decision, and the Ministry of Health once again banned FGM in public hospitals. The anti-FGM legislation, however, remains on hold. first UN Women's Conference in 1975 or in the 1979 UN Convention on All Forms of Discrimination Against Women. But as the situation in Egypt demonstrates, effective policy responses remain elusive. Violence stalks women throughout their lives, "from cradle to grave"--in the judgment of Human Development Report 1995, the UN's annual assessment of social and economic progress around the world. Gender-specific violence is almost a cultural constant, both emerging from and reinforcing the social relationships that give men power over women. This is most obvious in the implicit acceptance, across cultures, of domestic violence--of a man's prerogative to beat his wife. Largescale surveys in 10 countries, including Colombia, Canada, and the United States, estimate that as many as one-third of women have been physically assaulted by an intimate male partner. More limited studies report that rates of physical abuse among some groups in Latin America, Asia, and Africa may reach 60 percent or more. Belying the oft-cried clich about "family values," studies have shown that the biggest threat to women is domestic violence. In 1992, the Journal of the American Medical Association published a study that found that women in the United States are more likely to be assaulted, injured, raped, or murdered by a current or former male partner than by all other types of attackers combined. In Canada, a 1987 study showed that 62 percent of the women murdered in that year were killed by an intimate male partner. And in India, the husband or in-laws of a newly married woman may think it justified to murder her if they consider her dowry inadequate, so that a more lucrative match can be made. One popular method is to pour kerosene on the woman and set her on fire--hence the term "bride burning." One in four deaths among women aged 16 to 24 in the urban areas of Maharashtra state (including Bombay) is attributed to "accidental burns." About 5,000 "dowry deaths" occur in India every year, according to government estimates, and some observers think the number is actually much higher. Subhadra Chaturvedi, one of India's leading attorneys, puts the death toll at a minimum of 12,000 a year. The preference for sons, common in many cultures, can lead to violence against female infants--and even against female fetuses. In India, for example, a 1990 study of amniocentesis in a large Bombay hospital found that 95.5 percent of fetuses identified as female were aborted, compared with only a small percentage of male fetuses. (Amniocentesis involves the removal of a sample of amniotic fluid from the womb; this can be used to determine the baby's sex and the presence of certain inherited diseases.) Female infanticide is still practiced in rural areas of India; a 1992 study by Cornell University demographer Sabu George found that 58 percent of female infant deaths (19 of 33) within a 12-village region of Tamil Nadu state were due to infanticide. The problem is especially pronounced in China, where the imposition of the one-child-per-family rule has led to a precipitous decline in the number of girls: studies in 1987 and 1994 found a half-million fewer female infants in each of those years than would be expected, given the typical biological ratio of male to female births. Women are also the primary victims of sexual crimes, which include sexual abuse, rape, and forced prostitution. Girls are the overwhelming target of child sexual assaults; in the United States, 78 percent of substantiated child sexual abuse cases involve girls. According to a 1994 World Bank study, Violence Against Women: The Hidden Health Burden, national surveys suggest that up to one-third of women in Norway, the United States, Canada, New Zealand, Barbados, and the Netherlands are sexually abused during childhood. Often very young children are the victims: a national study in the United States and studies in several Latin American cities indicate that 13 to 32 percent of abused girls are age 10 and under. Rape haunts women throughout their lives, exposing them to unwanted pregnancy, disease, social stigma, and psychological trauma. In the United States, which has some of the best data on the problem, a 1993 review of rape studies suggests that between 14 and 20 percent of women will be victims of completed rapes during their lifetimes. In some cultures, a woman who has been raped is perceived as having violated the family honor, and she may be forced to marry her attacker or even killed. One study of female homicide in Alexandria, Egypt, for example, found that 47 percent of women murdered were killed by a family member following a rape. In war, rape is often used as both a physical and psychological weapon. An investigation of recent conflicts in the former Yugoslavia, Peru, Kashmir, and Somalia by the international human rights group, Human Rights Watch, found that "rape of women civilians has been deployed as a VIOLENCE IS A UNIVERSAL THREAT Egypt's confused and ambivalent response to FGM mirrors in many ways the intensifying international debate on all forms of violence against women. And even though FGM itself may seem just a grotesque anomaly to people brought up in cultures where it isn't practiced, FGM is grounded in attitudes and assumptions that are, unfortunately, all too common. Throughout the world, women's inferior social status makes them vulnerable to abuse and denies them the financial and legal means necessary to improve their situations. Over the past decade, women's groups around the world have succeeded in showing how prevalent this problem is and how much violence it is causing--a major accomplishment, given the fact that the issue was not even mentioned during the 2 ISS315 - PAGE 222 ANNUAL EDITIONS tactical weapon to terrorize civilian communities or to achieve `ethnic cleansing.'" Studies suggest that tens of thousands of Muslim and Serbian women in Bosnia have been raped during the conflict there. A growing number of women and girls, particularly in developing countries, are being forced into prostitution. Typically, girls from poor, remote villages are purchased outright from their families or lured away with promises of jobs or false marriage proposals. They are then taken to brothels, often in other countries, and forced to work there until they pay off their "debts"--a task that becomes almost impossible as the brothel owner charges them for clothes, food, medicine, and often even their own purchase price. According to Human Rights Watch, an estimated 20,000 to 30,000 Burmese girls and women currently work in brothels in Thailand; their ranks are now expanding by as many as 10,000 new recruits each year. Some 20,000 to 50,000 Nepalese girls are working in Indian brothels. As the fear of AIDS intensifies, customers are demanding ever younger prostitutes, and the age at which girls are being forced into prostitution is dropping; the average age of the Nepalese recruits, for example, declined from 1416 years in the 1980s, to 1014 years by 1994. worse. Not only must these women be cut and stitched repeatedly, on their wedding night and again with each childbirth, but sexual dysfunction and pain during intercourse are common. Infibulated women are also much more likely to have difficulties giving birth. Their labor often results, for instance, in vesico-vaginal fistulas-- holes in the vaginal and rectal areas that cause continuous leakage of urine and feces. An estimated 1.5 to 2 million African women have fistulas, with some 50,000 to 100,000 new cases occurring annually. Infibulation also greatly increases the danger to the child during labor. A study of 33 infibulated women in delivery at Somalia's Benadir Hospital found that five of their babies died and 21 suffered oxygen deprivation. Other forms of violence are taking a heavy toll as well. A 1994 national survey in Canada, for example, found that broken bones occurred in 12 percent of spousal assaults, and internal injuries and miscarriages in 10 percent. Long-term effects may be less obvious but they are often just as serious. In the United States, battered women are four to five times more likely than non-battered women to require psychiatric treatment and five times more likely to attempt suicide. And even these effects are just one part of a much broader legacy of misery. A large body of psychological literature has documented the erosion of self-esteem, of social abilities, and of mental health in general, that often follows in the wake of violence. And the problem is compounded because violence tends to be cyclical: people who are abused tend to become abusers themselves. Whether it's through such direct abuse or indirectly, through the destruction of family life, violence against women tends to spill over into the next generation as violence against children. Only a few studies have attempted to assign an actual dollar value to genderbased violence, but their findings suggest that the problem constitutes a substantial health care burden. In the United States, a 1991 study at a major health maintenance organization (a type of group medical practice) found that women who had been raped or beaten at any point in their lifetimes had medical costs two-and-a-half times higher during that year than women who had not been victimized. In the state of Pennsylvania, a health insurer study estimated that violence against women cost the health care system approximately $326.6 million in 1992. And in Canada, a 1995 study of violence against women, which examined not only medical costs, but also the value of community support services and lost work, put the annual cost to the country at Cdn $1.5 billion (US $1.1 billion). One important consequence of violence is its effect on women's productivity. In its World Development Report 1993, the World Bank estimated that in advanced market economies, 19 percent of the total disease burden of women aged 15 to 44-- nearly one out of every five healthy days of life lost--can be linked to domestic violence or rape. (Violence against women is just as pervasive in developing countries, but because the incidence of disease is higher in those regions, it represents only 5 percent of their total disease burden.) Similarly, a 1993 study in the United States showed a correlation between violence and lower earnings. After controlling for other factors that affect income, the study found that women who have been abused earn 3 to 20 percent less each year than women who have not been abused, with the discrepancy depending on the type of sexual abuse experienced and the number of perpetrators. Violence can also prevent women from participating in public life--a form of oppression that can cripple Third World development projects. Fear may keep women at home; for example, health workers in India have identified fear of rape as an impediment to their outreach efforts in rural sites. The general problem was acknowledged plainly in a UN report published in 1992, Battered Dreams: Violence Against Women as an Obstacle to Development: "Where violence keeps a woman from participating in a development project, force is used to deprive her of earnings, or fear of sexual assault prevents her from taking a job or attending a public function, development does not occur." Development efforts aimed at reducing fertility levels may also be affected, since gender-based violence, or the threat of it, may limit women's use of contraception. According to the 1994 World Bank study, a woman's contraceptive use often depends in large part on her partner's approval. A recurrent motive in much of this violence is an interest in preventing women from gaining autonomy outside the home. Husbands may physically prevent their wives from attending development meetings, or they may intimidate them into not seeking employment or accepting promotions at work. The World Bank study relates a chilling example of the way in which violence can be used to control THE HIDDEN COSTS OF VIOLENCE Whether it takes the form of enforced prostitution, rape, genital mutilation, or domestic abuse, gender-based violence is doing enormous damage--both to the women who experience it, and to societies as a whole. Yet activists, health officials, and development agencies have only recently begun to quantify the problem's full costs. Currently, they are focusing on two particularly burdensome aspects of the violence: the health care costs, and the effects on economic productivity. The most visible effects of violence are those associated with physical injuries that require medical care. FGM, for example, often causes severe health problems. Typically performed in unsterile environments by untrained midwives or barbers working without anesthesia, the procedure causes intense pain and can result in infection or death. Long-term effects include chronic pain, urine retention, abscesses, lack of sexual sensitivity, and depression. For the approximately 15 percent of mutilated women who have been infibulated, the health-related consequences are even 3 ISS315 - PAGE 223 Article 31. VIOLENCE AGAINST WOMEN women's behavior: "In a particularly gruesome example of male backlash, a female leader of the highly successful government sponsored Women's Development Programme in Rajasthan, India, was recently gang raped [in her home in front of her husband] by male community members because they disapproved of her organizing efforts against child marriage." The men succeeded in disrupting the project by instilling fear in the local organizers. der violence. The result: for the first time, violence against women was recognized as an abuse of women's human rights, and nine paragraphs on "The equal status and human rights of women" were incorporated into the Vienna Declaration and Programme of Action. More recently, 18 members of the Organization of American States have ratified the Inter-American Convention on the Prevention, Punishment and Eradication of Violence Against Women. Many activists consider this convention, which went into effect on March 5, 1995, the strongest existing piece of international legislation in the field. And the Pan American Health Organization (PAHO) has become the first development agency to make a significant financial commitment to the issue. PAHO has received $4 million from Sweden, Norway, and the Netherlands, with the possibility of an additional $2.5 million from the Inter-American Development Bank, to conduct research on violence and establish support services for women in Latin America. National governments are also drawing up legislation to combat various forms of gender violence. A growing number of countries, including South Africa, Israel, Argentina, the Bahamas, Australia, and the United States have all passed special domestic violence laws. Typically, these clarify the definition of domestic violence and strengthen protections available to the victims. In September 1994, India passed its "Pre-natal Diagnostic Techniques (Regulation and Prevention of Misuse) Act," which outlaws the use of prenatal testing for sex-selection. India is also developing a program to eradicate female infanticide. FGM is being banned in a growing number of countries, too. At least nine European countries now prohibit the practice, as does Australia. In the United States, a bill criminalizing FGM was passed by the Senate in May, but had yet to become law. More significant, perhaps, is the African legislation: FGM is now illegal in both Ghana and Kenya. It is true, of course, that laws don't necessarily translate into real-life changes. But it is possible that the movement to stop FGM will yield the first solid success in the struggle to make human rights a reality for women. Over the past decade, the InterAfrican Committee on Traditional Practices Affecting the Health of Women and Children, an NGO dedicated to abolishing FGM, has set up committees in 25 African countries. And in March 1995, Ghana used its anti-FGM statute to arrest the parents and circumciser of an eight-year-old girl who was rushed to the hospital with excessive bleeding. In Burkina Faso, some circumcising midwives have been convicted under more general legislation. These are modest steps, perhaps, but legal precedent can be a powerful tool for reform. In the United States, an important precedent is currently being set by a 19-yearold woman from the nation of Togo, in west Africa. Fleeing an arranged marriage and the ritual FGM that would accompany it, Fauziya Kasinga arrived in the United States seeking asylum in December 1994. She has spent much of the time since then in prison, and her request for asylum, denied by a lower court, is at the time of writing under appeal. People are eligible for asylum in the United States if they are judged to have a reasonable fear of persecution due to their race, religion, nationality, political opinions, or membership in a social group. However, U.S. asylum law makes no explicit provision for genderbased violence. In 1993, Canada became the world's first country to make the threat of FGM grounds for granting refugee status. Whichever way the decision on Kasinga's case goes, it will be adopted as a binding general precedent in U.S. immigration cases (barring the passage of federal legislation that reverses it). But even while her fate remains in doubt, Kasinga has already won an important moral victory. Her insistence on her right not to be mutilated--and on the moral obligation of others to shield her from violence if they can--has made the threat she faces a matter of conscience, of politics, and of policy. Given the accumulating evidence of how deeply gender-based violence infects our societies, in both the developing and the industrialized countries, we have little choice but to recognize it as the fundamental moral and economic challenge that it is. WOMEN BREAK THE SILENCE "These women are holding back a silent scream so strong it could shake the earth." That is how Dr. Nahid Toubia, Executive Director of the U.S.-based anti-FGM organization RAINBO, described FGM victims when she testified at the 1993 Global Tribunal on Violations of Women's Human Rights. Yet her statement would apply just as well to the millions of women all over the world who have been victims of other forms of violence. Until recently, the problem of gender-based violence has remained largely invisible. Because the stigma attached to many forms of violence makes them difficult to discuss openly, and because violence typically occurs inside the home, accurate information on the magnitude of the problem has been extremely scarce. Governments, by claiming jurisdiction only over human rights abuses perpetrated in the public sphere by agents of the state, have reinforced this invisibility. Even human rights work has traditionally confined itself to the public sphere and largely ignored many of the abuses to which women are most vulnerable. But today, the victims of violence are beginning to find their voices. Women's groups have won a place for "private sphere" violence on human rights agendas, and they are achieving important changes in both national laws and international conventions. The first major reform came in June 1993, at the UN Second World Conference on Human Rights in Vienna. In a drive leading up to the conference, activists collected almost half a million signatures from 124 countries on a petition insisting that the conference address gen- Toni Nelson is a staff researcher at the Worldwatch Institute. From World Watch, July/August 1996, pp. 3338. 1996 by the Worldwatch Institute. Reprinted by permission. 4 ISS315 - PAGE 224 AI INDEX: ACT 77/06/97 WHAT IS FEMALE GENITAL MUTILATION? The different types of mutilation Female genital mutilation (FGM) is the term used to refer to the removal of part, or all, of the female genitalia. The most severe form is infibulation, also known as pharaonic circumcision. An estimated 15% of all mutilations in Africa are infibulations. The procedure consists of clitoridectomy (where all, or part of, the clitoris is removed), excision (removal of all, or part of, the labia minora), and cutting of the labia majora to create raw surfaces, which are then stitched or held together in order to form a cover over the vagina when they heal. A small hole is left to allow urine and menstrual blood to escape. In some less conventional forms of infibulation, less tissue is removed and a larger opening is left. The vast majority (85%) of genital mutilations performed in Africa consist of clitoridectomy or excision. The least radical procedure consists of the removal of the clitoral hood. In some traditions a ceremony is held, but no mutilation of the genitals occurs. The ritual may include holding a knife next to the genitals, pricking the clitoris, cutting some pubic hair, or light scarification in the genital or upper thigh area. The procedures followed The type of mutilation practised, the age at which it is carried out, and the way in which it is done varies according to a variety of factors, including the woman or girl's ethnic group, what country they are living in, whether in a rural or urban area and their socio-economic provenance. The procedure is carried out at a variety of ages, ranging from shortly after birth to some time during the first pregnancy, but most commonly occurs between the ages of four and eight. According to the World Health Organization, the average age is falling. This indicates that the practice is decreasingly associated with initiation into adulthood, and this is believed to be particularly the case in urban areas. Some girls undergo genital mutilation alone, but mutilation is more often undergone as a group of, for example, sisters, other close female relatives or neighbours. Where FGM is carried out as part of an initiation ceremony, as is the case in societies in eastern, central and western Africa, it is more likely to be carried out on all the girls in the community who belong to a particular age group. The procedure may be carried out in the girl's home, or the home of a relative or neighbour, in a health centre, or, especially if associated with initiation, at a specially designated site, such as a particular tree or river. The person performing the mutilation may be an older woman, a traditional midwife or healer, a barber, or a qualified midwife or doctor. Girls undergoing the procedure have varying degrees of knowledge about what will happen to them. Sometimes the event is associated with festivities and gifts. Girls are exhorted to be brave. Where the mutilation is part of an initiation rite, the festivities may be major events for the community. Usually only women are allowed to be present. Sometimes a trained midwife will be available to give a local anaesthetic. In some cultures, girls will be told to sit beforehand in cold water, to numb the area and reduce the likelihood of bleeding. More commonly, however, no steps are taken to reduce the pain. The girl is immobilized, held, usually by older women, with her legs open. Mutilation may be carried out using broken glass, a tin lid, scissors, a razor blade or some other cutting instrument. When ISS315 - PAGE 225 2 infibulation takes place, thorns or stitches may be used to hold the two sides of the labia majora together, and the legs may be bound together for up to 40 days. Antiseptic powder may be applied, or, more usually, pastes -- containing herbs, milk, eggs, ashes or dung -- which are believed to facilitate healing. The girl may be taken to a specially designated place to recover where, if the mutilation has been carried out as part of an initiation ceremony, traditional teaching is imparted. For the very rich, the mutilation procedure may be performed by a qualified doctor in hospital under local or general anaesthetic. Geographical distribution of female genital mutilation An estimated 135 million of the world's girls and women have undergone genital mutilation, and two million girls a year are at risk of mutilation -- approximately 6,000 per day. It is practised extensively in Africa and is common in some countries in the Middle East. It also occurs, mainly among immigrant communities, in parts of Asia and the Pacific, North and Latin America and Europe. FGM is reportedly practised in more than 28 African countries (see FGM in Africa: Information by Country (ACT 77/07/97)). There are no figures to indicate how common FGM is in Asia. It has been reported among Muslim populations in Indonesia, Sri Lanka and Malaysia, although very little is known about the practice in these countries. In India, a small Muslim sect, the Daudi Bohra, practise clitoridectomy. In the Middle East, FGM is practised in Egypt, Oman, Yemen and the United Arab Emirates. There have been reports of FGM among certain indigenous groups in central and south America, but little information is available. In industrialized countries, genital mutilation occurs predominantly among immigrants from countries where mutilation is practised. It has been reported in Australia, Canada, Denmark, France, Italy, the Netherlands, Sweden, the UK and USA. Girls or girl infants living in industrialized countries are sometimes operated on illegally by doctors from their own community who are resident there. More frequently, traditional practitioners are brought into the country or girls are sent abroad to be mutilated. No figures are available on how common the practise is among the populations of industrialized countries. The physical and psychological effects of female genital mutilation Physical effects The effects of genital mutilation can lead to death. At the time the mutilation is carried out, pain, shock, haemorrhage and damage to the organs surrounding the clitoris and labia can occur. Afterwards urine may be retained and serious infection develop. Use of the same instrument on several girls without sterilization can cause the spread of HIV. More commonly, the chronic infections, intermittent bleeding, abscesses and small benign tumours of the nerve which can result from clitoridectomy and excision cause discomfort and extreme pain. Infibulation can have even more serious long-term effects: chronic urinary tract infections, stones in the bladder and urethra, kidney damage, reproductive tract infections resulting from obstructed menstrual flow, pelvic infections, infertility, excessive scar tissue, keloids (raised, irregularly shaped, progressively enlarging scars) and dermoid cysts. ISS315 - PAGE 226 3 First sexual intercourse can only take place after gradual and painful dilation of the opening left after mutilation. In some cases, cutting is necessary before intercourse can take place. In one study carried out in Sudan, 15% of women interviewed reported that cutting was necessary before penetration could be achieved.1 Some new wives are seriously damaged by unskilful cutting carried out by their husbands. A possible additional problem resulting from all types of female genital mutilation is that lasting damage to the genital area can increase the risk of HIV transmission during intercourse. During childbirth, existing scar tissue on excised women may tear. Infibulated women, whose genitals have been tightly closed, have to be cut to allow the baby to emerge. If no attendant is present to do this, perineal tears or obstructed labour can occur. After giving birth, women are often reinfibulated to make them "tight" for their husbands. The constant cutting and restitching of a women's genitals with each birth can result in tough scar tissue in the genital area. The secrecy surrounding FGM, and the protection of those who carry it out, make collecting data about complications resulting from mutilation difficult. When problems do occur these are rarely attributed to the person who performed the mutilation. They are more likely to be blamed on the girl's alleged "promiscuity" or the fact that sacrifices or rituals were not carried out properly by the parents. Most information is collected retrospectively, often a long time after the event. This means that one has to rely on the accuracy of the woman's memory, her own assessment of the severity of any resulting complications, and her perception of whether any health problems were associated with mutilation. Some data on the short and long-term medical effects of FGM, including those associated with pregnancy, have been collected in hospital or clinic-based studies, and this has been useful in acquiring a knowledge of the range of health problems that can result. However, the incidence of these problems, and of deaths as a result of mutilation, cannot be reliably estimated. Supporters of the practice claim that major complications and problems are rare, while opponents of the practice claim that they are frequent. Effects on sexuality Genital mutilation can make first intercourse an ordeal for women. It can be extremely painful, and even dangerous, if the woman has to be cut open; for some women, intercourse remains painful. Even where this is not the case, the importance of the clitoris in experiencing sexual pleasure and orgasm suggests that mutilation involving partial or complete clitoridectomy would adversely affect sexual fulfilment. Clinical considerations and the majority of studies on women's enjoyment of sex suggest that genital mutilation does impair a women's enjoyment. However, one study found that 90% of the infibulated women interviewed reported experiencing orgasm.2 The mechanisms involved in sexual enjoyment and orgasm are still not fully understood, but it is thought that compensatory processes, some of them psychological, may mitigate some of the effects of removal of the clitoris and other sensitive parts of the genitals. Psychological effects The psychological effects of FGM are more difficult to investigate scientifically than the physical ones. A small number of clinical cases of psychological illness related to genital mutilation have been reported.3 Despite the lack of scientific evidence, personal accounts of mutilation reveal feelings of anxiety, terror, humiliation and betrayal, all of which would be likely to have long-term negative effects. Some experts suggest that the shock and trauma of the operation may contribute to the behaviour described as "calmer" and "docile", considered positive in societies that practise female genital mutilation. ISS315 - PAGE 227 4 Festivities, presents and special attention at the time of mutilation may mitigate some of the trauma experienced, but the most important psychological effect on a woman who has survived is the feeling that she is acceptable to her society, having upheld the traditions of her culture and made herself eligible for marriage, often the only role available to her. It is possible that a woman who did not undergo genital mutilation could suffer psychological problems as a result of rejection by the society. Where the FGM-practising community is in a minority, women are thought to be particularly vulnerable to psychological problems, caught as they are between the social norms of their own community and those of the majority culture. Why FGM is practised Cultural identity "Of course I shall have them circumcised exactly as their parents, grandparents and sisters were circumcised. This is our custom." An Egyptian woman, talking about her young daughters 4 Custom and tradition are by far the most frequently cited reasons for FGM. Along with other physical or behavioural characteristics, FGM defines who is in the group. This is most obvious where mutilation is carried out as part of the initiation into adulthood. Jomo Kenyatta, the late President of Kenya, argued that FGM was inherent in the initiation which is in itself an essential part of being Kikuyu, to such an extent that "abolition... will destroy the tribal system".5 A study in Sierra Leone reported a similar feeling about the social and political cohesion promoted by the Bundo and Sande secret societies, who carry out initiation mutilations and teaching. Many people in FGM-practising societies, especially traditional rural communities, regard FGM as so normal that they cannot imagine a woman who has not undergone mutilation. Others are quoted as saying that only outsiders or foreigners are not genitally mutilated. A girl cannot be considered an adult in a FGM-practising society unless she has undergone FGM. Gender identity FGM is often deemed necessary in order for a girl to be considered a complete woman, and the practice marks the divergence of the sexes in terms of their future roles in life and marriage. The removal of the clitoris and labia -- viewed by some as the "male parts" of a woman's body -- is thought to enhance the girl's femininity, often synonymous with docility and obedience. It is possible that the trauma of mutilation may have this effect on a girl's personality. If mutilation is part of an initiation rite, then it is accompanied by explicit teaching about the woman's role in her society. "We are circumcised and insist on circumcising our daughters so that there is no mixing between male and female... An uncircumcised woman is put to shame by her husband, who calls her `you with the clitoris'. People say she is like a man. Her organ would prick the man..." An Egyptian woman 6 Control of women's sexuality and reproductive functions "Circumcision makes women clean, promotes virginity and chastity and guards young girls from sexual frustration by deadening their sexual appetite." Mrs Njeri, a defender of female genital mutilation in Kenya7 ISS315 - PAGE 228 5 In many societies, an important reason given for FGM is the belief that it reduces a woman's desire for sex, therefore reducing the chance of sex outside marriage. The ability of unmutilated women to be faithful through their own choice is doubted. In many FGM-practising societies, it is extremely difficult, if not impossible, for a woman to marry if she has not undergone mutilation. In the case of infibulation, a woman is "sewn up" and "opened" only for her husband. Societies that practise infibulation are strongly patriarchal. Preventing women from indulging in "illegitimate" sex, and protecting them from unwilling sexual relations, are vital because the honour of the whole family is seen to be dependent on it. Infibulation does not, however, provide a guarantee against "illegitimate" sex, as a woman can be "opened" and "closed" again. In some cultures, enhancement of the man's sexual pleasure is a reason cited for mutilation. Anecdotal accounts, however, suggest that men prefer unmutilated women as sexual partners. Beliefs about hygiene, aesthetics and health Cleanliness and hygiene feature consistently as justifications for FGM. Popular terms for mutilation are synonymous with purification (tahara in Egypt, tahur in Sudan), or cleansing (sili-ji among the Bambarra, an ethnic group in Mali). In some FGM-practising societies, unmutilated women are regarded as unclean and are not allowed to handle food and water. Connected with this is the perception in FGM-practising communities that women's unmutilated genitals are ugly and bulky. In some cultures, there is a belief that a woman's genitals can grow and become unwieldy, hanging down between her legs, unless the clitoris is excised. Some groups believe that a woman's clitoris is dangerous and that if it touches a man's penis he will die. Others believe that if the baby's head touches the clitoris during childbirth, the baby will die. Ideas about the health benefits of FGM are not unique to Africa. In 19th Century England, there were debates as to whether clitoridectomy could cure women of "illnesses" such as hysteria and "excessive" masturbation. Clitoridectomy continued to be practised for these reasons until well into this century in the USA. However, health benefits are not the most frequently cited reason for mutilation in societies where it is still practised; where they are, it is more likely to be because mutilation is part of an initiation where women are taught to be strong and uncomplaining about illness. Some societies where FGM is practised believe that it enhances fertility, the more extreme believing that an unmutilated woman cannot conceive. In some cultures it is believed that clitoridectomy makes childbirth safer. Religion FGM predates Islam and is not practised by the majority of Muslims, but has acquired a religious dimension. Where it is practised by Muslims, religion is frequently cited as a reason. Many of those who oppose mutilation deny that there is any link between the practise and religion, but Islamic leaders are not unanimous on the subject. The Qur'an does not contain any call for FGM, but a few hadith (sayings attributed to the Prophet Muhammad) refer to it. In one case, in answer to a question put to him by `Um `Attiyah (a practitioner of FGM), the Prophet is quoted as saying "reduce but do not destroy". Mutilation has persisted among some converts to Christianity. Christian missionaries have tried to discourage the practice, but found it to be too deep rooted. In some cases, in order to keep converts, they have ignored and even condoned the practice. ISS315 - PAGE 229 6 FGM was practised by the Falasha (Ethiopian Jews), but it is not known if the practise has persisted following their emigration to Israel. The remainder of the FGM-practising community follow traditional Animist religions. Testimony "I was genitally mutilated at the age of ten. I was told by my late grandmother that they were taking me down to the river to perform a certain ceremony, and afterwards I would be given a lot of food to eat. As an innocent child, I was led like a sheep to be slaughtered. Once I entered the secret bush, I was taken to a very dark room and undressed. I was blindfolded and stripped naked. I was then carried by two strong women to the site for the operation. I was forced to lie flat on my back by four strong women, two holding tight to each leg. Another woman sat on my chest to prevent my upper body from moving. A piece of cloth was forced in my mouth to stop me screaming. I was then shaved. When the operation began, I put up a big fight. The pain was terrible and unbearable. During this fight, I was badly cut and lost blood. All those who took part in the operation were half-drunk with alcohol. Others were dancing and singing, and worst of all, had stripped naked. I was genitally mutilated with a blunt penknife. After the operation, no one was allowed to aid me to walk. The stuff they put on my wound stank and was painful. These were terrible times for me. Each time I wanted to urinate, I was forced to stand upright. The urine would spread over the wound and would cause fresh pain all over again. Sometimes I had to force myself not to urinate for fear of the terrible pain. I was not given any anaesthetic in the operation to reduce my pain, nor any antibiotics to fight against infection. Afterwards, I haemorrhaged and became anaemic. This was attributed to witchcraft. I suffered for a long time from acute vaginal infections." Hannah Koroma, Sierra Leone 1 Lightfoot-Klein, H., "The Sexual Experience and Marital Adjustment of Genitally Circumcised and Infibulated Females in the Sudan", The Journal of Sex Research, 26 (3), pp. 375-392, 1989. 2 Lightfoot-Klein, H., Prisoners of Ritual: An Odyssey into Female Genital Circumcision in Africa, Haworth Press, New York, 1989. 3 Baasher, T.A., "Psychological Aspects of Female Circumcision", Traditional Practices Affecting the Health of Women and Children, Report of a seminar, 10-15 February, 1979, WHO-EMRO Technical Publication 2, WHO, Alexandria, Egypt, 1979, pp. 71-105. 4 Assaad, M.B., "Female Circumcision in Egypt: Social Implications, Current Research and Prospects for Change", Studies in Family Planning, 11:1, 1980, pp. 3-16. 5 Kenyatta, J., Facing Mount Kenya: The Tribal Life of the Kikuyu, Secker and Warburg, London, 1938. 6 Assaad, M.B., ibid. 7 Katumba, R., "Kenyan Elders Defend Circumcision", Development Forum, September, 1990, p. 17. ISS315 - PAGE 230 DALIT WOMEN: The Triple Oppression of Dalit Women in Nepal Durga Sob [Ed. Note: The author is the president of the Feminist Dalit Organisation (FEDO) in Kathmandu, Nepal.] Background Nepal is a country characterised not only by biodiversity but also by socio-cultural diversity. Meanwhile, Nepalese political and social life is primarily dominated by the Hindu religion, which divides Hindu society into four varnas, namely, Brahmins, Kshatriyas, Vaishyas and Sudras. Over a period of time, casteism developed a rigid hierarchical society with the purity and pollution of castes. In this manufactured caste hierarchy, Brahmins lie at the top, and Sudras, or Dalits, lie at the bottom of society. The word dalit literally means "a person immersed in a swamp." Traditionally, Dalits have been treated inhumanely as "Untouchables." Although untouchability was abolished by the New National Code of Nepal in 1963, its practice still continues. The people belonging to this community are living in a swamp of illiteracy, exploitation, marginalisation, absolute poverty and, above all, caste discrimination. Dalit women, however, are triply oppressed: (1) oppressed by the so-called high caste people, which equally affects both male and female Dalits, (2) oppressed by the design of the Hindu patriarchal system and (3) oppressed by Dalit males. It is estimated that the Dalit community constitutes 20 percent of the total population of the country, or four million people, and that the population of Dalit women is half of this figure, i.e., two million people. In general, Dalits are characterised as being illiterate, unemployed, landless, poor, ignorant, exploited, docile, unhygienic, dirty, sick and ignored by the rest of society. The Dalit community has lost its self-respect and dignity as a result of centuries of social discrimination, oppression, exploitation and suppression. Despite being marginalised, Dalits are skilled artisans. However, statistics have revealed that Dalits are far behind in the development process compared to other caste groups. Unlike other ethnic groups, Dalits are scattered throughout the country. In today's context of the globalisation of women's issues, Nepalese women from different segments of society are also raising their voices against discrimination and exploitation. The government has already established the Women Development Ministry to monitor national and international women's issues, which can be viewed as a good initiative. Unfortunately, the Dalit women's issue has not been recognised yet as a national issue. In addition, the so-called "mainstream" women's movement, led by high caste women, ignore Dalit women and their concerns. In general, the status of women in Nepal is very low, like in other South Asian countries. Among them though, Dalit women face the worst conditions and oppression. Dalit women are living a history of pain, agony, sorrow, misconduct, maltreatment and suffering. They are not only the victim of gender discrimination but also the victim of casteism. Moreover, the lives of Dalit women are spiralling downward from bad to worse. There is no controversy among development planners and workers that there has been very little impact on raising the status of Dalit women from the development initiatives implemented thus far in Nepal. The Condition of Dalit Women The difficult lives of Dalit women are perhaps best revealed by studying the social, economic, educational, health and political conditions of Dalit women, which are outlined below. Social Condition: Untouchability ISS315 - PAGE 231 Though outlawed since 1963 and made punishable by the Constitution of the Kingdom of Nepal in 1990, untouchability is still practised. Thus, Dalits are treated as socially untouchable even in a democracy. This can be observed around the periphery of the Kathmandu Valley and in the rural areas. Even in public places, such as schools, Dalit students face discrimination. The entire Dalit community is exploited and discriminated on the grounds of caste, but women, as noted earlier, are further victimised. Untouchability related to women is practised in many ways that affect all Dalit women every day. For example, when Dalit women fetch water from public water taps, wells, etc., they suffer from mental as well as physical assaults. Moreover, by traditional cultural practise, women usually are the member of the family that worships in the temple; but in Nepal, Dalit women are not allowed to enter the temples nor are they allowed to enter the house of upper caste people. Meanwhile, intercaste marriage is socially disapproved, and Dalit women are the principal victims of this system. If a girl from an upper caste family marries a lower caste boy, for instance, then she is accepted by the boy's family. However, when a marriage takes place between an upper caste boy and a lower caste girl, problems occur as she is not accepted by her husband's family. Subsequently, she is mentally and physically abused and abandoned in many cases. Economic Condition: Exploitation Most Dalits have their own traditional occupation, but they are economically exploited. It is estimated that more than 50 percent of the total population of the country lives below the absolute poverty line. Dalits, due to their history of discrimination, exploitation and abuse, constitute a large proportion of this number-approximately 90 percent of the total Dalit population of four million people. Since women have no economic power in the family, it clearly indicates the economic condition of Dalit women. These women have to work hard as labourers to earn a living, but they receive very little in return. Moreover, payment is mostly in kind, and their pay does not justify the intensity of the work. There is no doubt that Dalit women are more economically exploited than their upper caste women counterparts. Education Condition: Illiteracy The literacy rate of Dalit women is very low in comparison with high caste women. The present literacy rate of men is 66 percent, and the education rate for women is 30 percent. However, the literacy rate of the Dalit community is 16 percent; and for Dalit women, the literacy rate is only 7 percent. Among two million Dalit women, there are hardly 10 to 15 graduates and postgraduates. Ignorance, absolute poverty, caste and gender discrimination can be considered as the explanation for such statistics. Health Condition: Lowest Life Expectancy In Nepal, the life expectancy of women is lower than that of men (Nepal is one of the three countries in the world where women live less than men). Compared though to the so-called "upper caste people," the health condition of Dalits is very miserable. For example, a very backward Dalit caste-the Mushar of Terai -has a life span of 42 years against the national average of 55 years. Moreover, gynecological diseases, like a prolapsed uterus, are very common among Dalit women. They do not know much about birth control and spacing and become pregnant every year. Furthermore, because of illiteracy and ignorance, they live in filthy and unhygienic conditions which further deteriorates their health. The children of Dalits are also severely malnourished. As Dalits do not have easy access to clean drinking water in most places, they are compelled to drink polluted water and thus suffer from various gastrointestinal diseases. In this environment, both the mortality and fertility rates are high. Political Condition: Non-Representation Nepalese society is patriarchal, and the involvement of women in public life is not encouraged. This social prohibition applies to politics too. Thus, although Nepali women comprise 52 percent of the country's population, their representation in politics is among the lowest in Asia, i.e., as low as 5 ISS315 - PAGE 232 percent. This is in spite of the 1990 Nepalese Constitution in which there is a mandatory provision that 5 percent of all candidates put forward by the country's national parties should be women. However, there were no mandatory rules for women to be represented in local government. In 1997 though, 5 percent of the seats were reserved for women in local government, and yet this rule cannot embrace Dalit women. Dalits are about 20 percent of the total population, but Dalits are not represented in national-level politics. Presently, there are four nominated parliamentarians in the National Assembly, but there is none that have been elected to the Lower House of Parliament. If the representation of men is so negligible, then one can easily imagine the political participation of Dalit women. Sexual Exploitation: Trafficking Because of poverty, ignorance and illiteracy, Dalit women and young girls are compelled to be involved in prostitution. One of the Dalit castes-Badi-is regarded as the prostitute caste. In addition, there is the trafficking of girls. Most of the Dalit girls taken by the brokers are trafficked to Indian brothels. In addition, many women working in carpet factories, hotels and government and private offices are sexually harassed and exploited. Because of the untouchability problem related to caste, Dalit women are deprived of the opportunity of working in the moneymaking professions, i.e., opening teashops and restaurants. Their time is spent on earning subsistence wages, and they do not have time to think about improving their condition. Solutions We feel very uncomfortable in reporting that very few efforts have been made by the government of Nepal to eradicate the problems of Dalits in general and of Dalit women in particular. In recent years, particularly after the restoration of multiparty democracy, the women's movement has gained momentum with the emergence of many women's organisations and leaders. Unfortunately, none of these women's organisations have taken the issues and plight of two million Dalit women seriously. This is why we established an organisation dedicated to the rights and emancipation of Dalit women. As mentioned above, there is a serious lack of consciousness among the entire Dalit community about their fundamental human rights. Therefore, they accept all forms of discrimination and exploitation as God's grace to them. Meanwhile, the outside world has been silent about caste exploitation and discrimination. In recent years, we, a few educated Dalit women, have come forward to move the issue forward. We intend to influence the government, donor agencies and international non-governmental organisations (NGOs), enabling them to realise the harsh reality of Dalit women and to direct resources for the upliftment of these downtrodden women. We also plan to agitate at the local level against caste discrimination and violence and to join hands with other like-minded organisations. The vast problems of Dalits and Dalit women cannot be solved through the efforts of one or two organisations though. Therefore, we strongly feel that all national forces should join hands together. In this spirit, we would like to offer the recommendations below. (1) Various programmes should be launched to uplift the living conditions of two million Dalit women. (2) Scholarships should be provided from primary to higher education to all Dalit girls. (3) The practice of untouchability in all public places, like schools, water sources and teashops, should be punished with immediate effect. ISS315 - PAGE 233 (4) All laws and acts which discriminate against Dalits and Dalit women should be rescinded immediately, and a law should be formulated and enforced to discourage the practice of untouchability. (5) Intercaste marriage between Dalits and non-Dalits should be promoted and protected. (6) There should be reserved seats for Dalit women in appropriate constitutional bodies, and these seats should be reserved for Dalit women in elections from the local to the national level. (7) The government should abide by the U.N. International Convention on the Elimination of All Forms of Racial Discrimination. (8) Dalits and Dalit women should be represented in the National Human Rights Commission. (9) A separate commission at the national and the international level should be formed to identify the problems of Dalit women and to make recommendations to resolve these issues. As for national and international NGOs, it is recommended that they consider the steps that follow. (1) A series of in-depth studies should be undertaken about Dalit women. (2) All NGOs should declare Dalits as one of their target groups of development and should formulate specific programmes to improve their lives. (3) All human rights organisations should network with Dalit organisations and expose the human rights problems of Dalit women at the national and international level. (4) The government of Nepal should be pressured to implement all of the U.N. conventions on human rights. (5) International NGOs especially should give priority to Dalits and Dalit women in employment and involve them in the formulation of plans and programmes for the Dalit community. Posted on 2001-08-22 ISS315 - PAGE 234 Volume 18 - Issue 22, Oct. 27 - Nov. 09, 2001 India's National Magazine from the publishers of THE HINDU COVER STORY MANY FACES OF GENDER INEQUALITY An essay by Amartya Sen. Nobel Laureate Amartya Sen's work on gender inequality is of seminal importance. His work on the theory of the household represents the household not as an undifferentiated unit, but as a unit of cooperation as well as of inequality and internal discrimination. He has worked on problems of discrimination against women in the development process, on survivorship differentials between men and women under conditions of social discrimination against women, and on women's agency in the process of social development. Along with his academic collaborator Jean Drze, Professor Sen proposed and popularised the concept of "missing women" - estimated to exceed 100 million round the world - which has given us a new way of understanding and mapping the problem. In this Cover Story essay, which is based on the text of his inauguration lecture for the Radcliffe Institute at Harvard University, Professor Sen takes a comprehensive and deeply concerned look at the "many faces of gender inequality." Focussing on South Asia, he discovers in the data thrown up by the Census of 2001 an interesting phenomenon - a split India, "something of a social and cultural divide across India, splitting the country into two nearly contiguous halves, in the extent of antifemale bias in natality and post-natality mortality." He concludes by identifying the principal issues, emphasising the need to "take a plural view of gender inequality," and calling for a new agenda of action to combat and put an end to gender inequality. SHANKER CHAKRAVARTY Frontline features this important essay by Amartya Sen as its Cover Story. I. Seven Types of Inequality IT was more than a century ago, in 1870, that Queen Victoria wrote to Sir Theodore Martin complaining about "this mad, wicked folly of 'Woman's Rights'." The formidable empress certainly did not herself need any protection that the acknowledgment of women's rights might offer. Even at the age of eighty, in 1899, she could write to A.J. Balfour, "We are not interested in the possibilities of defeat; they do not exist." That, however, is not the way most people's lives go reduced and defeated as they frequently are by adversities. And within each community, nationality and class, the burden of hardship often falls disproportionately on women. The afflicted world in which we live is characterised by deeply unequal sharing of the burden of adversities between women and men. Gender inequality exists in most parts of the world, from Japan to Morocco, from Uzbekistan to the United States of America. However, inequality between ISS315 - PAGE 235 women and men can take very many different forms. Indeed, gender inequality is not one homogeneous phenomenon, but a collection of disparate and interlinked problems. Let me illustrate with examples of different kinds of disparity. (1) Mortality inequality: In some regions in the world, inequality between women and men directly involves matters of life and death, and takes the brutal form of unusually high mortality rates of women and a consequent preponderance of men in the total population, as opposed to the preponderance of women found in societies with little or no gender bias in health care and nutrition. Mortality inequality has been observed extensively in North Africa and in Asia, including China and South Asia. (2) Natality inequality: Given a preference for boys over girls that many male-dominated societies have, gender inequality can manifest itself in the form of the parents wanting the newborn to be a boy rather than a girl. There was a time when this could be no more than a wish (a daydream or a nightmare, depending on one's perspective), but with the availability of modern techniques to determine the gender of the foetus, sex-selective abortion has become common in many countries. It is particularly prevalent in East Asia, in China and South Korea in particular, but also in Singapore and Taiwan, and it is beginning to emerge as a statistically significant phenomenon in India and South Asia as well. This is high-tech sexism. KAMAL KISHORE/REUTERS A woman worker in New Delhi. (3) Basic facility inequality: Even when demographic characteristics do not show much or any anti-female bias, there are other ways in which women can have less than a square deal. Afghanistan may be the only country in the world the government of which is keen on actively excluding girls from schooling (it combines this with other features of massive gender inequality), but there are many countries in Asia and Africa, and also in Latin America, where girls have far less opportunity of schooling than boys do. There are other deficiencies in basic facilities available to women, varying from encouragement to cultivate one's natural talents to fair participation in rewarding social functions of the community. (4) Special opportunity inequality: Even when there is relatively little difference in basic facilities including schooling, the opportunities of higher education may be far fewer for young women than for young men. Indeed, gender bias in higher education and professional training can be observed even in some of the richest countries in the world, in Europe and North America. Sometimes this type of division has been based on the superficially innocuous idea that the respective "provinces" of men and women are just different. This thesis has been championed in different forms over the centuries, and has had much implicit as well as explicit following. It was presented with particular directness more than a hundred years before Queen Victoria's complaint about "woman's rights" by the Revd James Fordyce in his Sermons to Young Women (1766), a book which, as Mary Wollstonecraft noted in her A Vindication of the Rights of Women (1792), had been "long made a part of woman's library." Fordyce warned the young women, to whom his sermons were addressed, against "those masculine women that would plead for your sharing any part of their province with us," identifying the province of men as including not only "war," but also "commerce, politics, exercises of strength and dexterity, abstract philosophy and all the abstruser sciences."1 Even though such clear-cut beliefs about the provinces of men and women are now rather rare, nevertheless the presence of extensive gender asymmetry can be seen in many areas of education, training and professional work even in Europe and North America. (5) Professional inequality: In terms of employment as well as promotion in work and occupation, women often face greater handicap than men. A country like Japan may be quite ISS315 - PAGE 236 egalitarian in matters of demography or basic facilities, and even, to a great extent, in higher education, and yet progress to elevated levels of employment and occupation seems to be much more problematic for women than for men. In the English television series called "Yes, Minister," there is an episode where the Minister, full of reforming zeal, is trying to find out from the immovable permanent secretary, Sir Humphrey, how many women are in really senior positions in the British civil service. Sir Humphrey says that it is very difficult to give an exact number; it would require a lot of investigation. The Minister is still insistent, and wants to know approximately how many women are there in these senior positions. To which Sir Humphrey finally replies, "Approximately, none." (6) Ownership inequality: In many societies the ownership of property can also be very unequal. Even basic assets such as homes and land may be very asymmetrically shared. The absence of claims to property can not only reduce the voice of women, but also make it harder for women to enter and flourish in commercial, economic and even some social activities.2 This type of inequality has existed in most parts of the world, though there are also local variations. For example, even though traditional property rights have favoured men in the bulk of India, in what is now the State of Kerala, there has been, for a long time, matrilineal inheritance for an influential part of the community, namely the Nairs. K. GAJENDRAN At a family welfare centre in Tamil Nadu. (7) Household inequality: There are, often enough, basic inequalities in gender relations within the family or the household, which can take many different forms. Even in cases in which there are no overt signs of anti-female bias in, say, survival or son-preference or education, or even in promotion to higher executive positions, the family arrangements can be quite unequal in terms of sharing the burden of housework and child care. It is, for example, quite common in many societies to take it for granted that while men will naturally work outside the home, women could do it if and only if they could combine it with various inescapable and unequally shared household duties. This is sometimes called "division of labour," though women could be forgiven for seeing it as "accumulation of labour." The reach of this inequality includes not only unequal relations within the family, but also derivative inequalities in employment and recognition in the outside world. Also, the established fixity of this type of "division" or "accumulation" of labour can also have farreaching effects on the knowledge and understanding of different types of work in professional circles. When I first started working on gender inequality, in the 1970s, I remember being struck by the fact that the Handbook of Human Nutrition Requirement of the World Health Organisation (WHO), in presenting "calorie requirements" for different categories of people, chose to classify household work as "sedentary activity," requiring very little deployment of energy.3 I was, however, not able to determine precisely how this remarkable bit of information had been collected by the patrician leaders of society. II. Focussing on South Asia It is important to take note of the variety of forms that gender inequality can take. First, inequality between women and men cannot be confronted and overcome by any one set of all-purpose remedy. Second, over time the same country can move from one type of gender inequality to harbouring other forms of that inequity. I shall presently argue that there is new evidence that India is undergoing just such a transformation right at this time. Third, the different forms of gender inequality can impose diverse adversities on the lives of men and boys, in addition to ISS315 - PAGE 237 those of women and girls. In understanding the different aspects of the evil of gender inequality, we have to look beyond the predicament of women and examine the problems created for men as well by the asymmetric treatment of women. These causal connections, which (as I shall presently illustrate) can be very significant, can vary with the form of gender inequality. Finally, inequalities of different kinds can also, frequently enough, feed each other, and we have to be aware of their interlinkages. Even though part of the object of this paper is to discuss the variety of different types of gender inequality, a substantial part of my empirical focus will, in fact, be on two of the most elementary kinds of gender inequality, namely, mortality inequality and natality inequality. I shall be concerned, in particular, with gender inequality in South Asia, or the Indian subcontinent. While I shall separate out the subcontinent for special attention, I must also warn against the smugness of thinking that the United States or Western Europe is free from gender bias simply because some of the empirical generalisations that can be made about the subcontinent would not hold in the West. Given the many faces of gender inequality, much would depend on which face we look at. For example, India, along with Bangladesh, Pakistan and Sri Lanka, has had female heads of governments, which the United States or Japan has not yet had (and does not seem very likely to have in the immediate future, if I am any judge). Indeed, in the case of Bangladesh, where both the Prime Minister and the Leader of the Opposition are women, one might begin to wonder whether any man could possibly rise to a leadership position there in the near future. To take another example, I had a vastly larger proportion of tenured women colleagues when I was a Professor at Delhi University - as early as the 1960s - than I had at Harvard University in the 1990s, or presently have at Trinity College, Cambridge. To take another type of example (of a rather personal kind), in preparing my last book, Development as Freedom,4 when I was looking for a suitably early formulation of the contrast between the instrumental importance of income and wealth, on the one hand, and the intrinsic value of human life, on the other (a point of departure for my book), I found it in the words of Maitreyee, a woman intellectual depicted in the Upanishads (from the eighth century B.C.). The classic formulation of this distinction would, of course, come about four centuries later, from Aristotle, in Nicomachean Ethics, but it is interesting that the first sharp formulation of the value of living for men and women should have come from a woman thinker in a society that has not yet - three thousand years later - been able to overcome the mortality differential between women and men. Indeed, in the scale of mortality inequality, India - as well as Pakistan and Bangladesh - is close to the bottom of the league in gender disparity. And, as I shall presently argue, natality inequality is also beginning to rear its ugly head very firmly and very fast right at this time in the subcontinent. III. Exceptions and Trends In the bulk of the subcontinent, with only a few exceptions (such as Sri Lanka and the State of Kerala in India), female mortality rates are very significantly higher than what could be expected given the mortality patterns of men (in the respective age groups). This type of gender inequality need not entail any conscious homicide, and it would be a mistake to try to explain this large phenomenon by invoking the occasional cases of female infanticide that are reported from China or India; these are truly dreadful events when they occur, but they are relatively rare. Rather, the mortality disadvantage of women works mainly through a widespread neglect of health, nutrition and other interests of women that influence survival. It is sometimes presumed that there are more women than men in the world, since that is wellknown to be the case in Europe and North America, which have a female to male ratio of 1.05 or so, on the average (that is, about 105 women per 100 men). But women do not outnumber men in the world as a whole; indeed there are only about 98 women per 100 men on the globe. This ISS315 - PAGE 238 "shortfall" of women is most acute in Asia and North Africa. For example, the number of females per 100 males in the total population is 97 in Egypt and Iran, 95 in Bangladesh and Turkey, 94 in China, 93 in India and Pakistan, and 84 in Saudi Arabia (though the last ratio is considerably reduced by the presence of male migrant workers from elsewhere who come to Saudi Arabia). It has been widely observed that given similar health care and nutrition, women tend typically to have lower age-specific mortality rates than men do. Indeed, even female foetuses tend to have a lower probability of miscarriage than male foetuses have. Everywhere in the world, more male babies are born than female babies (and an even higher proportion of male foetuses are conceived compared with female foetuses), but throughout their respective lives the proportion of males goes on falling as we move to higher and higher age groups, due to typically greater male mortality rates. The excess of females over males in the population of Europe and North America comes about as a result of this greater survival chance of females in different age groups. BRENNAN LINSLEY/AP There is relatively little bias against women in terms of health care and social status in sub-Saharan Africa. However, in many parts of the world, women receive less attention and health care than men do, and particularly girls often receive very much less support than boys. As a result of this gender bias, the mortality rates of females often exceed those of males in these countries. The concept of "missing women" was devised to give some idea of the enormity of the phenomenon of women's adversity in mortality by focussing on the women who are simply not there, due to unusually high mortality compared with male mortality rates. The basic idea is to find some rough and ready way to understand the quantitative difference between (1) the actual number of women in these countries, and (2) the number we could expect to see if the gender pattern of mortality were similar in these countries as in other regions of the world that do not have a significant bias against women in terms of health care and other attentions relevant for survival. For example, if we take the ratio of women to men in sub-Saharan Africa as the standard (there is relatively little bias against women in terms of health care, social status and mortality rates in subSaharan Africa, even though the absolute numbers are quite dreadful for both men and women), then its female-male ratio of 1.022 can be used to calculate the number of missing women in women-short countries.5 For example, with India's female-male ratio of 0.93, there is a total difference of 9 per cent (of the male population) between that ratio and the standard used for comparison, namely, the sub-Saharan African ratio of 1.022. This yielded a figure of 37 million missing women already in 1986 (when I first did the estimation). Using the same sub-Saharan standard, China had 44 million missing women, and it was evident that for the world as a whole the magnitude of shortfall easily exceeded 100 million.6 Other standards and different procedures can also be used, as has been done by Ansley Coale and Stephan Klasen, getting somewhat different numbers, but invariably very large ones (Klasen's total number is about 80 million missing women).7 Gender bias in mortality does take an astonishingly heavy toll. How can this be reversed? Some economic models have tended to relate the neglect of women to the lack of economic empowerment of women. While Ester Boserup, an early feminist economist, discussed how the status and standing of women are enhanced by economic independence (such as gainful employment), others have tried to link the neglect of girls to the ISS315 - PAGE 239 higher economic returns for the family from boys compared with girls.8 I believe the former line of reasoning, which takes fuller note of social considerations that take us beyond any hard-headed calculation of relative returns from rearing girls vis-a-vis boys, is both appropriately broader and more promising, but no matter which interpretation is taken, women's gainful employment, especially in more rewarding occupations, clearly does play a role in improving the deal that women and girls get. And so does women's literacy, and other factors that can be seen as adding to the status, standing and voice of women in family decisions.9 An example that has been discussed in this context is the experience of the State of Kerala in India, which provides a sharp contrast with many other parts of the country in having little or no gender bias in mortality. Indeed, not only is the life expectancy of Kerala women at birth above 76 (compared with 70 for men), the female-male ratio of Kerala's population is 1.06 according to the 2001 Census (possibly somewhat raised by greater migration for work by men, but certainly no lower than the West European or North American ratios, which are around 1.05 or so). With its 30 million population, Kerala's example also involves a fair number of people. The causal variables related to women's empowerment can be seen as playing a role here, since Kerala has a very high level of women's literacy (nearly universal for the younger age groups), and also much more access for women to well paid and well respected jobs. One of the other influences of women's empowerment, namely a fertility decline, is also observed in Kerala, where the fertility rate has fallen very fast (much faster, incidentally, than China, despite the rigours of Chinese coercive measures in birth control), and Kerala's present fertility rate around 1.7 or 1.8 (roughly interpretable as an average of 1.7 or 1.8 children per couple) is one of the lowest in the developing world (about the same as in Britain and France, and much lower than in the United States). All these observations link with each other very well in a harmonious causal story. However, there is further need for causal discrimination in interpreting Kerala's experience. There are other special features of Kerala which may also be relevant, such as female ownership of property for an influential part of the Hindu population (the Nairs), openness to and interaction with the outside world (with the presence of Christians - about a fifth of the population - who have been much longer in Kerala - since around the fourth century - than they have been in, say, Britain, not to mention Jews who came to Kerala shortly after the fall of Jerusalem), and activist left-wing politics with a particularly egalitarian commitment, which has tended to focus strongly on issues of equity (not only between classes and castes, but also between women and men).10 IV. Issues that Need Investigation I now move away from the old - and by now much discussed - problems of gender bias in life and death (illustrated by the enormity of the size of "missing women") to other issues which are in need of greater investigation at this time. We begin by noting four substantial phenomena that happen to be quite widely observed in South Asia. (1) Undernourishment of girls over boys: At the time of birth, girls are obviously no more nutritionally deprived than boys are, but this situation changes as society's unequal treatment takes over from nature's non-discrimination. There has, in fact, been plenty of aggregative evidence on this for quite some time now.11 But this has been accompanied by some anthropological scepticism of the appropriateness of using aggregate statistics with pooled data from different regions to interpret the behaviour of individual families. However, there have also been some detailed and concretely local studies on this subject, which confirm the picture that emerges on the basis of aggregate statistics.12 One case study from India, which I myself undertook in 1983, along with Sunil Sengupta, involved the weighing of every child in two large villages. The time pattern that emerged from this micro study, which concentrated particularly on weight-for-age as the chosen indicator of nutritional level for children under five, brings out clearly how an initial condition of broad nutritional symmetry turns gradually into a situation of significant female disadvantage.13 The detailed local studies tend to confirm rather than contradict the picture that emerges from aggregate statistics. ISS315 - PAGE 240 In interpreting the causal process, it is important to emphasise that the lower level of nourishment of girls may not relate directly to their being underfed vis-a-vis boys. Often enough, the differences may particularly arise from the neglect of health care of girls compared with what boys get. There is, in fact, some direct information of comparative medical neglect of girls vis-a-vis boys in South Asia. Indeed, when I studied, with Jocelyn Kynch, admissions data from two large public hospitals in Bombay (Mumbai), it was very striking to find clear evidence that the admitted girls were typically more ill than boys, suggesting the inference that a girl has to be more stricken before she is taken to the hospital.14 Undernourishment may well result from greater morbidity, which can adversely affect both the absorption of nutrients and the performance of bodily functions. JORGE SILVA/REUTERS A malnourished mother and her daughter in Guatemala. (2) High incidence of maternal undernourishment: In South Asia maternal undernutrition is more common than in most other regions of the world.15 Comparisons of Body Mass Index (BMI), which is essentially a measure of weight for height, bring this out clearly enough, as do statistics of such consequential characteristics as the incidence of anaemia.16 (3) Prevalence of low birthweight: In South Asia, as many as 21 per cent of children are born clinically underweight (in accepted medical standards) - more than in any other substantial region in the world.17. The predicament of being low in weight in childhood seems often enough to begin at birth in the case of South Asian children. In terms of weight for age, South Asia has around 40 to 60 per cent children undernourished compared with 20 to 40 per cent undernourishment even in sub-Saharan Africa. The children start deprived and stay deprived. (4) High incidence of cardiovascular diseases: South Asia stands out as having more cardiovascular diseases than any other part of the third world. Even when other countries, such as China, have greater prevalence of the standard predisposing conditions, the Indian population seems to have more heart problems than these other countries have. It is not difficult to see that the first three observations are very likely causally connected. The neglect of the care of girls and of women in general and the underlying gender bias that they reflect would tend to yield more maternal undernourishment, and through that more foetal deprivation and distress, underweight babies, and child undernourishment. But what about the last observation - the higher incidence of cardiovascular diseases among South Asian adults? In interpreting it, we can, I would argue, draw on some pioneering work of a British medical team, led by Professor D.J.P. Barker.18 Based on English data, Barker has shown that low birth weight is closely associated with higher incidence, many decades later, of several adult diseases, including hypertension, glucose intolerance, and other cardiovascular hazards. The robustness of the statistical connections as well as the causal mechanisms involved in intrauterine growth retardation can, of course, be further investigated, but as matters stand these medical findings offer a possibility of causally interconnecting the different empirical observations related to South Asia, as I have tried to discuss in a joint paper with Siddiq Osmani.19 The application of this medical understanding to the phenomenon of high incidence of cardiovascular diseases in South Asia strongly suggests a causal pattern that goes from the nutritional neglect of women to maternal undernourishment, from there to foetal growth retardation and underweight babies, and thence to greater incidence of cardiovascular afflictions much later in adult life (along with the phenomenon of undernourished children in the shorter run). What begins as a neglect of the interests of women ends up causing adversities in the health and survival of all - even at an advanced age. ISS315 - PAGE 241 Given the uniquely critical role of women in the reproductive process, it would be hard to imagine that the deprivation to which women are subjected would not have some adverse impact on the lives of all - men as well as women and adults as well as children - who are "born of a woman" (as the Book of Job describes every person, not particularly daringly). Indeed, since men suffer disproportionately more from cardiovascular diseases, the suffering of women hit men even harder, in this respect. The extensive penalties of neglecting women's interests rebounds, it appears, on men with a vengeance. V. What Women's Agency Can Achieve These biological connections illustrate a more general point, to wit, gender inequality can hurt the interests of men as well as women. There are other - non-biological - connections that operate through women's conscious agency. The expansion of women's capabilities not only enhances women's own freedom and well-being, but also has many other effects on the lives of all.20 An enhancement of women's active agency can, in many circumstances, contribute substantially to the lives of all people - men as well as women, children as well as adults. As many studies have brought out, the greater empowerment of women tends to reduce child neglect and mortality, cut down fertility and overcrowding, and more generally, broaden social concern and care. These illustrations can be supplemented by considering the functioning of women in other areas, including in economic and political fields.21 Substantial linkages between women's agency and social achievements have been noted in many different countries. There is, for example, plenty of evidence that whenever social and economic arrangements depart from the standard practice of male ownership, women can seize business and economic initiative with much success. It is also clear that the result of women's participation is not merely to generate income for women, but also to provide many other social benefits that come from women's enhanced status and independence. The remarkable success of organisations like the Grameen Bank and the Bangladesh Rural Advancement Committee (BRAC) in Bangladesh is a good example of this, and there is some evidence that the high profile presence of women in social and political life in that country has drawn substantial support from women's economic involvement and from a changed image of the role of women. While the Revd James Fordyce might disapprove of "those masculine women," as he called them, straying into men's "province," the nature of modern Bangladesh reflects in many different ways the increasing agency of women. The precipitate fall of the total fertility rate in Bangladesh from 6.1 to 3.0 in the course of two decades (perhaps the fastest such fall in the world) is clearly related to the changed economic and social roles of women, along with increases in family planning facilities. There have also been cultural influences and developments in that direction.22 Similar changes can be observed also in parts of India where women's empowerment has expanded, with more literacy and greater economic and social involvements outside the home.23 VI. Behind a Split India While there is something to cheer in the developments I have just been discussing, and there is considerable evidence of a weakened hold of gender disparity in several fields in the subcontinent, there is also, alas, some evidence of a movement in the contrary direction, at least in one aspect of gender inequality, namely, natality inequality. This has been brought out particularly sharply by the early results of the 2001 decennial national Census of India, which are now available. Early results indicate that even though the overall female to male ratio has improved slightly for the country as a whole (with a corresponding reduction of the proportion of "missing women"), the female-male ratio for children has had a substantial decline. For India as a whole, the female-male ratio of the population under age 6 has fallen from 94.5 girls for hundred boys in 1991 to 92.7 girls per hundred boys in 2001. While there has been no such decline in some parts of the country (most notably Kerala), it has fallen very sharply in others, such as Punjab, Haryana, Gujarat and Maharashtra, which are among the richer Indian States. ISS315 - PAGE 242 Taking together all the evidence that exists, it is clear that this change reflects not a rise in female child mortality, but a fall in female births vis-a-vis male births, and is almost certainly connected with increased availability and use of gender determination of foetuses. Fearing that sex-selective abortion might occur in India, the Indian Parliament banned some years ago the use of sex determination techniques for foetuses, except when it is a by-product of other necessary medical investigation. But it appears that the enforcement of this law has been comprehensively neglected, and when questioned by Celia Dugger, the energetic correspondent of The New York Times, the police often cited difficulties in achieving successful prosecution thanks to the reluctance of mothers to give evidence of use of such techniques. I do not believe that this need be an insurmountable difficulty (other types of evidence can in fact be used for prosecution), but the reluctance of the mothers to give evidence brings out perhaps the most disturbing aspect of this natality inequality, to wit, the "son preference" that many Indian mothers themselves seem to have. This face of gender inequality cannot, therefore, be removed, at least in the short run, by the enhancement of women's empowerment and agency, since that agency is itself an integral part of the cause of natality inequality. Policy initiatives have to take adequate note of the fact that the pattern of gender inequality seems to be shifting in India, right at this time, from mortality inequality (the female life expectancy at birth is by now two years higher than male life expectancy in India) to natality inequality. Indeed, there is clear evidence that traditional routes of changing gender inequality, through using public policy to influence female education and female economic participation, may not serve as a path to the removal of natality inequality. A sharp pointer in that direction comes from countries in East Asia, which all have high levels of female education and economic participation. Despite these achievements, compared with the biologically common ratio across the world of 95 girls being born per hundred boys, Singapore and Taiwan have 92 girls, South Korea only 88, and China a mere 86. In fact, South Korea's overall female-male ratio for children is also a meagre 88 girls for 100 boys and China's 85 girls for 100 boys. In comparison, the Indian ratio of 92.7 girls for 100 boys (though lower than its previous figure of 94.5) still looks far less unfavourable.24 However, there are more grounds for concern than may be suggested by the current all-India average. First, there are substantial variations within India, and the all-India average hides the fact that there are States in India where the female-male ratio for children is very much lower than the Indian average. Second, it has to be asked whether with the spread of sex-selective abortion, India may catch up with - and perhaps even go beyond - Korea and China. There is, in fact, strong evidence that this is happening in a big way in parts of the country. There is, however, something of a social and cultural divide across India, splitting the country into two nearly contiguous halves, in the extent of anti-female bias in natality and post-natality mortality. Since more boys are born than girls everywhere in the world, even without sex-specific abortion, we can use as a classificatory benchmark the female-male ratio among children in advanced industrial countries. The female-male ratio for the 0-5 age group is 94.8 in Germany, 95.0 in the U.K., and 95.7 in the U.S., and perhaps we can sensibly pick the German ratio of 94.8 as the cut-off point below which we should suspect anti-female intervention. The use of this dividing line produces a remarkable geographical split of India. There are the States in the north and the west where the female-male ratio of children is consistently below the benchmark figure, led by Punjab, Haryana, Delhi and Gujarat (with ratios between 79.3 and 87.8), and also including, among others, Himachal Pradesh, Madhya Pradesh, Rajasthan, Uttar Pradesh, Maharashtra, Jammu and Kashmir, and Bihar (a tiny exception is Dadra and Nagar Haveli, with less than a quarter million people altogether). On the other side of the divide, the States in the east and the south tend to have female-male ratios that are above the benchmark line of 94.8 girls per 100 boys: with Kerala, Andhra Pradesh, West Bengal and Assam (each between 96.3 and 96.6), and also, among others, Orissa, Karnataka and the northeastern States to the east of Bangladesh (Meghalaya, Mizoram, Manipur, Nagaland, Arunachal Pradesh). ISS315 - PAGE 243 One significant exception to this neat pattern of adjoining division is, however, provided by Tamil Nadu, where the female-male ratio is just below 94, which is higher than the ratio of any State in the deficit list, but still just below the cut-off line used for the partitioning (94.8). The astonishing finding is not that one particular State seems to provide a marginal misfit, but how the vast majority of the Indian States fall firmly into two contiguous halves, classified broadly into the north and the west, on one side, and the south and the east, on the other. Indeed, every State in the north and the west (with the slight exception of the tiny Union Territory of Dadra and Nagar Haveli) has strictly lower female-male ratio of children than every State in the east and the south (even Tamil Nadu fits into this classification), and this indeed is quite remarkable. The pattern of female-male ratio of children produces a much sharper regional classification than does the female-male ratio of mortality of children, even though the two are also fairly strongly correlated. The female-male ratio in child mortality varies between 0.91 in West Bengal and 0.93 in Kerala, on one side, in the southern and eastern group, to 1.30 in Punjab, Haryana and Uttar Pradesh, with high ratios also in Gujarat, Bihar and Rajasthan, in the northern and western group. The north and the west have clear characteristics of anti-female bias in a way that is not present or at least not yet visible - in most of the east and the south. This contrast does not have any immediate economic explanation. The States with anti-female bias include rich ones (Punjab and Haryana) as well as poor States (Madhya Pradesh and Uttar Pradesh), and fast-growing States (Gujarat and Maharashtra) as well as growth failures (Bihar and Uttar Pradesh). Also, the incidence of sex-specific abortions cannot be explained by the availability of medical resources for determining the sex of the foetus: Kerala and West Bengal in the non-deficit list, both with the ratio of 96.3 girls to 100 boys (comfortably higher than the benchmark cut-off of 94.8), have at least as much medical facilities as in such deficit States as Madhya Pradesh or Rajasthan. If commercial facilities for sex-selected abortion are infrequent in Kerala or West Bengal, it is because of a low demand for those specific services, rather than any great supply side barrier. This suggests that we have to look beyond economic resources or material prosperity or GNP growth into broadly cultural and social influences. There are a variety of potential connections to be considered here, and the linking of these demographic features with the rich subject matter of social anthropology and cultural studies would certainly be important to pursue.25 There is perhaps a common link with politics as well. Indeed, it has been noted, in other contexts, that the States in the north and the west have, by and large, given much more room to religion-based sectarian politics than have the east or the south, where religion-centred parties have had very little success. For example, of the 197 members of Parliament from the Bharatiya Janata Party (BJP) and the Shiv Sena elected in 1999, as many 169 won from States in the north and the west. Even if we take out the BJP members who, though elected from Bihar or Madhya Pradesh, come from the recently formed relatively "eastern" States of Jharkhand and Chhatisgarh (which, incidentally, do have "eastern" female-male ratios above the benchmark line), the predominance of the north and the west in the representation of the Sangh Parivar remains strong. It is not easy to settle, without further scrutiny, how significant these regional, cultural or political associations are, and how (and even in which direction) the causal influences operate. But the remarkable geographical division of India into two largely contiguous parts in terms of female-male ratio among children (reflecting the combined influence of inequality in natality and post-natal mortality) does call for acknowledgement and further analysis. It would also be important to keep a close watch on whether the incidence of sex-specific abortions will significantly increase in States in which they are at this time quite uncommon. VII. Summing up I may end by trying briefly to identify some of the principal issues I have tried to discuss. First, I have argued for the need to take a plural view of gender inequality, which can have many different faces. The prominent faces of gender injustice can vary from one region to another, and also from one period to the next. ISS315 - PAGE 244 Second, the effects of gender inequality, which can impoverish the lives of men as well as women, can be more fully understood by taking detailed empirical note of specific forms of inequality that can be found in particular regions. Gender inequality hurts the interests not only of girls and grown-up women, but also of boys and men, through biological connections (such as childhood undernourishment and cardiovascular diseases at later ages) and also through societal connections (including in politics and in economic and social life). To have an adequate appreciation of the far-reaching effects of disparities between women and men, we have to recognise the basic fact that gender inequality is not one affliction, but many, with varying reach on the lives of women and men, and of girls and boys. There is also the need to reexamine and closely scrutinise some lessons that we have tended to draw from past empirical works. There are no good reasons to abandon the understanding that the impact of women's empowerment in enhancing the voice and influence of women does help to reduce gender inequality of many different kinds, and can also reduce the indirect penalties that men suffer from the subjugation of women. However, the growing phenomenon of natality inequality raises questions that are basically much more complex. When women in some regions themselves strongly prefer having boys to girls, the remedying of the consequent natality inequality calls at least for broader demands on women's agency, in addition to examining other possible influences. Indeed, in dealing with the new - "high tech" - face of gender disparity, in the form of natality inequality, there is a need to go beyond just the agency of women, but to look also for more critical assessment of received values. When anti-female bias in action (such as sex-specific abortion) reflects the hold of traditional masculinist values from which mothers themselves may not be immune, what is needed is not just freedom of action but also freedom of thought - in women's ability and willingness to question received values. Informed and critical agency is important in combating inequality of every kind. Gender inequality, including its many faces, is no exception. Based on the text of an inauguration lecture for the new Radcliffe Institute at Harvard University, on April 24, 2001. A shortened version of this paper was published in The New Republic on September 17, 2001; this is the full text. ENDNOTES 1. See William St. Clair, The Godwins and the Shelleys (New York: Norton, 1989), pp. 504-8. 2. Bina Agarwal, among others, has investigated the far-reaching effects of landlessness of women in many agricultural economies; see particularly her A Field of One's Own (Cambridge: Cambridge University Press, 1994). 3. World Health Organisation, Handbook of Human Nutrition Requirement (Geneva: WHO, 1974); this was based on the report of a high-level Expert Committee jointly appointed by the WHO and FAO - the Food and Agriculture Organisation. 4. Development as Freedom (New York: Knopf, and Oxford: Oxford University Press, 1999), Chapter 1. 5. Presented in my "More Than a Hundred Million Women Are Missing," The New York Review of Books, Christmas Number, December 20, 1990, and in "Missing Women," British Medical Journal, 304 (March 1992). 6. The fact that I had used the sub-Saharan African ratio as the standard, rather than the European or North American ratio, was missed by some of my critics, who assumed (wrongly as it happens) that I was comparing the developing countries with advanced Western ones; see for example Ansley Coale, "Excess Female Mortality ISS315 - PAGE 245 and the Balances of the Sexes in the Population: An Estimate of the Number of 'Missing Females'," Population and Development Review, 17 (1991). In fact, the estimation of "missing women" was based on the contrasts within the so-called third world, in particular between sub-Saharan Africa, on the one hand, and Asia and North Africa, on the other. The exact methods used were more elaborately discussed in my "Africa and India: What Do We Have to Learn from Each Other?," in Kenneth J. Arrow, ed., The Balance between Industry and Agriculture in Economic Development (London: Macmillan, 1988); and (with Jean Drze), Hunger and Public Action (Oxford: Clarendon Press, 1989). 7. Stephan Klasen, "'Missing Women' Reconsidered," World Development, 22 (1994). 8. See Ester Boserup, Women's Role in Economic Development (London: Allen & Unwin, 1970); M.R. Rosenzweig and T.P. Schultz, "Market Opportunities, Genetic Endowments, and Intrafamily Resource Distribution," American Economic Review, 72 (1982). 9. On this see my "Women and Cooperative Conflict," in Irene Tinker, Persistent Inequalities (New York: Oxford University Press, 1990). See also J.C. Caldwell, "Routes to Low Mortality in Poor Countries," Population and Development Review, 12 (1986); Jere Behrman and B.L. Wolfe, "How Does Mother's Schooling Affect Family Health, Nutrition, Medical Care Usage and Household Sanitation," Journal of Econometrics, 36 (1987); Jean Dreze and Amartya Sen, Hunger and Public Action (Oxford: Clarendon Press, 1989). 10. I have discussed these factors in my "More Than a Hundred Million Women Are Missing" (1990). See also Jean Dreze and Amartya Sen, India: Economic Development and Social Opportunity (Delhi: Oxford University Press, 1995), and particularly V.K. Ramachandran, "Kerala's Development Achievements," in Jean Dreze and Amartya Sen, eds., Indian Development: Selected Regional Perspectives (Delhi: Oxford University Press, 1996). 11. See the literature on this cited in Development as Freedom (1999). 12. One of the earliest and pioneering studies was by Lincoln Chen, E. Huq and S. D'Souza, "Sex Bias in the Family Allocation of Food and Health Care in Rural Bangladesh," Population and Development Review, 7 (1981). 13. See my joint paper with Sunil Sengupta, "Malnutrition of Rural Indian Children and the Sex Bias," Economic and Political Weekly, 19 (1983). 14. See my joint paper with Jocelyn Kynch, "Indian Women: Well-being and Survival," Cambridge Journal of Economics, 7 (1983), and also Resources, Values and Development (Cambridge, MA: Harvard University Press, 1984). 15. See Peter Svedberg, Poverty and Undernutrition: Theory and Measurement (Oxford: Clarendon Press, 2000), for an illuminating and thorough analysis of comparative nutrition in South Asia and sub-Saharan Africa. 16. See S.R. Osmani, "Poverty and Nutrition in South Asia," in ACC/SCN, Nutrition and Poverty (1997), and also Nutrition Policy Paper No. 16 (Geneva: WHO, 1997). This is the First Abraham Horowitz Lecture of the United Nations. See also the references to the literature cited by Osmani. 17. On this see Osmani, "Poverty and Nutrition in South Asia" (1997), and also the references cited there. 18. See D.J.P. Barker, "Intrauterine Growth Retardation and Adult Disease," Current Obstetrics and Gynaecology, 3 (1993); "Foetal Origins of Coronary Heart Disease," British Medical Journal, 311 (1995); Mothers, Babies and Diseases in Later Life (London: Churchill Livingstone, 1998). See also P.D. Gluckman, K.M. Godfrey, J.E. Harding, J.A. Owens, and J.S. Robinson, "Fetal Nutrition and Cardiovascular Disease in Adult Life," Lancet, 341 (1995). 19. Siddiq Osmani and Amartya Sen, "The Hidden Penalties of Gender Inequality: Fetal Origins of Ill-Health," ISS315 - PAGE 246 mimeographed, Trinity College, Cambridge, 2001. 20. On the extensive role and reach of capabilities of women, see particularly Martha Nussbaum, Women and Human Development: The Capabilities Approach (Cambridge: Cambridge University Press, 2000). 21. UNDP's Human Development Report 1995 (New York: United Nations, forthcoming: 1995) presents an intercountry investigation of gender differences in social, political and business leadership, in addition to reporting on gender inequality in terms of more conventional indicators. See also Sudhir Anand and Amartya Sen, "Gender Inequality in Human Development: Theories and Measurement," in UNDP, Background Papers: Human Development Report 1995 (New York: United Nations, 1996). 22. The complex influences that operate in fertility decline, including cultural adaptations, have been discussed by Alaka Basu and Sajeda Amin in "Conditioning Factors for Fertility Decline in Bengal: History, Language Identity, and Openness to Innovations," Population and Development Review, 26 (2000). 23. A recent study of local governmental decisions in India brings out the substantial nature of this change, as a consequence of women coming to occupy leadership positions in the "Panchayats" (local administrative bodies); see Raghabendra Chattopadhyay and Esther Duflo, "Women's Leadership and Policy Decisions: Evidence from a Nationwide Randomised Experiment in India," mimeographed, Department of Economics, MIT, 2001. 24. Note, however, that the Chinese and Korean figures cover children between 0 and 4, whereas the Indian figures relate to children between 0 and 6. However, even with appropriate age adjustment, the general comparison of female-male ratios holds in much the same way. 25. See, among other contributions, Irawati Karve, Kinship Organization in India (Bombay: Asia Publishing House, 1965); Pranab Bardhan, "On Life and Death Questions," Economic and Political Weekly, Special Number, 9 (1974); David Sopher, ed., An Exploration of India: Geographical, Perspectives on Society and Culture (Ithaca, NY: Cornell University Press, 1980); Barbara Miller, The Endangered Sex (Ithaca, NY: Cornell University Press, 1981); Tim Dyson and Mick Moore, "On Kinship Structure, Female Autonomy, and Demographic Behaviour in India," Population and Development Review, 9 (1983); Monica Das Gupta, "Selective Discrimination against Female Children in Rural Punjab," Population and Development Review, 13 (1987); Alaka M. Basu, Culture, the Status of Women and Demographic Behaviour (Oxford: Clarendon Press, 1992); Satish Balram Agnihotri, Sex Ratio Patterns in the Indian Population (New Delhi: Sage, 2000). Copyrights 2001, Frontline. Republication or redissemination of the contents of this screen are expressly prohibited without the written consent of Frontline ISS315 - PAGE 247 TABLE 1 WEEKLY EARNINGS OF WOMEN AND MEN: 1994 OCCUPATION REGISTERED NURSE ELEMENTARY SCHOOL TEACHER CASHIER GENERAL OFFICE CLERK HEALTH AIDE (U.S. DEPARTMENT OF LABOR) WOMEN $680.00 $621.00 $220.00 $367.00 $271.00 MEN $709.00 $650.00 $264.00 $403.00 $301.00 TABLE 2 WAGE GAP BY AGE: 1994 AGE 25-19 30-34 35-40 40-44 45-49 50-54 60-64 65+ % OF MEN'S EARNINGS 90.1% 78.3 73.8 66.6 63.3 62.9 67.7 61.1 (U.S. DEPARTMENT OF LABOR, 1998) ISS315 - PAGE 248 TABLE 3 WEEKLY WAGES BY GENDER: 1998 OCCUPATION MANAGEMENT & PROFESSIONAL TECHNICAL, SALES SERVICE OCCUPATIONS PRECISION PRODUCTION OPERATORS FARMING, FORESTRY, FISHING TOTAL (ABOVE AGE 16) (U.S. DEPARTMENT OF LABOR, 1998) WOMEN $647.00 $418.00 $290.00 $404.00 $321.00 $297.00 $596.00 MEN $865.00 $606.00 $388.00 $585.00 $452.00 $303.00 $455.00 ISS315 - PAGE 249 TABLE 4 INCOME BY OCCUPATION OCCUPATION EXECUTIVES, ADMINISTRATORS, & MANAGERS PROFESSIONAL SPECIALISTS LAWYERS AND JUDGES TECHNICAL SALES ADMINISTRATIVE SUPPORT AND CLERICAL PRECISION PRODUCTS POLICE AND FIREFIGHTERS FARMING, FISHING, FORESTRY FARM OPERATORS AND MANAGERS TOTAL MEN 34,755 36,261 61,780 27,849 23,197 23,835 23,907 40,033 15,865 21,809 25,862 WOMEN 51,351 51,654 114,947 40,546 37,248 31,153 31,631 44,284 18,855 20,658 36,679 ISS315 - PAGE 250 TABLE 5 INCOME BY AGE (U.S. CENSUS BUREAU, 1999) AGE 25 34 35 44 45 54 55 64 65 OLDER WOMEN 25,556 27,186 28,424 26,144 21,858 MEN 31,262 37,663 41,583 40,654 30,259 INCOME BY EDUCATION (U.S. CENSUS BUREAU, 1999) EDUCATION < 9TH GRADE 9TH 12TH HIGH SCHOOL SOME COLLEGE ASSOCIATE BACHELOR MASTER'S PROFESSIONAL PH.D. TOTAL (25 > ) 14,132 15,847 21,963 26,024 28,377 35,408 42,002 55,460 52,167 26,711 WOMEN 18,553 23,438 30,868 35,949 38,483 49,982 60,168 90,653 69,188 36,679 MEN ISS315 - PAGE 251 ISS315 - PAGE 252 ISS315 - PAGE 253 ...
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