Eastwood,+2001+_Pop+Matters_

Eastwood,+2001+_Pop+Matters_ - Demographic Transition and...

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Unformatted text preview: Demographic Transition and Poverty: Effectsvia: Ecdnornic Growth, Distribution, ' ' ' _ and Conversion _ sooner nasrw'oon AND MICHAEL Mirrors 1. THE HEART OF THE MATTER Demographic transition affects'pov'erty in several ways. The iiterature concentrates on one aspect of the transitionm-«populafion size, as it is increased by population growth and as it increases population density-find on one'effect of this, on povertyI via economic growth. The effect ofthe size aspect of transition on poverty is probably important and usually negativembut it is controversial, because contingent on socio- economic circumstances and scarcities in ways thatlrender it variable in space, and not very robust over time. More robust and important may be another aspect of the transition-e-changing age—suucmro—as the main driving force behind two other effects on poverty: via the distribution of consumption and income (CI), and via the efficiency with which the poor and near-poor convert CI into Well—being. Growth » effects of demographic transition, especially of changing fertility, on poverty, should be explored with both micro— and macro—ievel evidence. But the effect of high fertility on poverty via income distribution shouicl be explored mainly at macro levei; the main transmission mechanism—postuiateci by Malthusauis through macro markets for labour and food. (When unskilled labourers’ mean fertiiity is high, a labouring famiiy cannot, justby its own prudentiai restraint, safeguard itself against dear food and cheap work.) Conversion effects are best explored mainly at micro level. The modern demographic transition is manifested as sharp fails in chiid mortality and, significantly later,: in fertility. These falis (1-) first sharply raise, then lower, the rate of population grooms—tire popuiation—size aspect; and (2) first substantiaiiy ‘ While this does not prove causality, hundreds of empirical studies confirm that in today’s developing countries largeriioosehoids have higher poverty incidence (Krishnaii £934; Lloyd 1994; Lipton 1983a, 1994: 12~1 3). For example, in urban Colombia in the 19205, inthe poorest docile ofhouseholds, 78% contained 3 or more persons, as againém only 12% forail househoids (Birdsail 1979}. Recent evidence suggests that there are no large regional exceptions, as was sometimes ciaimed for West Africa, for example; the household surveys in Ghana, the Ivory Coast (Giewwe 1990; Kaimani 1993: 53.4) and Mauritania (Couiombe and Mackay 1994: 48), showastrong positive iink of household size to payerty incidence. Probablyail. or almost all, the 41 deveioping countries with reliable household suréeys show this relationship in both iii-ban and rural areas. Poverty intensity” also often increases with household size (cg; Bauer and Mason 1993: 34). 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Growth is the product of (1) extra output . per unit of investment ins—that is, the total marginal product of—physical and human capital and (2) the proportion of output that is saved (domestically, or by attracting net foreign savings to finance an import surplus). So we can separate the demographics ~+ growth —+ poverty sequence into effects via (1) the efficiency of extra capital, human or physical, generated by savings and (2) the savings rate. 0 Demographic transition can also alter inequality as it affects poverty: via income distribution between poor and nonnpoor (especially near-poor), and also {ifintem sity or severity is the poverty measure) between just—poor, poorer, and very poor.7 Poverty is not directly affected by ‘any old‘ inequality, for example, by the gap between the top 1 percent and the next—richest 9 percent.3 Section 3, using BL, shows that the demographics ks inequality ~a- poverty sequence can operate in two ways, here termed the dependency effect and the acquisition effect. 0 Thirdly, and almost ignored in the literature, each ‘dose’ of demographic change may have a once-forwall effect on current consumption poverty by altering the consumptionmincome ratio of the poor and nearwpoor. I So the effects of demographic transition on poverty are complex partly because ‘poverty’, ‘transition’, and ‘effects’ each have several connotations: 1. Poverty is measured by (a) incidence, (b) intensity, or (c) severity.9 2. Each of the three measures in a country can be affected by either of mo demo- graphic transition aspects: changing (a) population size or (b) age structure. (They are linked, but the links shift: age—specific fertility and mortality vary Over space and time). ' ' ‘ l 3. Each of the two demographic transition aspects can influence each of the three poverty measures via five effects: by changing: Economic growth, as the transition alters (a) savings rates or (b) the total derivative of output to physical andlor human capital; low—end inequality, as the transition alters, differently for the poor (or near-poor) and the nompoor, (c) dependency ratios, or '(d) incentives per 7 If many of the non-poor are near-poor, this hugely increases the impact of regressive redistribution {if it pushes many such people even very slightly below the poverty line) on poverty incidence, though the effect on intensity and severity may be tiny. Because incidence is arbad (though popular) measure of poverty, its use leads to counter-intuitive inferences about the effects on poverty incidence of redistribution (whether or not due to demographic change). Redistriburing consumption from the very poor to those just below the poverty line, Le. regressively among the poor, will push some of the just-poor above the line and thus reduce poverty incidence Redistributing income from the just-poor to the rich does not increase poverty incidence, 'only’ intensity and severity. ' ' I l 8 lt does, however, have a disproportionately large effect on the Gini coefficient. It'may well be the main mechanism at work in those few cases when a Kuznets‘curve has been Verified (Lccaillon etal. 1983‘). This underlines the need not to use Giui, Theil, etc. coefficients to measure inequality when we consider it as a cause, effect, or correlate of poverty. (It may be that top-end inequality affects poverty indirectly—cg. if such inequality arises from concentration ofluarltet power to buy unskilled laboiir andlor to sell items consumedbythepoor.) ' U l" ‘ I L ‘ J 9 This assumes We measure absolute poverty at one poverty line. Matters become even more involved with different (absolute) poverty lines, or relative or rubiecn've measures: ' ‘ ' 7‘ 3 Demographic Transition and Poverty 217 worker toacquire income; or (e) the part of poor people’s incomes that they consume, as the transition alters low-end consumption~income ratios. So there are at least 3 x 2 x 5, or 60, paths from demographic transition to poverty. Not one is clearly unimportant! Two more bricks must be put in place. Difl'erential demographic transition means, among other things, that the poor usually experience declining mortality and subse— quently falling fertility later, and with a longer lag between them, than the non-poor (see above). Mutual causation means that rapid population growth—with its usual accompaniments of early first births, large families, high child—adult ratios and near spacing of sihfingommay he not only a cause of poverty through the above mecha« nisrns, but also a consequence of povertymprobahly due largely to constraints on, and rational behavior by, the poor. ‘ 7 In this example, faster demographic transition is assumed to reduce poverty levels, and this reduction is assumed to accelerate transition. in fact, either part of the mutual causation might work the opposite way. More people could bring economies of scale of various sorts, for example, making it pay in some African economies to put in transport links that led to farm intensification; if this path is genuine, rapid demographic transition would slow down progress along it, toward faster growth and hence poverty reduction. Similarly, if faster population growth permits ‘infection efl’ects’ and economies of scale in research (Simon £986), this process too would be slowed down by. faster demographic transition. It is also possible to envisage circumstances where poverty reduction leads to faster, rather than-down, population growth; this was Malthus’s initial position, though one he drastically modified in his later work. _ _ Fartha Dasgupta (2600) has argued persuasively that—especially if we add the interaction of environmental change—it is not feasible to sort out the mutual demographioeconomic chains {from or to population, via growth and distri- bution, to or from poverty) by normal economic empirics. We seem to be reduced either to exchanging examples and anecdotes, or to econometrics inevitably dependent—because of degrees—of-freedom problems,- if nothing else—on selecting some variables (and some functional forms) and omitting others. Also time series for poverty are scarce, and usually on a time scale shorter than that of demographic change {for which normally only decennial census data are available—household surveys being usually one—off). , l ' The rest of this chapter is structured as follows. Section 3, the bulk of the chapter, condenses the results of EL‘V{11999); indicating large negative growth and distribu- tion etfectsij—of similar size-wot high fertility on poverty. Section 4 argues that the ‘conversion effect’ of highfertility 011.900: and near—poor people’s capacity to turn a given CI into capabilities and well—being is also negative and large. Section 5 sug— gests that the distrihution and conversion effects of fertility 'on poverty are likely to _ be more robustra'ud universal than the growth effect. 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Data and McthOds" Data Choices 0:: poverty,m there are three issues, BL’s choice ofconceptis ‘narrow' absolute private consumption poverty, PCP. A person suffers PCP if and, only if she falls below a fixed level of private consumptionmthe povertylineT—in the country and year of survey. Consumption indicates command. over resources more reliably and stably than income. It would be desirable to include free or subsidized consumption ofstate-provided, collective, or non-price—exciudable public goods, but this is seldom available from surveys, and raises valuation problems; hence per-person ‘paid~for’ {private} consumption29 is used. Elie choice of poverty line is the widely available, though arbitrary, ‘POVSO’: a level of private consumption that,_ if exchanged into dollars at rates adjusted for purchasingfpower parity, could command $30 per person per month (in 1985 prices) of the bundle of goods and services consumed in 1985 by an average citizen of ‘Earthia’.20 For adding up poverty below the line, EL use inci-. dence and intensity (incidence times proportion by which the average poor person’s consumption falls below the line). EL also need to measure real resource flows per person—to isolate the effect of fertility on distribution, and to act as dependent variable in analyzing the effect of fertility on growth. EL report equations using two such indicators: mean real GDP, because its growth is a policy target, and indicates a nation’s resource flow; and avcou, because its variance does significantly better than_variance of real per capita GDP in explaining international differences in pout-11y.21 The right measures and lags for fertility depend on the effects hypothesized on poverty, whether via growth or distribution. Possible indicators are: crude birth rate; 17 For full discussion, see EL. - “l A cross-section of the most recent available nationvwide household survey data—all post-1980 and in all but five cases post- 1987—is used for both poverty and avcou'. The data were kindly made available to BL by Ravaflion and Chen. who have screened them for reliability and nation—wide coverage (1996). Data are for 59 countries, 46 developing and 13 transitional. is in surveys used by EL, consumption includes peasant household enterprises’ self-consumed product (mostly staple crops), usually at retail value. Consumption per equivalent adult is preferable, but raist measurement problems (Deaton and Muellbauer 1989), is often unobtainable, and where obtainable Seldom ranks large groups differently from consumption per person. i ‘ 7. ' ' 1° Comparisons among countries of real average consumption (moon), real average GDP, and poverty lines are made using purchasing-power panties from the latest (5.6)1version,of the Penn World Tables. - 2‘ EL use avcou, alongside fertility indicators, to predict national poverty levels through the distribution channel. However, KS {aod‘fibwhere theyanalyze the poverty effect of fertility through the growth channel) also use fertility indicators to predict not AVCDN but mean realGDl’, and there they also estimate the elasticity of avcon with respect to mean real GDP. - ‘ ' Demographic Transition and Poverty 221 net birth rate, that is; crude birth rate not of infant deaths; dependency ratio; and total fertility rate. The net birth rate represents durable additions better, especially since many infant deaths-(in developing countries often 10—26% of all births} occur in the first few weeks of life; The net birth rate is used in KS’S analysis of the fertility- towgrowth link, and-in EL’s‘ analysis-of the growth channel.22 - Data Types rWhy did BL decide to teSt fertility—to—poverty links with macro data?- Probably only household surveyswoften, only panele provide data to estimate individual decisions underlying changes in fertility, and also in many socioeconomic variables, such as labour supply, that affect poverty (Schultz 1981 ). Edwever, a data set on PCP at an internatibnally comparable norm has recently become available; it is for countries. Even ifwe can get househoidwleveil poverty data, comparable data for fertility are hard to come by; vital events are infrequent, even in quite large sub« samples. Official fertility data from large samples, such as lndia’s Sample Registration Survey, are never made available at household level and'seldom even at reasonably disaggregated levels such as an lndian District (typically ZMS million people}. But all this merely says that it is easier to look for a lost key where there is a street~light—little use if it is elsewhere. There is a more positive reason for using macro data: that the main posited links between fertility and poverty operate only at national level, or via large interacting markets. The Malthus hypothesis-writer population growth increases poverty by depressing real wage rates, bidding up market labour supply and food demand—cannot be tested by using micro data that reveal only the impact of population characteristics on each household’s labour supply, food demand, and poverty. Further, internal migration weakens effects at subnational level, even in large geographical units; for instance, if an Indian State has higher fertility than its neigh— bors and this threatens higher poverty, one would expect migration to other States, diffusing the effect. Only cross-national analysis can capture such effects in macro markets. - EL use cross-section data because on time series on poverty are. inadequate. Ravaliion and Chen (1996) find pairs of observations on national poverty for 42 developing and transitional economies, but most of the pairs are separated by five years or fewer; only very few countries have reliable estimates of demographic change over such short periods.23 Until more long-term poverty (or more short-term demographic) data sets are available, international comparative work on the links between poverty and demography must rely on cross-section data. Econometric Issues—Causality In assessing whether there is reciprocal causation between the birth: rate andpoverty incidence, a problem arises: for the vast majority 22 In modeling the'discribution channel we tried a range of fertility indicators, and two other demo- graphic indicators (net death rate, namely, crude death rate less infant deaths, and population growth rate. Agaht'uet birth rate does beat in a Statistical sense, for reasons'discufised in Sect. 3.3). ‘ . 23 The only base for fertility data decennial censuses. Except in a cases such as India‘s Sample Registrationlsurveys,‘ annual data'are' inevitably arbitrary interpolations, giving no genuine information about fertility changes over short periods. 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Table 9.2. POV$30 Set afRegmsions —4.37 (---1.84) 0.843 LAVCON 234.9 (01' 170121310 25.76 (or —9.32 (—2.09) 1.87 (0.57) 1.59 (0.43) ~0.52 («—0.59) —0.14{~o.19) 10.19 (3111* 190.42 (01* NBR 1.051.592) NBRIO 0.851 mvcon 4.58‘10.101) Regressions -8.13 (-6.93) 3.41 {3.23)‘ —€}.64 (—2.6?)‘ ' 9.97 (3.78)' 0.854 197.99 (a? NBRIG 01.57 {01' . LAVCON 59,'except for regression 5 where s =. 38; (E!) values in parentheses next to the coafiicienm are Matias; they are p-values for the Wald statistics; (1:) all regressions are free from statistical problems (toss outlined in £11. 25); (d) Wald tests: in each case a joint test is performed of the nuli hypothesis that the coefficient on that term, and those on ail interaction terms inciuding it, are zero. Thus, ifPOVSSom a! + 22271va + a3‘NBR10 + (24"{mvoo1v‘NBRmL than thede test oftlie significance of NBRll'l uses the 111111: 1113 = fl and :24 == 0; (2) variable definitions in the Appendix. 18. moooa 11. LAVCON*?OPGIO . Adj. :13 12. DUMLATAM 13. DUMGB 14. PHY SIC 15. NURSE Independent variables 1. LAVCON 2. N33 3. NBRIO 4. NDR 6. POPGIO 7. LAVCON‘NBR 8. LAVCON‘NBRIO 9. LAVCON‘NDR 10. LAVCON'NDREG 5. NDREG Notes: (a) Sample size 15. PRIMENR 17. sacENa Wald tests Demographic Transition and Poverty 225 i 1,: ', l. i E E l . l [i .l :1 Equation isgows that lagged population grovvdrcan replace lagged not births quite ' successfully?l El. therefore consider whether tho intcrnational differences in poverty are being driven, not by NBR, but by not death rate (NDR) (with which NBR is highly - corrciated across countries), in eqns _4 to 5, which may be compared to eqns 1 to 2. NDRZG is insignificant in eqn 4. yet when both MR and NDRIG are included (in eqn 5), both appear significant. EL conclude. that the problem of causation makes eqn 5 uninterprctablc; not only are their: are piainly good reasons to expect two~ way causation between net deaths and pchcrtyf'2 but the Waid test statistics, unlike those for eqn 2, give no grounds for asserting that causation runs principaliy in one direction. _ ' _‘ _ ' Equation 6 introduces the set ofsociai variables to con 1. They achievesignificance neither individually nor collectively (but'sce the discussion of the Latin American dummy below). Equation 7 introduces a dummy variable for Guinea—Bissau. This dummy fails to achieve significance at the-.3 percent levei, yet the very large estimated excess poverty incidence is worth noting {and becomes significant for measures of poverty intensity, and incidence at $21). ' . 5 The effects are summarized in Tables 9.3 and 9.4. Because ofthe interactionbcnvecn " NBRIO and swoon, the parameter estimates in eqn 1 are not directly interpretable as marginal effects: for example, the marginal effect of a change in NBRIO on POV$30 depends on the level ofravcon. The negative sign on the interaction term implies, reasonably, that the poorer tho country, the more POV$30 is raised by extra not births. Table 9.3 reports the marginal effects of NBRLO on POV$30 at the 25th, 50th, and 75th percentile vaiucs of LAVCON: the effect at the median of LAVCON is of the order of 0.6 (corresponding to an elasticity of about 1.1). This may be put in context: the semi-interquartile range of-NBRIO is about 7 births per LOGO and the median ofNBR is about 4 per 1,000 below the median of NBRIO. So, for example, it is predicted that a hypothetical country at the medians ofNBRlO, NBR, and LAVCON would attain, by virtue of the fall of 4 per 1,060 in the net birth rate in the pre~survey decade, a fall of some 2.4 pcrccnt in POV$30 in the next decade via the distribution channel alone. Fora country at the 25th percentile of the LAVCON distribution, the predicted fall would be about 3.4 percent. The (diagnostic) eqn 2 apart, Table 9.3 also shows that the estimated effects of changes in NBRIO on POV$30 via distribution are fairly stabie to specification changes. :- , ' Tobie 9.4 rcportsthe responscof I’OV$3O to changes in mason. Quite apart from changes in I‘iBRIO, the functional form Of our equation implies that the elasticity of 3‘ Experimentation with the total fertfiily rate (TER) and the dependency ratio (DR) produced poor results. in the cascofTFR the eitpianation probabiy lies in the long and diffuse lagbctumen this variabie and CBR. For DR, as explained in Section 3.4, it is high variability of this variable across househoEdS—mratber than its average value—which would bdéxpected to be associated with high poverty at constant moon. 32 The poor have higher death rated-both became they have a highér proportion of persons in the ‘death-pronc‘ age groupofito '5 (because; they marry younger and have higher family—size norms} and because their nutrition and sanitation'incrcase agcopecific mortality: thus poverty causes net deaths. Poor households also feature higher death rates among workers, making a causai limit from lagged net deaths to poverty probable also. 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WHEN i'S'01OIHHN IZ'Z «Jch ' 9'2" :HGN GIG =OI‘d‘JN ZI'O ‘OIHHN IG‘FOIHSN 65‘s «)1qu -‘ film-MA qudwé’awza 2111 v.1 9314qu 33m "10 (waned u!) osmoafo 1943? am no wfla rmazssafiaafo :33 OE$AOJ 'E'6 31921 EST) iolfiflN 0929:0135»: ' ssh ionifiN 230 I Fertility Poverty, and the Family assets, where the rates of reward are raised, relative to labour, by Malthusia'n effects. Other things equal, these factor reward changes imply a- positive acquisition effect. 3.5. Updating KS’s ‘Growth Channel’ from Fertilityro Poverty EL have shown that ptwerty is higher in countries with {i} higher birth rates lagged ten years given nvcommthe ‘distribution channel’; or {ii} lower Neon given the lagged birth rate. EL now estimate the growth channel, that is, '(iii) the irnp'act that demo- graphic variables would have no poverty via the grthh rate of {mean GDP and thus) avcozv, even if distribution were unchanged. This involves estimating impacts of (I) fertility and population growth on growth of GDP per gases; that is. updating Kan (2) growth of GD? per person on growth of .wcou; (3)"‘growth of moon on level of moon, and hence on poverty at a given horizon. v 3 " For (1), EL use similar methods to KS, but with updated data, and a per- haps more appropriate sample. EL further reduce ambiguity about the direction of causation—from high current fertility to slower growth of GDP per person—and confirm a large and significant effect, not, contrary to KS, offset by faster growth of GDP per person resulting from other aspects of population increase (namely, lower death rates, or delayed effects of earlier high birth rates on growth, perhaps via extra labor input or savings). 30 the net negative impact of population increase on grbwth of GDP per person is more clear-cut than in the KS estimates: ‘ For (2), EL estimate the relationship between growth ofGDP and growth ofavcou. For (3)—using the fact that (1) and (2) give the path from differences in the birth rate to differences in growth of moon over the next ten years-MEL calculate the effect, on the level of AVCON after ten years, of this slower growth rate, and hence the effect of a sustained shift in a country's net birth rate on poverty via the growth channel alone, ten years later. 7 ‘ ‘ ' ' From Fertility to GDP Following KS, we estimated trend growth of real GDP per head, 1980—90, as a linear function of (i) its initial (1980) level and the square and cube of that level; (ii) mean net birth rate and netz’cleath rate in the period 1980—90, PNBR and I’NDR; (iii) net birth rate 15 yearsipreviously, that is, in 2965m75, i’NBRlS;42 (iv) interaction elfects between GDPlper head and the demographic ‘51 The 19805 are used {1) because census demographic data are more reliable than interpolations, (2) for comparability with the KS results. In predicting avccm growth from GDR growth we must use the years of nation-wide household surveys from which avcougrowth can be calculated (Ravaflion and Chen 1996). ' -' - 42 (I) These PNBRs and PNDRs differ somewhat from the NBRs and NDRs in Sect. 3.3. The poverty equations of Sect. 3.3 sought to explain international diti'erences in levels of poverty given‘nvcou, in a particular survey year. Hence the levels of NBR and NDR, in that year or ten years earlier, were used as explanators. Eat the growth equations of Sect. 3.5 seek to explain differences in the growth of'GDP'per person, and hence of moon and thus poverty, over the period 1939—90. Hence net birth rates over the period (e.g. PNBR), over the 19805 or over the decade 15 years prior, are used as explanators; (Of course PNBR and PNBRlS are not very different from, respectively, NBR in l985 and NBR in 1970). ' (2) The choice of lag length is somewhat arbitrage We chose-a ten~year=iagfor our poverty equations on the grounds that a longer lag would obscure the dependency-em (many poor people become workers ' Demographic Transition and Poverty 231 variables. As before, these interaction effectsf—vital to avoid irnposing assumptions of independence and thus biasing the estiirrateFmean that the impact of the coeffi— cients cannot be directly, that is, iridePendentiy, interpreted from their size, nor their significance from their tvstatistics. Accordingly we present the equation, predicting GDP grovvth from GDP level and the demographic variables, and thenw'mallowing for interactions-—the was statistics {indiCating significance level of the explanators), and elasticities ofg‘rowth to the 'explanators at the median netbirth rate of the 19803.43 We present only the e'fiuation, from our data, that most closely follovx's the original KS work for developing countries; we have tried other formulations without irnproving on this.44 Next,’We compare results from KS. We then discuss the result‘s.‘15 Equation 1:14.115 Grmvthfiquarion forthe 19305 I . GDPGR = c :- 6.75(GDP)[—-1.59I «i» l0.58(GDP2) [1.191 - 4 s.13(ooP3)[-1.1g} a 4.5(PNBR)[~2.17}** + 0.95{GDP*PNBR}[9.931 + 0.91(PNBR_£5)E0.44] — 0.15(GBP*PNBars){L-o.rs] — G.03(?NDR)E~—G.01] + B.60(GDP*PNDR){€}.S9] (i) Adj. R2 = 0.418; standard error m 1.96; 46 observations. Wald (PNBR) : ' 10.55 (0.oos)*;Wa1d (PNBRIS) :2 0.38 (0.327); Wald (PNDR) 2 0.63 (0.731); Wald (GDP) m 8.21(0.223). Equation 2: KS’s Grewth Equation for the 19805-45 GDPGR = c + 2.-94(GI3P)[1.46} -E— l.97(GDP2) {0.73} m 1.6(GDP3)[0.831 - 4.67(PNBR){4.48]* + anamorpnsmgrzs] + augmentsmlsr — 1.3(GDP*PNBR15)[2.44}** _ + 2.78{PNDR)[1.31I ~— 3.25(GDP*PNDR)[Z.74}"-‘_ (2) Adj. R2 = 0.370; 66 observations. Wald (PNBR) .—. (assert; Wald _ (PNBRIS) m..({3.092)‘f;-Wald (PNDR) = (G.005}*;Wald (GDP) = (0.000? aror'md age 10); we try (and, as it happens, reject) a 35—year lag in the growth equation to provide results directly comparable to those of KS,‘recognizing that their choice of lag length was, reasonable given their main posited ‘rnechanisr’n by which lagged birth rates might (positively) affect growth, namely, life—cycle savings. ' " - I ‘3 Chosen because this is the only significant explanatory variable; see below. "4 Our sample [46 here) is smaller than that'used by KS (66). There are 41 developing and 5 transitional countries in our sample; these comprise all those in the Sit-country sample. used in Sect. 3:3, with reliable PWT 5.6 and demographic data for the 19805. Our sample thus includes all feasible countries where we can later align‘the findings with reliable household-survey data on paverty. KS omit countries that are historically plannedi'highly resource- or-remittancedependenr, or with incomplete or grossly problematic data 06.1994: 86, 92). Also we use FWT figs-data for growth of GDP per person,-which improve upon the PM 5-5 data available-taxi I ' - -' ' ' :45 Reproduced from column 9 OE'KElleY and Schmidt (1994, table C4: 192). 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The results are similar: the estimates equivalent to those in the central cells oETablcs 9.5 and 9.6 are 2.30 and 4.45 for POVSZ! and 1.23 and 3.07 for POVGAP30. 3.6. Combined Results: Impact of National Fertility on Poverty at Ten— Year Horizon - The distribution effect is itself higher at lower levels ofavcon, as a result of the negative coefficient on the interaction term in the poverty equation: the estimated effect on $30 poverty incidence at the 25111, 50th, and 75th percentiles of svcon of a fall in the net birth rate of 5 per 1,000 can be calculated from Table 9.3 as 4.25, 3.05. and E32 percentage points respectively. Adding these numbers to those in Table 9.5 gives estimates of the total effect shown in Table 9.6; It is large. The absolute effect on P$30 incidence is highest for poor countries with high fertility.‘l9 Tables 9.5 and 9.6 show that the growth and distribution channels from fertility to poverty are of similar size. EL use a fall of 5 per 1,000 in the net birth rate to generate these numbers because this is similar both to the average fall achieved by the countries in the sample in the ten years preceding the survey, and to the send-interquartile range. So it is not absurd to imagine that a change of this order might be achievable over 10 to 15 years in countries still experiencing high fertility. The estimates dependon numbers derived from the poverty and growth equations and on the estimated elasticity of svcon to GDP per head—«all subject to error. But alternative specifications of the growth and poverty equations generate similar coef— ficients to those that underlie Table 9.6, and computations of the array of total. effects based on such alternatives produce broadly similar numbers. . The element in which EL have least confidence is the level] growth distinction that characterizes the distribution and growth effects. As far as the poverty equation is concerned, insufficiency of time-series data on poverty forced us into 3 ‘levels’ speci— fication. This excuse is not available for the growth equation, but there a comparison of EL’s results with those of KS reveals some doubt about whether per capita. GDP growth should be thought of as depending on the level or the change in fertility?“ Thc latter specification is equivalent to a ‘levels’ model'wlth a time trend and country 49 So, ofcpnrse, is their initial poverty incidence, in the last‘lable, the 9.07%!fall absolute incidence in the bottomrleft corner is likely to represent a srnaller proportion of those initial) povertyl. than does the 2.93% figure in the topwright corner. ‘ 7 7 5° The role of PNBRES in KS implEES that the fastest grovvlng countries are thoselwhEre‘fertility has fallen. " ' ' ' Demographic Transition and Poverty 235 Table 9.6. Total 51%;: on the POVSSO ofa 5 pacific!) Fall in Net Birth Rate: OuYear Horizon Percentage points 25211 percentile of Median of 75th percentile of GDP per capita GDP per capita GDP per capita 25th percentile of fertility 7.69 5.81 2.93 Median futility 3.28 6.23 .. . 3.20 75th percentile of fertility 9.87 6.91 3.57 I fixed effects.51 While more extensivetime—series data on poverty would be useful, it would be unduly optimistic tozcxpcct even good data to discriminate finely between alternative dynamic specifications of the link from fertility either to GDP per capita or to (conditional) poverty.52 4. THE ISSUE OF CONVERSION EFFICIENCY Does high fertility affect household conversion efficiency—that is, capacity 'to transform a given CI, per equivalent adult (EA), into welfare or capabilities (cg. health, schooling) per EA? Because poor families rationally choose more offspringfl (Sect. 4.1), this boils down to another issue. Are ‘economies of scale in consumption’ more, or less, important than ‘sib crowding’ in their effects on poor households’ cons version efficiency (Sect. 4.2)? There is a clear, large not negative effect in education (Sect. 4.3) andhealth and nutrition (Sect. 4.4). The evidence on transient and chronic poverty illuminates this debate (Sect. 4.5). Finally, declining state activity may interact with high fertility in reducing the conversion efficiency of the poor (Sect. 4.6). These demographic effects on Cl damage mainly women and children. The , differential effect on -wornen«-~together with the fact that the ‘double day’ and edu— cational disadvantage locks'female, more than male, poor into chronic poverty and non—empowerment—may justify the general perception that women are poorer in the developing world, even though most careful surveys find little difference in l’CP between men and women, or male—headed and female-headed households. Children’s concentration in large, poor households—30 1035 percent of persons are below a national povertyline in recent Indian NSS data, but 40 to 45 percent ofchildren—also 5‘ i.e. dy/dt = a + b dx/dt integrates to y m constant at -§- bx. _ 52 This doas not imply that better time-series data on poverty would not be useful; in particular, such I data allows country fixed effects to be removed by differencing (as in Ravalllon and Chen 1996). 53 Above, we argued that crosswnational data showed that the causal chain from high fertility to poverty Was statistically much mbre important than the reverse causation, when the process was medi- ated via low-antl- maldistributed CI. Here, we argue that individual households in food poverty are likely rationally to prefer higher fertility than are the bettermle so that-reverse causation is impor- tant when the'fertility—to—poverty process is mediated via low conversion cfliclcncy of CI into welfare and capabilities. There‘ls‘ no inconsistency. 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JET.“ ‘Ananod poo; amépgma 121.11 saidnoa m ‘sxsafifins (9551 gazing) qsapqSueg pug 2331:} u; SIM)ng ssagpgqa pm; 1310 50 Hopemgs amzadsap {19330 aql :pgcp sun 1312:; 32 max; uoddns 01 339 p10 11; ammme ages a ‘Anmtp [apes m ‘suog'suad 3.22 JON '(aumau; .man pgqa auogxog) 3503 hmnuoddo manna qfigq SLIqu pun ‘summl 5319;; fima (pamnoasgp zifiAeaq pat—z) paAepp ‘sBuyms afqepxogeun Bugzmbal tread-poo; sq; 91 31011131 swaas ‘(glm spur} pun mung) me; o; snads'md Janaq ngm an1 pm). ‘pazeanpa-xanaq ‘uaxplgqa lama; go 91192313312 sq; 559mg s‘uamom go 5130:) Almmmddo Aflupadsa—fiugmaj *pfilp go 9:803 Ma] £132; 932} mod—poo; sq; ‘snsoa 10; 5V 'saatraugmu 5321;16ng zinpe 12101; 19191 pm; ‘xoqe; p‘filp go saunas se Harpggqa Auteur mug 311103121“! mammamg, 01—91mm {BAqu HQAHRIEA qfigq {pane Anemone: mod-poo; sq: ‘osw uhgeuom pfltp asgm o; (bampgm mawémo: Amaadsa) am queaq QIqepaogego afiezioqs pug ‘uopmguas paq ‘xazemfitmeaqun {pp-A spasm; matted poo; {IRAgAms 3992:1143 go ‘pooB mumsan alqamp, 31.13 91mm; 0; map paau mod—poo; 31;; se ‘uaxpfiqs Anew may qfigq 9:; 01 pual ageing 'saAyemaqe homepage 93 figmqeqoxd [gems pug ‘sxsoa 3111 03 mien MOI ‘uarpgqa Anew meg mgauaq sq: or; anpm qfigq {puma 5112110532.: mod sq; uoggaauog mm}; 5.111381; 1mg iF‘s‘g'tauagxzag 5; 133121113 Eungsgo-Juvm (52167103 mod Ti; 243(12an nogsxamloa Bugzmpm u; snags ‘aq; Aq pagggdum 912 Aumod 1350 'samseam no Angus; 1.13m 50 snags aq1 ‘ugefie 332m :uopgn mumptm paanpuwmawd go 53393;; 311:1 03 hmqexaufm 13199.18 .1;an my» 31321331; . r' I .8??wa 91:1 PW “waver “1.119135 9% 238 Fertility, Poverty, and theFarnily education. Such relationships, apply much more strongly to households in the poverty zone than to wealthy households. in most studies, high fertility and large households ' especially damage the educational prospects of girls (Lloyd and Gage—Brandon 1993, for Ghana; Shreeniwas 1993, for Malaysia; implicitly, Gre'enhalgh 1985 for Taiwan; see also Lloyd 1994).59 4.4. Health, Nutrition, and the Impact oingh Fertility on Conversion Efficiency " ~ Large households tend to be less healthy and worse:nourished,_and to discriminate more among members. larger households provide less care per child, less access to health care, and more gender discrimination in food distribution (King11987; Desai 1993: £79; Mahmud and McIntosh 1980; Lloyd 1994), Much greaterrislt of underno- trition appears in larger households, for example, in the Philippines, Bangladesh, and Mali (King 1987). ‘In 12 of 16 countries the additionof asibling under age 5 hasa sig— nificant negative impact on children’s height-for—age standardized scores’ (Desai 1993: 165}. In one suggestive study, larger household size (and associated greater poverty risk) brings more damage to nutrition“3 in villages where average consumption is relatively low (Mahmud and Mcintosh 1980). As for health, ‘cornpetition between children {and} exhaustion of the mother’ are quantified ‘explanatory factors51 for the same reality: poverty‘ in Mali. Lloyd (1994) shows that the links are subtle—not just via female exhaustion and sib crowding, but because larger household size induces authoritarian, less equal, less altruistic households, more hierarchical by age and gender. 4.5. Relevance of Transient and Chronic Poverty The mechanics of high fertility, as a reducer of conversion efficiency ameng‘ low-. income households, is illuminated by growing evidence from household panels about the extent, nature, and lifevcycie victims of transient poverty Typically, 25 to 40 per- cent of persons in PCP (private consumption poverty) in the year before a survey are not normally in PCP (Ryan and Walker l990}. The transient poor are much likelier to be ultra—poor than the chronic poor (Gaiha and Kairni 1987). fChnrnin'g’ down from near-poverty towards ultra-poverty is iikeliesr when a household con- tains several under—fives and one worker; and in economic adjustment a household’s risk of being thus ‘churned .down’ is greatly increased by an extra birth, which also 59 The educational harm from high fertility, especially for girls, is usually more in towns, because there is less to damage. In general, when: few people, especiallyfew girls, are educated, the effect of Si!) numbers on their prospects is smaller, as in Baltistan (Lloyd 1294; Allisongt at. $989: 38—9). _ ‘ _ '_ _ 7 5° The study is in Bangladesh, where (as in North India and Pakistan) there is evidence of gender discrimination against little girls in food provision or health care. in such circumstances the nutritional damage, associated with larger and poorer families, appears to fall especially on these girls. .- ' 6: Lloyd (1994) has a more nuanced, socially contextualized account of how large families induce child deprivation. ' ' Demographic Transition and Poverty 239' cuts prospects of joining the (many) poor households who escape poverty during adjustment (Grootaert 1996; Grootaert er a1. 1997; Glewwe and Hall 1998). In short, the evidence from page! data about transient poverty seems to be that time distributions of a given (integral ofhousehoid lifetime) poverty over household life cycles concentrate it when members are pregnant or lactating women, or under- fives, and thus most exposed to irreversible consequences. For those with large or rising child numbers, declines in near—poverty C1 are thus converted into larger pro- portionate declines in welfare orcapability. So fertility reduction, apart from effects on PCP, reduces damage from given levels of poverty. If families with low PCP are helped to control fertility,52 their children benefit even if overall PCP indicators do not improve. In Thailand, children’s education'im'proved With falling household size, ‘household wealth level’ (not an ideal indicator of poverty) constant (Knodel 1993: 289). ~ 4.6. (State Retreat may Catalyse the Damage of High Fertility to Conversion Ejficienqr_ ' Rattan (1993) points out a ‘time warp’: the fiscal crises, and the disillusionment with the state, of the early 1980s have impaired public—sector activities such as agricultural research, perhaps for-a shortish period, but with long-term results. A similar time warp may have harmed children in big, poor households. Such children are especially dependent on public provision to correct their parents’ below~average ' ability to provide them with these semi-public goods. This has been shown for pri- mary education in Ghana (Lloyd and Gage-Brandon 1993) and preventive health . care‘(l_3esai 1993: l178},in many countries. Where adjustment causes governments to withdraw from providing child—related services, transient poverty ‘is likely to increase the vulnerability of children in large families’ (ihid, 178—911.63 _ Ilse delayed-action effectof undiscrirninating anti—statism, therefore, homes in on big, and often therefore poor, families seeking to maintain or upgrade their children’s human capitalfi‘hat makes it less likely that couples can, or will, escape poverty by choosing smaller families with better prospects of survival, health, or; education. ‘5. FERTILITY «a POVERTY: CONVERSION AND DISTRIBUTION EFFECTS VERSUS GROWTH EFFECTS The economics that the most intelligent literary figures come to take for granted suggests that the effect of fertility on poverty via distribution and conversion efficiency is deeply intuitive, The distribution effect is based on price theory (in labor and staples markets) so old, elementary, and apparently non-controversial that it was famously 5" Such help will usually take the form of changing incentives to favor high family size norms (cg. Via better access to education for poor girls), and may sometimrss also involve lowering the costs of contraception. 93:- A conunéntator (Andrea Cbrnia) suggests that in Eastern Europe and the former Soviet Union the rapid reduction of state provision may have reduced fertility and ‘linlted’ poverty to smaller family size. . 1:112:15 ammdanae go shuns pgoqaszwq apyu—uogzu g9 :35-peuaams.£1{rgam a mag $10322)ng Agra-15311350 aszqsiap pazgpmpums 2 mesach (9551);.11nbg pun 1333;3an rpasn an 53mm ‘ppqasnoq—ndm uoszad '«19d 10 ‘awoauym flogdumsum 2311:3415 am; sq m meadcie pmz Swab-@593 xsamodgo amqs 03 133321919}; 01532: meg 133113;: 30 :ueqs ‘naq‘L wags} pasn sghganbaugo xaergkaAaxmgm 48mm.“ EMBED} 3pm: sing, 99 (:31; 'qmms away-11113311! sflqyses s‘apioad {pg mg: as 11321113911; :0; past: 315 Janna swam); amp) :1qu Amp 321;} ‘3qu 8:29 Barman} u no Emery) Ammymrg ‘sammafum :meggpq sq; 1mm pump; éguug 9mm 3.12 snagsnpuoa 91Lan aq; ma $2 guns 533mm mmmaeugwoyzu panmdmg p112 ' Mamas pioqasnoq pauaans Amara: pug man moqwmuampmzdwg map mace: SEQ no mp pm; ‘ODEEU pm: wow: '[Buopamam‘g pm: 42mg “31% naysayssoxa pus sagas-2mg mm; asgxe sucgsnpuo: mu 9 V 7 - ' 111mm! so hgmbasrg no ‘uopgsodwoa feuogfim 10 12101935 513 go padsaz a! 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For example, cross-State and tune-series analysis for indie in 1958—91 shows that agricultural growth substantially reduced poverty, but industrial growth did not (Datt and Ravallion 1996)."57 - - 3. If policymakers choose an ‘uneQualizing’ growth path, they do not accelerate subsequent economic growth. Contrary to earlier views, countries with initially more unequal income or consumption probably experience slower subsequent growth.68 There is wider consensus that very unequal access to education (Birdsall et ul 1995) or operated farmland (Deininger, personal communication, 1997) retards subsequent growth. _. ‘ 4. Most evidence suggests that two sets of policies can be identified as growth— promotirlgz ‘openness’, that is, policies that reduce niarlcet distortions and other barriers, especially against international trade and investment; and creation of infra— structures, both human (mass health and education) and physical, for economic development.69 Such policies may not perform well in particular cases due tocivil vio~ lance, institutional failure. obstructed foreign markets, or remote or difficult tel-rain. However, across large cross-sections of countries and in the long run, both ‘openness’ and ‘infrastructure’ (or human capital) seem importantin explaining growth, whether technical progress is interpreted endogenously to it (Rome: 1986) or exogenously as in conditional convergence models (Burro l991; Radelet et al. 1997). 5. The above points are parts of the new consensus, to be ‘interacted' with the fertfiitngrowthwpoverty nexus. To them has to be added another point, which is at once controversial and puzzling: it is not clear that adiustment policies, notably increased ‘openness’, are poverty—reducing. in low-income economies with high labor-«capital ratios, such policies should, through the Heckscher—Ohlin mechanism, redistribute income toward the plentiful factor, labor, and thereby improve lowrend income distribution. Yet, even for countries with the highest labor—capital ratios, the controversy is about whether adjustment policies damage the poor (by worsening inequality more, and more surely, than they accelerate growth) (Cornia et al. 1987) or are neutral toward poverty and thus, in the long run, helpful to the poor as growth picks up (see point 3 above) (Sahn etul. 1996). Almost nobody argues that adjustment and liberalization, because they reduce low-end inequality, help the poor'even. if they do not promote gr0wth. Yet Heckscher—Ohlin implies just that."0 67 That may be due to the greater labouriutensityof agriculture, to labour-dutcnsive agricultural progress {the spread of irrigation followedbythe 'Green Revolution’), or to protectionistaudother policies reudEring industry capital-intensive. ' ' A . -. r ‘ 53 Alesiua and Rodrik (I994); Persson and Tabeliini (1994}; Clarke (£995); Bruno at aL. {1996}; but cf. Hongyi er of. (1995). 59 Government size, perhaps because ‘good’ for educational and communications infrastructure but ‘bad’ for price neutrality and openness, ‘is not robustly associated with good—or bad—subsequent economic performance (Levine and Renal: 1992). ,- - ‘ '. : _ 7“ One explanation of this puzzle is that‘openness’ does not reduceiowwd inequality because it attracts private foreign investment which brings globally generated, labor~saving technology Another is that the poorest countries lack the spread of education required for tradenexpa'nding laboninteusive to openness (Wood 1994). Demographic Transition and Poverty. 243 Probablyrthe most serious deficiency these ‘big new ideas’ of development eco~ nomics is their empirical base in studies that, except for Bloom and Wllliamson (1997), exclude demographics. The fertility transition is a central feature of develop» .ment, and is interwoven with growth, distribution, and induced technical progress. incorporating changes in human fertilitgas causes and asveffects of economic change, into the above ‘stylized'facts’ therefore seems lilter to be central to the research agenda ' of development economics. This task cannot even be commenced here. But how might the ‘new consensus’ be affected, if indeed poverty reduction in developing countries now depends importantly on reduced fertility? . 1.-' Table 9.6 calculates the effects oflfertility'change upon poor people by adding its effects in changing (i) distribution between poor and non~poor of a given level of real resource flows per. head, (ii) the latter’s rate of growth. This ignores any possible direct feedback from-growth to distribution; if, as used to be believed, there was a Kuznets curve, then a ion~income countrythat achieved faster growth would tend to worsen itsincom'e distribution. So a cut in fertility would lead to faster growth, but this-would carry a distributional penalty, so the poverty effects inTable 9.6 would be overestimates. If proposition 1 above is correct, this possibility can be put aside. If anything, feedbacks excluded from the model in Section 3 above are likely—Hover a longer time horizonj—to enhance the poverty effects. Though E133 and KS’s tests identified fertility change as cause, much more than effect, ofshort-run or concurrent economic change, economic growth and income equalization are normally associated with events that reduce fertility in the long run: most growth paths are associated with a rising opportunity cost of women’s time, and with better prospects and incentives for couples to produce fewer children; more equal income distribution also appears to be associated, in the long term, with lower Subsequent fertility (Lam E997; Repetto _ 1979). The elements of a virtuous circle are therefore present, an initial fall in fertility leading via faster growth and more equal distribution to further fertility declines later ‘ on. This may be dubbed the ‘fertility amplifier'. 2. The findings reported'in the previ0us paragraph, together with Section 3 above, suggest that governments that choose policies leading to' equalizing growth will find longer—term benefitsfiom the fertilityamplifier.‘ Equalizing growth will, ceteris permits-reduce fertility. This will lead to improvements in both growth and distribution. ' ' ' _ ' ' ' ' ‘ r ' 3. This would lose much of its force ifthe policy choice were between faster growth and more equality Then; the fertility effect of choosing more equality would be ambiguous. Conversely, our findings interact positively With evidence that inequality retards growth of moon and mean GDP, the implication being'ma't a country may become'loclced into a cycle of high inequality, high‘ fertility, and slow growth.” 4 and '5. 'l'lieoi‘y indicates ‘twoLw'ay’ gains to the poormvia faster growth, and via more equal distribution of given resource levelsufrom appropriate policy (open— ness, physical and humaniirifi-astructure—building) in developing countriBS'L'Eviclence 7‘ - Note that our‘result’s hnplyra statistical association between faster growth 'and more Equality, to tire . extent that bom‘foliow a fail‘in fertility. ‘ . - 344K930“: PH‘P 9‘1qu ' qfinonp uogeIndod Bupnpax ‘AixaAod ana pm: Aqgenbau; sasnaxau; £341}:an pagans —amn pampossa amp ameaaq ‘Jaflews sammaq pazdaaae s; Hogmpum lueufindm sq; }; alqeuymsns nopemdod sq; go azgs sq: 5&3;qu 30 [Mag 113MB Am: m ‘pu'v ‘paanpa: s; Azania; go 9191;21:1de sq: IIan ‘uogsnpuoa meufindai sq; pIBMOJ paqsnd aq 03 Apes: s; auo 3; 13er 0M} sueau: sgql figs 1912313 snag; Qq; 5331211.: 9 magmas u'; peuguwxa UOQBSHED angelnuma aqL 'LIOIS 2:;qu sq; axaM—Agsnogaaxd pagpunnb _ sauo 1:195:19; lquo 2119—4954013 uo snag; 31p ;} Hang 1312318 3; Auawd no 33339 53} ‘uogmqgnsgp 911mm; suasmm‘bmna} Jaqggq 31 "uogsnpuoa Jmaufindal, sggxeg ‘sgtp 01 pawn ‘pua ‘uoneindod femng summon lqflnomquaupxadug {Bug V gqmmfi Io; pafiaau axe ‘fiuplm-xsgx pm: ssauaAgmAug pmmaz 12m 3901;; finger:me Jo sumo; 131110 1121;; hugeuamxeau sq; {mm Jaqzafioxmfiigenba go sung} sums pun ‘qmmfi ‘uopnnpax hymn} Qumsuuoa 5913133 3110ng sq; Aq papaya aq ‘5‘»:va 13111; sggfi }0 pm: ‘aaumgaqu; }0 31131312311 {azuazmuaaofi pinqu mop; 53:10ng mp 50 may; amtimfiomap pug Inuogmqgmgp-amoauy sq), 312 mm ‘axmguafioumgn {mg-V Hg) autos pun 3:912pr uogsgAgp am squo ‘azmyuagougad [admoa 10 afiemoaua 333331303 autos ‘aidwexa 10:1 'Agewamapurg Ange; Jaexagtrg 39;de 3ng u; passmsg? 3901;; S8 qsns ‘saauanbés squexsowap pine saggy: aau'ezpaqug 3mg 5; sass; LPIBBSQJ gnu _ _ . 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'(5661') mum! ‘{E6613 virauapur 1266:) asp“: “(mm/{Mun}; 1266:) summer; ‘(ISGU nesssg-vamna 1266:) mini) 16960 vtvwawns 162—:2260 9.160.266 16661) mafia 1166660164351 ‘36“ )xopvmg ‘(696nsyqndaa uwyuywoa 1666:) DECERéGH £13923 19260 21.1041)? 219:) 16961) In)?! visas 1166:) VEQWOIOD ‘(Eééfl WW) 1266:) 9M3 ‘(ZGGU BEJSBFIK 16861) 1.12919 199—986!) flaw-mg 1161366” amazes 18961) eszafiw (saga); a; ‘salqum lupus, my“ s‘uogss‘afia; mfpasn agdxuvsqns ‘samunas 5g) uoyvnba warmer (a) saldmes JO sqsn 'ZV :qpuaddv 61.1ng sq; pun mm: flaming r 896 250 ‘14733—UNI I 4397‘?AK 12293-PA 10193-PA, 1E191—PE 7144-?1—1' 1305090 12455—RW i3431-CE £5526-T0 I IBBG-UG ' I4313-UG ’9663—UR ‘91 147W ‘13442~VN * E 2985—ZA 13540-ZIM - (2 vols.) (21.04.1995) Fertility, Poverty, and the Family Nigeria. Poverty in the Midst of Plenty; The Challenge of Growth with . . Inclusion: A World Bank Poverty Assessment (31.65.1996) Pakistan. Poverty Assessment (25.09.95) Pa raguay Poverty and the Sdcial'Secmrs in Paraguay: A Poverty Assessment (29.05.94) ‘ ‘ ' Paraguay. Pubiic Expenditure Review—‘JI‘he Social Sectors (16.06.92) Peru. Poverty Assasmeat and Social Policies and Programs for the Poor (95.85.1993) The Phifippines. The Challenge of Poverty (17.10.88) 7 Poland. Understanding Poverty in Poland (14.09.94) Ramada. Poverty Reduction 'and Sustainable Growth (1994) Sri Lanka. Poverty assessment (1995) ' '7 ' Togo. Overcoming the Crisis, Overcpming Poverty: A World Bank Perverty Assessment (25.05.1996) Uganda. Growing on: of Payeny (31.05.1993) Uganda. The Chafienge of Gfowth and vacnjy'Reduction (30.06.95) Uruguay. Poverty Assessment: Pube Social Expenditures and their Impact on the Income Disfribution (04.05.93) ' ' Venezuela. Poverty Smdylerom ngérafized Subsidies to Targeted Pro- grams (05.06.1993 ' ' L ' Wernam. Poverty Assessment and Sqategy‘(_23.91.1995) Zambia. i’overty Assess'mgmtz vois.) _(10.‘f1.j1994)' Zimbabwe. Achieving Shared. Growthi.C6untjry Ecanomic Membrshdum Appsndjx A4. Equatioris fa} PQVSZI (incidencs reiafive to 39$21 poverty line) and POW/“GA?3 (intensity relative to a $39 poverty line) Table A91. POVSZI Set'ofRagressions . Regressions Indepgndent variabies 8.45 (0.30) a -‘ i 7.1 (0.345 —2.95 {—0.34} 20.07 {1.25) 42.54 0—045) 5.775(071} 1. LAVCON ‘2. NBR 3. NBRIOI 4.” NDR .5. spasm '142 (0.15) 5.93 {2.89)‘ 5.05 (4.13)* «2.65 (—0.32) ——0.86 (—0.09) 4.44 (43)" 6.78 {1.9) 3.89 (4.0? J -—4.18(-~1.15) 39.79 01.15)" 6. PO?G10 7. LAVCON‘ 0.57 (0.70) N58 8. LAVCON' “1.23 (—2.74)“ —i.09 (*3.69)‘ —0.90 («~-3.78)‘I -l.3$ («1.64) M079 ("3543* NBREG 9. LAVCON‘ 0.16 (0.08) NDR 10. LAVCON‘ 0.54 {0.23} 0.88 (1.05) 'NEDRIO any? >35. «9.31323» Hummwoaboa “~333on <m1m§$ H N m g .m m E. $2502. {Pow filubwv. wOwOwo , . HM. chgfig mbm GUS. mbm 3&3» flmb 3.3%. Nam 3.03.. 35 nuke... . mbv 3:33 5. UCKQW 2.3 3.3» wmha 3.3% ambw Akrumvu Ermw 3&5. mpg 3&8: E. 33$an lo.mwm.w Alarm“: E. ngm obwmb 3&3 Hm. WEEme 0:3 3&5 G. mmOme I93 fiiPuov . Ire.» T193; >&. w”: $8 92m 93m $5 in: . 38 €me 83 $282 54002 550m $802 E5502 .2 ".3802 :93 m8... www.mu 3v. wfibm AS... 693 A8. 693, “.3... $93 A3. Zwmfio ZEN wOwao 2mg? Zwmfio mewo A. .. pm.” me. Pew 8.38; 3.5 “8.. . N98 AS. 3.3 AS. Pu.» 8.303.. . 299,5 76ch Zn.» 83mm 42mmme uhm 8.85.. :3 313mg Pwm 8:38 Pm». 8.53 . Zfiwwo . PM» 8.38 23mm Eng.»an awn H mw. g3 m2 mamammmoumirfinm R mm“ 3 ? mugaiwwmmws mm :0" naufimnna mu $81." mm. Sana m an Uczmw .5 Hamnnmmmou 9 3 £75 “.5 wuwawna :83 8 5a Sana—owns: a: Emma" "Ea. Ed H3532 mom Sta 5% $2 35 3 Human 98“ g ._ 825m Ema?»an a 3? mam .. an mm? 3 w: Humanism; 3.... man “3:. 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NOBAV'I 13:11pm! nogssalflarfi sazqunA 2.111511180qu 32;: u; 33::qu Hun vjci (1:91:36 us) Off/10g fa 13m 35;ch Jaafl‘g :suogssafiayjb 333 [as-AOJ 1.6V anal amazed“ put; noggsuvg; amdmgowag €93 3.53 (2.69)“ 2.58 (1.04? ——O.1FE-3 (-~2.25)** 0.253-3 {0.58) —0.73 {-2.55)" 0.05 (€31) 12.41 {1.21) 0.05 {-43.81} 0.40 (0.06} 9.682 mvoou 38.03 (91’ NBRIO 8.35 (0.017)" social variables 11.0 (0.051) 3.63 (amt worse; (—2.32)" 0.26E—3 (0.7a). -o.75 («2.50)” 0.05 (1.40)- -U.06 (—0.84) 4.67 (0.79) 0.682 social variables 13.59 (1.33) 35.94 (or 3.92 (0312)" 16.62 (o.oos)* LAVCON NBRIG 7.55 (1.33) ~31: (—1.58) 6.25 (2.52)” 0.75 {5.39) 4.27 {—2.33)" 4.73 (2.87)‘ 34.12 (5.02}* 100.14 (or 11.8 (0.003)‘ NBR 0.810 LAVCON' 4.64 (0.098) NDRIG from heterosketiasticity of resiouals (Harvey rest, 5% significance level). White‘s 2 and 4 which suffer from a problem of incorrect frmctional form (the test used —ratios are given in brackets for these two regressions, instead of the usual t—ratios. ~Va1ues for the Wald tests (see notes to Table 9.2); (c) ' means significant at 1% and " 9.31 (1.55) +4.80 91.94) 7.32 (2.91)‘ 1.0 (we) 4.52 («—2.53)** 36.17 (soar MVCON 81.35 {or NBR 5.56 (0.052) NBRIG 13.02 (0.001)‘ 0.784 m 38. As Guinea-Bisseu is not contained in tire sample 38, there is no DUMGE in regressions 5 Table 11.9.4. POVGAP Set of Regressions and 6; (b) values in parentheses next to the eoeflficients are t-rarios; they are p 5.50 (1.93) 2.52 {3.08)‘ “0.54 {—2.50" 5.23 {3.88)‘ 27.90 (may 0.801 LAVCON 90.99 m)" NBRlU 24.37 (m'I Regressions 6.68 (1. is) 2.63 (2.97)‘ “9.54 {—2.70)‘ 2.8.32 (3.14)‘ mvcou 71.15 Kl)" NBRIG 22.87 (0)“ 9379 sensor test, 5% significant level), and regressions 1 and 2 which suffer i. IAVCON 2. NBR 3. NBREO 4. NDR 7. LAVCON‘NBR 8. LAVCON’NBRJO 9. MVCON'NDR :0. LAVCON‘NDRIG S. NDRlO 6. POPGIO at 5%; (d) all regressions are free from statistical problems, apart from regressions Notes: (a) Sample size m 59, except for regressions 5 and 5 where s 11. MVCON‘PO?G10 i2. DUMLA’EAM 13. DUMGB l4. PHYSIC 15. NURSE heteroskedesticity-consistent t 16. PRIMENR 17. SECENR 18. MNDGINE Independent Adj. R3- variables Wald tests in Ramsey’s Demographic Transition and Poverty 255 Table A35. i’OVGAP Set of Regressions: Eflec: on the Level ofPOVGAP {in percent) oft: Um": Change in the - Demographic Wrinkles LAVCDN - Regression number ‘ r ..z ‘3 4 s s M2511: -NBRIO:0.4? 'Neiuowms NEIL- —o.e NBR:«70.71 NBR1020.63 NBRIG:0.61 percentile .NBRlG: 1.24 NDR10:1.17 At 50th .NBRIG:0.25 NBREQHLZS NEE-#632 NBR; —D.43 NBRIG:0.35 NBR‘lO:0.33 percentile NBRlCl:G.66 N01116: 9.69 At75th Newman: Mortimer}: NEE: 0:01 N311: ~o.ro N31110: 0.92 NBRiozom Name: 0.91 NBRIO: 9.14 ' Table A.9.6. POVGAP Set of Regressions: Efi‘ect ofChonges in LAVCON Regression number i 2 3 4 5 _ 6 Level effect at median —10.83 «11.917 —ll.54 —12.3(} "-10.73 — l 1.26 Elasticity al'mecli'en —2.1‘7 +2.38 ‘ “2.31 —2.4§ —2.15 “2.25 . Nata: Elasticifies in row 2 are caloulatecl with-median values of povgap (m 5%). References Attlburg, I). 1996, ‘Population Growth ami Poverty’. In D. Alilborg, A. Kelley, and K. Mason, eds, Thelmpoct ofPopizlotion Growth at; Well-being in Developitzg Countries. Berlin: Springer. Alesina,- A, anti D. Rodrik. 1994. ‘Distributive Politics and Ecohomic Growth’. Quarterly JaumalafEcanamics. 109: 465-99. ' - Allisoo, C., K. C. Cheong, and L. Yap. 1989. Rapid Population Growth in Pakistan: Causes and Consequences. Report No. 7522~PAK (EMENA). Washington: World Bank. Amend, 5., and R. Kanbur. 1993. ‘The Kuznets Process ané the Inequality-Development Reletionship’. foumal of Development Economics. 40: 25—52. Barro, R. E991. ‘Economic Growth in a Cross—Section of Countries'. Quarterly journal of Economics} 106 {2), 497—43. . Bauer, L, and A. Masorr. 1993. ‘Equivelence Scales, Costs of Children anti Poverty in the Philippines and Thailand’. In Lloyd 1993. Becker, G., andH. Lewis. 1973. ‘On the interaction between the Quantity and Quality of Children'. Journal of Political Economy 81 (2), pt. 2. Berndt. E. 1991. The Practice of Econometrics: Classic and Contemporary. Addison-Wesley Birdsail, N. 1979. ‘Silnlings and Schooling in Urban Colombia’. New Haven: Yale University, Ph.D. Unpub. ‘ . . . «mu—D. Ross, and R. Sabot. 1995. ‘loequality and Growth Reconsiclered: Lessoris from East _ Asia‘. World Bank Economic Review. 9 [3), 477-608. Bland-tot, D. 1988. ‘Estimating the Relationship bemeeo Population Growth and Aggregate Economic Growth in LDCerethadological Problems’. In Consequences ofRopid Population Growth in Developing Countries. 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