Undp regional bureau for africa undp rba 2014g ebola

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UNDP Regional Bureau for Africa (UNDP-RBA). 2014g. “Ebola Virus Disease Nexus: Let’s not lose sight of the factors hampering its containment”, United Nations Development Programme Africa Policy Note, Vol. 1, No. 6, 19 December 2014. UNDP Regional Bureau for Africa (UNDP-RBA). 2015a. The Macro-economic Impact of the Ebola Virus Disease in Guinea, Liberia and Sierra Leone, Draft Report, January 2014. UNDP Regional Bureau for Africa (UNDP-RBA). 2015b. “Confronting the Gender Impact of Ebola Virus Disease in Guinea, Liberia and Sierra Leone”, United Nations Development Programme Africa Policy Note, Vol. 2, No. 1, 30 January 2015. UNECA. 2014. “Socio-economic impacts of the Ebola Virus Disease on Africa”, December 2014. UNFPA. 2014. “Liberia’s Ebola outbreak leaves pregnant women stranded”, 26 August 2014, . org/news/liberias-ebola-outbreak-leaves-pregnant-women-stranded#sthash.zdvmnjF2.dpuf. United Nations Office of High Commissioner for Human Rights (OHCHR) West African Region Office (WARO). 2015. ‘A human rights perspective into the Ebola Outbreak’, February 2015.” UNOCHA. 2014a. UNOCHA Financial Tracking Services on the Ebola outbreak. [accessed on 22 October 2014]. UNOCHA. 2014b. United Nations Ebola Virus Disease Outbreak: Overview of Needs and Requirements, September 2014. WHO. 2014. Global Alert Response – EVD: Democratic Republic of Congo. 09_10_ebola/en/ WHO. 2015a. Global Alert and Response (GAR) – One year into the Ebola epidemic: a density, tenacious and unforgiving virus. January 2015. WHO. 2015b. “Situation summary by sex and age group”, . ebola-summary-age-sex-20150107?lang=en World Bank. 2014. “The Economic Impact of the 2014 Ebola Epidemic: Short- and Medium-Term Estimates for West Africa”. Washington, D.C. SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
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Annexes A woman in a soap cooperative in Kankan, Guinea. Photo by UNDP/Anne Kennedy
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83 Annexes Annex 1. The non-linear two-stage least square techniques This model uses non-linear two stage least square techniques. It uses Bloom and Mahal’s (1995) approach by building a model that captures the potential effect of the Ebola virus disease (EVD) on economic production. The model is specified as follows: Y it =α + γ i1 Y i(t–1) + γ i2 Y i(t–2) + β i X it + δZ j it . + ε it (1) Where: I equals 1 to 15, representing the 15 West African countries T represents the time period γ i is a vector of parameters Y it is the real gross domestic product per capita (GDP) α is a constant β i is a vector of parameters X i is a set of explanatory economic variables that determine Y it the total labour force education openness current account balance as a percentage of GDP δ is the coefficient of the variable that captures the influence of Ebola P j it is the probability for a country to detect an Ebola case under scenario j (j= no Ebola, Low, High).
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