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Identification of sources of variation in poverty outcomes

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  • Essama-Nssah, B.

Abstract

The international community has declared poverty reduction one of the fundamental objectives of development, and therefore a metric for assessing the effectiveness of development interventions. This creates the need for a sound understanding of the fundamental factors that account for observed variations in poverty outcomes either over time or across space. Consistent with the view that such an understanding entails deeper micro empirical work on growth and distributional change, this paper reviews existing decomposition methods that can be used to identify sources of variation in poverty. The maintained hypothesis is that the living standard of an individual is a pay-off from her participation in the life of society. In that sense, individual outcomes depend on endowments, behavior and the circumstances that determine the returns to those endowments in any social transaction. To identify the contribution of each of these factors to changes in poverty, the statistical and structural methods reviewed in this paper all rely on the notion of ceteris paribus variation. This entails the comparison of an observed outcome distribution to a counterfactual obtained by changing one factor at a time while holding all the other factors constant.

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  • Essama-Nssah, B., 2012. "Identification of sources of variation in poverty outcomes," Policy Research Working Paper Series 5954, The World Bank.
  • Handle: RePEc:wbk:wbrwps:5954
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    1. Facundo Alvaredo & Leonardo Gasparini, 2013. "Recent Trends in Inequality and Poverty in Developing Countries," CEDLAS, Working Papers 0151, CEDLAS, Universidad Nacional de La Plata.
    2. Inchauste, Gabriela & Azevedo, João Pedro & Olivieri, Sergio & Saavedra, Jaime & Winkler, Hernan, 2012. "When Job Earnings Are behind Poverty Reduction," World Bank - Economic Premise, The World Bank, issue 97, pages 1-6, November.
    3. Azevedo, Joao Pedro & Sanfelice, Viviane & Nguyen, Minh C., 2012. "Shapley Decomposition by Components of a Welfare Aggregate," MPRA Paper 85584, University Library of Munich, Germany.
    4. B. Essama-Nssah & Peter J. Lambert, 2013. "Counterfactual decomposition of pro-poorness using influence functions," Working Papers 309, ECINEQ, Society for the Study of Economic Inequality.
    5. Inchauste, Gabriela & Olivieri, Sergio, 2014. "Understanding Poverty Reduction in Bangladesh: A Micro-Decomposition Approach," Bangladesh Development Studies, Bangladesh Institute of Development Studies (BIDS), vol. 37(1-2), pages 21-56, March-Jun.
    6. B. Essama-Nssah & Saumik Paul & Léandre Bassolé, 2013. "Accounting for Heterogeneity in Growth Incidence in Cameroon Using Recentered Influence Function Regression," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 22(5), pages 757-795, November.
    7. World Bank Group, 2015. "Kyrgyz Republic," World Bank Publications - Reports 22957, The World Bank Group.

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