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Global Poverty Estimates

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Author Info

  • Camelia Minoiu
  • Shatakshee Dhongde

Abstract

Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across different estimation methods. The decline in poverty over the past decade is found to be robust across methodological choices.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 11/234.

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Length: 30
Date of creation: 01 Oct 2011
Date of revision:
Handle: RePEc:imf:imfwpa:11/234

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Keywords: National accounts; Income distribution; Developing countries; poverty line; surveys; sensitivity analysis; statistics; household surveys; survey; statistical methods; estimation method; income levels; distributional data; poverty monitoring; distribution of income; functional forms; poverty analysis; stata; statistical method; data sources; statistical techniques; measurement of poverty; estimation technique; lognormal distribution; measurement error; correlation; samples; household survey; distributive analysis; horizontal axis; consumption patterns; counting; rate of change; analysis of poverty; poverty statistics; statistical technique; representative samples; econometrics; sampling; data collection; time series; household members; confidence interval; probability; data limitations; consumption variables; functional form; estimates of income;

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References

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  1. Miguel Szekely & Nora Lustig & Martin Cumpa & Jose Antonio Mejia, 2004. "Do we know how much poverty there is?," Oxford Development Studies, Taylor & Francis Journals, vol. 32(4), pages 523-558.
  2. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2009. "Parametric Estimations of the World Distribution of Income," NBER Working Papers 15433, National Bureau of Economic Research, Inc.
  3. Mistiaen, Johan A. & Ravallion, Martin, 2003. "Survey compliance and the distribution of income," Policy Research Working Paper Series 2956, The World Bank.
  4. Martin Ravallion & Shaohua Chen & Prem Sangraula, 2009. "Dollar a Day Revisited," World Bank Economic Review, World Bank Group, vol. 23(2), pages 163-184, June.
  5. Villasenor, JoseA. & Arnold, Barry C., 1989. "Elliptical Lorenz curves," Journal of Econometrics, Elsevier, vol. 40(2), pages 327-338, February.
  6. Ravallion, Martin & Datt, Gaurav & van de Walle, Dominique, 1991. "Quantifying Absolute Poverty in the Developing World," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 37(4), pages 345-61, December.
  7. Shaohua Chen & Datt, Gaurav & Ravallion, Martin, 1993. "Is poverty increasing in the developing world?," Policy Research Working Paper Series 1146, The World Bank.
  8. Camelia Minoiu & Sanjay Reddy, 2008. "Kernel Density Estimation Basedon Grouped Data," IMF Working Papers 08/183, International Monetary Fund.
  9. Florent Bresson, 2009. "On The Estimation Of Growth And Inequality Elasticities Of Poverty With Grouped Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(2), pages 266-302, 06.
  10. Ahluwalia, Montek S. & Carter, Nicholas G. & Chenery, Hollis B., 1979. "Growth and poverty in developing countries," Journal of Development Economics, Elsevier, vol. 6(3), pages 299-341, August.
  11. Ben Jann, 2005. "KDENS: Stata module for univariate kernel density estimation," Statistical Software Components S456410, Boston College Department of Economics, revised 26 May 2008.
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Cited by:
  1. Sabina Alkire and Maria Emma Santos, 2013. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," OPHI Working Papers ophiwp059, Queen Elizabeth House, University of Oxford.

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