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Poverty comparisons with absolute poverty lines estimated from survey data

  • Simler. Kenneth R.
  • Arndt, Channing

"The objective of measuring poverty is usually to make comparisons over time or between two or more groups. Common statistical inference methods are used to determine whether an apparent difference in measured poverty is statistically significant. Studies of relative poverty have long recognized that when the poverty line is calculated from sample survey data, both the variance of the poverty line and the variance of the welfare metric contribute to the variance of the poverty estimate. In contrast, studies using absolute poverty lines have ignored the poverty line variance, even when the poverty lines are estimated from sample survey data. Including the poverty line variance could either reduce or increase the precision of poverty estimates, depending on the specific characteristics of the data. This paper presents a general procedure for estimating the standard error of poverty measures when the poverty line is estimated from survey data. Based on bootstrap methods, the approach can be used for a wide range of poverty measures and methods for estimating poverty lines. The method is applied to recent household survey data from Mozambique. When the sampling variance of the poverty line is taken into account, the estimated standard errors of Foster-Greer- Thorbecke and Watts poverty measures increase by 15 to 30 percent at the national level, with considerable variability at lower levels of aggregation." -- Authors' Abstract

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Paper provided by International Food Policy Research Institute (IFPRI) in its series FCND discussion papers with number 211.

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Date of creation: 2006
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Handle: RePEc:fpr:fcnddp:211
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  1. Russell Davidson & Jean-Yves Duclos, 2000. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Econometrica, Econometric Society, vol. 68(6), pages 1435-1464, November.
  2. Haddad, Lawrence James & Adato, Michelle, 2001. "How effectively do public works programs transfer benefits to the poor?," FCND briefs 108, International Food Policy Research Institute (IFPRI).
  3. Tarp, Finn & Simler, Kenneth & Matusse, Cristina & Heltberg, Rasmus & Dava, Gabriel, 2002. "The robustness of poverty profiles reconsidered," FCND discussion papers 124, International Food Policy Research Institute (IFPRI).
  4. Greer, Joel & Thorbecke, Erik, 1986. "A methodology for measuring food poverty applied to Kenya," Journal of Development Economics, Elsevier, vol. 24(1), pages 59-74, November.
  5. Atkinson, A B, 1987. "On the Measurement of Poverty," Econometrica, Econometric Society, vol. 55(4), pages 749-64, July.
  6. Blackorby, Charles & Donaldson, David, 1987. "Welfare ratios and distributionally sensitive cost-benefit analysis," Journal of Public Economics, Elsevier, vol. 34(3), pages 265-290, December.
  7. Ravallion, Martin & Lokshin, Michael, 2003. "On the utility consistency of poverty lines," Policy Research Working Paper Series 3157, The World Bank.
  8. Deaton, A. & Zaidi, S., 1999. "Guidelines for Constructing Consumption Aggregates for Welfare Analysis," Papers 192, Princeton, Woodrow Wilson School - Development Studies.
  9. Fafchamps, Marcel & Quisumbing, Agnes R., 1999. "Social roles, human capital, and the intrahousehold division of labor," FCND discussion papers 73, International Food Policy Research Institute (IFPRI).
  10. Kakwani, Nanak, 1993. "Statistical Inference in the Measurement of Poverty," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 632-39, November.
  11. Ravallion, Martin & Bidani, Benu, 1994. "How Robust Is a Poverty Profile?," World Bank Economic Review, World Bank Group, vol. 8(1), pages 75-102, January.
  12. Arndt, Channing & Simler, Kenneth R., 2005. "Estimating utility-consistent poverty lines," FCND briefs 189, International Food Policy Research Institute (IFPRI).
  13. Datt, Gaurav & Jolliffe, Dean, 2005. "Poverty in Egypt: Modeling and Policy Simulations," Economic Development and Cultural Change, University of Chicago Press, vol. 53(2), pages 327-46, January.
  14. Todd Benson & Charles Machinjili & Lawrence Kachikopa, 2004. "Poverty in Malawi, 1998," Development Southern Africa, Taylor & Francis Journals, vol. 21(3), pages 419-441.
  15. Bishop, John A & Chow, K Victor & Zheng, Buhong, 1995. "Statistical Inference and Decomposable Poverty Measures," Bulletin of Economic Research, Wiley Blackwell, vol. 47(4), pages 329-40, October.
  16. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
  17. Sen, Amartya K, 1976. "Poverty: An Ordinal Approach to Measurement," Econometrica, Econometric Society, vol. 44(2), pages 219-31, March.
  18. Gibson, John & Rozelle, Scott, 2003. "Poverty and Access to Roads in Papua New Guinea," Economic Development and Cultural Change, University of Chicago Press, vol. 52(1), pages 159-85, October.
  19. Zheng, Buhong, 2001. "Statistical inference for poverty measures with relative poverty lines," Journal of Econometrics, Elsevier, vol. 101(2), pages 337-356, April.
  20. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
  21. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August.
  22. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-66, May.
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