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Developing Poverty Assessment Tools Based on Principal Component Analysis: Results from Bangladesh, Kazakhstan, Uganda, and Peru

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  • Zeller, Manfred
  • Houssou, Nazaire
  • Alcaraz V., Gabriela
  • Schwarze, Stefan
  • Johannsen, Julia

Abstract

Developing accurate, yet operational poverty assessment tools to target the poorest households remains a challenge for applied policy research. This paper aims to develop poverty assessment tools for four countries: Bangladesh, Peru, Uganda, and Kazakhstan. The research applies the Principal Component Analysis (PCA) to seek the best set of variables that predict the household poverty status using easily measurable socio-economic indicators. Out of sample validations tests are performed to assess the prediction power of a tool. Finally, the PCA results are compared with those obtained from regressions models. In-sample estimation results suggest that the Quantile regression technique is the first best method in all four countries, except Kazakhstan. The PCA method is the second best technique for two of the countries. In comparison with regression techniques, PCA models accurately predict a large percentage of households. With regard to out-of sample validations, there is no clear trend; neither the PCA method nor the Quantile regression consistently yields the most robust results. The results highlight the need to assess the out-of-sample performance and thereby the robustness of a poverty assessment tool in estimating the poverty status of a new sample. We conclude that measures of relative poverty estimated with PCA method can yield fairly accurate, but not so robust predictions of absolute poverty as compared to more complex regression models.

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Bibliographic Info

Paper provided by International Association of Agricultural Economists in its series 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia with number 25396.

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Date of creation: 2006
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Handle: RePEc:ags:iaae06:25396

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Related research

Keywords: poverty assessment; targeting; principal component analysis; Bangladesh; Peru; Kazakhstan; Uganda; Food Security and Poverty; H5; Q14; I3;

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  1. Sahn, David E. & Stifel, David C., 2000. "Poverty Comparisons Over Time and Across Countries in Africa," World Development, Elsevier, vol. 28(12), pages 2123-2155, December.
  2. Ahmed, Akhter U. & Bouis, Howarth E., 2002. "Weighing what's practical: proxy means tests for targeting food subsidies in Egypt," Food Policy, Elsevier, vol. 27(5-6), pages 519-540.
  3. Ahmed, Akhter U. & Rashid, Shahidur & Sharma, Manohar & Zohir, Sajjad, 2004. "Food aid distribution in Bangladesh," FCND briefs 173, International Food Policy Research Institute (IFPRI).
  4. Grootaert, Christiaan & Braithwaite, Jeanine, 1998. "Poverty correlates and indicator-based targeting in Eastern Europe and the Former Soviet Union," Policy Research Working Paper Series 1942, The World Bank.
  5. Sharma, Manohar, 2000. "Microfinance," MP05 briefs 0, International Food Policy Research Institute (IFPRI).
  6. Filmer, Deon & Pritchett, Lant, 1998. "Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India," Policy Research Working Paper Series 1994, The World Bank.
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Cited by:
  1. Murendo, Conrad & Keil, Alwin & Zeller, Manfred, 2010. "Drought impacts and related risk management by smallholder farmers in developing countries: evidence from Awash River Basin, Ethiopia," Research in Development Economics and Policy (Discussion Paper Series) 114750, Universitaet Hohenheim, Department of Agricultural Economics and Social Sciences in the Tropics and Subtropics.

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