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


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


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.

Suggested Citation

  • Zeller, Manfred & Houssou, Nazaire & Alcaraz V., Gabriela & Schwarze, Stefan & Johannsen, Julia, 2006. "Developing Poverty Assessment Tools Based on Principal Component Analysis: Results from Bangladesh, Kazakhstan, Uganda, and Peru," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25396, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae06:25396
    DOI: 10.22004/ag.econ.25396

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    3. 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.
    4. 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.
    5. Ahmed, Akhter U. & Rashid, Shahidur & Sharma, Manohar & Zohir, Sajjad, 2004. "Food aid distribution in Bangladesh," FCND briefs 173, International Food Policy Research Institute (IFPRI).
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    Cited by:

    1. Guie Li & Zhongliang Cai & Ji Liu & Xiaojian Liu & Shiliang Su & Xinran Huang & Bozhao Li, 2019. "Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1099-1134, August.
    2. Muriithi, B. & Gichungi, H., 2018. "Effect of Technology Innovation on Gender Roles: A case of Fruit Fly IPM Strategy on Women s Decision Making in Mango Production and Marketing in Kenya," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277398, International Association of Agricultural Economists.
    3. 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.
    4. Mathieu J. P. Poirier & Karen A. Grépin & Michel Grignon, 2020. "Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(1), pages 1-46, February.
    5. Mohamed Bakhshoodeh, 2013. "Proxy Means Tests for Targeting Subsidies Scheme in Iran," Working Papers 795, Economic Research Forum, revised Nov 2013.
    6. Delalić Adela & Abdić Ademir & Halilbašić Muamer & Šćeta Lamija, 2020. "Assesing efficiency of targeting in social services in Federation of Bosnia and Herzegovina," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 56-74, May.

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