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How Accurate is Participatory Wealth Ranking (PWR) in Targeting the Poor? A Case Study from Bangladesh

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  • Zeller, Manfred
  • Feulefack, Joseph
  • Neef, Andreas

Abstract

PWR is a participatory poverty assessment method that uses the ratings of local reference groups concerning the relative poverty status of households in their community. This paper assesses the accuracy of PWR in predicting absolute (income) poverty, and compares PWR with three other poverty assessment methods. Using a village census in 8 villages located in three of the six divisions of Bangladesh, 1660 households have been scored using the PWR method. A randomly selected subsample of 320 households was interviewed with a questionnaire employing the Living Standard Measurement Survey (LSMS) method. The data allow the identification of households that have per-capita expenditures below the international poverty line of 1 dollar a day. Our results show that calibrated PWR scores can achieve an accuracy of 70 to 79 percent, i.e. up to 8 out of 10 households are correctly predicted as to whether they live in extreme poverty or not. As expected, the so-called Total Accuracy of PWR is higher if its scores are calibrated at lower geographical level, and highest if calibrated at the community level. For the case of Bangladesh, the results confirm the accuracy of PWR as a poverty targeting method for development policies and projects if used at the community level.

Suggested Citation

  • Zeller, Manfred & Feulefack, Joseph & Neef, Andreas, 2006. "How Accurate is Participatory Wealth Ranking (PWR) in Targeting the Poor? A Case Study from Bangladesh," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25237, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae06:25237
    DOI: 10.22004/ag.econ.25237
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    1. Sharma, Manohar, 2000. "Microfinance," MP05 briefs 0, International Food Policy Research Institute (IFPRI).
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    2. Noor Hidayah Zakaria & Rohayanti Hassan & Muhamad Razib Othman & Zalmiyah Zakaria & Shahreen Kasim, 2017. "A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(11), pages 450-458, November.

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    Keywords

    Food Security and Poverty;

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