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Proxy Means Testing vulnerability to measurement errors?

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  • Jules Gazeaud

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UCA [2017-2020] - Université Clermont Auvergne [2017-2020] - CNRS - Centre National de la Recherche Scientifique)

Abstract

Proxy Means Testing (PMT) is a popular method to target the poor in developing countries. PMT usually relies on survey-based consumption data and assumes they are measured with random errors – an assumption that has been challenged by recent literature. This paper brings causal evidence on the impact of non-random errors on PMT performances. Using a survey experiment conducted in Tanzania in which eight alternative consumption questionnaires were randomly distributed across households, I compare the performances of PMT relying on error-prone consumption data with those of a PMT using gold standard consumption data. Results show that non-random errors reduce the predictive performances of PMT by a magnitude ranging from 5 to 27 percent, which in turn induce a 10 to 34 percent increase in the incidence of targeting errors (using the typical PPP $1.25 poverty line). Impacts on the relative distribution of households are nonetheless small and non-significant.

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  • Jules Gazeaud, 2018. "Proxy Means Testing vulnerability to measurement errors?," Working Papers halshs-01887649, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01887649
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01887649
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    Cited by:

    1. Ameye, Hannah & De Weerdt, Joachim & Gibson, John, 2021. "Measuring macro- and micronutrient consumption in multi-purpose surveys: Evidence from a survey experiment in Tanzania," Food Policy, Elsevier, vol. 102(C).
    2. repec:wbk:wbrwps:10241 is not listed on IDEAS
    3. Joachim De Weerdt & John Gibson & Kathleen Beegle, 2020. "What Can We Learn from Experimenting with Survey Methods?," Annual Review of Resource Economics, Annual Reviews, vol. 12(1), pages 431-447, October.
    4. Thomas Pave Sohnesen & Peter Fisker & David Malmgren‐Hansen, 2022. "Using Satellite Data to Guide Urban Poverty Reduction," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 282-294, December.
    5. Ligon, Ethan & Trachtman, Carly, 2024. "Assessing Targeting Peformance: The Case of Ghana’s LEAP Program," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2zk0m608, Department of Agricultural & Resource Economics, UC Berkeley.
    6. Rolando Gonzales Martinez & Mariza Cooray, 2025. "Enhancing Poverty Targeting with Spatial Machine Learning: An application to Indonesia," Papers 2503.04300, arXiv.org.

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