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Correcting Sampling and Nonresponse Bias in Phone Survey Poverty Estimation Using Reweighting and Poverty Projection Models

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  • Zhang,Kexin
  • Takamatsu,Shinya
  • Yoshida,Nobuo

Abstract

To monitor the evolution of household living conditions during the COVID-19 pandemic, the World Bank conducted COVID-19 High-Frequency Phone Surveys in around 80 countries. Phone surveys are cheap and easy to implement, but they have some major limitations, such as the absence of poverty data, sampling bias due to incomplete telephone coverage in many developing countries, and frequent nonresponses to phone interviews. To overcome these limitations, the World Bank conducted pilots in 20 countries where the Survey of Wellbeing via Instant and Frequent Tracking, a rapid poverty monitoring tool, was adopted to estimate poverty rates based on 10 to 15 simple questions collected via phone interviews, and where sampling weights were adjusted to correct the sampling and nonresponse bias. This paper examines whether reweighting procedures and the Survey of Wellbeing via Instant and Frequent Tracking methodology can eliminate the bias in poverty estimation based on the COVID-19 High-Frequency Phone Surveys. Experiments using artificial phone survey samples show that (i) reweighting procedures cannot fully eliminate bias in poverty estimates, as previous research has demonstrated, but (ii) when combined with Survey of Wellbeing via Instant and Frequent Tracking poverty projections, they effectively eliminate bias in poverty estimates and other statistics.

Suggested Citation

  • Zhang,Kexin & Takamatsu,Shinya & Yoshida,Nobuo, 2023. "Correcting Sampling and Nonresponse Bias in Phone Survey Poverty Estimation Using Reweighting and Poverty Projection Models," Policy Research Working Paper Series 10656, The World Bank.
  • Handle: RePEc:wbk:wbrwps:10656
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    References listed on IDEAS

    as
    1. Fan Li & Kari Lock Morgan & Alan M. Zaslavsky, 2018. "Balancing Covariates via Propensity Score Weighting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 390-400, January.
    2. Sara Capacci & Mario Mazzocchi & Sergio Brasini, 2018. "Estimation of unobservable selection effects in on-line surveys through propensity score matching: An application to public acceptance of healthy eating policies," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-17, April.
    3. Czajka, John L, et al, 1992. "Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 117-131, April.
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