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Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements

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  • Dang,Hai-Anh H.
  • Kilic,Talip
  • Carletto,Calogero
  • Abanokova,Kseniya
Abstract
A key challenge with poverty measurement is that household consumption data are oftenunavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldomfeasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is acost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choicesand ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existingpoverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi,Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures householdutility consumption expenditures—as part of a basic imputation model with household-level demographic andemployment variables—provides poverty estimates that are not statistically significantly different from the true povertyrates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatialvariables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additionalcommunity-level predictors (available from survey and census data in Vietnam) related to educational achievement,poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in modelperformance, with certain models performing well for either urban areas or rural areas only. The paper providesoperationally-relevant and cost-saving inputs into thedesign of future surveys implemented with a poverty imputation objective and suggests directions for future research.

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  • Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9838
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    Cited by:

    1. Dang, Hai-Anh H & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    2. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    3. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    4. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    5. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    6. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.

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    More about this item

    Keywords

    Inequality; Educational Sciences; Health Care Services Industry; Demographics; Urban Housing; Urban Governance and Management; Municipal Management and Reform; Urban Housing and Land Settlements;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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