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Poverty imputation in contexts without consumption data: a revisit with further refinements

Author

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  • Dang, Hai-Anh H.
  • Kilic, Talip
  • Abanokova, Kseniya
  • Carletto, Calogero
Abstract
Survey-to-survey imputation has been increasingly employed to address data gaps for poverty measurement in poorer countries. We refine existing imputation models, using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. We find that adding household utility expenditures to a basic imputation model with household-level demographic and employment variables provides accurate estimates, which even fall within one standard error of the true poverty rates in many cases. The proposed imputation method performs better than several commonly used multiple imputation and machine learning techniques. Further adding geospatial variables improves accuracy, as does including additional community-level predictors (available from data in Vietnam) related to educational achievement, poverty, and asset wealth. Yet, within-country spatial heterogeneity exists, with certain models performing well for either urban areas or rural areas only. These results offer cost-saving inputs into future survey design.

Suggested Citation

  • Dang, Hai-Anh H. & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2024. "Poverty imputation in contexts without consumption data: a revisit with further refinements," LSE Research Online Documents on Economics 125798, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:125798
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    Cited by:

    1. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    2. 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).
    3. 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.
    4. Dang, Hai-Anh & 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," GLO Discussion Paper Series 1392, Global Labor Organization (GLO).
    5. 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.
    6. 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.

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    More about this item

    Keywords

    Ethiopia; Malawi; Nigeria; Tanzania; Vietnam; consumption; household surveys; poverty; Sub-Saharan Africa; survey-to-survey imputation; sub-Saharan Africa;
    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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