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Predicting poverty trends by survey-to-survey imputation: the challenge of comparability

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  • Astrid Mathiassen
  • Bjørn K. Getz Wold

Abstract

Poverty in low-income countries is usually measured using large and infrequent household consumption surveys. The challenge is to find methods to measure poverty rates more frequently. This study validates a survey-to-survey imputation method, based on a statistical model utilizing consumption surveys and light surveys to measure changes in poverty rates over time. A decade of poverty predictions and regular poverty estimates in Malawi provides a unique case study. The analysis suggests that this modelling approach works within the same context given that households’ demographic composition is included in the model. Predicting poverty using different surveys is challenging because of different aspects of comparability. A new way to account for seasonal coverage strengthens the model when imputing for surveys covering different seasons. It is important for national statistics offices and supporting agencies to prioritize maintaining consistency in the way data are collected in surveys to provide comparable trends over time.

Suggested Citation

  • Astrid Mathiassen & Bjørn K. Getz Wold, 2021. "Predicting poverty trends by survey-to-survey imputation: the challenge of comparability," Oxford Economic Papers, Oxford University Press, vol. 73(3), pages 1153-1174.
    • Handle: RePEc:oup:oxecpp:v:73:y:2021:i:3:p:1153-1174.
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    File URL: http://hdl.handle.net/10.1093/oep/gpab014
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    References listed on IDEAS

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    1. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    2. Hai-Anh H. Dang & Peter F. Lanjouw & Umar Serajuddin, 2017. "Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 939-962.
    3. Hai-Anh H. Dang & Peter F. Lanjouw, 2018. "Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 67(1), pages 131-170.
    4. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    5. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    6. Astrid Mathiassen, 2013. "Testing Prediction Performance of Poverty Models: Empirical Evidence from U ganda," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(1), pages 91-112, March.
    7. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
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    Cited by:

    1. Ibrahima Sarr & Hai-Anh H. Dang & Carlos Santiago Guzman Gutierrez & Theresa Beltramo & Paolo Verme, 2025. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 177(1), pages 207-251, March.
    2. Fernandez Romero,Jaime Estuardo & Olivieri,Sergio Daniel & Wambile,Ayago Esmubancha, 2024. "Reconstructing 2010–2022 Poverty and Inequality Trends in Bangladesh : A Statistical Matching Approach," Policy Research Working Paper Series 10749, The World Bank.
    3. Hai‐Anh H. Dang & Talip Kilic & Kseniya Abanokova & Calogero Carletto, 2025. "Poverty Imputation in Contexts Without Consumption Data: A Revisit With Further Refinements," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 71(1), February.
    4. Eri Nakamura & Kimitaka Nishitani & Fumitoshi Mizutani, 2023. "Do Consumers Really Pay for SDGs? Re-Evaluating Consumer Behaviour Using Surveys in the USA, Germany, and Japan," CESifo Economic Studies, CESifo Group, vol. 69(3), pages 158-176.
    5. World Bank, 2022. "The Concept and Empirical Evidence of SWIFT Methodology," World Bank Publications - Reports 38095, The World Bank Group.
    6. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    7. Brychka, Bohdan & Vyslobodska, Halyna & Voitovych, Nadiia, 2023. "Poverty in Ukraine: evolution of interpreting and analysis of impact factors," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(2), June.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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