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Challenges in predicting poverty trends using survey to survey imputation. Experiences from Malawi




Poverty in low-income countries is usually measured with large and infrequent household surveys. A challenge is to find methods to measure poverty more frequently. The objective of this study is to test a method for predicting poverty, based upon a statistical model utilizing consumption surveys and light annual surveys. A decade of poverty predictions and regular poverty estimates in Malawi provides us with a unique real-life experience to better understand the suitability of such approaches to monitor trends in poverty. The analysis from Malawi suggests that a modelling approach works per se, given that information on the household’s demographic composition is included in the model. The main challenge when predicting onto other surveys seems to be related to comparability between the surveys. Differences in implementation, questionnaire design and survey sample size are aspects that may contribute to incomparability of data collected between the surveys.

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  • Astrid Mathiassen & Bjørn K. Wold, 2019. "Challenges in predicting poverty trends using survey to survey imputation. Experiences from Malawi," Discussion Papers 900, Statistics Norway, Research Department.
  • Handle: RePEc:ssb:dispap:900

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    References listed on IDEAS

    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. Newhouse, D. & Shivakumaran, S. & Takamatsu, S. & Yoshida, N., 2014. "How survey-to-survey imputation can fail," Policy Research Working Paper Series 6961, The World Bank.
    3. Channing Arndt & Karl Pauw & James Thurlow, 2016. "The Economy-wide Impacts and Risks of Malawi's Farm Input Subsidy Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(3), pages 962-980.
    4. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    5. 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.
    6. Ulrik Beck & Karl Pauw Author-Name:Richard Mussa, 2015. "Methods matter: The sensitivity of Malawian poverty estimates to definitions,data, and assumptions," WIDER Working Paper Series 126, World Institute for Development Economic Research (UNU-WIDER).
    7. Arndt, Channing & Pauw, Karl & Thurlow, James, 2014. "The economywide impacts and risks of Malawi.s farm input subsidy programme," WIDER Working Paper Series 099, World Institute for Development Economic Research (UNU-WIDER).
    8. Chirwa, Ephraim & Dorward, Andrew, 2013. "Agricultural Input Subsidies: The Recent Malawi Experience," OUP Catalogue, Oxford University Press, number 9780199683529.
    9. Carr, Stephen, 2014. "The challenge of Africa’s nitrogen drought: Some indicators from the Malawian experience:," MaSSP policy notes 19, International Food Policy Research Institute (IFPRI).
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    More about this item


    Survey-to-survey imputation; poverty measurement; poverty model; household surveys; Malawi;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

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