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Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies

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  • Talip Kilic
  • Thomas Pave Sohnesen

Abstract

Based on a randomized survey experiment that was implemented in Malawi, the study finds that observationally‐equivalent, as well as same, households answer the same questions differently depending on whether they are interviewed with a short questionnaire or its longer counterpart. Statistically significant differences in reporting emerge across all topics and question types. In proxy‐based poverty measurement, these reporting differences lead to significantly different predicted poverty rates and Gini coefficients. The difference in poverty predictions ranges from 3 to 7 percentage points, depending on the model specification. A prediction model based only on the proxies that are elicited prior to the variation in questionnaire design yields identical poverty predictions irrespective of the short‐versus‐long questionnaire treatment. The results are relevant for estimating trends with questionnaires exhibiting inter‐temporal variation in design, impact evaluations administering questionnaires of different length and complexity to treatment and control samples, and development programs utilizing proxy‐means tests for targeting.

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  • 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.
    • Handle: RePEc:bla:revinw:v:65:y:2019:i:1:p:144-165
    DOI: 10.1111/roiw.12343
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    1. Thomas Pave Sohnesen & Niels Stender, 2017. "Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment," Poverty & Public Policy, John Wiley & Sons, vol. 9(1), pages 118-133, March.
    2. 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," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    3. Fiala, Nathan & Rose, Julian & Aryemo, Filder & Peters, Jörg, 2022. "The (very) long-run impacts of cash grants during a crisis," Ruhr Economic Papers 961, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Adan Silverio‐Murillo & Jose Roberto Balmori de la Miyar, 2022. "Remittances and domestic violence," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2274-2295, November.
    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. Abay, Kibrom A. & Berhane, Guush & Hoddinott, John F. & Tafere, Kibrom, 2021. "Assessing response fatigue in phone surveys: Experimental evidence on dietary diversity in Ethiopia," IFPRI discussion papers 2017, International Food Policy Research Institute (IFPRI).
    7. Joachim De Weerdt & John Gibson & Kathleen Beegle, 2020. "What Can We Learn from Experimenting with Survey Methods?," Annual Review of Resource Economics, Annual Reviews, vol. 12(1), pages 431-447, October.
    8. Brown, Caitlin & Ravallion, Martin & van de Walle, Dominique, 2018. "A poor means test? Econometric targeting in Africa," Journal of Development Economics, Elsevier, vol. 134(C), pages 109-124.
    9. Ligon, Ethan & Christiaensen, Luc & Sohnesen, Thomas P, 2020. "Should Consumption Sub-Aggregates be Used to Measure Poverty?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9b9929jh, Department of Agricultural & Resource Economics, UC Berkeley.
    10. Jeong, Dahyeon & Aggarwal, Shilpa & Robinson, Jonathan & Kumar, Naresh & Spearot, Alan & Park, David Sungho, 2023. "Exhaustive or exhausting? Evidence on respondent fatigue in long surveys," Journal of Development Economics, Elsevier, vol. 161(C).
    11. 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.
    12. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.
    13. Deepti Sharma & Hema Swaminathan & Rahul Lahoti, 2024. "Does it matter who you ask for time-use data?," WIDER Working Paper Series wp-2024-1, World Institute for Development Economic Research (UNU-WIDER).
    14. Fiala, Nathan & Masselus, Lise, 2022. "Whom to ask? Testing respondent effects in household surveys," Ruhr Economic Papers 935, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. 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.

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