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Telescoping Error in Recalled Food Consumption: Evidence from a Survey Experiment in Ethiopia
[Video-Based Behavioral Change Communication to Change Consumption Patterns: Experimental Evidence from Urban Ethiopia]

Author

Listed:
  • Gashaw T Abate
  • Alan de Brauw
  • John Gibson
  • Kalle Hirvonen
  • Abdulazize Wolle

Abstract

Telescoping errors occur if survey respondents misdate events from outside the reference period and include them in their recall. Concern about telescoping influenced the design of early Living Standards Measurement Study (LSMS) surveys, which used a two-visit interview format to bound food consumption recall. This design fell out of favor although not for evidence-based reasons. To measure the extent of telescoping bias on food consumption measures, a survey experiment was conducted in Addis Ababa, Ethiopia, randomly assigning households to either a two-visit bounded recall or a single visit unbounded recall. The average value of reported food consumption is 16 percent higher (95 percent CI: 7.4–25.9) in the unbounded single visit recall relative to the two-visit bounded recall. Most of the error is explained by difference in reported spending on less frequently consumed, protein-rich foods, so apparent food security indicators based on household diet diversity are likely overstated with unbounded recall.

Suggested Citation

  • Gashaw T Abate & Alan de Brauw & John Gibson & Kalle Hirvonen & Abdulazize Wolle, 2022. "Telescoping Error in Recalled Food Consumption: Evidence from a Survey Experiment in Ethiopia [Video-Based Behavioral Change Communication to Change Consumption Patterns: Experimental Evidence from," The World Bank Economic Review, World Bank, vol. 36(4), pages 889-908.
    • Handle: RePEc:oup:wbecrv:v:36:y:2022:i:4:p:889-908.
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    File URL: http://hdl.handle.net/10.1093/wber/lhac015
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    References listed on IDEAS

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    1. Rachael Meager, 2022. "Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature," American Economic Review, American Economic Association, vol. 112(6), pages 1818-1847, June.
    2. Meager, Rachael, 2022. "Aggregating distributional treatment effects: a Bayesian hierarchical analysis of the microcredit literature," LSE Research Online Documents on Economics 115559, London School of Economics and Political Science, LSE Library.
    3. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    4. John Gibson & Kathleen Beegle & Joachim De Weerdt & Jed Friedman, 2015. "What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 466-474, June.
    5. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    6. 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.
    7. Menno Pradhan, 2009. "Welfare Analysis with a Proxy Consumption Measure: Evidence from a Repeated Experiment in Indonesia," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 391-417, December.
    8. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    9. Backiny-Yetna, Prospère & Steele, Diane & Yacoubou Djima, Ismael, 2017. "The impact of household food consumption data collection methods on poverty and inequality measures in Niger," Food Policy, Elsevier, vol. 72(C), pages 7-19.
    10. Zezza, Alberto & Carletto, Gero & Fiedler, John L & Gennari, Pietro & Jolliffe, Dean M, 2017. "Food Counts. Measuring Food Consumption And Expenditures In Household Consumption And Expenditure Surveys (HCES)," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260886, European Association of Agricultural Economists.
    11. Friedman, Jed & Beegle, Kathleen & De Weerdt, Joachim & Gibson, John, 2017. "Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in Tanzania," Food Policy, Elsevier, vol. 72(C), pages 94-111.
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