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How survey-to-survey imputation can fail

Author

Listed:
  • Newhouse, D.
  • Shivakumaran, S.
  • Takamatsu, S.
  • Yoshida, N.

Abstract

This paper proposes diagnostics to assess the accuracy of survey-to-survey imputation methods and applies them to examine why imputing from the Household Income and Expenditure Survey into the Labor Force Survey fails to accurately project poverty trends in Sri Lanka between 2006 and 2009. Survey-to-survey imputation methods rely on two key assumptions: (i) that the questions in the two surveys are asked in a consistent way and (ii) that common variables of the two surveys explain a large share of the intertemporal change in household expenditure and poverty. In addition, differences in sampling design can lead validation tests to underestimate the accuracy of survey-to-survey predictions. In Sri Lanka, the causes of failure differ across sectors. In the urban sector, the primary culprit is differences between the two surveys in the design of the questionnaire. In the rural and estate sectors, the set of common variables in the prediction model does not adequately capture changes in poverty. The paper concludes that in Sri Lanka, survey-to-survey imputation between the Household Income and Expenditure Survey and the Labor Force Survey cannot produce accurate poverty estimates unless the Labor Force Survey adds additional questions on assets and is redesigned to use a questionnaire that is compatible with the Household Income and Expenditure Survey. Alternatively, a new welfare-tracking survey that satisfies these conditions could be established.

Suggested Citation

  • 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.
    • Handle: RePEc:wbk:wbrwps:6961
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    References listed on IDEAS

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    Cited by:

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    2. Jose Cuesta & Gabriel Lara Ibarra, 2017. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    3. Atamanov, Aziz & Tandon, Sharad & Lopez-Acevedo, Gladys & Vergara Bahena, Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them," IZA Discussion Papers 13363, Institute of Labor Economics (IZA).
    4. 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.
    5. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    6. Betti, Gianni & Molini, Vasco & Mori, Lorenzo, 2024. "An attempt to correct the underestimation of inequality measures in cross-survey imputation through generalized additive models for location, scale and shape," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    7. Jose Cuesta & Gabriel Lara Ibarra, 2018. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    8. Kathleen Beegle & Luc Christiaensen & Andrew Dabalen & Isis Gaddis, 2016. "Poverty in a Rising Africa," World Bank Publications - Books, The World Bank Group, number 22575, April.
    9. 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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