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Unit non-response in household wealth surveys


  • Osier, Guillaume


The Household Finance and Consumption Survey (HFCS) is a recent initiative from the Eurosystem to collect comparable micro-data on household wealth and indebtedness in the euro area countries. The Household Finance and Consumption Network (HFCN), which comprises the European Central Bank (ECB), national central banks (NCBs), and national statistical institutes (NSIs), is in charge of the development and implementation of the HFCS. The first round of the survey was successfully conducted between 2008 and 2011, and the results were published in April 2013. The second round is now under way and will cover all the euro area countries. This paper is a joint effort by several members of the HFCN to further investigate the issue of unit non-response in the HFCS, better describe and understand its patterns, measure its effects on the overall quality of the survey and, ultimately, propose strategies to mitigate them. The paper is divided into sections, the first section being the introduction. The second section draws up a list of the main possible sources of auxiliary information that can be relied on in order to analyse non-response patterns in the HFCS. It also presents summary indicators that can be used to quantify unit non-response. In the third section, based on the experience from the first wave of the HFCS, the report elaborates on good survey practices (e.g. interviewer training and compensation, use of incentives, persuasive contact strategies, etc.) to prevent unit non-response from occurring. The fourth section compares several reweighting strategies for coping with unit non-response a posteriori, in particular simple and generalised calibration methods. These methods are assessed with respect to their impact on the main HFCS-based estimates. Finally, based on the outcome of this empirical analysis, recommendations are made with regard to post-survey weighting adjustment in the HFCS. JEL Classification: C83

Suggested Citation

  • Osier, Guillaume, 2016. "Unit non-response in household wealth surveys," Statistics Paper Series 15, European Central Bank.
  • Handle: RePEc:ecb:ecbsps:201615

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    References listed on IDEAS

    1. Schouten, Barry & Shlomo, Natalie & Skinner, Chris J., 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    2. David Haziza & Jean‐François Beaumont, 2007. "On the Construction of Imputation Classes in Surveys," International Statistical Review, International Statistical Institute, vol. 75(1), pages 25-43, April.
    3. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    4. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    5. G. Blom, Annelies, 2009. "Nonresponse bias adjustments: what can process data contribute?," ISER Working Paper Series 2009-21, Institute for Social and Economic Research.
    6. Thomas Y. Mathä & Alessandro Porpiglia & Michael Ziegelmeyer, 2012. "The Luxembourg Household Finance and Consumption Survey (LU-HFCS): Introduction and Results," BCL working papers 73, Central Bank of Luxembourg.
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    Cited by:

    1. Rafael Wildauer & Jakob Kapeller, 2019. "Rank correction: a new approach to differential nonresponse in wealth survey data," Working Papers PKWP1921, Post Keynesian Economics Society (PKES).
    2. Sofie R. Waltl & Robin Chakraborty, 2022. "Missing the wealthy in the HFCS: micro problems with macro implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 169-203, March.
    3. Wildauer, Rafael & Heck, Ines & Kapeller, Jakob, 2023. "Was Pareto right? Is the distribution of wealth thick-tailed?," Greenwich Papers in Political Economy 38597, University of Greenwich, Greenwich Political Economy Research Centre.
    4. Benjamin Ferschli & Jakob Kapeller & Bernhard Schütz & Rafael Wildauer, 2017. "Bestände und Konzentration privater Vermögen in Österreich 2014/2015," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 43(4), pages 499-533.
    5. Anastasia Girshina & Thomas Y. Mathä & Michael Ziegelmeyer, 2017. "The Luxembourg Household Finance and Consumption Survey: Results from the 2nd wave," BCL working papers 106, Central Bank of Luxembourg.
    6. Jaanika Meriküll & Tairi Rõõm, 2022. "Are survey data underestimating the inequality of wealth?," Empirical Economics, Springer, vol. 62(2), pages 339-374, February.
    7. Apostel, Arthur & O'Neill, Daniel W., 2022. "A one-off wealth tax for Belgium: Revenue potential, distributional impact, and environmental effects," Ecological Economics, Elsevier, vol. 196(C).
    8. Jakob Kapeller & Rafael Wildauer, 2019. "Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data," ICAE Working Papers 101, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    9. Wildauer, Rafael & Kapeller, Jakob, 2022. "Tracing the invisible rich: A new approach to modelling Pareto tails in survey data," Labour Economics, Elsevier, vol. 75(C).

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    More about this item


    calibration; response propensity; sampling weights; unit non-response;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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