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The impact of questioning method on measurement error in panel survey measures of benefit receipt: evidence from a validation study

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  • Peter Lynn
  • Annette Jäckle
  • Stephen P. Jenkins
  • Emanuela Sala

Abstract

We assess measurement error in panel survey reports of social security benefit receipt, drawing on a unique validation study. Our aims are threefold. First, we quantify the incidence of measurement errors (under- and over-reporting). Second, we assess the extent to which this varies according to the questioning method that is used. Specifically, dependent interviewing has been proposed as a way to reduce under-reporting in some circumstances. We compare two versions of dependent interviewing with traditional independent interviewing in an experimental design. Third, we identify and assess new ways of reducing measurement error in panel surveys. We use data from a large-scale UK household panel survey and we consider six benefits. To assess the measurement error, a validation exercise was conducted, with administrative data on benefit receipt matched at the individual level to the survey microdata.
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  • Peter Lynn & Annette Jäckle & Stephen P. Jenkins & Emanuela Sala, 2012. "The impact of questioning method on measurement error in panel survey measures of benefit receipt: evidence from a validation study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 289-308, January.
    • Handle: RePEc:bla:jorssa:v:175:y:2012:i:1:p:289-308
    DOI: j.1467-985X.2011.00717.x
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    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    2. Stephen P. Jenkins & Lorenzo Cappellari & Peter Lynn & Annette Jäckle & Emanuela Sala, 2006. "Patterns of consent: evidence from a general household survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 701-722, October.
    3. Jäckle, Annette, 2008. "Measurement error and data collection methods: effects on estimates from event history data," ISER Working Paper Series 2008-13, Institute for Social and Economic Research.
    4. Ruth Hancock & Geraldine Barker, 2005. "The quality of social security benefit data in the British Family Resources Survey: implications for investigating income support take‐up by pensioners," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 63-82, January.
    5. Jäckle, Annette & Noah Uhrig, S.C. & Laurie, Heather, 2007. "The introduction of dependent interviewing on the British Household Panel Survey," ISER Working Paper Series 2007-07, Institute for Social and Economic Research.
    6. Lynn, Peter & Jäckle, Annette & Sala, Emanuela & P. Jenkins, Stephen, 2004. "Validation of survey data on income and employment: the ISMIE experience," ISER Working Paper Series 2004-14, Institute for Social and Economic Research.
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    Cited by:

    1. H. Xavier Jara & Marcelo Varela, 2017. "Tax-benefit microsimulation and income redistribution in Ecuador," WIDER Working Paper Series 177, World Institute for Development Economic Research (UNU-WIDER).
    2. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    3. Paul Fisher & Omar Hussein, 2023. "Understanding Society: the income data," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 377-397, December.
    4. Mike Brewer & Ben Etheridge & Cormac O’Dea, 2017. "Why are Households that Report the Lowest Incomes So Well‐off?," Economic Journal, Royal Economic Society, vol. 127(605), pages 24-49, October.
    5. Lugtig Peter & Jäckle Annette, 2014. "Can I Just Check...? Effects of Edit Check Questions on Measurement Error and Survey Estimates," Journal of Official Statistics, Sciendo, vol. 30(1), pages 45-62, March.
    6. Serena Pattaro & Nick Bailey & Chris Dibben, 2020. "Using Linked Longitudinal Administrative Data to Identify Social Disadvantage," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(3), pages 865-895, February.
    7. Jennifer Roberts & Karl Taylor, 2022. "New Evidence on Disability Benefit Claims in Britain: The Role of Health and the Local Labour Market," Economica, London School of Economics and Political Science, vol. 89(353), pages 131-160, January.
    8. Bruckmeier, Kerstin & Riphahn, Regina T. & Wiemers, Jürgen, 2019. "Benefit underreporting in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," IAB-Discussion Paper 201906, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Paul Fisher, 2019. "Does Repeated Measurement Improve Income Data Quality?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 989-1011, October.
    10. Bucks, Brian & Pence, Karen, 2015. "Wealth, pensions, debt, and savings: Considerations for a panel survey," Journal of Economic and Social Measurement, IOS Press, issue 1-4, pages 151-175.
    11. Bollinger, Christopher R. & Tasseva, Iva, 2023. "Income source confusion using the SILC," LSE Research Online Documents on Economics 119351, London School of Economics and Political Science, LSE Library.
    12. Jäckle, Annette & Johannes, Eggs, 2014. "Dependent interviewing and sub-optimal responding," ISER Working Paper Series 2014-32, Institute for Social and Economic Research.
    13. Luis Ayala & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The impact of different data sources on the level and structure of income inequality," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 583-611, September.
    14. H Xavier Jara & Marcelo Varela, 2019. "Tax-benefit Microsimulation and Income Redistribution in Ecuador," International Journal of Microsimulation, International Microsimulation Association, vol. 12(1), pages 52-82.
    15. R. Bollinger, Christopher & Valentinova Tasseva, Iva, 2022. "Income source confusion using the SILC," ISER Working Paper Series 2022-04, Institute for Social and Economic Research.
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    17. Tasseva, Iva Valentinova, 2016. "Evaluating the performance of means-tested benefits in Bulgaria," Journal of Comparative Economics, Elsevier, vol. 44(4), pages 919-935.

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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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