IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/38080.html
   My bibliography  Save this paper

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

Author

Listed:
  • Lynn, Peter
  • Jäckle, Annette
  • Jenkins, Stephen P.
  • Sala, Emanuela

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.

Suggested Citation

  • Lynn, Peter & Jäckle, Annette & Jenkins, Stephen P. & Sala, Emanuela, 2012. "The impact of questioning method on measurement error in panel survey measures of benefit receipt: evidence from a validation study," LSE Research Online Documents on Economics 38080, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:38080
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/38080/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    2. 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.
    3. 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.
    4. 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.
    5. 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].
    6. 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.
    7. 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.
    8. Tasseva, Iva Valentinova, 2016. "Evaluating the performance of means-tested benefits in Bulgaria," Journal of Comparative Economics, Elsevier, vol. 44(4), pages 919-935.
    9. 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).
    10. 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.
    11. 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.
    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. Paul Fisher & Omar Hussein, 2023. "Understanding Society: the income data," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 377-397, December.
    16. repec:esx:essedp:736 is not listed on IDEAS

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Lynn, Peter & Jäckle, Annette & Sala, Emanuela & P. Jenkins, Stephen, 2004. "The impact of interviewing method on measurement error in panel survey measures of benefit receipt: evidence from a validation study," ISER Working Paper Series 2004-28, Institute for Social and Economic Research.
    3. Korbmacher, Julie M. & Schröder, Mathis, 2013. "Consent when Linking Survey Data with Administrative Records: The Role of the Interviewer," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(2), pages 115-131.
    4. Lynn, Peter & Sala, Emanuela, 2005. "The impact of a mixed-mode data collection design on non response bias on a business survey," ISER Working Paper Series 2005-16, Institute for Social and Economic Research.
    5. SC Noah Uhrig & Emanuela Sala, 2011. "When Change Matters: An Analysis of Survey Interaction in Dependent Interviewing on the British Household Panel Study," Sociological Methods & Research, , vol. 40(2), pages 333-366, May.
    6. Jennifer C. Smith, 2015. "Pay Growth, Fairness, and Job Satisfaction: Implications for Nominal and Real Wage Rigidity," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(3), pages 852-877, July.
    7. Smith, Jennifer C., 2013. "Pay Growth, Fairness and Job Satisfaction: Implications for Nominal and Real Wage Rigidity," Economic Research Papers 270540, University of Warwick - Department of Economics.
    8. Lüthen Holger & Schröder Carsten & Grabka Markus M. & Goebel Jan & Mika Tatjana & Brüggmann Daniel & Ellert Sebastian & Penz Hannah, 2022. "SOEP-RV: Linking German Socio-Economic Panel Data to Pension Records," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(2), pages 291-307, April.
    9. Jäckle, Annette & Lugtig, Peter, 2011. "Can I just check…? Effects of edit check questions on measurement error and survey estimates," ISER Working Paper Series 2011-23, Institute for Social and Economic Research.
    10. Lynn, Peter & Jäckle, Annette & Sala, Emanuela & P. Jenkins, Stephen, 2004. "Linking household survey and administrative record data: what should the matching variables be?," ISER Working Paper Series 2004-23, Institute for Social and Economic Research.
    11. Sala, Emanuela & Noah Uhrig, S.C., 2009. "When change matters: the effect of dependent interviewing on survey interaction in the British Household Panel Study," ISER Working Paper Series 2009-09, Institute for Social and Economic Research.
    12. 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].
    13. Emanuela Sala & Peter Lynn, 2009. "The potential of a multi-mode data collection design to reduce non response bias. The case of a survey of employers," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(1), pages 123-136, January.
    14. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    15. Kaspar W thrich, 2013. "Set Identification of Generalized Linear Predictors in the Presence of Non-Classical Measurement Errors," Diskussionsschriften dp1304, Universitaet Bern, Departement Volkswirtschaft.
    16. Fabian T C Schmidt & Clemens M Lechner & Daniel Danner, 2020. "New wine in an old bottle? A facet-level perspective on the added value of Grit over BFI–2 Conscientiousness," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-25, February.
    17. Ruth Hancock & Marcello Morciano & Stephen Pudney & Francesca Zantomio, 2015. "Do household surveys give a coherent view of disability benefit targeting?: a multisurvey latent variable analysis for the older population in Great Britain," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 815-836, October.
    18. Thomas Pave Sohnesen, 2019. "Are you what you consume?: Impact of food, soft drinks, and coffee on cognitive and non-cognitive test scores," WIDER Working Paper Series wp-2019-117, World Institute for Development Economic Research (UNU-WIDER).
    19. Zhiguo Xiao & Jun Shao & Mari Palta, 2010. "GMM in linear regression for longitudinal data with multiple covariates measured with error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 791-805.
    20. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.

    More about this item

    Keywords

    dependent interviewing; longitudinal survey; social security; survey errors; under-reporting;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:38080. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.