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Patterns of Consent: Evidence from a General Household Survey

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

Abstract

We analyse consent patterns and consent bias in the context of a large general household survey, the 'Improving survey measurement of income and employment' (ISMIE) survey, also addressing issues that arise when there are multiple consent questions. Using a multivariate probit regression model for four binary outcomes with two incidental truncations, we show that there are biases in consent to data linkage with benefit and tax credit administrative records held by the Department for Work and Pensions, and with wage and employment data held by employers, and also in respondents' willingness and ability to supply their National Insurance Number. The biases differ according to the question considered, however. We also show that modelling consent questions independently rather than jointly may lead to misleading inferences about consent bias. A positive correlation between unobservable individual factors affecting consent to DWP record linkage and consent to employer record linkage is suggestive of a latent individual consent propensity.

Suggested Citation

  • Stephen P. Jenkins & Lorenzo Cappellari & Peter Lynn & Annette Jäckle & Emanuela Sala, 2005. "Patterns of Consent: Evidence from a General Household Survey," Discussion Papers of DIW Berlin 490, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp490
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    References listed on IDEAS

    as
    1. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    2. Stewart, Mark B & Swaffield, Joanna K, 1999. "Low Pay Dynamics and Transition Probabilities," Economica, London School of Economics and Political Science, vol. 66(261), pages 23-42, February.
    3. F. Thomas Juster & Richard Suzman, 1995. "An Overview of the Health and Retirement Study," Journal of Human Resources, University of Wisconsin Press, vol. 30, pages 7-56.
    4. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    5. Lynn, Peter & Jäckle, Annette & Jenkins, Stephen P. & Sala, Emanuela, 2004. "The effects of dependent interviewing on responses to questions on income sources," ISER Working Paper Series 2004-16, Institute for Social and Economic Research.
    6. Philip J. Smith & David C. Hoaglin & J. N. K. Rao & Michael P. Battaglia & Danni Daniels, 2004. "Evaluation of adjustments for partial non‐response bias in the US National Immunization Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(1), pages 141-156, February.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    8. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, September.
    9. Jenkins, Stephen P. & Lynn, Peter & Jäckle, Annette & Sala, Emanuela, 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.
    10. Jäckle, Annette & Sala, Emanuela & Jenkins, Stephen P. & Lynn, Peter, 2004. "Validation of survey data on income and employment: the ISMIE experience," ISER Working Paper Series 2004-14, Institute for Social and Economic Research.
    11. Schrapler, Jorg-Peter, 2003. "Respondent behaviour in panel studies: a case study for income-nonresponse by means of the British Household Panel Study (BHPS)," ISER Working Paper Series 2003-08, Institute for Social and Economic Research.
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    More about this item

    Keywords

    Informed consent; Household surveys; Consent bias; Selection bias; Multivariate probit; Incidental truncation; Data linkage; National Insurance number;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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