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Patterns of Consent: Evidence from a General Household Survey

Author

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

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    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. 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.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, March.
    4. 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.
    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. 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.
    7. 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 s7-s56.
    8. 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.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    10. 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.
    11. 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.
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    Cited by:

    1. 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.
    2. Dimitris Christelis & Raquel Fonseca, 2015. "Labor Market Policies and Self-Employment Transitions of Older Workers," Cahiers de recherche 1516, Chaire de recherche Industrielle Alliance sur les enjeux économiques des changements démographiques.
    3. Sala, Emanuela & Burton, Jonathan & Knies, Gundi, 2010. "Correlates of obtaining informed consent to data linkage: respondent, interview and interviewer characteristics," ISER Working Paper Series 2010-28, Institute for Social and Economic Research.
    4. 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.
    5. Keay, Myoung-Jin, 2016. "Partial copula methods for models with multiple discrete endogenous explanatory variables and sample selection," Economics Letters, Elsevier, vol. 144(C), pages 85-87.
    6. Christelis, Dimitris & Georgarakos, Dimitris & Haliassos, Michael, 2009. "Stockholding: From participation to location and to participation spillovers," CFS Working Paper Series 2009/02, Center for Financial Studies (CFS).
    7. Warnke, Arne Jonas, 2017. "An investigation of record linkage refusal and its implications for empirical research," ZEW Discussion Papers 17-031, ZEW - Leibniz Centre for European Economic Research.
    8. Sala, Emanuela & Knies, Gundi & Burton, Jonathan, 2013. "Propensity to consent to data linkage: experimental evidence from the Innovation Panel on the role of three survey design features," Understanding Society Working Paper Series 2013-05, Understanding Society at the Institute for Social and Economic Research.
    9. Coisnon, Thomas & Rousselière, Damien & Rousselière, Samira, 2018. "Information on biodiversity and environmental behaviors: a European study of individual and institutional drivers to adopt sustainable gardening practices," Working Papers 272611, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    10. Beste, Jonas, 2011. "Selektivitätsprozesse bei der Verknüpfung von Befragungs- mitProzessdaten : Record Linkage mit Daten des Panels „Arbeitsmarkt und soziale Sicherung“ und administrativen Daten der Bundesagentur für Arb," FDZ Methodenreport 201109_de, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    11. Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation," Stata Journal, StataCorp LP, vol. 6(2), pages 156-189, June.
    12. Cherkashin, Ivan & Demidova, Svetlana & Imai, Susumu & Krishna, Kala, 2009. "The inside scoop: Acceptance and rejection at the journal of international economics," Journal of International Economics, Elsevier, vol. 77(1), pages 120-132, February.
    13. Fertig, Michael & Görlitz, Katja, 2013. "Missing wages: How to test for biased estimates in wage functions?," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 269-271.
    14. Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 192-224.
    15. Christelis, Dimitris & Georgarakos, Dimitris & Haliassos, Michael, 2011. "Stockholding: Participation, location, and spillovers," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1918-1930, August.
    16. Al Baghal, Tarek & Knies, Gundi & Burton, Jonathan, 2014. "Linking administrative records to surveys: differences in the correlates to consent decisions," Understanding Society Working Paper Series 2014-09, Understanding Society at the Institute for Social and Economic Research.
    17. Sala, Emanuela & Lynn, Peter, 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.
    18. 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.
    19. Christelis, Dimitris & Georgarakos, Dimitris, 2013. "Investing at home and abroad: Different costs, different people?," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2069-2086.
    20. Angioloni, Simone & Kudabaev, Zarylbek & Ames, Glenn & Wetzstein, Michael, 2015. "Household Allocation of Microfinance Loans in Kyrgyzstan," 2015 Conference, August 9-14, 2015, Milan, Italy 210949, International Association of Agricultural Economists.
    21. Korbmacher, Julie M. & Schröder, Mathis, 2013. "Consent when Linking Survey Data with Administrative Records: The Role of the Interviewer," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 115-131.
    22. repec:mea:meawpa:12258 is not listed on IDEAS
    23. Jeremy Tanguy, 2013. "Collective and Individual Conflicts in the Workplace: Evidence from F rance," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 52(1), pages 102-133, January.
    24. Lynn, Peter & Jäckle, Annette & Jenkins, Stephen P. & Sala, Emanuela, 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.

    More about this item

    Keywords

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

    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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