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Work Disability, Work, and Justification Bias in Europe and the U.S

Author

Listed:
  • Arie Kapteyn

    (RAND)

  • James P. Smith

    (RAND)

  • Arthur van Soest

    (Tilburg University, Netspar & RAND)

Abstract

To analyze the effect of health on work, many studies use a simple self-assessed health measure based upon a question such as “do you have an impairment or health problem limiting the kind or amount of work you can do?” A possible drawback of such a measure is the possibility that different groups of respondents may use different response scales. This is commonly referred to as “differential item functioning” (DIF). A specific form of DIF is justification bias: to justify the fact that they don’t work, non-working respondents may classify a given health problem as a more serious work limitation than working respondents. In this paper we use anchoring vignettes to identify justification bias and other forms of DIF across countries and socio-economic groups among older workers in the U.S. and Europe. Generally, we find differences in response scales across countries, partly related to social insurance generosity and employment protection. Furthermore, we find significant evidence of justification bias in the U.S. but not in Europe, suggesting differences in social norms concerning work.

Suggested Citation

  • Arie Kapteyn & James P. Smith & Arthur van Soest, 2009. "Work Disability, Work, and Justification Bias in Europe and the U.S," Working Papers wp207, University of Michigan, Michigan Retirement Research Center.
  • Handle: RePEc:mrr:papers:wp207
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    Cited by:

    1. Hullegie, P.G.J., 2012. "Essays on health and labor economics," Other publications TiSEM dcc68fc9-7af1-4ba9-8f90-6, Tilburg University, School of Economics and Management.
    2. Alain Jousten & Mathieu Lefebvre, 2013. "Retirement Incentives in Belgium: Estimations and Simulations Using SHARE Data," De Economist, Springer, vol. 161(3), pages 253-276, September.
    3. Oh, Hyunseung & Reis, Ricardo, 2012. "Targeted transfers and the fiscal response to the great recession," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 50-64.
    4. Datta Gupta, Nabanita & Jürges, Hendrik, 2012. "Do workers underreport morbidity? The accuracy of self-reports of chronic conditions," Social Science & Medicine, Elsevier, vol. 75(9), pages 1589-1594.
    5. Seuring, Till & Serneels, Pieter & Suhrcke, Marc, 2019. "The impact of diabetes on labour market outcomes in Mexico: A panel data and biomarker analysis," Social Science & Medicine, Elsevier, vol. 233(C), pages 252-261.
    6. Monika Bütler & Eva Deuchert & Michael Lechner & Stefan Staubli & Petra Thiemann, 2015. "Financial work incentives for disability benefit recipients: lessons from a randomised field experiment," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-18, December.
    7. Messinis, George, 2013. "Returns to education and urban-migrant wage differentials in China: IV quantile treatment effects," China Economic Review, Elsevier, vol. 26(C), pages 39-55.
    8. Richard W. Johnson & Melissa M. Favreault & Corina Mommaerts, 2009. "Work Ability and the Social Insurance Safety Net in the Years Prior to Retirement," Working Papers, Center for Retirement Research at Boston College wp2009-28, Center for Retirement Research, revised Nov 2009.
    9. Andrew M. Jones; Nigel Rice, Silvana Robone; & Nigel Rice; & Silvana Robone:, 2012. "A comparison of parametric and non-parametric adjustments using vignettes for self-reported data," Health, Econometrics and Data Group (HEDG) Working Papers 12/10, HEDG, c/o Department of Economics, University of York.
    10. Teresa Bago d'Uva & Maarten Lindeboom & Owen O'Donnell & Eddy van Doorslaer, 2011. "Education‐related inequity in healthcare with heterogeneous reporting of health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(3), pages 639-664, July.
    11. Enrica Croda & Jonathan Skinner & Laura Yasaitis, 2018. "The Health of Disability Insurance Enrollees: An International Comparison," Working Papers 2018:28, Department of Economics, University of Venice "Ca' Foscari".

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy

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