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Identifying Trend and Age Effects in Sickness Absence from Individual Data: Some Econometric Problems


  • Biørn, Erik

    () (Dept. of Economics, University of Oslo)


When using data from individuals who are in the labour force to disentangle the empirical relevance of cohort, age and time effects for sickness absence, the inference may be biased, affected by sorting-out mechanisms. One reason is unobserved heterogeneity potentially affecting both health status and ability to work, which can bias inference because the individuals entering the data set are conditional on being in the labour force. Can this sample selection be adequately handled by attaching unobserved heterogeneity to non-structured fixed effects? In the paper we examine this issue and discuss the econometric setup for identifying from such data time effects in sickness absence. The inference and interpretation problem is caused, on the one hand, by the occurrence of time, cohort and age effects also in the labour market participation, on the other hand by correlation between unobserved heterogeneity in health status and in ability to work. We show that running panel data regressions, ordinary or logistic, of sickness absence data on certain covariates, when neglecting this sample selection, is likely to obscure the interpretation of the results, except in certain, not particularly realistic, cases. However, the fixed individual effects approach is more robust in this respect than an approach controlling for fixed cohort effects only.

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  • Biørn, Erik, 2010. "Identifying Trend and Age Effects in Sickness Absence from Individual Data: Some Econometric Problems," Memorandum 20/2010, Oslo University, Department of Economics.
  • Handle: RePEc:hhs:osloec:2010_020

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    More about this item


    Sickness absence; health-labour interaction; cohort-age-time problem; self-selection; latent heterogeneity; bivariate censoring; truncated binormal distribution; panel data;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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