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Age-Cohort-Time Effects in Sickness Absence: Exploring a Large Data Set by Polynomial Regression

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  • Biørn, Erik

    (Dept. of Economics, University of Oslo)

Abstract

Identification of equations explaining a continuous variable, e.g., the length of sickness absence spells, by age, cohort and time (ACT), subject to their definitional identity is reconsidered. Various extensions of a linear equation to polynomials are explored. If no interactions between the ACT variables are included, only the coefficients of the linear terms create identification problems. A data set with 4.5 million individual observations for long-term sickness absence in Norway is used. The sensitivity of the estimated marginal effects of cohort and age on the length of the absence spells, at the sample mean, is illustrated. Notable differences are found between linear and quadratic equations on the one hand and cubic and fourth-order polynomials on the other. Representing heterogeneity by cohort effects is compared with representing heterogeneity by random and fixed individual effects. On the whole, the age coefficients in the estimated regressions are quite sensitive to how heterogeneity is modeled.

Suggested Citation

  • Biørn, Erik, 2013. "Age-Cohort-Time Effects in Sickness Absence: Exploring a Large Data Set by Polynomial Regression," Memorandum 19/2013, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2013_019
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    File URL: https://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2013/memo-19-2013.pdf
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    References listed on IDEAS

    as
    1. David J. McKenzie, 2006. "Disentangling Age, Cohort and Time Effects in the Additive Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(4), pages 473-495, August.
    2. Erik Biørn & Simen Gaure & Simen Markussen & Knut Røed, 2013. "The rise in absenteeism: disentangling the impacts of cohort, age and time," Journal of Population Economics, Springer;European Society for Population Economics, vol. 26(4), pages 1585-1608, October.
    3. France Portrait & Rob Alessie & Dorly Deeg, 2002. "Disentangling the Age, Period, and Cohort Effects using a Modeling Approach," Tinbergen Institute Discussion Papers 02-120/3, Tinbergen Institute.
    4. Bronwyn Hall & Jacques Mairesse & Laure Turner, 2007. "Identifying Age, Cohort, And Period Effects In Scientific Research Productivity: Discussion And Illustration Using Simulated And Actual Data On French Physicists," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 16(2), pages 159-177.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    age-cohort-time problem; identification; sickness absence; sickness and gender; panel data; polynomial regression; interaction; heterogeneity;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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