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Identifying Age-Cohort-Time Effects, Their Curvature and Interactions from Polynomials: Examples Related to Sickness Absence

Author

Listed:
  • Biørn, Erik

    (Dept. of Economics, University of Oslo)

Abstract

In the paper is considered identification of coefficients in equations explaining a continuous variable, say the number of sickness absence days of an individual per year, by cohort, time and age, subject to their definitional identity. Extensions of a linear equation to polynomials, including additive polynomials, are explored. The cohort+time=age identity makes the treatment of interactions important. If no interactions between the three variables are included, only the coefficients of the linear terms remain unidentified unless additional information is available. Illustrations using a large data set for individual long-term sickness absence in Norway are given. The sensitivity to the estimated marginal effects of cohort and age at the samplemean, as well as conclusions about the equations’ curvature, are illustrated. We find notable differences in this respect between linear and quadratic equations on the one hand and cubic and fourth-order polynomials on the other.

Suggested Citation

  • Biørn, Erik, 2013. "Identifying Age-Cohort-Time Effects, Their Curvature and Interactions from Polynomials: Examples Related to Sickness Absence," Memorandum 08/2013, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2013_008
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    File URL: https://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2013/memo-08-2013.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    AGe cohort-time problem; Identification; Polynomial regression; Interaction; Age-cohort curvature; Panel data; Sickness absence;
    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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