Identifying Age-Cohort-Time Effects, Their Curvature and Interactions from Polynomials: Examples Related to Sickness Absence
AbstractIn 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.
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Bibliographic InfoPaper provided by Oslo University, Department of Economics in its series Memorandum with number 08/2013.
Length: 20 pages
Date of creation: 21 Mar 2013
Date of revision:
Contact details of provider:
Postal: Department of Economics, University of Oslo, P.O Box 1095 Blindern, N-0317 Oslo, Norway
Phone: 22 85 51 27
Fax: 22 85 50 35
Web page: http://www.oekonomi.uio.no/indexe.html
More information through EDIRC
AGe cohort-time problem; Identification; Polynomial regression; Interaction; Age-cohort curvature; Panel data; Sickness absence;
Find related papers by 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
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-09 (All new papers)
- NEP-DEM-2013-06-09 (Demographic Economics)
- NEP-ECM-2013-06-09 (Econometrics)
- NEP-HEA-2013-06-09 (Health Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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, vol. 26(4), pages 1585-1608, October.
- Biorn, Erik & Gaure, Simen & Markussen, Simen & Røed, Knut, 2010. "The Rise in Absenteeism: Disentangling the Impacts of Cohort, Age and Time," IZA Discussion Papers 5091, Institute for the Study of Labor (IZA).
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