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New Evidence on the Determinants of Absenteeism Using Linked Employer-Employee Data

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  • Georges Dionne
  • Benoit Dostie

Abstract

This paper provides new evidence on the determinants of absenteeism. The authors extend the typical labor-leisure model used to analyze the decision to skip work to include firm-level policy variables relevant to the absenteeism decision and uncertainty about the cost of absenteeism. Estimates based on data from Statistics Canada's Workplace Employee Survey (1999–2002), with controls for observed and unobserved demographic, job, and firm characteristics (including workplace practices), indicate that work arrangements were important determinants of absence. For example, the authors find strong evidence that standard weekday work hours, work-at-home options, and reduced workweeks were associated with reduced absence, whereas shift work and compressed work weeks were associated with increased absence.

Suggested Citation

  • Georges Dionne & Benoit Dostie, 2007. "New Evidence on the Determinants of Absenteeism Using Linked Employer-Employee Data," ILR Review, Cornell University, ILR School, vol. 61(1), pages 108-120, October.
  • Handle: RePEc:sae:ilrrev:v:61:y:2007:i:1:p:108-120
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    5. Per Johansson & Mårten Palme, 2002. "Assessing the Effect of Public Policy on Worker Absenteeism," Journal of Human Resources, University of Wisconsin Press, vol. 37(2), pages 381-409.
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    15. Abowd, John M. & Kramarz, Francis, 1999. "The analysis of labor markets using matched employer-employee data," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 40, pages 2629-2710 Elsevier.
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    More about this item

    JEL classification:

    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J29 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Other
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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