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Gender Differences in the Duration of Sick Leave: Economics or Biology

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  • Martín-Román, Ángel L.
  • Moral, Alfonso
  • Pinillos-Franco, Sara

Abstract

This study addresses the gender gap in workplace sick leave duration, focusing on the underlying economic and biological factors that contribute to this disparity. Using a novel methodological approach, we combine the stochastic frontier technique with an Oaxaca-Blinder-type decomposition to separate sick leave into medically justified and "opportunistic" days. Our analysis, based on detailed administrative data of workplace accidents in Spain, reveals that men and women recover at different rates for the same injuries, with biological differences explaining the majority of the observed gender gap. Additionally, we identify that men tend to use more sick leave days for reasons unrelated to health recovery. The findings offer valuable insights for policymakers and employers, providing an empirical foundation for targeted policies that reduce gender-based discrimination in the workplace and ensure fairer resource allocation. This research contributes to a deeper understanding of the gender gap in occupational health and offers implications for improving workplace equality.

Suggested Citation

  • Martín-Román, Ángel L. & Moral, Alfonso & Pinillos-Franco, Sara, 2024. "Gender Differences in the Duration of Sick Leave: Economics or Biology," GLO Discussion Paper Series 1542, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:1542
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    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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