How to evaluate the impact of part-time sick leave on the probability of recovering
This paper presents an econometric framework for analyzing part-time sick leave as a treatment method. We exemplify how the discrete choice one-factor model can address the importance of controlling for unobserved heterogeneity in understanding the selection into part-time/full-time sick leave and the probability to fully recover from a reduced work capacity. The results indicate that part-time sick listing increases the probability to recover compared to full-time sick listing when the expected time to recover is longer than 120 days.
|Date of creation:||13 Oct 2009|
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- Heckman, James, 2013.
"Sample selection bias as a specification error,"
Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
- Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
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