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The sick absence variable and the control variables are computed as in Section 5. To take into account the local labor market conditions we include 103 province of work dummies (and exclude region of birth dummies).
We tested the proportional hazards assumption by including an interaction of each diagnosis-specific absence variable with the log of follow-up time; in all cases, this interaction was not statistically significant.
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Methodological studies have materially altered thinking about the nature of absence variables.
The current discussion focuses on the operational and methodological decisions researchers make that impact the measurement properties of absence variables.
The coefficients of the work absence variables are negative, except for non-work and compulsory service for men.
This is because of the way that we selected young highly skilled men and constructed the work absence variables.
Note that the coefficients are estimated very precisely, and the coefficients of the maternity leave variables are significantly different from the coefficients of the other work absence variables.
Our focus is on investigating the differences across types of absences for men and women separately, as well as differences across men and women in the coefficients of specific types of absence variables.
In addition, we estimate models where the coefficients of the work absence variables are allowed to vary depending on whether individuals change firms when they re-enter employment after the work absence, or not.
Because of this research, the research community is now aware that absence variables frequently follow irregular data distributions [J. Appl. Psychol. 66 (1981) 574; J. Appl. Psychol. 74 (1989) 300].
When we included birth weight as a predictor in the model (results available on request), we found no association with age at menarche, nor did the results change for the father-absence variables.
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