Health Effects and the Status of Applicants vs

Incumbents

Up to this point, I have treated change in job status as if changing from a normal job into a high status job means the same as changing from a high status job into a normal job. This implies treating a promotion and a demotion as if the same mechanisms are at work. Is it a feasible assumption that climbing up the job ladder relates in the same way to health as tumbling down the job ladder?

The theory in section 2.8.1.2 states that this would be an oversimplification of health selection mechanisms if open and closed positions are considered. High status jobs are defined as closed positions. Therefore it is expected to be hard to get into such a position, because the applicants face a job competition or career tournament situation. The positive effect of a closed position for an employee is that incumbents are protected from competition. Health should therefore be among the criteria on which employers select their employees when looking for new incumbents of a high status job. However, once occupying such a position health should no longer affect job status. The incumbents can keep their position regardless of performance and health. Thus, I use survival analysis to estimate the effects of health on the separate events, high status

attainment and loss of high status job, to test hypotheses H4, H5, and H6.

The results presented in this section are based on a non-parametric discrete event-history or survival analysis described in section 4.6. The models estimate the “risk” to change job status from normal to high status and vice versa. All employees in normal jobs are treated as applicants, all those in high status jobs as incumbents. The event for applicants is the change into a high status job, for incumbents the event is changing into a regular job. After the event takes place or an observation is censored, the analysis ends. As in the previous sections the results are reported in log-odds. All models control for the same set of covariates as in the previous models.

Figure 5.16 shows the results of the analysis stratified by gender and by private and public sector. Just as a reminder: In the regular analysis health showed substantial effects only for women in the private sector.

For women in the private sector only applicants are selected according to their health status as proposed by hypothesis H4. Incumbents of high status jobs have a similarly higher risk of dropping out of their position with increasing health (sic!), which is a very surprising result.

Taking the low significance level into account only marginally reduces the concern about the finding, because the size of the effect is quite sizable. Men in the private sector do not face health selection as either incumbents or applicants, which supports hypothesis H4, but partially speaks against hypothesis H1.

For women in the public sector health does not play a role for job change neither as incumbents nor as applicants. The same holds true for men in the private sector. In the public sector there is a positive health effect on high status attainment as an applicant, and a negative effect as an incumbent, both insignificant due to small sample size. These results are supporting hypothesis H5 for women, stating that there is no health selection in the public sector. For men the picture remains unclear.

If we look at the effect of sickness absence in figure 5.17 we can see that the results are similar to the results from health as an independent variable. In the fixed-effects-logit analysis we could only find a significant effect of sickness absence on status change for men in the private sector. Sickness absence only matters for applicants for high status jobs, not for incumbents, which supports the theory. The effect vanishes once a person already is inside a closed position. In all other constellations there are no significant effects of sickness absence on the chance of either getting or losing a high status job. For incumbents of high status jobs in the public sector, the number of events was too low to report reliable estimates. However, the low number of drop-outs indicates that losing such a job is so rare, that it probably cannot be related to days of sickness absence.

Taken together the results reveal two things. First, the differentiation between incumbent and applicant makes sense not only in theoretical terms, but reveals more detailed structures in the empirical analyses as well. This counts for both visible and non-visible measures of health. Second, the results are very similar to the analyses using a fixed-effects-logit approach.

This speaks in favor of the stability and robustness of the models chosen. For women in the private sector the point estimates are almost the same in the survival analysis models as in the fixed-effects-logit models.

Key results from this section are: Subjective health has a positive impact on high status attainment for women in the private sector. The effects for women in the public sector are almost zero as are the effects for men in the private sector. For men in the public sector the results suggest strong selection effects, but sampling uncertainty is high.

Sickness absence matters for male applicants in the private sector. All other respective effects are negligible.

Figure 5.16.: Health Effect on Status Change Depending on the Position of Applicant or Incumbent

Incumbents Applicants

Incumbents Applicants

-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5

-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 Women, Private Sector Women, Public Sector

Men, Private Sector Men, Public Sector

95% Confidence-Interval Point Estimate

Note: The complete results of the regression can be found in tables A.10 and A.11 in the appendix.

Figure 5.17.: Effect of Sickness Absence on Status Change Depending on the Position of Applicant or Incumbent

Incumbents Applicants

Incumbents Applicants

-.2 -.15 -.1 -.05 0 .05 .1 .15 .2 -.2 -.15 -.1 -.05 0 .05 .1 .15 .2

-.2 -.15 -.1 -.05 0 .05 .1 .15 .2 -.2 -.15 -.1 -.05 0 .05 .1 .15 .2 Women, Private Sector Women, Public Sector

Men, Private Sector Men, Public Sector

95 % Confidence-interval Point Estimate

Note: The complete results of the regression can be found in tables A.12 and A.13 in the appendix.