Testing Health Selection vs. Social Causation

Male dominated Mixed Female dominated

5.7. Testing Health Selection vs. Social Causation

Up until this point, I have only looked at unidirectional approaches to modeling health selection effects. Now, I will take a further step and evaluate health selection versus social causation in one model. I will do this once in a cross-sectional perspective and once from a longitudinal fixed-effects approach. This chapter therefore reexamines hypotheses H1,H2a, andH5with a new method, and allows the first test of hypothesis H9d from the social causation perspective, testing for a directsocial status effect on health. In the next section, hypotheses H9a- H9c can also be tested using a decomposition approach that is based on the same models as in this section.

Figure 5.18 shows the effects of the cross-lagged fixed-effects regression, which is the next step from a unidirectional fixed-effects approach. The model simultaneously estimates the effects of health at time point t on job status att+1 and job status at t on health at t+1. It controls for the same variables as the models presented before and for time constant unobserved effects. The error terms of the dependent variables are allowed to be correlated.

We can see that the health effects are barely affected by allowing for reversed causality. The pattern of the results is the same as it has been with the unidirectional fixed-effects approach.

Health plays a role for women in the private sector, but not in the public sector. For both men in the private and in the public sector health has no substantial impact on the probability of a change in job status. So the interpretation that health effects are only found for women in context of competition holds true for this model specification as well.

Figure 5.18.: Health Effects - Allowing for Reversed Causality in a Cross-Lagged Model with Fixed-Effects

-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3

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.14 and A.15 in the appendix.

But what about social causation effects? Finding or not finding health selection effects in the model does not tell us whether social causation effects also exist. These results of the simultaneous equation, which tests the effects of job status on health, are presented in figure 5.19. While there are health selective effects for women in the private sector no social causation effects can be found for this constellation. Direct social status effects cannot be found for men either. However, for women in the public sector high status jobs seem to increase health. This is the only social causation effect which can be found.

Note, however that it is only the direct effect that was estimated. What I have not considered so far is that social causation might work indirectly via observed job characteristics, which are controlled for in the model. To see whether there is such an indirect social causation effect I divide the control variables into three groups. The first group are background characteristics which are not directly influenced by a change in job status. This includes all variables concerning household characteristics, characteristics of the occupation and employer, years of education

Figure 5.19.: Effects of High Status Jobs on Health - Allowing for Reversed Causality in a Cross-Lagged Model with Fixed-Effects

CLFE w A CLFE w C CLFE w BG CLFE w/o C

-.1 -.05 0 .05 .1 -.1 -.05 0 .05 .1

Women, Private Sector Women, Public Sector

Men, Private Sector Men, Public Sector

95% Confidence-Interval Point Estimate

Note:w/o C = without controls; w BG = with background characteristics (group 1); w C = with BG and job controls (group 2); w A = with anticipation effect (group 3). The complete results of the regression can be found in tables A.14 and A.15 in the appendix.

and years of full-time employment. The second group consists of variables measuring job characteristics which might change with job status. These are hours of work, overtime, and log. wage per hour. Third, the anticipation variables as social-psychological mediators are grouped together. Table 5.6 gives an overview.

Table5.6.:VariableGroupsforAssessingMediatedEffects VariableMechanismGroup AnticipationofJobLoss WorriesAboutJobSecuritysocialcausation-social-psychological3 ...ownEconomicSituationsocialcausation-social-psychological3 SatisfactionwithWorksocialcausation-social-psychological3 HumanCapital Educationthirdfactors-backgroundcharacteristics1 JobTenuresocialcausation-environmental-materialistic2 LaborMarketExperiencethirdfactors-backgroundcharacteristics1 Wagesocialcausation-environmental-materialistic2 LaborMarketEffortandEffortIntensity IndustryofEmployerthirdfactors-backgroundcharacteristics1 FirmSizethirdfactors-backgroundcharacteristics1 WorkHourssocialcausation-environmental-materialistic2 Overtimesocialcausation-environmental-materialistic2 EffortIntensity(Physical)thirdfactors-backgroundcharacteristics1 EffortIntensity(Psychological)thirdfactors-backgroundcharacteristics1 Non-LaborMarketEffort NumberofHHMembersthirdfactors-backgroundcharacteristics1 NumberofChildrenthirdfactors-backgroundcharacteristics1 MaritalStatusthirdfactors-backgroundcharacteristics1 HouseworkandChildcarethirdfactors-backgroundcharacteristics1 YoungestHHMemberthirdfactors-backgroundcharacteristics1 DemographyandPeriodEffects PeriodEffectsbasiccontrolvariable- Agebasiccontrolvariable- Age2basiccontrolvariable- EastGermanybasiccontrolvariable-

EastGermanybasiccontrolvariable-This division has the following advantages. If background characteristics mediate the effect, this lends credibility to the explanation that health inequalities between job status are at least partly explained by spurious correlation or third factors. If group two or three of the control variables mediate the effect, this points to a social causation explanation based on either environmental-material or social-psychological grounds. The remaining effects from above present social causation effects not explained by these factors.

Surprisingly, taking control variables into the model does not influence the estimation of the impact of job status on health. Regardless of whether we look at women or men, public or private sector, the effects remain constant. This indicates that confounders can probably only be found in a cross-sectional analysis, because any existing health inequalities which might be explained by third factors are explained by time constant factors. For this reason I ran the whole models one more time without using a fixed-effects approach. Figure 5.20 shows the results of these models. Indeed background characteristics which are rather stable over time can explain most of the effect that high job status has on health. For all contexts job status effects become insignificant and are reduced in magnitude when taking these background factors into account. Both groups of social causation variables further reduce the effect of job status but not as much as the background factors do.

Figure 5.20.: Effects of High Status Jobs on Health in Cross-Section - Allowing for Reversed Causality in a Cross-Lagged Model

CL w A CL w C CL w BG CL w/o C

-.04 -.02 0 .02 .04 .06 .08 -.04 -.02 0 .02 .04 .06 .08

-.04 -.02 0 .02 .04 .06 .08 -.04 -.02 0 .02 .04 .06 .08 Women, Private Sector Women, Public Sector

Men, Private Sector Men, Public Sector

95% Confidence-Interval Point Estimate

The last estimate, which should be interpreted, is the correlation of the error terms of the dependent variables. A high correlation would indicate that there is at least one factor determining health and high job status, which has not been modeled correctly. This could be an omitted variable or an unspecified path in the model. Whereas there is a significant correlation of about 0.4 in the basic cross-sectional approach, the correlation in the final fixed-effects-models is very low (0.04) and not significant. This supports my view that the cross-lagged panel fixed-effects model adequately captures the causal mechanisms that are at work be-tween health and job status. It is another indicator for the robustness of the results in my thesis.

For days of sickness absence the pattern is very similar to subjective health. Allowing for reversed causality does not have a strong influence on the estimated effects. Figure 5.21 presents the results from the cross-lagged fixed-effects model. Again there is only a significant effect for men in the private sector.

The reversed causal direction is presented in figure 5.22. Again, we see that there is no direct effect of job status when the set of control variables in introduced. If the controls are excluded stepwise we get the results in figure 5.21. The figure shows that the control variables do not have a substantial influence on the estimation of the coefficient of job status.

Only when we take a cross-sectional perspective we can that there is a effect of job status if the major control variables are not in the model (see figure 5.23). This association is however

not present in the longitudinal analysis.

Key results from this section are: The cross-lagged approach yields the same results as the unidirectional analysis of health selection. In the private sector, subjective health is important for women, and sickness absence is important for men. A direct effect of job status on health can be found for women in the public sector, but not for any other constellation.

Figure 5.21.: log. Days of Sickness Absence Effects - Allowing for Reversed Causality in a Cross-Lagged Model with Fixed-Effects

-.6 -.4 -.2 0 .2 .4 .6 -.6 -.4 -.2 0 .2 .4 .6

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.16 and A.17 in the appendix.

Figure 5.22.: Effects of High Status Jobs on log. Days of Sickness Absence - Allowing for Reversed Causality in a Cross-Lagged Model with Fixed-Effects

CLFE w A CLFE w C CLFE w BG CLFE w/o C

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

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

Men, Private Sector Men, Public Sector

95% Confidence-Interval Point Estimate

Figure 5.23.: Effects of High Status Jobs on log. Days of Sickness Absence in Cross-Section - Allowing for Reversed Causality in a Cross-Lagged Model

CL w A CL w C CL w BG CL w/o C

-.25 -.2 -.15 -.1 -.05 0 .05 -.25 -.2 -.15 -.1 -.05 0 .05

-.25 -.2 -.15 -.1 -.05 0 .05 -.25 -.2 -.15 -.1 -.05 0 .05 Women, Private Sector Women, Public Sector

Men, Private Sector Men, Public Sector

95% Confidence-Interval Point Estimate

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