Male dominated Mixed Female dominated
5.9. Decomposing Overall Health Inequalities
In this last part of the analyses, I try to attribute the overall health inequalities to different explanatory approaches. These are:
This presents a new perspective and also allows to test hypotheses H9a- H9c. I use the cross-lagged fixed-effects models in a slightly modified version (see section 4.8). This implies treating job status as if it were linear. Such a linear probability model has the advantage of reporting marginal effects as coefficients. The gross health inequalities which are decomposed in this section are measured by the cross-sectional association of health at time t with job status at time point t+1. This cross-sectional estimate of health inequalities does not presuppose any direction of causality, although technically health is used as the predicting variable.
In table 5.9 we can see to what degree the overall health inequalities, which can be found in a cross-sectional perspective, can be explained by taking specific variable sets as mediators.
For women in the private sector a quarter of the inequalities can be explained by observed background characteristics, whereas demography plays almost no role (ca. 3%, group 1).
Controlling for materialist-environmental social causation variables (group 2) does not explain much of the original health inequalities (3%). Taking social-psychological anticipation variables in account further explains about 6%, an estimate which is significantly positive according to the computed confidence interval (1.16; 11.29), but substantively small. State dependence, meaning the job status one year before (at time t) has a huge explanation power of about 55%. This suggests that time constant factors are extremely important as is also shown by the explanatory contribution of time constant factors, which is about 73% or almost three quarters, a highly significant effect. All time varying factors together only add up to 5% of the explana-tory power, a non-significant estimate. Evaluated on their own only the social-psychological dimension has a time varying influence, similar to the overall explanation power of 6%. This means that except for this group of variables things that change over the period of observation actually do nothing to mediate health inequalities. The remaining effect, after all mediators are taken into account, is the health selective part of health inequalities which amounts to about 21%, a significant estimate. Figure 5.244 gives a visual impression of the relative
contri-4Women in the private sector are the only group in which such a graphical representation is possible.
All other estimates include negative values that are hard to represent in a graph.
bution of time varying, time constant factors and health selection for women in the private sector.
Figure 5.24.: Women in the Private Sector: Proportion of Health Inequalities due to Factors which are...
73.24%
5.52%
21.24%
73.24%
Time Constant Time Varying Health Selection
For women in the public sector the picture looks different. Background factors and environmental-materialist factors are significant mediators of about 31% and 6% respectively.
State dependency is even a little higher than for women in the private sector (61%).
Using a longitudinal approach we can see that almost all health inequalities (99%) can be traced back to time constant factors, time varying factors or selection do not play a role. This is an extreme case where any change during the period of observation does not influence health inequalities any more.
For men in the private sector demography is actually hiding part of the overall health inequalities (17%). Background characteristics play a major role in mediating health inequal-ities with 41%. Environmental-materialist and social psychological factors both contribute significantly with about 8%. State dependency amounts to 62% of the health inequalities.
Longitudinally we see that time constant factors make up more than 90% of the health in-equalities. Time varying factors in their sum contribute nothing, although individually change in state (7%) and social-psychological factors (10%) play a significant mediator role. Health
selection is not significant.
Cross-sectionally the results are very similar for men in the public sector to men in the private sector. Environmental-materialist and social psychological factors have a similar size, but are not significant.
In the longitudinal perspective it is revealed that changes in demographics actually hide health inequalities. This is a sign for gerontocratic promotion structures which run counter to the fact that health deteriorates with age. Time constant factors make up 70% of health inequalities, time varying factors (except for demography) are not significant. Health selection is also not significant.
We can derive two major conclusions from this analysis. First, health selection is important only for women in the private sector. In this context, it explains about 20% of overall health inequalities. Second, explanations of health inequalities between regular and high status jobs vary widely between men and women and public and private sector. Depending on the context a different combination of background factors, social causation factors, and health selection determines the degree of health inequalities. Health selection factors are embedded in their social context. Their contribution to health inequalities cannot be judged to be high or low in general. Researchers have to make an effort to specify the contribution (or lack thereof) for the special case and the special context. Claiming insignificance of the contribution of health selection without a detailed analysis might be very misleading as this decomposition shows.
Table 5.9.: Decomposition of Health Inequalities - Subjective Health
(1) (2) (3) (4)
Women - Private Women - Public Men - Private Men - Public
CS DEMO -3.75 -4.80 -17.35 -18.10
[-9.32;1.82] [-14.64;5.03] [-27.02;-7.68] [-33.26;-2.94]
CS BG 21.93 33.80 41.56 44.63
[13.85;30.01] [16.95;50.66] [30.05;53.07] [26.69;62.56]
CS E-M 2.91 6.34 8.68 2.87
[-0.91;6.73] [0.40;12.28] [4.80;12.57] [-2.11;7.84]
CS S-P 6.22 7.58 8.43 6.70
[1.16;11.29] [-1.05;16.21] [4.20;12.65] [-1.10;14.50]
CS STATE 55.22 61.99 62.95 59.83
[32.09;78.35] [20.52;103.47] [42.65;83.25] [23.46;96.20]
TV DEMO 16.88 -27.46 -48.92 -157.98
[-6.77;40.53] [-83.89;28.97] [-122.81;24.97] [-305.29;-10.67]
TV BG -11.26 30.72 45.32 158.49
[-38.90;16.38] [-15.19;76.64] [-40.59;131.22] [-4.43;321.42]
TV E-M -8.82 1.12 -3.93 5.56
[-16.10;-1.55] [-26.37;28.61] [-10.86;2.99] [-11.19;22.31]
TV S-P 6.20 -8.34 9.50 7.94
[0.03;12.36] [-19.20;2.53] [4.26;14.74] [-2.99;18.87]
TV STATE 2.52 0.54 7.33 2.16
[-1.05;6.10] [-4.73;5.81] [3.68;10.98] [-3.78;8.10]
Total TC 73.24 99.34 92.50 70.93
[31.35;115.13] [23.46;175.23] [59.72;125.28] [10.42;131.44]
Total TV 5.51 -3.41 9.29 16.18
[-19.89;30.91] [-47.91;41.09] [-11.78;30.35] [-28.80;61.16]
Health Selection 21.24 4.07 -1.78 12.89
[0.72;41.76] [-31.67;39.80] [-16.49;12.92] [-16.55;42.33]
Note: CS: cross-sectional; TV = time varying; TC = time constant; DEMO = demography;
BG = background characteristics; E-M = environmental materialist factors; S-P = social psychological factors; STATE = state dependency
Table 5.10 contains the same results for the decomposition of health inequalities measured by log. sickness absence. Decomposing the inequalities in sickness absence yields the following re-sults. Forwomen in the private sectorthe cross-sectional perspective shows that background characteristics and social-psychological factors reduce inequalities tremendously (about 100%
and 50% respectively) while environmental-materialist factors actually invert health inequalities.
State dependency and demographic factors do not play a significant role. The longitudinal analyses show that both time constant and time varying factors are strongly associated with explaining health inequalities, but with opposing signs. Both estimates show huge ranges of the confidence-interval. Individually environmental-materialist and social-psychological factors have the same direction of effect as in the cross-sectional analysis. Health selection does not play a significant role.
Health inequalities in sickness absence ofwomen in the private sector can be decomposed in small to associations with background characteristics, and in large part to state dependency.
This is reflected in the longitudinal analysis as well. Time constant factors significantly reduce health inequalities, time varying factors and health selection do not play a role. Time varying social-psychological factors actually lead to a small increase in health inequalities.
The cross-sectional perspective on men in the private sector reveals, that a fifth of health inequalities can be attributed to background factors, a small portion (ca. 5%) to environmental-materialist factors, and a big portion to state dependency (ca. 60%). The longitudinal perspective shows some unusual results. First, on its own, change in background characteristics partly explains health inequalities. Then a change in state dependency makes also a significant, although small contribution (5%). That would mean that a change in job status at time point t partly mediates the fact that health at t influences job status at t+1.
Taken together time constant factors account for almost 34 of health inequalities. However, both health selection (7%) and other time varying factors (20%) also make a significant contribution.
Health selection is, relatively to other factors, less important in explaining health inequalities in sickness absence than in general health.
Formen in the public sectorwe can see that mostly state dependency (60%), background characteristics (25%) and to a small degree environmental-materialist factors (4%) are as-sociated with health inequalities. Interesting is that in the longitudinal perspective, no one time varying factor is significantly associated with health inequalities, together they explain an estimated third of health inequalities, with two thirds explained by time constant factors.
Health selection has no independent contribution.
Key results from this section are: Health selection explains health inequalities only in the private sector. For subjective health it explains about a fifth of overall health inequalities between female high and regular status job incumbents. For men and for sickness absence the contribution is only about 7%. Time constant factors are by far the most important explanatory force for health inequalities between job status. This indicates that during labor market trajectories job status only has limited independent impact on health inequalities, and health inequalities due to job status do not change much over time.
Table 5.10.: Decomposition of Health Inequalities - log. Days of Sickness Absence
(1) (2) (3) (4)
Women - Private Women - Public Men - Private Men - Public
CS DEMO 8.20 0.00 0.35 -0.86
Note: CS: cross-sectional; TV = time varying; TC = time constant; DEMO = demography;
BG = background characteristics; E-M = environmental materialist factors; S-P = social psychological factors; STATE = state dependency