Epidemiology, Public Health, and Sociology of Health
3.2.2. Studies from Health Economics
B¨ockerman & Ilmakunnas (2009) analyze the relationship between unemployment and self-rated health in Finland using the European Community Household Panel for Finland. Health is measured via the standard self-rated health survey item. They use a difference-in-difference estimation technique to assess whether unemployment affects health or the other way around.
In their diff-in-diff analysis and in various other model specifications they cannot find a sub-stantial effect of unemployment on health. However, subjective health proves to be a predictor of subsequent unemployment. The authors do not use causation or selection hypothesis as terms - this is rarely done in economics -, but their results clearly support the health selection hypothesis for Finnish unemployed. The strength of the study lies in its modeling techniques,
which control for time constant unobserved heterogeneity, and some observed time variant variables. On the other hand a SEM was not used.
A cross-sectional data set from Australia is the basis for Cai & Kalb’s (2006) analysis on the relationship between labor force participation and health. The health item used is the common self-rated health survey questions. They estimate simultaneous equation models for men and women separately. Their models control for a wide range of labor market and household related variables. They find a clear and strong effect of health on labor force participation, which is stronger for women and older study subjects. Social causation is also found in the parallel equation. Labor force participation is beneficial to subjective health status. The results point to a clear feedback mechanism in which bad health reduces labor force participation, and being out of the labor force reduces health status. The strength of their study lies in the usage of high quality data and suitable modeling techniques including controls for spurious correlation.
The study is one of the best when it comes to modeling the complex relationship between health and labor force participation. The cross-sectional nature of the study leaves room for improvement with regard to modeling of temporal priority.
In a further study Cai (2010) uses longitudinal data to reanalyze his previous study. He introduces a random effect structure to control for time constant unobserved heterogeneity.
Results show that health has a strong influence on labor force participation. The reverse causal direction holds only for men.
Haan & Myck (2009) use the German Socio-economic Panel Study (SOEP) to assess the relationship between health and non-employment. They use self-rated health as a health indicator and pool unemployed and non-employed as a labor market risk group. They estimate a simultaneous hazard equations model, controlling for various confounders in different specifi-cations. Similar to the results of Cai & Kalb (2006) they find a positive feedback mechanism between employment and health. Both social causation and health selection mechanisms can be detected. The strength of the study is clearly the use of high quality data with up-to-date SEM methods controlling for confounders and allowing for temporal ordering. The authors, however, seem to unnecessarily restrict their sample to men aged 30-59, probably the most over-studied group in the literature on labor market processes.
The Panel Study of Income Dynamics (PSID) is the basis for the analyses conducted by Haveman et al. (1994). They look at the interrelationship between wages, work hours, and health. Their health indicators are self-reported health and health related work limitations.
They estimate a complex three equation simultaneous equations model. They find that health limitations have a negative impact on work hours and wages while work hours have no effect on health. Their complex model tries to capture the difficult inter-relationship between health and labor market outcomes. However, they do not make use of the panel structure of the data
and they restrict their sample to white men aged 25-65 who are head of the household. This refers again to the most over-studied group in labor economics.
Lee (1982) also employs a simultaneous equations approach for wages and health. The health indicator is the self-rated health item and a report of health limitations. They use one wave of the National Longitudinal Survey of men aged 45-59. They find evidence that wages and health affect each other in a feedback mechanism. They introduce SEM at an early stage of the research literature, but use only cross-sectional data on men aged 45-59.
Summing up, we can note the following insights from the literature review. Most studies which look explicitly at SES and health either find no support for health selection or conclude that social causation is more important. A clear exception are studies on employment.
Regardless of the health indicator, various studies from different countries, using different methods found a clear link between health and labor force participation or unemployment.
The majority of those studies agree that the major part of this effect can be attributed to a health selection process. This means that the recent state of art allows us to draw the conclusion that participation on the labor market depends strongly on individuals’ health. The labor market is the most important source for income for most households and plays a crucial role for social integration. These results alone disqualify any claims that health selection is generally a negligible factor when looking at health inequalities.
Looking at the studies in the review we can see that different dimensions and indicators of health are used when assessing health inequalities. However, there seems to be no clear pattern whether some dimensions provide stronger evidence for social causation or health selection.
The use of varying indicators thus presents a problem, because it remains unclear what role the health measure actually plays. There are too few studies, and too many different health measures to make a statement about any clear trends at this point.
Education is used several times as a measure of SES. It has the advantage of being universally available. On the other hand, education changes very little after a certain age, leaving little room for health selective processes. Employment is another measure which is often used.
In its broadest sense it can also be applied to all individuals and is very responsive to other socio-economic conditions and to health. Occupation, or occupational group is the measure of SES which is used most often. Most studies use broad categories with six or less categories, sometimes adding a non-employment category. There are several possible reasons for this common use of occupation. First, it has a long tradition in health inequalities research to look for occupational differences in health. Second, occupation is rather stable, yet can change more than education, especially downward mobility is possible. Third, income can be seen as a derivate of occupation. In this sense occupation would be a more fundamental cause of health than income. Fourth, besides education, occupation is the measure which allows the easiest estimates of intergenerational mobility in addition or instead of intragenerational mobility.
One concern I have with several studies reviewed here is that they dismiss third factor explanations too easily. If health has common correlates with e.g. skill, or personal characteristics like locus of control, any association might be spurious. The responsible factors for the association might lie in childhood. If interactions of acquired childhood characteristics with changing environment over the life course is considered it is even unclear whether methods like fixed-effects can account for such spurious correlation. It is advisable to conduct sensitivity analyses, which indicate how strong a common background factor must be correlated with health and SES to account for estimated effects in the study (for such an approach see Do, Wang & Elliott 2013).
4. Methods
In this chapter I present the methodological approaches I choose to adress the formulated hypotheses. I explain my notion of causality, explain how I measure health, and describe various regression and decomposition models I employ.