Applying the Counterfactual Model

After this general discussion about causality, I will go into more detail about problems that arise when trying to estimate health selection effects on the labor market. I will first discuss the direction of causality and related problems, then spurious correlation and problems of measurement. Last, I will review in how far I can test causality and under what kind of limitations the estimates in the analysis section should be understood as causal.

3This means comparing those with the same probability to be in a certain health state, but differing in the actual fact that some are in this certain health state and others are not.

4.2.1. Direction of Causality

The most obvious problem with job status and health is the direction of causality. Usually health inequalities are explained by the social causation approach (see section 2.1.3). For my case that means explaining how resources or working conditions of workers in high status jobs are beneficial to their health, generating an overall higher health status for incumbents of high status jobs.

So how do I make sure that my estimates of health influencing job status are not merely artifacts produced by reversed causality, meaning job status influencing health?

The first step in excluding reverse causality is that all analyses presented in this thesis - if not stated otherwise - look at the effect of health at time point t on job status one year later (t+1). This ensures that the treatment (change in health) comes before the outcome (change in job status). However, this does not solve the problem of anticipation effects. It could be that a change in job status is expected or anticipated by the individuals (for a theory of anticipation, see Tavory & Eliasoph 2013). This might lead to the fact that the positive or negative psychological effects of status change already influence the individual at an earlier point in time. In addition, an adaption to the new situation with regard to resources might occur earlier than the actual change. In some cases it might be preceded by e.g. an increase in wages.

The second problem of reversed causality due to anticipation will be addressed in the following manner. I will introduce three variables into the regression equations which are supposed to capture the psychological effects of an anticipation of a job change. These variables measure satisfaction with work, worries about own economic situation, and worries about job security.

In combination with a measure for wages they should be adequately capturing any anticipation effects.

4.2.1.1. Closed Positions and Anticipation Effects

In my theory I argued that a distinction between open and closed positions on the labor market can yield important insights in the interaction of context and health selection.

The analysis of closed positions has an additional advantage. If the estimated health effects are due to the anticipation of the event - following the social causation instead of the health selectionhypothesis - there should be no differences between open and closed positions regarding the health effect.

This means that a certain worker would anticipate that he or she will receive a certain (positive or negative) labor market reward in the future. For my analysis this would just need to be an anticipation of a reward within the next year which is a plausible assumption. If the worker expects the rewards the psychological reaction could precede the actual reward. The methodological interesting part about a distinction between open and closed position is the following. There is no reason to assume that the effect of job loss or high status attainment

on health is different for incumbents of open or closed positions. Therefore the reaction should be the same as well. If the reaction is the same, then the estimated effects should be the same for open and closed positions.

With this in mind, I can counter the argument that the estimates in my model are simply anticipation effects of future labor market rewards. If we find that open positions show health effects and closed positions do not, it speaks against the anticipation hypothesis. If health effects are found both in open and closed positions, brushing off the anticipation hypothesis could be a mistake.

Bringing anticipation effects into the formal equation, we would view health as a function of the anticipation of the labor market reward:

LM R=α(e(H(A_{LM R}), E_{N LM})∗τ)^{σ∗|A−c∗d|}t (4.6) 4.2.1.2. Testing Causality in One Model

Another important step in estimating causal effects of health on job status is to consider both directions of causality at the same time. In section 3.1, I argued that one criterion for a

“good practice” study is exactly this simultaneous test of the health selection and the social causation hypothesis. This allows the evaluation of the hypotheses in a framework, which makes them comparable with regard to the magnitude of the estimated effects. The question can be addressed whether social causation effects play a bigger role than health selection effects in creating health inequalities between job status. Section 4.7 describes the respective statistical model.

4.2.2. Spurious Correlation

Another problem with establishing a causal relationship between health and job status is that there might be third factors influencing both health and job status. For example, the positive relationship between health and high job status could be caused by the education differential of the two groups. Individuals with higher education take more or more efficient care of their health and are therefore in better health. They are also much more likely to occupy a high status job. Another example would be the unobservable rate of health deterioration and unobserved skills or productivity which might cause the gradient in the relationship of job status and health (Case, Fertig & Paxson 2005). The correlation between health and job status could therefore be spurious.

Of course, spurious correlation is hardly a new problem in social sciences where experimental data are unavailable. What has to be done to uphold the conditional independence assumption is to control for all important factors which might influence both health and job status. Relevant factors for labor market rewards are captured in my theoretical model. For each construct

suitable indicators are chosen to make the conditional independence assumption as plausible as possible. In section 4.9.2 the control variables are listed and described.

4.2.3. Are the Estimated Effects Causal Effects?

So, can we interpret the effects estimated in these models as causal in nature? The study design and the statistical model of cross-lagged panel with fixed-effects adequately captures possible confounders from spurious correlation, simultaneity, and reversed causation. Therefore, I think that the proposed estimates represent the strongest test of causality available for the subject under investigation. Using different modeling approaches throughout the thesis further increases the reliability of the results, because they present a form of robustness check. The effects of health on job status, which are estimated are therefore treated as causal estimates in the rest of the thesis. The known limitations are explicated here and are not repeated every time in the results part.

4.3. Measuring Health - Methodological and Theoretical