6 Self-rated health and labour market participation
This section explores the relationship between self-rated health and labour market participation. It begins by revisiting the reasons for considering self-rated health and for using the various modelling approaches. Basic descriptive statistics related to self-rated health are then presented, before the results from the corresponding pooled (cross-sectional) models, as considered in the previous section, are summarised. The results of the fixed and correlated random effects (longitudinal) logistic regression models, and the equivalent models using an adjusted measure of self-rated health, are then discussed. Again, words such as “impact” and “effect” are used to describe relationships but do not denote causation. Full tables of results from the main models, including unweighted means and standard deviations for the self-rated health variable, can be found in Appendices E, F and G where the reference categories are labelled.
6.1 Models used
As outlined in Section 4, two measures of health are available in all three waves of SoFIE: chronic diseases; and self-rated health. Given the issues with both of these health measures, and the conclusion of an earlier literature review in the area, it is preferable to consider the relationships between both of these measures and labour force participation. This section therefore begins by reporting basic descriptive statistics related to self-rated health and then summarises the results from the corresponding pooled (cross-sectional) models as presented in the previous section. The results of the pooled logistic regression model are presented to enable comparison with both the equivalent models for self-rated health and with the subsequent panel models for self-rated health. Where results of the standard logistic regression models are discussed in this paper potential endogeneity bias should be remembered (as explained in Section 4.3.1).
The results of the fixed and correlated random effects (longitudinal) logistic regression models and the equivalent models using an adjusted measure of self-rated health are then presented. These models make use of the longitudinal nature of the data and aim to resolve some of the endogeneity issues identified in Section 4. Ideally these models would have been applied to the models including individual chronic diseases but owing to small numbers in some groups and that the diagnosis of chronic diseases is slow changing this was not possible. Unlike the standard logistic regression results (for which the assumptions may not be satisfied owing to endogeneity, thus possibility resulting in inconsistent (and biased) regression coefficients) the panel models account for some forms of endogeneity, and thus should produce estimates that are consistent and unbiased, if the model assumptions are satisfied.
In addition to the above, the health coefficients from the standard pooled regression, the fixed effects and the correlated random effects models are interpreted differently. The coefficients from the pooled regressions indicate how health levels are related to the chance of participation for a cross-section, while the health coefficients from the fixed and correlated random effects models use longitudinal data to indicate how health shocks are related to participation (although health level is also estimated in the latter model). The fixed effects model attempts to explain variation within (rather than between) respondents over time, making direct comparison of the odds ratios with those from the standard and random effects models problematic.
All three types of models identify a highly significant relationship between health and labour force participation; however, no model is perfect. The best model is found to be the fixed effects model. However, this model is not without its drawbacks. By definition a fixed effects model excludes all those for whom participation does not change over the period from the analysis, meaning there is no estimate of the relationship between health and participation for those continually inactive. Also the fixed effects model focuses on variation in participation for each respondent. This means that only within (rather than within and between) person variation is considered. Finally, there may be other types of endogeneity present that it is not possible to account for using a fixed effects model; for example, unobserved variables that change over time and are related to the explanatory variables. Assuming that this is not the case, the fixed effects model should produce estimates that are consistent (and unbiased).
The cross-sectional pooled regression considers the relationship between health state and participation for all respondents but does not consider within person variation. It is also not possible to control for any types of endogeneity so the results are likely to be biased. The correlated random effects model considers within and between person variation and includes an estimate of the average health level for respondents as well as looking at health shocks. However, if the assumption that the only correlation between health shocks and the unobserved variables that are fixed over time is through average health is not valid, or if average health is itself correlated with unobserved variables, then the coefficients from this model may be biased. Further, other types of endogeneity such as unobserved variables that change over time cannot be accounted for. Owing to the pros and cons of each of the models, and to allow comparisons between the models to be seen, all of the model results are presented in this section to illustrate the different types of relationships identified between health and labour force participation.
