7   Conclusion

This paper has examined the relationship between health and labour force participation. It found that health was significantly related to participation, using various health measures and even after accounting for certain types of endogeneity. Table 16 summarises the marginal effects from all the models considered.

Source: SoFIE Waves 1-3 Version 4, unweighted, Statistics New Zealand

Note:

1. All other variables in the models are fixed at the mean value for the whole sample.

2. *Significant at the 90% level. **Significant at the 95% level. ***Significant at the 99% level.

3. For the pooled regression the effect is of being in the health state rather than being in excellent health. For the fixed and random effects models the marginal effects for each health state are the effects of a health shock from excellent into that health state. The final marginal effects for the random effects model are the effects of spending all waves in a health state rather than all waves in excellent health.

Results of the standard pooled regression models that included individual chronic diseases indicated that there was insufficient evidence that those with asthma, high cholesterol, migraine or cancer were any less likely to be participating in the labour market than those without these diseases, once other factors were controlled for. In contrast, psychiatric conditions, stroke, heart disease, diabetes and high blood pressure were all associated with significant decreases in participation once other factors are held constant. Further, for psychiatric conditions, stroke and high cholesterol, the relationship with full-time work was higher than that for part-time work (ie, the chance of working full-time was reduced more than the reduction in the chance of working part-time), suggesting that not only is the presence of these diseases associated with lower participation but it is also associated with working fewer hours.

Psychiatric conditions for males were associated with the largest reduction in the chance of participation. This was the only disease where the relationship with labour force participation was significantly different by gender. When all other variables were fixed at their mean value, being a male with psychiatric conditions reduces labour market participation by 13.2 percentage points compared to that for males without psychiatric conditions. When all other variables were fixed at their mean value, being a female with psychiatric conditions was associated with a reduced labour market participation by 6.5 percentage points compared to that for females without psychiatric conditions. When all other variables were set at their mean level, being a male with psychiatric conditions was associated with a 1.5 percentage point reduction in participation compared to a female with psychiatric conditions. Following psychiatric conditions, for males the diseases that were associated with the largest fall in participation were strokes (a 12.3 percentage point reduction in labour market participation on average), heart disease (8.3 percentage point reduction), diabetes (6.7 percentage point reduction) and high blood pressure (1.8 percentage point reduction). The effect of the presence of disease did not differ significantly by gender, other than for psychiatric conditions.

These pooled regressions did not allow for possible endogeneity and as a result the coefficients may be biased. As the number of chronic diseases of interest diagnosed during the three waves of data available for analysis is relatively small, the paper moved to consider self-rated health. Fixed and correlated random effects models were used to allow for unobserved variables and an adjusted health measure was constructed to allow for possible rationalisation.

Results of the standard pooled regression models for self-rated health indicated that those in good, fair or poor health are significantly less likely to participate than those of excellent health. Being in good, fair or poor health was associated with a reduction in the chance of participating of 6.5, 22.2 and 49.6 percentage points respectively compared to being in excellent health. The only other variable for which the reduction in the chance of participating in the labour force is of a similar magnitude to that for fair or poor health is having a young child for females. This indicates the relative magnitude of the relationship between fair/poor health and participation. As with the individual chronic diseases, being in good, fair or poor health was associated with a larger reduction in the chance of working full-time than that for working part-time.

The fixed and correlated random effects panel models indicated that a negative health shock significantly reduced the chance of participation even when unobserved time-constant factors were controlled for. The coefficients for the fixed and correlated random effects model are higher (therefore the reduction in the chance of participation lower) than the pooled regression, suggesting possible unobserved variables that are correlated with health and participation. In the fixed effects model only a fair or poor health shock was associated with a significant reduction in participation; reducing the chance of participating by 12.7 and 34 percentage points respectively. The coefficients for the correlated random effects model indicate that a health shock to fair or poor health from excellent health significantly impacted on participation, reducing the chance of participating by 1.9 and 6.5 percentage points respectively. Further, even after controlling for the average time spent in each health state, health shocks were still found to be significantly related to participation. Spending all three waves in good, fair or poor health was associated with a 6.2, 12.7 and 20.1 percentage point reduction in the chance of participating.

All models indicate a significant relationship between health and labour force participation; as such the results complement each other. Tests suggested that the preferred model was the fixed effects model. If it is assumed that there are no unobserved variables that vary over time that are correlated with the explanatory variables, then estimates from this model are consistent (and unbiased). However, this model also had weakness and, owing to the slightly different things being estimated in the different models, results from all three models including self-rated health are informative.

An attempt was then made to remove possible rationalisation from the self-rated health variable. Results of the pooled, fixed and correlated random effects regression models using the adjusted health measure complement those from the unadjusted self-rated health models; that is, they indicate a significant relationship between adjusted health and participation above that from possible rationalisation. As with the longitudinal models that use unadjusted health, the impact of adjusted health on participation is reduced when unobserved time-constant variables are taken into account but remains significant.

The results do not control for unobserved variables that change over time. They also do not allow for the “feedback effect”; that is, that participation could influence health. As such, the results do not address causality but simply establish relationships between health and participation. An exploration of feasible instruments was conducted in order to try to instrument health thus making it possible to take into account variables that vary over time and causality, but no suitable instruments were found.