8   Discussion

8.1.1  Impact on the labour force

The results so far have considered the relationship between health and labour force participation at an individual level. For policy purposes, it is helpful to understand the potential impact of these relationships at the population level. While the magnitude of relationship between health and labour force participation is larger for those of poorer health, if the number in poorer health in the population is small then the estimated impact at the population level may not be large. It is important to remember again that, in this section, words such as “impact” and “effect” are used to describe relationships but do not attempt to explain causation.

Table 17 presents the estimated impact of different diseases and health states. These estimates are based on the marginal effects reported in Tables 3 and 13 and the estimated number of working age non-students in each group.[32] They therefore provide an indicator of the workforce impact of poor health. The marginal effects were estimated with the other variables set at the whole sample mean; that is, the figures estimate the additional number of people who may participate in the absence of poor health, if they have average values for the remaining characteristics.[33] The error margin around the estimated impact figures only considers error in the marginal effect. The proportion figures in the table illustrate the proportion of the number of participating working age non-students the count represents. The number of working age non-students estimated to be participating on average over the three waves of SoFIE is 1.84 million. Figures for all diseases and self-rated health states/shocks are reported even if they are not significant. The groups for which the number impacted, or the proportion, crosses zero indicate where the impact is not statistically significant. For the level of health this means that there was insufficient evidence to suggest that the chance of labour force participation for those in this health state was statistically different from those in the “best” health state. For the health shocks this means there is insufficient evidence to suggest that a negative health shock into this health state would significantly affect the chance of labour force participation. For those diseases or health states/shocks that are significant, the asterisks indicate the level of significance of the marginal effect. The categories that are not significant are excluded where totals are calculated. This is justifiable for the purpose of estimating the potential change in labour force participation that can be associated with the movement of those in these categories to better health, or the prevention of a health shock, as the models found insufficient evidence that there would be one.

As discussed in Section 6, the preferred model is the fixed effects model (for which results are assumed to be unbiased). Results from the standard logistic regression models may be biased owing to possible endogeneity. While the correlated random effects model attempts to account for some types of endogeneity, the results of this model may also be subject to bias. Despite this, impact figures are presented from all of these models to allow comparison of the model results and because the fixed effects model does not allow an estimate of the relationship between a constant health level and labour force participation.

Looking at the grouped chronic disease indicator from the pooled regression model indicates that if this group no longer had chronic diseases an additional 42,200 people may participate. This represents a 2.3% increase in the total number of people participating.

Moving on to consider individual chronic diseases, the table shows that the largest increase in the number of additional participants is for females with psychiatric conditions. This is despite the fact that the odds ratios and marginal effects are estimated to be of greater magnitude for stroke, heart disease, diabetes and males with psychiatric conditions. This illustrates the importance of the size of the group of interest when relating the results to the population as a whole. If no females suffered from psychiatric conditions it is estimated that an additional 9,500 people may participate; which represents a 0.5% increase in the total number of people participating. It should be remembered that as the disease groups are not independent (ie, a person may have diabetes as well as heart disease) the number impacted cannot be summed across all diseases.

The results of the pooled logistic regression for self-rated health in Table 17 illustrate the estimated additional number of people who would participate if they had excellent health, as opposed to the health state listed. So if all those people with good health had excellent health an additional 26,400 people may participate; which represents a 1.4% increase in the total number of people participating. Again, this illustrates that, while the marginal effects and odds ratios are higher for those in fair or poor health, the biggest potential increase in participation comes from those in good health. Overall, an additional 66,800 people may participate if they had excellent health; a 3.6% increase in the total number of people participating.

As explained previously, there may be unobserved variables that impact labour force participation and/or health. The logistic regressions do not account for this. Despite this, the estimates from the pooled models give an indication of the possible impact of health on participation. To try to control for unobserved time-constant variables, panel models were used. Their interpretation is slightly different from the pooled models. The results for the fixed effects model for self-rated health in Table 17 illustrate the additional number of people who may participate in the absence of negative health shocks.[34] That is, if during an annual period there were no negative health shocks, an additional 12,700 people may participate; which represents a 0.7% increase in the total number of people participating. While the coefficients and odds ratio from this model reported earlier were those for a health shock from excellent to a lower health state, other health shocks are possible and these health shocks are accounted for in these figures. For example, if there were no health shocks into poor health (from any of the higher health states) then an additional 5,200 people may participate.