Health and Labour Force Participation

This paper examines the relationship between health and labour force participation using data from the first three waves of the Survey of Family, Income and Employment (SoFIE) (2002/05). Using various health measures, the results show that health is significantly related to labour force participation, even after accounting for certain types of endogeneity. The results of the standard regression models including individual chronic diseases indicate that five out of the nine chronic diseases considered have a significant negative relationship with labour force participation once other factors are controlled for. These diseases are: psychiatric conditions (depression, manic depression or schizophrenia); stroke; heart disease; diabetes and high blood pressure. For psychiatric conditions, stroke and diabetes the negative relationship with full-time work is larger than that for parttime work (ie, the chance of working full-time rather than being inactive is reduced more than the reduction in the chance of working part-time rather than being inactive). This suggests that the presence of these diseases is associated not only with lower participation but also with working fewer hours. Various modelling techniques and a more general measure of overall health (self-rated health) are then used to account for possible endogeneity. The results of these models indicate that poorer self-rated health is associated with a reduced chance of participating in the labour force. The relationship between self-rated health and labour market participation is found to be significant even when time-constant unobserved variables are controlled for and when self-rated health is adjusted to account for possible rationalisation of labour force participation using self-rated health. More specifically, a health shock (measured using adjusted or unadjusted self-rated health) was found to be associated with a reduction in the chance of participating. While the results from all models are in a similar direction, they have different strengths and the preferred estimators are those from the fixed effects model. Using various assumptions, the model results were used to estimate the impact at the economy level. The point estimates from these models indicate that if there was an improvement in health (ie, no negative health shocks and/or everyone had excellent average health) an additional 12,700 to 66,800 people may participate; that represents a 0.7% to 3.6% increase in the total number of people participating. Based on the limitations of the models discussed in the paper it is more sensible to assume that, if there was an improvement in health, the additional number of people who may participate is likely to be between 5,300 and 38,700; that is, a 0.3% to 2.1% increase in the total number of people participating. The results do not control for unobserved variables that vary 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 only establish relationships between health and participation. Feasible instruments were explored to try to instrument health, thus making it possible to take into account both unobserved variables that change over time and causality, but no suitable instrument was found.