8.2  Factors associated with health status

In this section we analyse factors associated with health status. In each case (physical, mental and overall) the continuous score (H^p, H^m and H^o) was regressed on a common set of explanatory variables (Z_i), with a view to identifying the factors that are most strongly associated with health status, either positively or negatively.

H^p = β^mH^m + Σβ_i^pZ_i + ε^p (9)

H^m = β^pH^p + Σβ_i^mZ_i + ε^m (10)

H^o = Σβ_i^oZ_i + ε^o (11)

Figure 11: Factors associated with health status

Source:  Health, Work and Retirement Survey

Notes:
¹ The numerical scores refer to positive and negative effects at the 1% (=3), 5% (=2) and 10% (=1) levels of statistical significance.
² A summary of the regression results is given in Appendix Tables C. 11-13. Complete results are available on request.

Summary results from these three regression models for the different health scores, for the working group[24] and broken down by male and female are in Appendix Tables C.11–13.

The overall results are summarised in Figure 11 where bars to the right are factors that are associated with better health and those to the left with poorer health. The length of the bars is proportional to their statistical significance, so that a bar of three units in length is associated with a variable whose regression coefficient was significant at the 1% level. Bars of lengths one and two refer in turn to the 10% and 5% significance levels.

More education and labour force participation are associated with a higher probability an individual will have better physical and mental health. Finding such associations does not carry implications of causality. Working is significantly associated with better health, but, as we will show in Section 9, those with better mental and physical health are more likely to be working. In short, poorer health is associated with a lowering of the likelihood of working, but working is associated with better health. Those individuals with higher income and wealth are more likely to be healthy. Again, however, their ability to accumulate wealth may well be associated with their health status (recall Table 5-1). In each of the three models reported in Figure 11, being Māori relative to NZ European increases the likelihood of poorer health.

Stephens et al (2008) report similar findings based on the HWR data. In particular they find wealth is positively associated with physical but not mental health. They also report that retirement is associated with poorer scores for both mental and physical health.

Benzeval and Judge (2001) use longitudinal data from the British Household Panel Survey to study the relation between income and health. They distinguish between permanent and transitory income as well as addressing the possibility of reverse causation. They find that long-term income is more important for health than current income; that the level of income is more important than changes in income; that decreases in income are more important than increases; and persistent poverty is more harmful to health than occasional episodes.

The question of causation between health and income has been the subject of extensive debate. A clearer understanding of the relationship is important for public policy. Would policies that improved the incomes of a target group be expected to lead to improved health status? The possibility always exists that the measured effect of income on health might be owing to reverse causality; ie, those with better health will have higher labour force participation and command higher wages, resulting in greater income. The standard approach to dealing with this endogeneity has been to use a two stage instrumental variables model. It is expected that this would purge income of the effect of health status. The challenge has always been to find suitable instruments; ie, variables associated with income but not health. Ettner (1996) uses the state unemployment rate from cross-sectional data for the USA, together with work experience, parental education and spouse characteristics. Her results support the hypothesis that the direction of causality is from income to health. However, such models remain susceptible to the choice of instruments, and as Currie and Madrian (1999).note, the evidence remains mixed

Dave, Rashad and Spasojevic (2006) use longitudinal panel data from the Health and Retirement Study in the USA to examine the effects of retirement on physical and mental health. Partly as a result of changes in lifestyle after retirement, including less physical activity and fewer social interactions, they report robust evidence that both physical and mental health deteriorates. Complete retirement is associated with 5% to 16% increase in difficulties of mobility and daily activity, a 5% to 6% increase in illnesses and a 6% to 9% decline in mental health.