Panel data methods can be used to deal with time invariant unobservable determinants of health and income that cloud the conclusions that can be drawn from Granger causality analysis, although this is not straightforward using the nonlinear estimators appropriate for modeling often discrete and categorical health measures and when dynamics and long term relationships are taken into account. Using British data to estimate a dynamic random effects model of SAH, Contoyannis et al (2004) find that health varies with income averaged over time but not with current income (see Table 18.6 for all studies providing evidence from Europe). This might be interpreted as indicating that health responds to changes in permanent income but not to transitory income shocks. As such, it is consistent with the argument made above that sudden income surprises observed over a short period may not provide variation in economic circumstances that is relevant to the determination of health. Sustained differences in income that influence long term behaviour seem more relevant to the evolution of health.
However, caution is called for since it is not possible to separate the effect of individual income averaged over a panel from that of time invariant correlated unobservables.
Frijters et al (2005) exploit the largely exogenous income variation generated by the reunification of Germany in 1990 that resulted in sudden large income gains to virtually the whole population of the former East Germany. Reverse causality cannot be eliminated because the East German component of the panel only started in 1990 and so reunification cannot be used as an instrument. Fixed effects models of reported health satisfaction reveal positive effects of income on health in the West but, surprisingly, in the East, where the income variation was much greater, these effects are only observed for males. However, all
estimated effects are very small. Taking into account that the estimates are potentially upwardly biased by the failure to eliminate reverse causality, this study suggests that income does not have a substantial causal impact on health (satisfaction) in Germany.
Using data aggregated at the level of birth cohorts, Deaton and Paxson (2001) find strong negative effects of income on all-cause US mortality in the period 1976-1996. The effects appear strongest in middle age and in young men. But these findings are not uncontroversial.
It is difficult to rule out reverse causality in cohort models and the authors’ use of education as an instrument for income is easily criticized. Moreover, the same authors do not find any coherent or stable effects of cohort income on cohort mortality in England and Wales (1971-1998) (Deaton, Paxson 2004). They conclude that the observed correlated cohort income growth and mortality decline in both countries does not necessarily reflect a causal effect of the former on the latter but more plausibly arises from technological advances and the emergence of new diseases, such as AIDS, that affect age groups differentially. In this case, the main identifying assumption of the cohort approach – that age effects on mortality are constant through time – is invalid. This rather negative conclusion has not kept others from adopting a similar approach. Adda et al (2009) study the health effect of permanent income innovations arising from structural changes in the UK economy in the 1980s and 1990s that are assumed to be exogenous. They find that cohort incomes have little effect on a wide range of health outcomes, but do lead to increases in mortality: a 1% increase in income is estimated to lead to 0.7-1 more deaths per 100,000 persons among the prime aged (30-60) population in any given year. This result is in sharp contrast to Deaton and Paxson’s finding of no mortality effect for the UK, and a negative effect of income on mortality for the US.
The authors point out that their finding is consistent with Ruhm (2000, 2003), but the latter estimates the effect of transitory income changes arising from the business cycle, while they focus on more permanent income shocks.
Identification of the health effect of windfall gains in income or wealth is rather more transparent and has been a popular strategy adopted in recent studies. The reasoning is that because prizes, lottery wins, investment returns or inheritances are unanticipated, they are more plausibly exogenous to the evolution of health. While this might be true, one may question the relevance of windfall gains to understanding the large differences in morbidity and mortality between the rich and the poor that are likely to arise from sustained differences in health behaviour, and perhaps access to medical care, over many years.
Smith (2007) exploits large wealth gains accumulated by US stockholders during the stock market runups of the late 1980s and 1990s to estimate effects on the onset of major and minor chronic conditions, while conditioning on baseline health, income and wealth. He does not deal with unobserved heterogeneity and so uses the language of prediction, not causation.
Wealth changes (positive or negative) do not predict health changes. Using the same PSID data but instrumenting wealth by inheritances, Meer et al (2003) also find no significant effect on health. The same negative result emerges from three studies that test for a response of health to inheritance induced changes in wealth using data on older (50+) individuals from the HRS (Michaud and van Soest 2008, Kim and Ruhm 2012, Carman 2013). Allowing for a rich lag structure and unobserved heterogeneity, Michaud and van Soest (2008), as was noted in section 3.7, find a significant effect of health on wealth, but they find no evidence of a causal effect of (contemporaneous or lagged) wealth on either SAH or chronic conditions.39Carman (2013) finds that health is only correlated with inheritances that are anticipated, the exogeneity of which may be doubted.
Perhaps surprisingly, a few European studies do find positive health effects resulting from lottery wins. Using a Swedish panel and instrumenting a measure of permanent income (average income over 15 years) with average lottery winnings, Lindahl (2005) estimates that
39Inheritances are only used as an instrument for wealth in the models that test for a contemporaneous effect.
an income increase of 10 percent generates a fall in morbidity and a rather spectacular 2-3 percentage point decrease in the probability of dying within 5-10 years. One may be sceptical of the credibility of such a large effect, which exceeds even the raw correlation between income and mortality. Using British data, Gardner and Oswald (2007) find that two years after a win of between £1000 and £120,000, the GHQ index of mental health increased by 1.4 points, on a scale of 36 points. The effect is only significant for males and, surprisingly, for higher income individuals. Using a few more waves of the same data, Apouey and Clark (2013) find that lottery winnings have no significant effect on SAH, but a large positive effect on mental health.
[Table 18.6 here]