Wilkinson (1992) finds a correlation of -.86 between life expectancy and income inequality and hypothesized that income inequality should affect health most strongly in wealthier nations, since social factors become stronger indicators of health after the epidemiological transition. Others argue that the relationship between income inequality and health – that social cohesion powerfully affects health may be correct, but the relationship between social cohesion and income inequality may be weak or absent (Mellor and Milyo 2001), or the pathways from inequality to health do not exist. Critics also point that any relationship between income inequality and health may be a relic of the curvilinear relationship between individual income and health (Gravelle 1998; Gravelle et al. 2000; cf. Wolfson et al. 1999), though some argue that this curvilinear individual-level relationship, whereby health returns to increased income are larger at low income levels, merely explains the aggregate-level relationship (Deaton 2002; Lynch and Davey Smith 2002)
Jason Beckfield used a larger sample (692 observations from 115 countries over the 1947-1996 period) to determine whether income inequality harm health. He used Life Expectancy and Infant Mortality as the dependent variable and income inequality as the key exogeneous variable. The dependent variables correlate strongly (r = -.92). The Gini coefficient is used as a standard measure of inequality. He also showed results from models that use the share of income received by the poorest quintile of the population, in order to avoid some of the problems of over-reliance on one measure of inequality. However, he noted that the choice of income inequality measure matters little in inequality health research. In the bivariate models, income inequality has a statistically significant effect on life expectancy and infant mortality (one-tailed tests). In the models that introduce controls for GDP, year, and measurement, the income inequality coefficients shrink, but they retain their statistical significance. This sharp reduction in the size of the income inequality effect indicates that earlier bivariate studies may have generated biased results. The multivariate OLS results suggest that income inequality affects health, but that the effect may be overstated without controls. In fixed-effects models that capture unmeasured heterogeneity, the relationship between income inequality and health disappears. The coefficient for income inequality fails to reach significance in either the bivariate or the multivariate models, suggesting that heterogeneity bias may have affected the results of previous model. Though there is less within-country than between-country variation on income inequality in this sample, the changes in income inequality that are observed are not associated with changes in population health. Supplemental analysis using data from the United States, United Kingdom, Australia, and New Zealand, where “convincing and robust evidence exists that inequality is rising” (Moran 2003:366), shows no evidence that rising income inequality has harmed health. The result holds same for the models that are estimated using the share of income received by the poorest quintile of the population rather than the Gini coefficient. In the OLS models, the effect of income inequality on health is statistically significant but small. The effect on life expectancy is about the same size as in the Gini models, and the effect on infant mortality is somewhat smaller. In fixed-effects models that control for unmeasured heterogeneity, the income inequality effect again fails to reach significance. Jason also determined whether the Inequality – Health hypothesis hold true for wealthier countries. For this he used a sub sample of countries with real GDP per capita greater than $6,000 and found that in the OLS models, there is a statistically significant effect of income inequality on health. However, the income inequality coefficient again loses statistical significance in the fixed-effects models. Although previous work finds an inequality-health association in less-developed and more-developed countries, some view the hypothesis as more applicable to rich countries than poor countries.

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