National Income Inequality and Local Poverty: Correlates of Health Care Spending
I recently posted an essay on the Health Affairs blog entitled “Inequality is at the Core of High Health Care Spending: A View from the OECD.” It explores the relationship between GDP and health care spending in the OECD countries, adjusts spending for price differences and demonstrates that the residual excess spending in the US, which accounts for 31% of total US health care spending, can be explained by the high degree of income inequality in the US as compared with other countries. It notes further that, while the US spends more on health care, it spends less on social services, and concludes by saying, “It is difficult not to connect the dots from inadequate social spending to excess poverty and income inequality to more chronic illness and higher health care spending. These dots reside in the core of the OECD onion, and the failure to cope with them is placing an unsustainable burden on our health care system.”
John Goodman called attention to this blog post on his National Center for policy Analysis blog. In a follow-up blog, he posted a message from Angus Deaton, a distinguished professor at Princeton, authority on the relationship between income and health and author of a recent book, “The Great Escape: Health, Wealth, and the Origins of Inequality.” Professor Deaton took issue with my conclusion that income-inequality is at the core of health care spending, pointing out that in his studies of mortality in states and metropolitan statistical areas (MSAs) with Darren Lubotsky, the correlation between income inequality and mortality stems from the failure to adjust for the density of African Americans, and that “inequality and mortality are uncorrelated across space in other settings where race is not a salient factor.”
I believe that Professor Deaton was referring to settings in the US, such as states, cities and neighborhoods, which I shall return to. But there is no question that income-inequality correlates strongly with both maternal and child mortality among OECD countries, even after excluding the US, which is the only OECD country with substantial numbers of blacks. The studies of health care spending that I reported were at the level of OECD countries, and I stand firmly behind them. At the level of countries, income inequality is at the core of high health care spending.
What about states, cities and neighborhoods? A high density of blacks in these smaller units of analysis proves to be a surrogate for poverty and income inequality. For example, Deaton and Lubotsky’s least equal states were LA and MS, which have high densities of blacks, while the most equal were NH and VT, which have few. But there is more than the density of blacks differentiating them. Moreover, both blacks and whites are affected. Deaton and Lubotsky noted that “white mortality rates are higher in places where the fraction black is higher.” Similarly, in a study of poverty and health care utilization in Milwaukee and Los Angeles, my colleagues and I found that whites living in poor neighborhoods with a high density of blacks had the same high health care utilization as their black neighbors. A high density of blacks is a marker for concentrated poverty, and concentrated poverty is the strongest correlate of health care utilization at the local level. It can be measured by income and education and often by black race, but make no mistake. It is concentrated poverty that is the operative factor at the local level.
Richard Wilkinson and Kate Pickett addressed the differences between local and national measures in their remarkable book, “The Spirit Level: Why Greater Equality Makes Societies Stronger.” They noted that in their review of nearly 170 studies of income inequality and health, the units analyzed varied substantially, from neighborhoods to towns to states or regions and to countries. While there was overwhelming evidence that inequality was related to health at the level of whole countries, the results were mixed when the measures were of smaller areas. They point out that “what marks out neighborhoods with poor health is not the inequality within them. It is, instead that they are unequal in relation to the rest of society….We should perhaps regard the scale of material inequalities in a society as providing the skeleton round which cultural differences are formed.”
Thus, at an international level, the measure of societal inequality is income inequality, and it is a strong correlate of health and health care utilization. At the level of neighborhoods, the best correlate is concentrated poverty, and it is best measured by the income-race-education triad, but within that triad, income and education always trump race. Inconsistencies often occur when data are collected at the level of MSAs or states because they are simply aggregates of neighborhoods yet subject to the national skeleton, and, unfortunately, aggregation of neighborhoods into units varying as much as CA, with 40M people, and VT, with <1M, leads to marginally significant and often conflicting conclusions. National income inequality and local poverty are the best correlates of health care utilization.