Academic journal article NBER Reporter

Health, Income, and Inequality. (Research Summaries)

Academic journal article NBER Reporter

Health, Income, and Inequality. (Research Summaries)

Article excerpt

Richer, better-educated people live longer than poorer, less-educated people. According to calculations from the National Longitudinal Mortality Survey which tracks the mortality of people originally interviewed in the CPS and other surveys, people whose family income in 1980 was greater than $50,000, putting them in the top 5 percent of incomes, had a life-expectancy at all ages that was about 25 percent longer than those in the bottom 5 percent, whose family income was less than $5,000. Lower mortality and morbidity is associated with almost any positive indicator of socioeconomic status, a relationship that has come to be known as "the gradient." African-Americans have higher but Hispanic Americans lower mortality rates than whites; the latter is known as the "Hispanic paradox," so strong is the presumption that socioeconomic status is protective of health. Not only are wealth, income, education, and occupational grade protective, but so are several more exotic indicators. One study found that life-spans w ere longer on larger gravestones, another that winners of Oscars live nearly four years longer than those who were nominated but did not win.

Many economists have attributed these correlations to the effects of education, arguing that more educated people are better able to understand and use health information, and are better placed to benefit from the healthcare system. Economists also have emphasized the negative correlation between socioeconomic status and various risky behaviors, such as smoking, binge drinking, obesity, and lack of exercise. They have also pointed to mechanisms that run from health to earnings, education, and labor force participation, and to the role of potential third factors, such as discount rates, that affect both education and health.

Epidemiologists argue that the economists' explanations at best can explain only a small part of the gradient; they argue that socioeconomic status is a fundamental cause of health. They frequently endorse measures to improve health through manipulating socioeconomic status, not only by improving education but also by increasing or redistributing incomes. Fiscal policy is seen as an instrument of public health, an argument that is reinforced by ideas, particularly associated with Richard Wilkinson, that income inequality, like air pollution or toxic radiation, is itself a health hazard. Even if economic policy has no direct effect on health, the positive correlation between health and economic status implies that social inequalities in wellbeing are wider than would be recognized by looking at income alone. (1)

Income and Education among Cohorts and Individuals

Christina Paxson and I (2) looked at the relationship between health and economic status among American birth cohorts. We focused on the idea that health is determined by an individual's income relative to other members of a reference group whose membership typically is unobserved by the analyst. Even if income inequality has no direct effect on health, the fact that the reference groups are not observed means that the slope of the relationship between health and income depends on the ratio of the between-to-within group components of income inequality. For example, if doctors' health depends on the income of other doctors, and economists' health on the income of other economists, then the health-to-income relationship in the pooled data will flatten if the average incomes of the two groups pulls apart. (3)

Among birth cohorts there is a strong protective effect of income on mortality; the elasticity of mortality rates with respect to income is approximately -0.5. These estimates are consistent with estimates from the individual data in the National Longitudinal Mortality Study (NLMS), and show much the same pattern over the life cycle, with income most highly protective against mortality in middle age, in the mid-40s for women and the mid50s for men. Although it is difficult to test for reverse causality in the cohort data, we can experiment in the NLMS by looking at the effects of income at the time of interview on the probability of death over an interval some years later, thus eliminating or at least reducing the effects of including in the sample people who are already sick, and whose income is already reduced by the illness that will later kill them. …

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