title: Mortality, inequality and race in American cities and states.
abstract: A number of studies have found that mortality rates are positively correlated with income inequality across the cities and states of the US. We argue that this correlation is confounded by the effects of racial composition. Across states and Metropolitan Statistical Areas (MSAs), the fraction of the population that is black is positively correlated with average white incomes, and negatively correlated with average black incomes. Between-group income inequality is therefore higher where the fraction black is higher, as is income inequality in general. Conditional on the fraction black, neither city nor state mortality rates are correlated with income inequality. Mortality rates are higher where the fraction black is higher, not only because of the mechanical effect of higher black mortality rates and lower black incomes, but because white mortality rates are higher in places where the fraction black is higher. This result is present within census regions, and for all age groups and both sexes (except for boys aged 1-9). It is robust to conditioning on income, education, and (in the MSA results) on state fixed effects. Although it remains unclear why white mortality is related to racial composition, the mechanism working through trust that is often proposed to explain the effects of inequality on health is also consistent with the evidence on racial composition and mortality.
authors.affiliation: Center for Health and Wellbeing, 347 Wallace Hall, Princeton University, Princeton, NJ 08544, USA. deaton@princeton.edu
authors.forename: Angus
authors.initials: A
authors.lastname: Deaton
authors.forename: Darren
authors.initials: D
authors.lastname: Lubotsky
mesh_terms: D000293:Adolescent; D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data*; D000368:Aged; D002391:Catchment Area, Health; D019352:Censuses; D002648:Child; D002675:Child, Preschool; D002947:Cities; D003430:Cross-Sectional Studies; D003710:Demography; D005260:Female; D005843:Geography; D006801:Humans; D007182:Income / Q000145:classification / Q000706:statistics & numerical data*; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D009026:Mortality; D012044:Regression Analysis; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data*