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We assess the extent to which differences in socio-economic status are associated with racial and ethnic gaps in a fundamental measure of population health: the rate at which infants die. Using micro-level Vital Statistics data from 2000 to 2004, we examine mortality gaps of infants born to white, black, Mexican, Puerto Rican, Asian, and Native American mothers. We find that between-group mortality gaps are strongly and consistently (except for Mexican infants) associated with maternal marital status, education, and age, and that these same characteristics are powerful predictors of income and poverty for new mothers in U.S. Census data. Despite these similarities, we document a fundamental difference in the mortality gap for the three high mortality groups: whereas the black-white and Puerto Rican-white mortality gaps mainly occur at low birth weights, the Native American-white gap occurs almost exclusively at higher birth weights. We further examine the one group whose IMR is anomalous compared to the other groups: infants of Mexican mothers die at relatively low rates given their socio-economic disadvantage. We find that this anomaly is driven by lower infant mortality among foreign-born mothers, a pattern found within many racial/ethnic groups. Overall, we conclude that the infant mortality gaps for our six racial/ethnic groups exhibit many commonalities, and these commonalities suggest a prominent role for socio-economic differences.

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naive regression that includes a group indicator variable. Our analysis yields two principal findings. We show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We also show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences when compared to OLS regression, therefore understating unexplained differences. We then provide three empirical examples that explore the practical importance of our analytic results.