Consistent Divisions or Methodological Decisions? Assessing the U.S. Racial Hierarchy Across Outcomes

Abstract

Scholars have offered a range of perspectives on the twenty-first century racial landscape with little consensus about either the current state of the U.S. racial hierarchy or its future trajectory. We offer a more comprehensive assessment, using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to study racial stratification across a number of socioeconomic outcomes. We pay particular attention to the robustness of results across different categorization schemes that account for self-identification and interviewer classification, as well as racial fluidity. Although we observe that White and Asian Americans generally have the best socioeconomic outcomes, on average, while Black Americans and American Indians have the worst, we also find meaningful differences in patterns of stratification both across outcomes and depending on how race is operationalized. These differences in stratification are reflected in the estimated number of strata as well as the rank order of racial categories. Our results suggest that ongoing debates about the nature of the U.S. racial hierarchy can be partly explained by methodological decisions about which outcomes to study and how best to measure race. Supplementary Information The online version contains supplementary material available at 10.1007/s12552-021-09351-2.

Regimes beyond the One-Drop Rule: New Models of Multiracial Identity

“…Importantly, they reveal a shared socioeconomic hierarchy, where single-ancestry groups are arranged in the following descending order of status: (1) Asian, (2) White, (3) Black, (4) Indigenous, and (5) Hispanic. This finding departs from the typical racial hierarchy in existing research, which tends to find Black Americans and Indigenous Americans at the bottom of the socioeconomic hierarchy, and Hispanic Americans in the middle (Guluma and Saperstein 2022). This difference likely stems from two sources: first, we use reported ancestry rather than racial self-identification.…”

Measuring Hispanics/Latinxs: Racial Heterogeneity and Its Consequences for Modeling Social Outcomes in U.S. Population Samples

“…To evaluate the specifications, I use a variation of Guluma and Saperstein’s (2022) approach to comparing different racial specifications on their effectiveness in modeling racial stratification. I use two model-fit criteria, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), to make comparisons between (1) the non-nested specifications (e.g., the official Add Health specification and the ECLO specification) and (2) the nested specifications (e.g., the ECLO specification without Latinx-heterogeneity measures and the ECLO specification with the skin-tone measures).…”