Emma Williams-Baron, Aliya Saperstein
Sociological Science July 9, 2026
10.15195/v13.a32
Abstract
Most quantitative studies of U.S. inequality rely on single measures of race and do not transparently describe them. However, inconsistencies between measures can yield conclusions that differ both substantively and statistically. We ask: when faced with multiple ways to categorize respondents, how should researchers choose? We conduct intersectional analyses of five inequality outcomes, using the 1979 National Longitudinal Survey of Youth, which offers several measures of self-identification and external classification. Strikingly, we find the survey’s screener race variable, ubiquitous in prior research, is never empirically preferred based on model fit across outcomes spanning the labor market (wages, salary, and unemployment), health (depression), and education (school discipline). Instead, the top-performing measure varies by gender, outcome, and fit statistic. The range of potential researcher decisions and the absence of a clear gold-standard highlights the need for greater transparency and more thoughtful decision-making when researchers operationalize race—whether racial categorization is central to the analysis or included primarily as a control variable. To that end, we offer a roadmap of key considerations inequality researchers can consult when designing their approach.
Most quantitative studies of U.S. inequality rely on single measures of race and do not transparently describe them. However, inconsistencies between measures can yield conclusions that differ both substantively and statistically. We ask: when faced with multiple ways to categorize respondents, how should researchers choose? We conduct intersectional analyses of five inequality outcomes, using the 1979 National Longitudinal Survey of Youth, which offers several measures of self-identification and external classification. Strikingly, we find the survey’s screener race variable, ubiquitous in prior research, is never empirically preferred based on model fit across outcomes spanning the labor market (wages, salary, and unemployment), health (depression), and education (school discipline). Instead, the top-performing measure varies by gender, outcome, and fit statistic. The range of potential researcher decisions and the absence of a clear gold-standard highlights the need for greater transparency and more thoughtful decision-making when researchers operationalize race—whether racial categorization is central to the analysis or included primarily as a control variable. To that end, we offer a roadmap of key considerations inequality researchers can consult when designing their approach.
 | This work is licensed under a Creative Commons Attribution 4.0 International License. |
Supplemental Materials
Reproducibility Package: Data and code for reproducing the results presented in this article are publicly
available in an Open Science Framework repository here: https://doi.org/10.17605/OSF.IO/K3RZT. Data may also be accessed through the NLSY Investigator site at: https://www.nlsinfo.org/investigator.
- Citation: Williams-Baron, Emma, Aliya Saperstein. 2026. “A Roadmap for Inequality Research: Transparency, Intersectionality, and Multiple Measures of Race” Sociological Science 13: 825-863.
- Received: September 20, 2025
- Accepted: May 18, 2026
- Editors: Arnout van de Rijt, Kristian B. Karlson
- DOI: 10.15195/v13.a32