Why Treatment Prevalence Matters: Overcoming a Blind Spot in Experimental Inequality Research

Abstract Experimental methods increasingly inform sociological inequality research; however, a blind spot often remains when experimental findings are translated into group-level disparities. This article addresses treatment prevalence—the proportion of individuals within different groups who receive a given treatment outside the experimental setting—as a largely overlooked parameter linking experimental treatment effects to macro-level inequality patterns. We demonstrate how incorporating treatment prevalence enhances the interpretation of experimental results by drawing on three recent sociological studies concerning ethnic inequality in social interactions, gender inequality in academic hiring, and social inequality in higher-education enrolment. Our analysis reveals that substantial group-level disparities can emerge even when treatment effects are equal across groups. Conversely, treatments producing stronger positive effects for disadvantaged groups may nevertheless exacerbate inequality when treatment receipt differs across groups. We introduce a heuristic framework and sensitivity tool to help researchers systematically explore how treatment effects and treatment prevalences jointly shape macro-level disparities. Beyond advancing the study of existing inequalities, our framework has immediate practical implications for evaluating inequality-reducing interventions, which must consider not only treatment effects but also which groups receive treatments. More broadly, our approach offers a pathway for integrating experimental and observational methods in future social stratification and inequality research.