Identity and Employee Earnings: A Two-Step Method for Parsing and Estimating Market Returns for Multiple Identities

Researchers have studied how outcomes relate to identity and are increasingly considering complex identities. To study individuals with more than one identity, we focus on dissertators in the United States whose work has one or more identities (academic fields). Our novel estimation method leverages a two-step process to characterize the earnings of interdisciplinary dissertators as functions of the identities they acquire as graduate students (by studying more than one academic field). We estimate a first-stage regression of log earnings for monodisciplinarians and then regress log earnings for interdisciplinarians on functions of the first-stage coefficients. We find that (i) interdisciplinarians have lower earnings than disciplinarians; (ii) interdisciplinarians’ earnings are more strongly related to primary than secondary disciplines; and, (iii) interdisciplinarians’ earnings are positively related to the higher-paid discipline, especially for those entering industry. Our two-step method provides a framework for parsing and estimating the varied impacts of multiple identities across a wide range of contexts.