Workforce automation risks across race and gender in the United States

Abstract

AbstractAlthough the effects of automation on the future of work have received considerable attention, little research has been conducted on the costs of this technological transformation for different populations of workers. This article makes an important contribution as one of the first to analyze the intersectional effects of workforce automation across race and gender in the United States. Multilevel survey data models are employed using two distinct measures of automation job displacement risk for over 1.4 million Americans across 385 occupations. This research demonstrates that the intersection of race and gender matters for individual automation risks. Education, age, disability, and nativity are also significant. These findings indicate that labor market outcomes of job automation will be based not only on differences in human capital but critically on socially constructed identities as well.