As policymakers and researchers aim to understand the drivers of disparate outcomes and racial and gender inequalities in the labor market, the role of early career work experiences remains an underexamined issue. One barrier to analyzing the long-term impact of first jobs on workers’ lifetime earnings and career progression has been limited data sources that link workers’ occupations and employers over time.

Through the creation of a new linked dataset, this project seeks to understand how features and skill requirements of a worker’s first job shape wage and career trajectories and how these outcomes vary by race, gender, educational attainment, geography, and other worker characteristics. Led by researchers at the RAND Corporation, the project will use machine learning techniques to link occupational data in the American Community Survey with wage and employment data in the Longitudinal Employer-Household Dynamics Survey to build a novel dataset for current and future research.

Through this new dataset, the project team will also characterize initial jobs and occupations that offer workers greater opportunities for advancement and mobility. The results of this research will inform policies related to initial job placement for workers entering the labor market, employer practices that shape the design and skill requirements of entry-level jobs, and worker decisionmaking about the types of initial jobs to pursue.