Disaggregated Earnings Trajectories in the U.S. Labor Market: New Evidence from Linked Longitudinal Data

This project investigates the economic outcomes and trajectories of the fastest growing demographic group in the United States and, in particular, its workforce in science and engineering. To date, there is no consensus on fundamental questions about the labor market experiences and trajectories for this group. Empirical evidence of their labor market outcomes is scattered and mixed despite their high educational attainment. Moreover, this study investigates their labor market experiences disaggregating across gender, nativity, and national origin. The project also yields implications for reductions to barriers to incorporation and intergenerational mobility.

This study uses the restricted-access linked longitudinal survey data in NSF's Scientists and Engineers Statistical Data System (SESTAT) and Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) data and Decennial Census/American Community Survey (ACS) data. This enables it to overcome previous data limitations, which have made it difficult to accurately study the outcomes and trajectories of workers in this demographic group. Because diversity is a hallmark of this population, failing to disaggregate the population results in biased or incomplete narratives. The project adapts two widely used statistical methods—multi-level growth-curve models and group-based trajectory models—to represent individual heterogeneity in earnings trajectories in a population. Findings from these life-course trajectory models reveal economic inequality, such as across workers’ national origin and generation groups, as well as between this and other demographic groups.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2620351
Principal Investigator: Xi Song
Funds Obligated: $152,472
State: NY