We use U.S. linked survey and administrative data to investigate whether the sex composition of a firm's executives affects the earnings of new mothers. Our empirical strategy compares the earnings trajectories of new mothers to observably similar coworkers who did not give birth around that time. On average, mothers earn almost $2,000 less per quarter two years after birth, but the magnitude of these losses is unrelated to the female share of executives at the mothers' employer. Our results suggest that increasing the representation of women in firms' leadership positions will not reduce the motherhood penalty.

We consider sorting in the labor market, that is, whether high- or low-productivity workers and firms tend to match with each other, and how this varies over time using U.S. linked employer–employee data. Composition changes of workers and firms move in opposite directions over the business cycle. During and after recessions, low-rank workers are less likely to work, while the employment share of low-rank firms increases. The agreement between worker and firm ranks increases in the early stages of labor market downturns.

Abstract The share of the U.S. population that receives business income has increased substantially in recent decades. At the same time, worker hire and separation rates declined, with worrying implications for productivity and wage growth. In this paper, we explore the relationship between business income (BI) receipt and labor reallocation. We show that BI recipients are largely excluded from existing measures of labor reallocation. Including BI recipients reduces the measured decline from 1994 to 2014 in the hire and separation rates by 8.3–8.7%, respectively, primarily among jobs that were secondary sources of income or short in duration. We present evidence that worker transitions between wage and salary jobs and BI represent labor reallocation, as opposed to reclassification of employees as independent contractors.

The challenges faced by military veterans transitioning into the civilian labor force are a subject of ongoing concern to policymakers. The Census Bureau’s Veteran Employment Outcomes (VEO) are experimental statistics on Army veterans’ labor market outcomes one, five, and 10 years after discharge, by military occupation, rank, demographics (age, sex, race, ethnicity, education), industry and geography of employment. These statistics are generated by linking veteran records provided by the U.S. Army to national administrative data on jobs at the U.S. Census Bureau. Coverage of the data is all enlisted soldiers in the Army who completed their initial term of service and were discharged between 2000 and 2015 (about 650,000 veterans). Although VEO currently cover only Army veterans, these statistics could potentially be expanded to other service branches. These new data highlight the broad distribution of labor market outcomes for recent Army veterans, highlighting the role of industry and military occupation in post-military earnings outcomes. Some key findings from these new statistics include the following:

The economic questions that can be addressed using many of the large administrative employer-employee linked data sets with workers’ earnings have been limited by the absence of information on workers’ base wages, variable compensation, hours or weeks worked, and other factors determining workers’ earnings. This paper presents a set of machine learning methods that identify each worker’s unobserved persistent base wages, paydays weeks, and annual bonuses from the worker’s quarterly earnings. I then implement and evaluate the quality of these methods using quarterly earnings data in the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) dataset, an employer-employee linked dataset for the United States. Using the estimated nominal wages of workers in 30 U.S. states, I document four patterns of nominal wage adjustment: i) estimated persistent wage changes exhibit downward nominal wage rigidity, ii) optimal real wage cuts are suppressed by downward nominal wage rigidity, iii) workers’ nominal raises follow a Taylor-like pattern, with the probability of a wage raise spiking every four quarters, and iv) the timing of workers’ annual raises are synchronized within the firm. ∗I am deeply indebted to my dissertation committee: Borağan Aruoba, John Haltiwanger, Judy Hellerstein, and Henry Hyatt and my graduate director, John Shea, for their support and guidance. I also want to thank Katharine Abraham, Joonkyu Choi, Leland Crane, Cynthia Doniger, Joanne Gaskell, Jessica Goldberg, Ethan Kaplan, Erika McEntarfer, Michael Murray, Felipe Saffie, Kristin Sandusky, Matthew Staiger, Lesley Turner, Larry Warren, the seminar participants at the University of Maryland, the Bureau of Labor Statistics, Bates College, and the U.S. Census Bureau, and the LEHD group at the Center for Economic Studies in the U.S. Census Bureau for engaging with me on my research. I am grateful for the financial support of the Center for Retirement Research and the Betancourt Fellowship. The opinions expressed herein are those of the author alone and do not necessarily reflect the view of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential data are disclosed. See U.S. Census Bureau Disclosure Review Board bypass numbers: DRB-B0073-CED-20190910, DRB-B0069-CED-20190725, DRB-B0037-CED-20190327, CBDRB-2018-CDAR-061. Email: murrase@umd.edu

Six weeks after the national emergency declaration for the COVID-19 pandemic, unemployment insurance (UI) claims as a share of total state employment differed by as much as 24 percentage points across states. This paper examines three explanations for these dramatic differences: statewide stay-at-home orders, the industry composition of states' employment, and the historical utilization of states' UI systems. We find that i) the surge in UI claims began and often peaked before states enacted stay-at-home orders; ii) relative to other states, states that would eventually enact stay-at-home orders had high UI claim rates before the orders were enacted; iii) six weeks after the national emergency declaration, statewide stay-at-home orders accounted for less than 30% of the difference in cumulative initial UI claims between states that ever-versus-never enacted statewide stay-at-home orders; iv) industry-related exposure to the COVID-19 pandemic can account for twice as much of the state-level variation in UI claims relative to stay-at-home orders; and v) states with greater historical utilization of their UI systems by unemployed workers tended to both receive more UI claims and process these claims more quickly at the onset of the COVID-19 pandemic.