Papers Containing Tag(s): 'Longitudinal Employer Household Dynamics'
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Viewing papers 1 through 10 of 262
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Working PaperNew U.S. Business Establishments: Surging or Stalling?
June 2026
Working Paper Number:
CES-26-36
Since the 1990s, the Bureau of Labor Statistics (BLS) has reported much more rapid growth in U.S. private sector employer establishments than has the Census Bureau' the gap reached roughly 1.6 million by 2023. Using linked BLS-Census microdata, we document two main drivers. First, a large and growing number of employers providing services to the elderly and persons with disabilities are in scope for the BLS frame but not the Census Bureau's. Second, many firms appear with substantially more establishments in the BLS frame. These discrepancies substantially affect the measured establishment size distribution and quantitative policy analysis.View Full Paper PDF
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Working PaperRemote Work and Residential Sorting: IV Evidence From Expiring Office Leases
June 2026
Working Paper Number:
CES-26-34
How has remote work reshaped residential sorting and housing demand, and what are the implications for state and local governments? To estimate causal effects, I propose a novel instrument for remote work that exploits quasi-random variation in the timing and size of office lease expirations, captured through a Bartik-style exposure measure at the residential block level. Expirations allow tenant firms to reduce office space and switch employees to remote work, generating strong first-stage effects. Remote work causes modest increases in housing and property tax expenditures in exchange for space, homeownership, and public schools, but not other neighborhood characteristics. It significantly increases migration, particularly out of cities and states that levy income taxes. At the neighborhood level, higher 2020 remote work shares cause subsequent residential turnover, demographic clustering, and property tax revenue windfalls. Taken together, the results indicate that remote work induces migration consistent with Tiebout sorting, and accounts for 10% of migration since 2020. Residential choices and tax bases now depend less on employment proximity and more on affordability and tax-benefit linkage.View Full Paper PDF
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Working PaperEmployees in the US Nonprofit Sector
May 2026
Working Paper Number:
CES-26-33
The nonprofit sector employs roughly 10% of the American workforce, making it the third largest workforce behind the retail and manufacturing sectors. Despite this, relatively little is known about its employees. This paper is the first to use comprehensive administrative tax data, covering the near-universe of workers in the US, to quantify and explain the causes of the nonprofit pay differential. Unconditionally, we find the nonprofit earnings penalty to be 12% relative to for-profit workers. Estimating an 'AKM' worker-firm job ladder model, we show that most of the penalty is causal and not driven by selection. We also document considerable heterogeneity across industries, both in terms of earnings premia/penalties and worker selection, and show that nonprofit and for-profit earnings have been converging over time.View Full Paper PDF
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Working PaperEmployment and Earnings Trajectories of HUD Program Participants
May 2026
Working Paper Number:
CES-26-31
Federal housing assistance programs, such as those run by the U.S. Department of Housing and Urban Development (HUD), have been shown to reduce rent burden and improve housing stability for program participants, which may in turn have downstream impacts on their labor market attachment and career trajectories. However, existing studies from individual cities or states provide mixed evidence on the association of housing assistance with labor market outcomes. By linking HUD administrative records to matched employee-employer earnings records from the Longitudinal Employer-Household Dynamics (LEHD) program, we document how the labor market trajectories of program participants change as they enter and exit federal housing assistance programs, examining outcomes over a 14-year window surrounding entry or exit. In our analysis of entry, we find that the employment rates and earnings of first-time HUD program participants begin to increase upon entering a HUD program, which represents a reversal of prior declining trends in these outcomes. Suggestive of a positive association, these increases in employment and earnings trends exceed those of low-income non-participants from the American Community Survey (ACS). In our analysis of exits, we find that program participants who eventually leave a HUD program have increasing pre-exit trends in employment and earnings that then flatten upon exiting. Comparing these negative changes in trend to the relatively stable trajectories of those who remain in HUD programs throughout the analysis suggests that exits are associated with diminished employment and earnings trajectories.View Full Paper PDF
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Working PaperYou're (not) Hired: Artificial Intelligence and Early Career Hiring in the Quarterly Workforce Indicators
April 2026
Working Paper Number:
CES-26-27
Using detailed tabulations from matched employer-employee administrative data, I document evidence of an immediate, sizable, and persistent decrease in the level of early career (22-24 year old) hires following introduction of ChatGPT within the industry-state cells that are most exposed to AI. The decline in hires is the primary cause of large observed declines in employment over the subsequent period. Regressionadjusted employment of early career workers in the most AI-exposed quintile of industry-state cells declined by 12% over the 10 quarters following the introduction of ChatGPT, even as employment in lessexposed industries has remained stable. The rate of hiring largely recovered by early 2025, attributable to a smaller employment base. Earnings growth of early career workers in the most exposed industries slowed slightly relative to those in less exposed industries. Although the most AI-exposed quintile of detailed industries is dominated by a handful of industry sectors, I find that the association of higher AI exposure with reduced early career employment and fewer hires is observed across most sectors of the economy. Timing of effects in event studies is consistent with an immediate effect on hiring following introduction of ChatGPT. However, triple difference estimates provide some evidence of earlier trend shifts on employment, hiring, and separations around the onset of the COVID pandemic. I discuss potential explanations, including the increase in remote work and increased educational attainment among workers in AI-exposed occupations. Nonetheless, job gains to early career workers and backfill hires show evidence of discontinuous decline at the time of ChatGPT's release in comparison to older workers in the same industries. A local projections analysis at the NAICS industry group level shows that industries with high AI exposure are not particularly sensitive to unexpected fluctuations in monetary policy on average relative to other industries in employment, hiring, or separations. A historical decomposition suggests that up to one quarter of relative early career employment declines through 2025q2 may be attributable to monetary policy shocks through 2023, but the analysis does not find evidence that these shocks can explain the rapid decline in hires at the most AI-exposed firms in comparison to others.View Full Paper PDF
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Working PaperUnemployment Insurance Extensions, Labor Market Concentration, and Match Quality
April 2026
Working Paper Number:
CES-26-24
I investigate whether the effects of UI extensions are different for workers exposed to higher levels of local labor market concentration, a potential source of employer market power. I exploit measurement error in state unemployment rates that led to quasi-random assignment of UI durations in the U.S. during the Great Recession. Using matched employer-employee data from the Longitudinal Employer-Household Dynamics program, I find that UI extensions lengthen nonemployment durations by one week and cause economically meaningful but not statistically significant increases in earnings. The UI-earnings effect is significantly lower at higher levels of concentration, while there is no difference in the UI-duration effect. The lower UI-earnings effect is driven by the extremes of the distribution of concentration. My results suggest that match improvements from UI are attenuated at higher levels of concentration.View Full Paper PDF
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Working PaperThe Evolving Impact of Founders on Startup Employee Retention
March 2026
Working Paper Number:
CES-26-21
Founders are known to attract prospective employees by signaling their startup's mission, culture, and potential. But do they also shape who stays? And if so, does the founder's influence diminish as the startup matures? Using matched employer-employee data from the U.S. Census, we address these questions, especially focusing on cases of founder premature death to identify plausibly exogenous exits. We find that founder departures significantly increase employee turnover. These effects are stronger in older and larger startups. Further analyses show that the impact of founder departure is more salient among employees who had longer shared tenure or have the same sex as the founder. These patterns suggest that employees develop complementarities with founders over time'an alignment in skills, relationships, or culture'that reinforce founders' influence as startups mature.View Full Paper PDF
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Working PaperStatus Inconsistency and Geographic Mobility in the United States
March 2026
Working Paper Number:
CES-26-20
This study examines how neighborhood status and individual status jointly shape geographic mobility in the United States. Drawing on restricted-use American Community Survey data, we conceptualize neighborhood status as the relative standing of a census tract's median family income compared to demographically similar reference neighborhoods, and individual status as a household's relative income rank within its tract. Building on comparison theory and status inconsistency perspectives, we test whether mismatches between neighborhood and individual status influence short-distance (within-county) and long-distance (between-county) mobility. Multinomial logistic models reveal that disadvantaged neighborhood status increases within-county mobility, particularly when paired with high individual status, supporting spatial assimilation arguments. Conversely, low individual status in high-status neighborhoods heightens mobility, consistent with relative deprivation theory rather than status signaling. Results suggest that status inconsistency plays a central role in residential decision-making and that neighborhood status primarily affects short-distance mobility. The findings advance research on stratification and internal migration by integrating relative contextual and positional mechanisms.View Full Paper PDF
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Working PaperNeighborhood Racial Status and White Out-Mobility
March 2026
Working Paper Number:
CES-26-19
Drawing on American Community Survey data, this study examines how whites' relative socioeconomic standing vis-'-vis nonwhite neighbors affects the association between minority presence and white out-mobility. Moving beyond the racial preferences versus racial proxy debate, we integrate group competition and contact theories with status theory to conceptualize 'racial status' as whites' first-order income rank relative to the subgroup status of Black, Hispanic, and Asian residents at the census tract level. Multilevel linear probability models show that whites lacking advantaged status are generally more likely to move. However, the positive association between Black or Asian concentration and white departure is weaker among status-disadvantaged whites, while the negative association with Hispanic concentration is stronger. These patterns lend greater support to contact theory than to group competition theory. By foregrounding relative status, the study demonstrates that racial and socioeconomic mechanisms are intertwined in shaping white residential mobility.View Full Paper PDF
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Working PaperHow Do Neighborhoods and Firms Affect Intergenerational Mobility?
March 2026
Working Paper Number:
CES-26-18
We use data from the Longitudinal Employer Household Dynamics linked to the 2000 Census to study intergenerational earnings mobility in the United States. We augment the standard intergenerational transmission model relating children's log earnings to those of their parent with an additional term representing mean log parent earnings in the childhood neighborhood. The between-neighborhood intergenerational relationship is twice as strong as the within-neighborhood relationship, even after adjusting for measurement error in parents' earnings. Moreover, mean earnings of the parents in a neighborhood capture over 80% of the variation in unrestricted neighborhood effects that reflect differences in 'absolute mobility'. Next, we use an AKM framework to decompose parents', children's, and neighboring parents' earnings into person effects and establishment premiums. Children's person effects are mainly influenced by parents' and neighbors' person effects, whereas children's establishment premiums are mainly influenced by parents' and neighbors' establishment premiums. These patterns point to separate channels for human capital and access to jobs in the intergenerational transmission process. Finally, we explore the implications for the Black-white earnings gap. Neighborhoods explain 30% of the Black-white gap in children's earnings conditional on parents' earnings, operating largely through gaps in average person effects. Conditional on neighborhood average earnings, children from neighborhoods with higher Black shares achieve higher adult earnings.View Full Paper PDF
