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.
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Pay, Employment, and Dynamics of Young Firms
July 2019
Working Paper Number:
CES-19-23
Why do young firms pay less? Using confidential microdata from the US Census Bureau, we find lower earnings among workers at young firms. However, we argue that such measurement is likely subject to worker and firm selection. Exploiting the two-sided panel nature of the data to control for relevant dimensions of worker and firm heterogeneity, we uncover a positive and significant young-firm pay premium. Furthermore, we show that worker selection at firm birth is related to future firm dynamics, including survival and growth. We tie our empirical findings to a simple model of pay, employment, and dynamics of young firms.
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Unemployment 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.
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Abandoning the Sinking Ship: The Composition of Worker Flows Prior to Displacement
August 2002
Working Paper Number:
tp-2002-11
declines experienced by workers several years before displacement occurs. Little attention, however,
has been paid to other changes in compensation and employment in firms prior to the actual
displacement event. This paper examines changes in the composition of job and worker flows
before displacement, and compares the "quality" distribution of workers leaving distressed firms to
that of all movers in general.
More specifically, we exploit a unique dataset that contains observations on all workers over
an extended period of time in a number of US states, combined with survey data, to decompose
different jobflow statistics according to skill group and number of periods before displacement.
Furthermore, we use quantile regression techniques to analyze changes in the skill profile of workers
leaving distressed firms. Throughout the paper, our measure for worker skill is derived from
person fixed effects estimated using the wage regression techniques pioneered by Abowd, Kramarz,
and Margolis (1999) in conjunction with the standard specification for displaced worker studies
(Jacobson, LaLonde, and Sullivan 1993).
We find that there are significant changes to all measures of job and worker flows prior to
displacement. In particular, churning rates increase for all skill groups, but retention rates drop
for high-skilled workers. The quantile regressions reveal a right-shift in the distribution of worker
quality at the time of displacement as compared to average firm exit flows. In the periods prior
to displacement, the patterns are consistent with both discouraged high-skilled workers leaving the
firm, and management actions to layoff low-skilled workers.
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Is it Who You Are, Where You Work, or With Whom You Work? Reassessing the Relationship Between Skill Segregation and Wage Inequality
June 2002
Working Paper Number:
tp-2002-10
In a recent paper, Kremer & Maskin (QJE, forthcoming) develop an assignment model in
which increases in the dispersion and mean of the skill distribution can lead simultaneously
to increases in wage inequality and skill segregation. They then present evidence that,
concurrent with rising wage inequality, wage segregation increased for production workers in
the United States between 1975 and 1986. My paper argues that relying on wages as a proxy
for skill may be problematic. Using a newly developed longitudinal dataset linking virtually
the entire universe of workers in the state of Illinois to their employers, I decompose wages
into components due, not only to person and firm heterogeneity, but also to the characteristics
of their co-workers. Such "co-worker effects" capture the impact of a weighted sum of the
characteristics of all workers in a firm on each individual employee's wage. While rising wage
segregation can result from greater skill segregation, it may also be due to changes in the
variance of co-worker effects in the economy, or to changes in the covariance between the
person, firm, and co-worker components of wages.
Due to the limited availability of demographic information on workers, I rely on the
person specific component of wages to proxy for co-worker "skills." Because these person
effects are unknown ex ante, I implement an iterative estimation approach where they are
first obtained from a preliminary regression that excludes any role for co-workers. Because
virtually all person and firm effects are identified, the approach yields consistent estimates
of the co-worker parameters. My estimates imply that a one standard deviation increase
in both a firm's average person effect and experience level is associated, on average, with
wage increases of 3% to 5%. Firms that increase the wage premia they pay workers appear
to do so in conjunction with upgrading worker quality. Interestingly, the average effect
masks considerable variation in the relative importance of co-workers across industries. After
allowing the co-worker parameters to vary across 2 digit industries, I find that industry
average co-worker effects explain 26% of observed inter-industry wage differentials. Finally,
I decompose the overall distribution of wages into components due to persons, firms, and coworkers.
While co-worker effects do indeed serve to exacerbate wage inequality, the tendency
for high and low skilled workers to sort non-randomly into firms plays a considerably more
prominent role.
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The Distributional Effects of Minimum Wages: Evidence from Linked Survey and Administrative Data
March 2018
Working Paper Number:
carra-2018-02
States and localities are increasingly experimenting with higher minimum wages in response to rising income inequality and stagnant economic mobility, but commonly used public datasets offer limited opportunities to evaluate the extent to which such changes affect earnings growth. We use administrative earnings data from the Social Security Administration linked to the Current Population Survey to overcome important limitations of public data and estimate effects of the minimum wage on growth incidence curves and income mobility profiles, providing insight into how cross-sectional effects of the minimum wage on earnings persist over time. Under both approaches, we find that raising the minimum wage increases earnings growth at the bottom of the distribution, and those effects persist and indeed grow in magnitude over several years. This finding is robust to a variety of specifications, including alternatives commonly used in the literature on employment effects of the minimum wage. Instrumental variables and subsample analyses indicate that geographic mobility likely contributes to the effects we identify. Extrapolating from our estimates suggests that a minimum wage increase comparable in magnitude to the increase experienced in Seattle between 2013 and 2016 would have blunted some, but not nearly all, of the worst income losses suffered at the bottom of the income distribution during the Great Recession.
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Displaced workers, early leavers, and re-employment wages
November 2002
Working Paper Number:
tp-2002-18
In this paper, we lay out a search model that takes explicitly into account the
information flow prior to a mass layoff. Using universal wage data files that allow
us to identify individuals working with healthy and displacing firms both at
the time of displacement as well as any other time period, we test the predictions
of the model on re-employment wage differentials. Workers leaving a "distressed"
firm have higher re-employment wages than workers who stay with the
distressed firm until displacement. This result is robust to the inclusion of controls
for worker quality and unobservable firm characteristics.
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Firm Dynamics and Assortative Matching
May 2014
Working Paper Number:
CES-14-25
I study the relationship between firm growth and the characteristics of newly hired workers. Using Census microdata I obtain a novel empirical result: when a given firm grows faster it hires workers with higher past wages. These results suggest that productive, fast-growing firms tend to hire more productive workers, a form of positive assortative matching. This contrasts with prior research that has found negligible or negative sorting between workers and firms. I present evidence that this difference arises because previous studies have focused on cross-sectional comparisons across firms and industries, while my results condition on firm characteristics (e.g. size, industry, or firm fixed effects). Motivated by the empirical findings I develop a search model with heterogeneous workers and firms. The model is the first to study worker-firm sorting in an environment with worker heterogeneity, firm productivity shocks, multi-worker firms, and search frictions. Despite this richness the model is tractable, allowing me to characterize assortative matching, compositional dynamics and other properties analytically. I show that the model reproduces the positive firm growth-quality of hires correlation when worker and firm types are strong complements in production (i.e. the production function is strictly log-supermodular).
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Cyclical Reallocation of Workers Across Employers by Firm Size and Firm Wage
June 2015
Working Paper Number:
CES-15-13
Do the job-to-job moves of workers contribute to the cyclicality of employment growth at different types of firms? In this paper, we use linked employer-employee data to provide direct evidence on the role of job-to-job flows in job reallocation in the U.S. economy. To guide our analysis, we look to the theoretical literature on on-the-job search, which predicts that job-to-job flows should reallocate workers from small to large firms. While this prediction is not supported by the data, we do find that job-to-job moves generally reallocate workers from lower paying to higher paying firms, and this reallocation of workers is highly procyclical. During the Great Recession, this firm wage job ladder collapsed, with net worker reallocation to higher wage firms falling to zero. We also find that differential responses of net hires from non-employment play an important role in the patterns of the cyclicality of employment dynamics across firms classified by size and wage. For example, we find that small and low wage firms experience greater reductions in net hires from non-employment during periods of economic contractions.
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The Measurement of Human Capital in the U.S. Economy
April 2002
Working Paper Number:
tp-2002-09
We develop a new approach to measuring human capital that permits the distinction of both observable
and unobservable dimensions of skill by associating human capital with the portable part
of an individual's wage rate. Using new large-scale, integrated employer-employee data containing
information on 68 million individuals and 3.6 million firms, we explain a very large proportion
(84%) of the total variation in wages rates and attribute substantial variation to both individual
and employer heterogeneity. While the wage distribution remained largely unchanged between
1992-1997, we document a pronounced right shift in the overall distribution of human capital.
Most workers entering our sample, while less experienced, were otherwise more highly skilled, a
difference which can be attributed almost exclusively to unobservables. Nevertheless, compared
to exiters and continuers, entrants exhibited a greater tendency to match to firms paying below
average internal wages. Firms reduced employment shares of low skilled workers and increased
employment shares of high skilled workers in virtually every industry. Our results strongly suggest
that the distribution of human capital will continue to shift to the right, implying a continuing
up-skilling of the employed labor force.
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Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files
January 2017
Working Paper Number:
CES-17-34
Commuting flows and workplace employment data have a wide constituency of users including urban and regional planners, social science and transportation researchers, and businesses. The U.S. Census Bureau releases two, national data products that give the magnitude and characteristics of home to work flows. The American Community Survey (ACS) tabulates households' responses on employment, workplace, and commuting behavior. The Longitudinal Employer-Household Dynamics (LEHD) program tabulates administrative records on jobs in the LEHD Origin-Destination Employment Statistics (LODES). Design differences across the datasets lead to divergence in a comparable statistic: county-to-county aggregate commute flows. To understand differences in the public use data, this study compares ACS and LEHD source files, using identifying information and probabilistic matching to join person and job records. In our assessment, we compare commuting statistics for job frames linked on person, employment status, employer, and workplace and we identify person and job characteristics as well as design features of the data frames that explain aggregate differences. We find a lower rate of within-county commuting and farther commutes in LODES. We attribute these greater distances to differences in workplace reporting and to uncertainty of establishment assignments in LEHD for workers at multi-unit employers. Minor contributing factors include differences in residence location and ACS workplace edits. The results of this analysis and the data infrastructure developed will support further work to understand and enhance commuting statistics in both datasets.
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