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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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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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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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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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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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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Industry Wage Differentials: A Firm-Based Approach
August 2023
Working Paper Number:
CES-23-40
We revisit the estimation of industry wage differentials using linked employer-employee data
from the U.S. LEHD program. Building on recent advances in the measurement of employer wage premiums, we define the industry wage effect as the employment-weighted average workplace premium in that industry. We show that cross-sectional estimates of industry differentials overstate the pay premiums due to unmeasured worker heterogeneity. Conversely, estimates based on industry movers understate the true premiums, due to unmeasured heterogeneity in pay premiums within industries. Industry movers who switch to higher-premium industries tend to leave firms in the origin sector that pay above-average premiums and move to firms in the destination sector with below-average premiums (and vice versa), attenuating the measured industry effects. Our preferred estimates reveal substantial heterogeneity in narrowly-defined industry premiums, with a standard deviation of 12%. On average, workers in higher-paying industries have higher observed and unobserved skills, widening between-industry wage inequality. There are also small but systematic differences in industry premiums across cities, with a wider distribution of pay premiums and more worker sorting in cities with more highpremium firms and high-skilled workers.
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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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Comparing Measures of Earnings Instability Based on Survey and Adminstrative Reports
August 2010
Working Paper Number:
CES-10-15
In Celik, Juhn, McCue, and Thompson (2009), we found that estimated levels of earnings instability based on data from the Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP) were reasonably close to each other and to others' estimates from the Panel Study of Income Dynamics (PSID), but estimates from unemployment insurance (UI) earnings were much larger. Given that the UI data are from administrative records which are often posited to be more accurate than survey reports, this raises concerns that measures based on survey data understate true earnings instability. To address this, we use links between survey samples from the SIPP and UI earnings records in the LEHD database to identify sources of differences in work history and earnings information. Substantial work has been done comparing earnings levels from administrative records to those collected in the SIPP and CPS, but our understanding of earnings instability would benefit from further examination of differences across sources in the properties of changes in earnings. We first compare characteristics of the overall and matched samples to address issues of selection in the matching process. We then compare earnings levels and jobs in the SIPP and LEHD data to identify differences between them. Finally we begin to examine how such differences affect estimates of earnings instability. Our preliminary findings suggest that differences in earnings changes for those in the lower tail of the earnings distribution account for much of the difference in instability estimates.
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Estimating the "True" Cost of Job Loss: Evidence Using Matched Data from Califormia 1991-2000
June 2009
Working Paper Number:
CES-09-14
Estimates of the cost of job displacement from survey and administrative data differ markedly. This paper uses a unique match of data between the Displaced Worker Survey (DWS) and administrative wage records from California to examine the sources of this discrepancy. When we use similar estimation methods and account for measurement error in survey wages correlated with worker demographics, estimates of earnings losses at displacement are similar from both datasets and significantly larger than those based on the DWS alone. Also correcting for measurement errors in reported displacements suggests both sources of such estimates may yield lower bounds for the true cost of displacement.
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