This paper examines the labor market returns to earning industry-certified credentials in the manufacturing sector. Specifically, we are interested in estimating the impact of a manufacturing credential on wages, probability of employment, and probability of employment specifically in the manufacturing sector post credential attainment. We link students who earned manufacturing credentials to their enrollment and completion records, and then further link them to their IRS tax records for earnings and employment (Form W2 and 1040) and to the American Community Survey and decennial census for demographic information. We present earnings trajectories for workers with credentials by type of credential, industry of employment, age, race and ethnicity, gender, and state. To obtain a more causal estimate of the impact of a credential on earnings, we implement a coarsened exact matching strategy to compare outcomes between otherwise similar people with and without a manufacturing credential. We find that the attainment of a manufacturing industry credential is associated with higher earnings and a higher likelihood of labor market participation when we compare attainers to a group of non-attainers who are otherwise similar.
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Who Scars the Easiest? College Quality and the Effects of Graduating into a Recession
September 2024
Working Paper Number:
CES-24-47
Graduating from college into a recession is associated with earnings losses, but less is known about how these effects vary across colleges. Using restricted-use data from the National Survey of College Graduates, we study how the effects of graduating into worse economic conditions vary over college quality in the context of the Great Recession. We find that earnings losses are concentrated among graduates from relatively high-quality colleges. Key mechanisms include substitution out of the labor force and into graduate school, decreased graduate degree completion, and differences in the economic stability of fields of study between graduates of high- and low-quality colleges.
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The Gender Pay Gap and Its Determinants Across the Human Capital Distribution
June 2023
Working Paper Number:
CES-23-31R
This paper links American Community Survey data and postsecondary transcript records to examine how the gender pay gap varies across the distribution of education credentials for a sample of 2003-2013 graduates. Although recent literature emphasizes gender inequality among the most-educated, we find a smaller gender pay gap at higher education levels. Field-of-degree and occupation effects explain most of the gap among top bachelor's graduates, while work hours and unobserved channels matter more for less-competitive bachelor's, associate, and certificate graduates. We develop a novel decomposition of the child penalty to examine the role of children in explaining these results.
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Does Federally-Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms
January 2018
Working Paper Number:
CES-18-02
We study the job training provided under the US Workforce Investment Act (WIA) to adults and dislocated workers in two states. Our substantive contributions center on impacts estimated non-experimentally using administrative data. These impacts compare WIA participants who do and do not receive training. In addition to the usual impacts on earnings and employment, we link our state data to the Longitudinal Employer-Household Dynamics (LEHD) data at the US Census Bureau, which allows us to estimate impacts on the characteristics of the firms at which participants find employment. We find moderate positive impacts on employment, earnings and desirable firm characteristics for adults, but not for dislocated workers. Our primary methodological contribution consists of assessing the value of the additional conditioning information provided by the LEHD relative to the data available in state Unemployment Insurance (UI) earnings records. We find that value to be zero.
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Virtual Charter Students Have Worse Labor Market Outcomes as Young Adults
June 2023
Working Paper Number:
CES-23-32
Virtual charter schools are increasingly popular, yet there is no research on the long-term outcomes of virtual charter students. We link statewide education records from Oregon with earnings information from IRS records housed at the U.S. Census Bureau to provide evidence on how virtual charter students fare as young adults. Virtual charter students have substantially worse high school graduation rates, college enrollment rates, bachelor's degree attainment, employment rates, and earnings than students in traditional public schools. Although there is growing demand for virtual charter schools, our results suggest that students who enroll in virtual charters may face negative long-term consequences.
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Diversity and Labor Market Outcomes in the Economics Profession
July 2022
Working Paper Number:
CES-22-26
While the lack of gender and racial diversity in economics in academia (for students and professors) is well-established, less is known about the overall placement and earnings of economists by gender and race. Understanding demand-side factors is important, as improvements in the supply side by diversifying the pipeline alone may not be enough to improve equity in the profession. Using the Survey of Earned Doctorates (SED) linked to Longitudinal Employer-Household Dynamics (LEHD) jobs data, we examine placements and earnings for economists working in the U.S. after receiving a PhD by gender and race. We find enormous dispersion in pay for economists within and across sectors that grows over time. Female PhD economists earn about 12 percent less than their male colleagues on average; Black PhD economists earn about 15 percent less than their white counterparts on average; and overall underrepresented minority PhD economists earn about 8 percent less than their white counterparts. These pay disparities are attenuated in some sectors and when controlling for rank of PhD granting institution and employer.
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School Discipline and Racial Disparities in Early Adulthood
June 2021
Working Paper Number:
CES-21-14
Despite interest in the role of school discipline in the creation of racial inequality, previous research has been unable to identify how students who receive suspensions in school differ from unsuspended classmates on key young adult outcomes. We utilize novel data to document the links between high school discipline and important young adult outcomes related to criminal justice contact, social safety net program participation, post-secondary education, and the labor market. We show that the link between school discipline and young adult outcomes tends to be stronger for Black students than for White students, and that inequality in exposure to school discipline accounts for approximately 30 percent of the Black-White disparities in young adult criminal justice outcomes and SNAP receipt.
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Universal Preschool Lottery Admissions and Its Effects on Long-Run Earnings and Outcomes
March 2023
Working Paper Number:
CES-23-09
We use an admissions lottery to estimate the effect of a universal (non-means tested) preschool program on students' long-run earnings, income, marital status, fertility and geographic mobility. We observe long-run outcomes by linking both admitted and non-admitted individuals to confidential administrative data including tax records. Funding for this preschool program comes from an Indigenous organization, which grants Indigenous students admissions preference and free tuition. We find treated children have between 5 to 6 percent higher earnings as young adults. The results are strongest for individuals from the lower half of the household income distribution in childhood. Likely mechanisms include high-quality teachers and curriculum.
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An Evaluation of the Gender Wage Gap Using Linked Survey and Administrative Data
November 2020
Working Paper Number:
CES-20-34
The narrowing of the gender wage gap has slowed in recent decades. However, current estimates show that, among full-time year-round workers, women earn approximately 18 to 20 percent less than men at the median. Women's human capital and labor force characteristics that drive wages increasingly resemble men's, so remaining differences in these characteristics explain less of the gender wage gap now than in the past. As these factors wane in importance, studies show that others like occupational and industrial segregation explain larger portions of the gender wage gap. However, a major limitation of these studies is that the large datasets required to analyze occupation and industry effectively lack measures of labor force experience. This study combines survey and administrative data to analyze and improve estimates of the gender wage gap within detailed occupations, while also accounting for gender differences in work experience. We find a gender wage gap of 18 percent among full-time, year-round workers across 316 detailed occupation categories. We show the wage gap varies significantly by occupation: while wages are at parity in some occupations, gaps are as large as 45 percent in others. More competitive and hazardous occupations, occupations that reward longer hours of work, and those that have a larger proportion of women workers have larger gender wage gaps. The models explain less of the wage gap in occupations with these attributes. Occupational characteristics shape the conditions under which men and women work and we show these characteristics can make for environments that are more or less conducive to gender parity in earnings.
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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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Differences in Self-employment Duration by Year of Entry & Pre-entry
November 2016
Working Paper Number:
carra-2016-09
Self-employment is associated with entrepreneurship and a motivation to pursue an opportunity. Previous research indicates that people also become self-employed because of limited opportunities in the wage sector. Using a unique set of data that links the American Community Survey to Form 1040 and W-2 records, this paper extends the existing literature by examining self-employment duration for five consecutive entry cohorts, including two cohorts who entered self-employment during the Great Recession. Severely limited labor market opportunities may have driven many in the recession cohorts to enter self-employment, while those entering self-employment during the boom may have been pursuing opportunities under favorable market conditions. To more explicitly test the concept of "necessity" versus "opportunity" self-employment, we also examine the pre-entry wage labor attachment of entrants. Specifically, we ask whether an association exists between wage labor attachment and the duration of self-employment. We also explore whether the demographic/socio-economic characteristics and self-employment exit behavior of the cohorts are different, and if so, how. We find evidence consistent with the existence of "necessity" vs. "opportunity" self-employment types.
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