Is the labor market for US researchers experiencing the best or worst of times? This paper analyzes the market for recently minted Ph.D. recipients using supply-and-demand logic and data linking graduate students to their dissertations and W2 tax records. We also construct a new dissertation-industry 'relevance' measure, comparing dissertation and patent text and linking patents to assignee firms and industries. We find large disparities across research fields in placement (faculty, postdoc, and industry positions), earnings, and the use of specialized human capital. Thus, it appears to simultaneously be a good time for some fields and a bad time for others.
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Scientific Talent Leaks Out of Funding Gaps
February 2024
Working Paper Number:
CES-24-08
We study how delays in NIH grant funding affect the career outcomes of research personnel. Using comprehensive earnings and tax records linked to university transaction data along with a difference-in-differences design, we find that a funding interruption of more than 30 days has a substantial effect on job placements for personnel who work in labs with a single NIH R01 research grant, including a 3 percentage point (40%) increase in the probability of not working in the US. Incorporating information from the full 2020 Decennial Census and data on publications, we find that about half of those induced into nonemployment appear to permanently leave the US and are 90% less likely to publish in a given year, with even larger impacts for trainees (postdocs and graduate students). Among personnel who continue to work in the US, we find that interrupted personnel earn 20% less than their continuously-funded peers, with the largest declines concentrated among trainees and other non-faculty personnel (such as staff and undergraduates). Overall, funding delays account for about 5% of US nonemployment in our data, indicating that they have a meaningful effect on the scientific labor force at the national level.
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Measuring the Characteristics and Employment Dynamics of U.S. Inventors
September 2022
Working Paper Number:
CES-22-43
Innovation is a key driver of long run economic growth. Studying innovation requires a clear view of the characteristics and behavior of the individuals that create new ideas. A general lack of rich, large-scale data has constrained such analyses. We address this by introducing a new dataset linking patent inventors to survey, census, and administrative microdata at the U.S. Census Bureau. We use this data to provide a first look at the demographic characteristics, employer characteristics, earnings, and employment dynamics of inventors. These linkages, which will be available to researchers with approved access, dramatically increases the scope of what can be learned about inventors and innovative activity.
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Double-Pane Glass Ceiling: Commercial Engagement and the Female-Male Earnings Gap for Faculty
September 2025
Working Paper Number:
CES-25-68
I use administrative data from universities (UMETRICS) linked to the universe of confidential W-2 and 1040-C tax records to measure faculty commercial engagement and its role in female-male earnings gaps. Female faculty are 20 percentage points less likely to engage commercially, with the entire gap driven by self-employment. The raw earnings gap is $63,000 on a base of $162,000 and non-university earnings account for $18,000 (29 percent) of this total. Thus, while university pay explains most of the gap, commercial engagement substantially amplifies it. Earnings gaps appear in all components of non-university pay ' self-employment, and work for incumbent, young/startup, high-tech, and non-high-tech firms ' and remain large, though attenuated, after controlling publications, patents, field, university, scientific resources, age, marital status, childbearing, and demographics. Gaps widen as faculty move up the earnings distribution, and commercial engagement becomes a larger contributor. Men and women engage with similar industries, but men earn more in all shared industries.
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University Innovation, Local Economic Growth, and Entrepreneurship
June 2012
Working Paper Number:
CES-12-10
Universities, often situated at the center of innovative clusters, are believed to be important drivers of local economic growth. This paper identifies the extent to which U.S. universities stimulate nearby economic activity using the interaction of a national shock to the spread of innovation from universities - the Bayh-Dole Act of 1980 - with pre-determined variation both within a university in academic strengths and across universities in federal research funding. Using longitudinal establishment-level data from the Census, I find that longrun employment and payroll per worker around universities rise particularly rapidly after Bayh-Dole in industries more closely related to local university innovative strengths. The impact of
university innovation increases with geographic proximity to the university. Counties surrounding universities that received more pre-Bayh-Dole federal funding - particularly from the Department of Defense and the National Institutes of Health - experienced faster employment growth after the law. Entering establishments - in particular multi-unit firm expansions - over the period from 1977 to 1997 were especially important in generating long-run employment growth, while incumbents experienced modest declines, consistent with creative destruction. Suggestive of their complementarities with universities, large establishments contributed more substantially to the total 20-year growth effect than did small establishments.
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Business Dynamic Statistics of Innovative Firms
January 2017
Working Paper Number:
CES-17-72
A key driver of economic growth is the reallocation of resources from low to high productivity activities. Innovation plays an important role in this regard by introducing new products, services, and business methods that ultimately lead to increased productivity and rising living standards. Traditional measures of innovation, particularly those based on aggregate inputs, are increasingly unable to capture the breadth and depth of innovation in modern economies. In this paper, we describe an effort at the
US Census Bureau, the Business Dynamics Statistics of Innovative Firms (BDS-IF) project, which aims to address these challenges by extending the Business Dynamics Statistics data to include new measures of innovative activity. The BDS-IF project will produce measures of firm, establishment, and employment flows by firm age, firm size, and industry for the subset of firms engaged in activities related to innovation. These activities include patenting and trademarking, the employment of STEM workers, and R&D expenditures. The exibility of the underlying data infrastructure allows this measurement agenda to be extended to include copyright activity, management practices, and high growth firms.
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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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Estimating the Local Productivity Spillovers from Science
January 2017
Working Paper Number:
CES-17-56
We estimate the local productivity spillovers from science by relating wages and real estate
prices across metros to measures of scienti c activity in those metros. We address three fundamental challenges: (1) factor input adjustments using wages and real estate prices, along with Shepards Lemma, to estimate changes metros' productivity, which must equal changes in unit production cost; (2) unobserved differences in metros/causality using a share shift index that exploits historic variation in the mix of research in metros interacted with trends in federal funding for specific fields as an instrument; (3) unobserved differences in workers using data on the states in which people are born. Our estimates show a strong positive relationship between wages and scientifc research and a weak positive relationship for real estate prices. Overall, we estimate high rate of return to research.
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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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Rising Import Tariffs, Falling Export Growth: When Modern Supply Chains Meet Old-Style Protectionism
January 2020
Working Paper Number:
CES-20-01
We examine the impacts of the 2018-2019 U.S. import tariff increases on U.S. export growth through the lens of supply chain linkages. Using 2016 confidential firm-trade linked data, we document the implied incidence and scope of new import tariffs. Firms that eventually faced tariff increases on their imports accounted for 84% of all exports and represented 65% of manufacturing employment. For all affected firms, the implied cost is $900 per worker in new duties. To estimate the effect on U.S. export growth, we construct product-level measures of import tariff exposure of U.S. exports from the underlying firm micro data. More exposed products experienced 2 percentage point lower growth relative to products with no exposure. The decline in exports is equivalent to an ad valorem tariff on U.S. exports of almost 2% for the typical product and almost 4% for products with higher than average exposure.
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Trade Liberalization and Labor-Market Outcomes: Evidence from US Matched Employer-Employee Data
September 2022
Working Paper Number:
CES-22-42
We use matched employer-employee data to examine outcomes among workers initially employed within and outside manufacturing after trade liberalization with China. We find that exposure to this shock operates predominantly through workers' counties (versus industries), that larger own industry and downstream exposure typically reduce relative earnings, and that greater upstream exposure often raises them. The latter is particularly important outside manufacturing: while we find substantial and persistent predicted declines in relative earnings among manufacturing workers, those outside manufacturing are generally predicted to experience relative earnings gains. Investigation of employment reactions indicates they account for a small share of the earnings effect.
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