We examine how migrant workers impact firm performance using administrative data from the United States. Exploiting an unexpected change in firms' likelihood of securing low-wage workers through the H-2B visa program, we find limited crowd-out of other forms of employment and no impact on average pay at the firm. Yet, access to H-2B workers raises firms' annual revenues and survival likelihood. Our results are consistent with the notion that guest worker programs can help address labor shortages without inflicting large losses on incumbent workers.
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The Impact of Immigration on Firms and Workers: Insights from the H-1B Lottery
April 2024
Working Paper Number:
CES-24-19
We study how random variation in the availability of highly educated, foreign-born workers impacts firm performance and recruitment behavior. We combine two rich data sources: 1) administrative employer-employee matched data from the US Census Bureau; and 2) firm level information on the first large-scale H-1B visa lottery in 2007. Using an event-study approach, we find that lottery wins lead to increases in firm hiring of college-educated, immigrant labor along with increases in scale and survival. These effects are stronger for small, skill-intensive, and high-productivity firms that participate in the lottery. We do not find evidence for displacement of native-born, college-educated workers at the firm level, on net. However, this result masks dynamics among more specific subgroups of incumbents that we further elucidate.
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Starting Up AI
March 2024
Working Paper Number:
CES-24-09R
Using comprehensive administrative data on business applications over the period 2004- 2023, we study business applications (ideas) and the resulting startups that aim to develop AI technologies or produce goods or services that use, integrate, or rely on AI. The annual number of new AI-related business applications is stable between 2004 and 2011, but begins to rise in 2012 with further increases from 2016 onward into the Covid-19 pandemic and beyond, with a large, discrete jump in 2023. The distribution of these applications is highly uneven across states and sectors. AI business applications have a higher likelihood of becoming employer startups compared to other applications. Moreover, businesses originating from these applications exhibit higher revenue, average wage, and labor share, but similar labor productivity and lower survival rate, compared to other businesses. While it is still early in the diffusion of AI, the rapid rise in AI business applications, combined with the better performance of resulting businesses in several key outcomes, suggests a growing contribution from AI-related business formation to business dynamism.
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Minimum Wages and the Rise of the Robots
July 2026
Working Paper Number:
CES-26-42
This paper studies how minimum wage policy affects firms' adoption of automation'technologies. Using both state-level measures of robot exposure and novel plant-level'data on industrial robot imports linked to U.S. Census microdata from 1992'2021,'we show that increases in minimum wages raise the likelihood of robot adoption in'manufacturing. Our preferred identification exploits discontinuities at state borders,'comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly'8 percent relative to the mean.
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Unemployment Insurance, Wage Pass-Through, and Endogenous Take-Up
September 2025
Working Paper Number:
CES-25-59
This paper studies how unemployment insurance (UI) generosity affects reservation wages, re-employment wages, and benefit take-up. Using Benefit Accuracy Measurement (BAM) data, we estimate a cross-sectional elasticity of reservation wages with respect to weekly UI benefits of 0.014. Exploiting state variation in Pandemic Unemployment Assistance (PUA) intensity and the timing of federal supplements, we find that expanded benefits during COVID-19 increased reservation wages by 8'12 percent. Using CPS rotation data, we also document a 9 percent rise in re-employment wages for UI-eligible workers relative to ineligible workers. Over the same period, the UI take-up rate rose from roughly 30 to 40 percent; Probit estimates indicate that higher benefit levels, rather than changes in observables, account for this increase. A directed search model with an endogenous filing decision replicates these facts: generosity primarily operates through the extensive margin of take-up, which mutes the pass-through from benefits to wages.
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Do SBA Loans Create Jobs? Estimates from Universal Panel Data and Longitudinal Matching Methods
September 2012
Working Paper Number:
CES-12-27
This pape reports estimates of the effects of the Small Business Administration (SBA) 7(a) and 504 loan programs on employment. The database links a complete list of all SBA loans in these programs to universal data on all employers in the U.S. economy from 1976 to 2010. Our method is to estimate firm fixed effect regressions using matched control groups for the SBA loan recipients we have constructed by matching exactly on firm age, industry, year, and pre-loan size, plus kernel-based matching on propensity scores estimated as a function of four years of employment history and other variables. The results imply positive average effects on loan recipient employment of about 25 percent or 3 jobs at the mean. Including loan amount, we find little or no impact of loan receipt per se, but an increase of about 5.4 jobs for each million dollars of loans. When focusing on loan recipients and control firms located in high-growth counties (average growth of 22 percent), places where most small firms should have excellent growth potential, we find similar effects, implying that the estimates are not driven by differential demand conditions across firms. Results are also similar regardless of distance of control from recipient firms, suggesting only a very small role for displacement effects. In all these cases, the results pass a "pre-program" specification test, where controls and treated firms look similar in the pre-loan period. Other specifications, such as those using only matching or only regression imply somewhat higher effects, but they fail the pre-program test.
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After the Storm: How Emergency Liquidity Helps Small Businesses Following Natural Disasters
April 2024
Working Paper Number:
CES-24-20
Does emergency credit prevent long-term financial distress? We study the causal effects of government-provided recovery loans to small businesses following natural disasters. The rapid financial injection might enable viable firms to survive and grow or might hobble precarious firms with more risk and interest obligations. We show that the loans reduce exit and bankruptcy, increase employment and revenue, unlock private credit, and reduce delinquency. These effects, especially the crowding-in of private credit, appear to reflect resolving uncertainty about repair. We do not find capital reallocation away from neighboring firms and see some evidence of positive spillovers on local entry.
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Expectations versus Reality in Business Formation
February 2026
Working Paper Number:
CES-26-11
Using administrative data on 17 million U.S. business applications linked to outcomes, we compare potential entrants' expectations about employer entry and first-year employment with realizations. On average, applicants overestimate employment, mainly because many expect to enter but do not. Among those who expect and achieve entry, employment is typically underestimated. Expected employment predicts entry and realized employment, but conditional on entry realized employment rises less than one-for-one with expectations. Expectation errors are highly heterogeneous and systematically related to application characteristics and local economic conditions, and they predict near-term employment outcomes. A parsimonious model with heterogeneous priors, learning, and pre-entry selection rationalizes these patterns.
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CTRL+EXP+DEL: the Domestic Costs of U.S. Export Controls
August 2026
Working Paper Number:
CES-26-50
National-security restrictions on exports constrain exporters' market access, but empirical evidence on their domestic costs is limited. We leverage data on export transactions that includes the product classifications used to administer U.S. export controls to document a doubling in the share of U.S. exports subject to controls between 2010 and 2024 and a skewed firm-size distribution among firms that export controlled products. We then exploit the 2014 U.S. restriction on exports to Russia as a natural experiment to examine the impact of export controls on firm-level outcomes. These controls not only reduced U.S. firms' probability of exporting targeted goods to Russia but also had indirect effects: they reduced affected firms' exports of other products and to other destinations and lowered affected firms' average payroll. Our findings imply that the domestic costs of export controls extend beyond direct restrictions.
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Food Fight: U.S. Exporters' Adjustments to Russia's 2014 Agricultural Import Ban
December 2025
Working Paper Number:
CES-25-79
This paper examines the impact of Russia's 2014 food-import ban on U.S. firms that exported banned products to Russia. Using confidential customs transaction data, we implement triple-difference and dosage-response approaches to identify how firms adjust to the sudden loss of a market. Following the ban, treated firms experienced a 30 percentage-point decrease in the probability of exporting banned food to Russia relative to control firms. However, there is substantial heterogeneity by pre-ban reliance on the Russian market: heavily reliant firms were significantly less likely to survive once the ban was in place, and survivors experienced large reductions in revenue (19%) and total export value (49%) for each standard deviation increase in Russian market exposure. We find evidence of export redirection to neighboring countries, though it is insufficient to offset losses. Any negative impacts on survivors dissipate by five years post-ban.
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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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