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Who Hires Whom? Entrepreneurial Backgrounds and Labor Market Opportunities
July 2026
Working Paper Number:
CES-26-45
What are the implications of unequal access to entrepreneurial careers for labor markets? Using data from the U.S. Census and LinkedIn profiles, we document that entrepreneurs are significantly more likely to hire workers from similar social backgrounds (gender, race, age, education, etc.). These effects are quantitatively large across several demographic dimensions. For example, female employee share at female-founded startups is 36.4pp higher after controlling for industry-by-metro area-by-cohort fixed effects, with corresponding estimates of 51.2pp for Blacks, 37.3pp for Hispanics, and 11.3pp for non-college individuals. Large effects are present in high-growth startups, across industries and occupations, and remain stable across new firm cohorts. In addition, we find that these differences persist out to at least 20 years. We use wage data and an AKM research design to untangle whether the relative differences are driven by labor demand or labor supply effects. We find that demand drives the differences: group-specific wage decompositions show that new firms pay higher relative wages to individuals from similar backgrounds to the entrepreneur. Using these estimates, we calibrate a model of entrepreneurship with heterogeneous ability and production functions, and assess the impacts on relative wage from reducing access barriers to entrepreneurship.
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The Evolving Impact of Founders on Startup Employee Retention
March 2026
Working Paper Number:
CES-26-21
Founders are known to attract prospective employees by signaling their startup's mission, culture, and potential. But do they also shape who stays? And if so, does the founder's influence diminish as the startup matures? Using matched employer-employee data from the U.S. Census, we address these questions, especially focusing on cases of founder premature death to identify plausibly exogenous exits. We find that founder departures significantly increase employee turnover. These effects are stronger in older and larger startups. Further analyses show that the impact of founder departure is more salient among employees who had longer shared tenure or have the same sex as the founder. These patterns suggest that employees develop complementarities with founders over time'an alignment in skills, relationships, or culture'that reinforce founders' influence as startups mature.
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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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Business Owners and the Self-Employed: 33 Million (and Counting!)
September 2025
Working Paper Number:
CES-25-60
Entrepreneurs are known to be key drivers of economic growth, and the rise of online platforms and the broader 'gig economy' has led self-employment to surge in recent decades. Yet the young and small businesses associated with this activity are often absent from economic data. In this paper, we explore a novel longitudinal dataset that covers the owners of tens of millions of the smallest businesses: those without employees. We produce three new sets of statistics on the rapidly growing set of nonemployer businesses. First, we measure transitions between self-employment and wage and salary jobs. Second, we describe nonemployer business entry and exit, as well as transitions between legal form (e.g., sole proprietorship to S corporation). Finally, we link owners to their nonemployer businesses and examine the dynamics of business ownership.
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Technifying Ventures
July 2025
Working Paper Number:
CES-25-49
How do advanced technology adoption and venture capital (VC) funding impact employment and growth? An analysis of data from the US Census Bureau suggests that while both advanced technology use and VC funding matter on their own for firm outcomes, their joint presence is most strongly correlated with higher employment levels. VC presence is linked with a high increase in employment, though primarily among a limited subset of firms. In contrast, technology adoption is associated with a smaller rise in employment, yet it influences a considerably larger number of firms. A model of startups is created, focusing on decisions to use advanced technology and seek VC funding. The model is compared with firm-level data on employment, advanced technology use, and VC investment. Several thought experiments are conducted using the model. Some experiments assess the importance of advanced technology and VC in the economy. Others examine the reallocation effects across firms with different technology choices and funding sources in response to shifts in taxes and subsidies.
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Startup Dynamics: Transitioning from Nonemployer Firms to Employer Firms, Survival, and Job Creation
April 2025
Working Paper Number:
CES-25-26
Understanding the dynamics of startup businesses' growth, exit, and survival is crucial for fostering entrepreneurship. Among the nearly 30 million registered businesses in the United States, fewer than six million have employees beyond the business owners. This research addresses the gap in understanding which companies transition to employer businesses and the mechanisms behind this process. Job creation remains a critical concern for policymakers, researchers, and advocacy groups. This study aims to illuminate the transition from non-employer businesses to employer businesses and explore job creation by new startups. Leveraging newly available microdata from the U.S. Census Bureau, we seek to gain deeper insights into firm survival, job creation by startups, and the transition from non-employer to employer status.
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The Metamorphosis of Women Business Owners: A Focus on Age
November 2024
Working Paper Number:
CES-24-71
Due to their growth, increasing performance, and significant contributions to the United States economy, women-owned businesses have spurred the interest of policymakers, researchers, and advocacy groups. Using various data products from the Census Bureau's Business Demographics Program, this study examines how women business ownership changes over time by age. We find that young owners experienced growth in ownership between 2012 and 2020 and that younger employer businesses were mostly owned by women under the age of 35 in 2021. We show that among women aged 45 to 54 and those aged 55 to 64 ownership rates declined 5.5% and 4.8% between 2012 and 2020, implying an acceleration in the drop out of entrepreneurship for mid to late career age groups. We also show that older owners operate most businesses in capital-intensive industries, had more prior businesses, and higher rates of selling their most recently started businesses. Finally, we find that age groups often characterized as childbearing ages found balancing work and family as key drivers of their decision to start a business.
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Garage Entrepreneurs or just Self-Employed? An Investigation into Nonemployer Entrepreneurship
October 2024
Working Paper Number:
CES-24-61
Nonemployers, businesses without employees, account for most businesses in the U.S. yet are poorly understood. We use restricted administrative and survey data to describe nonemployer dynamics, overall performance, and performance by demographic group. We find that eventual outcome ' migration to employer status, continuing as a nonemployer, or exit ' is closely related to receipt growth. We provide estimates of employment creation by firms that began as nonemployers and become employers (migrants), estimating that relative to all firms born in 1996, nonemployer migrants accounted for 3-17% of all net jobs in the seventh year after startup. Moreover, we find that migrants' employment creation declined by 54% for the cohorts born between 1996 to 2014. Our results are consistent with increased adjustment frictions in recent periods, and suggest accessibility to transformative entrepreneurship for everyday Americans has declined.
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Household Wealth and Entrepreneurial Career Choices: Evidence from Climate Disasters
July 2024
Working Paper Number:
CES-24-39
This study investigates how household wealth affects the human capital of startups, based on U.S. Census individual-level employment data, deed records, and geographic information system (GIS) data. Using floods as a wealth shock, a regression discontinuity analysis shows inundated residents are 7% less likely to work in startups relative to their neighbors outside the flood boundary, within a 0.1-mile-wide band. The effect is more pronounced for homeowners, consistent with the wealth effect. The career distortion leads to a significant long-run income loss, highlighting the importance of self-insurance for human capital allocation.
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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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