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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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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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AI Adoption in America: Who, What, and Where
September 2023
Working Paper Number:
CES-23-48R
We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of 'superstar' cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing 'AI divide' if early patterns persist.
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The Local Origins of Business Formation
July 2023
Working Paper Number:
CES-23-34
What locations generate more business ideas, and where are ideas more likely to turn into businesses? Using comprehensive administrative data on business applications, we analyze the spatial disparity in the creation of business ideas and the formation of new employer startups from these ideas. Startups per capita exhibit enormous variation across granular units of geography. We decompose this variation into variation in ideas per capita and in their rate of transition to startups, and find that both components matter. Observable local demographic, economic, financial, and business conditions accounts for a significant fraction of the variation in startups per capita, and more so for the variation in ideas per capita than in transition rate. Income, education, age, and foreign-born share are generally strong positive correlates of both idea generation and transition. Overall, the relationship of local conditions with ideas differs from that with transition rate in magnitude, and sometimes, in sign: certain conditions (notably, the African-American share of the population) are positively associated with ideas, but negatively with transition rates. We also find a close correspondence between the actual rank of locations in terms of startups per capita and the predicted rank based only on observable local conditions ' a result useful for characterizing locations with high startup activity.
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The Transformation of Self Employment
February 2022
Working Paper Number:
CES-22-03
Over the past half-century, while self-employment has consistently accounted for around one in ten of the United States workforce, its composition has changed. Since 1970, industries with high startup capital requirements have declined from 53% of self-employment to 23%. This same time period also witnessed declines in 'hometown' local entrepreneurship and the probability of the self-employed being among top earners. Using 2016 data, we show that high startup capital requirements are linked with lower profitability at small scales. The transition away from high startup capital industries appears most closely linked to changes in small business production functions and less due to advantageous reallocation to other opportunities, growth in returns-to-scale among large businesses, or a worsening of financing conditions and debt levels.
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Business Applications as a Leading Economic Indicator?
May 2021
Working Paper Number:
CES-21-09R
How are applications to start new businesses related to aggregate economic activity? This paper explores the properties of three monthly business application series from the U.S. Census Bureau's Business Formation Statistics as economic indicators: all business applications, business applications that are relatively likely to turn into new employer businesses ('likely employers'), and the residual series -- business applications that have a relatively low rate of becoming employers ('likely non-employers'). Growth in applications for likely employers significantly leads total nonfarm employment growth and has a strong positive correlation with it. Furthermore, growth in applications for likely employers leads growth in most of the monthly Principal Federal Economic Indicators (PFEIs). Motivated by our findings, we estimate a dynamic factor model (DFM) to forecast nonfarm employment growth over a 12-month period using the PFEIs and the likely employers series. The latter improves the model's forecast, especially in the years following the turning points of the Great Recession and the COVID-19 pandemic. Overall, applications for likely employers are a strong leading indicator of monthly PFEIs and aggregate economic activity, whereas applications for likely non-employers provide early information about changes in increasingly prevalent self-employment activity in the U.S. economy.
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High Frequency Business Dynamics in the United States During the COVID-19 Pandemic
March 2021
Working Paper Number:
CES-21-06
Existing small businesses experienced very sharp declines in activity, business sentiment, and expectations early in the pandemic. While there has been some recovery since the early days of the pandemic, small businesses continued to exhibit indicators of negative growth, business sentiment, and expectations through the first week of January 2021. These findings are from a unique high frequency, real time survey of small employer businesses, the Census Bureau's Small Business Pulse Survey (SBPS). Findings from the SBPS show substantial variation across sectors in the outcomes for small businesses. Small businesses in Accommodation and Food Services have been hit especially hard relative to those Finance and Insurance. However, even in Finance and Insurance small businesses exhibit indicators of negative growth, business sentiment, and expectations for all weeks from late April 2020 through the first week of 2021. While existing small businesses have fared poorly, after an initial decline, there has been a surge in new business applications based on the high frequency, real time Business Formation Statistics (BFS). Most of these applications are for likely nonemployers that are out of scope for the SBPS. However, there has also been a surge in new applications for likely employers. The surge in applications has been especially apparent in Retail Trade (and especially Non-store Retailers). We compare and contrast the patterns from these two new high frequency data products that provide novel insights into the distinct patterns of dynamics for existing small businesses relative to new business formations.
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Entrepreneurial Teams: Diversity of Skills and Early-Stage Growth
December 2020
Working Paper Number:
CES-20-45
We use employer-employee linked data to track the employment histories of team members prior to startup formation for a full cohort of new firms in the U.S. Using pre-startup industry experience to measure skillsets, we find that startups that have founding teams with more diverse collective skillsets grow faster than peer firms in the same industries and local economies. A one standard deviation increase in teams' skill diversity is associated with an increase in five-year employment (sales) growth of 16% (10%) from the mean. The effects are stronger among startups in innovative industries and among startups facing greater ex-ante uncertainty. Moreover, the results are robust to a variety of approaches to address the endogeneity of team composition. Overall, our results suggest that teams with more diverse collective skillsets adapt their strategies more successfully in the uncertain environments faced by (innovative) startup firms.
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Founding Teams and Startup Performance
November 2019
Working Paper Number:
CES-19-32
We explore the role of founding teams in accounting for the post-entry dynamics of startups. While the entrepreneurship literature has largely focused on business founders, we broaden this view by considering founding teams, which include both the founders and the initial employees in the first year of operations. We investigate the idea that the success of a startup may derive from the organizational capital that is created at firm formation and is inalienable from the founding team itself. To test this hypothesis, we exploit premature deaths to identify the causal impact of losing a founding team member on startup performance. We find that the exogenous separation of a founding team member due to premature death has a persistently large, negative, and statistically significant impact on post-entry size, survival, and productivity of startups. While we find that the loss of a key founding team member (e.g. founders) has an especially large adverse effect, the loss of a non-key founding team member still has a significant adverse effect, lending support to our inclusive definition of founding teams. Furthermore, we find that the effects are particularly strong for small founding teams but are not driven by activity in small business-intensive or High Tech industries.
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Demographic Origins of the Startup Deficit
July 2019
Working Paper Number:
CES-19-21
We propose a simple explanation for the long-run decline in the startup rate. It was caused by a slowdown in labor supply growth since the late 1970s, largely pre-determined by demographics. This channel explains roughly two-thirds of the decline and why incumbent firm survival and average growth over the lifecycle have been little changed. We show these results in a standard model of firm dynamics and test the mechanism using shocks to labor supply growth across states. Finally, we show that a longer startup rate series imputed using historical establishment tabulations rises over the 1960-70s period of accelerating labor force growth.
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