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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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High-Growth Firms in the United States: Key Trends and New Data Opportunities
March 2024
Working Paper Number:
CES-24-11
Using administrative data from the U.S. Census Bureau, we introduce a new public-use database that tracks activities across firm growth distributions over time and by firm and establishment characteristics. With these new data, we uncover several key trends on high-growth firms'critical engines of innovation and economic growth. First, the share of firms that are high-growth has steadily decreased over the past four decades, driven not only by falling firm entry rates but also languishing growth among existing firms. Second, this decline is particularly pronounced among young and small firms, while the share of high-growth firms has been relatively stable among large and old firms. Third, the decline in high-growth firms is found in all sectors, but the information sector has shown a modest rebound beginning in 2010. Fourth, there is significant variation in high-growth firm activity across states, with California, Texas, and Florida having high shares of high-growth firms. We highlight several areas for future research enabled by these new data.
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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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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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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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Nonemployer Statistics by Demographics (NES-D):
Exploring Longitudinal Consistency and Sub-national Estimates
December 2019
Working Paper Number:
CES-19-34
Until recently, the quinquennial Survey of Business Owners (SBO) was the only source of information for U.S. employer and nonemployer businesses by owner demographic characteristics such as race, ethnicity, sex and veteran status. Now, however, the Nonemployer Statistics by Demographics series (NES-D) will replace the SBO's nonemployer component with reliable, and more frequent (annual) business demographic estimates with no additional respondent burden, and at lower imputation rates and costs. NES-D is not a survey; rather, it exploits existing administrative and census records to assign demographic characteristics to the universe of approximately 25 million (as of 2016) nonemployer businesses.
Although only in the second year of its research phase, NES-D is rapidly moving towards production, with a planned prototype or experimental version release of 2017 nonemployer data in 2020, followed by annual releases of the series. After the first year of research, we released a working paper (Luque et al., 2019) that assessed the viability of estimating nonemployer demographics exclusively with administrative records (AR) and census data. That paper used one year of data (2015) to produce preliminary tabulations of business counts at the national level. This year we expand that research in multiple ways by: i) examining the longitudinal consistency of administrative and census records coverage, and of our AR-based demographics estimates, ii) evaluating further coverage from additional data sources, iii) exploring estimates at the sub-national level, iv) exploring estimates by industrial sector, v) examining demographics estimates of business receipts as well as of counts, and vi) implementing imputation of missing demographic values.
Our current results are consistent with the main findings in Luque et al. (2019), and show that high coverage and demographic assignment rates are not the exception, but the norm. Specifically, we find that AR coverage rates are high and stable over time for each of the three years we examine, 2014-2016. We are able to identify owners for approximately 99 percent of nonemployer businesses (excluding C-corporations), 92 to 93 percent of identified nonemployer owners have no missing demographics, and only about 1 percent are missing three or more demographic characteristics in each of the three years. We also find that our demographics estimates are stable over time, with expected small annual changes that are consistent with underlying population trends in the U.S.. Due to data limitations, these results do not include C-corporations, which represent only 2 percent of nonemployer businesses and 4 percent of receipts.
Without added respondent burden and at lower imputation rates and costs, NES-D will provide high-quality business demographics estimates at a higher frequency (annual vs. every 5 years) than the SBO.
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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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Who Gains from Creative Destruction? Evidence from High-Quality Entrepreneurship in the United States
October 2019
Working Paper Number:
CES-19-29
The question of who gains from high-quality entrepreneurship is crucial to understanding whether investments in incubating potentially innovative start-up firms will produce socially beneficial outcomes. We attempt to bring new evidence to this question by combining new aggregate measures of local area income inequality and income mobility with measures of entrepreneurship from Guzman and Stern (2017). Our new aggregate measures are generated by linking American Community Survey data with the universe of IRS 1040 tax returns. In both fixed effects and IV models using a Bartik-style instrument, we find that entrepreneurship increases income inequality. Further, we find that this increase in income inequality arises due to the fact that almost all of the individual gains associated with increased entrepreneurship accrue to the top 10 percent of the income distribution. While we find mixed evidence for small positive effects of entrepreneurship lower on the income distribution, we find little if any evidence that entrepreneurship increases income mobility.
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Gender Differences in Self-employment Duration: the Case of Opportunity and Necessity Entrepreneurs
September 2019
Working Paper Number:
CES-19-24
A strand of the self-employment literature suggests that those 'pushed' into self-employment out of necessity may perform differently from those 'pulled' into self-employment to pursue a business opportunity. While findings on self-employment outcomes by self-employed type are not unanimous, there is mounting evidence that performance outcomes differ between these two self-employed types. Another strand of the literature has found important gender differences in self-employment entry rates, motivations for entry, and outcomes. Using a unique set of data that links the American Community Survey to administrative data from Form 1040 and W-2 records, we bring together these two strands of the literature. We explore whether there are gender differences in self-employment duration of self-employed types. In particular, we examine the likelihood of self-employment exit towards unemployment versus the wage sector 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 wage labor attachment (or weeks worked in the wage sector) in the year prior to becoming self-employed. We find that, within the cohorts we examine, there are gender differences in the rate at which men and women depart self-employment for either wage work or non-participation, but that the patterns are dependent on pre self-employment wage-sector attachment and cohort effects.
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