The business entry literature typically observes firms only at the first hire. We provide a new perspective using linked administrative microdata tracking the universe of U.S. business applications and their transition into employer firms. We model entry as a two-stage process: pursuit of a business idea (proxied by a business application) and implementation (transition). Results show these margins are distinct and associate differently with local conditions. While both margins matter, high-startup locations are characterized by high application intensity, whereas low-startup locations exhibit low transition rates, suggesting geographic disparities in entry arise from different dynamics at each stage of the entrepreneurial process.
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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 Formation: A Tale of Two Recessions
January 2021
Working Paper Number:
CES-21-01
The trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and COVID-19 Recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 Recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise but this is dampened by a change in the composition of applications in 2020 towards applications that are more likely to be nonemployers.
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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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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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Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers
December 2018
Working Paper Number:
CES-18-52
This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the 'Business Formation Statistics (BFS),' that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.
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How Firms Respond to Business Cycles: The Role of Firm Age and Firm Size
June 2013
Working Paper Number:
CES-13-30
There remains considerable debate in the theoretical and empirical literature about the differences in the cyclical dynamics of firms by firm size. This paper contributes to the debate in two ways. First, the key distinction between firm size and firm age is introduced. The evidence presented in this paper shows that young businesses (that are typically small) exhibit very different cyclical dynamics than small/older businesses. The second contribution is to present evidence and explore explanations for the finding that young/small businesses were hit especially hard in the Great Recession. The collapse in housing prices accounts for a significant part of the large decline of young/small businesses in the Great Recession.
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The Geographic Concentration of New Firm Formation and Human Capital: Evidence from the Cities
February 2003
Working Paper Number:
CES-03-05
The role of education and human capital externalities is a key variable in theories of economic growth. However, the mechanism by which these externalities are realized has not been fully investigated. We examine the relationship between area differences in the levels of human capital and subsequent differences in new firm start-up rates. Firm start-ups are usually based on an innovation (in product, process, or market) that derives from utilization of new knowledge. We find that the new firm start-up rates in areas that function as integrated labor and consumer markets (city plus surrounding commuter area) are (1) positively related to the share of adults with college degrees, and also (2) positively related to higher levels of existing establishments in the same industry and area sector. The finding that higher concentrations of existing establishments in the same industry segment were strongly associated with higher startup rates suggests that spillover of relevant knowledge from other local business owners/managers and researchers within each industry contributes to greater innovation and growth in the area.
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Extending Place-Based Innovation Policy to Rural Areas: Using Oaxaca-Blinder Decomposition to Identify Endowment Deficiencies
July 2026
Working Paper Number:
CES-26-48
Place-based policy is premised on the idea that productive investments that address endowment deficiencies may release latent comparative advantages, stimulating growth while reducing regional disparities. Extending place-based policy to the domain of innovation is conceptually challenging given conventional wisdom that settlement size is the principal determinant of agglomeration economies that fuels innovation. However, growing concern that more places are being left behind by the innovation economy has prompted greater interest in ways to expand the geography of innovation. Using confidential firm-level data from the 2018 Annual Business Survey, this study examines differences in self-reported innovation activity between firms in rural and urban counties to identify possible endowment deficiencies. We do this by employing the Oaxaca-Blinder decomposition method to quantify the urban-rural innovation gap to better understand the relative contributions of firm characteristics, owner characteristics, and county-level factors.
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The Role of Start-Ups in StructuralTransformation
January 2016
Working Paper Number:
CES-16-38
The U.S. economy has been going through a striking structural transformation'the secular reallocation of employment across sectors'over the past several decades. We propose a decomposition framework to assess the contributions of various margins of firm dynamics to this shift. Using firm-level data, we find that at least 50 percent of the adjustment has been taking place along the entry margin, owing to sectors receiving shares of start-up employment that differ from their overall employment shares. The rest is mostly the result of life cycle differences across sectors. Declining overall entry has a small but growing effect of dampening structural transformation.
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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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