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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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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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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Agglomerative Forces and Cluster Shapes
June 2012
Working Paper Number:
CES-12-09
We model spatial clusters of similar firms. Our model highlights how agglomerative forces lead to localized, individual connections among firms, while interaction costs generate a defined distance over which attraction forces operate. Overlapping firm interactions yield agglomeration clusters that are much larger than the underlying agglomerative forces themselves. Empirically, we demonstrate that our model's assumptions are present in the structure of technology and labor flows within Silicon Valley and its surrounding areas. Our model further identifies how the lengths over which agglomerative forces operate influence the shapes and sizes of industrial clusters; we confirm these predictions using variations across both technology clusters and industry agglomeration.
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Clusters and Entrepreneurship
September 2010
Working Paper Number:
CES-10-31
This paper examines the role of regional clusters in regional entrepreneurship. We focus on the distinct influences of convergence and agglomeration on growth in the number of start-up firms as well as in employment in these new firms in a given region-industry. While reversion to the mean and diminishing returns to entrepreneurship at the region-industry level can result in a convergence effect, the presence of complementary economic activity creates externalities that enhance incentives and reduce barriers for new business creation. Clusters are a particularly important way through which location-based complementarities are realized. The empirical analysis uses a novel panel dataset from the Longitudinal Business Database of the Census Bureau and the U.S. Cluster Mapping Project (Porter, 2003). Using this dataset, there is significant evidence of the positive impact of clusters on entrepreneurship. After controlling for convergence in start-up activity at the region-industry level, industries located in regions with strong clusters (i.e. a large presence of other related industries) experience higher growth in new business formation and start-up employment. Strong clusters are also associated with the formation of new establishments of existing firms, thus influencing the location decision of multiestablishment firms. Finally, strong clusters contribute to start-up firm survival.
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Matching State Business Registration Records
to Census Business Data
January 2020
Working Paper Number:
CES-20-03
We describe our methodology and results from matching state Business Registration Records (BRR) to Census business data. We use data from Massachusetts and California to develop methods and preliminary results that could be used to guide matching data for additional states. We obtain matches to Census business records for 45% of the Massachusetts BRR records and 40% of the California BRR records. We find higher match rates for incorporated businesses and businesses with higher startup-quality scores as assigned in Guzman and Stern (2018). Clerical reviews show that using relatively strict matching on address is important for match accuracy, while results are less sensitive to name matching strictness. Among matched BRR records, the modal timing of the first match to the BR is in the year in which the BRR record was filed. We use two sets of software to identify matches: SAS DQ Match and a machine-learning algorithm described in Cuffe and Goldschlag (2018). We find preliminary evidence that while the ML-based method yields more match results, SAS DQ tends to result in higher accuracy rates. To conclude, we provide suggestions on how to proceed with matching other states' data in light of our findings using these two states.
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Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain?
October 2008
Working Paper Number:
CES-08-37
Why are some places more entrepreneurial than others? We use Census Bureau data to study local determinants of manufacturing startups across cities and industries. Demo- graphics have limited explanatory power. Overall levels of local customers and suppliers are only modestly important, but new entrants seem particularly drawn to areas with many smaller suppliers, as suggested by Chinitz (1961). Abundant workers in relevant occupations also strongly predict entry. These forces plus city and industry fixed effects explain between sixty and eighty percent of manufacturing entry. We use spatial distributions of natural cost advantages to address partially endogeneity concerns.
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