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Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers

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  • Kimberly Bayard
  • Emin Dinlersoz
  • Timothy Dunne
  • John Haltiwanger
  • Javier Miranda
  • John Stevens

Abstract

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.

Suggested Citation

  • Kimberly Bayard & Emin Dinlersoz & Timothy Dunne & John Haltiwanger & Javier Miranda & John Stevens, 2018. "Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers," NBER Working Papers 24364, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24364
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    References listed on IDEAS

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    Cited by:

    1. Ufuk Akcigit & Emin Dinlersoz & Jeremy Greenwood & Veronika Penciakova, 2019. "Synergizing Ventures," 2019 Meeting Papers 36, Society for Economic Dynamics.
    2. Patel, Pankaj C., 2019. "Minimum wage and transition of non-employer firms intending to hire employees into employer firms: State-level evidence from the US," Journal of Business Venturing Insights, Elsevier, vol. 12(C).
    3. Catherine Buffington & Jason Fields & Lucia Foster, 2021. "Measuring the Impact of COVID-19 on Businesses and People: Lessons from the Census Bureau’s Experience," Working Papers 21-02, Center for Economic Studies, U.S. Census Bureau.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups

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