Can Financing Constraints Explain the Evolution of the Firm Size Distribution?


This paper exploits a comprehensive data set on business credit decisions to examine the importance of financing constraints for the evolution of the firm size distribution. The survey of small business finances provides information on whether a firm was in need of external financing. Firms without access to external financing—either because they were denied credit or because they did not apply for credit because they expected to be denied credit—are significantly smaller. To tighten the link between financing constraints and firm dynamics, I estimate the effect of financing constraints on subsequent employment growth and find that firms without access to external financing exhibit up to 3.5 % points lower annual employment growth than do their unconstrained counterparts. These findings suggest that financing constraints are a potentially important factor for understanding firm dynamics.

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  1. 1.

    See Hall (1987), Evans (1987), and, more recently, Neumark et al. (2011). Moreover, recent studies have shown that the firm size distribution of younger firms is skewed to the right and that the size distribution becomes more symmetric with firm age (Cabral and Mata 2003; Angelini and Generale 2008).

  2. 2.

    See Cooley and Quadrini (2001), Albuquerque and Hopenhayn (2004), and Clementi and Hopenhayn (2006).

  3. 3.

    Lentz and Mortensen (2008) estimate a firm growth model with Danish data and find that positive selection of more productive firms accounts for 53 % of aggregate growth. Foster et al. (2008) argue that new (young) firms have a considerable productivity advantage.

  4. 4.

    A detailed description of the SSBF sampling procedure can be found in the methodology reports. Firms were asked to use tax data and previously were sent worksheets to answer the questions. The data and methodology reports to all surveys can be downloaded at

  5. 5.

    See US Business Dynamics Statistics, www2.census.go/ces/bds/estab/bds_e_sz_release.xls.

  6. 6.

    Establishment and firm data do not necessarily match, as firms can own more than one establishment and what constitutes a firm does not necessarily match the definition of an establishment.

  7. 7.

    Average firm age increased from 13.3 years in the SSBF 1998 to 14.3 years in the SSBF 2003, after falling from 14.5 years in the SSBF 1993. The median also increased by 1 year from 11 to 12 years. The vast majority of the firms are very small and owner-managed (94 %). Less than 30 % of the firms reported annual sales of more than $500,000. Thirty-five percent of small businesses were located in the South, 24 % in the West, 21 % in the Midwest, and 20 % in the Northeast. Roughly four out of five firms had their headquarters or main office in urban areas, 44.5 % (1998: 49.4 %) of firms are proprietorships, 8.7 % (7 %) partnerships, 31 % (23.9 %) C-Corporations, and 15.8 % (19.8 %) S-Corporations. Bitler et al. (2001) and Mach and Wolken (2006) provide descriptive statistics that summarize the financial services that were used by small businesses for the SSBF 1998 and 2003, respectively.

  8. 8.

    In 2003 the average number of employees for firms that applied for new credit lines was 13.3 (median 5), while firms that renewed credit had (on average) 17.5 employees (median 7). The same pattern holds for firm age. Applicants for new lines of credit were established (on average) 14.3 years (median 12 years) ago, while firms that renewed credit were (on average) 19.2 years (median 17 years) in business.

  9. 9.

    About 25 % of the firms provided more than one type of collateral.

  10. 10.

    For three observations, sales were not reported in the SSBF.

  11. 11.

    Sutton (1997) surveys the older literature.

  12. 12.

    Most commonly they use panel data to estimate the relationship between Tobin’s q, cash flow, and investment. See Hubbard (1998) for a dated survey. More recently, the literature has exploited variation in the violation of debt covenants and self-reported measures of financing constraints; see Campello et al. (2011) and Berrospide and Meisenzahl (2015).

  13. 13.

    The monetary and time cost of credit applications can be considerable. Banks require business and personal financial statements in the application process. Business financial statements include balance sheet information, information on accounts receivable, organizational form, and other business characteristics. Personal financial statements ask for a detailed description of assets and liabilities, including the net value of the private residence, total net worth, and previous bankruptcies. For detailed application forms, see Cavalluzzo and Wolken (2005).

  14. 14.

    To avoid ambiguities, I drop observations that report that they were “sometimes” turned down in their most recent application.

  15. 15.

    The sectoral composition between the participants and non-participants shows only minor differences.

  16. 16.

    As was pointed out above, since the 1998 survey did not record credit renewals, the pooled sample is biased against finding a systematic difference between firms that secured external finance and firms that were denied credit.

  17. 17.

    The largest p value was 0.012 for the age group 1–6 years excluding “discouraged borrowers.”

  18. 18.

    This observation also holds in all firm age group subsamples.

  19. 19.

    Townsend (1979) shows that in the presence of informational frictions, collateral is a key determinant for credit. Osotimehin and Pappadà (2010) find that access to credit is a function of net worth.

  20. 20.

    Campello et al. (2011) and Berrospide and Meisenzahl (2015) document that firms without access to their line of credit invest less.

  21. 21.

    Unfortunately, the NETS database does not provide information on why a firm went out of business; for instance, because of bankruptcy or retirement of the owner. However, if a company went out of business for reasons that were unrelated to its business, such as the retirement of the owner, then access to credit cannot explain this drop in employment, and the estimated coefficient on access to credit would be estimated to be zero. Mach and Wolken (2012) use matched SSBF-NETS data to study the effect of credit on firm survival and find a positive relationship between access to credit and firm survival.

  22. 22.

    Ono and Uesugi (2009) discuss the importance of collateral and personal guarantees for small business lending.

  23. 23.

    The results for the sample of firms with the actual number of employees known are comparable.

  24. 24.

    Osotimehin and Pappadà (2010) estimate a model of access to credit, including discouraged borrowers, on French data and find that access to credit helps to understand firm dynamics in France. This additional international evidence suggests that the results presented here hold more generally.

  25. 25.

    For instance, Lentz and Mortensen (2008) argue that positive selection of more productive firms accounts for much of aggregate firm growth patterns. Foster et al. (2008) find that new (young) firms have a considerable productivity advantage. Warusawitharana (2012) provides a simple product quality ladder model to match firm dynamics.


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I would like to thank Larry Christiano, Martin Eichenbaum, Traci Mach, Robin Prager, Missaka Warusawitharana, Larry White, and participants at the Royal Economic Society Meeting for helpful comments. All errors are mine. The opinions expressed are those of the author and do not necessarily reflect the view of the Board of Governors of the Federal Reserve System.

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Correspondence to Ralf R. Meisenzahl.

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Meisenzahl, R.R. Can Financing Constraints Explain the Evolution of the Firm Size Distribution?. Rev Ind Organ 48, 123–147 (2016).

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  • Financing constraints
  • Firm size distribution
  • Firm growth

JEL Classification

  • L11
  • L25