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Regulation, entrepreneurship, and firm size

Journal of Regulatory Economics volume 61, pages 108–134 (2022)Cite this article

Abstract

We empirically investigate the theory that regulatory growth within an industry disproportionately burdens small businesses relative to their larger competitors. Using RegData 3.0, we find that a 10% increase in industry-specific regulatory restrictions is associated with a 0.5% reduction in the number of firms regardless of firm size, but a 0.6% reduction in employment only among small firms. We also find that consecutive years of high regulatory growth amplify the associated negative effects of future regulations on the number and employment of small firms, but we find no amplifying effects for large firms. Finally, we find that higher regulatory growth rates are associated with lower job destruction rates among establishments owned by large firms. These findings are consistent with the Public Choice theory of regulation and imply that regulatory growth leads to fewer small businesses and reduced small business employment, with minimal negative impacts on large businesses.

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Data availability

The data that support the findings of this study are openly available from QuantGov at https://www.quantgov.org/download-interactively and from Census at https://www.census.gov/programs-surveys/susb/data/tables.html.

Notes

  1. See Davis et al. (1998) for more details on the Davis-Haltiwanger-Schuh transformation.

  2. Bailey and Thomas (2017) find that a 10 percent increase in industry-specific regulatory restrictions is associated with a 0.5 percent reduction in the birth rate of all firms (pooled together). They also find that a 10 percent increase in industry-specific regulatory restrictions is associated with a 0.6 percent reduction in new hires among all firms (pooled together).

  3. This rate fell to just below eight percent in the midst of the Great Recession.

  4. The authors tested three models—nearest neighbors and random forests being the other two—using five-fold cross-validation. The authors chose the logit model because it performed the best of the three.

  5. For the sake of space, we must omit many important details regarding the development and accuracy of RegData. A more detailed explanation can be found in McLaughlin and Sherouse (2019).

  6. We chose SUSB over County Business Patterns for two reasons: (1) SUSB has data at the firm and establishment levels, while CBP has data only at the establishment level (which means its data misrepresent the total number of “businesses”); and (2) the Census Bureau recommends that County Business Patterns data not be used as a time series.

  7. Although the BDS dataset contains firm startup and shutdown data, it is limited to the 4-digit NAICS industry level. This tradeoff between granularity and period coverage explains the nearly identical size of the SUSB panel (4550 observations) and BDS panel (4715 observations). However, the dynamism measures of interest are only available at the 4-digit NAICS industry level in both the SUSB and BDS. Consequently, the BDS panel used in this paper is over four times larger than the SUSB panel used by Goldschlag and Tabarrok (2018).

  8. Although SUSB includes the category 0–4 employees, the data omit nonemployer firms. This implies that the small business share of the total workforce (as opposed to workers at employer firms) is greater than indicated by the numbers listed here.

  9. Bailey and Thomas (2017) alternatively use contemporaneous and lagged measures of industry regulation and obtain nearly identical results.

  10. It is also conceivable that firms could enter (or exit) periods of inactivity, whereby all employees are temporarily laid off (or re-hired), thereby affecting industry firm counts. Likewise, changes in industry classification criteria or methodological changes by the Census Bureau could affect firm counts in rare cases.

  11. In log–log models, the dependent variable, say ln(y), is regressed on a covariate of interest, say ln(x), and other log- transformed covariates. The coefficient on ln(x) has an elasticity interpretation: it reveals the percent change in y that results from a one percent change in x. If this model is first differenced, we now regress Δln(y) on Δln(x) and the first difference of the remaining logged covariates. Note that the coefficient on Δln(x) remains unchanged by the transformation and therefore retains the same elasticity interpretation. The inclusion of fixed effects in Eq. (5) does not alter this interpretation, but we include them per our identification strategy.

  12. We have estimated Models (5) to (11) using the alternative measure of small business (i.e., fewer than 100 employees) and the estimation results are very close to those provided in Tables 4, 5, 6 and 7. Although not reported, these alternative results are available from the authors upon request.

  13. The establishment startup rate is defined as 100 times the current number of new establishments divided by the average number of existing establishments in the current and prior period.

  14. These results are not included but are available from the authors upon request.

  15. The job creation rate is defined as 100 times the current number of new jobs created by establishments divided by the average number of existing jobs at establishments in the current and prior period.

  16. Goldschlag and Tabarrok (2018) find mixed results. When regressing the job creation rate for all firms on the natural log of current regulation, they find no statistically significant association between said variables. However, when including one and two period lags of the log of regulation, they find a positive and statistically significant association between log regulations lagged one period and the startup rate. They conclude that “the results suggest that lagged regulation indices are no better able to account for the decline [in the job creation rate] than regulation at time t.”.

  17. The job destruction rate is defined as 100 times the current number of jobs eliminated by establishments divided by the average number of jobs at establishments in the current and prior period.

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Authors and Affiliations

  1. Perdue School of Business, Salisbury University, 1101 Camden Ave., Salisbury, MD, 21801, USA

    Dustin Chambers

  2. Mercatus Center at George Mason University, 3434 Washington Blvd., Arlington, VA, 22201, USA

    Dustin Chambers

  3. Mercatus Center at George Mason University, 3434 Washington Blvd., Arlington, VA, 22201, USA

    Patrick A. McLaughlin

  4. George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

    Tyler Richards

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  1. Dustin Chambers
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  2. Patrick A. McLaughlin
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Correspondence to Tyler Richards.

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Chambers, D., McLaughlin, P.A. & Richards, T. Regulation, entrepreneurship, and firm size. J Regul Econ 61, 108–134 (2022). https://doi.org/10.1007/s11149-022-09446-7

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Keywords

  • Entrepreneurship
  • Regulation
  • Regulatory accumulation
  • Small business
  • Firm size
  • Industry concentration

JEL classification

  • L51
  • L53
  • D73