Employment and output effects of federal regulations on small business

• Published date: 01 October 2023
• Author: Dustin Chambers,Jang‐Ting Guo
• Date: 01 October 2023
• DOI: http://doi.org/10.1111/1468-0106.12353

ORIGINAL MANUSCRIPT

Employment and output effects of federal

regulations on small business

Dustin Chambers

1

| Jang-Ting Guo

2

1

Department of Economics and Finance,

Salisbury University, Salisbury, Maryland

2

Department of Economics, University of

California, Riverside, California

Correspondence

Jang-Ting Guo, Department of

Economics, University of California,

Sproul Hall 3133, Riverside, CA 92521,

USA.

Email: guojt@ucr.edu

Abstract

This paper examines the disparate impact of US federal

regulations on small businesses. Using a two-sector

dynamic general equilibrium model, we obtained two

implications of higher regulation on small firms that

have yet to be empirically tested in the published litera-

ture. First, as regulations increase, small firms’share of

employment shrinks. Second, as regulations rise, small

firms’share of total output falls. Using a panel of

industry-specific US regulatory restrictions, we found

that a 10% increase in federal regulations was associ-

ated with an approximate 0.8% reduction in small

firms’share of industry employment and a nearly 1.5%

decline in small firms’share of industry output.

KEYWORDS

dynamic general equilibrium, firm size, industry concentration,

regulation, small business

1|INTRODUCTION

It is well known that tracing the economic impact of federal regulations on households and

firms is a difficult and contentious task. Well-designed regulations may enhance social welfare

by reducing negative externalities and/or correcting market failures, while poorly conceived

regulations (“red tape”) may yield few if any benefits. Nonetheless, business regulations impose

additional compliance costs on firms. For example, Dawson & Seater, (2013) report that

between 1949 and 2011, federal regulations resulted in an accumulated loss of goods and ser-

vices totaling US$38.8 trillion. Of additional concern, there is strong reason to suspect that these

*We would like to thank Jin-Tan Liu (Editor), an Associate Editor, and an anonymous referee for helpful comments

and suggestions.

Received: 13 January 2020 Revised: 5 October 2020 Accepted: 17 December 2020

DOI: 10.1111/1468-0106.12353

Pac Econ Rev. 2023;28:503–518. wileyonlinelibrary.com/journal/paer © 2021 John Wiley & Sons Australia, Ltd 503

high costs have disproportionately burdened smaller firms, despite the enactment of federal

laws that promote partial or complete small business exemptions (e.g. the Regulatory Flexibility

Act of 1980 and the Small Business Regulatory Enforcement Fairness Act of 1996). Crain and

Crain (2014) estimate that small businesses (with fewer than 50 employees) faced average com-

pliance costs of US$11,724 per employee as compared to US$9,083 for large businesses (with

more than 100 employees).

1

Given the importance of small business as a source of economic dynamism, innovation, job

growth, and social mobility, it is surprising that few academic studies have investigated the out-

sized impact of regulations on these critically important businesses. Moreover, previous

research that has examined this topic is primarily empirical, typically lacking formal theoretical

models to motivate its regression specifications or results. Therefore, our paper seeks to partially

fill this gap in the literature by analysing a two-sector dynamic general equilibrium model that

generates empirically testable predictions regarding the disparate impact of federal regulations

on small and large firms. Consistent with our model, this paper presents empirical evidence

that higher regulations reduce both small firms’share of employment and output within the

US economy.

Although it has been long understood that economies of scale in regulatory compliance

costs may give larger firms an advantage over their smaller competitors, a lack of industry-

specific regulation data has hampered the empirical examin ation of this topi c. Early

research either relied on crude proxies for the level of federal regulation like page counts in

the Code of Federal Regulations (CFR) (see, e.g., Dawson & Seater, 2013) or on potentially

biased feedback from surveys sent to small business owners, as noted by Kitching, Hart, and

Wilson (2015). Fortunately, we are able to empirically test the effects of regulations on small

businesses by utilizing a relatively new database called RegData, which was constructed

using machine learning algorithms that mined the CFR for language consistent with regula-

tions and probabilistically matched these regulations with the NAICS-coded industries to

which they most likely apply (see McLaughlin & Sherouse, 2017 for details). Indeed, several

recent studies have used RegData to examine the general impact of federal regulations on

entrepreneurship, with conflicting results. Bailey and Thomas (2017)findthatgreater

industry-specific regulations are associated with a reduction in the entry of new firms, with

the greatest impact affecting smaller firms. Similarly, Chambers, McLaughlin, and

Richards (2018) find that an increase in industry-specific regulations is associated with a

reductioninboththenumberandtheemployment of small firms, whereas large firms (with

500 or more employees) are unaffected. Gutierrez and Philippon (2019) demonstrate that

lobbying and regulations are largely responsible for the decline in the e lasticity of entr y with

repect to Tobin's Q a nd that regulati ons have reduced bo th the entry and gro wth of small

firms relative to th eir larger compe titors. In a notabl e departure from th e abovementione d

papers, Goldschlag and Tabarrok (2018) modify their dependent variables (new firm forma-

tion and hiring) by way of the Davis–Haltiwanger–Schuh transformation, which is claimed

to be a more robust measure of dynamism.

2

The resulting regression models, despite using a

right-hand-side structure very similar to that in Bailey and Thomas (2017)andChambers

et al. (2018) and the same underlying data sources (i.e., RegData and the Census of

U.S. Business), fail to find a statistically significant association between federal regulations

and the transformed measures of new firm formation or hiring. This lack of consensus,

which may result from differences in the regression models’dependent variables, under-

scores the need for a theory to generate testable empirical hypotheses, which, in turn, may

provide guidance for appropriate empirical specifications.

504 CHAMBERS AND GUO