Abstract
Most researchers observe a negative association between regulatory accumulation and traditional measures of entrepreneurship (i.e., firm startups and job formation). However, when measuring business activity with metrics common to the dynamism literature, researchers fail to find a significant association between regulation and startup activity. After ruling out differences in unit of measure, industry aggregation, and regulation measurement, we demonstrate that differences in entrepreneurship and dynamism measurement are potentially responsible for the conflicting results. However, we do find empirical evidence that the relationship between regulations and job creation rates varies across industries. In industries with high job creation rates in the prior period, increased regulations are associated with lower job creation rates in the current period. However, the opposite is true in industries with low job creation rates in the prior period (i.e., increased regulations are associated with higher job creation rates in the current period). Depending upon the vintage of the regulation dataset (i.e., RegData 2.0), a similar pattern holds for the startup rate.
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Notes
For a detailed description of the machine learning algorithms and methodology used to construct recent versions of RegData, please see McLaughlin and Sherouse (2019). Early versions of RegData that preceded the use of machine learning algorithms are described in Al-Ubaydli and McLaughlin (2017). Machine learning algorithms were implemented in RegData 2.1 and subsequent versions.
Because the Census Bureau’s Statistics of US Businesses data only report NAICS-coded birth and death data for establishments, BT use establishment data as a proxy for firm-level behavior.
An analytical comparison of log new hires with the job creation rate produces analogous results. Log firm births in BT have no dynamism analog in GT (i.e., GT do not use an establishment death rate), and the job destruction rate in GT has no entrepreneurship analog in the BT study (i.e., BT do not examine log employment losses).
GT show in an appendix that the establishment creation rate and the firm birth rate are highly correlated.
See Al-Ubaydli and McLaughlin (2017) for an explanation of a sample Industry Regulation Index. This same baseline index was used in both studies.
RegData 2.2 is described in some detail in McLaughlin and Sherouse (2019). The machine learning algorithms described there are the same as those used to produce RegData 2.1.
Note that the dependent variable in Eq. (5) is not the growth rate of firm births (i.e., \(\mathrm{\Delta ln}({B}_{it})\)).
Dividing the coefficient on log-regulation (\(\beta\)) by 100 yields the percentage-point change in the startup rate associated with a 1% change in industry regulation.
The prior number of firms within the industry (\({F}_{it-1}\)) is predetermined by assumption, so a change in current regulation has no impact on past levels of entrepreneurship, that is, \(\frac{\partial {F}_{it-1}}{\partial \mathrm{ln}({R}_{it})}=0\). An argument can be made that some regulations are predictable (e.g., they are proposed long in advance or are part of a known policy agenda), and hence, expectations regarding future regulations may drive earlier entry and exit decisions. In such a case, Eq. (6) pertains to unanticipated regulatory changes.
It is worth noting that one cannot nest the GT and BT models and derive an augmented regression model analogous to Eq. (12) in which log firm births is the dependent variable and the independent variables consist of fixed effects, a lagged dependent variable, log regulations, and the interaction of log regulations and the lagged dependent variable.
We considered the sample average startup rate (10.16%) and added/subtracted multiples of the startup rate’s standard deviation (5.27%). Since startup rates cannot be negative, we truncated the lowest value at 0%.
At the NAICS 4-digit level, 65.9% of industry-year startup rate observations are less than 11.1%, while virtually all startup rate observations (99.3%) are below 26.7%.
We considered the sample average job creation rate (14.16%) and added/subtracted multiples of the job creation rate’s standard deviation (5.82%).
At the NAICS 4-digit level, 63.8% of industry-year job creation rate observations are less than 15.7%, while virtually all job creation rate observations (99.9%) are below 47.2%.
As a final robustness check, we augment Equation (12) by adding the lag of log establishments to the set of covariates. If BT suffers from omitted variable bias due to its use of establishment births rather than the startup rate, then the addition of this independent variable would correct the estimates reported in Table 4. The coefficient estimates of lagged log establishments are negative and statistically significant in all four cases. This is consistent with the findings of Law and McLaughlin (2021) who find strong evidence that larger industries, whether measured by number of employees or firms, tend to be more regulated than industries with fewer firms (or employees). Rather than weaken our results, the inclusion of the lag of log establishments strengthens our results. The positive association between RegData 2.2 regulations and the startup rate becomes statistically insignificant, while the remaining results retain their statistical significance and the resulting percentage point change in the startup rate (job creation rate) for a 1% increase in regulations is smaller (i.e., the curves in Figures 1B and 2 shift downward). These estimation results are not reported here but are available from the authors upon request.
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Chambers, D., McLaughlin, P.A. & Sherouse, O. Regulation, entrepreneurship, and dynamism. Empir Econ 64, 2449–2466 (2023). https://doi.org/10.1007/s00181-022-02321-6
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DOI: https://doi.org/10.1007/s00181-022-02321-6