Abstract
We examine the geographical and temporal effects of the technological changes that led to the U.S. shale oil and gas boom. We assess changes in U.S. county rates of entrepreneurship and survival rates of existing businesses across different industries in response to the innovations that led to energy development in counties with shale resources. We employ a panel difference-in-differences approach and rely on the geological determination of the location of shale resources and the unexpected innovation in shale extraction as our source of exogeneity. We find that temporal impacts obscure effects that would look small if we only examined average effects. Namely, new firm formation and sales initially decrease in boom regions, followed by a positive trend after the initial disruption. While new firm formation eventually recovers after many years, the overall impact on business dynamism is negative, suggesting that the areas most affected by this technological change may not benefit.
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Notes
Tsvetkova and Partridge (2017) relatedly focus on differential employment multiplier effects from oil and gas self-employment compared to oil and gas wage and salary employment, but they did not consider the effects on firm creation as in this paper, nor did they consider new incorporated businesses, which we cover in this study.
In terms of the shale oil boom, the main shock is due to the innovations that made hydraulic fracking economically feasible and producing the “shale revolution.” Yet, there would also be additional productivity effects as the technique matured that also enhanced oil and gas development, but these are second-order effects by comparison when considering the recent booms.
These studies tend to find economic impacts much larger than conventional direct difference-in-differences econometric methodologies. For example, estimates of the employment multiplier associated with shale development using direct difference-in-differences methodologies range from 0.75 (Tsvetkova and Partridge 2016) to 1.3 (Weinstein 2014). Hartley et al. (2015) suggest the multiplier may be as large as 3.7 (they find that as much as 125,000 total jobs may have been created as a result of drilling 5482 directional/fractured wells in Texas—using EMSI data, we know that over 34,000 direct oil and gas jobs were created during this time resulting in a multiplier of 125,000/34,000 = 3.7). This 3.7 employment multiplier is the result of their estimate of each rig creating as much as 23 jobs. Whereas, Agerton et al. (2017) estimate even higher numbers estimating each rig creates 31 jobs in the short run and 315 in the long run. Applying Agerton et al.’s (2017) results to Texas in 2011 would suggest a multiplier in the short run of 5 and nearly 50 in the long run.
We take the change in energy employment and divide by total employment in the county because county population varies so greatly that an increase of 100 oil and gas jobs matters more in areas with less population compared to high population areas. This allows us to scale the change in oil and gas employment as a share of total employment to make comparability easier.
The oil and gas production and employment used to define when a state’s boom period began came from the U.S. Energy Information Administration and Bureau of Labor Statistics.
Another recent paper (Paredes et al. 2015) measuring the impact of shale wells on the level of employment faces similar issues. Their panel fixed effect regression (and propensity score matching) approach helps address level fixed effects but not fixed effects that occur in growth rates.
The Infogroup compiles this information by first detecting businesses through numerous sources, such as over 4300 yellow and white pages, county-level public sources, utility connects and disconnects, real estate tax assessor data, and web research. It then calls every U.S. company every year. An independent audit found it is similar and in many cases higher quality to other private business-level datasets such as the National Establishment Time-Series dataset. For more information about the data, go to http://www.infogroup.com/data.
As noted above, with production and well data as used in some of the previous literature measuring the economic impact of the shale boom is more indirect and imprecise method to measure the intensity of an energy boom.
To calculate this overall effect, we take the coefficient on △energy/totemp (5.457) times the standard deviation of △energy/totemp (0.00736) and add it to the coefficient on △energy/totemp × Trend (− 0.583) times the standard deviation of △energy/totemp × Trend (0.073) times 10 years into the trend to get − 0.385 or approximately − 38.5%.
To calculate the overall effect during the boom period, we add the standard effect (− 0.385) to the coefficient of Boom Period × △energy/totemp (− 8.774) times the standard deviation of Boom Period × △energy/totemp (0.0054) and add to that the coefficient of Boom Period × △energy/totemp × Trend (0.762) times the standard deviation of Boom Period × △energy/totemp × Trend (0.0687) times 10 to get 0.091 or approximately 9%.
To calculate this effect, we take the coefficient on △in Industrial Mix (0.0085) times the standard deviation of △in Industrial Mix (4.562) to get 0.0388 or approximately 3.9%.
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Partridge, M., Rohlin, S.M. & Weinstein, A.L. Firm formation and survival in the shale boom. Small Bus Econ 55, 975–996 (2020). https://doi.org/10.1007/s11187-019-00162-9
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DOI: https://doi.org/10.1007/s11187-019-00162-9