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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
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%.
References
Acs, Z., & Armington, C. (2004). Employment growth and entrepreneurial activity in cities. Regional Studies, 38(8), 911–927. https://doi.org/10.1080/0034340042000280938.
Agerton, M., Hartley, P. R., Medlock, K. B., III, & Temzelides, T. (2017). Employment impacts of upstream oil and gas investment in the United States. Energy Economics, 62, 171–180. https://doi.org/10.1016/j.eneco.2016.12.012.
Armington, C., & Acs, Z. (2002). The determinants of regional variation in new firm formation. Regional Studies, 36(1), 33–45. https://doi.org/10.1080/00343400120099843.
Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86, 630–640 http://www.jstor.org/stable/2118216.
Bartik, T. J. (1991). Who benefits from state and local development policies? Kalamazoo, MI: W.E. Upjohn Institute.
Bertrand, O., & Zitouna, H. (2008). Domestic versus cross-border acquisitions: which impact on the target firms’ performance? Applied Economics, 40(17), 2221–2238. https://doi.org/10.1080/00036840600949397.
Betz, M., Farren, M., Lobao, L., & Partridge, M. (2015). Coal mining, economic development, and the natural resources curse. Energy Economics., (50), 105–116. https://doi.org/10.1016/j.eneco.2015.04.005.
Black, D., McKinnish, T., & Sanders, S. (2005). The economic impact of the coal boom and bust. The Economic Journal, 115(503), 449–476. https://doi.org/10.1111/j.1468-0297.2005.00996.x.
Brown, J. (2014). Production of natural gas from shale in local economies: a resource blessing or curse? Economic Review, 99(1), 119–147.
Bulte, E. & Damania, R. (2008). Resources for sale: corruption, democracy and the natural resource curse. The B.E. Journal of Economic Analysis & Policy, 8(1). doi:https://doi.org/10.2202/1935-1682.1890.
Clementi, G. L., & Palazzo, B. (2016). Entry, exit, firm dynamics, and aggregate fluctuations. American Economic Journal: Macroeconomics, 8(3), 1–41. https://doi.org/10.1257/mac.20150017.
Donegan, M., & Lowe, N. (2008). Inequality in the creative city: is there still a place for “old-fashioned” institutions? Economic Development Quarterly, 22, 46–62. https://doi.org/10.1177/0891242407310722.
Douglas, S., & Walker, A. (2017). Coal mining and the resource curse in the Eastern United States. Journal of Regional Science, 57(4), 568–590. https://doi.org/10.1111/jors.12310.
Duranton, G., & Puga, D. (2001). Nursery cities: urban diversity, process innovation, and the life cycle of products. American Economic Review, 91(5), 1454–1477. https://doi.org/10.1257/aer.91.5.1454.
Feldman, M. P., & Audretsch, D. B. (1999). Innovation in cities: science-based diversity, specialization and localized competition. European Economic Review, 43(2), 409–429. https://doi.org/10.1016/S0014-2921(98)00047-6.
Finegold, D. (1999). Creating self-sustaining, high skill ecosystems. Oxford Review of Economic Policy, 15, 60–81. https://doi.org/10.1093/oxrep/15.1.60.
Florida, R. (2005). The flight of the creative class: the new global competition for talent. London: Harper Collins.
Freeman, D. G. (2009). The ‘resource curse’ and regional US development. Applied Economics Letters, 16(5), 527–530. https://doi.org/10.1080/13504850601032107.
Glaeser, E., Rosenthal, S., & Strange, W. (2010). Urban economics and entrepreneurship. Journal of Urban Economics, 67, 1–14. https://doi.org/10.1016/j.jue.2009.10.005.
Goetz, S., & Rupasingha, A. (2009). Determinants of growth in non-farm proprietor densities in the US, 1990–2000. Small Business Economics, 32, 425–438. https://doi.org/10.1007/s11187-007-9079-5.
Goetz, S. J., Partridge, M.D., Rickman, D.S. & Majumdar, S. (2011). Sharing the gains of local economic growth: race to the top vs. race to the bottom economic development policies. Environment and Planning C: Politics and Space, 29(3). doi:https://doi.org/10.1068/c1077r.
Greenstone, M., Hornbeck, R., & Moretti, E. (2010). Identifying agglomeration spillovers: evidence from winners and losers of large plant openings. Journal of Political Economy, 188(3), 536–598. https://doi.org/10.1086/653714.
Hanson, A., & Rohlin, S. (2011). Do location-based tax incentives attract new business establishments. Journal of Regional Science, 51(3), 427–449. https://doi.org/10.1111/j.1467-9787.2010.00704.x.
Hanson, A., & Rohlin, S. (2013). Do spatially targeted redevelopment programs spillover? Regional Science and Urban Economics, 43(1), 86–100. https://doi.org/10.1016/j.regsciurbeco.2012.05.002.
Harrison, B., Kelley, M. R., & Gant, J. (1996). Specialization versus diversity in local economies: the implications for innovative private-sector behavior. Cityscape: A Journal of Political Development and Research, 2(2), 61–93 https://www.jstor.org/stable/20868411.
Hartley, P. R., Medlock, K. B., Temzelides, T., & Zhang, X. (2015). Local employment impact from competing energy sources: shale gas versus wind generation in Texas. Energy Economics, 49, 610–619. https://doi.org/10.1016/j.eneco.2015.02.023.
Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: evidence from firms’ patents, profits, and market value. The American Economic Review, 76(5), 984–1001. https://doi.org/10.3386/w1815.
Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 108(3), 577–598. https://doi.org/10.2307/2118401.
James, A., & Aadland, D. (2011). The curse of natural resources: an empirical investigation of U.S. counties. Resource and Energy Economics, 33(2), 440–453. https://doi.org/10.1016/j.reseneeco.2010.05.006.
Kelley, M. R. & Helper, S. (1999). Firm size and capabilities, regional agglomeration, and the adoption of new technology. Economics of Innovation and New Technology, 8(1–2), 79–103.
Kelsey, T., Partridge, M. D., & White, N. (2016). Unconventional gas and oil development in the United States: economic experience and policy issues. Applied Economic Perspectives and Policy, 38(2), 191–214. https://doi.org/10.1093/aepp/ppw005.
Kilkenny, M., & Partridge, M. D. (2009). Export sectors and rural development. American Journal of Agricultural Economics, 91, 910–929. https://doi.org/10.1111/j.1467-8276.2009.01320.x.
Lee, N., & Rodriguez-Pose, A. (2013). Innovation and spatial inequality in Europe and USA. Journal of Economic Geography, 13, 1–22. https://doi.org/10.1093/jeg/lbs022.
Light, A. & Munk, R. (2016). Business ownership vs. self-employment. (May 22, 2016). https://doi.org/10.1016/S0166-0462(98)00010-6.
Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.
Marchand, J. (2012). Local labor market impacts of energy boom-bust-boom in Western Canada. Journal of Urban Economics, 71, 165–174. https://doi.org/10.1016/j.jue.2011.06.001.
Munasib, A., & Rickman, D. (2015). Regional economic impacts of the shale gas and tight oil boom: a synthetic control analysis. Regional Science and Urban Economics, 50, 1–17. https://doi.org/10.1016/j.regsciurbeco.2014.10.006.
Nadiri, I. (1993). Innovations and technological spillovers. Working paper 423. Cambridge: National Bureau of Economic Research. https://doi.org/10.3386/w4423.
Papyrakis, E., & Gerlagh, R. (2004). The resource curse hypothesis and its transmission channels. The Journal of Comparative Economics, 32(1), 827–838. https://doi.org/10.1016/j.jce.2003.11.002.
Papyrakis, E., & Gerlagh, R. (2007). Resource abundance and economic growth in the United States. European Economic Review, 51(4), 1011–1039. https://doi.org/10.1016/j.euroecorev.2006.04.001.
Paredes, D., Komarek, T., & Loveridge, S. (2015). Income and employment effects of shale gas extraction windfalls: evidence from the Marcellus region. Energy Economics, 47, 112–120. https://doi.org/10.1016/j.eneco.2014.09.025.
Partridge, M., & Olfert, R. (2011). The winners’ choice: sustainable economic strategies for successful 21st century regions. Applied Economic Perspectives and Policy, 33(2), 143–178. https://doi.org/10.1016/j.eneco.2014.09.025.
Rodriguez-Pose, A. (1999). Innovation prone and innovation averse societies: economic performance in Europe. Growth and Change, 30, 75–105. https://doi.org/10.1111/0017-4815.00105.
Rodriguez-Pose, A., & Crescenzi, R. (2008). Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Regional Studies, 42(1), 51–67. https://doi.org/10.1080/00343400701654186.
Romer, P. M. (1986). Increasing returns and long run growth. Journal of Political Economy, 94, 1002–1037. https://doi.org/10.1086/261420.
Sachs, J. D., & Warner, A. M. (1995). Natural resource abundance and economic growth. NBER working paper no. 5398. Cambridge: National Bureau of Economic Research. https://doi.org/10.3386/w5398.
Sachs, J. D., & Warner, A. M. (2001). Natural resources and economic development: the curse of natural resources. European Economic Review, 45, 827–838. https://doi.org/10.1016/S0014-2921(01)00125-8.
Sobel, R. L., & Hall, J. C. (2008). Institutions, entrepreneurship, and regional differences in economic growth. American Journal of Entrepreneurship, 1, 69–96.
Stephens, H., Partridge, M., & Faggian, A. (2013). Innovation, entrepreneurship and economic growth in lagging regions. Journal of Regional Science, 53(5), 778–812. https://doi.org/10.1111/jors.12019.
Torvik, R. (2002). Natural resources, rent seeking and welfare. Journal of Development Economics, 67, 455–470. https://doi.org/10.1016/S0304-3878(01)00195-X.
Tsvetkova, A., & Partridge, M. D. (2016). Economics of modern energy boomtowns: do oil and gas shocks differ from shocks in the rest of the economy? Energy Economics, 59, 81–95. https://doi.org/10.1016/j.eneco.2016.07.015.
Tsvetkova, Alexandra and Mark D. Partridge. (2017) “Shale revolution and entrepreneurship: an empirical assessment of the relationship between energy sector expansion and self-employment in US counties.” Energy Journal, (141): 423–434.
U.S. BLS. Quarterly census of employment and wages. (n.d.). https://www.bls.gov/cew/#databases. Accessed 28 June 2018.
U.S. EIA. U.S. natural gas imports. (n.d.). http://www.eia.gov/dnav/ng/hist/n9100us2A.htm. Accessed 13 Feb 2017.
U.S. BLS. Establishment age and survival data. (n.d.). https://www.bls.gov/bdm/bdmage.htm. Accessed 13 Feb 2017.
U.S. EIA. (n.d.). “Natural gas expected to surpass coal in mix of coal used for U.S. generation in 2016.” http://www.eia.gov/todayinenergy/detail.php?id=25392.. Accessed 13 Feb 2017.
U.S. EIA. U.S. imports of crude oil and petroleum products. (n.d.). https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=pet&s=mttimus1&f=a. Accessed 13 Feb 2017.
Weber, J. G. (2012). The effects of natural gas boom on employment and income in Colorado, Texas, and Wyoming. Energy Economics, 34(5), 1580–1588. https://doi.org/10.1016/j.eneco.2011.11.013.
Weber, J. G. (2014). A decade of natural gas development: the makings of a resource curse? Resource and Energy Economics, 37, 168–183. https://doi.org/10.1016/j.reseneeco.2013.11.013.
Weinstein, A. L. (2014). Local labor market restructuring in the shale boom. Journal of Regional Analysis and Policy, 44(1), 71–92.
Weinstein, A. L. & Partridge, M. D. (2011). The economic value of shale natural gas in Ohio. Swank Program in Rural-Urban Policy Summary and Report. Columbus, OH: Ohio State University.
Weinstein, A. L., & Partridge, M. D. (2015). Economic implications of unconventional fossil fuel production. Book chapter for. In D. Albrecht (Ed.), Our energy future, socioeconomic implications and policy options for rural America (pp. 19–39). New York: Routledge.
Weinstein, A. L., Partridge, M. D., & Tsvetkova, A. (2018). Follow the money: aggregate, sectoral and spatial effects of an energy boom on local earnings. Resources Policy, 55, 196–209. https://doi.org/10.1016/j.resourpol.2017.11.018.
Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurship, innovation, and economic growth. Small Business Economics, 24, 335–350. https://doi.org/10.1007/s11187-005-2000-1.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11187-019-00162-9