This fact sheet series is based on the original research from the project “Maximizing the Gains of Old and New Energy Development for America’s Rural Communities.” This series summarizes the research into six chronological fact sheets to inform the reader of economic impacts related to energy development.
The U.S. shale energy boom has increased interest from local leaders in how the expanding oil and gas sector affects local economic performance. As described in fact sheets 2 and 3, communities anchored in the energy economy are more exposed to economic shocks than well-diversified economies. Due to the volatile nature of energy-based economies, these communities often experience a boom bust cycle that is difficult to predict. The research team developed a model to measure the economic impact of energy development, and believes that this model is the most accurate tool to predict local impacts. This fact sheet summarizes the variables used in the model and the corresponding results.
Overview of the Model
The research team conducted an in-depth nationwide literature review of all related energy studies. As a result, the team developed an economic impact analysis model with the primary data source acquired from Economic Modeling Services, Inc. (EMSI). EMSI provided information on county level employment and earnings data disaggregated at the four-digit industry level. The team also obtained data from other publicly available sources. The model looked at all U.S. areas between 1993 and 2013, examining the total employment as well as employment in 14 energy related sectors. By analyzing the data, the researchers were able to compare the change effects that were occurring across industry sectors. Based on this analysis and the literature review, the research team defined specific variables that they wanted to measure to see if there was a relationship consistent with a boom bust economy.
Economic Model Variables
What makes this model unique in comparison to previous research is that the scope combines a variety of variables including community size, time frame, energy resource potential, economic sector, geography, historical intensity of energy infrastructure, and a host of other demographic and educational attainment variables. The following is the summary of the variables:
- Metro vs. Non-Metro: The metro vs. non-metro variable considers population density and the agglomeration effects of new energy development. A metropolitan statistical area consists of one or more counties containing a city with a population greater than 50,000.
- Time Difference: The time difference variable considers the dynamic time path for the energy boom over a 1, 3, 6, and 10 year time period. This variable helps to assess the economic impacts of newly developed energy related sectors along with new supply chains that could be established and reveals other sectors that could be crowded out.
- Instrumental Variable (IV) Approach: The instrumental variable considers the energy resource potential of a geographical area while accounting for the endogeneity bias. Three measures including the thickness of shale deposits, historical drilling intensity, and the recoverable resource reserves are used as instruments.
- Industry Mix: The industry mix variable considers the change in other industry sectors to determine the influence of new energy development. The industry mix variable can demonstrate the multiplier effect of the new energy development in comparison to economic shocks from non-energy sectors.
- Mining 85: The mining 85 variable considers existing energy infrastructure as a result of historical energy development. This variable takes into account the economic impact related to the construction of energy related infrastructure.
The research team’s model is very complex and indicates that there is no single factor that can best predict the economic impact of an energy shock. But the model provides a comprehensive approach to a multi-faceted economic issue. Detailed analysis, discussion, and tables of the modelling results are available in the full report titled “Economics of Modern Energy Boomtowns: Do Oil and Gas Shocks Differ from Shocks in the Rest of the Economy.” Below is a summary of the key findings:
Impact on non-metro counties
- Positive effects are shown to increase with time, peaking at six years and declining afterwards.
- Between 2001 and 2013 an average county experienced a 0.1% annual job growth due to the energy sector.
- Construction, transportation and warehousing, wholesale trade, accommodation and food, and real estate are the industries that benefited most in terms of new employment. In contrast, manufacturing and agriculture lost some jobs.
- Comparing the job creation effects of energy shocks to equal-sized shocks in the rest of economy, growth elsewhere in the economy generally had larger net positive spillovers.
Impact on metro counties
- The effects of energy sector expansion on metropolitan employment are less pronounced compared to nonmetropolitan counties.
- After one year, estimates show a crowding out of jobs in other sectors of the economy as the energy sector expands.
- Crowding out causes the manufacturing sector to decline as a result of energy sector expansion.
- Employment in transportation and warehousing sectors increase after one year.
Based on the research model, energy booms are relatively small compared to the rest of the economy. Given a long-term perspective, a diversified local economy has a greater growth potential compared to an economy relying on jobs from an energy boom. Local governments should consider a comprehensive approach to economic development and not an overreliance on the energy sector as an answer to viable economic growth. Fact sheets 5 and 6 outline economic development and community planning strategies in further detail.
This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Agriculture and Food Research Initiative, under grant #11400612 titled “Maximizing the Gains of Old and New Energy Development for America’s Rural Communities.” The authors acknowledge and appreciate the support of both the USDA AFRI grant and the project team from The Ohio State University, College of Food, Agricultural, and Environmental Sciences, and Department of Agricultural, Environmental and Development Economics. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.
Tsvetkova, A. and Partridge, M. (2015). Economics of modern energy boomtowns: do oil and gas shocks differ from shocks in the rest of the economy? (MPRA Working Paper No. 65754). Retreived from The Munich Personal RePEc Archive website: https://mpra.ub.uni-muenchen.de/65754/.