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The influence of oil and natural gas employment on local retail spending: evidence from Oklahoma panel data

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Abstract

Recent volatility in oil and natural gas markets has led to questions about how local businesses and government revenues might be affected. This paper uses a panel dataset of Oklahoma counties during 2003–2012 to quantify the relationship between changes in oil and natural gas employment and retail spending. One particularly noteworthy contribution is the use of sub-categories of retail sales (to the two-digit Standard Industrial Classification code) to assess whether specific retail sectors are more responsive to changes in oil and gas activity. Our fixed effects panel regression model reveals that variations in mining employment impact aggregate retail expenditures, with an overall elasticity of 0.02. These results are driven by the grocery and furniture sectors, with elasticities of 0.05–0.06, and are even stronger when the analysis is limited to counties considered to be mining-dependent. We also show that spillover effects exist, with the grocery and furniture expenditures highly impacted by mining employment in neighboring counties as well as their own.

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Fig. 1

Source: Oklahoma Corporation Commission

Fig. 2

Source: Bureau of Economic Analysis, Upjohn Labor Institute, Economic Research Service

Fig. 3

Source: Upjohn Labor Institute, Oklahoma Tax Commission

Fig. 4

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Notes

  1. Only thirty-eight states allow local governments to levy sales taxes.

  2. The Oklahoma Tax Commission 2014–2015 annual report puts this figure at 7% ($683 M in gross production oil and gas taxes; $9778 M total collections) (Oklahoma Tax Commission 2016).

  3. Raimi and Newell (2016) indicate that sales tax revenue is not included in their analysis due to limited data and methodological issues.

  4. Note that the aggregate retail sales amount collected is simply the sum of collections for SIC codes 52 through 59.

  5. ERS defines counties as mining dependent if 13 percent or more of the average annual labor and proprietor’s earnings were derived from mining, or at least 8 percent of jobs were in mining. The 2015 versions of the ERS definitions are used, which are derived from 2010 to 2012 BEA data.

  6. As the data section below indicates, we drop the two primary metropolitan counties (Oklahoma and Tulsa) because of their outlying retail sales and also LeFlore county due to inconsistent retail collection data. LeFlore has only limited mining employment and oil/gas production.

  7. A micropolitan place is defined by the Office of Management and Budget as a city whose population is more than 10,000 but less than 50,000.

  8. Note that these county-level rates are actually weighted city-level tax rates across a county.

  9. Note that the MSA variable will be dropped if a fixed effects specification is preferred, since it will be perfectly collinear with the county fixed effect.

  10. Twenty-six of the 77 counties are officially defined as mining dependent, but we drop Oklahoma and Tulsa.

  11. Recall that when fixed effects are used, controls that are constant over time cannot be included. For this reason, the MSA variable is not shown in Table 3.

  12. In the random effects version of the model, the MSA variable was highly statistically significant for all sectors except grocery.

  13. Purchase prices for oil and natural gas are taken from the US Energy Information Administration (Domestic Crude Oil price by state and Natural Gas spot price for Henry Hub).

  14. In using the queen contiguity matrix, all 74 counties in our sample included at least one neighbor defined as a micropolitan or metropolitan county according to the 2013 ERS classifications. Thus, all counties include at least one neighbor with a larger retail base.

  15. Our data do not include any years associated with the oil and gas downturn (post-2014); however, the elasticities are representative of the relationship between mining employment and retail spending in years where oil and gas activity was low (2003–2005).

  16. Note that the oil-dependent county elasticity of 0.086 in Table 4 would raise this increase to $3.3 M.

  17. 2% of the $3.3 M in footnote 16 implies revenue growth of $65,000.

  18. Hotels are included in the two-digit NAICS code 72.

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Acknowledgements

Funding was provided by National Institute of Food and Agriculture (Grant No. Hatch Project 1018233).

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Authors and Affiliations

  1. Department of Agricultural Economics, Oklahoma State University, Stillwater, OK, 74075, USA

    Brian E. Whitacre, Dylan L. Johnston, David W. Shideler & Notie H. Lansford

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  1. Brian E. Whitacre
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Correspondence to Brian E. Whitacre.

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Appendices

Appendix 1

See Table 7.

Table 7 Lagged panel regression results (full sample of 74 counties)
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Appendix 2

See Table 8.

Table 8 Panel regression results using value of production instead of employment (full sample of 74 counties)
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Whitacre, B.E., Johnston, D.L., Shideler, D.W. et al. The influence of oil and natural gas employment on local retail spending: evidence from Oklahoma panel data. Ann Reg Sci 64, 133–157 (2020). https://doi.org/10.1007/s00168-019-00962-7

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