The political economy of local fracking bans

Abstract

Concerns about harmful effects arising from the increased use of hydraulic fracturing (fracking) to extract underground fuel resources has led to efforts to ban the practice. Many townships in western New York, which lies above the gas-rich Marcellus shale formation, have enacted bans or moratoria using local zoning laws. Using spatial econometric techniques, we examine factors related to townships’ choice to adopt fracking bans and document the importance of spatial dependence when analyzing fracking bans. We find education levels, the poverty rate, and veterans groups are associated with an increased probability of a township banning or putting a moratorium on fracking. Our results inform the literature on NIMBYism as well as helping explain why local fracking bans were so pervasive despite the positive economic effect of fracking on local labor markets.

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Notes

  1. 1.

    For a discussion of why the flammable faucet water was not related to fracking, we refer the reader to Schumacher and Morrissey (2012).

  2. 2.

    We apologize to Walsh et al. (2015) for the short attention we give to their fine paper. We only became aware of it close to the end of the refereeing process.

  3. 3.

    Map of municipal movements against fracking at: http://www.fractracker.org/map/us/new-york/moratoria/.

  4. 4.

    All explanatory variables except for the three obtained from the U.S. Geological Survey were obtained from the U.S. Census Bureau.

  5. 5.

    For example, a high poverty area can have low unemployment if many workers have migrated elsewhere or left the labor force. See Cebula and Vedder (1973) and Cebula et al. (2014b) for migration in response to economic opportunity and business cycle concerns, respectively.

  6. 6.

    It is also possible that college educated individuals are more likely to have jobs unassociated with fracking and thus might be voting in a more parochial manner. Our aggregated data does not allow us to separate between these hypotheses. College educated individuals are also more likely to have higher incomes. Due to the collinearity between incomes and education, however, we cannot include both.

  7. 7.

    Veterans have volunteered to fight fracking in Ohio (Renault 2016) but might be for fracking because they don’t like foreign wars for oil.

  8. 8.

    In our empirical section, we calculate separate coefficients for each of the spatially lagged independent variables, some of which may be significant, which would be evidence of spatial autocorrelation in the error term.

  9. 9.

    We do not directly compare our results to those of Walsh et al. (2015) because they do not employ a spatial Durbin model and calculate the direct, indirect, and total effects.

  10. 10.

    10.31 × 0.0070 = 0.072.

  11. 11.

    Even if local policymakers only care about the direct effects of explanatory variables, it is still important to use spatial approaches where appropriate in order to not mistakenly attribute indirect marginal effects to direct explanatory variables.

  12. 12.

    6.23 × 0.0179 = 0.1115.

  13. 13.

    6.23 × 0.0325 = 0.2024.

  14. 14.

    2.98 ×− 0.018 = − 0.0536.

  15. 15.

    2.98 ×− 0.00327 = − 0.097.

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Acknowledgments

Hall would like to acknowledge general research support from the Center for Free Enterprise at West Virginia University.

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Correspondence to Joshua C. Hall.

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Hall, J.C., Shultz, C. & Stephenson, E.F. The political economy of local fracking bans. J Econ Finan 42, 397–408 (2018). https://doi.org/10.1007/s12197-017-9420-z

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Keywords

  • Fracking bans
  • Spatial autocorrelation

JEL Classification

  • H73
  • Q48
  • R52