Unpacking the intensity of policy conflict: a study of Colorado’s oil and gas subsystem

Abstract

This article applies the Policy Conflict Framework (PCF) to describe and explain the characteristics of policy conflict within the oil and gas subsystem in Colorado. We use data from a survey of policy actors to assess three cognitive characteristics of policy conflict: divergence in policy positions, perceived threats from opponents’ positions, and an unwillingness to compromise. Aggregating these indicators across policy actors in the subsystem, we find a moderately high level of policy conflict intensity, but we also find substantial variation in the characteristics of policy conflict across policy actors. To help explain this variation, we examine how interpersonal and intrapersonal attributes of policy actors relate to the characteristics of policy conflict. In particular, we find that insular policy actor networks, interest group affiliations, and rigidity of risk and benefit perceptions associate more consistently with conflict characteristics than political views, education, or experience. We conclude with a discussion of the strengths and limitations of this first application of the PCF and reiterate the need for theoretically and empirically rigorous measures of policy conflict.

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Notes

  1. 1.

    Additionally, the dimensions of policy conflict may or may not be correlated. For example, a policy actor could be divergent in their policy positions but may perceive low threats from their opponents and be willing to compromise.

  2. 2.

    The initial target list of respondents was 630 individuals. After eliminating bounced emails from the list and individuals who were not actively involved in the issue, the final population was 453.

  3. 3.

    The response rates by organizational affiliation are local government (60 of 127 = 47%), industry (51 of 123 = 41%), environmental nonprofits (31 of 61 = 51%), state government (21 of 28 = 75%), legal professionals (17 of 34 = 50%), organized citizen groups (9 of 18 = 50%), university/consultants (12 of 30 = 40%), industry nonprofits (6 of 12 = 50%), other nonprofits (5 of 9 = 56%), media (1 of 6 = 17% response rate), federal government (0 of 4 = 0%), and “other” (0 of 1 = 0%).

  4. 4.

    Access to the full survey instrument is available from the authors.

  5. 5.

    These three dimensions form a conflict composite index that combines individual respondents’ scores on the three dimensions of the cognitive characteristics of conflict along a concord–conflict spectrum. Unlike typical measures of latent variables where correlated items in the scale are expected to load onto an abstract concept, we conceptualize the concord–conflict spectrum as comprised of three dimensions that might or might not be correlated. For example, perceptions of threats might or more not be correlated with divergent policy positions.

  6. 6.

    The three cognitive characteristics of policy conflict also interrelate. Spearman’s Rho correlations show significant correlations (p < 0.000) with correlations between 0.36 and 0.48. This suggests that policy actors are more likely to feel threatened and unwilling to compromise when they have divergent policy positions. It remains an empirical question whether such correlations will also be found in other studies or the implications of such correlations on the dynamics of policy conflicts.

  7. 7.

    Tests for collinearity and outliers were also done using the ordinary least square models. Mean variance inflation factor (VIF) values for each of the models were under 2. Tests for influential cases included assessing the Cook’s distance score and DFBETA scores for each of the variables. No cases on the five models exhibited Cook’s distance values greater than 1, or absolute value scores of the DFBETA’s greater than 1. As more conservative measures, cases with absolute values of the DFBETA scores exceeding 2/sqrt(n), or Cook’s distance scores greater than 4/n were evaluated (n = 168). Several cases in each model marginally exceeded the conservative thresholds. For the purposes of this research, we chose not to drop cases in the models, as the data arguably are within the range of the design of the survey instrument that we used to collect the data. All models were also run with and without the cases with Cook’s D scores that exceeded 0.5 (1 case in the level of government model; 1 case in the unwillingness to compromise model; and 2 cases in conflict composite index model) and removal of these cases did not change any of the significant variables identified in the results, or the direction of signs.

  8. 8.

    We also ran models with a variable for “extreme” political views in lieu of political views, which was created by taking the absolute value of the political view variable. While the “extreme political view” variable was significant and positive in the combined model, it was not significant in any of the individual models and the robustness of the models was not significantly improved. The coefficients were all positive, however, across all models.

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Acknowledgements

This research was supported by the AirWaterGas Sustainability Research Network funded by the National Science Foundation under Grant No. CBET-1,240,584. Any opinion, findings, and conclusions or recommendations expressed in this article are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Correspondence to Tanya Heikkila.

Appendix

Appendix

See Table 3.

Table 3 Descriptive statistics

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Heikkila, T., Weible, C.M. Unpacking the intensity of policy conflict: a study of Colorado’s oil and gas subsystem. Policy Sci 50, 179–193 (2017). https://doi.org/10.1007/s11077-017-9285-1

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Keywords

  • Policy conflict
  • Oil and gas development
  • Hydraulic fracturing
  • Policy process
  • Policy theory