Advocacy Coalition Resources and Strategies in Colorado Hydraulic Fracturing Politics

ABSTRACT

This article examines how members of competing coalitions seek to influence public policy. Past research examined coalitions and policy change, but offered limited understanding about the relationships between coalitions’ resources, strategies, and policy change. The case study focuses on the establishment of various regulations related to hydraulic fracturing in Colorado. Data were collected using interviews (n = 14) and a survey (n = 137) of policy actors asking about their beliefs, resources, and strategies in April 2013. This study found that members of the coalition proposing stricter regulations were successful. This winning coalition more frequently engaged in a wider range of strategies and had greater public support compared to their opponents. The study also found that among the winning coalition members, leadership and information technology resources were associated with the majority of their strategies, but financial resources did not have a significant relationship with any strategy.

KEYWORDS:

• Advocacy coalition framework
• energy policy
• environmental policy
• policy process

Acknowledgments

The author is grateful for comments and edits from Chris Weible, Remington Purnell, and three anonymous reviewers of this article.

Notes

Policy actors are individuals who engage in nontrivial, multiple activities to influence public policy (Sabatier and Jenkins-Smith 1999).

A breakdown of the organizational categories, response rates, and total responses for each category are as follows: academics and consultants 33% (15), environmental organizations 36% (17), federal government 34% (12), industry and professional associations 38% (7), local government 38% (37), oil and gas service providers and operators 37% (25), organized citizen groups 53% (9), regional government 33% (3), state government 27% (10), and other 34% (2). Total 34% response rate, with 137 respondents. Ten media actors were contacted to complete the survey but none of them ever responded.

The factor loadings using the 20 empirical beliefs range from 0.70 to 0.90 and have an overall Cronbach’s alpha of 0.94, demonstrating internal consistency of the latent variable.

One hundred and thirty-three was the number of respondents who completed all of the policy core belief questions on the survey.

The maximum possible distance was eight units, which is a disagreement between 1 and 5 on normative position and between 1 and 5 on problem severity between any two policy actors. The minimum distance was 0, which is complete agreement between policy actors.

Tabu search cluster analysis has a goodness of fit measure (R²) for determining the number of clusters. The R² for two coalitions was .51, while for three clusters it was .50 and identified only one policy actor for the third cluster.

The Shapiro–Wilk test for normality was conducted and all variables are found to be significant (p ≥ .001). A Levene’s test for homogeneity of variance was run on the resources and none are significant (p ≥ .001).

The Shapiro–Wilk test for normality was conducted and all variables were found to be significant (p ≥ .001). A Levene’s test for homogeneity of variance was run and one strategy, protests, was significant (p ≥ .001). Therefore, the Games–Howell post hoc test was utilized and the variation remained significant.