Policy expansion in local government environmental policymaking: The case of high-volume hydraulic fracturing (HVHF) policy in New York

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Laurence Ferguson
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$high-volume hydraulic fracturing (HVHF)$ Nguyen Long, Madeline Gottlieb, Michael

1 Policy expansion in local government environmental policymaking: The case of high-volume hydraulic fracturing (HVHF) policy in New York Gwen Arnold, Le Anh Nguyen Long, Madeline Gottlieb, Michael Bybee, and Nikita Sinha University of California, Davis Department of Environmental Science and Policy APPAM, November 3-5, Washington

2 Overview Main question: What factors encouraged municipalities in New York to expand on policy innovation? Main answers: Anti-HVHF policy expansion appears facilitated by Jurisdiction-level administrative and civic capacity Learning from peer and neighbor jurisdictions Timing: Being a late adopter 2

4 Municipal anti-hvhf policy passage in NY,

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8 HVHF policy innovation and expansion Policy innovation = adoption of at least one anti-hvhf policy. Policy expansion = a focal government s adoption of more than one policy during a diffusion cycle (Boehmke and Witmer 2004). At least 72 municipalities experienced expansion,

9 Motivation When do jurisdictions double down on policy priorities and paths? Is policy expansion affected by... Learning? Advocacy resources? Civic or jurisdictional capacity? Need or suitability for HVHF? This question and issue are understudied Policy adoption scholarship has minimally addressed expansion. Scholars studying local anti-hvhf policymaking in New York have only examined adoption (innovation).

10 Existing scholarship: Basic descriptives Arnold et al. (2016) Anti policies 279 Pro policies 60 Total policies 349 Barnes (2013) Dokshin (2016) Walsh et al. (2015) Zirogiannis et al. (2015) Municipalities passing anti Municipalities passing pro (157) Total passing Scope Entire state; all munis Utica overlaying; only towns Entire state; cities and towns Time Not specified March July 2013 Focus Innovation, expansion Entire state; all munis Through Nov. 13 Entire state; all munis Not specified Innovation Innovation Innovation Innovation

11 Existing scholarship: Core findings All scholars have investigated innovation, not expansion. Blank cells = variable was not considered or not significant. Variables considered by only one paper are mainly excluded. Barnes (2013) Dokshin (2016) Walsh et al. (2015) Zirogiannis et al. (2015) Democratic vote Education/income/ SES + + (inc.) + (ed.) Past drilling Population Population density

12 Existing scholarship: Core findings Prior ordinances in county Barnes (2013) Dokshin (2016) Walsh et al. (2015) Zirogiannis et al. (2015) Shales (assumed) Unemployment/ rural county Village + Well proximity/ well count + Wetlands/watershed/ aquifer, +

13 Hypotheses Factor More innovation More expansion Less innovation Less expansion H1: More neighbor policy influence Learning (social) X X H2: Earlier initial policy adoption Learning (social, evaluative) X H3: More exposure to advocacy Learning (social, evaluative) X H4: More liberalism Capacity X X H5: Greater economic need Need/suitability X X

14 Data collection : Acquisition of local HVHF policies, sourced from... Food and Water Watch, Frac Tracker, Joint Landowners Coalition of New York, Keuka Lake Citizens Against Hydrofracking, NYSLLD FOIL requests to obtain full texts and determine/verify date of passage Acquisition and analysis of public meeting minutes to ensure that policy action was motivated by HVHF 2014 survey of municipal clerks exploring local HVHF policy entrepreneurship (modeled on Schneider and Teske 1992, 1993a, 1993b; Schneider and Teske 1994) 481 respondents, 31% response rate 23% of responding municipalities reported policy entrepreneurs Other data from.. County election boards, U.S. Census, NYS Comptroller, NYS Department of Environmental Conservation, USDA NASS (and others)

15 Descriptive statistics Anti-HVHF policy entrepreneur (0, 1) Learning External/county-wide pressure (number of municipalities in county adopting policies) Learning Democratic vote share Capacity Drilling activity, (production and permitting within jurisdiction s borders, all well types, oil and gas) Economic need/suitability Full sample (n=1539) Expansion sample (n=192) Policy entrepreneur sample (n=481) (0.42) 5.57 (6.96) (8.11) 5.43 (6.91) (9.13) (10.84) (8.85) 1.70 (1.36) 1.14 (1.02) 0.59 (1.59)

16 Descriptive statistics Education/income (sum of standardized variables) Capacity Homeownership Capacity Municipal revenue (per capita, logged, average) Capacity Population density (logged) Economic need/suitability Full sample (n=1539) Expansion sample (n=192) Policy entrepreneur sample (n=481) (1.78) (1.34) 0.08 (1.75) 0.75 (0.14) 0.76 (0.14) 0.76 (0.13) 6.53 (0.85) 6.37 (0.68) 6.53 (0.89) 5.57 (1.90) 5.00 (1.49) 5.42 (1.91)

17 Descriptive statistics Pro-HVHF policy action in municipality Learning OR economic need/suitability Shales (0, 1) Economic need/suitability Southern Tier (0, 1) Economic need/suitability Unemployment Economic need/suitability Full sample (n=1539) Expansion sample (n=192) Policy entrepreneur sample (n=481) 0.03 (0.17) 0.05 (0.21) 0.03 (0.17) 0.69 (0.46) 0.96 (0.20) 0.68 (0.47) 0.17 (0.37) 0.07 (0.25) 0.18 (0.39) 0.07 (0.03) 0.07 (0.03) 0.07 (0.03) Year of first adoption (1=08, 5=12) (0.106) --

18 Modeling Logit (for 0, 1 DV); negative binomial and truncated negative binomial Model I: Innovation DV: 1 = adopted any anti policy, 0 = adopted none; n=1539 Run with policy entrepreneur (I PE); n=481 Model E: Expansion DV is number of policies adopted: 0, 1-2, 3-6 Municipalities taking 0 are dropped; n=192 Run with (E T) and without (E NT) time and with policy entrepreneur (E NT PE) Model IH: Innovation/expansion (hurdle model) DV is number of policies adopted: 0, 1-2, 3-6; n=1539 Run with policy entrepreneur (I H PE and E H NT PE); n=481

19 Red = unexpected, statistically significant finding Anti-HVHF policy entrepreneur Learning External/county-wide pressure Learning Democratic vote share Capacity Drilling activity, Need/suitability Education/income Capacity Homeownership Capacity/control I I PE I H I H PE E NT E T E H NT E H T -- + *** -- + *** *** + *** + *** + *** + * + * + ** *** + *** + *** + *** + *** + *** + * + ** + * + ** + *** + *** + *** + *** * *

20 I I PE I H I H PE E NT E T E H NT E H T Municipal revenue Capacity Population density (logged) Need/suitability Pro-HVHF policy action Learning OR need/suitability Shales Need/suitability Southern Tier Need/suitability * * *** *** *** ** * * + * + * + * + *** + *** + *** + *** *** *** *** ** Unemployment Need/suitability Year of first adoption (1=08, 5=12) Learning * * *** -- + ***

21 Fit statistics I I PE I E H NT E NT E T E H T LR or Wald χ *** *** *** 27.69*** 43.00*** 39.47*** McFadden s R N It is not possible to include the policy entrepreneur variable in the expansion models because the number of cases is However, bivariate analyses suggest that policy entrepreneurship does not substantially explain policy expansion. The null hypothesis of no relationship between these variables cannot be rejected by bivariate negative binomial regression or a χ 2 test. Spearman s correlation yields a ρ of 0.02 (p 0.897).

22 Results H1, social learning, supported: More neighbor policy influence more innovation, more expansion H2, social/evaluative learning, not supported: Earlier initial policy adoption more expansion Municipalities that adopted later were significantly more likely to expand.

23 Results H3, social/evaluative learning, supported: More exposure to advocacy more innovation, NOT more expansion. Municipalities with anti-hvhf policy entrepreneurs (PEs) were more likely to innovate. No relationship between expansion and anti-hvhf PE activity. H4, capacity, mixed support: Municipalities with more liberalism more innovation, more expansion. Democratic partisanship appears to facilitate innovation but not expansion. Greater education and income appear to facilitate expansion but not innovation.

24 Results H5, economic need/suitability, mixed to weak support: Municipalities with greater economic need/suitability for HVHF less innovation, less expansion Being in the Southern Tier and having higher unemployment discouraged anti-hvhf innovation but did not impact expansion. Pro-HVHF policy action facilitated anti-hvhf innovation but did not impact expansion. Lower population density discouraged innovation and expansion in two models. Drilling activity facilitated anti-hvhf innovation AND expansion.

25 Key take-aways Social learning from peers/neighbor jurisdictions appears to facilitate both policy innovation and expansion. Late adopters, rather than early ones, were more likely to expand. Civic and jurisdictional capacity, rather than economic need/suitability for HVHF, may best explain policy expansion.

26 Challenges, issues, and future work Data Road use, wellhead, and zoning policies: Which to count? How to measure local advocacy Municipal board/council partisanship and turnover Barnes (2013) found that Independent and Republican-dominated town boards adopted fewer anti-hvhf policies. Modeling and additional analyses Continue testing hurdle model specifications Examine trends within types of policies (moratorium, ban, zoning, resolution) Explore systematic differences between early, middle, and late adopters Possibly investigate county-level influences

27 G notes (not for presentation) Put in models (based on review of other papers; we already have these data) Municipality type County The other drilling variables (wells ever, and wells ) Presence of a pro-hvhf policy entrepreneur, not just a pro policy CONSIDER putting in the success of the policy entrepreneurs, but this will really hurt the n Gather data so we can put in model Landowner coalition presence, maybe manufacturing/industry (though has not been sig in extant models) Dokshin found significant the presence of a local university; important? Distance to a major highway Distance to PA border Some kind of water variable

28 To sum up ABSTAIN > Southern Tier > Shales >Population density ADOPT > External Influence >Timing (-) > Policy Entrepreneur > Democratic Vote Share > Education > Drilling EXPAND > External Influence > Timing (-) > Education > Drilling

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30 Hypotheses Results ABSTAIN ADOPT EXPAND ABSTAIN ADOPT EXPAND > Southern Tier > Shales >Unemployment rate >Drilling > External Influence >Timing > Policy Entrepreneur > Democratic Vote Share > External Influence > Timing > Democratic Vote Share > Education > Southern Tier > Shales >Population density > External Influence >Timing (-) > Policy Entrepreneur > Democratic Vote Share > External Influence > Timing (-) > Education > Drilling > Education > Education > Drilling

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Determinants of policy entrepreneur success in New York s local fracking struggles

$Determinants of policy entrepreneur success in New York s local fracking struggles$ Determinants of policy entrepreneur success in New York s local fracking struggles Gwen Arnold, University of California, Davis Department of Environmental Science and Policy (gbarnold@ucdavis.edu) APSA,