Examining the Policy Learning Dynamics of Atypical Policies with an Application to State Preemption of Local Dog Laws

Fix Michael P. () and Mitchell Joshua L.
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Fix Michael P.: Georgia State University, Political Science, Atlanta, GA 30302-3965, USA
Mitchell Joshua L.: University of Arkansas, Fayetteville, AR 72701-4002, USA

Statistics, Politics and Policy, 2017, vol. 8, issue 2, 223-247

Abstract: Most of the literature on policy diffusion focuses on palpable issues such as economic or morality policies. As such, we know little about the mechanisms of diffusion for preemption of atypical policies such as animal regulations that lack a clear economic or ideological motivation. In this article, we propose and test a theory of conditional policy learning to explain the diffusion of atypical policies. We posit that a type of policy learning is occurring here, but that states only look to their neighbors when certain policy specific factors are present in their state. His theory is then applied to examine the dynamics of state adoption of laws preempting local Breed Specific Legislation from 1988 to 2014. Using an exponential model, two policy learning and two conditional learning hypotheses are tested. This study finds that policy learning is occurring through both external and internal pathways. This advances the literature by demonstrating that preemption occurs through the learning mechanism, but this learning effect is conditioned on policy relevant factors within the state.

Date: 2017
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DOI: 10.1515/spp-2017-0009

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