Doctoral Dissertation Research in Political Science: Policy Tool Bundling: Predicting the Selection of Economic Development Policy Instruments Using a Multivariate Probit Analysis

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Modeling choices of economic development incentive tools requires integration of a complex system of political and economic considerations and accounting for relationships among specific instruments within a bundle of policy options. Previous studies of policy instrument choice focus almost exclusively on individual policy tools in isolation. This neglects the reality that multiple tools work in conjunction with one another. Relationships among economic development incentives can result from local officials' pursuit of efficiencies, such as scale economies, or their pursuit of political gains, from satisfying ideological or constituency demands. The selection of any policy alternative is not independent of the other policy options. Just as with economic goods, policy tools can operate as complements or substitutes. This dissertation develops a theory of policy tool bundling to explain the configurations of development policies cities adopt to promote their economic or political interests. The policy bundling approach builds on extant research from the tools of government and policy diffusion literatures by investigating how factors internal to local governments and external pressures from neighboring jurisdictions influence the set of policy instruments selected. Pooled, longitudinal data is employed to identify codependences among incentives policies for Georgia cities. The longitudinal nature of the study allows predictions of the influence of previous and current economic policies choices while avoiding spurious relationships. Focusing on one state facilitates the inclusion of diffusion variables capturing the actions of surrounding communities. The influences of political institutions, particularly the presence of professional managers and at-large council representation are expected to encourage policy bundling based on efficiency concerns. The presence of elected executives and district representation are expected to encourage bundling based on political interests. A Bayesian multivariate probit model is used to predict economic policy choice because it allows simultaneous estimation of multiple policy adoptions and identification substitutive and complementary effects. The intellectual merit of this dissertation is derived from the synthesizing of the policy tools and diffusion policy theoretical frameworks to explain policy bundling. Systematic investigation of policy interdependencies is absent from extant empirical research in both areas. The theory of policy bundling developed in this research seeks to fill this lacuna and can be extended to numerous policy areas including economic development, environmental regulation, tax policy, and numerous other government initiatives which utilize multiple instruments simultaneously. Thus, this research promises to benefit both academics and practitioners and expand our understanding of how political institutions influence policy outcomes.