We conduct a series of simulations to compare how various strategies for seeding a policy in the American states affect the rate at which that policy spreads. Using empirically derived parameters of the policy diffusion process, we simulate the diffusion of a hypothetical policy after seeding the policy in just a handful of states. We compare these strategies to seeding the ten states the RWJF monitored during the states' implementation of the Affordable Care Act of 2010. We attempt to mimic the choices that policy advocates make when deciding which states to target with their resources. Our results indicate that focusing on innovative states, that is, those that tend to adopt new policies faster, offers a valuable boost in the speed of diffusion. Even better, though, is a strategy that targets policy leaders.

Widening, asymmetric polarization is evident in both the U.S. Congress and state legislatures. Recent work unveils a new dimension to this polarization story: newly elected Republican women are driving this polarization. Women are more likely to legislate on women’s issues than men, yet women’s shared interest in representing women doesn’t preclude their identity as partisans. In this article, we explore the effect of today’s political climate on state legislators’ policy representation of women’s issues. We ask what effect does gendered polarization have on women’s issues? To test this, we evaluate bill sponsorship in the states on the quintessential “women’s issue” of abortion. Our research design focuses on bill introductions and uses on an original dataset of pro- and anti-abortion rights bill introductions, which we analyze using an event count model. We find that overall polarization leads to the introduction of fewer restrictive abortion bills, but as polarization between women lawmakers grows, legislators are more likely to introduce anti-abortion rights legislation. Gender polarization has consequences on the types of bills legislators introduce and for how scholars should study polarization.

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ACKNOWLEDGEMENTSMany people contributed to this project in tangible and intangible ways. First, thank you to my dissertation committee members, Tracy Osborn, Chuck Shipan, Julie Pacheco, and Elizabeth Menninga for taking the time to be a part of this project.This dissertation benefited greatly from your invaluable critiques and suggestions, and your advice helped me mature as a scholar. I cannot thank you enough. I am especially grateful to my advisor, Fred Boehmke, who not only reviewed drafts of this project but also provided much needed encouragement, motivation, and support throughout graduate school.I also thank my family, writing this dissertation would have been a seemingly impossible task without you. To my husband, Andy Rury, your patience knows no bounds. Thanks to you, I never have to regret not taking chances and for that, I will be eternally grateful. To my parents, Andy and Grace Matthews, thank you for supporting me during this long journey and not expecting me to be anything other than who I am. And thank you to my aunt, Lydia Matthews, for daring me to dream from an early age. You opened my eyes to a world beyond Iowa's borders; I would not be where I am today if we had not toured New England colleges together. ii ABSTRACT State supreme courts are autonomous institutions with significant power. Yet, despite this authority, state supreme courts routinely rely on one another to explain why and how they reached their decisions. This puzzle of why state supreme courts cite each other in their opinions led me to pose two questions. First, under what conditions do state supreme courts cite other states supreme courts? And second, to whom do they turn for guidance? To answer these questions, I propose a new theory for evaluating state supreme court citations, the social learning model. I borrow policy diffusion's learning mechanism and I pair it with network theory and methods to explain peer-to-peer state supreme court citations practices. I argue that courts are social actors who interact, influence, and learn from one another, and the citations are communications by and between the courts.To model citations between courts, I apply a temporal exponential random graph network analysis model or TERGM. TERGMs simulate the evolution of the state-to-state citation network by including aspects of both the courts and the network structure. I argue that only by understanding how networks and issue areas evolve can we begin to understand how courts and justices make decisions. The network approach to citations specifically tests these endogenous relationships, it also directly models the complex dependencies of citation networks.My findings demonstrate the courts became more connected over time and no single state supreme court leader emerges. I find that citations are endogenous; what iii one court does affects other courts. I also discover that the area of law matters a lot and it is insufficient to pool all legal issues into a single model. Finally, state supreme courts do not cite state supreme courts who ...

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