Connected courts

Abstract

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 ...