Standing the Test of Time: The Breadth of Majority Coalitions and the Fate of U.S. Supreme Court Precedents

Should a strategic Justice assemble a broader coalition for the majority opinion than is necessary, even if that means accommodating changes that move the opinion away from the author’s ideal holding? If the author’s objective is to durably move the law to his or her ideal holding, the conventional answer is no, because there is a cost and no corresponding benefit. We consider whether attracting a broad majority coalition can placate future courts. Controlling for the size of the coalition, we find that cases with ideologically narrow coalitions are more likely to be treated negatively by later courts. Specifically, adding either ideological breadth or a new member to the majority coalition results in an opinion that is less likely to be overruled, criticized, or questioned by a later court. Our findings contradict the conventional wisdom regarding the coalition-building strategy of a rational and strategic opinion author, establishing that the author has an incentive to go beyond the four most ideologically proximate Justices in building a majority coalition. And because of later interpreters’ negative reactions to narrow coalitions, the law ends up being less ideological than the Justices themselves

Discrete Measurement, Continuous Time and Event History Modeling

Most even history models used in political science assume the time being analyzed is continuous. Discrete measurement causes this assumption to be violated. The violation of this assumption is shown to introduce non-trivial bias to parameter estimates. Analysis of discrete-measured data as interval-censored is shown to greatly reduce this bias. The empirical properties of the bias introduced by discrete measurement and the interval-censoring correction are explored through Monte-Carlo simulations and a replication of the analysis of civil war duration from (Fearon 2004). I also demonstrate that analyzing discrete-measured continuous-time data as interval-censored is a better approach than the discrete-time models proposed in (Box-Steffensmeier and Jones 2004). The conclusion of the analysis is that event-history analysis of continuous-time variables should always be implemented as interval-censored estimation

Modeling interdependence in collective political decision making

Fundamental to many accounts of decision-making within political institutions is the interdependence between simultaneous choices. For instance, members in a legislature are hypothesized to take voting cues from party leaders, and the chief justice of the U.S. Supreme Court is thought to vote with the majority on the merits so as to assign opinion authorship. In this thesis I show that none of the conventional methods that have been used by political scientists for testing theories of simultaneous interdependence are statistically sound. I then propose a machine-learning algorithm that finds unmodeled interdependence in discrete-choice data. Next, I develop a novel statistical model that allows the researcher to explain -- in a methodologically appropriate manner -- the probability that an actor makes a particular choice as well as the probability that a collective-decision occurs in a particular form. In the last chapter of my dissertation, I demonstrate that U.S. Supreme Court case outcomes are interdependent and that the U.S. Supreme Court is best characterized as an institution striving to produce an ideologically optimal body of law rather than ideologically optimal independent case outcomes

Persistent Policy Pathways: Inferring Diffusion Networks in the American States

The transmission of ideas, information, and resources forms the core of many issues studied in political science, including collective action, cooperation, and development. While these processes imply dynamic connections among political actors, researchers often cannot observe such interdependence. One example is public policy diffusion, which has long been a focus of multiple subfields. In the American state politics context, diffusion is commonly conceptualized as a dyadic process whereby states adopt policies (in part) because other states have adopted them. This implies a policy diffusion network connecting the states. Using a dataset of 187 policies, we introduce and apply an algorithm that infers this network from persistent diffusion patterns. The results contribute to knowledge on state policy diffusion in several respects. Additionally, in introducing network inference to political science, we provide scholars across the discipline with a general framework for empirically recovering the latent and dynamic interdependence among political actors