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  1. Subnational governments in the United States have taken the lead on many aspects of the response to the COVID-19 pandemic. Variation in government activity across states offers the opportunity to analyze responses in comparable settings. We study a common and informative activity among state officials—state legislators’ attention to the pandemic on Twitter. We find that legislators’ attention to the pandemic strongly correlates with the number of cases in the legislator’s state, the national count of new deaths, and the number of pandemic-related public policies passed within the legislator’s state. Furthermore, we find that the degree of responsiveness to pandemic indicators differs significantly across political parties, with Republicans exhibiting weaker responses, on average. Lastly, we find significant differences in the content of tweets about the pandemic by Democratic and Republican legislators, with Democrats focused on health indicators and impacts, and Republicans focused on business impacts and opening the economy.
  2. Abstract The significance and influence of U.S. Supreme Court majority opinions derive in large part from opinions’ roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology—the citation exponential random graph model, for which we provide user-friendly software—enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citations should incorporate both the effects of case characteristics and the structure of past citations.
  3. Theories in political science are most commonly tested through comparisons of means via difference tests or regression, but some theoretical frameworks offer implications regarding other distributional features. I consider the literature on models of policy change, and their implications for the thickness of the tails in the distribution of policy change. Change in public policy output is commonly characterized by periods of stasis that are punctuated by dramatic change—a heavy-tailed distribution of policy change. Heavy-tailed policy change is used to differentiate between the incrementalism and punctuated equilibrium models of policy change. The evidentiary value of heavy-tailed outputs rests on the assumption that changes in inputs are normally distributed. I show that, in order for conventional assumptions to imply normally distributed inputs, variance in the within-time distribution of inputs must be assumed to be constant over time. I present this result, and then present an empirical example of a possible aggregate policy input—a major public opinion survey item—that exhibits over-time variation in within-time variance. I conclude that the results I present should serve as motivation for those interested in testing the implications of punctuated equilibrium theory to adopt more flexible assumptions regarding, and endeavor to measure, policy inputs.
  4. Recent work on legislative politics has documented complex patterns of interaction and collaboration through the lens of network analysis. In a largely separate vein of research, the field experiment—with many applications in state legislatures—has emerged as an important approach in establishing causal identification in the study of legislative politics. The stable unit treatment value assumption (SUTVA)—the assumption that a unit’s outcome is unaffected by other units’ treatment statuses—is required in conventional approaches to causal inference with experiments. When SUTVA is violated via networked social interaction, treatment effects spread to control units through the network structure. We review recently developed methods that can be used to account for interference in the analysis of data from field experiments on state legislatures. The methods we review require the researcher to specify a spillover model, according to which legislators influence each other, and specify the network through which spillover occurs. We discuss these and other specification steps in detail. We find mixed evidence for spillover effects in data from two previously published field experiments. Our replication analyses illustrate how researchers can use recently developed methods to test for interference effects, and support the case for considering interference effects in experiments on state legislatures.