During the COVID-19 pandemic, governors preempted local governments at unprecedented levels. A rich literature examines state preemption of local governments, but gubernatorial preemption – and the strategies governors use to do so – remain understudied. This paper examines what institutional and political factors influenced governors’ preemption style during the pandemic by analyzing a dataset of over 1,200 COVID-19 executive orders, classified by their style of preemption: ceiling, floor, or vacuum. Governors in states with high local autonomy rely on ceiling and floor preemptions. Republican governors are likelier to issue ceiling preemptions that bind local governments’ hands. Governors in states with ideologically dissimilar local governments tend to issue vacuum preemptions. When non-preempting previsions are dropped from the analysis, local autonomy does not significantly affect issuing one type of preemption over another. On the other hand, Republican governors are more likely to issue both ceiling and floor preemptions over vacuum preemptions. Governors in states with high ideological asymmetry are less likely to issue ceiling and floor preemptions over vacuum preemptions. These findings provide insight into gubernatorial behavior, interactions between state and local governments, and how theories of federalism can teach us more about how governments respond to crises.

The increasing nationalization of state and local politics alongside polarization and gridlock at the federal level have led states to become sites where policymaking on national hot button issues occurs. This political climate calls for a reconsideration of existing accounts of state identities, which posit that state identities are generally weak and apolitical in their content. This study considers the following questions: To what extent do respondents identify with their state? How does their state identity compare with other politically salient groups, like national identity, partisanship, race, and gender? To what extent and under what conditions are political considerations associated with state identities? How do results compare across different measures of state identification? Results show that a majority of respondents say that being from their state is an important part of their identity and the proportion saying so is similar to the proportion saying their race, class, and political party are important. Although politics may not come to mind first when respondents consider why their state is important, it relates to general feelings of connectedness, particularly for people in the political majority in their state, and being in the political majority is associated with increased levels of state identification. Results are similar across different measures of state identity. Closed- and open-ended questions show politics emerges most clearly when people explain why their state is not important to their identity. I discuss the implications of these findings and offer thoughts for future research.

Are workers effective lawmakers? Throughout American history, some politicians and elites have argued that white-collar Americans are more qualified than working-class Americans to govern. To date, however, we know relatively little about the legislative effectiveness of working-class lawmakers. I develop a theory of class-based electoral selection that links class-based discrimination in elections to legislators’ performance in office. I argue that working-class candidates face class-based biases in elections that make it more difficult to emerge and successfully win elective office. As a result, I expect the working-class candidates who do become lawmakers to be equally or more effective than their white-collar colleagues. To test these expectations, I create a data set merging the occupational background of more than 14,000 individual state legislators with their state legislative effectiveness score (SLES). The resulting data set includes more than 50,000 state legislator-term specific observations. Consistent with my expectations, I find that working-class lawmakers do not underperform white-collar lawmakers. Further, I provide evidence that, across various models and specifications, the gap between working-class and white-collar legislators’ effectiveness is negligible.

Legislative staffers are an invisible force in legislative bodies that provide every imaginable service. It is doubtful that modern legislatures could operate without them. Prior studies of Congressional staffers have found evidence that staffers not only aid but also exert an independent influence on the policy-making process through network effects. In this article, I test if this extends to state legislative staffers using novel data from shared staffer networks in Arizona, Indiana, and New Mexico. I argue that, compared to their Congressional counterparts, state legislative staffers are more akin to ‘clerks’ than ‘political professionals’ and this limits their ability to independently influence policymaking at the state level.

Identifying racial disparities in policy and politics is a pressing area of research within the United States. Where early work made use of identifying potentially noisy correlations between county or precinct demographics and election outcomes, the advent of Bayesian Improved Surname Geocoding (BISG) vastly improved estimation of race by employing voter lists. Machine Learning (ML)-modified BISG in turn offers accuracy gains over the static – and potentially outdated – surname dictionaries present in traditional BISG. However, the extent to which ML might substantively alter the policy and political implications of redistricting is unclear given its improvements in voter race estimation. Therefore, we ascertain the potential gains of ML-modified BISG in improving the estimation of race for the purpose of redistricting majority-minority districts. We evaluate an ML-modified BISG program against traditional BISG estimates in correctly estimating the race of voters for creating majority-minority congressional districts within North Carolina and Georgia, and in state assembly districts in Wisconsin. Our results demonstrate that ML-modified BISG offers substantive gains over traditional BISG, especially in diverse political geographic units. Further, we find meaningful improvements in accuracy when estimating majority-minority district racial composition. We conclude with recommendations on when and how to use the two methods, in addition how to ensure transparency and confidence in BISG-related research.

Research on the activities and influence of interest groups in state legislatures faces a data problem: we are missing a comprehensive, systematic dataset of interest groups’ policy preferences on state legislation. We address this gap by introducing the Dataset on Policy Choice and Organizational Representation in the United States (CHORUS). This dataset compiles over 13 million policy positions stated by tens of thousands of interest groups and individuals on bills in 17 state legislatures over the past 25 years. We describe the process used to construct CHORUS and present a new network science technique for analyzing policy position data from interest groups: the layered stochastic block model, which groups similar interest groups and bills together, respectively, based on patterns in the policy positions. Through two demonstrative applications, we show the utility of these data, combined with our novel analytical approach, for understanding interest group configurations in different state legislatures and policy areas.