This article addresses the interstate differences in outcomes from the coronavirus disease COVID-19 pandemic by focusing on state capacity. State capacity refers to states’ ability to create and implement policy. We posit that states want to limit death and destruction within their borders. COVID-19 created an instance in which states had a shared, preferred outcome but had very different levels of success. Using a novel measure of state capacity that allows for subnational comparisons – and is independent of ideological political will – we show that states with greater capacity experienced fewer excess deaths during 2020 and more successfully distributed vaccines in early 2021. The findings are robust to various measures of partisanship, social capital, geography, and demographics. Our work bridges US state politics literature and comparative politics literature on state capacity, and it contributes to research on the politics of pandemics.

Since 2017, Republican lawmakers in a growing number of US states have formed ideological intraparty organizations, modeled after the US House Freedom Caucus, that seek to move state policy further rightward. What explains the appearance of these state freedom caucuses, and what kinds of lawmakers are more likely to join them? We show that the creation of these caucuses was initially motivated by concerns that state-level legislative Republican parties are too ideologically heterogeneous but has since been driven by conservative entrepreneurs seeking to spread freedom caucuses nationally. We also provide evidence that conservative legislators are more likely to join a new state freedom caucus, as one would expect, but also that, in a few states, lawmakers who are more electorally vulnerable lawmakers or lack internal influence have also been more likely to join. These findings underscore how state-level ideological caucuses can appeal to members’ multiple goals and serve as instruments of vertical polarization in a federal system.

While a large body of research explores the federal-level influences over distributive politics decisions, very little attention has been given to the active role state and local governments play in the geographic distribution of federal funds. Before presidents, legislators, and agency leaders can influence the selection of federal grants, state and local governments must expend time and resources to submit grant proposals. We focus on grant applications as our unit of analysis and advance a theory that congressional representation influences the grant application behavior of state and local governments. We analyze US Department of Transportation grant applications and awards from 2009 to 2022 and find evidence that congressional representation meaningfully influences state-level grant application behavior. States apply more aggressively for federal transportation grants when represented by senators in the Senate majority party, and states apply more efficiently for grants when represented by a senator holding an advantageous committee leadership post.

Judges are not the first political officials that come to mind when one considers the role of social media in modern politics. Following in the wake of some prominent judicial personalities adopting Twitter, however, a growing number of state high court judges have adopted and established more public personas on the platform. Judges use Twitter in substantively different ways than traditional elected officials (Curry and Fix 2019); however, little is understood about how the use of such social media platforms affects broader judicial networks. Recognizing that judges, like typical social media users, may aspire to expand their networks to build and appeal to broader audiences, we contend that active participation in judicial Twitterverse could yield personal and professional advantages. Here, we address a currently unexplored question: To what extent have judges formed a distinctive “judicial network,” on Twitter, and what discernible patterns present in these networks? Leveraging the unique structure of social media, we collect comprehensive network data on judging using Twitter and analyze what institutional and social factors impact greater power within the judicial network. We find that early adoption, electoral concerns, and connective links between judges all impact the strength of the judicial network, highlighting the complex motivations driving judicial Twitter engagement, and the significance of network building in judges’ social media strategies and its potential impact on career advancement.

We investigate voter preferences for changes in voting rules, focusing specifically on the creation of citizen-initiative processes that were originally adopted in South Dakota in 1898 and eventually enacted by half of the states. Various claims have been advanced about why the process was adopted and who supported or opposed it, but without presenting evidence from referenda where voters approved the creation of the process. We test these claims by examining county-level election returns from South Dakota’s 1898 referendum that created the first statewide initiative process in the United States. We find that support for the initiative process was generally higher among groups that are disadvantaged in various ways by existing representative institutions and perceive advantages in creating direct democratic institutions capable of bypassing representative processes. These findings stand in contrast to the notion that the adoption of constitutional rules will be relatively free from calculations rooted in self-interest and perceived advantage from the rules changes.

The development of multilevel regression and poststratification (MRP) has allowed scholars to more accurately estimate subnational public opinion using national polls. However, MRP generally recovers less accurate estimates from polls whose respondents are selected using cluster sampling – also called area-probability sampling. This is in part because cluster-sampled polls rely on a complex form of random sampling focused on national representativeness that may result in small or unrepresentative subsamples in subnational geographies. This has limited MRP’s usefulness in subnational opinion estimation in several contexts, including historical polls in the US, where cluster-sampling was common into the 1980s, and large academic studies in many countries today. In this paper, I propose two approaches to improve estimation from MRP with cluster-sampled polls. The first is pooling data from multiple surveys to produce a larger sample of clusters. The second is clustered MRP (CMRP), which extends MRP by modeling opinion using the geographic information included in a survey’s cluster-sampling procedure. Using simulations, I show that both methods improve upon traditional MRP, and I validate them using historical polls in the US

We introduce a dynamic dataset of all communications by state election officials (EOs) on social media during the 2022 election cycle and develop metrics to assess the effectiveness of trust-building strategies on voter confidence. We employ quantitative manual content analysis of 10,000 organic posts from 118 state EOs’ accounts on Facebook, Instagram, and Twitter between September 10 and November 30, 2022, and code for the presence of variables that measure EOs’ efforts to combat misinformation and build trusted networks of communications. The measures we present here address two questions: (1) How much coordination was there among states in terms of incorporating the #TrustedInfo2022 campaign, promoted by the National Association of Secretaries of State, in their social media communications, and (2) How much of states’ social media communications explicitly signaled that EOs are trusted sources of information? We demonstrate the applicability of our data on research that evaluates the impact of trust-building campaigns on voter confidence in elections, which is grounded on theories of deliberative democracy and democratic listening.