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

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.

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.

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.

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.

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.

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.

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.

The US has experienced runaway economic inequality since the 1970s, yet there is no strong public support for government efforts that serve to narrow the growing disparities between citizens. Why? I point to the role of rising racial diversity. I argue Americans believe in conditional equality, where they support equalizing policies as long as they perceive the beneficiaries as people like themselves. However, as the country grows more diverse, citizens are less likely to perceive those around them as people like themselves. Using time-series cross-sectional data of the American states, I demonstrate that as racial diversity increases, the likelihood the public will respond to increasing inequality by supporting bigger government declines. This study provides evidence for the mechanism usually implied but rarely tested by studies of diversity and policy: mass preferences.

As judicial elections become increasingly expensive, recusal has emerged as a way to address concerns about the impartiality of judges who receive contributions from lawyers or potential litigants. While it is unclear if strict recusal rules are the best remedy for conflicts of interest created by contributions, they may disincentivize potential donors from investing in judicial campaigns by negating their potential goal of influencing decisions. We consider whether donor behavior in judicial campaigns – especially for those donors most likely to be interested in specifically currying favor with judges – responds to differences in recusal standards. Using data from 219 state supreme court races in 22 states from 2010 to 2020, we find that states with stricter recusal rules attract fewer campaign donations to judicial races, and states with more lax rules attract more overall and, most especially, for attorney donors.

Municipal and state governments are often constitutionally bound to ask voters to approve new government debt through voting on bond referendums. Generally, politicians expect voters to balk at higher-cost bonds and be more willing to approve lower-cost bonds. However, there is minimal research on how the amount of a bond affects voter support. We implement a survey experiment that presents respondents with hypothetical ballots, in which the cost of proposed bonds, the number of bonds on the ballot, and the order in which they are presented, are all randomized. Our results suggest that support is not responsive to the amount of the bond, even when the cost is well outside what is typical and within the bounds of what the government can afford. In contrast, we find other aspects of the ballot matter significantly more for bond referendum approval. The more bonds on the ballot and being placed lower on the ballot both reduce support significantly.

Many studies of policy diffusion focus on what factors affect a policy’s adoption. Few studies specifically test the mechanism and two of the most common explanations – learning and emulation – have not been tested outside of legislatures. While judicial scholars have applied policy diffusion to several types of laws, we know little about the motivation behind why policies spread from court to court. One unexplored area is the relationship between courts. This short article analyzes the two mechanisms most likely to affect peer institutions: learning and emulation. Using network analysis methods on an original dataset of state supreme court citations from 1960 to 2010, I provide evidence that courts are learning from and not emulating each other, but the mechanism is policy-specific.

Emerging health crises challenge and overwhelm federal political systems (Greer et al. 2020, Global Public Health 15: 1413–6). Within the context of COVID-19, states and governors took charge in the absence of a coordinated federal response. The result was uneven policy responses and variance in health-related and economic outcomes. While existing research has explored public evaluations of state COVID-19 policies, we explore primary care physicians’ trust in state government for handling the pandemic, as well as their evaluations of their state government’s treatment responsibility for the pandemic and their state’s policy response. We find that general preferences for the role of the federal/state government in addressing the pandemic are shaped by individual-level physician partisanship. Specific evaluations of state policy responsiveness are influenced by whether physicians’ partisan preferences matched their governor. We also find, however, that Republican physicians were critical of Republican governors and physicians were less partisan than the general public. At least within public health, there are limits to the influence of partisan identity on expert (physician) political evaluations.

We examine the influence of national politics and changing racial demographics on school board elections. We identify the districts where candidates campaigned on Critical Race Theory, COVID response, or parent control/transparency (what we call “conflict elections”) and examine two related questions. First, what characteristics define school districts that had elections involving these issues? Second, in the places that had conflict elections, how frequently did “conflict candidates” win, and what factors influenced their odds of winning? Utilizing a unique dataset of all school board elections in Wisconsin in 2022, we find that Republican presidential vote share is positively related to both the probability that a school district had a conflict election and that a conflict candidate won. We also find that in communities where the white population declined between 2010 and 2020, there was a higher likelihood that a conflict candidate won compared to communities where the size of the white population grew. Overall, our analysis confirms that school board elections are increasingly mirroring nationalized trends in other elections.

Inequalities in terms of who participates in politics yield policy outcomes that fail to reflect the interests of the broader public. Because these processes fail to engage the full citizenry in political decision-making processes, they are also markers of an anemic civic culture. Advocates of participatory budgeting (PB) – a process implemented at the subnational level in thousands of cities in the United States and beyond that invites residents to participate directly in the process of allocating public resources for local projects – argue that it can alleviate these inequalities. They argue that features of the PB process make it ripe for engaging new participants in the political process and weaving a more inclusive social fabric. We examine the correlates of interest in participating in PB using a survey of Cook County residents. We also consider the extent to which the policy priorities of those who are interested in participating diverge from those who are less interested. Although we find evidence that the process is particularly appealing to younger respondents and those who identify as Latine or Black (as opposed to White), we also find that interest is higher among those with higher socioeconomic status and those who perceive conditions in their neighborhood to already be good. Our evidence also suggests that inequalities in who is interested in participating may not radically affect policy outcomes. However, those who decline to participate cannot reap the broader social and political benefits advocates hope the PB process can foster.

What is the effect of divided government on issue attention in state legislatures? Much of the research on divided government examines how it affects the enactment of significant legislation but does not consider other effects on legislative behavior. In this article, I propose a new theory of the relationship between divided government and legislative activity. Regardless of partisan control, reelection-minded legislators face pressure to deliver benefits to voters, yet divided government can make substantive policy change difficult. As a result of these competing pressures, under divided government legislators increasingly turn their focus to bills that benefit their local constituents, which are seen as easier to enact and allow them to engage in advertising, credit claiming, and position taking. Consistent with this theory, I find that under divided government, legislators introduce bills at the same rate, but the type of legislation introduced shifts away from statewide policy changes and towards district-specific legislation.

We explore electoral explanations for U.S. governors’ willingness to commute death sentences in their state. Across descriptive tests and pre-registered regression specifications, we find little evidence that election timing or term limits affect either the probability of commuting death sentences or the proportion of such sentences governors might commute. However, we do find evidence that governors are more likely to commute sentences – and commute sentences for a higher proportion of defendants – during the “lame duck” period after their successor’s election but before their inauguration.

This article examines the relationship between legislative civility and legislative productivity in US state legislatures. The research employs data from the National Survey of State Legislative Lobbyists and from the State Policy Innovation and Diffusion (SPID) database. The former dataset is used to generate an overall civility index for each state as developed by Kettler et al. The SPID database allows one to measure the legislative productivity of a state legislature. Employing these data, negative binomial and Poisson regression models reveal that state legislatures rated as more civil by their own lobbyists produced significantly more pieces of noteworthy legislation than those legislative bodies rated as less civil. These results suggest that the quality of internal legislative dynamics matters for legislative productivity.

This article presents a short summary of the conclusions we report in a longer manuscript (available in our Supplementary Material) subjecting Lagodny et al.’s new measure of state policy mood to the same set of face validity and construct validity tests we applied earlier to Enns and Koch’s measure. We encourage readers to read this longer manuscript, which contains not only the conclusions herein, but also the evidence justifying these conclusions, before accepting or rejecting any claims we make. Our results show that the characteristics of Enns and Koch’s measure that led us to be doubtful that it is valid are also present in Lagodny et al.’s new measure – leaving us just as doubtful that Lagodny et al.’s measure is valid. Moreover, the low correlation between Lagodny et al.’s measure and Enns and Koch’s measure, combined with evidence from replications of seven published studies that the two measures frequently yield quite different inferences about the impact of policy mood on public policy, indicate that Lagodny et al.’s claim that both their measure and Enns and Koch’s measure are valid is wrong; either neither measure is valid, or one is valid and the other is not. Moreover, extending the replications to include not only Lagodny et al.’s and Enns and Koch’s measures, but also Berry et al.’s measure and Caughey and Warshaw’s measure of mass economic liberalism, shows that each of the four measures yields a substantive conclusion about the effect of policy mood that is dramatically different than each of the other three measures. This suggests that the goal of developing a measure of state policy mood that would be widely accepted as valid remains elusive.

This article concludes an exchange on developing and improving longitudinal estimates of state-level public opinion in the United States by introducing the U.S. Partisanship and Presidential Approval Dataset, which combines more than 1.1 million survey responses from 1948 to 2020 into a single harmonized “mega poll.”