Using data from nearly 5,000 votes cast by more than 400 judges in courts of last resort from all 50 states, we investigate whether there is a relationship between a state's judicial retention method and the likelihood that a judge votes to join a precedent-overturning majority. We find that relative to judges retained by institutions such as judicial commissions or state legislatures, those retained via either partisan or retention elections are significantly more likely to join majorities that overturn precedent. Most of this effect is due to behavior in high-profile cases that garner media attention. We find little evidence that an impending election moderates these effects. Finally, we find no evidence that judges retained via nonpartisan elections treat precedent differently than their institutionally retained colleagues.

This study employs state-level panel data between 1978 and 2000 to explore the relationship between transparency, media market penetration, class bias in voter participation, and welfare effort in the United States. I present empirical evidence that the effect of transparency—operationalized as state fiscal transparency—on state welfare effort is conditional on class bias in voter participation. Specifically, I present evidence that in states where transparency and class bias increased over time, state welfare effort significantly declined. These results are robust to the inclusion of controls for other determinants of redistribution that traditionally vary with geography such as governor partisanship, legislator ideology, citizen ideology, gross state product (GSP), and state demographic characteristics, and are robust across several alternate model specifications. My findings suggest that increased transparency does not automatically improve the condition of socioeconomically-disadvantaged citizens and that transparency may have welfare-reducing effects in societies with increasing participatory gulfs between the most and least advantaged citizens.

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.

The regression discontinuity design (RDD) is a valuable tool for identifying causal effects with observational data. However, applying the traditional electoral RDD to the study of divided government is challenging. Because assignment to treatment in this case is the result of elections to multiple institutions, there is no obvious single forcing variable. Here, we use simulations in which we apply shocks to real-world election results in order to generate two measures of the likelihood of divided government, both of which can be used for causal analysis. The first captures the electoral distance to divided government and can easily be utilized in conjunction with the standard sharp RDD toolkit. The second is a simulated probability of divided government. This measure does not easily fit into a sharp RDD framework, so we develop a probability restricted design (PRD) which relies upon the underlying logic of an RDD. This design incorporates common regression techniques but limits the sample to those observations for which assignment to treatment approaches “as-if random.” To illustrate both of our approaches, we reevaluate the link between divided government and the size of budget deficits.

Identifying the geographic constituencies of representatives is among the most crucial, yet challenging, aspects of state and local politics research. Regularly changing district lines, incomplete data, and computational obstacles can present barriers to matching individuals to their respective districts. Geocoding residential addresses is the ideal method for matching purposes. However, cost constraints can limit its applicability for many researchers, leading to geographic assignment methods that use polygonal units, such as ZIP codes, to estimate constituency membership. In this study, we quantify the trade-offs between three geographic assignment matching methods – centroid, geographic overlap, and population overlap matching – on the assignment of individual voters to state legislative districts. We confirm that population overlap matching produces the highest accuracy in assigning voters to their state legislative districts when polygonal location data are all that is available. We validate this finding by improving model estimates of lobbying influence through a replication analysis of Bishop and Dudley (2017), “The Role of Constituency, Party, and Industry in Pennsylvania’s Act 13,” State Politics and Policy Quarterly 17 (2): 154–79. Our replication suggests that distinguishing between out-of-district and in-district donations reveals a greater impact for in-district lobbying efforts. We make evident that population overlap assignment can confidently be used to identify constituencies when precise location data is not available.

The State Politics and Policy Data Archive is a free-access, web-based archive of publication-related datasets focusing on comparative American state politics and policy research. This essay describes the utility of the archive, provides instruction on its use, and requests that readers contribute data.

In the face of the intensifying global climate crisis, the US has failed to implement comprehensive policies to mitigate greenhouse gas emissions. During the 2000s, the shale oil and gas extraction (i.e., “fracking”) revolution highlighted the American energy economy. Is the fracking boom partially to blame for US lagging on climate policy? Political economy theory suggests that economic resources are primary drivers of policy outcomes. In this paper, I originally evaluate that claim in the context of the American states, the governments most powerful to mitigate emissions while the federal government faces gridlock. I first introduce an original measure of one state-level climate policy: adoption of the low-emission vehicle (LEV) policy from 1991 to 2015. I then frame the US fracking boom of the mid-to-late 2000s as a natural experiment, employing a difference-in-difference design to compare the effects of fracking on two climate policies across the American states – LEV and renewable electricity policy. Results yield evidence of a causal impact of the fracking boom on state LEV adoption and more suggestive evidence of an impact on renewable electricity mandates. I conclude by arguing that efforts to evaluate the influence of business on policy should account for “structural power” mechanisms.

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.

College students in the U.S. have long faced barriers to college town registration and voting. Arguably, these barriers raise the costs associated with electoral participation for students living away from home. Despite ongoing debate about where students should vote, the extent to which college town restrictions limit participation by students has never been estimated. This article is the first to demonstrate that college town registration barriers implemented by the U.S. states reduce participation in national elections.

Most research studying minority representation concludes that minorities enjoy better representation when they constitute a larger share of a constituency, but only through the partisanship and race/ethnicity of the representative. Other research finds that minorities receive worse representation when they constitute a larger share of a constituency. We argue that minority composition will have an independent effect on representation, but that this effect will differ depending on the representative's partisanship. We apply our theory to Latino composition and state legislative voting on immigration policy and find that Latino composition has no effect on voting among Democratic legislators, who are less likely to vote in a restrictive direction on immigration than Republicans regardless of the Latino composition in their district. However, Republicans are more likely to vote to restrict immigration as Latinos comprise a larger share of their district. Our findings suggest that scholars should consider the moderating effect of legislator partisanship when examining minority composition and representation.

Executives are important elites, and ideology is important to elite behavior, but measurement challenges and a focus on the presidency have kept scholars from fully exploring executive ideology. This article advocates studying US governors to learn more about executive ideology. It provides an overview of the data scholars can use to measure gubernatorial preferences, and highlights Bonica’s campaign finance-based ideology scores (CFscores) as offering the greatest coverage and allowing common-scale comparisons with other actors. As a validation exercise, I find that CFscores explain within-party variation in other measures and predict the decisions that governors make when in office. Then, I run a preliminary test of the substantive importance of executive ideology. Four models explain state policy liberalism as a function of executive, legislative, and citizen ideology. Gubernatorial preferences emerge as most predictive of the three. These results encourage greater investigation into the role of executive ideology in the policy process.