The parties as networks approach has become a critical component of understanding American political parties. Research on it has so far mainly focused on variation in the placement of candidates within a network at the national level. This is in part due to a lack of data on state-level party networks. In this article, I fill that gap by developing state party networks for 47 states from 2000 to 2016 using candidate donation data. To do this, I introduce a backboning network analysis method not yet used in political science to infer relationships among donors at the state level. Finally, I validate these state networks and then show how parties have varied across states and over time. The networks developed here will be made publicly available for future research. Being able to quantify variation in party network structure will be important for understanding variation in party-policy linkages at the state level.

Washington and California adopted the Top-Two Primary in 2008 and 2012, respectively. Under this new system, all candidates regardless of party affiliation run against each other, narrowing the field down to the top two for the general election. In some jurisdictions, the general election features two candidates from the same party. Ten percent of California voters chose not to vote in the 2016 U.S. Senate election which featured two Democrats. Using data from the Cooperative Congressional Election Study (2012-2016), I find that among those who vote in the national November elections, orphans, or voters without a copartisan candidate on the ballot are more likely to undervote, opting out of voting in their congressional race. Levels of undervoting are nearly 20 percentage points higher for orphaned voters compared to non-orphaned voters. Additionally, voters who abstain perceive more ideological distance between themselves and the candidates compared to voters who cast a vote. These findings support a multi-step framework for vote decisions in same-party matchups: voters are more likely to undervote if they are unable to vote for a candidate from their party (partisan model), but all voters are more likely to vote for a candidate when they perceive ideological proximity (ideological model).

Are the origins of trust in state government different from the reasons why people trust the national government? I argue that trust in state government has distinctive origins, tied to differences in how states operate within a federal system of government. Leveraging variations in the character of the states, I consider whether trust in state government is a function of its proximity to citizens, people's relative preferences for smaller government, and the homogeneity of state electorates. Using responses to the 2017 Cooperative Congressional Election Study, I show feelings of trust in state government follow not only from state political conditions and economic performance but also from the distinctive character of the states. These findings challenge prior accounts that argue that diffuse trust in state government reflects only how people feel about the national government, and highlight how large states and small states face different challenges in cultivating trust in state government.

Scholars of comparative constitution-making and direct democracy agree that economic conditions affect public support for constitutional reform but disagree as to how. Prospect theory suggests both approaches may be correct, depending on the political and economic context in which voters operate. Fourteen states periodically ask their citizens whether to call a state constitutional convention, making this the oldest form of direct democracy in the United States. We test our theory in preelection polls in two of these states and a survey experiment. According to the results, negative perceptions of economic and government performance increase support for conventions when voters view them as opportunities to correct problems. On the other hand, if a convention represents a chance to improve on an acceptable status quo, voters with positive performance evaluations become more supportive. Our findings contribute to the heuristics literature and inform normative debates over direct democracy and popular constitutionalism.

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.