Candidate Choice without Party Labels: New Insights from Conjoint Survey Experiments with Alexander Coppock. 2018. Political Behavior 40(3):571-591.
In the absence of party labels, voters must use other information to determine whom to support. The institution of nonpartisan elections, therefore, may impact voter choice by increasing the weight that voters place on candidate dimensions other than partisanship. We hypothesize that in nonpartisan elections, voters will exhibit a stronger preference for candidates with greater career and political experience, as well as candidates who can successfully signal partisan or ideological affiliation without directly using labels. To test these hypotheses, we conducted conjoint survey experiments on both nationally representative and convenience samples that vary the presence or absence of partisan information. The primary result of these experiments indicates that when voters cannot rely on party labels, they give greater weight to candidate experience. We find that this process unfolds differently for respondents of different partisan affiliations: Republicans respond to the removal of partisan information by giving greater weight to job experience while Democrats respond by giving greater weight to political experience. Our results lend microfoundational support to the notion that partisan information can crowd out other kinds of candidate information.
Is Divided Government a Cause of Legislative Delay? with Justin Phillips. 2018. Quarterly Journal of Political Science 13(2): 173-206.
Despite the compelling theoretical prediction that divided government decreases legislative performance, the empirical literature has struggled to identify a causal effect. We suspect that a combination of methodological challenges and data limitations are to blame. Here, we revisit this empirical relationship. Rather than relying on traditional measures of legislative productivity, however, we consider whether divided government affects the ability of lawmakers to meet critical deadlines—specifically, the ability of state lawmakers to adopt an on-time budget (as mandated by state law). By focusing on delay instead of productivity we avoid measurement problems, particularly the challenges inherent in measuring the supply of and demand for legislation. To assess the causal effect of divided government, we develop and implement a regression discontinuity design (RDD) that accounts for the multiple elections that produce unified or divided government in separation-of-powers systems. Our RDD approach yields compelling evidence that divided government is a cause of delay. We also evaluate and find support for a new hypothesis that divided government is more likely to lead to lead to delay when the personal and political costs that stalemate imposes on politicians are low.
Does electing a business owner or executive have an effect on public policy? Business interests have a long history of involvement and influence in American politics, but we know relatively little about business owners and executives in public office. With an original dataset of 3,257 mayoral candidates from 263 U.S. cities between 1950 and 2007, I provide a fresh account of descriptive representation in American cities and investigate the effect of electing a business executive mayor on local fiscal policy. Business owners and executives are extraordinarily well represented in city halls across the U.S., making up nearly 32\% of the mayors in my sample. I find that business executive mayors do shape municipal fiscal policy by shifting the allocation of expenditures across policy areas, investing in infrastructure while curtailing redistributive spending. Notably, my results suggest that business executive is not simply a proxy for Republican partisanship.
America’s Mayors: Descriptive Representation in U.S. Cities
Despite advances in descriptive representation, U.S. politicians are still overwhelmingly white and male, and the wealth gap between elected officials and their constituents continues to grow. These circumstances are especially concerning in light of considerable evidence supporting a link between descriptive and substantive representation. In this paper, I examine descriptive representation in U.S. cities. Drawing on an original dataset that includes gender, race, occupational background, and political experience for more than 3,000 mayoral candidates, I provide a comprehensive account of who runs for mayor and who serves. Covering 248 cities and more than 50 years, these data indicate that like politicians at higher levels of government, mayors tend to be white and male with prior political experience and white-collar careers. Business owners and executives are especially well represented, accounting for about 32% of the candidates in the sample. Despite their numbers, I find little evidence to suggest that business owners and executives win at higher rates than other candidates. However, business owners and executives make up a larger share of mayoral candidates in cities with reform institutions. In particular, candidates in council-manager cities are systematically more likely to have a background as a business owner or executive than candidates from cities with a mayor-council government.
A Regression Discontinuity Design for Studying Divided Government
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 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.
Social Class and Representation in American Cities
Despite advances in descriptive representation, the wealth gap between U.S. elected officials and their constituents continues to grow. I investigate whether and how the overrepresentation of the affluent shapes public policy in American cities. In recent years, political scientists’ renewed attention to social class and inequality has focused primarily on national politics, finding that the underrepresentation of the working class leads to more conservative policies. In light of considerable evidence supporting a link between descriptive and substantive representation, questions about social class and inequality seem especially pressing at the local level for two key reasons. First, the public depends on municipal government for essential services that affect their health and safety. Second, poor and working-class residents likely have fewer resources to “vote with their feet” by leaving cities with subpar services or regressive taxes, fees, and fines. Drawing on an original dataset that includes gender, race, occupational background, and political experience for more than 3,000 mayoral candidates from 250 cities over 60 years, I provide a comprehensive account of who runs for office and who serves in city government. Overall, these data indicate that like politicians at higher levels of government, mayors tend to be white and male with prior political experience and white-collar careers. I combine this extensive dataset with municipal public finance data to investigate the effect of the underrepresentation of the working class on local fiscal policy.