PreprintPDF Available

Divided, But on What? Parties, Preferences, and Policy Stasis

  • Trinity University
Preprints and early-stage research may not have been peer reviewed yet.


Prominent accounts of American politics posit both partisan and preference-based conflict as sources of policymaking dysfunction, but empirical tests rarely adjudicate between these accounts. In this paper, we examine the relative importance of these sources in the context of divided government and policymaking in the U.S. states. Using a new dataset of state-level gridlock intervals, we compare the role of preference-based gridlock to other, non-preference-based sources of policy stasis. Re-examining a variety of outcomes explored in existing studies of divided government, we find that, apart from a few exceptions, preference-based gridlock appears not to be the primary mechanism through which divided government affects the policymaking outcomes we examine. This study highlights the importance of understanding the mechanisms through which partisan differences operate when positing solutions to policy stasis in the United States.
A preview of the PDF is not available
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Cambridge Core - American Studies - Can America Govern Itself? - edited by Frances E. Lee
Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the effect modifier and other covariates, and including these interactions can lead to unstable estimates due to overfitting. In this paper, we explore the usefulness of machine learning algorithms for stabilizing these estimates and show how many off-the-shelf adaptive methods lead to two forms of bias: direct and indirect regularization bias. To overcome these issues, we use a post-double selection approach that utilizes several lasso estimators to select the interactions to include in the final model. We extend this approach to estimate uncertainty for both interaction and marginal effects. Simulation evidence shows that this approach has better performance than competing methods, even when the number of covariates is large. We show in two empirical examples that the choice of method leads to dramatically different conclusions about effect heterogeneity.
Regression discontinuity design could be a valuable tool for identifying causal effects of a given party holding a legislative majority. However, the variable “number of seats” takes a finite number of values rather than a continuum and, hence, it is not suited as a running variable. Recent econometric advances suggest the necessary assumptions and empirical tests that allow us to interpret small intervals around the cut-off as local randomized experiments. These permit us to bypass the assumption that the running variable must be continuous. Herein, we implement these tests for US state legislatures and propose another: whether a slim-majority of one seat had at least one state-level district result that was itself a close race won by the majority party.
While most common‐space estimations rely upon members who served in both the House and Senate as “bridges” to scale the remaining members, this assumes that these “bridge members” do not change their preferences when they change chambers. Such an assumption conflicts with standard notions of representation, that is, that legislators’ votes reflect (at least to some degree) the wishes of their constituents. We examine the constancy of this common‐space voting assumption by focusing on a subset of House members who move to the Senate: those who come from statewide House districts. Using these members as the bridge actors—and thus bridging by constituency explicitly—in a one‐dimensional item response theory model, we find that the standard assumption of chamber switchers in common‐space estimations is technically, but immaterially, false. While there are statistically distinguishable differences in House and Senate voting records for chamber switchers, they are not sufficiently large to meaningfully undermine bridging.
While previously polarization was primarily seen only in issue-based terms, a new type of division has emerged in the mass public in recent years: Ordinary Americans increasingly dislike and distrust those from the other party. Democrats and Republicans both say that the other party’s members are hypocritical, selfish, and closed-minded, and they are unwilling to socialize across party lines. This phenomenon of animosity between the parties is known as affective polarization. We trace its origins to the power of partisanship as a social identity, and explain the factors that intensify partisan animus. We also explore the consequences of affective polarization, highlighting how partisan affect influences attitudes and behaviors well outside the political sphere. Finally, we discuss strategies that might mitigate partisan discord and conclude with suggestions for future work. Expected final online publication date for the Annual Review of Political Science Volume 22 is May 11, 2019. Please see for revised estimates.
Despite the compelling theoretical prediction that divided government decreases legislative performance, the empirical literature has struggled to identify a causal e ect. 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 a ects 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 e ect 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 delay when the personal and political costs that stalemate imposes on politicians are low.
Given pervasive gridlock at the national level, state legislatures are increasingly the place where notable policy change occurs. Investigating such change is difficult because it is often hard to characterise policy change and use observable data to evaluate theoretical predictions; it is subsequently unclear whether law-making explanations focusing on the US Congress also apply to state legislatures. We use several measures of state policy outcomes to examine lawmaking in state legislatures across nearly two decades, and we argue for using simulation studies to connect theoretical predictions to empirical specifications and help interpret the theoretical relevance of estimated correlations. Doing so reveals that the observed law-making outcomes we study are most consistent with law-making models emphasising the importance of the chamber median and the powers of the governor rather than those that focus on the preferences of the majority party.