While longstanding theories of political behavior argue that voters do not possess sufficient political knowledge to hold their elected representatives accountable, recent revisionist studies challenge this view, arguing that voters can both follow how their representatives vote and use that information intelligently. We apply the revisionist account to the study of Supreme Court nominations in the modern era. Using survey data on the nominations of Clarence Thomas, Sonia Sotomayor, and Elena Kagan, we ask whether voters can and do hold senators accountable for their votes on Supreme Court nominees. While our results for Thomas are ambiguous, we find strong evidence for accountability in the cases of Sotomayor and Kagan. In particular, we show that voters on average can correctly recall the votes of their senators on these nominees, and that correct recall is correlated with higher levels of education and political knowledge. We then show that voters are more likely to both approve of and vote to re-elect their senator if he or she casts a vote on Sotomayor and Kagan that is in line with voters’ preferences. Finally, we show this effect is quite sizable, as it rivals the effect of agreement on other high-profile roll call votes. These results have important implications for both the broader study of representation and for understanding the current politics of Supreme Court nominations.

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

Does the potential for a successful private sector career induce legislators to leave office? How does this affect the representation voters receive? I show that when former US senators—who now work as lobbyists—become more successful, currently serving senators with similar characteristics are more likely to take private sector employment. I replicate all results on data from the House. A number of tests suggest that senators react to the opportunity costs of holding office. Investigating selection effects, I find that legislative specialists are attracted the most in the Senate. Preliminary evidence suggests that the least wealthy respond most strongly in the House. This suggests that the revolving door shapes the skill set of legislators and the representation voters receive.

How do governing parties respond in terms of ideological positioning when voted out of office? We study both theoretically and empirically the factors that shape parties’ responses following a loss. Studying national elections in advanced industrialized democracies over the past 70 years, we show that parties tend to counter their pre-election shifts, and do so particularly strongly following defeat. The extent of these ideological shifts is more limited in parties with a larger selectorate voting on the party leadership. Moreover, we find that subsequent to loss, parties are less likely to run on a centrist platform. Notably, shifting away from the center is associated with a higher probability of returning to power. We then introduce a dynamic model of party leadership selection and platform positioning. The model produces patterns of ideological positions over time that are consistent with our empirical findings.

This study investigates how individuals may develop more or less strong beliefs in the indivisibility of a disputed territory and how such beliefs may influence their policy preferences toward resolving the dispute. Using a survey experiment in Japan, we find that historical ownership strengthens respondents’ beliefs in territorial indivisibility. Furthermore, those who hold the strongest belief in territorial indivisibility are much less likely to support bilateral negotiation and more likely to support contentious policies, including but not limited to military actions. Finally, we explore external validity of the findings by analyzing respondents who had a real dispute in mind during the survey with China, South Korea, and Russia, respectively.

Twitter is a prominent communication tool for politicians with two potential uses: as a “substitute” channel to circumvent constraints from other political arenas, or as an “amplifier” that reinforces party messages. Using a novel dataset containing tweets and parliamentary speeches by members of parliament (MPs) in seven countries, we estimate politicians’ positions and intra-party dissent on European integration. We find that MPs’ sentiment about Europe on Twitter is a valid measure of their party's position, while also uncovering intra-party disagreements. Our results suggest that most MPs amplify the partisan message, but MPs who participate less in parliamentary debate tend to have larger differences with their party on Twitter. Social media thus can free politicians from their party's grip.

Although the literature on US Supreme Court agenda-setting is sizable, justice-vote-level multivariate analyses of certiorari are almost exclusively limited to samples of discussed cases from 1986 to 1993. Moreover, these studies have done very little to explore justice-level heterogeneity on certiorari. Here, we address these lacunae by analyzing the predictors of individual justices’ cert votes on all paid cases from the 1939, 1968, and 1982 terms. We find substantial justice-level heterogeneity in the weight that justices place on the standard set of forces shaping the cert vote. We also show that some of this heterogeneity is associated with justices’ experience and ideological extremism, largely in theoretically predicted ways. In closing, we sound a note of caution on drawing conclusions about effects of justice attributes, when the number of justices is relatively small.

How well are voters represented and what explains how well voters are represented? Answering these questions requires unpacking the mechanisms by which voters choose elected officials and elected officials implement policies. Though spatial theories of voting and legislative bargaining provide a broad framework for understanding these mechanisms, testing these theories involves a measurement problem where multiple political actors and outcomes must be located. We develop a technique for estimating policy outcomes, status quo locations, the ideology of elite political actors, and the ideology of voters, on a common scale. Using our new estimates, we demonstrate a similar level of incongruence for tax policies and spending policies. The incongruence arises for different reasons however—tax policies are over-responsive to the position of the median voter. Contrarily, spending policies are under-responsive and barely correlated with the position of the median voter. In examining the underlying mechanisms for policy change, we find that while the positions of elected officials over-respond to the median voter, the changing composition of state government has little immediate impact on policy. Instead, policy outcomes respond to long-term trends in the composition of government.

Individuals' attitudes about gender roles have been shown to be associated with a wide range of political outcomes. It is therefore crucial to better understand what shapes these attitudes. This note takes advantage of a randomized survey experiment embedded in the 2018 wave of the European Social Survey (ESS) to investigate how differences in education levels between partners influence the “gender childcare bias”—the extent to which individuals disapprove more of women working full time with children under three than men. Although male and female respondents exhibit an equally strong gender childcare bias on average, we find clear-cut evidence that the bias varies asymmetrically across the household education gap for women and men. In particular, positive household education gaps lead to a smaller gender childcare bias for female respondents, whereas the opposite holds for male respondents. Our findings are more in line with a resource-bargaining approach than a gender identity approach to the formation of gender role attitudes.

A growing body of empirical research shows an association between public support for the US Supreme Court and both judicial independence and congressional court curbing activity. At the same time, studies of jurisdiction stripping show Congress’ efforts to limit federal courts’ jurisdiction are principally related to courts’ workloads rather than ideological differences between courts and Congress. Here, the authors connect these streams of inquiry by testing the hypothesis of a negative relationship between public support for the Supreme Court and jurisdiction-stripping legislation. Contrary to prior studies, the authors find a positive relationship between Americans’ confidence in the Supreme Court and jurisdiction stripping. This result indicates the need for additional research on the interactions among public opinion, federal courts, and Congress.

There has been a proliferation of research with human participants in violent contexts over the past ten years. Adhering to commonly held ethical principles such as beneficence, justice, and respect for persons is particularly important and challenging in research on violence. This letter argues that practices around research ethics in violent contexts should be reported more transparently in research outputs, and should be seen as a subset of research methods. We offer practical suggestions and empirical evidence from both within and outside of political science around risk assessments, mitigating the risk of distress and negative psychological outcomes, informed consent, and monitoring the incidence of potential harms. An analysis of published research on violence involving human participants from 2008 to 2019 shows that only a small proportion of current publications include any mention of these important dimensions of research ethics.

To what extent can civil rights NGOs protect ethnic minorities against unequal treatment? We study this question by combining an audit experiment of 1260 local governments in Hungary with an intervention conducted in collaboration with a major Hungarian civil rights NGO. In the audit experiment we demonstrated that Roma individuals were about 13 percent-points less likely to receive responses to information requests from local governments, and the responses they received were of substantially lower quality. The intervention that reminded a random subset of local governments of their legal responsibility of equal treatment led to a short-term reduction in their discriminatory behavior, but the effects of the intervention dissipated within a month. These findings suggest that civil rights NGOs might face substantive difficulties in trying to reduce discrimination through simple information campaigns.

It is fairly well-known that proper time series analysis requires that estimated equations be balanced. Numerous scholars mistake this to mean that one cannot mix orders of integration. Previous studies have clarified the distinction between equation balance and having different orders of integration, and shown that mixing orders of integration does not increase the risk of type I error when using the general error correction/autoregressive distributed lag (GECM/ADL) models, that is, so long as equations are balanced (and other modeling assumptions are met). This paper builds on that research to assess the consequences for type II error when employing those models. Specifically, we consider cases where a true relationship exists, the left- and right-hand sides of the equation mix orders of integration, and the equation still is balanced. Using the asymptotic case, we find that the different orders of integration do not preclude identification of the true relationship using the GECM/ADL. We then highlight that estimation is trickier in practice, over finite time, as data sometimes do not reveal the underlying process. But, simulations show that even in these cases, researchers will typically draw accurate inferences as long as they select their models based on the observed characteristics of the data and test to be sure that standard model assumptions are met. We conclude by considering the implications for researchers analyzing or conducting simulations with time series data.

Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.

A flurry of current interest in time series has focused on clarifying equation balance, fractional integration, and cointegration testing. Despite this, a number of recent suggestions may continue to lead scholars toward incorrect inferences. In this comment, I investigate the likelihood of drawing both correct and incorrect inferences under a variety of stationary and non-stationary data-generating processes. I extend previous work in this area by focusing on both short- and long-run effects using several popular model specifications. Given these findings, I conclude by offering a variety of recommendations to practitioners about how they can best specify their model.

The papers in this symposium use Monte Carlo simulations to demonstrate the consequences of estimating time series models with variables that are of different orders of integration. In this summary, I do the following: very briefly outline what we learn from the papers; identify an apparent contradiction that might increase, rather than decrease, confusion around the concept of a balanced time series model; suggest a resolution; and identify a few areas of research that could further increase our understanding of how variables with different dynamics might be combined. In doing these things, I suggest there is still a lack of clarity around how a research practitioner demonstrates balance, and demonstrates what Pickup and Kellstedt (2021) call I(0) balance.