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.

Using survey vignettes and scaling techniques, we estimate common socio-cultural and European integration dimensions for political parties across the member states of the European Union. Previous research shows that party placements on the economic left-right dimension are cross-nationally comparable across the EU; however, the socio-cultural dimension is more complex, with different issues forming the core of the dimension in different countries. The 2014 wave of the Chapel Hill Expert Survey included anchoring vignettes which we use as “bridge votes” to place parties from different countries on a common liberal/authoritarian dimension and a separate common scale for European integration. We estimate the dimensions using the Bayesian Aldrich–McKelvey technique. The resulting scales offer cross-nationally comparable, interval-level measures of a party's socio-cultural and EU ideological positions.

Can data on government coercion and violence be trusted when the data are generated by state itself? In this paper, we investigate the extent to which data from the California Department of Corrections and Rehabilitation (CDCR) regarding the use of force by corrections officers against prison inmates between 2008 and 2017 conform to Benford's Law. Following a growing data forensics literature, we expect misreporting of the use-of-force in California state prisons to cause the observed data to deviate from Benford's distribution. Statistical hypothesis tests and further investigation of CDCR data—which show both temporal and cross-sectional variance in conformity with Benford's Law—are consistent with misreporting of the use-of-force by the CDCR. Our results suggest that data on government coercion generated by the state should be inspected carefully before being used to test hypotheses or make policy.

Firms in the USA rely on highly skilled immigrants, particularly in the science and engineering sectors. Yet, the recent politics of immigration marks a substantial change to US immigration policy. We implement a conjoint experiment that isolates the causal effect of nativist, anti-immigrant, pronouncements on where skilled potential-migrants choose to immigrate to. While these policies have a significantly negative effect on the destination choices of Chilean and UK student subjects, they have little effect on the choices of Indian and Chinese student subjects. These results are confirmed through an unobtrusive test of subjects’ general immigration destination preferences. Moreover, there is some evidence that the negative effect of these nativist policies are particularly salient for those who self-identify with the Left.

Although covert warfare does not readily lend itself to scientific inquiry, new technologies are increasingly providing scholars with tools that enable such research. In this note, we examine the effects of drone strikes on patterns of communication in Yemen using big data and anomaly detection methods. The combination of these analytic tools allows us to not only quantify some of the effects of drone strikes, but also to compare them to other shocks. We find that on average drone strikes leave a footprint in their aftermath, spurring significant but localized spikes in communication. This suggests that drone strikes are not a purely surgical intervention, but rather have a disruptive impact on the local population.

When the United States intervenes in foreign countries, the lives of both foreign combatants and foreign civilians are put at risk. I examine two rhetorical strategies, the use of sanitized and dehumanizing language that can influence the public's support of foreign intervention. In the context of foreign policy, sanitized language operates by obscuring casualties of war, while dehumanizing language operates by devaluing the lives of groups of individuals. Drawing on data from two experiments, I find that sanitized language operates through creating less of an emotional reaction toward casualties of war, which causes individuals to adopt more hawkish foreign policy attitudes. I find that dehumanizing language also leads to more hawkish foreign policy attitudes, but, contrary to expectations, does not lead to increased disgust or anger toward dehumanized groups.

Research on presidential power delineates between a modern era of relative autonomy and an earlier period of congressional dominance. What drove this change? Unlike prior arguments about presidential entrepreneurship and the rise of the United States as a global power, we attribute the emergence of the modern presidency partially to an institutional change—the adoption of direct election of senators that culminated in the 17th Amendment. With direct election, senators were selected by individual voters rather than state legislators. These senators answered to a new principal—the general public—that was (in the aggregate) less informed and less interested in foreign policy. As a result, senators had less incentive to constrain presidential foreign policy preferences. We find evidence for this shift in the relationship between the piecemeal adoption of direct election and senate votes to delegate foreign policy authority to the executive. The implication is that the direct election of senators played an underappreciated role in the emergence of the modern presidency.

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.

Previous research reports that parties in established European democracies learn from and emulate the successful election strategies of foreign incumbents, i.e., successful parties are influential abroad. We theorize that—in addition to incumbency (or success)—exchange takes place through transnational party alliances in the European Union. Relying on party manifesto data and spatial econometric analyses, we show that belonging to the same European Parliament (EP) party group enhances learning and emulation processes between national political parties. Estimated short- and long-term effects are approximately two and three times greater when foreign incumbents are in the same EP party group compared to other foreign incumbents. Our results have implications for our understanding of how transnational party groups influence national parties’ policy positions.

When separation is a problem in binary dependent variable models, many researchers use Firth's penalized maximum likelihood in order to obtain finite estimates (Firth, 1993; Zorn, 2005; Rainey, 2016). In this paper, I show that this approach can lead to inferences in the opposite direction of the separation when the number of observations are sufficiently large and both the dependent and independent variables are rare events. As large datasets with rare events are frequently used in political science, such as dyadic data measuring interstate relations, a lack of awareness of this problem may lead to inferential issues. Simulations and an empirical illustration show that the use of independent “weakly-informative” prior distributions centered at zero, for example, the Cauchy prior suggested by Gelman et al. (2008), can avoid this issue. More generally, the results caution researchers to be aware of how the choice of prior interacts with the structure of their data, when estimating models in the presence of separation.

People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.

How does the Rule of Four affect Supreme Court decisions? We show two effects of changing a “hearing pivot” justice who is decisive for case selection. First, a court with more extreme hearing pivots will hear cases with more moderate precedents. For example, as the conservative hearing pivot becomes more extreme, the court hears a broader range of cases with liberal status quo precedents. Second, more extreme hearing pivots shrink dispositional majorities and lead to more polarized rulings. If the median justice becomes more extreme without changing the hearing pivots, then rulings are more extreme. The effect on the range of cases heard, however, is smaller than that from changing hearing pivots. Finally, we show that case selection can also depend on non-median, non-hearing-pivot justices. Replacing an extreme justice with someone even more extreme can lead to a smaller set of heard cases, as final rulings can shift away from the binding hearing pivot, making status quo precedents more appealing.

Previous study demonstrates that partisans perceive in-party news outlets as fair, and out-party news outlets as unfair. However, much of this study relies on one-shot designs. We create an ecologically valid design that randomly assigns participants to news feeds within a week-long online news portal where the balance of in-party and out-party news outlets has been manipulated. We find that sustained exposure to a feed that features out-party news media attenuates Democrats' beliefs that Fox News is unfair, but the same is not true for Republican's perceptions of MSNBC's fairness. Unexpectedly, repeated exposure to in-party news did increase Republicans' beliefs that Fox News is unfair. This study updates our understanding of partisan news effects in a fragmented online news environment.

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.

Delegation is a well-known feature of policymaking in separation of powers systems. Yet despite the importance of this activity, there is little systematic evidence about how many major laws in the United States actually delegate policymaking authority to administrators in federal agencies. Using a database of agency regulatory activity along with text searches, we examine significant US federal enactments from 1947 to 2016 to see which of these laws delegate to agencies. We find that nearly all major laws—more than 99 percent—contain delegation. We also find that the number of agencies receiving delegation in each law has increased over time.

I present a novel measure of partisan alignment between firms and employees. This measure is constructed using data matching 1,691,790 US federal campaign contribution filings of 85,109 individuals to the donations of 874 Political Action Committees (PACs) of publicly listed US companies between 2003 and 2016. The alignment measure shows that employee and employer contributions are highly correlated. Furthermore, firm- and occupation-level factors are significantly associated with firm–employee alignment. Uniquely, these new data can be easily linked to external data on industries, firms, and occupations and consequently allow for in-depth analysis of precisely how companies can influence employees’ politics.

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.

We examine citizens' evaluations of majoritarian and proportional electoral outcomes through an innovative experimental design. We ask respondents to react to six possible electoral outcomes during the 2019 Canadian federal election campaign. There are two treatments: the performance of the party and the proportionality of electoral outcomes. There are three performance conditions: the preferred party's vote share corresponds to vote intentions as reported in the polls at the time of the survey (the reference), or it gets 6 percentage points more (fewer) votes. There are two electoral outcome conditions: disproportional and proportional. We find that proportional outcomes are slightly preferred and that these preferences are partly conditional on partisan considerations. In the end, however, people focus on the ultimate outcome, that is, who is likely to form the government. People are happy when their party has a plurality of seats and is therefore likely to form the government, and relatively unhappy otherwise. We end with a discussion of the merits and limits of our research design.

Close ties between politicians and businesses affect firms’ performance and political outcomes, and while direct political control over firms has been curtailed by tightened regulation, political connections remain ubiquitous in many countries. Yet, it is unclear through which channels these linkages are maintained in strictly regulated environments. I speculate that one such channel of political control over firms is politicians’ ability to influence corporate appointment decisions. To test the claim, I employ survival models that analyze chairpersons’ turnovers in 90 Spanish savings banks between 1985 and 2010 and find strong evidence for electoral appointment cycles: bank chairpersons are more likely to lose office shortly after regional elections and when new governments enter office.