This paper examines whether voters’ experience of extreme weather events such as flooding increases voting in favor of climate protection measures. While the large majority of individuals do not hold consistent opinions on climate issues, we argue that the experience of natural disasters can prime voters on climate change and affect political behavior. Using micro-level geospatial data on natural disasters, we exploit referendum votes in Switzerland, which allows us to obtain a behavioral rather than attitudinal measure of support for policies tackling climate change. Our findings indicate a sizeable effect for pro-climate voting after experiencing a flood: vote-share supporting pro-climate policies can increase by 20 percent. Our findings contribute to the literature exploring the impact of local conditions on electoral behavior.

Authoritarian leaders around the world often fight against corruption in an effort to win public support. Conventional wisdom holds that this strategy works because leaders can signal their benevolent intentions by removing corrupt officials. We argue that fighting against corruption can undermine regime support. By revealing scandals of corrupt officials, corruption investigations can alter citizens' beliefs about public officials and lead to disenchantment about political institutions. We test this argument by examining how China's current anti-corruption campaign has changed citizens' public support for the government and the Communist Party. We analyze the results of two original surveys conducted before and during the campaign, and employ a difference-in-differences strategy to show that corruption investigations, at the margin, suppress respondents' support for the central government and party. We also examine our respondents' prior and posterior beliefs, and the results support our updating mechanism.

While Amazon's Mechanical Turk (MTurk) has reduced the cost of collecting original data, in 2018, researchers noted the potential existence of a large number of bad actors on the platform. To evaluate data quality on MTurk, we fielded three surveys between 2018 and 2020. While we find no evidence of a “bot epidemic,” significant portions of the data—between 25 and 35 percent—are of dubious quality. While the number of IP addresses that completed the survey multiple times or circumvented location requirements fell almost 50 percent over time, suspicious IP addresses are more prevalent on MTurk than on other platforms. Furthermore, many respondents appear to respond humorously or insincerely, and this behavior increased over 200 percent from 2018 to 2020. Importantly, these low-quality responses attenuate observed treatment effects by magnitudes ranging from approximately 10 to 30 percent.

Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that is related to Qualitative Comparative Analysis (QCA) but, contrary to the latter, is custom-built for analyzing causal structures with multiple outcomes. So far, however, CNA has only been capable of processing dichotomous variables, which greatly limited its scope of applicability. This paper generalizes CNA for multi-value variables as well as continuous variables whose values are interpreted as membership scores in fuzzy sets. This generalization comes with a major adaptation of CNA’s algorithmic protocol, which, in an extended series of benchmark tests, is shown to give CNA an edge over QCA not only with respect to multi-outcome structures but also with respect to the analysis of non-ideal data stemming from single-outcome structures. The inferential power of multi-value and fuzzy-set CNA is made available to end users in the newest version of the R package cna.

While the structure of party competition evolves slowly, crisis-like events can induce short-term change to the political agenda. This may be facilitated by challenger parties who might benefit from increased attention to issues they own. We study the dynamic of such shifts through mainstream parties’ response to the 2015 refugee crisis, which strongly affected public debate and election outcomes across Europe. Specifically, we analyse how parties changed their issue emphasis and positions regarding immigration before, during, and after the refugee crisis. Our study is based on a corpus of 120,000 press releases between 2013 and 2017 from Austria, Germany, and Switzerland. We identify immigration-related press releases using a novel dictionary and estimate party positions. The resulting monthly salience and positions measures allow for studying changes in close time-intervals, providing crucial detail for disentangling the impact of the crisis itself and the contribution of right-wing parties. While we provide evidence that attention to immigration increased drastically for all parties during the crisis, radical right parties drove the attention of mainstream parties. However, the attention of mainstream parties to immigration decreased toward the end of the refugee crisis and there is limited evidence of parties accommodating the positions of the radical right.

This research note investigates how mainstream party strategies affect the success of radical right parties (RRPs). It is a widespread view that mainstream party accommodation of radical right core issue positions would reduce the radical right's success. Empirical evidence for this claim, however, remains inconclusive. Using party level data as well as micro-level voter transitions between mainstream and RRPs, we re-evaluate the effectiveness of accommodative strategies and also test whether they work contingent on specific conditions, e.g., the newness of radical right challengers or the existence of a cordon sanitaire. We do not find any evidence that accommodative strategies reduce radical right support. If anything, our results suggest that they lead to more voters defecting to the radical right. Our findings have important implications for the study of multi-party competition as they challenge what has become a core assumption of this literature: that accommodative strategies reduce niche party success.

Inattentive respondents introduce noise into data sets, weakening correlations between items and increasing the likelihood of null findings. “Screeners” have been proposed as a way to identify inattentive respondents, but questions remain regarding their implementation. First, what is the optimal number of Screeners for identifying inattentive respondents? Second, what types of Screener questions best capture inattention? In this paper, we address both of these questions. Using item-response theory to aggregate individual Screeners we find that four Screeners are sufficient to identify inattentive respondents. Moreover, two grid and two multiple choice questions work well. Our findings have relevance for applied survey research in political science and other disciplines. Most importantly, our recommendations enable the standardization of Screeners on future surveys.

This paper uses a regression discontinuity design to estimate the degree to which incumbents scare off challengers with previous officeholder experience. The estimates indicate a surprisingly small amount of scare-off, at least in cases where the previous election was nearly tied. As Lee and others have shown (and as we confirm for our samples) the estimated party incumbency advantage in these same cases is quite large—in fact, it is about as large as the average incumbency advantage for all races found using other approaches. Drawing from previous estimates of the electoral value of officeholder experience, we thus calculate that scare-off in these cases accounts for only about 5–7 percent of the party incumbency advantage. We show that these patterns are similar in elections for US House seats, statewide offices and US senate seats, and state legislative seats.

Human rights monitoring reports play important roles both in the international human rights regime and in productions of human rights data. However, human rights reports are produced by organizations subject to formal and informal pressures that may influence the topics considered salient for attention and scrutiny. We study this potential using structural topic models (STMs), a method used for identifying the latent topical dimensions of texts and assessing the effects of covariates on these dimensions. We apply STMs to a corpus of 6298 State Department Country Reports on Human Rights Practices (1977–2012), identifying a plausible set of topics including killings and disappearances, freedoms of expression and movement, and labor rights, among others. We find that these topics vary markedly both over time and space. We also find that while US domestic politics play no systematic role in shaping topic prevalence, US allies tend to receive more attention to violations of physical integrity rights. These results challenge extant research, and illustrate the usefulness of STM methods for future study of foreign policy documents. Our findings also highlight the importance of topical attention shifts in documents that monitor and evaluate countries.

Media freedom has motivated substantial political activism, yet there are surprising gaps in the related academic literature, in part because of a lack of historic and consistent data. We introduce the newly expanded Global Media Freedom Dataset, which includes 196 countries from 1948 to 2012, and demonstrate how it can be used to test previous hypotheses and assumptions about the correlates of media freedom.

Although a voluminous literature has shed light on the relationship between economic conditions and government accountability, most studies in this literature have implicitly assumed that the actions of competing political parties are either irrelevant or that they cancel each other out. In this paper, we take an important first step toward relaxing this strong assumption. We develop and test a set of theoretical propositions from the issue competition literature about the amount of emphasis that parties place on the economy during election campaigns. We test these propositions with an estimation technique that properly situates the motivations of rival elites within the context of spatial party competition using a spatial autoregressive model. On a sample of 22 advanced democracies from 1957 to 2006, we find strong support for the proposition that parties with a greater role in economic policymaking respond to worsening economic conditions by increasing their emphasis on the economy during election campaigns. We also find strong evidence of spatial contagion effects as parties respond positively to the campaign strategies of ideologically proximate parties. This finding reveals a fundamental link in the chain of economic accountability and has important implications for the study of party competition.

Well-established methods exist for measuring party positions, but reliable means for estimating intra-party preferences remain underdeveloped. While most efforts focus on estimating the ideal points of individual legislators based on inductive scaling of roll call votes, this data suffers from two problems: selection bias due to unrecorded votes and strong party discipline, which tends to make voting a strategic rather than a sincere indication of preferences. By contrast, legislative speeches are relatively unconstrained, as party leaders are less likely to punish MPs for speaking freely as long as they vote with the party line. Yet, the differences between roll call estimations and text scalings remain essentially unexplored, despite the growing application of statistical analysis of textual data to measure policy preferences. Our paper addresses this lacuna by exploiting a rich feature of the Swiss legislature: on most bills, legislators both vote and speak many times. Using this data, we compare text-based scaling of ideal points to vote-based scaling from a crucial piece of energy legislation. Our findings confirm that text scalings reveal larger intra-party differences than roll calls. Using regression models, we further explain the differences between roll call and text scalings by attributing differences to constituency-level preferences for energy policy.

Digital literacy is receiving increased scholarly attention as a potential explanatory factor in the spread of misinformation and other online pathologies. As a concept, however, it remains surprisingly elusive, with little consensus on definitions or measures. We provide a digital literacy framework for political scientists and test survey items to measure it with an application to online information retrieval tasks. There exists substantial variation in levels of digital literacy in the population, which we show is correlated with age and could confound observed relationships. However, this is obscured by researchers’ reliance on online convenience samples that select for people with computer and internet skills. We discuss the implications of these measurement and sample selection considerations for effect heterogeneity in studies of online political behavior. We argue that there is no universally applicable formula for selecting a given non-probability sample or operationalization of the concept of digital literacy; instead, we conclude, researchers should make theoretically informed arguments about how they select both sample and measure.

Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

Do authoritarian governments always censor news about protests to prevent unrest from spreading? Existing research on authoritarian politics stresses the danger that information spread within the society poses for a regime. In particular, media and Internet reports of social unrest are deemed to threaten authoritarian rule, as such reports may incite more protests and thus spread instability. We show that such reasoning is incomplete if social protests are targeted at local officials. Allowing media the freedom to report local protests may indeed lead to protest diffusion, but the increased probability of citizen protest also has two potential benefits for the regime: (1) identifying and addressing more social grievances, thus releasing potential revolutionary pressure on the regime; (2) forcing local officials to reduce misbehavior, thus reducing underlying social grievances. For authoritarian governments whose survival is vulnerable to citizen grievances, allowing the media to report social protests aimed at local governments can therefore enhance regime stability and protect its interests under many circumstances. We construct a game-theoretic model to analyze the problem and illustrate the argument with examples from China.

How do marginal voters differ from regular voters? This article develops a method for comparing the partisan preferences of regular voters to those marginal voters whose turnout decisions are influenced by exogenous factors and applies it to two sources of variation in turnout in the United States—weather and election timing. In both cases, marginal voters are over 20 percentage points more supportive of the Democratic Party than regular voters—a significant divide. The findings suggest that the expansion or contraction of the electorate can have important consequences. Moreover, the findings suggest that election results do not always reflect the preferences of the citizenry, because the marginal citizens who may stay home have systematically different preferences than those who participate. Finally, the methods developed in the article may enable future researchers to compare regular and marginal voters on many different dimensions and in many different electoral settings.

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.

In small samples, maximum likelihood (ML) estimates of logit model coefficients have substantial bias away from zero. As a solution, we remind political scientists of Firth's (1993, Biometrika, 80, 27–38) penalized maximum likelihood (PML) estimator. Prior research has described and used PML, especially in the context of separation, but its small sample properties remain under-appreciated. The PML estimator eliminates most of the bias and, perhaps more importantly, greatly reduces the variance of the usual ML estimator. Thus, researchers do not face a bias-variance tradeoff when choosing between the ML and PML estimators—the PML estimator has a smaller bias and a smaller variance. We use Monte Carlo simulations and a re-analysis of George and Epstein (1992, American Political Science Review, 86, 323–337) to show that the PML estimator offers a substantial improvement in small samples (e.g., 50 observations) and noticeable improvement even in larger samples (e.g., 1000 observations).

The efficiency of local public goods provision and the functioning of local democracy crucially depend on the size and number of local jurisdictions. This article empirically analyzes voluntary municipal mergers in Finland. Our main focus is on aspects that have been somewhat neglected in prior empirical work: whether local democracy considerations, representation and voter preferences are involved in shaping the resulting municipal structure. The main results imply that some municipalities are forced to merge due to fiscal pressure and have to trade off political power to be accepted by their partners. The study also finds that the median voter's distance from services matters, while population size does not. The latter, somewhat surprising, observation is possibly explained by existing municipal co-operation, which already exhausts potential economies of scale.

Even though social investment is highly popular, welfare state recalibration remains an uphill battle. When resources are scarce in times of austerity, welfare recalibration involves multidimensional trade-offs. Existing research primarily studied preferences toward individual policies or trade-offs in specific policy fields, failing to capture citizens’ overall social policy priorities. Using two novel survey experiments in three European countries, we show that citizens have clear social policy priorities: pensions and education enjoy a high, family policies a medium, and labor market policies a low priority. However, policy constituencies differ in their relative priorities. Our findings suggest that welfare state recalibration is difficult because trade-offs are unpopular, and distributive conflicts in mature welfare states are mainly about distributing resources to specific social groups.