Research on autocratic regimes in comparative politics and international relations often uses categorical typologies of autocratic regimes to distinguish among different forms of autocracy. This paper introduces historical data on dozens of features of dictatorships to estimate latent dimensions of autocratic rule. We identify three time-varying dimensions of autocracy that correspond to ideal types proposed in the literature: party dominance, military rule, and personalism. We show that dimensions of autocratic rule are orthogonal to commonly-used measures of democracy–autocracy, and compare these dimensions to existing typologies of autocracies, showing that time-varying information on personalism is unique. We discuss a measurement model of personalism and illustrate the time-varying features of this measure in applied research on conflict initiation and regime collapse.

Contemporary dictionary-based approaches to sentiment analysis exhibit serious validity problems when applied to specialized vocabularies, but human-coded dictionaries for such applications are often labor-intensive and inefficient to develop. We demonstrate the validity of “minimally-supervised” approaches for the creation of a sentiment dictionary from a corpus of text drawn from a specialized vocabulary. We demonstrate the validity of this approach in estimating sentiment from texts in a large-scale benchmarking dataset recently introduced in computational linguistics, and demonstrate the improvements in accuracy of our approach over well-known standard (nonspecialized) sentiment dictionaries. Finally, we show the usefulness of our approach in an application to the specialized language used in US federal appellate court decisions.

The Transatlantic Trade and Investment Partnership between the European Union and the US is highly technical. Still, the negotiations triggered large-scale protests among citizens with very diverse socioeconomic backgrounds. Why has a complex issue such an enormous mobilizing effect although the economic consequences are either unclear or favorable for the participating economies? We argue that the transparency of negotiations is an important consideration for people evaluating the negotiation outcome. Conducting a survey experiment, we show that non-transparent decision-making decreases citizens' appraisal of the agreement independent of its outcome: A non-transparent negotiation is, on average, almost 16 percent less likely to find public approval than a transparent but otherwise identical agreement. Our findings have important implications for democratic decision-making.

Recent years have seen a renaissance of conjoint survey designs within social science. To date, however, researchers have lacked guidance on how many attributes they can include within conjoint profiles before survey satisficing leads to unacceptable declines in response quality. This paper addresses that question using pre-registered, two-stage experiments examining choices among hypothetical candidates for US Senate or hotel rooms. In each experiment, we use the first stage to identify attributes which are perceived to be uncorrelated with the attribute of interest, so that their effects are not masked by those of the core attributes. In the second stage, we randomly assign respondents to conjoint designs with varying numbers of those filler attributes. We report the results of these experiments implemented via Amazon's Mechanical Turk and Survey Sampling International. They demonstrate that our core quantities of interest are generally stable, with relatively modest increases in survey satisficing when respondents face large numbers of attributes.

We study the effects of municipal mergers using novel geocoded data on local public sector jobs and local politicians' place of residence. We find that the mergers had no effects on municipal expenditures overall after eight years. However, the mergers led to highly unequal geographic political representation in the post-merger councils among the merged municipalities. Small and politically marginalized municipalities experienced a substantial reduction in local public jobs in administration and health and social care services relative to the municipalities with stronger representation. Development of house prices suggests that the quality of the service-tax bundle deteriorated in these politically marginalized municipalities.

Much of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975 to 2005. We illustrate the measure's utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.

The relationship between participation in revolt and individuals' economic conditions is among the most debated in political science. While conventional economic theory suggests that those who face the poorest economic prospects are most inclined to fight, extant evidence is decidedly mixed. We address this puzzling variation by analyzing the interplay between macro-structural conditions and individuals' micro-level circumstances. Under conditions of severe group repression, we show how a “glass-ceiling” logic may operate: among the repressed group, those with relatively high productive potential may be most motivated to revolt. We test this with in-depth analysis of participation in the 1993–2003 Burundian insurgency. The data are consistent with numerous implications of the glass-ceiling logic and inconsistent with extant alternative explanations.

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.

The use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

Event data provide high-resolution and high-volume information about political events and have supported a variety of research efforts across fields within and beyond political science. While these datasets are machine coded from vast amounts of raw text input, the necessary dictionaries require substantial prior knowledge and human effort to produce and update, effectively limiting the application of automated event-coding solutions to those domains for which dictionaries already exist. I introduce a novel method for generating dictionaries appropriate for event coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and machine learning to reduce the prior knowledge and researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describe that domain. I evaluate the method with the production of a novel event dataset on cybersecurity incidents.

List experiments are a widely used survey technique for estimating the prevalence of socially sensitive attitudes or behaviors. Their design, however, makes them vulnerable to bias: because treatment group respondents see a greater number of items (J + 1) than control group respondents (J), the treatment group mean may be mechanically inflated due simply to the greater number of items. The few previous studies that directly examine this do not arrive at definitive conclusions. We find clear evidence of inflation in an original dataset, though only among respondents with low educational attainment. Furthermore, we use available data from previous studies and find similar heterogeneous patterns. The evidence of heterogeneous effects has implications for the interpretation of previous research using list experiments, especially in developing world contexts. We recommend a simple solution: using a necessarily false placebo statement for the control group equalizes list lengths, thereby protecting against mechanical inflation without imposing costs or altering interpretations.

In the wake of the 2008 financial crisis, macroeconomic policy returned to the political agenda, and the influence of Keynesian ideas about fiscal stimulus rose (and then fell) in expert circles. Much less is known, however, about whether and when Keynesian prescriptions for countercyclical spending have any support among the general public. We use a survey experiment, fielded twice, to recover the extent to which UK respondents hold such countercyclical attitudes. Our results indicate that public opinion was countercyclical—Keynesian—in 2016. We then use Eurobarometer data to estimate the same basic parameter for the population for the period 2010–2017. The observational results validate our experimental findings for the later period, but also provide evidence that the UK population held procyclical views at the start of the period. Thus, there appear to be important dynamics in public opinion on a key macroeconomic policy issue.

At least two theories have been offered that explain the rise of affective polarization. Some scholars, relying on social identity theory, argue that as the relevance of party identification increased, Americans became more likely to see their in-party in positive terms and the out-party in negative terms. Other scholars argue that affective polarization is a reaction to increasingly extreme political actors. This study seeks to arbitrate between these two theories of affective polarization through a survey experiment which asks respondents to rate candidates whose party (or lack thereof) and ideology (or lack thereof) is randomly assigned. In line with the policy-oriented view of affective polarization, respondents reacted far more strongly to ideology than party, especially if it was the ideology of the member of the out-party.

Subnational analyses of political preferences are substantively relevant and offer advantages for causal inference. Yet, our knowledge on regional political preferences across Europe is limited, not least because there is a lack of adequate data. The rich Eurobarometer (EB) data is a promising source for European-wide regional information. Yet, it is only representative for the national level. This paper compares state-of-the-art methods for estimating regional preferences from nationally representative EB data, validating predictions with regionally representative surveys. Our analysis highlights a number of challenges for estimating regional preferences across Europe, such as data availability, variable selection, and over-fitting. We find that predictions are best using a Bayesian additive regression tree with synthetic post-stratification.

We analyze a two-election political agency model with moral hazard and adverse selection in which candidates have observable traits such as job qualifications that help voters to imperfectly predict politicians' types. Candidate's traits can help voters select good types of politicians. However, selecting candidates with good traits can reduce accountability because the voter cannot commit to throw out politicians with good traits who produce bad outcomes. This result explains when and why voters may sometimes select candidates who they believe are less likely to be competent.

Facebook ads are increasingly used by political scientists as a method of survey recruitment. A key advantage is said to be the ability to recruit targeted audiences defined by demographics, political beliefs, location, and numerous other attributes. The same feature has been decried by non-researchers concerned about potential racial discrimination and foreign influence in elections. The extent to which these ads actually reach their targets, however, is unknown. Using a series of six surveys and 20 targeted ads, I show these ads regularly fail to reach their targets. The success rate ranges from 23 to 99 percent, and ads targeted toward groups defined by self-reported data and broader geographic locations are generally more successful.