Search

Recent scholarship on affective polarization documents partisan animosity that extends to people's everyday lives. But does partisan dislike go so far as to deny fundamental rights to political opponents? We study this question through a moral dilemma that gained worldwide notoriety during the COVID-19 pandemic: triage decisions on the allocation of intensive medical care. Using a conjoint experiment in Brazil, Germany, Italy, Poland, and the United States, we study the influence of patients' partisanship in addition to commonly discussed factors determining access to intensive medical care. We find that while participants' choices are consistent with a utilitarian heuristic, revealed partisanship is a robust decision criterion across most countries. Supporters of left or right political camps are more likely to withhold support from partisan opponents, with right-party supporters being more strongly penalized. Our findings offer new comparative evidence on affective polarization in non-political contexts.

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified—even among investigated individuals—absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.

Digital contact tracing apps have been introduced globally as an instrument to contain the COVID-19 pandemic. Yet, privacy by design impedes both the evaluation of these tools and the deployment of evidence-based interventions to stimulate uptake. We combine an online panel survey with mobile tracking data to measure the actual usage of Germany's official contact tracing app and reveal higher uptake rates among respondents with an increased risk of severe illness, but lower rates among those with a heightened risk of exposure to COVID-19. Using a randomized intervention, we show that informative and motivational video messages have very limited effect on uptake. However, findings from a second intervention suggest that even small monetary incentives can strongly increase uptake and help make digital contact tracing a more effective tool. published

Comparative scholars of legislative politics continue to face the challenge of measuring a key theoretical concept: conflict at the level of legislative bills. We address this challenge with a multilingual sentiment-based approach and show that such a measure can effectively capture different types of parliamentary conflict. We also demonstrate that an automated translation of the dictionary yields valid results and therefore greatly facilitates comparative work on legislatures. Our applications show that a sentiment approach recovers government-opposition dynamics in various settings. The use of a simple, translatable sentiment dictionary opens up the possibility of studying legislative conflict in bill debates across languages and countries.

Scholars often use voting data to estimate central bankers' policy preferences but consensus voting is commonplace. To get around this, we combine topic-based text analysis and scaling methods to generate theoretically motivated comparative measures of central bank preferences on the U.S. Federal Open Market Committee leading up to the financial crisis in a way that does not depend on voting behavior. We apply these measures to a number of applications in the literature. For example, we find that FOMC members that are Federal Reserve Bank Presidents from districts experiencing higher unemployment are also more likely to emphasize unemployment in their speech. We also confirm that committee members on schedule to vote are more likely to express consensus opinion than their off schedule voting counterparts and show that it is Dovish rather than Hawkish members who are more likely to want to amend the official monetary policy statement.

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians that police observe but do not investigate. In this paper, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, rendering many quantities of interest unidentified---even among investigated individuals---absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.

During the last decade, much of political behaviour research has come to be concerned with the impact of the Internet, and more recently social networking sites such as Facebook, on political and civic participation. Although existing research generally finds a modestly positive relationship between social media use and offline and online participation, the majority of contributions rely on cross-sectional data, so the causal impact of social media use remains unclear. The present study examines how Facebook use influences reported political participation using an experiment. We recruited young Greek participants without a Facebook account and randomly assigned a subset to create and maintain a Facebook account for a year. In this paper we examine the effect of having a Facebook account on diverse modes of online and offline participation after six months. We find that maintaining a Facebook account had clearly negative consequences on reports of offline and online forms of political and civic participation.

The extensive use of social media for protest purposes was a distinctive feature of the recent protest events in Spain, Greece, and the United States. Like the Occupy Wall Street protesters in the United States, the indignant activists of Spain and Greece protested against unjust, unequal, and corrupt political and economic institutions marked by the arrogance of those in power. Social media can potentially change or contribute to the political communication, mobilization, and organization of social movements. To what extent did these three movements use social media in such ways? To answer this question a comparative content analysis of tweets sent during the heydays of each of the campaigns is conducted. The results indicate that, although Twitter was used significantly for political discussion and to communicate protest information, calls for participation were not predominant. Only a very small minority of tweets referred to protest organization and coordination issues. Furthermore, comparing the actual content of the Twitter information exchanges reveals similarities as well as differences among the three movements, which can be explained by the different national contexts.

This article introduces the global Pro-Government Militias Database (PGMD). Despite the devastating record of some pro-government groups, there has been little research on why these forces form, under what conditions they are most likely to act, and how they affect the risk of internal conflict, repression, and state fragility. From events in the former Yugoslavia, Iraq, Sudan, or Syria and the countries of the Arab Spring we know that pro-government militias operate in a variety of contexts. They are often linked with extreme violence and disregard for the laws of war. Yet research, notably quantitative research, lags behind events. In this article we give an overview of the PGMD, a new global dataset that identifies pro-government militias from 1981 to 2007. The information on pro-government militias (PGMs) is presented in a relational data structure, which allows researchers to browse and download different versions of the dataset and access over 3,500 sources that informed the coding. The database shows the wide proliferation and diffusion of these groups. We identify 332 PGMs and specify how they are linked to government, for example via the governing political party, individual leaders, or the military. The dataset captures the type of affiliation of the groups to the government by distinguishing between informal and semi-official militias. It identifies, among others, membership characteristics and the types of groups they target. These data are likely to be relevant to research on state strength and state failure, the dynamics of conflict, including security sector reform, demobilization and reintegration, as well as work on human rights and the interactions between different state and non-state actors. To illustrate uses of the data, we include the PGM data in a standard model of armed conflict and find that such groups increase the risk of civil war.

Automated and statistical methods for estimating latent political traits and classes from textual data hold great promise, because virtually every political act involves the production of text. Statistical models of natural language features, however, are heavily laden with unrealistic assumptions about the process that generates these data, including the stochastic process of text generation, the functional link between political variables and observed text, and the nature of the variables (and dimensions) on which observed text should be conditioned. While acknowledging statistical models of latent traits to be “wrong,” political scientists nonetheless treat their results as sufficiently valid to be useful. In this article, we address the issue of substantive validity in the face of potential model failure, in the context of unsupervised scaling methods of latent traits. We critically examine one popular parametric measurement model of latent traits for text and then compare its results to systematic human judgments of the texts as a benchmark for validity.

This paper applies theories of international socialization to examine the impact of European Union contact on the professional socialization of public officials in Central and Eastern Europe. Based on a survey of officials in seven new member states, the paper finds that daily work on European Union issues is associated with favourable attitudes towards merit-based civil service governance. The distinction between types of European Union contact shows that officials dealing with ‘reception’-related European Union activities such as the transposition and implementation of European Union policies develop more meritocratic attitudes. By contrast, ‘projection’-related activities that involve personal contact with European Union officials have no effect. The paper concludes that the small but consistent impact of European Union contact on professional socialization promotes the silent professionalization of public administration in Central and Eastern Europe.

Scholars estimating policy positions from political texts typically code words or sentences and then build left-right policy scales based on the relative frequencies of text units coded into different categories. Here we reexamine such scales and propose a theoretically and linguistically superior alternative based on the logarithm of odds- ratios. We contrast this scale with the current approach of the Comparative Manifesto Project (CMP), showing that our proposed logit scale avoids widely acknowledged flaws in previous approaches. We validate the new scale using independent expert surveys. Using existing CMP data, we show how to estimate more distinct policy dimensions, for more years, than has been possible before, and make this dataset publicly available. Finally, we draw some conclusions about the future design of coding schemes for political texts.

Why do some minorities seek affirmative action while others pursue territorial autonomy or secession, given similar conditions at the substate level? This article attempts to unpack the puzzle of minority radicalization, focusing on group claim-making as an important dynamic that has been overlooked by much of the recent quantitative literature on ethnic conflict. To address this gap, the authors introduce a new `claims' variable, which codes the demands made by groups in the Minorities at Risk dataset for three five-year periods from 1985 to 2000. The authors examine the relationship between minority claim-making and rebellion and conclude that they are similar but distinct forms of group mobilization. Groups use claims as a means of bargaining with the center; relative power, therefore, has a critical influence on the extremity of demands that groups advance against the government. The authors test this model against alternative arguments using ordinal logit analysis and find that factors related to strategic power — including a history of autonomy, outside military support, and territorial concentration — are all positively correlated with a group's propensity to advance more extreme demands. This study shows that minorities with greater power vis-à-vis the center are more likely to both rebel and mobilize around separatist demands. However, minority rebellion — unlike separatist claims — may also be triggered by group deprivation, indicating that violent resistance may be driven by grievances as well as opportunities.

Event data, the sequences of dated categorised events between identifiable actors typically harvested from newswire, are a vital source of information about interacting political actors (Quarterly, 1983; Schrodt and Gerner, 2001; Schrodt, 2012). They offer a finer time scale and greater actor and spatial specificity than perhaps any other systematic data source. From a statistical perspective they are also undeniably awkward (see Schrodt, 1994, for useful discussion).

Coding non-manifesto documents as if they were genuine policy platforms produced at election time clearly raises serious issues with error when these codings are used in the standard manner to estimate left-right policy positions. In addition to the long term solution of improving the document base of the Manifesto Project identified by Gemenis (2012), we argue that immediate gains in manifesto-based estimates of policy positions can be realised by using the confrontational logit scales from Lowe et al. (2011), which addresses the problems of scale content and scale construction that are exacerbated by but not unique to the problems found in proxy documents.

The establishment of Weberian-type bureaucracies has been one of the major objectives of the transformation of the post-communist state. Weberian bureaucracies with their emphasis on centralized hierarchies, office specialization, rule orientation and administrative staff characterized by merit recruitment, protection from political dismissal, predictable careers and salaries, and an elevated formal and social status have been a hallmark of the modernization in Western capitalist democracies. Weberian-type bureaucracies are typically seen as beneficial if not as a necessary condition for the consolidation of democracy (Linz and Stepan 1996), economic development and the prevention of public sector corruption (Evans and Rauch 1999).

Chen Shui-bian achieved an international reputation for his promotion of Taiwan independence. Whilst that reputation may have been well earned, the analyses on which this conclusion is based are frequently flawed in two ways. First, by using an undifferentiated notion of independence, they tend to conflate sovereignty with less threatening expressions of Taiwanese identity and pro-democracy discourse. Second, by failing to take into account the impact of immediate strategic context, analysts ignore a fundamental element of democratic political communication. In our empirical analysis of more than 2,000 of Chen's speeches, we seek to avoid both flaws by unpacking the concept of independence and taking into account Chen's strategic relationship with his primary audiences. Our findings challenge popular portrayals of Chen, but more importantly they have strong implications for policy makers and students of political rhetoric with regard to current and future ROC presidents.

Wordscores is a widely used procedure for inferring policy positions, or scores, for new documents on the basis of scores for words derived from documents with known scores. It is computationally straightforward, requires no distributional assumptions, but has unresolved practical and theoretical problems. In applications, estimated document scores are on the wrong scale and the theoretical development does not specify a statistical model, so it is unclear what assumptions the method makes about political text and how to tell whether they fit particular text analysis applications. The first part of the paper demonstrates that badly scaled document score estimates reflect deeper problems with the method. The second part shows how to understand Wordscores as an approximation to correspondence analysis which itself approximates a statistical ideal point model for words. Problems with the method are identified with the conditions under which these layers of approximation fail to ensure consistent and unbiased estimation of the parameters of the ideal point model.

This chapter sketches out some new ways to look at conflict data sets. Since political scientists have more computer power available to them than at perhaps any point in the past, the paper emphasizes methods whose principle feature is that they purchase substantive realism at the cost of more compute cycles, not more advanced statistics. Existing statistical theory is sufficient to perform much more realistic analyses than are typically performed, but it is not necessarily found in the standard location. Most of the models and methods described here can be found in other guises in the field of machine learning. Political methodology is often accused of importing techniques wholesale from other disciplines, particularly econometrics, and by introducing machine learning as another field worth mining, this paper continues a long tradition. 1 The sections below consider new approaches to two important sources of information about conflict: event data and conflict databases. Event data remain underused in international relations, in part because of the apparently narrow range of inferences they license using existing methods. Hopefully, widening the range of available methods should earn this sort of data a wider audience, and help integrate them more fully into quantitative international relations research. Conflict data sets, e.g. the State Failure, and Correlates of War datasets (Esty et al., 1998; Singer and Diehl, 1990), in contrast, are widely used to provide data for regression analysis and this application is well-understood. Trappl and colleagues have recently applied case-based reasoning methods to making predictions (see Chapter 11 in this volume). We describe how this work might itself be generalized in a way that allows it to be integrated back into regular statistical methodology using probabilistic expert systems. 1. But it is a good one; methodology should always be about ensuring a model represents your theory, not somebody else’s, so ultimately it is the theory that matters, and any model that effectively represents it is good enough, wherever it came from.

We present new statistical methods for evaluating information extraction systems. The methods were developed to evaluate a system used by political scientists to extract event information from news leads about international politics. The nature of this data presents two problems for evaluators: 1) the frequency distribution of event types in international event data is strongly skewed, so a random sample of newsleads will typically fail to contain any low frequency events. 2) Manual information extraction necessary to create evaluation sets is costly, and most effort is wasted coding high frequency categories. We present an evaluation scheme that overcomes these problems with considerably less manual effort than traditional methods, and also allows us to interpret an information extraction system as an estimator (in the statistical sense) and to estimate its bias.

Ballard et al. show how control structures using minimal state can be made flexible enough for complex cognitive tasks by using deictic pointers, but they do so within a specific computational framework. We discuss broader implications in cognition and memory and provide biological evidence for their theory. We also suggest an alternative account of pointer binding, which may better explain their limited number.

Regulation plays a central role in modern governance; yet, we have limited knowledge of how subjects of regulation—particularly, private actors—act in the face of potentially adverse regulatory decisions. Here, we document and examine a novel lobbying strategy in the context of competition regulation, a strategy that exploits the regulator’s finite administrative capacities. Companies with merger cases under scrutiny by the European Commission’s Directorate General for Competition appear to be employing a strategy of ‘spamming the regulator,’ through the strategic and cumulative submission of economic expert assessments. Procedural pressures may result in an undeservedly favourable assessment of the merger. Based on quantitative and qualitative analyses of an original dataset of all complex merger cases in the European Union 2005–2020, we present evidence of this new strategy and a possible learning process among private actors. We suggest remedies to ensure regulatory effectiveness in the face of this novel strategy.