Search

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.

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.

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.

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.

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.

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.