Kevin M. Quinn

Multiparty electoral competition in the Netherlands and Germany: A model based on multinomial probit

A typical assumption of electoral models of party competition is that parties adopt policy positions so as to maximize expected vote share. Here we use Euro-barometer survey data and European elite-study data from 1979 for the Netherlands and Germany to construct a stochastic model of voter response, based on multinomial probit estimation. For each of these countries, we estimate a pure spatial electoral voting model and a joint spatial model. The latter model also includes individual voter and demographic characteristics. The pure spatial models for the two countries quite accurately described the electoral response as a stochastic function of party positions. We use these models to perform a thought experiment so as to estimate the expected vote maximizing party positions. We go on to propose a model of internal party decision-making based both on pre-election electoral estimation and post-election coalition bargaining. This model suggests why the various parties in the period in question did not adopt vote maximizing positions. We argue that maximizing expected vote will not, in general, be a rational party strategy in multiparty political systems which are based on proportional representation.

Voter Choice in Multi-Party Democracies: A Test of Competing Theories and Models

We contrast social-structural theories of voting behavior with spatial theories of voting behavior to explain voter choice in the Netherlands and Great Britain. We hypothesize that voting behavior is best explained by the spatial theory of voting. Markov chain Monte Carlo (MCMC) simulation is used to estimate multinomial probit (MNP) and multinomial logit (MNL) models of voter choice, for which we calculate Bayes factors for the purpose of model comparison. We find that the joint social-structural/spatial model is the best explanatory model in the Netherlands. Our results indicate that the MNP model outperforms the MNL model in our Dutch sample. In Great Britain, on the other hand, a purely spatial model is the best explanatory model, and our MNL model outperforms our MNP model. These results suggest the question of whether to employ MNL or MNP depends crucially on the data at hand.

Measuring Explicit Political Positions of Media

We amass a new, large-scale dataset of newspaper editorials that allows us to calculate fine-grained measures of the political positions of newspaper editorial pages. Collecting and classifying over 1500 editorials adopted by 25 major US newspapers on 495 Supreme Court cases from 1994 to 2004, we apply an item response theoretic approach to place newspaper editorial boards on a substantively meaningful — and long validated — scale of political preferences. We validate the measures, show how they can be used to shed light on the permeability of the wall between news and editorial desks, and argue that the general strategy we employ has great potential for more widespread use.

The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods—one a statistical model that relies on general case characteristics, and the other a set of independent predictions by legal specialists. The basic result is that the statistical model did better than the legal experts in forecasting the outcomes of the Term’s cases: The model predicted 75% of the Court’s affirm/reverse results correctly, while the experts collectively got 59.1% right. These results are notable, given that the statistical model disregards information about the specific law or facts of the cases. The model’s relative success was due in large part to its ability to predict more accurately the important votes of the moderate Justices (Kennedy and O’Connor) at the center of the current Court. The legal experts, by contrast, did best at predicting the votes of the more ideologically extreme Justices, but had difficulty predicting the centrist Justices. The relative success of the two methods also varied by issue area, with the statistical model doing particularly well in forecasting “economic activity” cases, while the experts did comparatively better in the “judicial power” cases. In addition to reporting the results in detail, the Essay explains the differing methods

Competing Approaches to Predicting Supreme Court Decision Making

Political scientists and legal academics have long scrutinized the U.S. Supreme Courts work to understand what motivates the justices. Despite significant differences in methodology, both disciplines seek to explain the Courts decisions by focusing on examining past cases. This retrospective orientation is surprising. In other areas of government, for example, presidential elections and congressional decision making, political scientists engage in systematic efforts to predict outcomes, yet few have done this for court decisions. Legal academics, too, possess expertise that should enable them to forecast legal events with some accuracy. After all, the everyday practice of law requires lawyers to predict court decisions in order to advise clients or determine litigation strategies. The authors thank Michael Cherba, Nancy Cummings, David Dailey, Alison Garvey, Nick Hershman, and Robin Rimmer for their assistance. Their project is supported in part by National Science Foundation grants SES-0135855 and SES 0136679. The foundation bears no responsibility for the results or conclusions.

Exit Polling and Racial Bloc Voting: Combining Individual-Level and R X C Ecological Data

Despite its shortcomings, cross-level or ecological inference remains a necessary part of some areas of quantitative inference, including in United States voting rights litigation. Ecological inference suffers from a lack of identification that, most agree, is best addressed by incorporating individual-level data into the model. In this paper we test the limits of such an incorporation by attempting it in the context of drawing inferences about racial voting patterns using a combination of an exit poll and precinct-level ecological data; accurate information about racial voting patterns is needed to assess triggers in voting rights laws that can determine the composition of United States legislative bodies. Specifically, we extend and study a hybrid model that addresses two-way tables of arbitrary dimension. We apply the hybrid model to an exit poll we administered in the City of Boston in 2008. Using the resulting data as well as simulation, we compare the performance of a pure ecological estimator, pure survey estimators using various sampling schemes and our hybrid. We conclude that the hybrid estimator offers substantial benefits by enabling substantive inferences about voting patterns not practicably available without its use.

Improving the presentation and interpretation of online ratings data with model-based figures

Online ratings data are pervasive, but are typically presented in ways that make it difficult for consumers to accurately infer product quality. We propose an easily understood presentation method that has the virtue of incorporating a parametric model for the underlying ratings data. We illustrate the method with new data on the content quality of news outlets, and demonstrate its reliability and robustness with an experiment of online users and a simulation study. Our simple approach is easy to implement and widely applicable to any presentation of ratings data.

Tracking the Dynamic Evolution of Participants Salience in a Discussion

We introduce a technique for analyzing the temporal evolution of the salience of participants in a discussion. Our method can dynamically track how the relative importance of speakers evolve over time using graph based techniques. Speaker salience is computed based on the eigenvector centrality in a graph representation of participants in a discussion. Two participants in a discussion are linked with an edge if they use similar rhetoric. The method is dynamic in the sense that the graph evolves over time to capture the evolution inherent to the participants salience. We used our method to track the salience of members of the US Senate using data from the US Congressional Record. Our analysis investigated how the salience of speakers changes over time. Our results show that the scores can capture speaker centrality in topics as well as events that result in change of salience or influence among different participants.

Assessing Political Positions of Media

Although central to understanding the role of the media, few quantitative measures of the political positions of media exist. Collecting and classifying editorials adopted by 23 major U.S. newspapers on 495 Supreme Court cases from 1994-2004, we apply an item response theoretic approach to place newspapers on a substantively meaningful - and long validated - scale of political preferences. Our results provide significant insights into the study of the media. We show that 17 of the 23 papers are more likely to the left of the median Justice for this period, but also find considerable evidence that this may be an artifact of the liberalness of urban, elite, high circulation papers.

Does Encouragement Matter in Improving Gender Imbalances in Technical Fields? Evidence from a Randomized Controlled Trial

Does encouragement help address gender imbalances in technical fields? We present the results of one of the first and largest randomized controlled trials on the topic. Using an applied statistics conference in the social sciences as our context, we randomly assigned half of a pool of 3,945 graduate students to receive two personalized emails encouraging them to apply (n = 1,976) and the other half to receive nothing (n = 1,969). We find a robust, positive effect associated with this simple intervention and suggestive evidence that women responded more strongly than men. However, we find that women’s conference acceptance rates are higher within the control group than in the treated group. This is not the case for men. The reason appears to be that female applicants in the treated group solicited supporting letters at lower rates. Our findings therefore suggest that “low dose” interventions may promote diversity in STEM fields but may also have the potential to expose underlying disparities when used alone or in a non-targeted way.