Papers Under Review
Voter Expectations of Government Formation in Coalition Systems: The Importance of the Information Context. European Journal of Political Research: revise & resubmit (with
Shaun Bowler and
Gail McElroy).
Can voters in multi-party systems predict which coalition will form the government with any degree of accuracy? To date, studies which explore voter expectations of coalition formation have emphasized individual level attributes, such as education, but the context of information that voters experience at the time the coalitions are forming should also be consequential in enabling (or handicapping) voters in forming expectations. We examine the relative effects of individual level attributes (e.g. education, cognitive mobilization) versus contextual factors (e.g. information availability) in 19 German state elections and 3 German general elections between 2009 and 2017. We find that the ease of identifiability of alternative future governments varies significantly across multi-party systems. We find that respondents are more likely to predict governments that they would like to see in office, that have a higher probability of receiving a majority of seats, and that consist of ideologically proximate parties. Combining survey data with a novel indicator of coalition signals, measured through a quantitative text analysis of newspaper coverage, we also find that voters consider positive pre-election coalition signals when predicting the government. Finally, we find that the information environment is much more relevant for correct coalition predictions than individual-level characteristics of respondents. While individual attributes do influence predictive ability, these factors are strongly dominated by the context in which the prediction is taking place. The information environment has by far the largest effect on predicting coalition outcomes. Our results have implications for the literature on strategic voting in multiparty settings, as well as the literature on accountability.
Analyzing the Incumbency Advantage across Contexts: Evidence from 70 Years of Irish Local and General Elections. Electoral Studies: revise & resubmit (with
Michael Jankowski).
Do candidates elected in a system using proportional representation profit from an incumbency advantage? Do we observe different incumbency effects in national and local elections? And do high-quality competitors affect the reelection of marginally elected candidates? Analyzing seven decades of Irish local (1942–2019) and general national elections (1937–2020), we reassess the mixed evidence on the incumbency advantage under proportional representation and in national and sub-national elections. Additionally, we exploit the ‘dual mandate’ characteristic of Irish local elections to test whether the presence of national-level politicians in local elections decreases the reelection probability of marginally elected local candidates. By applying the Regression Discontinuity Design, we find that the incumbency advantage is stronger in Irish local than in national elections, and that the bonus on the local level increased over time. Finally, the advantage decreases with the presence of a high-quality list competitor. Results suggest that future research on the incumbency advantage should pay more attention to the impact of contextual factors.
Social Media and Political Agenda Setting (with
Fabrizio Gilardi,
Theresa Gessler, and
Maël Kubli).
What is the role of social media in political agenda setting? Digital platforms have reduced the gatekeeping power of traditional media and, potentially, they have increased the capacity of various kinds of actors to shape the agenda. We study this question in the Swiss context by examining the connections between three agendas: the traditional media agenda, the social media agenda of parties, and the social media agenda of politicians. Specifically, we validate and apply supervised machine-learning classifiers to categorize 2.78 million articles published in 84 newspapers, 6,500 tweets posted on official party accounts, and 210,000 tweets posted by politicians on their own accounts from January 2018 until December 2019. We first use the classifier to measure the salience of the four most relevant issues of the period: the environment, Europe, gender equality, and immigration. Then, using a VAR approach, we analyze the relationship between the three agendas. Results show that not only do the traditional media agenda, the social media agenda of parties, and the social media agenda of politicians influence one another but, on balance, they cancel each other out, such that no single agenda drives the debate. There is one important exception: for the environment issue, the social media agenda of parties is more predictive of the traditional media agenda than vice-versa, by a significant difference of two percentage points. These findings underscore how closely different agendas are tied together, but also show how exogenous events both constrain and enable parties to push their specific agendas.
Evidence for the Irrelevance of Irrelevant Events (with
Liam Kneafsey).
Do ‘irrelevant events’ affect voting behaviour? Moving beyond the case of the United States, we investigate whether sports results influence political opinions. We match constituency-level election data from Irish general elections between 1922 and 2020 and local elections between 1942 and 2019 with the results of matches in the Gaelic football and hurling championships, the most popular sports in Ireland. A difference-in-differences design does not suggest any relationship between match results of regional teams and support for incumbent candidates or government parties. These findings also hold when applying an ‘unexpected event during survey design’ approach to representative surveys conducted throughout two seasons. Our results have important implications for the literature on retrospective voting, opinion formation, and citizen competence.
Building Research Infrastructures to Study Digital Technology and Politics: Lessons from Switzerland (with
Fabrizio Gilardi,
Lucien Baumgartner,
Clau Dermont,
Karsten Donnay,
Theresa Gessler,
Maël Kubli, and
Lucas Leemann).
The effects of digital technology on political processes is an important phenomenon that, due to several structural problems, remains poorly understood. A key problem is the lack of adequate research infrastructures to study digital technology and politics, or the lack of access. We first discuss the challenges many social scientists face and then present the infrastructure we built in Switzerland to overcome them, using COVID-19 as an example. We conclude by discussing seven lessons we learned: automatization is key; avoid data hoarding; outsource some parts of the infrastructure, but not others; focus on substantive questions; share data in the context of collaborations; engage in targeted public outreach; collaboration beats competition. We hope that our experience will be helpful to other researchers pursuing similar goals.
Working Papers
Efficient and Scalable Analysis of Political Text (with
Kenneth Benoit,
Patrick Chester, and
Michael Laver).
Estimating policy positions from political text is a core element in many empirical analyses of political competition. This has traditionally been achieved using classical content analysis, which requires (costly) human experts to read and make judgements about all text in some corpus. Benoit et al. (2016) showed that crowd workers can label political texts as effectively as experts, but much faster and more cheaply. However, crowdsourced text analysis still requires judgements about every sentence in every text by multiple crowd workers, limiting its scalability to large text corpora. Unsupervised machine learning requires human “curation” of texts based on policy content, to allow ex-post human interpretation of results. Supervised machine learning methods, in contrast, leverage a relatively small training set of text labelled by humans, whether experts or crowd workers, to analyze a potentially huge volume of text out of sample, making this a much more scalable research tool. In this paper, we evaluate the effectiveness of different supervised machine learning algorithms using training sets labelled by humans, whether experts or crowd workers, to analyze both party manifestos and legislative speeches. We first replicate a widely used left-right scale derived from classical text analysis by human experts. We then exploit the flexibility crowd sourced labels to estimate "new" policy dimensions. Our results are encouraging, suggesting that supervised machine learning based on limited training data is a viable, fast, cheap and scalable method for analyzing large political text corpora out of sample.
Multimodal Determinants of Voter Impression Formation of Female Political Candidates During Televised Debates (with
Constantine Boussalis,
Travis G. Coan, and
Mirya R. Holman).
Political debates often serve as the most important campaign event of elections. Viewers evaluate participants not just by the words they say, but also via non-verbal cues, including facial displays of emotions and vocal pitch. But not all candidates are equally likely to provide these non-verbal cues and candidate characteristics can shape how voters react to this communication. In this paper, we draw on role congruity expectations and focus on how gender shapes the use of and reactions to non-verbal communication via a multimodal evaluation of the most powerful woman in the world: Angela Merkel. Using four full-length debate videos from German federal elections (2017, 2013, 2009, 2005), we employ computer vision, machine learning, text analytic, and statistical methods to automatically extract second-by-second measurements of political candidates’ facial displays of emotion, vocal pitch, and speech sentiment. While Merkel displays emotions at a similar rate to her male competitors, she is much less likely to express anger and emotes less over time. We combine second-by-second emotions data with continuous response measures of support for candidates recorded by live audiences. Controlling for viewer political attitudes and demographic characteristics, we find that voters punish Merkel for her displays of anger and reward her happiness expression.
Ongoing Projects
Explaining (In)Congruence Between Politicians’ Campaign Promises and Subsequent Legislative Priorities (with
Naofumi Fujimura).
Social Media and Policy Responses to the COVID-19 Pandemic in Switzerland (with
Fabrizio Gilardi,
Theresa Gessler, and
Maël Kubli).
The Compass of Irish Politics is Moving to the Left (with
Aidan Regan).
Causes and Electoral Effects of Nostalgic Rhetoric: A Cross-National Analysis of Party Communication (with
Sven-Oliver Proksch).
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