Working Papers
Prospective and Retrospective Campaign Rhetoric. (Winner of the 2018 Manifesto Corpus Conference Best Paper Award)
Voters attribute credit and blame to parties, and parties should strategically react to voters’ attributions of responsibility in their campaign communication. However, so far, researchers have not analysed the circumstances under which parties focus on ‘retrospective’ statements about the past and present, and
‘prospective’ statements with references about the future. Applying a novel measure of ‘retrospective’ and ‘prospective’ campaign rhetoric to 569 national party manifestos from nine democracies and 644 manifestos from German regional elections, I find support for most theoretical expectations. Parties devote, on average, around half of their manifestos to descriptions of the past and present. Incumbents use manifestos to claim credit for past achievements, whereas the opposition points to negative developments. The opposition rarely frames the future significantly more negatively than incumbents. Only parties with low office aspiration in regional elections tend to write ‘doomsday’ manifestos. The results uncover a new dimension of party competition and have important implications for our understanding of representation and responsibility attribution.
Media Coverage of Campaign Promises Throughout the Electoral Cycle.
A growing body of work concludes that governments fulfil a majority of their campaign pledges. However, only a minority of voters believes that politicians try to keep their promises, and many voters struggle to accurately recall the fulfilment or breaking of salient campaign pledges. I argue that this disparity between the public perception and empirical evidence is partially driven by the information voters receive in the media. I expect that only few statements on promises mention the fulfilment or breaking of a pledge, and that news outlets focus more on broken than on fulfilled promises. Additionally, I posit that partisan outlets employ more positive sentiment in statements about promises when the endorsed party holds office. Based on a new text corpus of over 480,000 sentences on political promises published in 25 newspapers during 33 electoral cycles in Australia, Canada, Ireland, and the United Kingdom, I find support for these expectations. Across the four countries, newspapers report between 1.5 and 2 times more on broken than on fulfilled promises. Tabloids, but not broadsheet newspapers, express more negative sentiment in statements on promises when the endorsed party is not in government. The results have important implications for studying negative information in mass media, election pledges, and the linkage between voters and parties.
Reassessing an Established Concept Through Crowd-Sourced Text Coding.
A growing body of research analyses whether crowd workers can reproduce the ‘gold standard’ of expert-generated data. Based on the case of election pledges, I show how crowd-coding can also test for differences in perceptions of a concept between groups of experts and instructed non-experts. Comparing the most extensive reliability exercise carried out by nine pledge scholars to 3,660 codings generated by 90 crowd workers reveals considerable disagreement within and between both groups. Moreover, the carefully instructed and continuously monitored non-experts have a much broader understanding of election promises, which has important implications for analysing pledge fulfilment. The approach illustrates that crowd-coding could be used across all subfields of political science to reassess the measurement validity of a concept.
Scalable Analysis of Political Text Using Machine Learning (with Kenneth Benoit, Patrick Chester, and Michael Laver).
Estimating policy positions from political text is a core element in many analyses of political competition. Classical content analysis requires (costly) human experts to read and make judgements about all texts in the corpus. Benoit et al. (2016) showed that crowd workers can, much faster and more cheaply, label political texts as effectively as experts. Crowdsourced text analysis still requires judgements about every sentence in every text by multiple crowd workers, however, 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 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 very 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.
Expectation of Coalition Formation in Multi-Party Settings (with Shaun Bowler and Gail McElroy).
In coalition settings, when voters cast a ballot do they have a sense of who will form the government? The answer to this question is of relevance to questions of accountability; voters may well vote against a particular incumbent government but presumably would like some sense of what alternative will replace it. The answer is also relevant to the literature on strategic voting in coalition systems. Typically, studies of these topics rely on national level elections. But national level settings are themselves somewhat atypical electoral settings. First, these are likely to be information rich environments and provide unusually good conditions for voters to form expectations about the range of possible outcomes. In addition, studies which compare across national contexts often contain a great deal of institutional and cultural variation which muddy the focus on expectations. For these reasons, we use pre-election surveys from 19 German state elections between 2009 and 2017, in addition to three federal elections, to explore how voters form expectations in multi-party settings. We observe large variation in the ability of voters to predict actual government formation, ranging from 10 to 75 per cent. Both on the national and the subnational level we find robust evidence for ‘wishful thinking’ when predicting a government. We also find that public opinion polls heavily influence voter expectations about government formation. Our results have implications for the role of strategic voting in multiparty settings and, also, for an understanding of the ‘simple act of voting’ in complex settings.
Challenger Quality as a Moderator of the Incumbency Advantage: Evidence From a Natural Experiment in the Republic of Ireland from 1942 to 2014 (with Michael Jankowski).
We address the question of how the quality of list competitors affects the incumbency advantage. We argue that incumbents are less likely to benefit from holding office when list competitors are well-known and of high quality. To test this mechanism, we exploit the special ‘dual mandate’ characteristic of Irish local elections. Until 1999, members of the national parliament (TDs) were allowed to be represented in local and national parliaments simultaneously. By applying the regression discontinuity design to local elections between 1942 and 2014, we demonstrate that marginally elected politicians perform significantly worse when a TD competes for votes on the same list. Moreover, the incumbency advantage has become larger after the dual mandate was abolished. Our results highlight the moderating impact of high-quality competitors on the incumbency bonus.
Campaigns and the Selection of Policy-Seeking Representatives (with Shaun Bowler and Gail McElroy).
Can voters learn meaningful information about candidates from their electoral campaigns? As with job market hiring, voters, like employers, cannot know the productivity of candidates, challengers in particular, when they elect them. The real productivity of representatives only reveals itself after the election. We explore if the information revealed during the ‘hiring process’ is a good signal of the legislative effort of elected representatives. In the incomplete information environment of election campaigns candidates should turn to credible signals to indicate their “type” to voters. Campaigns – and campaigning – are means by which candidates can, in principle, signal their motivations to voters. Is a candidate’s behaviour on the campaign trail informative about their behaviour and effort as a legislator? Does it, for example, reveal whether a candidate is more policy-seeking than office seeking? Using evidence from the European Parliament we show that campaign activity prior to the election is not related to policy-seeking behaviour in the legislature post-election. The finding also holds in two national-level settings and across a variety of measures of legislative effort. Those who campaign harder do seem more likely to win the election, but campaign effort seems to provide a poor guide to what the winner does once elected.
Ongoing Research
Who’s Willing to Compromise? Investigating the Effect of Parties’ Compromise Rhetoric on Voters’ Preferences (with Carolina Plescia and Mariken van der Velden).
Right-Wing Populism and Social Media in Direct Democratic Campaigns
(with Fabrizio Gilardi, Clau Dermont, and Theresa Gessler).
Those Were the Days: Identifying and Explaining Nostalgia in Parties’ Campaign Communication (with Sven-Oliver Proksch).
Does Issue Emphasis in Election Campaigns Correspond to Politcians’ Legislative Behaviour? Evidence from Japan between 2003 and 2017 (with Naofumi Fujimura and Daisuke Hakiai).
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