This article presents a new method for estimating positions of political parties across country- and time-specific contexts by introducing a latent variable model for manifesto data. We estimate latent positions and exploit bridge observations to make the scales comparable. We also incorporate expert survey data as prior information in the estimation process to avoid ex post facto interpretation of the latent space. To illustrate the empirical contribution of our method, we estimate the left-right positions of 388 parties competing in 238 elections across twenty-five countries and over sixty years. Compared to the puzzling volatility of existing estimates, we find that parties more modestly change their left-right positions over time. We also show that estimates without country- and time-specific bias parameters risk serious, systematic bias in about two-thirds of our data. This suggests that researchers should carefully consider the comparability of party positions across countries and/or time.
Research has shown that emotions matter in politics, but we know less about when and why politicians use emotive rhetoric in the legislative arena. This article argues that emotive rhetoric is one of the tools politicians can use strategically to appeal to voters. Consequently, we expect that legislators are more likely to use emotive rhetoric in debates that have a large general audience. Our analysis covers two million parliamentary speeches held in the UK House of Commons and the Irish Parliament. We use a dictionary-based method to measure emotive rhetoric, combining the Affective Norms for English Words dictionary with word-embedding techniques to create a domain-specific dictionary. We show that emotive rhetoric is more pronounced in high-profile legislative debates, such as Prime Minister’s Questions. These findings contribute to the study of legislative speech and political representation by suggesting that emotive rhetoric is used by legislators to appeal directly to voters.
We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics.
This article analyses the circumstances under which the European Commission implements its legislative programme on time. Similar to many national governments the European Commission announces an annual Work Programme, where it identifies important legislation it plans to propose within 12 or 18 months. This study is based on an original dataset of 233 legislative proposals listed in the Work Programme in the period 2005-2012. I show that the Commission implements at least 94% of its legislative programme, where 76% of the proposed legislation is formally introduced within the deadline. The empirical analysis provides evidence that procedural and technical complexity decreases the probability of timely implementation. In addition, proposals listed in Work Programmes that allow for the introduction of some proposals within the extended deadline of 18 months are more likely to be introduced on time. The size of the gridlock interval, as defined in spatial models, does not have a statistically significant effect.
This article analyzes how uncertainty about the location of the pivotal actor influences the outcome of Commission proposals. We argue that the Commission is an imperfect agenda-setter and expect that Commission proposals are more likely to fail when uncertainty increases in the bicameral legislature of the Council and the European Parliament. Considering all legislative acts decided under the co-decision procedure proposed in the period from November 1993 until December 2009, we focus on withdrawal of Commission proposals as failures. In the empirical analysis we distinguish between electoral and procedural uncertainty and provide evidence that both types of uncertainty explain withdrawal of Commission proposals.
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