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Delegation in the European Union (EU) involves a series of principal-agent problems, and the various chains of delegation involve voters, parties, parliaments, governments, the European Commission and the European Parliament. While the literature has focused on how government parties attempt to monitor EU affairs through committees in national parliaments and through Council committees at the EU level, much less is known about the strategies opposition parties use to reduce informational deficits regarding European issues. This article argues that the European Parliament (EP) offers opposition parties an arena to pursue executive oversight through the use of written parliamentary questions. Using a novel dataset on parliamentary questions in the EP, this article examines why Members of the European Parliament (MEPs) ask questions of specific Commissioners. It transpires that MEPs from national opposition parties are more likely to ask questions of Commissioners. Questions provide these parties with inexpensive access to executive scrutiny. This finding has implications for the study of parliamentary delegation and party politics inside federal legislatures such as the EP. Adapted from the source document.

Legislative speeches are an important part of parliamentary activity in the European Parliament (EP). Using a new dataset on EP speeches, this paper offers an explanation for participation in legislative debates. We argue that floor speeches partially serve as a communication tool between members of parliament, their national parties, and their European political groups. EP group dissidents often go on record by taking the floor when there is a conflict between their national party and their European political group. In this instance, members give speeches for two reasons: to explain their national party's position to other members of their EP political group, and to create a positive record for themselves in the eyes of the national party to serve their own reelection purposes.

This article examines how national parties and their members position themselves in European Parliament (EP) debates, estimating the principal latent dimension of spoken conflict using word counts from legislative speeches. We then examine whether the estimated ideal points reflect partisan conflict on a left-right, European integration or national politics dimension. Using independent measures of national party positions on these three dimensions, we find that the corpus of EP speeches reflects partisan divisions over EU integration and national divisions rather than left-right politics. These results are robust to both the choice of language used to scale the speeches and to a range of statistical models that account for measurement error of the independent variables and the hierarchical structure of the data. Adapted from the source document.

This article examines how national parties and their members position themselves in European Parliament (EP) debates, estimating the principal latent dimension of spoken conflict using word counts from legislative speeches. We then examine whether the estimated ideal points reflect partisan conflict on a left–right, European integration or national politics dimension. Using independent measures of national party positions on these three dimensions, we find that the corpus of EP speeches reflects partisan divisions over EU integration and national divisions rather than left–right politics. These results are robust to both the choice of language used to scale the speeches and to a range of statistical models that account for measurement error of the independent variables and the hierarchical structure of the data.

Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing text‐based methods face challenges in producing valid and reliable time‐series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time‐series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.

The role of domestic public opinion is an important topic in research on international negotiations, yet we know little about how exactly it manifests itself. We focus on government rhetoric during negotiations and develop a conceptual distinction between implicit and explicit manifestations of public opinion. Drawing on a database of video recordings of negotiations of the Council of the European Union and a quantitative text analysis of government speeches, we find that public opinion matters implicitly, with the exact pattern depending on governments' stance toward the EU. Pro-EU governments are responsive to public opinion in their support for compromises and attempts to stall negotiations, whereas Euroskeptic governments tend to remain silent when confronted with a public positively disposed toward the EU. Our results show that although governments implicitly represent public opinion, they do not systematically invoke their voters explicitly, suggesting the public matters but in different ways than often assumed.

The analysis of political texts from parliamentary speeches, party manifestos, social media, or press releases forms the basis of major and growing fields in political science, not least since advances in "text-as-data" methods have rendered the analysis of large text corpora straightforward. However, a lot of sources of political speech are not regularly transcribed, and their on-demand transcription by humans is prohibitively expensive for research purposes. This class includes political speech in certain legislatures, during political party conferences as well as television interviews and talk shows. We showcase how scholars can use automatic speech recognition systems to analyze such speech with quantitative text analysis models of the "bag-of-words" variety. To probe results for robustness to transcription error, we present an original "word error rate simulation" (WERSIM) procedure implemented in$R$. We demonstrate the potential of automatic speech recognition to address open questions in political science with two substantive applications and discuss its limitations and practical challenges.

The analysis of political texts from parliamentary speeches, party manifestos, social media, or press releases forms the basis of major and growing fields in political science, not least since advances in text-as-data methods have rendered the analysis of large text corpora straightforward. However, a lot of sources of political speech are not regularly transcribed, and their on-demand transcription by humans is prohibitively expensive for research purposes. This class includes political speech in certain legislatures, during political party conferences as well as television interviews and talk shows. We showcase how scholars can use automatic speech recognition systems to analyze such speech with quantitative text analysis models of the bag-of-words variety. To probe results for robustness to transcription error, we present an original word error rate simulation (WERSIM) procedure implemented in . We demonstrate the potential of automatic speech recognition to address open questions in political science with two substantive applications and discuss its limitations and practical challenges.