Measuring Interest Group Influence Using Quantitative Text Analysis

The analysis of interest group influence is crucial in order to explain policy outcomes and to assess the democratic legitimacy of the European Union. However, owing to methodological difficulties in operationalizing influence, only few have studied it. This article therefore proposes a new approach to the measurement of influence, drawing on quantitative text analysis. By comparing interest groups’ policy positions with the final policy output, one can draw conclusions about the winners and losers of the decision-making process. In order to examine the applicability of text analysis, a case study is presented comparing hand-coding, WORDSCORES and Wordfish. The results correlate highly and text analysis proves to be a powerful tool to measure interest groups’ policy positions, paving the way for the large-scale analysis of interest group influence.

[1]  Andreas Dür.  Measuring Interest Group Influence in the EU , 2008 .

[2]  Christine Mahoney,et al.  Brussels Versus the Beltway: Advocacy in the United States and the European Union , 2008 .

[3]  D. Randall.  Lobbying and Policy Change: Who Wins, Who Loses, and Why , 2010 .

[4]  I. Budge,et al.  Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945-1998 , 2001 .

[5]  Andreas Dür,et al.  Inclusion without Influence? NGOs in European Trade Policy , 2007 .

[6]  Irina. Michalowitz.  What determines influence? Assessing conditions for decision-making influence of interest groups in the EU1 , 2007 .

[7]  W. Lowe,et al.  Understanding Wordscores , 2008, Political Analysis.

[8]  Cornelia Woll.  Leading the Dance? Power and Political Resources of Business Lobbyists , 2007 .

[9]  M. Laver,et al.  Measuring National Delegate Positions at the Convention on the Future of Europe Using Computerized Word Scoring , 2005 .

[10]  Tom Louwerse.  Mapping Policy Preferences II: Estimates for Parties, Electors, and Governments in Eastern Europe, European Union and OECD 1990–2003 , 2009 .

[11]  C. Mahoney.  Lobbying Success in the United States and the European Union , 2007 .

[12]  Slava Mikhaylov Trinity,et al.  Coder Reliability and Misclassification in Comparative Manifesto Project Codings∗ , 2008 .

[13]  F. U. Pappi,et al.  The organization of influence on the EC's common agriculturalpolicy: A network approach , 1999 .

[14]  M. Laver,et al.  Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.

[15]  Sven-Oliver Proksch,et al.  A Scaling Model for Estimating Time-Series Party Positions from Texts , 2007 .

[16]  D. Finke,et al.  With a little help from the state: interest intermediation in the domestic pre-negotiations of EU legislation , 2007 .