Title: Lobbying as a collective enterprise: winners and losers of policy formulation in the European Union
Abstract: Abstract Why does lobbying success in the European Union (EU) vary across interest groups? Even though this question is central to the study of EU policy-making, only few have dealt with it. The small number of existing studies is moreover characterized by a multitude of hypotheses and contradictory findings. This article aims to overcome these shortcomings by presenting a theoretical exchange model that identifies information supply, citizen support and economic power of entire lobbying camps as the major determinants of lobbying success. The hypotheses are empirically evaluated based on a large new dataset. By combining a quantitative text analysis of interest group submissions to Commission consultations with an online survey among interest groups, the theoretical expectations are tested across a large number of policy issues and interest groups while controlling for individual interest group and issue characteristics. The empirical analysis confirms the theoretical expectations indicating that lobbying is a collective enterprise. Keywords: European Commissioninfluenceinterest groupslobbyinglobbying successquantitative text analysis ACKNOWLEDGEMENTS Research for this article was financially supported by the Graduate School of Economic and Social Sciences at the University of Mannheim, the Landesstiftung Baden-Württemberg, the German Academic Exchange Programme and the Volkswagen Foundation. I would like to thank the anonymous reviewers and numerous colleagues and friends who commented on earlier drafts of the article or the entire research project, most notably Gema García Albacete, Doreen Allerkamp, Christian Arnold, Patrick Bayer, Simona Bevern, Tanja Dannwolf, Oshrat Hochman, Thomas Meyer, Sven-Oliver Proksch, Iñaki Sagarzazu, Daniel Stegmüler, Bettina Trüb and Arndt Wonka. Special thanks go to Sabine Saurugger, Thomas Gschwend and, in particular, Berthold Rittberger for continuous invaluable support throughout the research process. Notes I performed a principal component factor analysis which confirms that the two indicators measure the same underlying latent variable. Further information about the construction of the survey and about the operationalization of citizen support and economic power can be found in Klüver (forthcoming, 2013). The factor analysis retained only one factor according to the Kaiser criterion. While the listwise deletion estimates are unbiased if the MCAR assumption holds, the estimates are relatively inefficient no matter which assumption characterizes the missingness (King et al. 2001 King, G., Honaker, J., Joseph, A. and Scheve, K. 2001. Analzying incomplete political science data: an alternative algorithm for multiple imputation. American Political Science Review, 95(1): 49–69. [Web of Science ®] , [Google Scholar]: 51).
Publication Year: 2013
Publication Date: 2013-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 101
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