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Biased access? Exploring selection to advisory committees

Published online by Cambridge University Press:  13 November 2014

Anne Rasmussen*
Affiliation:
University of Copenhagen, Department of Political Science, Copenhagen K, Denmark Leiden University, Department of Public Administration, The Hague, The Netherlands
Vlad Gross
Affiliation:
Sciences Po Paris, Centre d’études européennes, Paris, France
*
*E-mail: ar@ifs.ku.dk
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Abstract

The existing literature on bias in interest group access faces the challenge that there is often no clear benchmark for judging whether a given distribution of interest groups involved in policy making is biased. To tackle this challenge, we link two new data sets on registered European Union (EU) interest groups and membership of the advisory committees of the European Commission and examine the factors affecting selection to these committees. Our approach allows us to qualify the conclusions of the existing literature. We see that, even if business interests dominate advisory committees, they are not generally privileged over other group types in the selection processes and their degree of access varies considerably between policy areas. Instead, supranational interest groups enjoy selection privileges, which are particularly pronounced on permanent committees. Finally, we find some evidence that, even though lobbying budgets are important for getting access, their value varies across group types. In this way, our study has implications for future theory building on resource exchanges between interest group types and decision makers.

Information

Type
Research Article
Copyright
© European Consortium for Political Research 2014 
Figure 0

Table 1 Group types

Figure 1

Table 2 Geographical scope

Figure 2

Figure 1 Predicted probability of having seats on advisory committees by group type (model 3A).

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Table 3 Logistic regression predicting access

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Table 4 Negative binomial regression predicting number of advisory committee seats

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Figure 2 Predicted count of seats on advisory committees by DG area and group type (models 5A and 5D).

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Table 5 Negative binomial regression predicting number of advisory committee seats on advisory committees per DG

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Figure 3 Predicted count of seats on advisory committees by geographical scope (model 4B).

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Figure 4 Predicted count of seats on advisory committees by lobby budget (model 4B).

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Figure 5 Predicted probability of having seats on advisory committees for different group types by lobby budget (model 3C).

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Table 6 Negative binomial regression predicting number of advisory committee seats on temporary and permanent advisory committees

Supplementary material: File

Rasmussen and Gross Supplementary Material

Appendix

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