Published online by Cambridge University Press: 03 December 2018
We experimentally investigate the informational theory of legislative committees (Gilligan and Krehbiel 1989). Two committee members provide policy-relevant information to a legislature under alternative legislative rules. Under the open rule, the legislature is free to make any decision; under the closed rule, the legislature chooses between a member’s proposal and a status quo. We find that even in the presence of biases, the committee members improve the legislature’s decision by providing useful information. We obtain evidence for two additional predictions: the outlier principle, according to which more extreme biases reduce the extent of information transmission; and the distributional principle, according to which the open rule is more distributionally efficient than the closed rule. When biases are less extreme, we find that the distributional principle dominates the restrictive-rule principle, according to which the closed rule is more informationally efficient. Overall, our findings provide experimental support for Gilligan and Krehbiel’s informational theory.
We are grateful to Andreas Blume, Colin Camerer, Yuk-fai Fong, Jean Hong, David Huffman, Navin Kartik, Joel Sobel, Lise Vesterlund, Joel Watson, Emanuel Vespa, Jonathan Woon, the editor, and four anonymous referees for their valuable comments and suggestions. We also express our gratitude to the conference and seminar participants at the 3rd Haverford Meeting on Behavioral and Experimental Economics, the 2015 ESA North American Meeting, the 12th International Conference of the Western Economic Association International, HKUST, UCSD, the Institute of Economics at Academia Sinica, the University of Arizona, the University of Pittsburgh, and the Kiel Institute of Economics for their helpful comments and suggestions. We thank Amanda Eng for excellent research assistance. This study is supported by a grant from the Research Grants Council of Hong Kong (Grant No. GRF-16502015). Replication materials can be found on Dataverse at: https://doi.org/10.7910/DVN/OWQNVF.