Replication data for: How Transparency Kills Information Aggregation: Theory and Experiment

Principal Investigator(s): View help for Principal Investigator(s) Sebastian Fehrler; Niall Hughes

Name	File Type	Size	Last Modified
data_for_replication			10/25/2021 03:32:PM
LICENSE.txt	text/plain	14.6 KB	10/12/2019 06:49:PM

Project Citation:

Fehrler, Sebastian, and Hughes, Niall. Replication data for: How Transparency Kills Information Aggregation: Theory and Experiment. Nashville, TN: American Economic Association [publisher], 2018. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E114353V1

Project Description

Summary: We investigate the potential of transparency to influence committee decision-making. We present a model in which career concerned committee members receive private information of different type-dependent accuracy, deliberate, and vote. We study three levels of transparency under which career concerns are predicted to affect behavior differently and test the model's key predictions in a laboratory experiment. The model's predictions are largely borne out—transparency negatively affects information aggregation at the deliberation and voting stages, leading to sharply different committee error rates than under secrecy. This occurs despite subjects revealing more information under transparency than theory predicts.

Scope of Project

Subject Terms: Laboratory Experiment; Group Decision-Making

JEL Classification:
      C92 Design of Experiments: Laboratory, Group Behavior
      D72 Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
      D82 Asymmetric and Private Information; Mechanism Design
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

Geographic Coverage: Switzerland

Time Period(s): 5/8/2013 – 5/30/2013

Universe: Students form ETH Zurich and University of Zurich who are interested in participating in laboratory experiments (suject pool of DeSciL).

Data Type(s): experimental data

Methodology

Unit(s) of Observation: Individual Student,

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Published Versions

V1 [2019-10-12]

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