Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach

Stephen Hansen, Michael McMahon and Andrea Prat

The Quarterly Journal of Economics, 2018, vol. 133, issue 2, 801-870

Abstract: How does transparency, a key feature of central bank design, affect monetary policy makers’ deliberations? Theory predicts a positive discipline effect and negative conformity effect. We empirically explore these effects using a natural experiment in the Federal Open Market Committee in 1993 and computational linguistics algorithms. We first find large changes in communication patterns after transparency. We then propose a difference-in-differences approach inspired by the career concerns literature, and find evidence for both effects. Finally, we construct an influence measure that suggests the discipline effect dominates.

Date: 2018
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Working Paper: Transparency and Deliberation within the FOMC: a Computational Linguistics Approach (2015)
Working Paper: Transparency and Deliberation within the FOMC: A Computational Linguistics Approach (2014)
Working Paper: Transparency and Deliberation within the FOMC: a Computational Linguistics Approach (2014)
Working Paper: Transparency and Deliberation within the FOMC: a Computational Linguistics Approach (2014)
Working Paper: Transparency and deliberation within the FOMC: a computational linguistics approach (2014)
Working Paper: Transparency and deliberation within the FOMC: a computational linguistics approach (2014)
Working Paper: Transparency and deliberation within the FOMC: A computational linguistics approach (2014)
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