Central Bank Transparency with (semi-)public Information: Laboratory Experiments - Trabelsi, Emna; Hichri, Walid (Journal of behavioral and experimental economics, 2021)
•Two laboratory experiments are run to test central bank partial transparency strategies: “fragmented information” strategy and “partially hidden information” strategy.•We provide original evidence that the “fragmented information” strategy outperforms the “partially hidden information” strategy in terms of social welfare, as central banks can better control the mean squared action error and the dispersion of agents’ behavior with the former strategy.•The central bank can alleviate agents’ overreaction to public information by one or other form of partial transparency.•The distribution of the weights assigned on the (semi-)public signal emphasizes heterogeneity of behavior entailed by boundedly rational reasoning.•Learning mechanisms to improve performance and behavior differs with respect to the game played by the subjects. Strategies of public announcements pose challenges to central banks. Theory shows that full transparency is not always good. In this paper, we propose to assess two forms of partial public disclosure by central banks (“fragmented information” and “partially hidden information” strategies) in two beauty contest games, as well as a scenario where public information is fully disclosed. Based on laboratory experiments, we offer original evidence that the “fragmented information” strategy outperforms the “partially hidden information” strategy in terms of social welfare, as central banks can better control the mean squared distance of agents’ actions from the true state of fundamentals (i.e., Mean squared action error) and the dispersion of agents’ behavior with “fragmented information”, while both partial transparency strategies similarly alleviate agents’ overreaction to fully disclosed public information. We also find that divergence from the Nash equilibrium emphasizes heterogeneity of behavior that is entailed by boundedly rational reasoning, especially in early periods of a game. Further, we build on choice reinforcement and belief-based learning models to better understand how subjects learn over time to improve their performance. How well those learning models fit the data depends on the game played by the subjects.