Economics at your fingertips  

Identifying Structural Models of Committee Decisions With Heterogeneous Tastes and Ideological Bias

Yonghong An and Xun Tang

Journal of Business & Economic Statistics, 2017, vol. 35, issue 3, 452-469

Abstract: In practice, members of a committee often make different recommendations despite a common goal and shared sources of information. We study the nonparametric identification and estimation of a structural model, where such discrepancies are rationalized by the members’ unobserved types, which consist of ideological bias while weighing different sources of information, and tastes for multiple objectives announced in the policy target. We consider models with and without strategic incentives for members to make recommendations that conform to the final committee decision. We show that pure-strategy Bayesian Nash equilibria exist in both cases, and that the variation in common information recorded in the data helps us to recover the distribution of private types from the members’ choices. Building on the identification result, we estimate a structural model of interest rate decisions by the Monetary Policy Committee (MPC) at the Bank of England. We find some evidence that the external committee members are less affected by strategic incentives for conformity in their recommendations than the internal members. We also find that the difference in ideological bias between external and internal members is statistically insignificant. Supplementary materials for this article are available online.

Date: 2017
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Identifying Structural Models of Committee Decisions with Heterogeneous Tastes and Ideological Bias (2013) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1080/07350015.2015.1084309

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

Page updated 2022-05-02
Handle: RePEc:taf:jnlbes:v:35:y:2017:i:3:p:452-469