A Learning-Based Approach to Evaluating Boards of Directors

57 Pages Posted: 13 Aug 2015 Last revised: 7 Oct 2018

See all articles by Lea Henny Stern

Lea Henny Stern

University of Washington - Michael G. Foster School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: September 30, 2018

Abstract

Directors matter, contrary to the “rubber-stamp” view of board governance. The gender of a director also matters, as does the individual’s specific function on the board. This paper uses a Bayesian learning model in the spirit of Pan, Wang and Weisbach (PWW, 2015) to study outstanding questions on boards. This framework reveals that a director has a statistically significant impact on governance-related uncertainty that is about one-third the impact that PWW find for new CEOs. The results help delineate what matters in governance by outlining the channels through which directors make a difference. For instance, while women on boards are not as influential on average, they are especially important when the firm faces acute monitoring needs, consistent with findings in Adams and Ferreira (2009).

Keywords: Board of Directors, Corporate Governance, Bayesian Learning, Director Turnover

JEL Classification: G30, G34, G39, M12, M51

Suggested Citation

Stern, Lea Henny, A Learning-Based Approach to Evaluating Boards of Directors (September 30, 2018). Available at SSRN: https://ssrn.com/abstract=2642294 or http://dx.doi.org/10.2139/ssrn.2642294

Lea Henny Stern (Contact Author)

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

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