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).
Can an algorithm assist firms in their nominating decisions of corporate directors? We construct algorithms tasked with making out-of-sample predictions of director performance. We run tests of the quality of these predictions and show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably unpopular directors are more likely to be male, have held more directorships, have fewer qualifications, and larger networks than the directors the algorithm recommends. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help firms improve their governance.