This paper reexamines the relation between firm value and board structure. We find that complex firms, which have greater advising requirements than simple firms, have larger boards with more outside directors. The relation between Tobin's Q and board size is U-shaped, which, at face value, suggests that either very small or very large boards are optimal. This relation, however, arises from differences between complex and simple firms. Tobin's Q increases (decreases) in board size for complex (simple) firms, and this relation is driven by the number of outside directors. We find some evidence that R&D-intensive firms, for which the firm-specific knowledge of insiders is relatively important, have a higher fraction of insiders on the board and that, for these firms, Q increases with the fraction of insiders on the board. Our findings challenge the notion that restrictions on board size and management representation on the board necessarily enhance firm value.

We provide empirical evidence of a strong causal relation between managerial compensation and investment policy, debt policy, and firm risk. Controlling for CEO pay-performance sensitivity (delta) and the feedback effects of firm policy and risk on the managerial compensation scheme, we find that higher sensitivity of CEO wealth to stock volatility (vega) implements riskier policy choices, including relatively more investment in R&D, less investment in PPE, more focus, and higher leverage. We also find that riskier policy choices generally lead to compensation structures with higher vega and lower delta. Stock-return volatility has a positive effect on both vega and delta.

This paper reports a laboratory experiment that examines price formation in the single call market. The experiment design is intended to enhance the predictive power of the Bayesian Nash equilibrium (BNE) theory for this trading institution. The data support several qualitative implications of the BNE, especially when subjects compete against Nash 'robot' opponents, but subjects' behavior is not as responsive to changes in the pricing rule as the BNE predictions. Offers tend to reveal more of the underlying values and costs than predicted, particularly when subjects are experienced. A simple learning model accounts for several of the deviations from BNE.

Regulatory programs that restrict output levels often impose restrictions on the transfer of rights to produce or to use particular inputs. In this paper, we use a unique cross-section, time-series data set from North Carolina to quantify the welfare effects of transfer restrictions for poundage quota under the U.S. flue-cured tobacco program. We find that the deadweight costs of such restrictions are small but that the distributional effects are substantial. We analyze congressional testimony on quota transfer legislation and conclude that our estimates of the distributional effects are consistent with expressed views of market participants.