I develop a theory of stock-based compensation contracts for the chief executive officers (CEOs) of firms and confront the theoretical predictions with recent CEO compensation data. The model characterizes the optimal contract for a CEO whose reputation evolves as signals of the executive's ability are observed by shareholders. Using various proxies for CEO reputation, I show a positive and economically meaningful relationship between stock-based pay-sensitivities and CEO reputation. The findings are robust to controls for CEO age, firm size, the dollar variability of the stock returns, and industry effects.

The credit rating industry has historically been dominated by just two agencies, Moody's and Standard & Poor's, leading to long-standing legislative and regulatory calls for increased competition. The material entry of a third rating agency (Fitch) to the competitive landscape offers a unique experiment to empirically examine how increased competition affects the credit ratings market. What we find is relatively troubling. Specifically, we discover that increased competition from Fitch coincides with lower quality ratings from the incumbents: Rating levels went up, the correlation between ratings and market-implied yields fell, and the ability of ratings to predict default deteriorated. We offer several possible explanations for these findings that are linked to existing theories.

Principal-agent theory suggests that a manager should be paid relative to a benchmark that removes the effect of market or sector performance on the firm's own performance. Recently, it has been argued that such indexation is not observed in the data because executives can set pay in their own interests; that is, they can enjoy “pay for luck” as well as “pay for performance.” We first show that this argument is incomplete. The positive expected return on stock markets reflects compensation for bearing systematic risk. If executives’ pay is tied to market movements, they can only expect to receive the market-determined return for risk-bearing. This argument, however, assumes that executive pay is tied to bad luck as well as to good luck. If executives can truly influence the setting of their pay, they will seek to have their performance benchmarked only when it is in their interest, namely, when the benchmark has fallen. Using industry benchmarks, we find significantly less pay for luck when luck is down (in which case, pay for luck would reduce compensation) than when it is up. These empirical results are robust to a variety of alternative hypotheses and robustness checks, and they suggest that the average executive loses 25–45% less pay from bad luck than is gained from good luck.

Using a large data set of performance goals employed in executive incentive contracts, we find that a disproportionately large number of firms exceed their goals by a small margin as compared to the number that fall short of the goal by a similar margin. This asymmetry is particularly acute for earnings goals, when compensation is contingent on a single goal, when the pay-performance relationship around the goal is concave-shaped, and for grants with non-equity-based payouts. Firms that exceed their compensation target by a small margin are more likely to beat the target the next period and CEOs of firms that miss their targets are more likely to experience a forced turnover. Firms that just exceed their Earnings Per Share (EPS) goals have higher abnormal accruals and lower Research and Development (R&D) expenditures, and firms that just exceed their profit goals have lower Selling, General and Administrative (SG&A) expenditures. Overall, our results highlight some of the costs of linking managerial compensation to specific compensation targets.