This article illustrates an incentive-aligning role of debt in the presence of optimal compensation contracts. Owing to information asymmetry, value-maximizing compensation contracts allow managerial rents following high investment outcomes. The manager has an incentive to increase these rents by choosing investments that generate greater information asymmetry. An aptly chosen debt level mitigates this incentive, because investments that generate greater information asymmetry have more volatile outcomes. The greater volatility would make the debt risky, causing the shareholders to focus on high outcomes and therefore compensation contracts that reduce managerial rents. At the optimum, the manager avoids opportunistic investments, and the shareholders offer value-maximizing compensation contracts. Empirically, the analysis predicts a negative relationship between leverage and market-to-book that is reversed at extreme market-to-book ratios, a negative relationship between leverage and profitability, a negative relationship between leverage and pay-for-performance, and a positive relationship between pay-for-performance and investment opportunities.

This article illustrates an incentive-aligning role of debt in the presence of optimal compensation contracts. Owing to information asymmetry, value-maximizing compensation contracts allow managerial rents following high investment outcomes. The manager has an incentive to increase these rents by choosing investments that generate greater information asymmetry. An aptly chosen debt level mitigates this incentive, because investments that generate greater information asymmetry have more volatile outcomes. The greater volatility would make the debt risky, causing the shareholders to focus on high outcomes and therefore compensation contracts that reduce managerial rents. At the optimum, the manager avoids opportunistic investments, and the shareholders offer value-maximizing compensation contracts. Empirically, the analysis predicts a negative relationship between leverage and market-to-book that is reversed at extreme market-to-book ratios, a negative relationship between leverage and profitability, a negative relationship between leverage and pay-for-performance, and a positive relationship between pay-for-performance and investment opportunities.

Key indicators of managerial performance are frequently subjective, that is, they are difficult to specify and/or verify for contracting purposes. When a principal must rely on subjective information to create incentives for a group of agents, discretionary bonus pools are shown to be optimal mechanisms. Despite their optimality, however, discretionary bonus pools entail an additional agency cost relative to the benchmark of optimal contracts based on objective and verifiable information. Our analysis identifies circumstances under which this additional agency cost is small, for example, the subjective information signals are precise, or the number of agents participating in the bonus pool increases. When incentive schemes can be based on both objective and subjective performance indicators, the relative weights to be placed on alternative signals are shown to differ from the ones predicted by models with objective signals only. We also demonstrate that correlation in measurement errors has a different impact on the structure of optimal incentive schemes when the performance indicators are merely subjective.

In this paper, we analyze how financial analysts generate information, make decisions about firm coverage, and try to maintain their forecasting accuracy after the passage of Regulation Fair Disclosure (“Reg FD”). Using the model developed by Barron, Kim, Lim, and Stevens 1998, we find that analysts are investing more effort in idiosyncratic information discovery. In order to do this, individual analysts appear to be reducing coverage for well‐followed firms while increasing coverage of firms that were less followed prior to Reg FD. Analysts who had preferential links with firms that they covered, such as analysts from large brokerage houses, tend to have greater forecast accuracy in the pre‐FD period. However, these analysts are unable to sustain their forecasting superiority in the post‐FD period, which suggests that there has been a leveling of the information playing field among analysts. Overall, our results reflect a trend toward greater reliance on idiosyncratic information discovery on part of the financial analysts.

Integrated risk management for financial institutions requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. We construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions that capture essential empirical features of these risks such as skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk. We then compare the copula-based method with several conventional approaches to computing risk.

We show how data from an evaluation in which subjects are randomly assigned to some treatment versus a control group can be combined with nonexperimental methods to estimate the differential effects of alternative treatments. We propose tests for the validity of these methods. We use these methods and tests to analyze the differential effects of labor force attachment (LFA) versus human capital development (HCD) training components with data from California’s Greater Avenues to Independence (GAIN) program. While LFA is more effective than HCD training in the short term, we find that HCD is relatively more effective in the longer term.

Due to the new regulatory guidelines known as Basel II for banking and Solvency 2 for insurance, the financial industry is looking for qualitative approaches to and quantitative models for operational risk. Whereas a full quantitative approach may never be achieved, in this paper we present some techniques from probability and statistics which no doubt will prove useful in any quantitative modelling environment. The techniques discussed are advanced peaks over threshold modelling, the construction of dependent loss processes and the establishment of bounds for risk measures under partial information, and can be applied to other areas of quantitative risk management.1This paper was presented as an invited contribution at the meeting “Implementing an AMA for Operational Risk”, Federal Reserve Bank of Boston, May 18–20, 2005.1