We argue that some powerful CEOs induce boards to shift the weight on performance measures toward the better performing measures, thereby rigging incentive pay. A simple model formalizes this intuition and gives an explicit structural form on the rigged incentive portion of CEO wage function. Using U.S. data, we find support for the model's predictions: rigging accounts for at least 10% of the compensation to performance sensitivity and it increases with CEO human capital and firm volatility. Moreover, a firm with rigged incentive pay that is one standard deviation above the mean faces a subsequent decrease of 4.8% in firm value and 7.5% in operating return on assets.

We examine gender differences in misconduct punishment in the financial advisory industry. There is a “gender punishment gap”: following an incident of misconduct, female advisers are 20% more likely to lose their jobs and 30% less likely to find new jobs, relative to male advisers. The gender punishment gap is not driven by gender differences in occupation, productivity, nature of misconduct, or recidivism. The gap in hiring and firing dissipates at firms with a greater percentage of female managers and executives. We also explore the differential treatment of ethnic minority men and find similar patterns of “in-group” tolerance.

We document the economywide extent of misconduct among financial advisers and the associated labor market consequences. Seven percent of advisers have misconduct records, and this share reaches more than 15 percent at some of the largest firms. Roughly one-third of advisers with misconduct are repeat offenders. Approximately half of advisers lose their jobs after misconduct. The labor market partially undoes firm-level discipline by rehiring such advisers. Firms that persistently engage in misconduct coexist with firms that have clean records. We show that this phenomenon may be explained by some firms “specializing” in misconduct and catering to unsophisticated consumers.

We find that regulators can implement identical rules inconsistently due to differences in their institutional design and incentives, and this behavior may adversely impact the effectiveness with which regulation is implemented. We study supervisory decisions of U.S. banking regulators and exploit a legally determined rotation policy that assigns federal and state supervisors to the same bank at exogenously set time intervals. Comparing federal and state regulator supervisory ratings within the same bank, we find that federal regulators are systematically tougher, downgrading supervisory ratings almost twice as frequently as do state supervisors. State regulators counteract these downgrades to some degree by upgrading more frequently. Under federal regulators, banks report worse asset quality, higher regulatory capital ratios, and lower return on assets. Leniency of state regulators relative to their federal counterparts is related to costly outcomes, such as higher failure rates and lower repayment rates of government assistance funds. The discrepancy in regulator behavior is related to different weights given by regulators to local economic conditions and, to some extent, differences in regulatory resources. We find no support for regulator self-interest, which includes “revolving doors” as a reason for leniency of state regulators.

This paper theoretically and empirically studies the interaction of search and application approval in credit markets. Risky borrowers internalize the probability that their application is rejected and behave as if they had high search costs. Thus, “overpayment” may be a poor proxy for consumer sophistication since it partly represents rational search in response to rejections. Contrary to standard search models, our model implies (1) endogenous adverse selection through the search and application approval process, (2) a possibly non‐monotone or non‐decreasing relationship between search and realized interest, default, and application approval rates, and (3) search costs estimated from transaction prices alone are biased. We find support for the model's predictions using a unique data set detailing search behavior of mortgage borrowers. Estimating the model, we find that screening is informative and search is costly. Counterfactual analyses reveal that tightening lending standards and discrimination through application rejection both increase equilibrium interest rates. This increase in realized interest rates is in part due to strategic complementarity in bank rate setting.

We develop a conceptual framework and an empirical methodology to analyze the effect of rising interest rates on the value of U.S. bank assets and bank stability. We mark-to-market the value of banks’ assets due to interest rate increases from Q1 2022 to Q1 2023, revealing an average decline of 10 %, totaling about $2 trillion in aggregate. We present a model illustrating how asset value declines due to higher rates can lead to self-fulfilling solvency runs even when banks’ assets are fully liquid. Banks with high asset losses, low capital, and, critically, high uninsured leverage are most fragile. A case study of the failed Silicon Valley Bank confirms the model insights. Our empirical measures of bank fragility suggest that, in the absence of regulatory intervention, many U.S. banks would have been at risk of self-fulfilling solvency runs.

We investigate the functioning of internal capital markets in Indian Business Groups. We document that intragroup loans are an important means of transferring cash across group firms and are typically used to support financially weaker firms. Evidence suggests that an important reason for providing support may be to avoid default by a group firm and consequent negative spillovers to the rest of the group. Consistent with such spillovers, the first bankruptcy in a group is followed by significant drops in external financing, investments and profits of other firms in the group and an increase in their bankruptcy probability.

We argue that internal capital market imperatives of business groups i.e., reallocation of capital across group firms, influences an affiliated firm's dividend policy. Intuition is developed in a model in which business group insiders distribute dividends from cash-rich firms and use their share of payout to invest in other affiliated firms. Employing multi-country panel-data, we find support for this channel: Dividends by a group firm are positively related with equity-financed investments by its affiliated firms. Results are corroborated by exploiting variation in a firm's investment opportunity generated by changes in import tariff policy: a shock to investment opportunity of an affiliated firm is propagated to dividend policies of other firms in its group.

We study the frictions in dealer-intermediation in residential real estate through the lens of "iBuyers," technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid, and in which automated valuation models have low pricing error. These facts suggest that iBuyers' speedy offers may come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand and quantify the economic forces underlying the tradeoffs of dealer intermediation in this market.