Knowledge that Transforms

We study the determinants of value creation in U.S. commercial banks. We develop novel measures of individual banks’ productivities at collecting deposits and making loans that we relate to bank market values. We find that deposit productivity is responsible for two-thirds of the value of the median bank and most variation in value across banks. Variation in productivity is driven by differences across banks in technology, customer demographics, and market power. We also find evidence of synergies between deposit-taking and lending. Our findings suggest that there is significant heterogeneity in banks’ ability to capture value by manufacturing safe assets.

Using procurement auctions and register data, we find that temporary demand shocks have long-term effects for startups. Startups that win a procurement auction have 20% higher sales and employment and are more profitable than startups that narrowly lose an auction, even several years after the contract work has ended. There are no such effects for mature firms. The effects for startups are large: about 50% of the contract value is transmitted into long-term sales. Our analysis suggests learning-by-doing as a plausible mechanism. Overall, our results point to the importance of path dependence in shaping the long-term outcomes of startups.

We ask whether the correlation between mortgage leverage and default is due to moral hazard (the causal effect of leverage) or adverse selection (ex ante risky borrowers choosing larger loans). We separate these information asymmetries using a natural experiment resulting from the contract structure of option adjustable-rate mortgages and unexpected 2008 divergence of indexes that determine rate adjustments. Our point estimates suggest that moral hazard is responsible for 40% of the correlation in our sample, while adverse selection explains 60%. We calibrate a simple model to show that leverage regulation must weigh default prevention against distortions due to adverse selection.

Some credit booms result in financial crises. While excessive risk-taking could plausibly explain the boom-to-bust cycle, many investors do not anticipate increasing risk. We show that credit booms may be misunderstood as being driven by high productivity because opaque bank assets disguise risk incentives. Balanced funding relative to productive prospects can sustain prudent lending (good boom), whereas funding imbalances may induce high risk exposure and boost asset prices (bad boom) or lead to asset underpricing and insufficient lending (missed boom). Rational agents drawing inference from prices make mistakes that can amplify the effect of funding imbalances and propagate risk.

We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30%.

Based on textual analysis and a comparison of cybersecurity risk disclosures of firms that were hacked to others that were not, we propose a novel firm-level measure of cybersecurity risk for all U.S.-listed firms. We then examine whether cybersecurity risk is priced in the cross-section of stock returns. Portfolios of firms with high exposure to cybersecurity risk outperform other firms, on average, by up to 8.3% per year. Yet, high-exposure firms perform poorly in periods of high cybersecurity risk. Reassuringly, the measure is higher in information-technology industries, correlates with characteristics linked to firms hit by cyberattacks, and predicts future cyberattacks.

The return expectations of public pension funds are positively related to cross-sectional differences in past performance. This positive relation operates through the expected risk premium, rather than the expected risk-free rate or inflation rate. Pension funds act on their beliefs and adjust their portfolio composition accordingly. Persistent investment skills, risk taking, efforts to reduce costly rebalancing, and fiscal incentives from unfunded liabilities cannot fully explain the reliance of expectations on past performance. The results are consistent with extrapolative expectations, since the dependence on past returns is greater when executives have personally experienced longer performance histories with the fund.

This paper proposes an aggregation scheme of subjective bond return expectations based on the historical accuracy of professional interest rate forecasters. We use disaggregated survey data on bond returns and document large disagreement in the cross-sectional distribution and persistence in forecast accuracy. Our aggregate subjective belief proxy outperforms equal weighting schemes, and its dynamics are significantly different from statistical forecasting models. With this measure in hand, we study the relationship between quantities of risk and subjective expectations of excess returns and demonstrate a strong link between the two, even if such a relationship is difficult to detect using realized returns.

We document that a more generous unemployment insurance (UI) system shifts labor supply from safer to riskier firms and reduces the compensating wage differentials that risky firms need to pay. Consequently, a more generous UI system increases risky firms’ value and fosters entrepreneurship by reducing new firms’ labor costs. Exploiting a UI reform in Brazil that affects only part of the workforce allows us to compare labor supply for workers with different degrees of UI protection within the same firm, sharpening the identification of the results. Altogether, our results suggest that UI provides a transfer system from safe to risky firms.

We investigate the effects of opioid abuse on municipal finance. We employ instrumental variables, border discontinuity difference-in-differences regressions, and coarsened exact matching to identify consistent causal effects, while controlling for variation in economic conditions and demographics. Opioid abuse lowers credit ratings, increases new offer yields, and reduces bond issuance. Reversal of these effects following effective antiopioid legislation further supports causality. Differential effects due to investor heterogeneity suggest that opioid abuse affects municipal finance through a capital supply channel. Overall, we conclude that local opioid abuse impedes municipalities’ access to capital and thus hurts their ability to provide public services and infrastructure.