This paper examines whether targeted credit rationing by banks can disrupt the operations of firms likely to generate negative externalities. We exploit a major regulatory initiative in the United States—Operation Choke Point—which targeted bank relationships with firms in industries with a high risk for fraud and money laundering. Using supervisory loan-level data, we find that targeted banks reduce lending and terminate relationships with affected firms. However, these firms fully compensate by substituting lending from non-targeted banks at similar terms, resulting in no changes in total debt, investment, or profitability. Our results suggest that targeted credit rationing fails to promote change.

We document the role that inside investment plays in managerial compensation and hedge fund performance. Merging against a comprehensive dataset of US hedge funds, we find that funds with greater inside investment outperform on a factor-adjusted basis. We emphasize the role of capacity constraints in explaining this result: insider funds are smaller, are less likely to accept inflows in response to positive returns, and are more likely to be closed to outside investors. These results suggest that managers earn outsize rents by operating trading strategies further from their capacity constraints when managing their own money.

 Media Coverage: Harvard Law School Forum, Bloomberg View, Institutional Investor, ValueWalk, Reuters

Implementing a state-of-the-art machine learning technique for causal inference using text analysis, we document that women are under-recognized for the quality of the patents they are granted. For the equivalent patent written by a female, a male would have received 12% more citations and 16% more patents in the top decile. Both female inventors and examiners tend to cite patents with female lead inventors more. Underrecognition of female-authored patents likely has economic implications for the allocation of talent in the economy. We conclude by outlining future applications of this new causal inference technique to finance and economics.

This study empirically evaluates the relationship between health state-contingent assets and their ability to facilitate greater survival rates. Using transaction-level data from the life settlement market in a quasi-experimental setting, this study finds that wealth in particularly poor states of health leads to a significant increase in survival rates. This relationship is stronger for people in fragile health and those living furthest from hospitals. Improved survival rates are independent from the severity of the disease diagnoses and the social-economic status of the policyholders. These findings provide novel evidence in support of financial solutions aimed at the rising cost of healthcare.

We study the real effects of environmental activist investing. Using plant-level data in a quasi-experimental setting, we find that firms targeted by environmental activist investors reduce their toxic releases, greenhouse-gas emissions, and cancer-causing pollution through preventative efforts. Improvements in air quality within a one-mile of targeted plants suggest potentially important externalities to local economies. We provide evidence supporting the external validity of environmental activism while also ruling out reporting biases, forms of selection, and other alternative hypotheses. Overall, our study suggests that engagements are an effective tool for long-term shareholders to address climate change risks.

We use widely adopted voluntary ESG disclosure standards and leading machine learning techniques to quantify and compare the ESG content across a comprehensive sample of public firms’ ESG reports and their 10-Ks. ESG information in both documents is financially material, and the amount of material information has increased over time. While both documents feature financially material ESG information, ESG reports uniquely offer additional material insights not found in the 10-K. Studying the market reaction around the public disclosure of ESG reports, we find the release of reports and the intensity of material information in the reports do not predict changes in returns in the short run. Studying the long term implications, we find that reports with novel material information are related to decreased downside firm risks in subsequent years. Overall, our study underscores the unique value of ESG report data and sheds light on its practical relevance for investors.

We study production responses to emission capping regulation on manufacturing firms. We find that firms reduced their pollution as they transitioned from self-generated to externally sourced electricity, shifted toward producing less coal-intensive products, and increased their abatement expenditures. Firms preserved profitability by increasing their production of higher-margin products. However, firms in highly polluting industries produced fewer products. In the aggregate, we document lower product variety, higher markups, an altered firm-size distribution, and lower business formation. Our findings highlight both the mechanisms behind how mandated pollution reduction can be effective and its costs, suggesting a loss in agglomeration externalities.

In the United States, corporations were directly responsible for more than 30 percent of the country’s 2022 total emissions, or approximately 2 billion metric tons of CO2 equivalents (1). Worried in part about the potential regulatory threats that companies face, investors and asset managers have called for greater transparency of publicly traded firms’ climate impacts. These calls have sparked a debate about whether government agencies should require such disclosures, or whether market pressures are sufficient to drive disclosure necessary for investors to make informed decisions (2). We show that these disclosures are, in fact, unreliable. Examining 900 emissions disclosures from 276 of the largest U.S. firms with available data, we find that 60% of these disclosures are eventually restated by the firm, a rate of restatement that has been consistent during the sample period. Furthermore, firms are more likely to understate than overstate, and the value of the understated emissions, on average, is more than twice the value of overstated emissions, suggesting that at least some of the misstatements are not errors. During the decade that we examine, the total gross understated public-firm emissions is 265 million tons, equivalent to the total emissions of Taiwan in 2020. Our results demonstrate the failure of market forces to produce reliable corporate environmental data.

This paper studies whether ESG funds trade off the long-term sustainability of portfolio firms for greater short-term financial performance. ESG funds reveal their preference for superior returns by voting against E&S proposals when it is uncertain whether these proposals will pass. We find that consistent with their financial incentives, active ESG funds and non-ESG-focused institutions are more likely to oppose E&S proposals. However, investors do not appear to respond to such differential voting patterns by withdrawing capital. Overall, our results highlight that investors’ conflicting objectives of advancing sustainability while achieving superior returns can impede improvements in corporate sustainability.

This paper measures the cost of biased retirement expectations for investors in target-date funds. Using a nationally representative survey that follows households for nearly three decades, we document those survey respondents systematically underestimate their long-run labor participation on average by 4.8 years, with these errors having meaningful cross-sectional relationships with the respondent’s health, earnings, and wealth. We use these insights to build a life-cycle model of target-date funds to measure the costs of biased expectations. Model calibrations suggest that errors in expectations compound over time, costing the median respondent 4% of wealth at retirement, equivalent to 0.2% a year in losses.

 The conventional view is that capital requirements, those intended to reduce bank risks, can have a negative impact on bank lending. This paper reconsiders this view by studying the interaction between capital requirements and corporate governance. Our model highlights how capital requirements can have directionally different effects on lending, depending on the extent that management is protected against shareholder actions. Taking our model to the data, we find divergent lending practices for banks with stronger protections compared to their weaker counterparts. These results highlight the need for regulators to consider the impact of corporate governance when implementing bank capital requirements.

All investors face the same decision problem: either invest for themselves or delegate their portfolio problem to an outside investor. Typically, asset managers can communicate their superior knowledge to attract capital. However, such communication comes with the risk of revealing the particulars of their valuable information without a commitment from potential investors. I explore this fundamental investment-delegate problem through developing an entropy-based model of communication, where investors endogenously determine to be a principal or an agent in a highly generalizable setting.