Knowledge that Transforms

We examine companies’ voluntary adoption of sustainability disclosure standards developed by the Sustainability Accounting Standards Board (SASB). Specifically, we study which company characteristics help explain the use of SASB standards and examine whether voluntary use is associated with sustainability-related activities and market outcomes. We find that peer behavior, sustainability-focused institutional ownership, company size, and existing sustainability reporting practices are key determinants associated with SASB adoption. Moreover, SASB adoption appears to be a highly persistent disclosure choice. It is significantly associated with better sustainability performance, such as lower sustainability violations, greenhouse gas emissions, and pollution levels, particularly when the SASB standards identify those issues as financially material for the company's industry. Finally, SASB adoption is associated with more extensive sustainability disclosure and greater price informativeness, consistent with investors getting additional firm-specific information from SASB-based reporting.

Consider a setting where N players, partitioned into K observable types, form a directed network. Agents’ preferences over the form of the network consist of an arbitrary network benefit function (e.g., agents may have preferences over their network centrality) and a private, or dyadic, component which is additively separable in own links. This latter component allows for unobserved heterogeneity in the costs of sending and receiving links across agents (respectively out- and in- degree heterogeneity) as well as homophily/heterophily across the K types of agents. In contrast, the network benefit function allows agents’ preferences over links to vary with the presence or absence of links elsewhere in the network (and hence with the link formation behavior of their peers). In the null model, which excludes the network benefit function, links form independently across dyads in the manner described by Charbonneau (2017) among others. Under the alternative, there is interdependence across linking decisions (i.e., strategic interaction). We show how to test the null with power optimized in specific directions. These alternative directions include many common models of strategic network formation (e.g., “connections” models, “structural hole” models etc.). Our random utility specification induces an exponential family structure under the null which we exploit to construct a similar test which exactly controls size (despite the the null being a composite one with many nuisance parameters). We further show how to construct locally best tests for specific alternatives without making any assumptions about equilibrium selection. To make our tests feasible, we introduce a new MCMC algorithm for simulating the null distributions of our test statistics.

Do impact investors seek impact, or merely “impact wash”? We provide systematic evidence on the nonfinancial determinants of impact investing. Impact investors focus on firms aligned with the priorities of the federal government and disproportionately invest in economically disadvantaged regions. While we find high levels of coinvestment between impact and traditional investors, we also show that impact investors influence the strategies of their traditional coinvestors, are more likely to fund firms in nascent industries, and invest countercyclically. Finally, we characterize investment heterogeneity based on a novel classification of impact investment theses, with a focus on climate, environment, and jobs and equity.

This study examines the relation between exposure to social tax discontent—public expressions of frustration about others’ tax avoidance on social media—and individual tax avoidance. Economic and psychological theories predict bidirectional effects: discontent may reduce avoidance by inducing internal sanctions (e.g., shame and guilt) or increase avoidance by normalizing noncompliance through perceived prevalence. Using a novel, large-scale measure of tax discontent derived from geolocated Twitter data and a language classification model, we find that increases in social tax discontent are associated with reductions in estimated tax avoidance. The effect is stronger among high-income groups, in response to tweets targeting wealthy individuals, and in areas with higher political engagement and social media activity. Our results reinforce the importance of taxpayers’ social and behavioral motivations and suggest that jurisdictions’ efforts to manage taxpayer beliefs and leverage social norms can act as a complementary tool to traditional enforcement. Data Availability: The data used in this study are derived from public and third-party sources described in the text: the IRS Statistics of Income, the U.S. Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages, the Bureau of Economic Analysis, Google Trends, and the National Neighborhood Data Archive. Twitter/X data were collected via the Twitter Application Programming Interface (API) under its academic-access terms and are subject to Twitter’s/X’s terms of use; they cannot be redistributed by the authors, but the collection and classification procedures are described in Section IV.

Despite persistent academic and regulatory concerns, collateralized loan obligations (CLOs) have consistently demonstrated resiliency over the past two decades. We document that firms whose loans are acquired by CLOs are less likely to experience adverse credit events within 12 months of inclusion in the CLO portfolio, particularly when CLOs maintain institutional-level relationships with originating banks (through repeated transactions) or when individual-level relationships exist (as evidenced by personnel flows between originating banks and CLOs). The effects of these relationships are more pronounced when banks likely possess superior private information about borrowers. Furthermore, conditional upon filing for bankruptcy, borrowers whose loans are held by CLOs with institutional- or individual-level relationships are more likely to successfully reorganize under Chapter 11. Collectively, our findings highlight the dual information channels, both institutional and personnel based, that mitigate information frictions in the leveraged loan market. Data Availability: Data are available from commercial sources cited in the text.

Deviations from covered interest rate parity (CIP) are often linked to limits to arbitrage, yet trading volumes surge during periods of apparent no-arbitrage violations. We show that these distortions stem from constraints on non-U.S. agents’ access to wholesale U.S. dollar markets and reflect a premium for unencumbered synthetic dollar funding: non-U.S. banks substitute secured USD borrowing with FX swaps to meet regulatory requirements. A shadow cost-augmented CIP condition holds, implying no riskless arbitrage. U.S. dealers extract rents on dollar provision while non-U.S. customers bear $10.4 billion in additional annual hedging costs. Our results illustrate how intermediary constraints segment global dollar funding.

This paper studies the unemployment of physical capital—defined as idle units searching to be traded—and its macroeconomic implications. I provide evidence documenting that capital unemployment is large, volatile, and increases during economic downturns. I construct a capital-accumulation model that explains these patterns, with trading frictions in capital markets, which give rise to equilibrium capital unemployment, and financial shocks, which lead to large fluctuations in trading probabilities and capital unemployment. Using the model, I show that trading frictions and capital unemployment matter for aggregate dynamics. First, unemployed capital affects aggregate investment dynamics: Downturns characterized by large increases in unemployed capital are followed by investment slumps, because the economy tends to recover by absorbing existing unemployed capital rather than by producing new capital goods. Second, capital unemployment constitutes a propagation mechanism from financial shocks to economic activity, which shows up at the aggregate level as measured total factor productivity.