Knowledge that Transforms

Academic freedom is often equated with protection from political and economic interference. Authoritative definitions are broader. They combine protection from external interference, peer-based evaluation, and institutional arrangements for scholarly self-governance. Drawing on Honneth’s (2014) theory of democratic institutions, this paper develops the second and third elements and integrates all three within a recognition-theoretical framework that reconceptualizes academic freedom as social freedom. Using accounting as a case, we reconstruct two transformations that shape the discipline today: the theory revolution of the 1960s, which consolidated peer-based epistemic recognition, and the journal ranking revolution of the 1990s, which reorganized recognition around performance metrics and publication hierarchies. We show that academic freedom can be hollowed out from within without being formally revoked and argue that a discipline weakened from within is less able to resist external threats. Defending academic freedom requires attention to architectures of recognition through which scholarly worth and truth are organized.

This paper provides evidence that the mean and dispersion of manufacturers’ inventory growth, as reflected in accounting disclosures, convey valuable information about subsequent changes in economic output growth. At the same time, I find that although government statistical agencies and professional forecasters incorporate mean inventory growth in their estimates or forecasts, they fail to account for its dispersion. I develop and calibrate a heterogeneous-firm model to show that the leading information embedded in macro-level inventory moments arises from firms’ asymmetric responses to news shocks at the micro level. The model also suggests that the failure to incorporate dispersion is likely due to the noise in the dispersion signal used by statistical agencies and forecasters. Data Availability: Data are available from the public sources cited in the text.

We examine commonly used indicators of aggressive non-GAAP exclusions and find that the majority perform poorly at identifying low-quality exclusions in terms of decision usefulness for investors. We propose a new firm-quarter-specific indicator that identifies instances in which GAAP earnings quality is high (i.e., when firms have less need to provide non-GAAP metrics) but managers disclose non-GAAP earnings anyway. Our new indicator is easy to calculate, requires minimal data, and performs far better at identifying low-quality exclusions than indicators used in prior research. Using our indicator, we find instances in which managers exclude earnings components that are decision useful, consistent with regulators’ concerns about the quality of some non-GAAP earnings disclosures. Our results are robust to a variety of specification checks. Data Availability: Data are derived from a combination of publicly available sources referenced in the article and third-party subscription data bases.

Policymakers claim that combating tax avoidance can help reduce industry concentration by leveling the playing field between industry leaders and their competitors. We test the validity of this claim by using administrative data on industry concentration and exploiting the staggered introduction of anti-tax avoidance regulations across 17 European countries. Although these regulations significantly reduce tax avoidance, we find no statistically or economically meaningful effect on industry concentration. Further tests indicate that our nonresults stem from a genuine lack of effect rather than a lack of statistical power and that our inferences are robust to multiple research designs. The sole exception is in industries with both high levels of leader tax-avoidance advantages and multinational presence, but even in these industries, effect sizes are modest and fall short of regulatory benchmarks. Overall, our findings cast doubt on the idea that broad-based anti-tax avoidance regulations can materially influence industry concentration.

We collect data on ranges of hypothetical asset liquidation values disclosed in U.S. Bankruptcy Court filings. We use this historical information to construct a firm-specific measure, “RecRisk,” which captures asset recovery risk through the uncertainty surrounding asset valuations in liquidation events. We document that higher RecRisk is associated with smaller syndicated loan amounts as a percentage of available collateral, more and tighter performance covenants, and increased loan spreads for borrowers with high credit risk. High RecRisk borrowers also experience lower secondary loan market prices and reduced liquidity for loans with high credit risk. When borrowers become financially distressed, high RecRisk is further associated with declining loan prices and reduced ownership by Collateralized Loan Obligations, the dominant investors in the leveraged loan market. Overall, our results indicate that loan contract terms and prices reflect recovery risk faced by lenders. Data Availability: Data are available from the sources cited in the text. The authors can provide the RecRisk measure at the firm-year level upon request.

This study examines the determinants and consequences of broker in-house ESG research. Using manually collected brokerage ESG research data, we find that the provision of in-house ESG research is positively related to both demand-side factors (client demand, internal demand, and local ESG awareness) and supply-side factors (broker size and coverage of sustainable industries). Regarding the consequences of such research, we find that in-house ESG research is associated with improved earnings forecast accuracy in industries where ESG is more salient. Further, the market reaction to earnings forecast revisions is stronger when equity analysts have access to in-house ESG research. Finally, we observe a consistent pattern suggesting that ESG analysts support equity analysts beyond forecast accuracy, as equity analysts with access to in-house ESG research issue more accurate target prices, more informative recommendation upgrades for ESG-heavy industries, and discuss ESG issues more extensively in their analyst reports. Data Availability Statement: The data used in this study are obtained from publicly available sources identified in the paper, as well as job posting data licensed from Lightcast. Sample construction procedures are described in the paper. Researchers interested in the job posting data should contact Lightcast directly, as access is subject to licensing restrictions.

We examine how foreign non-income tax shocks affect U.S. product markets. Our setting is the politically motivated tariffs on U.S. whiskey exports levied during the 2018 trade war, which created an exogenous negative foreign demand shock for domestic producers. Using a difference-in-differences design we show that, on average, U.S. whiskey producers decrease U.S. product prices and thus increase domestic sales volume following the export tariffs. However, we find evidence of strategic pricing, as producers increase prices of locally produced products in primary production states (Kentucky and Tennessee) and more significantly decrease prices in states where whiskey consumption is less popular. Further, we show that U.S. whiskey producers implement smaller product price decreases in states with greater tariff-related media exposure and reduce advertising spending nationwide but not in Kentucky and Tennessee. Taken together, these findings provide timely evidence regarding how foreign trade restrictions impact the U.S. product market and consumer outcomes. Data Availability: NielsenIQ Retail Scanner Data are available through the Kilts Center for Marketing at the University of Chicago Booth School of Business (http://research.chicagobooth.edu/nielsen). Kantar Advertising Insights data are available by subscription. GDELT data are available at https://www.gdeltproject.org/.

This paper examines circuitousness, which reflects related information being spread throughout a narrative as opposed to being grouped together. Circuitousness in the MD&A is higher for underperforming firms facing an imminent recovery, a difficult scenario for managers to describe and reconcile. Such firms’ disclosures are accompanied by heightened information processing activity, such as EDGAR downloads and analyst forecast revisions. Additionally, initial return reactions are faster but incomplete, consistent with recoveries and the ensuing circuitousness increasing processing costs that delay price discovery. Changes in institutional ownership are concentrated among quasi-indexers, who rely on public disclosure but rebalance only gradually. Circuitousness is incrementally and more consistently predictive than other textual characteristics that are related but are typically associated with obfuscation, such as the Fog index and repetition. Overall, circuitousness in financial disclosure signals that a turnaround is imminent and corresponds to more costly information processing.

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.

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.