Knowledge that Transforms

Quality regulation seeks to ensure quality and foster competition by reducing vertical differentiation, but it may also have adverse effects on market structure. We study this trade-off in the context of pharmaceutical bioequivalence, which is the primary quality standard for generic drugs. By exploiting the introduction of bioequivalence in Chile, we find that stronger regulation reduced the number of drugs in the market by 18% and increased average paid prices by 13%. We estimate a model of drug entry, certification, and demand to study how drug quality, aversion to generics, and certification costs shape the equilibrium effects of quality regulation. We find that quality regulation increased demand for generic drugs by resolving asymmetric information and reducing aversion to unbranded generics, which induced the entry of high-quality drugs in place of low-quality drugs. Consumer welfare increased despite higher prices and a lower number of firms. We compare minimum quality standards with alternative designs of quality regulation, including quality disclosure.

We study competition between two payment instruments: bank deposits and digital platform tokens. Using a Hotelling model that integrates the theory of two-sided markets and payment economics, we assess how this competition is affected by a public payment infrastructure that makes private payment instruments interoperable. We show that interoperability, whether implemented as a fast payment system (FPS) or a central bank digital currency (CBDC), may eliminate payment access exclusion and increase trade volumes and social welfare, but at the cost of some disintermediation. When new payment service providers (PSPs) enter and compete for consumers, they soften competition for merchants: incumbent intermediaries then charge merchants higher fees and earn higher profits. This contrasts with entry of PSPs on the merchant side, which disciplines incumbent fees instead.

We exploit two regulatory changes, one under US GAAP and the other under IFRS, that mandate improved disclosure of pension asset allocation. While the changes are otherwise similar, US GAAP retains the expected rate of return (“ERR”) on pension assets, which preparers can more easily manipulate if asset allocation remains opaque, while IFRS eliminates the ERR. We find that the IFRS standard improves pension asset transparency more than the US standard, which only mandates higher-quality disclosure without changing recognition or measurement and hence leaves unchanged preparers’ incentives to obfuscate. The US standard elicits a mixed response that reduces transparency in some respects. We isolate the relative deterioration in US firms’ transparency to firms with stronger incentives to inflate ERRs. Overall, our study highlights the potential for standard setters to learn from each other by comparing their regulatory experience on a specific accounting topic with clearly identifiable managerial incentives.

This paper identifies a novel channel—corporate fraud—through which local firms protect themselves during anti-mafia periods. We initially explore the impact of the anti-mafia campaign on mafia-like forces captured by accepted mafia-like criminal cases. The results indicate that the average number of accepted mafia-like criminal cases increases by 10.03%. More importantly, the accepted mafia-like criminal cases committed by firms also exhibit a notable increase, mainly within the first 30 months of the anti-mafia campaign. We then theorize that the anti-mafia campaign may have two contradictory effects on corporate fraud: deterrence and self-protection. The main results show that the self-protection effect prevails over the deterrence effect and that firms are more likely to commit fraud to strategically protect themselves from potential losses. The baseline findings reveal that the fraud occurrence of an average firm increases by 44.07% during anti-mafia periods.

How do recent advances in Generative Artificial Intelligence (AI) affect firm value? We construct the first measure of firms' workforce exposures to Generative AI and show that an “Artificial‐Minus‐Human” portfolio earned 5% in the two weeks following the release of ChatGPT. The labor‐exposure effect is more pronounced for firms with greater data assets and is distinct from the effect of firms' product exposures to AI. We assess whether exposed workforces are substituted or complemented by Generative AI based on whether their exposed tasks are core or supplemental. Examining firms' labor demand and profitability following the release of ChatGPT supports a labor‐technology substitution channel.

What are the consequences of making central bank deposits accessible to actors beyond the banking sector? We collect novel micro data on deposit accounts historically held with the Bank of England by individuals and firms other than banks to study their role in the financial panic of 1866. We document that the use of deposit accounts at the central bank soared during the crisis, while commercial banks permanently lost funding. The expansion in the number of active accounts was fueled by individuals, but the intensive margin increase in balances came almost entirely from firms. Recourse to central bank deposits was heterogeneous and persisted well beyond the panic, reflecting easy access, low opportunity costs, and positive network externalities. At the same time, retail borrowers seeking liquidity support from the Bank during the crisis preferred receiving loan proceeds in the form of cash rather than deposits.

This paper asks about the existence, determinants, and implications of toxic content in financial social media. Using machine learning algorithms to measure toxicity in Seeking Alpha articles and comments, we find measurable toxicity concentrated primarily in user-generated comments rather than articles. Comment toxicity is greater for firms with more investor attention or uncertainty. We document three key results about toxicity in capital markets. First, toxicity displays a feedback loop in platform participation: past toxicity is associated with more future toxic contributors for a given firm. Second, firms receiving more toxic comments have greater retail trading volume but less informative retail trades, especially when comments disagree. Third, toxicity is associated with slower price discovery around earnings announcements. Our findings suggest that financial social media toxicity relates to both user behavior and market outcomes and raise important considerations for platform governance in financial markets. Data Availability: The Seeking Alpha articles and comments used in this study were obtained directly from Seeking Alpha under a data-sharing arrangement and are proprietary; we are not permitted to redistribute them. These data may be requested from Seeking Alpha. Compustat, Execucomp, CRSP, and New York Stock Exchange (NYSE) Trade and Quote (TAQ) data are available through Wharton Research Data Services (WRDS) to subscribing institutions.