ABSTRACT How do investor reactions to the mandatory disclosure of firm tax information influence managers' reporting choices? To answer this question, we build a structural model of reporting bias, which we test with a unique data set of Turkish firms that have been mandated to publicly disclose their tax returns since 2009. We find that both book and tax income numbers are informative to investors. Structural estimates are consistent with disclosure of the tax report reducing both market incentives to manage book earnings and investor uncertainty about underlying firm value. Tax disclosure is associated with higher reported tax income, though the increase is not statistically significant. Counterfactual estimates suggest that book‐tax‐conformity could change financial and tax reporting biases, but lead to a lower quality information environment relative to the separate disclosure of book and tax reports.
ABSTRACT We examine whether legal protection for civic participation affects corporate pollution. Polluters may deter public discipline through strategic lawsuits. Anti‐SLAPP (Strategic Lawsuit Against Public Participation) statutes mitigate this threat by enabling prompt dismissal and recovery of legal costs. Using a difference‐in‐differences design, we find that enacting an anti‐SLAPP statute in a state significantly reduces toxic emissions from that state by increasing local public pressure. The reduction is greater when the statutes provide stronger protection, when local stakeholders have stronger incentives and abilities to discipline polluters, and when local public pressure increases more after the enactment. Plants lower emissions by improving abatement rather than by reducing output or relocating emissions. Our paper underscores the role of legal protection in environmental governance and informs policy debates over anti‐SLAPP legislation.
ABSTRACT We examine the spillover effects of local initial public offerings (IPOs) on new business formation. An IPO in a local area is associated with a 1%–4% increase in new business registrations, and this effect is particularly pronounced in counties facing higher economic uncertainty. New business registrations are significantly influenced by the extent of EDGAR downloads related to the IPO firm's public disclosures and the information in the IPO firm's S‐1 disclosure. The findings highlight the role of IPOs in conveying crucial information through signaling of potential success prospects and additional provision of information through disclosures. A field survey of 503 entrepreneurs further supports these conclusions.
This study examines the relation between taxes and business investment using the setting of an unexpected and economically significant federal corporate income tax rate increase in Canada known colloquially as the Halloween Massacre of 2006. This tax increase only applies to firms organized as income trusts, but not corporations. Using a difference-indifferences design, we find that investment by income trusts decreases about 0.67% for each 1 percentage point increase in the tax rate, translating into over $10 billion in reduced aggregate investment. In decomposing total investment, our results reveal that capital expenditures exhibit an immediate decrease while there is a delayed decrease in acquisition activity. Our results are concentrated in high-investment income trusts that are most sensitive to the link between investment and taxes. Our study helps resolve conflicting prior results by providing robust evidence that tax increases have an economically significant and negative causal effect on business investment.
We examine how analysts’ information acquisition and processing differ when analysts have access to AI resources, focusing on investment banks’ AI investments. We propose and test a two-step framework, which is informed by in-depth interviews with analysts. First, consistent with AI facilitating automation-assisted public information processing, we show that AI investments are associated with more timely earnings forecasts following 10-K filings, particularly after the implementation of iXBRL, which increases the machine readability of filings. Second, we show that analysts reallocate the time and capacity freed by automation toward acquiring and incorporating private information, supported by several sets of evidence: AI investments (1) are associated with higher quality and bolder earnings forecasts, particularly when private information is more important and accessible to analysts; (2) are associated with an expansion of analyst coverage to new firms and industries; and (3) are associated with higher information seeking efforts, particularly greater participation in earnings conference calls. Additionally, exploiting the launch of AskResearchGPT at Morgan Stanley, an in-house generative AI designed for research, we find results consistent with our main analyses. Overall, our study provides insights into the potential for AI to reshape the human value of sell-side analysts.
