Little is known about how the SEC selects its targets for investigation. We study this subject using a new database of formal SEC investigations. We predict and find that case selection is associated with a firm's (i) likelihood of regulatory noncompliance, (ii) exposure to private sector scrutiny, and (iii) conspicuous public trigger events. The relationship between investigations and regulatory noncompliance and private sector scrutiny preferences is sensitive to SEC constraints, whereas the relationship with triggers is not. We also examine the association between investigation motives and enforcement actions, an important SEC outcome reported to Congress. While regulatory noncompliance-motivated and public trigger-motivated investigations are more likely to result in public charges, specifically when the SEC is constrained, private sector scrutiny-motivated investigations are less likely to result in public charges. Finally, investigation rates of potential targets are associated with the career trajectories of SEC personnel, while investigation outcomes are not.

The patent system grants inventors temporary monopoly rights in exchange for a public disclosure detailing their innovation. These disclosures are meant to allow others to recreate and build on the patented innovation. We examine how the quality of these disclosures affects follow-on innovation. We use the plausibly exogenous assignment to patent applications of examiners who differ in their enforcement of disclosure requirements as a source of variation in disclosure quality. We find that some examiners are significantly more lenient with respect to patent disclosure quality requirements, and that patents granted by these examiners include significantly lower-quality disclosures and generate significantly less follow-on innovation. Overall, our evidence suggests that high-quality patent disclosures create knowledge spillovers that spur follow-on innovation.

ChatGPT frequently appears in the media, with many predicting significant disruptions, especially in the fields of accounting and auditing. Yet research has demonstrated relatively poor performance of ChatGPT on student assessment questions. We extend this research to examine whether more recent ChatGPT models and capabilities can pass major accounting certification exams including the Certified Public Accountant (CPA), Certified Management Accountant (CMA), Certified Internal Auditor (CIA), and Enrolled Agent (EA) certification exams. We find that the ChatGPT 3.5 model cannot pass any exam (average score across all assessments of 53.1%). However, with additional enhancements, ChatGPT can pass all sections of each tested exam: moving to the ChatGPT 4 model improved scores by an average of 16.5%, providing 10-shot training improved scores an additional 6.6%, and allowing the model to use reasoning and acting (e.g., allow ChatGPT to use a calculator and other resources) improved scores an additional 8.9%. After all these improvements, ChatGPT passed all exams with an average score of 85.1%. This high performance indicates that ChatGPT has sufficient capabilities to disrupt the accounting and auditing industries, which we discuss in detail. This research provides practical insights for accounting professionals, investors, and stakeholders on how to adapt and mitigate the potential harms of this technology in accounting and auditing firms.

Distributed, rather than co‐located, teams increasingly perform audit work, raising regulator concerns that distributed team communication issues may affect audit quality. We investigate upward communication (i.e., raising issues to supervisors), a key communication dimension related to audit quality. In Study 1, we survey 69 senior auditors to establish that distributed team upward communication suffers. Furthermore, distributed team auditors identify less with their teams and struggle to know when and how to speak up. Because both are linked to inhibited upward communication, we next experimentally test firm practices with the potential to attenuate these problems. Study 2, an experiment with 128 staff auditors and interns, reveals that making auditors' professional identities salient improves upward communication on co‐located teams with strong team identities but not on distributed teams with weak team identities. Study 3, an experiment with 58 staff auditors, replicates a key Study 2 finding with a design that makes no reference to team identity strength. Specifically, in the presence of a salient professional identity, upward communication is significantly higher for co‐located relative to distributed team auditors. Study 4, an experiment with 69 staff auditors, focuses solely on distributed teams. It indicates that several distributed team management practices, including one‐on‐one and standing daily meetings, encourage upward communication. Finally, Study 5, informal interviews with eight audit seniors, corroborates key survey and experimental findings. Combined, our results provide insights into why distributed team communication suffers, refine audit voice theory, and provide regulators, practitioners, and researchers with multiple paths to improve audit quality.

Social protection programs have become increasingly widespread in low- and middle-income countries, with their own distinct characteristics to match the environments in which they are operating. This paper reviews the growing literature on the design and impact of these programs. We review how to identify potential beneficiaries given the large informal sector, the design and implementation of redistribution and income support programs, and the challenges and potential of social insurance. We use our frameworks as a guide for consolidating and organizing the existing literature and also to highlight areas and questions for future research. (JEL E26, H23, I13, I32, I38, O12, O16)

The Securities and Exchange Commission encourages the presentation of information or data in graphical form to improve users' ability to understand financial disclosures. We find a dramatic increase in the disclosure of both qualitative and quantitative infographics in 10-K filings over time and substantial cross-sectional variation in firms' choices regarding image types, data content, and the placement of infographics within 10-Ks. We provide evidence on factors associated with firms’ use of infographics and explore the persistence with which they are disclosed over time. Finally, we investigate the relation between the use of infographics and uncertainty in capital markets.

We study how U.S. manufacturing firms’ investment responds to tariff reductions in supplier industries. Our estimates, based on tariff reductions following multinational trade agreements, suggest that a hypothetical 10% reduction of all upstream tariffs would increase downstream investment by 4% to 6%. This estimate is not explained by decreasing uncertainty and stems from tariff reductions for homogeneous and low-R&D inputs, consistent with the investment response resulting from cost reductions rather than superior foreign technology embodied in imported inputs. Evidence from an instrumental variable estimation using the sudden increase in Chinese import penetration suggests that import competition also increases downstream investment.

We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual consumers. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market‐level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market‐level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which nonprice product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when these product characteristics are endogenous and not instrumented. We discuss implications of these results for applied work.

We examine the structural stability of Gaussian shadow rate term structure models in a sample of Treasury yields that includes the “effective lower bound” (ELB) period from 2008 to 2015. After highlighting the challenges of testing for structural breaks in a latent-factor model, we proceed to document various pieces of empirical evidence for a structural break. As one of several practical implications, the expected policy rate paths during ELB years are notably shallower in our model that accommodates a structural break compared with a model that imposes structurally stability.