We propose a more expansive analysis of accounting regulation, highlighting the significance of a regulatory network and the importance of examining the recursive processes involved in the transition from formal law to practice. Our analysis is grounded in the sociology of law and is exemplified by the case of a corporate disclosure regulation for Canada's extractive industry. The paper examines how the regulator, in concert with intermediary actors, works to clarify and influence the practice of law, what we refer to as operationalization. In providing a historically and socially contextualized analysis, the paper treats regulation as a dynamic process that includes multistakeholder working groups, legislative debates, regulatory procedures, and shifting meanings of compliance and enforcement. We examine operationalization as a distinct regulatory dynamic and explore its significance for an expanded understanding of accounting regulation.

In this paper, I examine what we know and don’t know about both private and public workforce development in the United States. I highlight three of the most important categories of programs and policy: (i) workforce development in accredited higher education institutions, particularly community colleges; (ii) other publicly funded or private training and services, including “sectoral training” that targets specific high-demand sectors of the economy; and (iii) on-the-job or work-based learning, including apprenticeships. I summarize the theoretical literature on workforce development and a broad landscape of the three key categories. I synthesize the empirical literature on workforce development, beginning with comparisons of different data sources, outcome measures, and empirical methods used before reviewing the literature on estimated impacts in each of the three categories. I then consider the international evidence on workforce development and how public efforts differ between the United States and other industrial countries before concluding. (JEL I23, I26, J24, J31, M53)

We examine how removing user anonymity affects social media’s ability to generate value-relevant information for the stock market. Using a difference-in-differences design that exploits the differential timing of adopting real-name verification policies by the two most popular investment-related social media websites in China, we find that content on the treated site becomes significantly more informative about future stock returns and earnings after the policy takes effect. This effect is primarily driven by continuing users who post more actively in the pre-period. Although these users post less after the policy, the informational content of their posts increases, suggesting greater prudence in expressing opinions. Our results strengthen for firms that attract regulatory scrutiny. Overall, our study suggests that real-name verification policies can discipline internet users and potentially improve the informativeness of investment-related social media, particularly in emerging markets where retail investors predominate.

We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.

We extract and describe corporate-issued guidance contained in over 23,000 LSEG Guidance Reports of S&P 1,500 firms from 2005 to 2021. Our sample contains 1.735 million Guidance Reports guidance instances that span over 180 guided items and fall into three broad categories: (1) qualitative topics, (2) consolidated financial statements, and (3) other key performance indicators. We identify research opportunities arising from Guidance Reports’ rich features, including quantitative or qualitative form, underpinning text, disclosure channels, and source speakers. We also compare Guidance Reports to the commonly used I/B/E/S Guidance database, which covers only quantitative guidance for 13 items. Approximately 1.494 million Guidance Reports instances fall outside I/B/E/S Guidance’s coverage, and even among overlapping items, only a subset is translated to I/B/E/S Guidance based on LSEG cost–benefit considerations. Our findings suggest researchers should be aware of the extent and nature of I/B/E/S Guidance omissions when studying guidance.

We build a quantitative spatial model in which some workers can substitute on-site effort with work done from home. Ability and propensity to telecommute vary by education and industry. We quantify our framework to match the distribution of jobs and residents across 4,502 U.S. locations. Then, we simulate permanent increases in the attractiveness and productivity of telework that lead to greater adoption of hybrid and fully remote work. To validate our model, we show that our results are positively correlated with local changes in residents, jobs, and housing costs since 2019. The rise of telework results in a rich non-monotonic pattern of reallocations of residents and jobs within and across cities. Workers who can telecommute experience welfare gains, and those who cannot suffer losses. Broader access to jobs reduces wage inequality across residential locations, and heralds a partial reversal in the spatial concentration of talent and spending power known as the “Great Divergence”.

Natives are expected to respond to the wage impact of immigration by moving across markets. We argue that the observed impact depends not only on the size of the native response but also on which natives choose to respond. Specifically, a nonrandom response produces a selection bias. We document its empirical relevance by showing that the strong feminization of the French immigrant workforce reduced the employment of native women, leading to sizable compositional shifts and no correlation between immigration and female wages. Adjusting for selection bias results in a wage elasticity that becomes negative for women and similar to men.

We examine the impact of auditors’ reputation impairments on their private-client market share to explore how conducting low-quality audits affects auditors’ broader client portfolios. Prior evidence implies that an audit office loses public-client market share after a client announces a restatement. However, auditors’ private clients may be less concerned about auditor reputation and quality, given that they have lower agency costs and their financial statement users are often creditors that can rely on direct monitoring to narrow information asymmetry. Also, differences between public and private company audits cast doubt on whether public-client restatements are relevant to private clients. We find that the private-client market share of a Big Four audit office falls by, on average, 5 percent the year after a public client announces a restatement. This evidence suggests that Big Four offices cannot simply replace lost public-client revenue with private-client revenue after suffering reputation damage.