Knowledge that Transforms

Taxation is a central economic policy tool, with governments increasingly using tax policy to stimulate local economic growth and also regulate multinational firms. We review the empirical literature that studies the effect of tax policies on firms’ investment, employment, and other real outcomes. Building on the neoclassical theory of corporate taxes and tangible investment, we propose an organizing framework for our review that captures the wide set of tax policies and firm responses examined in accounting research. This framework highlights four dimensions along which accounting scholars contribute to the literature: (i) documenting the role of financial reporting incentives as a moderating factor in firms’ real responses, (ii) studying firms’ reporting versus real responses, (iii) quantifying real effects of tax disclosure regulations, and (iv) improving measurement of firms’ tax status and proxies for investment and employment. We identify open questions for future research and suggest new international, federal, and local settings that may help uncover underlying mechanisms driving observed economic phenomena. Specifically, we encourage scholars to further distinguish firms’ reported and real responses to tax changes and improve measurement of these outcomes, especially in settings related to environmental taxation or settings in which tax avoidance and real outcomes are closely linked.

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading on long-run stock price informativeness. Overall, our findings indicate that a significant number of investors use ChatGPT in ways that influence their trading decisions and market outcomes. Future research can investigate the mechanisms underlying these GenAI effects and the potential risks of using GenAI for trading.

Financial analysts closely follow a firm’s operations and assess the risks that it faces. In this paper, we examine whether analysts incorporate ESG risks into their stock recommendations and target prices. Specifically, we use a unique firm-day level dataset on negative ESG risk incidents to proxy for unobservable risk assessments of analysts. We find that analyst outputs predict future ESG incidents, suggesting that analysts incorporate ESG risks into their models. Our results are robust to controlling for ESG incidents that firms experienced in the past, and are stronger in more transparent information environments, and in the presence of more guidance on ESG issues from the Sustainability Accounting Standards Board. Importantly, we find that analysts incorporate ESG risks through adjusting discount rates rather than cash flow estimates. Overall, our results highlight the ability of financial analysts to synthesize and integrate ESG risks into their research.

This paper examines complexity in CEO compensation contracts. We develop a measure of compensation complexity and provide empirical evidence that complexity has increased substantially over time. We document that complexity results not only from factors reflecting efficient contracting, but also from external pressures from compensation consultants, institutional investors, proxy advisors, and attempts to benchmark to peers, with these external factors having greater impact in more recent years. Examining consequences of contract complexity, we find an association with lower future firm performance that is related to the influence of external factors on compensation design. We further find this relation is partially mitigated when a contract's performance metrics are more highly correlated, consistent with information processing costs hampering decision-making. Collectively, these findings confirm concerns raised by investors and the media regarding compensation complexity and can inform boards in their design of CEO pay packages.

We offer empirical evidence on the role of accounting information in shaping the incentives to produce fake news. We document that fake news authors strategically (1) publish their articles near earnings announcements, leveraging the widespread market attention these events attract, and (2) within the near-announcement window, avoid publishing post-announcement when investors are less susceptible to fake news due to the disclosure of accounting information. In extending our analyses to the broader accounting information environment, we find that fake news authors are less likely to target firms with more robust accounting information and elicit lower market reactions when doing so. These results highlight both ex-ante and ex-post roles that accounting information plays in safeguarding firms from financial disinformation.

We use textual analysis of mandatory accounting filings to develop firm-level, time-varying measures of exposure to individual government agencies including the Securities Exchange Commission (SEC) and Internal Revenue Service (IRS). The measures vary predictably across industries and with agency-specific events such as the Sarbanes Oxley Act at the SEC and budget cuts at the IRS. The measures positively relate to undisclosed agency investigations and financial statement downloads. Firms' total exposure across government agencies negatively relates to their profitability, consistent with exposure to government agencies imposing net costs. Consistent with a causal interpretation of these results, the positive stock market reaction to the surprise election of Donald Trump, who promised to reduce the power of government agencies, positively varies with firms' exposure to government agencies. As initial applications of our measures, we demonstrate that expanded SEC oversight increases firms' stock liquidity and reduced IRS oversight decreases firms’ effective tax rates.

We investigate a 2012 comply-or-explain regulation implemented by China’s Shanghai Stock Exchange. The regulation requires eligible firms to pay 30% of their current-year profits as cash dividends or explain the reasons why they do not meet this requirement through a public conference call. Using firms listed on the Shenzhen Stock Exchange as a control group, our difference-in-differences estimates suggest that firms subject to the regulation decreased tunneling, irrespective of whether they complied by paying or disclosing. Further analyses suggest that the reduction in tunneling is partially attributed to enhanced regulatory monitoring over explaining firms and the constraint on excess cash of paying firms. These findings offer novel policy insights into how a flexible comply-or-explain form of regulation can mitigate agency costs between controlling and minority shareholders in a weak institutional environment.

We use data from TransUnion, a large U.S. credit bureau covering millions of individual consumer loans, to examine the transition to the Current Expected Credit Loss (CECL) accounting standard and to provide novel evidence about the impact that raising reserve requirements has on banks’ pricing and lending decisions in the U.S. consumer lending market. We find that greater reserve requirements following the adoption of CECL induce a statistically significant but economically moderate increase in loan interest rates. The effects are more pronounced for weakly-capitalized banks and even more so for underprivileged individuals borrowing from weakly-capitalized banks. Our evidence informs the ongoing policy debate between standard setters and members of the financial industry about the potential effects of CECL on credit markets.