Knowledge that Transforms

Financial statement fraud generates many negative effects, including reducing people's willingness to participate in the stock market. If it also stigmatizes accounting, it may similarly adversely affect the quantity and quality of workers willing to become accountants, thereby potentially creating negative effects for years to come. We examine the impact of fraud on the labor force entering the accounting profession, which is a key input into the production of accounting information (i.e., the output). Using data describing millions of college students across the United States, we find incoming students are actually more likely to major in accounting when local frauds occur during their formative years. These students are also more likely to have attributes desired by the accounting profession (e.g., high academic aptitude) and are more likely to subsequently serve in public accounting and become Certified Public Accountants. In the context of other fields (i.e., all college majors), we find that fraud similarly spurs interest in other business disciplines, but not in majors outside of business schools. Those attracted to other business disciplines, however, generally possess different traits. Specifically, students entering accounting are distinctively more likely to exhibit values espoused by the accounting profession, including a predisposition to public service and less commercial orientation. Thus, nonpecuniary motives appear to uniquely drive accounting student enrollment following fraud. Collectively, our findings suggest that, while fraud is unmistakably bad, it appears to have the positive unintended consequence of attracting labor into business disciplines and, in accounting, increasing the prevalence of desirable traits among entrants.

Firms are increasingly implementing performance–potential assessment systems, whereby supervisors evaluate employees’ current performance and future‐oriented potential (i.e., promotion prospects). Whether retention is greatest for high performance–high potential (HiPo) employees under such a system is an empirical question—while the “promise” of a promotion may aid retention, these employees likely face attractive outside options. Using data from a multinational firm, I find that HiPos generally depart at a lower rate than lower performing non‐HiPos. Among shorter tenured employees, I find the strongest evidence that potential matters to departures, above and beyond performance. In several instances, the rate of departure is lower when assessed potential is higher, holding performance constant. This is the case both among higher performers, where high potential leads to HiPo status, and among lower performers where high potential affords no such status. My findings suggest that HiPo status and potential ratings may help in facilitating employees’ early sorting decisions.

This paper studies whether and how environmental, social, and governance (ESG) disclosure regulations imposed on banks generate transmission effects along the lending channel. I use a setting of U.S. firms borrowing from non‐U.S. banks and exploit the staggered adoption of ESG disclosure regulations in banks’ home countries. I find that exposed borrowers of affected banks improve their environmental and social (E&S) performance following the disclosure mandate. Consistent with banks enhancing both their engagement and selection activities, affected banks impose more environmental action covenants in loan contracts, and they are more likely to terminate a borrower with bad E&S records following the regulation. Further evidence shows that the transmission effects are stronger when a disclosure regulation is well‐enforced (as indicated by a greater increase in banks’ disclosure) and among borrowers with greater switching costs. Collectively, the findings document the role of lending relationships in transmitting the real effect of ESG disclosure regulations from banks to borrowing firms.

We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first‐step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.

This study examines whether taxpayers intentionally avoid Internal Revenue Service (IRS) third‐party reports. In 2017 an IRS amendment created a quasi‐exogenous shock that reduced third‐party tax reporting of pari‐mutuel gambling winnings from certain types of wagers. I consider the effect that this rule change had on taxpayer behavior. Using a difference‐in‐differences research design comparing thoroughbred racing in the United States to Canada, I find a 27% increase in gambler's investment into wager‐types that became less likely to trigger third‐party reports. Further, I provide evidence that this effect was because of third‐party reporting, not withholding, and was stronger in more informed gambling populations. These findings suggest that taxpayers knowingly avoid third‐party reports, enabling underreporting of income to the IRS. This has important policy implications because underreported individual income is the largest driver of the $496 billion annual gap between legal tax liability and actual tax collections in the United States.

Beginning in 2018, U.S. public firms were required to report the ratio of the chief executive officer's (CEO) compensation to their median employee's compensation in the annual proxy statement. Exploiting the staggered reporting of pay ratios, we find little evidence that total CEO compensation changes in response to pay ratio disclosure reform. However, we do find that boards significantly adjust the mix of compensation awarded by reducing the sensitivity of CEO pay to equity price changes, particularly when the CEO is likely to garner media scrutiny, and by reducing reliance on stock‐based and other compensation components that are most susceptible to media coverage surrounding the pay ratio disclosure. Firms ultimately disclosing higher pay ratios garner more media coverage around the filing of their proxy statement, and more negative‐toned coverage in the subsequent month. Finally, we find evidence that greater pay disparity is associated with greater selling activity by retail investors and more negative say‐on‐pay votes following pay ratio reform, consistent with a broad set of investors responding to public scrutiny resulting from pay ratio disclosures.

We examine the deterrence effect of the Dodd–Frank whistleblower provision on accounting fraud. To facilitate causal inference, we use state False Claims Acts (FCAs), under which whistleblowing about accounting fraud at a firm invested in by a state's pension fund can result in monetary rewards from that state's government. We divide our sample into firms exposed and not exposed to whistleblowing risk from a state FCA during the 2008–2010 period that preceded the 2011 SEC implementation of the Dodd–Frank whistleblowing provision. We hypothesize that firms already exposed to a state FCA whistleblower law are less affected by the Dodd–Frank whistleblower provision. Using the companies exposed to a state FCA as control firms in our Dodd–Frank tests, the remaining firms constitute the treatment sample. We find that exposure to Dodd–Frank reduces the likelihood of accounting fraud of treatment firms by 12%–22% relative to control firms, but do not find that it affects audit fees.

We analyze the disposition of auditor‐proposed adjustments to financial statements. Our analyses address concerns, expressed by regulators and others, that auditors and their clients fixate on quantitative thresholds and overlook qualitative factors in assessing the materiality of discovered misstatements. Using a large sample of Public Company Accounting Oversight Board (PCAOB)‐inspected audits, we examine the frequency with which management records versus waives auditor‐proposed adjustments and whether waiving‐proposed adjustments ha consequences for reporting reliability and the audit process. We find waived adjustments are linked to lower financial reporting quality measured by material misstatements, to incentives to meet/beat earnings targets, and to the audit process, as measured by higher next‐period audit effort and fees and higher next‐period proposed adjustments. These effects on the audit process are consistent with auditors responding to the increased risk associated with waived adjustments. In an exploratory analysis, we find that controlling for the amount of proposed adjustments, auditor resignations are negatively associated with waived adjustments.

We use machine learning methods and high‐dimensional detailed financial data to predict the direction of one‐year‐ahead earnings changes. Our models show significant out‐of‐sample predictive power: the area under the receiver operating characteristics curve ranges from 67.52% to 68.66%, significantly higher than the 50% of a random guess. The annual size‐adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02% to 9.74%. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.

We examine how tax cuts that benefit some firms are related to the economic performance of their direct competitors. Consistent with tax cuts decreasing the cost of initiating competitive strategies, we find that a decrease in the tax burden for only a specific group of firms in the U.S. economy (i.e., “rivals”) has a negative economic effect on the performance of its direct competitors not directly exposed to the same tax cut (i.e., “competitors”). This negative externality is stronger when the relatively higher taxed competitors (1) are financially constrained, (2) operate in more competitive markets, (3) have similar products to their lower taxed rivals, (4) face rivals that retain more of their cash tax savings due to lower dividends and share repurchases, and (5) face lower taxed, but financially constrained, rivals. We also find that shareholders and lenders price the negative externality manifested in these competitors’ economic performance.