This study exploits the implementation of IRS Schedule UTP to examine how linking tax return disclosures to financial reporting for income taxes affects firms' reporting decisions. Using confidential tax return data and public financial statement data, I find that after imposition of Schedule UTP reporting requirements, firms report lower financial reporting reserves for uncertain income tax positions, but do not claim fewer income tax benefits on their federal tax returns. The reduction in reserves is concentrated among multinational firms and firms with larger reserves prior to Schedule UTP. These findings suggest that some firms changed their financial reporting for uncertain tax positions to avoid Schedule UTP reporting requirements without changing the underlying positions. In contrast with prior studies, this evidence represents a permanent, rather than a temporary, tax-induced reporting change. My results imply that linking tax return disclosures to financial reporting can have unintended effects on firms' reporting decisions.

Although balanced budget rules are widely used throughout the world, there is considerable debate on whether and how they impact fiscal outcomes. Existing research shows that states with strict balanced budget rules address deficits by raising taxes and curbing expenditures. However, little is known about whether politicians can meet budget rules by shifting resources inter-temporally or by transferring revenues from funds not subject to balanced budget rules into funds that are required to meet a balanced budget. We show that, in addition to increasing taxes and cutting expenditures, states with strict balanced budget rules sell public assets and transfer resources across government funds to close the budget shortfall. Our findings suggest that current budget deficits not only influence the current-period taxpayers, but also impact future taxpayers and other funds within the government. The results complement existing research by expanding our understanding of the effects of balanced budget restrictions on politicians' fiscal actions.

We examine the impact of distance on internet search, and the effect of the “local bias” in search on the stock market response around earnings announcements. We find significant local bias in search behavior. Motivated by theories explaining local bias, local information advantage, and familiarity bias, we predict and find that firms with higher local bias in search experience higher bid-ask spreads, lower trading volumes, and lower earnings response coefficients at the time of earnings announcements, consistent with non-local investors relying more than locals on public information announcements. Consistent with local information advantage, we find that in the week prior to the announcement, firms with higher local bias have higher bid-ask spreads, higher trading volumes, and returns that are more predictive of the coming earnings surprise. Consistent with familiarity bias, firms with higher local bias in search experience stronger post-earnings announcement drift. We use unique predictions, propensity score matching, and two-stage least squares to identify the effects of local bias separately from the effects of overall visibility. Overall, we show there is significant local bias in search, and that this local bias has a significant impact on the market response around earnings announcements.

We examine the response of informed market participants to the informational signal of auditor changes. Using propensity score matching and difference-in-differences research designs, we document that loan spreads increase by 22 percent on bank loans initiated within a year after auditor changes, increasing direct loan costs by approximately $6.6 million. We also find a significant increase in upfront and annual fees and the probability of pledging collateral, consistent with an increase in screening and monitoring by banks. The increase in spreads is significant for client-initiated auditor changes, with or without disagreements with the auditor, as well as for auditor resignations. Further, the significant increase in loan spreads is documented for upward, lateral, and downward auditor changes. Our results are robust to other proxies for financial reporting quality. Finally, we find no effect resulting from the forced auditor changes due to Arthur Andersen. Collectively, these results suggest that voluntary auditor changes increase information risk, which is priced in private credit markets.

We test whether tax avoidance strategies are associated with greater firm risk. We find that low tax rates tend to be more persistent than high tax rates and that measures of tax avoidance commonly used in the literature are generally not associated with either future tax rate volatility or future overall firm risk. Our evidence suggests that, on average, corporate tax avoidance is accomplished using strategies that are persistent and do not increase firm risk. We also find that the volatility of cash tax rates is associated with future stock volatility, suggesting that tax rate volatility and overall firm risk are related.

We report the results of four experiments investigating the relationship between (1) the quality of an audit, (2) jurors' assessments of the standard of prudent care (SOC) against which audit quality is compared, and (3) jurors' negligence verdicts. Experiment 1 operationalizes audit quality by varying the sample size used in audit testing, and provides evidence that jurors anchor their assessment of SOC on audit quality, producing a “competitive mediation” in which audit quality reduces the potential for a negligence verdict directly, but increases that potential indirectly by increasing SOC. Experiment 2 generalizes this finding to a setting that operationalizes audit quality by varying the size of adjustment the auditor required. Experiments 3 and 4 extend these results to a setting in which SOC is elicited after jurors make negligence verdicts. Overall, these experiments provide insight into the role of SOC in constraining and justifying negligence verdicts. Data Availability: Contact the authors.

Online labor markets allow rapid recruitment of large numbers of workers for very low pay. Although online workers are often used as research participants, there is little evidence that they are motivated to make costly choices to forgo wealth or leisure that are often central to addressing accounting research questions. Thus, we investigate the validity of using online workers as a proxy for non-experts when accounting research designs use more demanding tasks than these workers typically complete. Three experiments examine the costly choices of online workers relative to student research participants. We find that online workers are at least as willing as students to make costly choices, even at significantly lower wages. We also find that online workers are sensitive to performance-based wages, which are just as effective in inducing high effort as high fixed wages. We discuss implications of our results for conducting accounting research with online workers. Data Availability: Contact the authors.

Commonly used Jones-type discretionary accrual models applied in quarterly settings do not adequately control for nondiscretionary accruals that naturally occur due to firm growth. We show that the relation between quarterly accruals and backward-looking sales growth (measured over a rolling four-quarter window) and forward-looking firm growth (market-to-book ratio) is non-linear. Failure to control for the effects of firm growth and performance on innate accruals leads to excessive Type I error rates in tests of earnings management. We propose simple refinements to Jones-type models that deal with non-linear growth and performance effects and show that the expanded models are well-specified and exhibit high power in quarterly settings where one is testing for earnings management. The expanded models are able to identify the presence of earnings management in a sample of restatement firms. Our findings have important implications for the use of discretionary accrual models in earnings management research.

Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent relative to the best current techniques. Improved fraud prediction can result in meaningful benefits, such as improving the ability of the SEC to detect fraudulent filings and improving audit firms' client portfolio decisions.

An extensive literature examines the causes and effects of financial misconduct based on samples drawn from four popular databases that identify restatements, securities class action lawsuits, and Accounting and Auditing Enforcement Releases (AAERs). We show that the results from empirical tests can depend on which database is accessed. To examine the causes of such discrepancies, we compare the information in each database to a detailed sample of 1,243 case histories in which regulators brought enforcement actions for financial misrepresentation. These comparisons allow us to identify, measure, and estimate the economic importance of four features of each database that affect inferences from empirical tests. We show the extent to which each database is subject to these concerns and offer suggestions for researchers using these databases.