Knowledge that Transforms

This paper investigates how consulting services affect audit quality, from the perspective of knowledge- and expertise-sharing between employees. Semistructured interviews with 16 audit partners reveal that consulting expertise is used in 60–80 percent of audit engagements, with the main rationale for such collaboration being knowledge-sharing and improved audit quality. We leverage a comprehensive office-level dataset of employment profiles covering 86 percent of all employees at large U.S. public accounting firms to systematically investigate the effect of consulting employees on audit quality. We document that a one standard deviation increase in the share of consulting employees in an office results in a 2.6 percentage point reduction in restatements (a decrease of 19 percent relative to the baseline). This effect is strongest when consulting employees have skills complimentary to auditors, e.g., technical and human resources skills, and when consultants have specific industry expertise in the same industry as the audit client. Data Availability: Data from common sources, such as Audit Analytics, can be purchased from the providers. Data on firm-level measures of workforce characteristics and skills are available from the authors upon request, conditional on approval from the data provider (Cognism).

Despite the potential of algorithms to improve judgment quality, recent research suggests that individuals may be averse to algorithmic use. We experimentally examine whether and how managers’ use of an algorithm-advised performance rating is influenced by rating valence and the decision rights managers have to adjust the algorithm. We find that managers are less willing to use an algorithm to evaluate subordinate performance when it advises a low, rather than high, rating. We further show that when the algorithm-advised rating is low, allowing managers to adjust how the algorithm computes the rating, compared with adjusting the rating itself or not allowing any adjustment, increases algorithmic use. Further analyses show this effect to be consistent with managers’ increased understanding of an algorithm when involved in its computation. Our findings inform organizations’ implementation of performance evaluation algorithms by showing how rating valence and decision rights jointly influence managers’ use of the algorithms.

We exploit a one-month period when SEC activity largely stopped during a U.S. government shutdown to examine whether variation in SEC scrutiny affects its ability to enforce insider trading. Difference-in-differences analyses suggest insiders earn abnormal profits during the shutdown, and the findings are robust to using different control periods and groups. We estimate that it takes roughly one week before the abnormally profitable trading begins, consistent with insiders updating their beliefs regarding the duration and disruption of the shutdown. Supporting the claim that SEC regulatory activity drops with the shutdown and does not fully recover afterward, we find a decline in the frequency of insider trading enforcement releases, investigations, and comment letter issuances after the SEC resumes operations. Our study speaks to the SEC insider trading enforcement literature and economic consequences of a regulatory discontinuity in a divisive political climate. Data Availability: All data are available from public sources.

I examine how mandatory position disclosure of claimholders’ economic interests affects Chapter 11 bankruptcy outcomes. Exploiting a regulation that increased disclosure by creditors and equityholders on certain committees, I find that position disclosure is associated with a decrease in the length of bankruptcy cases, especially the duration of negotiations between claimholders across classes. Further, I show that position disclosure is associated with lower post-bankruptcy recidivism. Contrary to the concerns expressed by critics, I find little evidence that position disclosure reduced claimholders’ participation in committees or decreased trading in the market for bankruptcy claims. My findings highlight the overall benefits of position disclosure in facilitating negotiations during bankruptcy. Data Availability: Data are available from the public sources cited in the text.

I use a unique setting in the municipal bond market to examine the impact of compliance auditing on bond yields. SEC Rule 15c2-12 requires bond issuers to provide annual financial and operating disclosures to investors over the life of the bonds. Due to concerns of noncompliance, Louisiana passed a state law requiring auditors to test for compliance with Rule 15c2-12. I find that bond yields in the municipal bond market decrease by 21 basis points after the audit law for previously noncompliant issuers in Louisiana. I find similar results when I separately examine the primary and secondary markets. I also find that previously noncompliant issuers file 75.2 percent more annual disclosures after the audit law, suggesting that the audit law improved compliance with Rule 15c2-12. Overall, my findings inform policymakers and regulators on the role of auditing over compliance with disclosure regulation as a mechanism to protect investors.

Using a large sample of executive performance equity grants over 2006–2019, we provide a comprehensive representation of the single- and multi-metric vesting schemes used by U.S. public firms and investigate the incentives provided by alternative functional forms of vesting formulas that combine earnings with stock returns and other nonearnings targets. Our results indicate that, compared to earnings-vesting single- and multi-metric summative grants, binding schemes that require the contemporaneous achievement of both earnings and nonearnings targets for grant vesting limit executive fixation on earnings and the associated incentives to maximize grant payouts by managing earnings around the targets. The findings confirm the expectation of different incentive effects from alternative (linear versus nonlinear) aggregations of multiple performance targets in the grant vesting formulas, with binding schemes being more effective in mitigating the risk of executives prioritizing earnings relative to other targets in multi-metric grants.

This study examines the impact of ex ante litigation risk on auditor hiring and retention. Using employee data from LinkedIn, we find ex ante accounting-related litigation risk is associated with fewer auditors joining and more auditors leaving an audit office, whereas we fail to find any impact of nonaccounting litigation risk. Moreover, ex ante accounting-related litigation risk is associated with audit firms' hiring less experienced and less educated auditors, suggesting ex ante litigation risk impacts the quality of auditing hires. We also find that the impact of ex ante litigation risk is concentrated in audit offices with more outside job opportunities and in those that are more susceptible to changes in litigation risk. Our results highlight the impact of ex ante litigation risk on audit labor supply, providing insights concerning the unintended consequences of increasing auditors' legal liability. Data Availability: All data used in this study are based on publicly available information obtained through the services or authors cited in the manuscript.

Revenue-sharing contracts allow firms that are distant from their target markets to leverage sellers' local expertise. Although these contracts align incentives in operational decisions, they also introduce the potential for sellers to underreport revenues. We analyze film-level box office data from 7,309 Chinese cinemas and find that cinemas report significantly lower revenues for foreign films than for comparable domestic films, consistent with foreign producers being less able to monitor reported revenues due to geographic distance. The underreporting of foreign films is lower in cities with widespread mobile payments, in multi-unit cinemas, and when foreign films have more predictable revenues, suggesting institutional factors that increase detection likelihood can mitigate underreporting. Further tests indicate the lower reported box office revenues of foreign films is not due to government intervention. Our findings provide novel evidence of product-level misreporting under revenue-sharing contracts and offer insights on mitigating these risks in international markets. Data availability: Data are available via the sources specified in the paper. The authors greatly appreciate the data supplied by EntGroup (http://english.entgroup.com.cn/enbase.html) but are not able to share the data based on the agreement with Entgroup.

Firms are facing increasing pressure to provide information about their Corporate Social Responsibility (CSR) commitment. Firms however differ in the quality of how they communicate CSR-related efforts to stakeholders as well as in the intensity of their CSR contracting. We examine the relationship between CSR disclosures and contracting and argue that the designs of the two practices are complements in signaling a CSR commitment. Using hand-collected data to capture disclosure quality of CSR reports and intensity of CSR contracting (i.e., scope, importance, and degree to which CSR metrics are incorporated) for S&P 500 firms, we show that firms indeed align the design choices of the two practices. In addition, firms using both practices intensively are associated with a stronger CSR commitment and more credible CSR disclosures. Finally, we document that firms that face higher credibility concerns show stronger complementarity between the design of these two practices. Data Availability: All data are available from public sources mentioned in the text.

Accounting misstatements are often detected with substantial delays, leading to “look-ahead bias” in model predictions if the detection lag is not considered. Moreover, the misstatement data-generating process is evolving due to regulatory regime shifts, further complicating the evaluation of model predictions. We design an approach that accounts for detection lags and continuously updates models to adapt to the changing data-generating process. By comparing with the conventional approach that ignores detection lags, we show that the look-ahead bias can substantially inflate prediction performance. We also demonstrate that although leaving a temporal gap between training and test samples can mitigate the look-ahead bias, it sacrifices the model’s predictive power by disconnecting the dynamic data-generating process between training and test periods. We further implement a trading strategy to evaluate the practical utility of the continuously updating approach. Our study presents a new conceptual lens for understanding and evaluating misstatement prediction models. Data Availability: Data are available from the public sources identified in the study.