This study examines whether differences in proxies for audit quality between Big 4 and non-Big 4 audit firms could be a reflection of their respective clients’ characteristics. In our analyses, we use three audit-quality proxies—discretionary accruals, the ex ante cost-of-equity capital, and analyst forecast accuracy—and employ propensity-score and attribute-based matching models in attempt to control for differences in client characteristics between the two auditor groups while estimating the audit-quality effects. Using these matching models, we find that the effects of Big 4 auditors are insignificantly different from those of non-Big 4 auditors with respect to the three audit-quality proxies. Our results suggest that differences in these proxies between Big 4 and non-Big 4 auditors largely reflect client characteristics and, more specifically, client size. We caution the reader that this study has not resolved the question, although we hope that it encourages other researchers to explore alternative methodologies that separate client characteristics from audit-quality effects.

No abstract

This study examines whether auditor industry specialization, measured using the auditor's within-industry market share, improves audit quality and results in a fee premium. After matching clients of specialist and nonspecialist auditors on a number of dimensions, as well as only on industry and size, there is no evidence of differences in commonly used audit-quality proxies between these two groups of auditors. Moreover, there is no consistent evidence of a specialist fee premium. The matched sample results are confirmed by including client fixed effects in the main models, examining a sample of clients that switched auditors, and using an alternative proxy that aims to capture the auditor's industry knowledge. The combined evidence in this study suggests that * University of Miami, School of Business Administration. Accepted by Philip Berger. This paper is based on the first chapter of my dissertation at the University of Toronto, Rotman School of Management. I gratefully acknowledge the guidance provided by my co-chairs Gordon Richardson and Ping Zhang, and the other members of my dissertation committee, Jeffrey Callen and Gus De Franco, as well as the comments provided by my external examiner Michel Magnan. I thank an anonymous reviewer,

No abstract

No abstract

The new generation of expanded audit reports includes disclosures about significant matters in a company's financial reporting and its audit. These disclosures are a landmark change in auditors' responsibility to provide information to the public. I examine expanded reports in various jurisdictions, why they became mandatory, what the evidence from their implementation is, and whether they have fulfilled the expectations of regulators and other stakeholders. Expanded reports are intended to increase the information content and usefulness of audit opinions, to increase external monitoring of auditors and management, and to foster a more open conversation between auditors and users of financial reporting. However, existing regulatory requirements, conflicting auditors' incentives to provide new information, and evidence from the expanded reports' implementation call into question whether these objectives have been met. It is my hope that expanded reports are only a first step towards enhanced auditor reporting.

Accounting studies often encounter observations with extreme values that can influence coefficient estimates and inferences. Two widely used approaches to address influential observations in accounting studies are winsorization and truncation. While expedient, both depend on researcher-selected cutoffs, applied on a variable-by-variable basis, which, unfortunately, can alter legitimate data points. We compare the efficacy of winsorization, truncation, influence diagnostics (Cook's Distance), and robust regression at identifying influential observations. Replication of three published accounting studies shows that the choice impacts estimates and inferences. Simulation evidence shows that winsorization and truncation are ineffective at identifying influential observations. While influence diagnostics and robust regression both outperform winsorization and truncation, overall, robust regression outperforms the other methods. Since robust regression is a theoretically appealing and easily implementable approach based on a model's residuals, we recommend that future accounting studies consider using robust regression, or at least report sensitivity tests using robust regression. JEL Classifications: C12; C13; C18; C51; C52; M41. Data Availability: Data are available from the public sources cited in the text.

No abstract