A large auditing literature concludes that Big N auditors provide higher audit quality than non-Big N auditors. Recently, however, a high-profile study suggests that propensity score matching (PSM) on client characteristics eliminates the Big N effect [Lawrence A, Minutti-Meza M, Zhang P (2011) Can Big 4 versus non-Big 4 differences in audit-quality proxies be attributed to client characteristics? Accounting Rev. 86(1):259–286]. We conjecture that this finding may be affected by PSM’s sensitivity to its design choices and/or by the validity of the audit quality measures used in the analysis. To investigate, we examine random combinations of PSM design choices that achieve covariate balance, and four commonly used audit quality measures. We find that the majority of these design choices support a Big N effect for most of the audit quality measures. Overall, our findings show that it is premature to suggest that PSM eliminates the Big N effect. This paper was accepted by Suraj Srinivasan, accounting.

Do Client Characteristics Really Drive the Big N Audit Quality Effect? New Evidence from Propensity Score Matching

An extensive accounting and finance literature examines the causes and effects of financial misreporting or misconduct based on samples drawn from four popular databases that identify restatements, securities class action lawsuits, and Securities and Exchange Commission (SEC) Accounting and Auditing Enforcement Releases (AAERs). We show, however, 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 the SEC brought enforcement action for financial misrepresentation. These comparisons allow us to identify, measure, and estimate the economic importance of four characteristics of each database that affect inferences from empirical tests. First, these databases contain information on only the event that is used to proxy for misconduct (e.g., restatements), so they omit other relevant announcements that affect a researcher’s interpretation and use of the events. Second, the initial public revelation of financial misconduct occurs, on average, months before the initial coverage in these databases, leading to discrepancies in event study measures and pre/post comparison tests. Third, most of the events captured by these databases are unrelated to financial fraud, and efforts to cull out non-fraud events yield heterogeneous results. Fourth, the databases omit large numbers of events they were designed to capture. We show the extent to which each database is subject to these concerns and offer suggestions for researchers seeking to use these databases.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2917524

Proxies and Databases in Financial Misconduct Research

Capital market accounting research (CMAR) studies routinely encounter observations taking on extreme values that are likely to affect statistical inferences. Ex-ante univariate approaches such as truncation and winsorization are the most common methods used in CMAR to mitigate the effect of extreme data values. While expedient, each relies on researcher-selected cut-offs which possibly alter legitimate observations in the process. More importantly, there is no empirical evidence in CMAR that either approach is effective at identifying and dealing with the effect of influential observations. We document the efficacy and trade-offs associated with using winsorization, truncation, and robust regression to address the effects of influential observations in CMAR. We first replicate three published CMAR studies to show how the approaches can yield different estimates and statistical inferences. We then use simulations to compare the approaches in controlled settings where we hold the data-generating process constant. The results indicate that robust regression generally outperforms winsorization and truncation, especially in the presence of unusual (or infrequent) economic events that are correlated with the dependent and independent variables of interest. The findings lead us to recommend that future CMAR studies consider using robust regression, or at least report sensitivity/robustness tests using robust regression, especially because robust regression focuses on overall model fit to deal with influential observations, in addition to being relatively straight-forward to implement in typical CMAR settings.

Influential Observations and Inference in Accounting Research

This study assesses whether the accrual-generating process is adequately described by a linear model with respect to a range of underlying determinants examined by prior literature. We document substantial departures from linearity across the distributions of accrual determinants, including measures of size, performance, and growth. To incorporate non-linear relations, we employ a recently developed multivariate matching approach (entropy balancing) to adjust for determinants in place of relying on a linear model. Entropy balancing identifies weights for the control sample to equalize the distribution of determinants across treatment and control samples. In simulations drawing random samples from deciles where a linear model displays poor fit, we find that entropy balancing significantly improves accrual model specification by reducing coefficient bias relative to linear and propensity-score matched models. Consistent with entropy balancing retaining sufficient power, we find that its estimates detect seeded accrual manipulations and explain variation in accruals around equity issuances.

Entropy-balanced accruals

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. JEL Classifications: G38; K22; K42; M41.

Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008–2014. We document a significant increase in the use of PSM from zero studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composi...

Propensity Score Matching in Accounting Research

Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008-2014. We document a significant increase in the use of PSM from 0 studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods.

This study investigates whether the market for audit clients penalizes auditors following association with low-quality audits. Specifically, we examine whether audit offices experience a loss in local market share following client restatements. We document that the frequency of restatement announcements within an office-year (“contamination”) is inversely related to subsequent year-over-year change in local market share. Further analysis indicates that restatements impair the office's ability to both attract and retain audit clients. We find that this effect is strongest in high competition markets and diminished in low competition markets. We also examine auditor retention decisions at the client level and find that the likelihood of auditor dismissal increases with contamination, even for non-restating clients. We also find that, on average, clients dismissing their auditor select less contaminated audit offices. Taken together, our results suggest that market forces penalize auditors for asso...

/pdf/do-clients-avoid-contaminated-offices-the-economic-2g9ppxdcym.pdf

Do Clients Avoid “Contaminated” Offices? The Economic Consequences of Low-Quality Audits

This study provides evidence on how local office growth affects audit quality. We predict that significant recent growth will temporarily stress office resources, leading to a negative relation between office-level growth and audit quality. To test this prediction, we examine a sample of 17,062 firm-year observations from 2005-2010. Results indicate a consistent negative relation between changes in volume of audit work and audit quality. Specifically, clients of offices that experience increases in workload over the prior year have greater absolute discretionary accruals as well as an increased likelihood of restatement. Our tests also indicate that the effect of office growth is transient and vanishes after one year. We find limited evidence that the size of the auditor’s national network of offices partially mitigates the negative effects of office growth on audit quality. We further show that proxies for audit quality are negatively related to office-level growth from new and existing clients. These findings are robust to controls for client and auditor characteristics as well as alternative specifications of growth. Taken together, evidence indicates that while larger offices provide higher audit quality, the benefits of office size are not realized immediately and rapid growth temporarily impairs audit quality. These results are informative to regulators concerned with audit quality and to practitioners charged with adjusting to office growth.

Growing Pains: Audit Quality and Office Growth

This study provides evidence on how local office growth affects audit quality. We predict that significant recent growth will temporarily stress office resources, leading to a negative relation between office-level growth and audit quality. To test this prediction, we examine a sample of 17,062 firm-year observations from 2005 to 2010. Results indicate a consistent negative relation between changes in volume of audit work and audit quality. Specifically, clients of offices that experience increases in workload over the prior year have greater absolute discretionary accruals as well as an increased likelihood of restatement. Our tests also indicate that the effect of office growth is transient and vanishes after one year. We find limited evidence that the size of the auditor's national network of offices partially mitigates the negative effects of office growth on audit quality. We further show that proxies for audit quality are negatively related to office-level growth from new and existing clients. These findings are robust to controls for client and auditor characteristics as well as alternative specifications of growth. Taken together, evidence indicates that while larger offices provide higher audit quality, the benefits of office size are not realized immediately and rapid growth temporarily impairs audit quality. These results are informative to regulators concerned with audit quality and to practitioners charged with adjusting to office growth.

Quinn Thomas Swanquist

Papers

Propensity Score Matching in Accounting Research

Propensity Score Matching in Accounting Research

Do Clients Avoid “Contaminated” Offices? The Economic Consequences of Low-Quality Audits

Growing Pains: Audit Quality and Office Growth

Growing Pains: Audit Quality and Office Growth