Knowledge that Transforms

This study investigates the practice of re-audits—where an incoming audit firm re-audits the prior year’s financial statements previously examined by a predecessor audit firm. We examine the costs and benefits of such re-audits. We find that re-audits are more likely when the risks of misstatement are high and when incoming auditors can expect to win more clients by identifying material misstatements overlooked by predecessor auditors. Our results show that incoming auditors who perform re-audits are more successful in winning new clients from predecessor auditors. Moreover, re-audits result in significantly more restatements. However, consistent with the resource-intensive nature of re-audits, we also find that they are associated with longer audit delays and higher audit fees. Overall, we conclude that re-audits have important consequences for financial statement users, incoming auditors, and predecessor auditors.

OLS estimates of linear regression models become imprecise when distributional assumptions about the regression errors are not strictly met. Such situations frequently arise in applied research due to the heavy-tailed distributions of dependent variables. Using simulated data and replication settings, we show how robust regression estimation can produce more policy-relevant inferences by increasing the precision of estimates, improving test power, and tightening confidence intervals. We provide guidance to researchers on when and how to apply robust estimation as alternative to OLS and how to combine robust regression estimators with fixed effects and clustered standard errors. Given the non-random nature of observations typically downweighted by robust regression estimators, we also illustrate the importance of inspecting the robust regression weights and discuss how these weights can provide useful insights about heterogeneity in treatment effects or relations of interest.