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 composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods. Data Availability: All data used are available from sources cited in the text.

In the absence of random treatment assignment, the selection of appropriate control variables is essential to designing well-specified empirical tests of causal effects. However, the importance of control variables seems under-appreciated in accounting research relative to other methodological issues. Despite the frequent reliance on control variables, the accounting literature has limited guidance on how to select them. We evaluate the evolution in the use of control variables in accounting research and discuss some of the issues that researchers should consider when choosing control variables. Using simulations, we illustrate that more control is not always better and that some control variables can introduce bias into an otherwise well-specified model. We also demonstrate other issues with control variables, including the effects of measurement error and complications associated with fixed effects. Finally, we provide practical suggestions for future accounting research. Data Availability: All data used are publicly available from sources cited in the text.

Consulting service revenues recently surpassed audit revenues as the primary income source for the largest accounting firms. Since SOX limits the provision of consulting services to audit clients, this shift in revenues implies that firms and many clients likely choose between audit and consulting relationships. We explore the implications of this by developing and validating a measure of client-level consulting needs that can likely be fulfilled by accounting firms, which we refer to as “consulting opportunities.” As predicted, we find that consulting opportunities relate positively to auditor switches. We also find that consulting opportunities relate negatively to subsequent Big 4 auditor selection—the firms focusing most on consulting—but we fail to find evidence that consulting opportunities relate to deteriorations in audit quality. Together, our results suggest that legislation limiting firms' ability to deliver consulting services to audit clients may have reduced audit market concentration without discernably impacting quality. Data Availability: All data used are publicly available from sources cited in the text.