Propensity Score Matching in Accounting Research

• 成果类型: Article
• 署名作者: Shipman, Jonathan E.; Swanquist, Quinn T.; Whited, Robert L.
• 署名单位: University of Arkansas System; University of Arkansas Fayetteville; University System of Georgia; Georgia State University; University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/accr-51449
• 发表日期: 2017
• 页码: 213-244
• 关键词: financial-reporting quality; cash flow forecasts; big 4 auditors; INTERNAL CONTROL; Causal Inference; equity incentives; earnings quality; Dividend policy; Sarbanes-Oxley; Tax avoidance

摘要

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.