When good balance goes bad: A discussion of common pitfalls when using entropy balancing

For many accounting research questions, empirical researchers cannot randomly assign observations to treatment conditions or identify a quasi-experimental setting. In these cases, entropy balancing (Hainmueller 2012) is an increasingly popular statistical method for identifying a control sample that is nearly identical to the treated sample with respect to observable covariates. In this paper, we compare entropy balancing’s approach of reweighting control sample observations to ordinary least squares and propensity score matching. We demonstrate that researchers applying entropy balancing in empirical settings involving panel data with features common in accounting research may encounter implementation issues that render the resulting estimates sensitive to relatively minor changes in the control sample or the research design. Using the setting of estimating the Big-N audit fee premium, we empirically demonstrate these issues and propose solutions.