Measuring Real Activity Management

Abstract

To test hypotheses about earnings management many studies investigate managers’ manipulation of real activities (real earnings management, REM). Tests using measures of abnormal REM hinge critically on the measurement of normal real activities. Yet there is no systematic evidence on the statistical properties of commonly-used REM measures. We provide such evidence by documenting the Type I error rates and power of the test of the REM measures commonly used in the literature. We find these measures are often mis-specified with Type I error rates that deviate from the nominal significance level of the test, especially in samples of firms with extreme performance or firm characteristics. We also compare the specification and power of traditional REM measures with performance-matched REM measures to see if the latter provide better-specified and more powerful tests. While performance-matched REM measures are not immune from mis-specification in all settings, in general, they are better specified under the null hypothesis (i.e., in terms of Type I errors) than are traditional REM measures. Comparisons of the power to detect abnormal REM reveal that neither approach, traditional or performance-matched, is consistently more powerful than the other in terms of detecting abnormal REM ranging from 1% to 10% of (lagged) total assets. The absence of a dominant approach to measure abnormal REM leads us to recommend that future researchers report results using both traditional and performance-matched measures so that readers are able to clearly assess the reliability of the inferences drawn about the magnitude and significance of the abnormal REM documented in a given study.

Keywords: real activity management, real earnings management, earnings management, real activity models, test specification, Type I errors, Type II errors, power of the test, meet or beat, earnings benchmarks, model specification

JEL Classification: M41, C12, C15, M42