Test power properties of within-firm estimators of ownership and board-related explanatory variables with low time variation

Abstract

Corporate governance research is often limited in its ability to employ within-firm estimators, which address time-invariant endogeneity, when the variables of interest exhibit low time variation (for example, ownership and board independence). The problem is further exacerbated if data for multiple points in time needs to be hand-collected. We offer simulation-based methodological guidance to improve the statistical power of within-firm estimators in the presence of low time variation. We illustrate the usefulness of our simulation results by replicating two influential studies on ownership and board independence and extending them with a within-firm estimator. Based on widely used databases as well as a novel granular database, we document the different degrees and nature of time variation of ownership and board independence across jurisdictions and subgroups by listed status, family control and complexity of ownership structure. Researchers can use our findings to optimize the hand-collection and pre-processing of governance data and thereby increase statistical power and/or to distinguish whether lack of significance is due to low time variation as opposed to absence of a true relationship between their governance variable of interest and the respective outcome.

CEO power and CSR-linked compensation for corporate environmental responsibility: UK evidence

“…This requires the selection of valid instruments correlated with CEO variables but uncorrelated with environmental performance. Board size and board independence are relatively stable over time and may be much less likely to be impacted by firm performance than other governance variables, which is widely recognised in corporate governance research (e.g., Armstrong et al 2010;Brown et al 2011;Al-Shaer et al 2017;Nuber and Velte, 2021;Boutchkova et al 2022). Therefore, we, follow prior research in our choice of instrumental variables (IVs) (Ben-Amar et al 2017;Nuber and Velte, 2021), selecting the industry average of board size, BODSIZE and board independence, BOD-IND.…”