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.