The study of a causal interpretation of board and firm characteristics, that is, a hidden dependence relationship on the causal inference among board and firm characteristics, is an important but unaddressed issue in the corporate governance literature. Using diverse advanced statistical methods and focusing on Tobin's Q, we find that (i) not all board variables previously found to be significant are "robust" to latent variable data analysis, and (ii) those variables that are consistently significant differ markedly in latent structural equation analysis. Our analyses provide researchers interested in board issues with an important caveat: Focusing on the dependence structure of available board variables affected by latent factors may introduce a new horizon in corporate finance.
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© 2020 by the authors.
- Board structure
- Causal inference
- Corporate governance
- Directed acyclic graph
- Functional principal component analysis
- Gaussian copula marginal regression
- Latent variable analysis
- Structural equation modeling