The connection between efficiency and copula is discussed by showing that a copula can be employed to decompose the efficiency content of a multivariate distribution into marginal and dependence components. The idea of association measures is used to show that empirical linear correlation underestimates the amplitude of the actual correlation in the case of non-Gaussian marginals. The mutual efficiency is shown to provide an upper bound for the asymptotic empirical log-likelihood of a copula.