A Monte Carlo study of ranked efficiency estimates from frontier models

Parametric stochastic frontier models yield firm‐level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm‐level efficiency? This study compares the techniques of estimating (a) the conditional mean of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are easier to estimate (less noisy) in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance in the stochastic frontier model.