The sensitivity of efficiency scores to input and other choices in stochastic frontier analysis: an empirical investigation

Abstract

Productivity and efficiency analysis have gained substantial attention in many industries over the last two decades, and stochastic frontier analysis has been one of the most popular analytical approaches. The abundant model choices in stochastic frontier analysis make it difficult to select the best option and compare studies. The main purpose of this study is to examine the sensitivity of technical and scale efficiency estimates to choices around input-output combinations, functional forms, distributional assumptions and estimation methods in stochastic frontier analysis, using data from an Australian fishery to illustrate these effects. We estimated 252 stochastic frontier models using combinations of variable choice, functional form and distributional assumptions. A second stage analysis was conducted to examine the effects of model choices on statistical properties of technical and scale efficiency. The results show that estimates of technical and scale efficiency are most sensitive to distributional assumptions and the choice of time effects. In particular, the assumption of time-invariant efficiency produced significantly higher technical efficiency (20 percentage points) and scale efficiency (8 percentage points) scores than time-varying efficiency models in our analysis. We also find that the choice of fixed input variables can significantly affect the average efficiency estimates, by as much as 5 percentage points, but mean efficiency was not significantly affected by the choice of variable inputs. Our findings suggest that caution should be taken when comparing findings of stochastic frontier studies using different distributional and fixed input assumptions.