How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

Monte Carlo experimentation is a well-known approach used to test the performance of alternative
methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric
or non-parametric methods tested, experiments to date have been developed assuming single output
multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology.
Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on
scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable
two-output two-input production data can be generated using a parametric output distance function
approach. A variable returns to scale translog technology satisfying regularity conditions is used for this
purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement
when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation
between true and estimated scale efficiency is dramatically low when DEA analysis is performed with
small samples and wide output ratio variations.