Abstract
•Using a MC experiment, we study the performance of efficiency estimation methods.•We show that combination approaches are a useful alternative to a singular method.•Taking the maximum estimate of common methods minimizes the risk of underestimation.•Taking the maximum estimate can also improve the precision of efficiency estimation.•We give recommendations for efficiency estimation for regulation purposes and beyond.
Data envelopment analysis (DEA) and stochastic frontier analysis (SFA), as well as combinations thereof, are widely applied in incentive regulation practice, where the assessment of efficiency plays a major role in regulation design and benchmarking. Using a Monte Carlo simulation experiment, this paper compares the performance of six alternative methods commonly applied by regulators. Our results demonstrate that combination approaches, such as taking the maximum or the mean over DEA and SFA efficiency scores, have certain practical merits and might offer a useful alternative to strict reliance on a singular method. In particular, the results highlight that taking the maximum not only minimizes the risk of underestimation, but can also improve the precision of efficiency estimation. Based on our results, we give recommendations for the estimation of individual efficiencies for regulation purposes and beyond.
