Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes
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Date
2018
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Abstract
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 an 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.
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Keywords
data envelopment analysis, network operators, regulation, efficiency analysis, stochastic frontier analysis