Explaining inefficiency in nonparametric production models: the state of the art
Luiza Badin (),
Cinzia Daraio () and
Leopold Simar ()
Annals of Operations Research, 2014, vol. 214, issue 1, 5-30
The performance of economic producers is often affected by external or environmental factors that, unlike the inputs and the outputs, are not under the control of the Decision Making Units (DMUs). These factors can be included in the model as exogenous variables and can help to explain the efficiency differentials, as well as improve the managerial policy of the evaluated units. A fully nonparametric methodology, which includes external variables in the frontier model and defines conditional DEA and FDH efficiency scores, is now available for investigating the impact of external-environmental factors on the performance. In this paper, we offer a state-of-the-art review of the literature, which has been proposed to include environmental variables in nonparametric and robust (to outliers) frontier models and to analyse and interpret the conditional efficiency scores, capturing their impact on the attainable set and/or on the distribution of the inefficiency scores. This paper develops and complements the approach of Bădin et al. ( 2012 ) by suggesting a procedure that allows us to make local inference and provide confidence intervals for the impact of the external factors on the process. We advocate for the nonparametric conditional methodology, which avoids the restrictive “separability” assumption required by the two-stage approaches in order to provide meaningful results. An illustration with real data on mutual funds shows the usefulness of the proposed approach. Copyright Springer Science+Business Media, LLC 2014
Keywords: DEA; Two-stage; Conditional efficiency; Robust frontiers; Bootstrap; Sub-sampling; Mutual funds (search for similar items in EconPapers)
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Working Paper: Explaining inefficiency in nonparametric production models: the state of the art (2014)
Working Paper: Explaining Inefficiency in Nonparametric Production Models: the State of the Art (2011)
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