Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal

Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal

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Article ID: iaor201530462
Volume: 249
Issue: 2
Start Page Number: 771
End Page Number: 783
Publication Date: Mar 2016
Journal: European Journal of Operational Research
Authors: ,
Keywords: management, datamining, manufacturing industries
Abstract:

This paper introduces an original methodology, derived by the robust order‐m model, to estimate technical efficiency with spatial autocorrelated data using a nonparametric approach. The methodology is aimed to identify potential competitors on a subset of productive units that are identified through spatial dependence, thus focusing on peers located in close proximity of the productive unit. The proposed method is illustrated in a simulation setting that verifies the territorial differences between the nonparametric unconditioned and the conditioned estimates. A firm‐level application to the Italian industrial districts is proposed in order to highlight the ability of the new method to separate the global intangible spatial effect from the efficiency term on real data.

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