Addressing spatial dependence in technical efficiency estimation: A Spatial DEA frontier approach

TL;DR

This paper proposes a Spatial DEA frontier method to account for spatial dependence in efficiency estimation, revealing regional disparities and polarization in European regions from 2000-2014.

Contribution

It introduces a novel spatially-aware DEA model that reduces bias and captures regional performance correlations not addressed by standard DEA methods.

Findings

SpDEA scores show bimodal distribution, unlike standard DEA.

Spatial dependence significantly influences regional efficiency estimates.

Results provide new insights into European regional disparities and income polarization.

Abstract

This paper introduces a new specification for the nonparametric production-frontier based on Data Envelopment Analysis (DEA) when dealing with decision-making units whose economic performances are correlated with those of the neighbors (spatial dependence). To illustrate the bias reduction that the SpDEA provides with respect to standard DEA methods, an analysis of the regional production frontiers for the NUTS-2 European regions during the period 2000-2014 was carried out. The estimated SpDEA scores show a bimodal distribution do not detected by the standard DEA estimates. The results confirm the crucial role of space, offering important new insights on both the causes of regional disparities in labour productivity and the observed polarization of the European distribution of per capita income.