Nonparametric spatial frontier models for productivity analysis: evidence from EU regions

This paper proposes a novel nonparametric panel data framework for estimating conditional production frontiers and efficiency measures that explicitly accounts for spatial interdependencies. By integrating recent advances in nonparametric frontier estimation with spatial panel data analysis, the proposed approach offers a flexible and robust framework for assessing productivity and efficiency in the presence of spatial interactions, explicitly accounting for both global and local spatial effects. By extending recently developed tools for estimating Malmquist productivity indices to conditional nonparametric frontier efficiency models, we provide a refined decomposition of productivity growth into technological change, efficiency change, and scale effects within a fully nonparametric framework. Applying this framework to a comprehensive dataset on European regions, we provide new evidence on spatial patterns of productivity growth and efficiency dynamics across the EU. The results reveal marked heterogeneity in regional performance and highlight the crucial role of spatial spillovers in shaping productivity outcomes. Ignoring these interdependencies can lead to mismeasurement of productivity trends, reinforcing the value of our proposed spatial nonparametric frontier approach for policy and performance analysis.