Work

Spatial DEA with independent neighbours’ inputs: healthcare efficiency patterns in Europe

Year: 2025

Type: article

Abstract: This study presents a new method – SDEA-INI (spatial data envelopment analysis-independent neighbours’ inputs) – that integrates spatial interactions and non-discretionary input restrictions into data envelopment analysis to address the issue of uncontrollable spatial inputs. Monte Carlo simulations show that spatial data envelopment analysis (SDEA) outperforms data envelopment analysis (DEA) under spatial dependence, while SDEA-INI is best when inputs are uncontrollable. We employ this novel approach to evaluate healthcare efficiency across European regions. Our results reveal an inefficient core (Germany, Austria) and a more efficient periphery (Central, Eastern and Southern Europe). Our approach supports resource allocation and fosters greater cooperation and cohesion across Europe’s diverse healthcare systems.

Source: Figshare

DOI: 10.6084/m9.figshare.30933523

Authors: Olejnik, Jakub, Żółtaszek, Agata, Olejnik, Alicja

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W7116841969

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