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Assessing regional economic Performance: Fuzzy multi-criteria decision making and panel regression approach

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  • Profiroiu, Constantin Marius
  • Profiroiu, Alina Georgiana
  • Constantin, Daniela-Luminția
  • Nica, Ionuț
  • Delcea, Camelia
  • Brişcariu, Maria Roxana

Abstract

This study presents a novel methodological framework that integrates Fuzzy Multi-Criteria Decision-Making (MCDM) techniques with panel regression analysis to assess regional economic performance in Romania. The primary objective of this research is to evaluate and prioritize key economic policies aimed at addressing regional disparities. By applying Fuzzy TOPSIS and Fuzzy Electre methods, the study approaches a more flexible and detailed evaluation of policy effectiveness, accounting for the uncertainty and variability inherent in regional economic data. Additionally, panel regression analysis is used to explore the relationships between socioeconomic factors and regional GDP growth, while Granger causality tests identify significant interdependencies among key variables. The case study, covering the period from 2003 to 2022, shows significant regional disparities, with Bucharest-Ilfov emerging as the most economically advanced region, while South-West Oltenia and West regions face considerable challenges. The results highlight the importance of regional factors, such as the number of postgraduate students and IT infrastructure, as key drivers of economic growth. This research contributes to the literature by proposing a hybrid approach that integrates fuzzy logic with econometric analysis, providing a robust tool for formulating evidence-based, region-specific policies aimed at fostering balanced economic development across Romania.

Suggested Citation

  • Profiroiu, Constantin Marius & Profiroiu, Alina Georgiana & Constantin, Daniela-Luminția & Nica, Ionuț & Delcea, Camelia & Brişcariu, Maria Roxana, 2025. "Assessing regional economic Performance: Fuzzy multi-criteria decision making and panel regression approach," Socio-Economic Planning Sciences, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:soceps:v:100:y:2025:i:c:s0038012125000941
    DOI: 10.1016/j.seps.2025.102245
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