Tag: C38

THE ECONOMIC PERFORMANCES OF MOROCCAN REGIONS: A TOPSIS AND SPATIAL AUTOCORRELATION METHODS

Hamdi EL ASLI

Laboratory of Economy & Management, Polydisciplinary Faculty of Khouribga (25000), Sultan Moulay Slimane University of Beni Mellal (23000), Morocco

hamdielasli@gmail.com

Mohamed AZEROUAL

Laboratory of Economy & Management, Polydisciplinary Faculty of Khouribga (25000), Sultan Moulay Slimane University of Beni Mellal (23000), Morocco

m.azeroual@usms.ma

Alae MOHAMMED MOURAI

Laboratory of Economy & Management, Polydisciplinary Faculty of Khouribga (25000), Sultan Moulay Slimane University of Beni Mellal (23000), Morocco

alae.mourai@gmail.com

Mounya CHAHBOUNE

Laboratory of Economy & Management, Polydisciplinary Faculty of Khouribga (25000), Sultan Moulay Slimane University of Beni Mellal (23000), Morocco

c.mounya@gmail.com

Abdelhak OULALA

Laboratory of Economy & Management, Polydisciplinary Faculty of Khouribga (25000), Sultan Moulay Slimane University of Beni Mellal (23000), Morocco

oulala1981@gmail.com

Abstract

This paper investigates the economic performance of Morocco’s twelve regions from 2015 to 2022, combining a temporal and spatial analysis methods, and focusing on five key regional macroeconomic indicators: GDP per capita, HFCE per capita, contribution to national growth, start-ups created, and the activity rate. While previous studies have examined regional disparities using MCDM or spatial statistics, none have combined TOPSIS with spatial autocorrelation to evaluate regional economic-entrepreneurial performance in Morocco under its new administrative division, which enables ranking of regional competitiveness and detection of clustering patterns. Findings show that Casablanca-Settat consistently ranks in the top twelve, solidifying its position as the country’s economic capital, followed alternately by the northern Tanger-Tétouan-Al Hoceima and the emergent Rabat-Salé-Kénitra regions, while the southern regions remain at the bottom. Marrakech-Safi was severely affected by the disruption of tourist cash flows under the Covid-19 crisis, before it gradually recovered post-2020. Similarly, Béni Mellal-Khénifra progressed significantly, largely due to its phosphate exports, agro-oil industry, and remittances’ inflows, until 2020, when it retrograded remarkably. Spatial analysis reveals that Moroccan regions exhibit high autocorrelation, with both, top and low ranked regions identified by the TOPSIS method clustering together. Results can inform region-specific development strategies, equitable resource allocation, entrepreneurship promotion, and spatial regional planning. However, limitations such as the restricted set of indicators, short interval, and methodological constraints suggest future research directions that integrate broader social, environmental, and innovation variables, extend the sample interval, and apply advanced comparative and econometric approaches.

Keywords: Morocco, regions, economy, TOPSIS, spatial autocorrelation

JEL classification: C38, L26, R11, R12

pp. 93-114

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MARGINALISED ZONES AS STATISTICAL INSTRUMENTS TO NAVIGATE PERMACRISIS IMPACTS IN EUROPEAN REGIONS

Cristina LINCARU

PhD, FeRSA, Department of Labour Market, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

cristina.lincaru@yahoo.de

ORCID ID: 0000-0001-6596-1820

Gabriela TUDOSE

PhD, Senior Researcher, II-nd degree, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

gabriela_tudose@yahoo.com

ORCID ID: 0000-0002-340-9987

Adriana GRIGORESCU

PhD Full Professor, SNSPA; Director of Global Economy & Governance Interdisciplinary Research Platform; AOSR; INCE; LEAD Cambridge, MA; UCLM Spain

adrianagrigorescu11@gmail.com

ORCID ID: 0000-0003-4212-6974

Speranța PÎRCIOG

PhD, Scientific Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

pirciog@incsmps.ro

ORCID ID: 0000-0003-0215-038X

Cristina STROE

Senior Researcher II-nd degree, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

cristina.radu@incsmps.ro

ORCID ID: 0000-0001-8384-6084

Abstract

In the context of overlapping and interrelated crises—economic, ecological, social, and geopolitical—European regions are confronted with new governance challenges. Marginalised zones, often treated as residual spaces in policy discourse, must be reimagined as analytical and governance instruments in the transition toward sustainability and territorial resilience. This article explores how marginalised areas can be conceptualised and operationalised through spatial statistical methodologies and policy frameworks that support just transition processes. Drawing on a critical review of empirical studies and strategic European and Romanian documents, we synthesise the main tools used to identify territorial disparities, such as Principal Component Analysis (PCA), clustering algorithms, fuzzy logic, spatial econometrics, and machine learning. We confirm that these methods allow for more nuanced territorial diagnostics and typologies, which are essential for evidence-based and place-based policies. The article advances a transdisciplinary framework that repositions marginalised zones as strategic levers in adaptive territorial governance. Ultimately, we argue for a paradigm shift: from periphery to policy, where marginalised regions evolve from passive recipients of aid to active instruments of just transition.

Keywords: Marginalised regions, Just transition, Spatial inequality, Territorial resilience, Governance instruments, PCA, Clustering, Fuzzy logic, Regional typologies, Permacrisis

JEL classification: R11, R58, O18, Q56, C38

pp.155-165

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DETECTING INTERREGIONAL PATTERNS IN TOURISM SEASONALITY OF GREECE: A PRINCIPAL COMPONENTS ANALYSIS APPROACH

Dimitrios TSIOTAS

Adjunct Lecturer, Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, Amfissa, 33100, Greece, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece, tsiotas@aua.gr

tsiotas@uth.gr

Thomas KRABOKOUKIS

Ph.D. candidate, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece

tkrabokoukis@uth.gr

Serafeim POLYZOS

Professor, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece

spolyzos@uth.gr

Abstract

Tourism seasonality is a complex phenomenon incorporating a temporal, a spatial, and a socioeconomic (ontological) dimension. This paper builds on principal component analysis (PCA) to provide an integrated methodological framework for studying all three dimensions of tourism seasonality. The proposed method classifies the seasonal patterns of tourism demand of the Greek prefectures into regional groups, which are examined in terms of their geographical and socioeconomic characteristics. The study aims to configure distinguishable seasonal profiles in terms of their socioeconomic attributes. The proposed method is applied to monthly data of tourism overnight stays for the period 1998-2018 and detects seven principal components described by diverse socioeconomic attributes. The overall analysis proposes a useful tool for tourism management and regional policy, it advances PCA to be used as a tool of regional classification, and it incorporates a multivariate consideration based on the socioeconomic evaluation of the principal components. The proposed methodology develops an integrated framework dealing with complexity describing socioeconomic research and particularly tourism seasonality.

Keywords: regional development; seasonal classification; spatiotemporal patterns; pattern recognition.

JEL classification: C18, C38, O52, R10, R58, Z30

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