Posts Tagged ‘C38’

PATTERNS OF MAINLY TOURISM SECTORS AT LOCAL LEVEL BY EMPLOYEE’S CHARACTERISTICS USING GIS MULTIVARIATE CLUSTERING ANALYSIS – ROMANIA CASE STUDY

Cristina LINCARU

Dr, FeRSA, Department of Labour Market, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0001-6596-1820

cristina.lincaru@yahoo.de

Speranța PÎRCIOG

Dr, Scientific Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0003-0215-038X

pirciog@incsmps.ro

Draga ATANASIU

Senior Researcher, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0002-9695-8592

incsmps1@incsmps.ro

Cristina STROE

Senior Researcher, Department of Social Policies, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0001-8384-6084

cristinaradu@incsmps.ro

Vasilica CIUCĂ

Dr, Dr, General Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0003-4687-6377

silviaciuca@incsmps.ro

Adriana GRIGORESCU

Dr., Department of Public Management, National University of Political Studies and Public Administration,  Correspondent Member of Academy of Romanian Scientists, Bucharest, Romania ORCID ID: 0000-0003-4212-6974

adrianagrigorescu11@gmail.com

Abstract

The tourism sector, before the Corona Strikes, works as a inclusive development engine for many countries’ economies and labour markets. In a global world, with increasing travel opportunities, tourism offers both labours intensive and knowledge-intensive activities, across many economic sectors. Tourism is a spatially dependent sector and also a tradable one. The Methodology for tourism statistics (Eurostat 2014),  Tourism Satellite Accounts (TSA 2010) and The International Recommendations for Tourism Statistics 2008 (IRTS 2008) differentiate the “mainly tourism” industries at four digits. We identify the natural cluster by number and pattern, at 3189 local spatial units (NUTS 5) by eight attribute variable employees: gender (male, female), age (youth, adult and aged) and education detained level (low, medium and high). Sectors are detailed at two digits only (H51- Air transport, I55 – Hotels and other accommodation facilities and N79-Activities of tourist agencies and tour operators; other reservation services and tourist assistance). Romanian National Institute of Statistics provides 2011 Census data. We apply the Multivariate Clustering Analysis with K Means algorithm as a Spatial Statistical Tool in Arc Gis Pro 2.3, an unsupervised machine learning an Artificial Intelligence technique, appropriate for Big Data. Clusters resulted illustrates natural hidden patterns of local labour markets pooling in the sense of Urban& Jacobian economies, but also some insight regarding the Morettian externalities sources. These results are useful for Regions Smart Specialisation Strategies development of human resources & talents to increase innovation capabilities and inclusive job creation, but also for a prompt recovery post-Covid Pandemic.

Keywords: tourism, labour force characteristics, Multivariate Clustering Analysis, local labour markets, regional specialisation, education level, age and gender analysis

JEL classification: J210, C38, R23

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CHARACTERIZATION OF AGRICULTURAL SYSTEMS IN THE EUROPEAN UNION REGIONS: A FARM DIMENSION- COMPETITIVENESS-TECHNOLOGY INDEX AS BASE

Vítor João Pereira Domingues MARTINHO

Coordinator Professor with Aggregation, Agricultural School, Polytechnic Institute of Viseu, 3500 Viseu, Portugal

vdmartinho@esav.ipv.pt

Abstract

The agricultural realities across the several regions belonging to the European Union (EU) present some significant differences, in terms of the socioeconomics, cultural, structural and environmental dimensions. In general, because it is difficult to consider all the realities, within each country, sometimes the public decision makers, in Europe, need to design common approaches for all countries and regions, despite there being some decentralization of decisions. In this scenario, this study aims to identify and characterize the main agricultural systems in the European Union, using statistical information available in the FADN (2017), for the periods 2007-2009 and 2012-2013. This was done, considering the utilized agricultural area (farm dimension), the machinery (farm technology and innovation) and the farm net value added (farm competitiveness) as principal indicators. From these variables the European Union countries and regions were grouped into agricultural systems through cluster approaches and based on a farm dimension-competitiveness-technology index obtained with factor analysis. This approach was complemented by spatial analysis, through the observation of spatial autocorrelation between European Union countries, as determining the farming characteristics in neighboring countries. Of stressing, the relevant differences in the farm characteristics, not only, among European countries, but also, inside each member state between regions. On the other hand, of highlighting the adequacy of the index considered as representative of the farming particularities.

Keywords: European Union regions, Farm Accountancy Data Network (FADN), Spatial approaches, Factor and clusters analysis

JEL classification: C21, C38, O13, O52, Q10
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