Archive for June, 2020

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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IS STABILITY FOR REGIONAL DISPARITIES OF UNEMPLOYMENT RATES TRULY MYSTERIOUS? AN ANALYSIS FROM STATISTICAL APPROACH

Tsunetada HIROBE

Professor, Department of Economics, Meikai University, 1 Akemi, Urayasu, Chiba 279-8550, Japan

tsune@meikai.ac.jp

Abstract

The paper analyzes the peculiar phenomenon of regional disparities brought by the changes in the geographical distribution of US unemployment rates. Specifically, we investigate the characteristics concerning the gap of that regional distribution especially focusing upon the statistical analysis by mainly an exploratory way. Reduction in disparities or Expansion in disparities usually involves reducing or increasing the overall level of distribution, and the so-called relative disparity between all states of the U.S. shows an extremely stable transition of distribution within a certain range. This is a mysterious phenomenon that is also shown in any other country in the world. One of the reasons that the regional distribution of unemployment rates becomes stable is derived from the robustness of that geographical distribution; this is one of the reasons that the unemployment rate does not fluctuate significantly. Even if that robustness deteriorates for some reason, then the unemployment rate updates the values of minimum and maximum, or only just the range of variation expands; the relative disparities between regions tend to be offset by increases or decreases in the same direction as a result. Since that range is usually very limited, the gap frequently fluctuates up and down within a confined extent and it does not necessarily converge or diverge to a specific point; it would constantly change within the allowable fluctuation range depending on the socio-economic situation.

Keywords: unemployment rate, regional disparity, convergence, equilibrium, stability

JEL classification: C13, C15, J69, R12, R19

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REGIONAL CONVERGENCE: THEORY AND EMPIRICS

Stilianos ALEXIADIS *

Ministry of Rural Development & Foods, Department of Agricultural Policy & Documentation, Division of Agricultural Statistics, Room 519, 2 Acharnon Street, 104 32, Athens, Greece, Tel: ++30 210 2125925

salexiadis7@aim.com

Abstract

One of the most controversial issues in regional science is regional convergence. Do regions converge? Why the existing inequalities across regions persist overtime, despite some movements towards convergence. Such questions had bred an extensive literature. In this paper, a model of regional convergence focusing on technological factors is developed. This model is tested using data for the EU-27 regions. A possible explanation for these results is offered and suggests that might afford an interesting policy conclusion.

Keywords: Regional convergence, Technological gaps, Technology adoption

JEL classification: R10

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* The findings, interpretations and conclusions are entirely those of the author and, do not necessarily represent the official position, policies or views of the Ministry of Rural Development & Foods and/or the Greek Government.