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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GROSS JOB – CREATION AND GROSS JOB – DESTRUCTION DETERMINANTS: EMPIRICAL ANALYSE AT MICRO FIRMS DATA LEVEL

Tadeu LEONARDO

Faculty of Economy – Univercity Jos´e Eduardo dos Santos UJES-Angola

tad.eufeca@hotmail.com

Francisco DINIZ

Centre for Transdisciplinary Development Studies (CETRAD); Quinta dos Prados, 5000-801 Vila Real, Portugal, http://www.cetrad.info/

fdiniz@utad.pt

Abstract

This study analyses gross job-creation and gross job-destruction determinants at the firm level for a panel of Portuguese micro firms across four industry sectors, using the Ordinary Leat Square and Fixed Effect econometrics model to analyse a database consisting on 15.686 micro firms, for time period going from 2010 to 2017. It was found that laggard gross job-creation, assets tangibility, financial leverage, profits and the fact firms belong to the construction sector determines gross job-creation. Regarding gross job-destruction,  its was found that this variable is determined by its laggard variable, firm’s size, worker’s tenure and the fact the firm belongs to the hotels and restaurants sector. Finally, findings suggest that a resource-based approach explains gross job-creation and gross job-creation for micro firms by using microdata. This study contributes to the state of the art on the determinants of employment and firing at micro firms’ level as it investigates the importance of the independent variables in explaining micro firm’s labour demand in Portugal.

Keywords: Gross job-creation, gross job-destruction, micro firms, Portugal

JEL classification: M10, O14, O18, O44

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BALANCED SCORECARD AS A TOOL FOR EVALUATING THE INVESTMENT ATTRACTIVENESS OF REGIONS COMPRISING THE ARCTIC ZONE OF THE RUSSIAN FEDERATION

Vladimir MYAKSHIN

Professor,Northern (Arctic) Federal University,Russia,Arkhangelsk,Naberezhnaya Severnnoy Dviny17

mcshin@yandex.ru

Vladimir PETROV

Professor, St. Petersburg State Forestry University, Russia, St. Petersburg, Institutsky pereulok 5

wladimirpetrov@mail.ru

Abstract

Prerequisite to sound investment decision-making is the availability of reliable, objective information on earlier investments and which economic sectors they have benefitted, as well as methods allowing for multi-faceted analysis of investment performance. This study aims to elaborate a balanced scorecard to reflect the performance of and the trends in the investment activity ongoing in the regions that comprise the Arctic Zone of the Russian Federation. Methodologically, the study relies on a systemic, balanced approach; balanced scorecard concept; and foreign and domestic practices of estimating regional investment attractiveness. The study is novel in that it has achieved a customized balanced scorecard that allows for analyzing the RF Arctic regions’ investment attractiveness from various perspectives, while also allowing to identify these regions’ major investment-related challenges and promising investment opportunities. Further, the study contributes to the scientific soundness of strategies that seek better investment image. Among key outcomes of this study is the economic model that uses the said balanced scorecard to measure the RF Arctic regions’ investment attractiveness with regard to investment stakeholders (public authorities, investors, population). The outcomes of this study are expected to be used as guidance by the public authorities in the RF Arctic regions when shaping local investment policies. The prospects of this study lie in further improvement of the contents and the structure of the balanced scorecard as the Russian economy progresses in its development and, hence, improved models will be required for measuring its regions’ investment appeal.

Keywords: The Arctic Zone of the Russian Federation, investment activity, investment risks, investment climate, investment policy, investment attractiveness, important investment aspects, estimation of investment attractiveness, balanced scorecard, regional economic system.

JEL classification: D29, L50, L52, L90

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