Tag: C18

TECHNOLOGIZATION PROCESSES AND SOCIAL AND ECONOMIC GROWTH: MODELING THE IMPACT AND PRIORITIES FOR STRENGTHENING THE TECHNOLOGICAL COMPETITIVENESS OF THE ECONOMY

Taras VASYLTSIV

Professor at the Department of Social and Humanitarian Development of the Regions, Dolishniy Institute of Regional Research of the National Academy of Sciences of Ukraine, Lviv, Ukraine;

tgvas77@ukr.net

Olha MULSKA

Senior Research Fellow at the Department of Social and Humanitarian Development of the Regions, Dolishniy Institute of Regional Research of the National Academy of Sciences of Ukraine, Lviv, Ukraine

oliochka.mulska@gmail.com

Volodymyr PANCHENKO

Professor at the Department of Pedagogy and Management of Education, Volodymyr Vynnychenko Central Ukrainian State Pedagogical University, Kropyvnytskyi, Ukraine

op_panchenko@ukr.net

Maryana KOHUT

Associate Professor at the Department of International Economic Relations and Marketing, Lviv National Agrarian University, Dubliany, Ukraine

maryana_kohut@i.ua

Volodymyr ZAYCHENKO

Associate Professor at the Faculty of Economics and Management, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

zaichenko.v78@gmail.com

Olha LEVYTSKA

Senior Research Fellow at the Department of Social and Humanitarian Development of the Regions, Dolishniy Institute of Regional Research of the National Academy of Sciences of Ukraine, Lviv, Ukraine

o.levytska@gmail.com

Abstract

The methodology of integral assessment of the technological competitiveness state of the economy has been developed, which includes a system of indicators in the areas of the country’s readiness for economy digitization, the quality of innovation activity institutions, the state of digital knowledge dissemination. The integral values of technological competitiveness of the economy for the countries of the European Union and Ukraine have been calculated. A dynamic grouping of countries according to the level of technological competitiveness of the economy has been carried out. Modelling the impact of the parameters of technological competitiveness of the national economy on the basic parameters of social and economic development such as GDP per capita, share of high-tech exports, capital investment and quality of life of population has been realized. The strategic priorities and means of introduction of the collective contractual organizational and institutional system for providing technologization in the processes of social and economic growth of the country (the casestudy of Ukraine) are substantiated.

Keywords: innovation and technological development, competitiveness of the social and economic system, economic integration, prerequisites, factors of technologization

JEL classification: O32, O38, O47, C18, C51

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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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OUTPUT, GROWTH, AND CONVERGENCE IN A GREATIVE REGION: AN ANALYSIS OF SOME MEASUREMENT ISSUES

Amitrajeet A. BATABYAL

Department of Economics, Rochester Institute of Technology, 92 Lomb Memorial Drive, Rochester, NY 14623-5604, USA.

aabgsh@rit.edu

Abstract

We study some measurement issues that arise when analyzing the long run behavior of the  jth creative region’s time t log output per creative class member (yj(t)) when this region is part of an aggregate economy of j=1,…N creative regions. We focus first (second) on absolute (relative) convergence. In the absolute (relative) convergence case, the N creative regions are similar (dissimilar) in that they all have the same (different) balanced growth path (BGP) level of log output per creative class member denoted by yBGP(yjBGP) In the absolute convergence case, we analyze how to estimate the speed of convergence parameter (σ) and then discuss the relationship between the variance of yj(t) and that of yj(0) In the relative convergence case, we study the error associated with estimating σ using the methodology of the absolute convergence case. Finally, suppose yjBGP= a + bXj where Xj is an explanatory variable such as creative capital and a and b are positive constants. Here, we study how to estimate b from our knowledge of the coefficients of a related cross-region growth regression.

Keywords: Convergence, Creative Capital, Economic Growth, Measurement, Output

JEL classification: R11, C18

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