ANALYSIS OF CONVERGENCE OF EUROPEAN REGIONS WITH THE USE OF COMPOSITE INDEX

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

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ISSN: 1234-7655
eISSN: 2450-0291

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VOLUME 16 , ISSUE 2 (June 2015) > List of articles

ANALYSIS OF CONVERGENCE OF EUROPEAN REGIONS WITH THE USE OF COMPOSITE INDEX

Joanna Górna * / Karolina Górna *

Keywords : economic convergence/divergence, spatial autocorrelation, spatial econometric model, composite index.

Citation Information : Statistics in Transition New Series. Volume 16, Issue 2, Pages 265-278, DOI: https://doi.org/10.21307/stattrans-2015-014

License : (CC BY 4.0)

Published Online: 31-October-2017

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ABSTRACT

Convergence study is related to several crucial issues. One of those problems is an individual character of every region in the selected area, as the regions established accordingly to the European classification system NUTS-2 are not homogeneous. Therefore, while analysing convergence in the European Union, regions with extremely dissimilar characteristics (for example GDP per capita) are taken under consideration. Absolute β-convergence means that all of the investigated regions tend to the same level of economic growth. Thus, among the regions with highly differential amounts of the examined variables the convergence hypothesis can be rejected. Due to the heterogeneity in the conducted investigation a classification based on the composite index will be used so that the convergence clubs could be established. Several approaches to convergence will be used according to those regimes. Moreover, there will be an attempt to indicate the determinants that differentiate the selected regions, such as: expenditure on R&D, HRST, quantity of patents, employment, participation of people in tertiary education among all employees. This will allow the analysis of conditional β-convergence to be conducted. In the investigation some methods and models offered by the spatial statistics and econometrics will be used. There are empirical proofs that geographical location has a great impact on the processes of economic growth. Consequently, spatial dependencies will be analysed as well.

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