Classifying Hungarian sub-regions by their competitiveness

Transcription

1 Classifying Hungarian sub-regions by their competitiveness Péter KOVÁCS, lecturer, Department of Statistics and Demography, Miklós LUKOVICS, lecturer, Institute of Economics and Economic Development, Faculty of Economics and Business Administration, University of Szeged, Hungary The 25th SCORUS Conference on Regional and Urban Statistics and Research Wroclaw, Polland, August 30th September 1rst, 2006

2 Framework of the presentation Introduction of Hungary and Szeged The notion of regional competitiveness The pyramid model of regional competitiveness Aims of our study The methodology of our classification Typology of the Hungarian sub-regions Results Conclusions

3 Hungary, Szeged Hungary is a member of the European Union since 1 st May, 2004 Szeged is located near the southern border of Hungary; just two hours from the capital, Budapest Szeged, situated on the banks of the river Tisza, is known as the City of Sunshine as it averages 2,000 hours of sunshine annually Population without students: 160,000 Known worldwide for its Pick Salami and the spice paprika Level of territorial units NUTS 2 = region 7 NUTS 3 = county 19 + Budapest (capital) NUTS 4 = subregion Budapest (capital)

4 University of Szeged The university of Szeged is ranked 202nd 301st among the best universities of the world academic survey 2004 of the Shanghai Jiao Tong University Internationally recognized academic achievements Nobel Prize laureate Albert Szent-Györgyi (Medical Sciences) Student population: 30,000 More than 1000 international students annually

5 Regional disparities Catching-up process of Hungary at the national level Discernible process at the regional level: regional disparities are widening because the growth of the most developed territorial units is increasing while the less favoured territorial units are lagging behind. During the beginning of the 1990s after the transformation of the political system in Hungary the regional differences sharpened remarkably. This process hasn t stopped by the beginning of the 21st century. Since the process of economic development comes along with the growth of regional disparities for a while.

6 Common opinion said Highest development phase: Budapest and the West Underdevelopment phase: East borderland

7 Working definition of regional competitiveness (EC 1999) The ability of companies, industries, regions, nations and supra-national regions to generate, while being exposed to international competition, relatively high income and employment levels (EC pp. 75)

8 The pyramidal model of regional competitiveness Target Quality of life Standard of living Basic Categories Regional performance Gross Regional Product Labour productivity Employment rate Development factors Research and technological development Infrastructure and human capital Foreign direct investment Small and medium-sized enterprises Institutions and social capital Success determinants Economic structure Innovative activity Regional accessibility Skills of work force Social structure Decision centres Environment Regional identity

9 Some highlighted typologies of regions Porter s competitive advantage types Postfordist region types Cambridge region types OECD-region types Developed region Innovationdriven Knowledge creation Hubs of knowledge Regions with increasing returns Urban Investmentdriven Mediumdeveloped region Knowledge transfer Regions as production sites Intermediate Underdevelop ed region Factor-driven Neofordist Non-productive region Rural

10 How to measure the competitiveness in the case of sub-regions? Income generated in the region labour productivity employment rate (Basic categories) The model contains the three named indicators of the standard competitiveness definition (GDP per capita, labour productivity, employment rate) and other chosen indicators representing the basic categories, development factors and success determinants of the pyramid model. The selection of the later indicators has been driven by economic theories and the principles of competitiveness.

11 How to measure the competitiveness in the case of sub-regions? An attempt to create three, approximately homogeneous groups of sub-regions, based on the highlighted typologies of regions In choosing indicators we followed the logic of the pyramidal model: we described each basic category, development factor and success, what resulted in 64 indicators determinant with at least three or four variables. We performed a complex analysis with the help of multivariable data analysing methods Two methods were applied: Principal Component analysis and cluster analysis

12 The steps of the multivariate analysis 1. Select variables from the 64 variables by their amount of information in their category. 2. Making an objective weight system in the pyramid for the selected variables, and the categories. 3. Cluster analysis: make 3 homogenous clusters by the weighted (& selected) variables, weighted categories. 4. Calculating the Red-indicator

13 Select variables Every categories were characterized principal component. (Amount of information at least 75-80%) Dropped the variables with low communalities and loading variables.

14 Making an objective weight system Porter: regression model Depend variable: GDP Independent variables: factors Weights: regression coefficients Our model: Use more variables to characterize the competitiveness The model was represented principal components Communalities: coefficients of determination

15 Making an objective weight system Weights: the square root of the communalities Coefficients of the correlation between the variables & the model

16 Cluster analysis Cluster analysis is performed with the same 64 weighted and standardized variables as those used for multidimensional scaling the non-hierarchical K-means method was picked Budapest, by itself, consists the most developed cluster 54 sub-regions classified in the medium developed cluster 113 relatively underdeveloped sub-regions

17 The classification of sub-regions really established relatively homogeneous groups

18 Red indicator The indicator is based on the eigenvalues of the correlation matrix. The Red indicator can also be quantified without knowing the eigenvalues: the value of the Red indicator is the quadratic mean of the elements outside the main diagonal of the correlation matrix R. The Red indicator measures the redundancy of the studied database of the given size. it quantifies the average correlation of the entire database. the Red indicators can only be used to determine how redundant individual databases are, but one cannot make a direct statement as to which of these has more useful data. Red=0,42 In this study the Red indicator resembles a generalized redundancy index between the variables and the pyramid model representing principal components.

19 Conclusions The use of various methods with different logics lead to similar results The pyramidal model and the methodology based on it are suitable to make regional competitiveness measurable and outline the possibilities of economic development

20 THANK YOU FOR YOUR ATTENTION! Péter Kovács Miklós Lukovics