The European economic space: some new aspects of regionalization

Emilija Manić, Đorđe Mitrović, Svetlana Popović


European continent is very diverse in physical, demographic, economical and social aspects. Different processes that shaped European continent during the past times produced different economical environment across the whole continent. Economic regionalization is especially complex because of economic space dynamism. This made regional disparities within existing geographical regions so big that changeability of economic regions boundaries could not be overlooked.

The paper provides a completely new aspect of the economical regionalization, using Data Envelopment Analysis method (DEA). Some relevant economic (financial and macroeconomic stability), demographic and social indicators have been chosen to calculate composite index (Regional Development Index - RDI), considering each of these categories through calculated sub-indexes. The given methodology is developed for the purpose of revealing regional disparities within existing European economic regions and provides an excellent tool for evaluating efficiency of possible regional and economic policies.

Key words: economic region, Data Envelopment Analysis, Regional Development Index, regionalization

Full Text:



Ali, A.I., Lerme, C.S.& Nakosteen, R.A. (1993). Assessment of Intergovernmental revenue transfers. Socio-Economics Planning Sciences, 27, 1, pp. 109-118.

Berry, B.J.L. (1968). Numerical Regionalization of Political-Economic Space. Geographia Polonica, 15, pp.27-35.

Charnes, A., Cooper, W. &Rhodes, W. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(4), 429-444.

Chang, P-L, Hwang, S-N. & Cheng, W-Y. (1995). Using Data Envelopment Analysis to measure the achievement and change of regional development in Taiwan. Journal of Envirnomental Management, 43, pp. 49-66.

Cherchye, L., Moesen W., Rogge, N.& Puyenbroeck, T. V. (2007). An introduction to 'Benefit of doubt’ composite indicators. Social Indicators Research, 82(1), 111-145.

Cherchye, L., Moesen W., Rogge, N., Puyenbroeck, T. V., Saisana, M., Saltelli, A., Liska R. & Tarantola, S. (2008). Creating Composite Indicators with DEA and Robustness Analysis: The Case of Technology Achievement Index. The Journal of Operational Research Society, 59 (2), 239-251.

Cook, W. D., Tone, K. and Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.

Cook, W. D. and Zhu, J. (2015). “DEA Cross Efficiancy” in Zhu, J. (ed.) Data Envelopment Analysis - A Handbook of Models and Methods, Springer

Core Team R (2016). R: A language and environment for statistical computing. R Vienna: Foundation for Statistical Computing,

Dinc, M. & Stough, R.R. (1997). Intertemporal DEA: using DEA to examine local government efficiency in Fairfax, County, Virginia, U.S.A. Working papaer, The Institute of Public Policy, Georg Mason Univeristy.

Doyle, J. &Green, R. (1994). Efficiency and Cross-Efficiency in DEA: Derivations, Meanings and Uses. The Journal of the Operational Research Society,45(5), 567-578.

Fridmann, J., Alonso, W. (1974). Regional Development and Planning: A Reader. Boston: M.I.T. Press.

Fujita, M., Krugman, P., Vanables, A. (1999). The Spatial Economy. Boston: M.I.T. Press.

Fusco, E. (2015). Enhancing non-compensatory composite indicators: A directional proposal. European journal of operational research, 242(2), 620-630.

Garfield, R. (2001).Economic, Sanctions, Health and Welfare in Federal Republic of Yugoslavia 1990-2000.Belgrade: OCHA and UNICEF Belgrade.

Garrison, W.L. (1956). Applicability of statistical inference to geographical research. Geography Review, 46, pp. 427-429.

Golany, B. & Roll, Y. (1989). An Application Procedure for DEA. Omega,17(3), 237-250.

Haynes, K.E., Stough, R.R. & Shroff, H.F.E. (1990). New methodology in context: Data Envelopment Analysis. Computers, Environment and Urban Systems, 14, 2, pp. 85-88.

Historical Statistics of the World Economy 2008,

Isard, W. (1960). Methods of Regional Analysis: an introduction to Regional Science. New York: Papers and Proceedings of the Regional Science Association.

Krugman, P. (1991). Increasing Returns and Economic Geography. Journal of Political Economy, 99, pp. 483–99.

Manic, E., Popović S. & Molnar D. (2012). Regional Disparities and Regional Development: The Case of Serbia, Mittelungen der Osterreichishen Geographischen Gesellschaft, Band 154, Wien 2012, 191 – 210.

Manic, E., Popovic, S. & Mitrovic, D. (2016). Is There a “Chinese Wall” in Europe? A Glance at the Development of Socio-Economic Disparities in Europe.Mitteilungen der Österreichischen Geographischen Gesellschaft, Band 158. Jg. (Jahresband), 133-148.

Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Homan, A. & Giovannini, E. (2005). Handbook on constructing composite indicators: Methodology and user guide. OECD statistics working papers 2005/3, OECD, statistics directorate.

Mitrović, Đ. (2015). Broadband Adoption, Digital Divide, and the Global Economic Competitiveness of Western Balkan Countries, Economic Annals, 60(207), 95-116.

OECD (2008). Handbook on Constructing Composite Indicators – METHODOLOGY AND USER GUIDE. Retrieved from

Popovic, S. (2010).Financial Crisis and the Growth Model in SEE Region, The Challenges of Economic Science and Practice in the 21st Century, The Faculty of Economics, Niš, October 2010.

Popović, S., Manić, E. & Mitrović, Đ. (2016). The Republic of Serbia in the region: Analysis of socio-economic performances, International Scientific Conference: The Priority Directions of National Economy Development, The Faculty of Economics, Niš, October 2016

Poter, M.E. (1998). Location, clusters and the “new” micro-economics of competition. Business-Economics, 33,1, pp. 7-13.

Ristic, B., Trifunovic, D. & Tanaskovic, S. (2010). Competitiveness of Transitional Countries during the Global Crisis. In Fifth International Conference of the School of Economics and Business in Sarajevo (ICES2010). Proceedings (p. 1J). University of Sarajevo, School of Economics and Business.

Sarafoglou, N. & Haynes, K.E. (1990). Regional efficinece of building sector research in Sweden: an introduction. Computer, Environment and Urban Systems, 14, 2, pp.117-132.

Stimson, R.J., Stough, R.R & Roberts B.H. (2006). Regional Economic Development: Analysis and Planning Strategy, second edition. New York: Springer.

Tarabusi, C. E. & Guarini, G. (2013). An unbalance adjustment method for development indicators. Social indicators research, 112(1), 19-45.

Тошић, Д. (2012). Принципи регионализације. Београд: Универзитет у Београду Географски факултет.

Trifunović, D., Ristić, B., Ivković, M., Tanasković, S., Italiano, L., & Tattoni, S. (2009). FDI’s Impact on Transitional Countries, Serbia as a Rational Choice: the FIAT-ZASTAVA Case. Transition Studies Review, 16(2), 269-286.

Trifunović, D. (2010). Optimal Auction Mechanisms with Private Values. Economic Annals, 55(184), 71-112.

Trifunović, D. (2011). Single Object Auctions with Interdependent Values. Economic Annals, 56(188), 125-170.

Vidoli, F., Fusco, E. & Mazziotta, C. (2015). Non-compensability in composite indicators: a robust directional frontier method. Social indicators research, 122(3), 635-652.

The United Nations Economic Commission for Europe UNECE (1999). Post-war Reconstruction and Development in South-East Europe, Economic Survey of Europe, 1999, No. 2, p. 3,

World Bank. World Development Indicators Database 2016.

World Bank. Global Financial Development Database (GFDD) 2016, .

Zhou, P., Ang B.W. & Poh, K.L. (2006). Comparing aggregating methods for constructing the composite environmental index: An objective measure. Ecological Economics, 59(3), 305-311.

Zhou, P., Ang B.W. & Poh, K.L. (2007). A mathematical programming approach to constructing composite indicators. Ecological Economics, 62(2), 291-297.


  • There are currently no refbacks.