Diverse Effects of FDI in Regional Innovation Systems: Synergy Measurement, Complexity Theory, and Entropy Statistics

29 Pages Posted: 26 Apr 2013

See all articles by Balázs Lengyel

Balázs Lengyel

Hungarian Academy of Sciences (HAS) - Institute of Economics CERS-HAS (IEHAS); International Business School - Budapest (IBS)

Loet Leydesdorff

University of Amsterdam - Amsterdam School of Communication Research (ASCoR)

Date Written: April 23, 2013

Abstract

The aim of this study is to show regional differences as local effects of foreign investments in the dual economy of Hungary. We first introduce three knowledge functions (knowledge exploitation, knowledge exploration, and organizational control) of innovation systems. These functions are operationalized using entropy statistics. Regional innovation systems can then be expected to self-organize in terms of a synergy — that is, reduction of uncertainty — among these functions. Analyzing one-, two-, three-, and four-dimensional entropies of high-tech and medium-tech firms, and knowledge-intensive services, we measure the synergy among the distributions of geographical addresses, organizational size (number of employees), and technologies (NACE codes of the OECD). Synergy is defined as mutual information among these three dimensions; the fourth dimension is used to bring internationalization into the model. Our results show that regional innovation systems in Hungary are self-organized differently, in relation to a small set of foreign firms that have an overwhelming positive effect in regions between the Hungarian capital and the Austrian border. Foreign direct investment, however, has negative effects on local economies and disturbs the domestic synergy in the lagging Eastern and Southern provinces of the country.

Keywords: regional innovation system, knowledge function, synergy, entropy, foreign firms

JEL Classification: B52, O18, P25, R12

Suggested Citation

Lengyel, Balázs and Leydesdorff, Loet, Diverse Effects of FDI in Regional Innovation Systems: Synergy Measurement, Complexity Theory, and Entropy Statistics (April 23, 2013). Available at SSRN: https://ssrn.com/abstract=2255521 or http://dx.doi.org/10.2139/ssrn.2255521

Balázs Lengyel (Contact Author)

Hungarian Academy of Sciences (HAS) - Institute of Economics CERS-HAS (IEHAS) ( email )

Budaorsi ut 45
Budapest, 1112
Hungary

International Business School - Budapest (IBS) ( email )

Záhony utca 7.
Budapest, 1031
Hungary

Loet Leydesdorff

University of Amsterdam - Amsterdam School of Communication Research (ASCoR) ( email )

PO Box 15793
Amsterdam, 1001 NG
Netherlands

HOME PAGE: http://www.leydesdorff.net

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