Determinants of National Innovation Systems: Policy implications for developing countries
Frank L Bartels
Unit Chief and Deputy to the Director, Strategic Research and Regional Analysis Unit, United Nations Industrial Development Organization (UNIDO), Vienna, Austria
Hinrich Voss
Roberts Academic Research Fellow, Centre for International Business, University of Leeds, Centre for International Business (CIBUL), Leeds University Business School, Leeds, UK
Suman Lederer
Research Associate, Strategic Research and Regional Analysis Unit, UNIDO, Vienna, Austria
Christopher Bachtrog
Research Associate, Strategic Research and Regional Analysis Unit, UNIDO, Vienna, Austria
PP: 2 - 18
Abstract
The importance of National Innovation Systems (NIS) for the economic performance of a country is widely acknowledged. In order to close the development gap, it is crucial for developing countries to identify the degree to which knowledge institutions, governments and business corporations determine NIS and deduce relevant innovation policies from this. We model and estimate their relationship from a network perspective for NIS of 46 developed economies and emerging markets at the macro level using secondary data from a series of international measures. We find that, while the structural dynamics of knowledge management, decision-making, government-business relations and the market are crucial to NIS behaviour, overall innovation in this context is dominated by market forces. This implies developing economies should focus on establishing an institutional environment that supports well market which, subsequently, supports the domestic NIS and economic growth.
Keywords
National Innovation Systems, network determinants, performance indicators
Article Text
While recent research has emphasised the importance of National Innovation Systems or National Systems of Innovation (NIS/NSI)[1] to the economic health of countries (Block and Keller 2008). The roots of the empirical work on NIS are found in investigations into the interactions between human capital, diffusion of technology and economic growth (Nelson and Phelps, 1966; Arrow, 1996; Solow, 1960) and further back in terms of Marshallan industrial districts (Marshall, 1920). Despite the wealth of conceptual and theoretical contributions, statistical assessments that use dynamic network analysis, structural equation modelling (SEM), and multiple regressions to model NIS are relatively few. This paper examines the phenomenon respective to the literature and provides a statistical framework analysis. It uses cross-sectional data of indices of competitiveness, technology, industrial performance and human development for 26 developed countries and 20 emerging markets economies (EMEs) (See Appendix I).
The research attempts to identify the factors that actually depict National Innovation Systems and establish how the constructs can be measured. The underlying assumption is that certain determinants of coherent and high performing NIS exist in developed economies and emerging markets and these determinants can provide valuable insights to policy for, and management of, NIS in developing countries with not so high-performing economies.
Within the debate concerning the institutions or technology roots of economic growth (Nelson and Phelps 1966) NIS has become especially important in national policy since 1980 (European Commission 2004). This is reflected not only in public policy support for Small and Medium-sized enterprises (SMEs) as sources of innovation (Audretsch 2004) but also by the Community Innovation Surveys of the EU[2]. NIS literature has expanded to cover multi-faceted dimensions in the economics of technology, industrial performance and competitiveness and innovation (including regional innovation systems, industrial dynamics and structural change). According to Asheim and Gentler (2005), innovative activities and the economic geographies that conform to the efficiency and effectiveness of knowledge links are crucial to development. Though the dynamics of that knowledge and spatial concentration of economic relations are based on intellectual assets (Cohen, Nelson and Walsh, 2000). These concepts inform our research. NIS are prevalent in industrialised and emerging market economies. Nevertheless several developing countries aspire to increase the appreciation of innovation to develop their economies. (See African Union Summit 2007 on science and technology for Africa's development). Therefore, empirical understanding of NIS in industrialised and emerging market economies carry significant implications for developing countries. The international business dimensions of NIS (e.g., technology transfer via licensing or foreign direct investment] are equally important; this, however, is beyond the scope of the paper.
The paper is organised as follows. The seminal literature on NIS is reviewed in section 2. A model of NIS for analysis is presented in section 3. In section 4, we illustrate the approach used for identifying, standardising and analysing the data. Results of the factor and multiple regression analyses are presented in section 5. The discussion of the results is presented in section 6. Section 7 concludes.
[1] Both these two phrasings appear consistently in the literature (see Munk and Vintergaard 2004; Lundvall 1995).
[2] The EU Community Innovation Statistical Surveys have been conducted on a four yearly basis since 1993. The coverage has increased from 15 countries in 2000/2001 to 25 countries of the EU in 2008 (http://www. eurostatepp.eurostat.ec.europa.eu/portal/page/portal/microdata/cis; accessed 21 June 2011).
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