The Efficiency of Triple-Helix Relations in Innovation Systems: Measuring the Connection between a Country’s Net Income and Its Knowledge Base
Economies, like Russia, blessed with resource abundance, do not usually perform well during the period of commodity price boom. The optimal policy of managing resource revenues prescribes to commit the permanent income rule to smooth the resource dividend in efficiency units and to smooth the real exchange rate. During the commodity price boom, Russia followed partially this prescribed policy, but the situation changed after the crash of oil and gas prices in 2014. Possible ways to overcome the consequences of low oil and gas prices are discussed, paying particular attention to the lack of economic complexity and the need for diversification and capabilities for growth and development of the Russian economy.
The article provides an overview of methods for measuring the technological progress developed in the last 50–60 years, and analyzing the performance of national economies. A special place is given to production functions as a tool that allows one to give aggregated estimates of technological progress in the framework of different ways, including frontier analysis, i.e., (the analysis of stochastic efficiency frontier requires the SFA approach and the envelope method requires the DEA approach) and nonedge analysis (Solow residual). We have analyzed the feasibility of expanding the traditional threefactor model of production functions, including labor, physical, and human capital through the addition of two indicators, i.e., institutional development and provision of infrastructure. A review of variables available in global statistical databases and ability to approximate these production factors has been presented.
The effectiveness of the Triple Helix model of innovations can be evaluated in bits of information using the TH indicator of synergy based on information theory. However synergy, measured in bits of information can’t be straightforwardly interpreted in economic terms. The present paper is an attempt to establish a connection between synergy and other growth relating economic measure, such as complexity indices. The synergy distribution among 31 Chinese territorial districts is compared with corresponding distribution of complexity. The latter are calculated with three different complexity measures and on different datasets. Synergy and complexity show substantial linear relationship with each other. These complexity measures are further tested with their ability to predict future GDP per capita growth using employment, income, and investment data for 31 territorial districts of China and 19 industries. The results of regression analysis suggests that the accuracy of growth forecast can be substantially improved when exploiting links of different origin in bipartite networks in comparison with export oriented approach.