An empirical investigation of the National Innovation System (NIS) using Data Envelopment Analysis (DEA) and the TOBIT model

The goal of this paper is to investigate national innovation systems’ input–output components and to model a robust efficiency measurement using the DEA Bootstrap technique. Most of the previous NIS studies are descriptive and little emphasis is given to complex analysis. In our previous study, we evaluated the innovation performance of 20 emerging and developed countries, from the point of view of technical efficiency. This study makes an important contribution using the DEA Bootstrap technique, whereby we rank the countries based on bias-corrected estimation parallel to conventional DEA efficiency. The efficiency scores obtained from this technique show which countries are considered to be innovation leaders because their innovation performance is efficient under both constant and variable returns to scale in the process of transforming innovation inputs into innovation outputs. We suggest some key policy implications that can be learned from these innovation leaders. Subsequently, we apply the Tobit model to explain inefficiency. Based on the Tobit regression model, the DEA CRS technical efficient score of inefficient countries could be improved through three main variables: the secondary school enrolment ratio; the labour force (ages 15–65), as a percentage of the total population; and domestic credit expansion by the business sector, as a percentage of GDP.

[1]  L. Singh Innovations and Economic Growth in a Fast Changing Global Economy: Comparative Experience of Asian Countries , 2006 .

[2]  Paul W. Wilson,et al.  FEAR: A software package for frontier efficiency analysis with R , 2008 .

[3]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[4]  B. Dalum National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning , 1992 .

[5]  P. Romer Endogenous Technological Change , 1989, Journal of Political Economy.

[6]  N. Rosenberg Sources of Innovation in Developing Economies: Reflections on the Asian Experience , 2013 .

[7]  Minge Xie,et al.  Bootlier-plot: Bootstrap based outlier detection plot , 2003 .

[8]  P. W. Wilson,et al.  Statistical Inference in Nonparametric Frontier Models: The State of the Art , 1999 .

[9]  Maxim Kotsemir Measuring National Innovation Systems Efficiency – A Review of DEA Approach , 2013 .

[10]  Munshi Naser Ibne Afzal,et al.  KBE frameworks and their applicability to a resource-based country: The case of Brunei Darussalam , 2012 .

[11]  Munshi Naser Ibne Afzal,et al.  An empirical productivity analysis of ASEAN economies intransition towards knowledge-based economy , 2013 .

[12]  J. Bos,et al.  Producing Innovations: Determinants of Innovativity and Efficiency , 2011 .

[13]  G. Dosi,et al.  Technical Change and Economic Theory , 1989 .

[14]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[15]  Charles Edquist,et al.  Economic Development and the National System of Innovation Approach , 2003 .

[16]  Yuezhou Cai,et al.  Factors Affecting the Efficiency of the BRICSs' National Innovation Systems: A Comparative Study Based on Dea and Panel Data Analysis , 2011 .

[17]  L. Leydesdorff The Knowledge-Based Economy: Modeled, Measured, Simulated , 2006 .

[18]  M. Frenz,et al.  Does Multinationality Affect the Propensity to Innovate? An Analysis of the Third UK Community Innovation Survey , 2007 .

[19]  The relationship between a physician incentive plan and departmental performance in a Taiwan hospital , 2013 .

[20]  John A. Mathews,et al.  National innovative capacity in East Asia , 2005 .

[21]  K. Green National innovation systems: a comparative analysis , 1996 .

[22]  Jeffrey L. Furman,et al.  The Determinants of National Innovative Capacity , 2000 .

[23]  Al-Ani,et al.  Using Data Envelopment Analysis To Measure Cost Efficiency With an Application on Islamic Banks , 2006 .

[24]  C. Edquist Systems of Innovation: Technologies, Institutions and Organizations , 1997 .

[25]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[26]  Jiancheng Guan,et al.  Modeling the relative efficiency of national innovation systems , 2012 .

[27]  Jorge Oliveira Pires,et al.  Productivity of Nations: A Stochastic Frontier Approach to TFP Decomposition , 2004 .

[28]  Wen-Min Lu,et al.  Dea Performance Measurement of the National Innovation System in Asia and Europe , 2010, Asia Pac. J. Oper. Res..

[29]  M. Lessnoff Capitalism, Socialism and Democracy , 1979 .

[30]  P. Intarakumnerd,et al.  National innovation system in less successful developing countries: the case of Thailand , 2002 .

[31]  Roger Lawrey,et al.  Evaluating the comparative performance of technical and scale efficiencies in knowledge-based economies (KBEs) in ASEAN: a data envelopment analysis (DEA) application , 2012 .

[32]  Feinson National innovation systems overview and country cases , 2016 .

[33]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[34]  Roger Lawrey,et al.  A measurement framework for knowledge-based economy (KBE)efficiency in ASEAN: a data envelopment (DEA) window approach , 2012 .

[35]  An Australian Measuring a Knowledge-based Economy and Society , 2002 .

[36]  Markus Balzat,et al.  Recent trends in the research on national innovation systems , 2004 .

[37]  P. W. Wilson,et al.  Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models , 1998 .

[38]  M. Porter The Competitive Advantage Of Nations , 1990 .

[39]  Xiaohui Liu,et al.  Knowledge spillovers, absorptive capacity and total factor productivity in China’s manufacturing firms , 2012 .