This paper applies the production framework associated with the data envelopment analysis (DEA) method to evaluate the relative efficiency of R&D activities across countries. R&D capital stocks and manpower are treated as inputs while patents and academic publications are considered as outputs. A three-stage approach, which involves using DEA for evaluating efficiency and using Tobit regressions for controlling the external environment, is applied to 30 countries in recent years. The results show that less than one-half of the countries are fully efficient in R&D activities and that more than two-thirds are at the stage of increasing returns to scale. Most countries have a more significant advantage in producing SCI cum EI publications than in generating patents.
R&D activity is a well-organized process of knowledge creation, production, diffusion, and application. It entails innovation in scientific technology, in management measures, and in social and political systems, etc. OECD (2003) defined the investment in knowledge as the sum of R&D expenditure, expenditure for higher education and investment in software. Since R&D investment is one of the most crucial elements in promoting scientific and technological progress, any country that uses the resources inefficiently could bear a penalty in the form of achieving a much slower progress. Furthermore, if R&D resources are not used effectively, additional investment may be of little help in stimulating progress. However, the relevant literature has focused primarily on efforts at new R&D investment and comparatively little attention has been given to the effective use of the resources, particularly at the national level, once they are in place. This is a potentially important omission, since the very conditions responsible for scientific and economic backwardness may operate through the poor management of R&D activities. Understanding the nature of R&D efficiency/inefficiency is important for designing policies to improve resource allocation.
In this paper, we attempt to fill in this gap by examining the efficiency of national R&D activities. We propose a three-stage approach, which involves applying data envelopment analysis (DEA) for estimating efficiency and Tobit regressions for controlling the external environment. Following Pakes and Griliches (1984) and Griliches (1990), this paper considers R&D to be a production process and regards each country as a decision-making unit (DMU) conducting R&D. By setting up an inter-country R&D innovation production framework and using the DEA model and Tobit regression iteratively, our three-stage approach can identify and separate the intrinsic technical inefficiency in the R&D process from the external effects stemming from the operating environment, which differs substantially from country to country.1 The sample in this paper consists of thirty (30) countries that engage in R&D activities intensively. In addition, slack and advantage analyses provide detailed assessments on each country's R&D resource allocation.
There has been a large amount of literature devoted to discussing the effects of R&D investment on raising productivity and profits at the firm and industry levels. Mansfield, 1980 and Mansfield, 1988, Terleckyj (1982), Griliches (1986), Meliciani (2000), Hartmann (2003), Timmer (2003), and Gonzalez and Gascon (2004) provided evidence from many industries in various countries. Feller (1990) and Adams and Griliches (2000) emphasized the importance of the productivity of basic research in universities.
Only in recent years have a few examples in the literature discussed R&D efficiency by using quantitative approaches with regard to R&D at the firm level, Zhang et al. (2003) applied the stochastic frontier analysis (SFA) approach to the R&D efforts of Chinese firms to examine the difference in efficiency among various types of ownership. As for academic research, Korhonen et al. (2001) and Cherchye and Vanden Abeele (2005) applied the DEA technique to evaluate the efficiency of university R&D in Finland and the Netherlands, respectively.
The rest of the paper is organized as follows. Section 2 presents a summary of the overall methodology and discusses the R&D production framework and the DEA model of the efficiency measure. Section 3 gives a description of the data management and the hypotheses tested in the paper. Section 4 presents the empirical results of the three-stage approach. A comparison of the relative efficiency scores for Stage I and Stage III as well as advantage analysis are also performed in this section. The final section provides a summary and the conclusion.
