Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach
Introduction
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, 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.
Section snippets
An overall summary of the methodology
This paper sets up an inter-country innovation production framework for R&D activities in 30 countries. Each country is regarded as a DMU that employs R&D manpower and physical resources as inputs to produce countable outputs. Inspired by Fried et al. (1999), we propose a three-stage approach to analyze the relative efficiency of R&D production. In the first stage, the input-oriented DEA model is applied to estimate the inter-country efficiency frontier of R&D activities. Technical efficiency
Data and hypotheses
A total of 30 countries form the sample used in this study. Twenty-three (23) of them are OECD members and seven (7) are non-OECD economies. Quantitative R&D input and output data for these countries are collected and processed. Official data compiled and released by international organizations and governments, such as OECD, UNESCO, the World Intellectual Property Organization, and the U.S. Patent and Trademark Office, etc., are used. A list of countries and detailed data sources are given in
Empirical results of the three-stage approach
The efficiency of an R&D unit to transform inputs into outputs is influenced by its technical proficiency, which is mostly under its control, and external operating environment, which is usually beyond its control. The purpose of the three-stage approach is to obtain a measure of net technical efficiency after controlling for the exogenous features of the environment.15
Implications of the findings and concluding remarks
Large amounts of resources have been devoted to scientific R&D in many countries. However, if R&D resources are not used effectively, additional investment may be of little help in stimulating scientific and technological progress. This paper proposes a three-stage approach to evaluate, in a production framework, the relative technical efficiency of R&D across 30 countries, mainly by controlling for country variations in external environments. The commonly used non-parametric DEA technique is
Acknowledgements
This paper is part of a joint research project carried out when the first author was a visiting scholar at Western Michigan University, under the financial support of the Fulbright Program, Grant No. 68428298, USA, and the National Science Council, Grant No. 42115F, Taiwan. The authors would like to thank two anonymous referees of the Journal and E. Alvi, D. Alexander, M. Higgins, J. Kimmel, and seminar participants at Western Michigan University and at the 2005 Taipei Conference on Efficiency
References (32)
- et al.
Measuring the efficiency of decision making units
European Journal of Operational Research
(1978) - et al.
On research efficiency: a micro-analysis of Dutch university research in economic and business management
Research Policy
(2005) Universities as engines of R&D-based economic growth: they think they can
Research Policy
(1990)- et al.
Sources of productivity growth in the Spanish pharmaceutical industry (1994–2000)
Research Policy
(2004) - et al.
Value efficiency analysis of academic research
European Journal of Operational Research
(2001) Technological development and rates of return to investment in a catching-up economy: the case of South Korea
Structural Change and Economic Dynamics
(2003)- et al.
A study on the R&D efficiency and productivity of Chinese firms
Journal of Comparative Economics
(2003) - et al.
Research productivity in a system of universities
- et al.
Profit efficiency among Basmati rice producers in Pakistan Punjab
American Journal of Agricultural Economics
(1989) - et al.
Computing inequality: have computers changed the labor market?
The Quarterly Journal of Economics
(1998)