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...two-factor learning and diffusion models to estimate the effect of learning by doing and learning by research on technical progress for a range of technologies in four stages of development. We find learning patters broadly in line with the perceived view of technical progress. The results generally show higher learning by research than learning by doing rates. Moreover, we do not find any development stage where learning by doing is stronger than learning by research. We show that simple learning by doing curves overstate the effect of learning in particular for newer technologies. Finally, we find little substitution potential between learning by doing and research for most technologies.
1. INTRODUCTION
The importance of technological progress as a major force behind factor productivity and economic growth is well established in the literature. The focus of early literature on science and technology was, however, on the effect and measurement of technical change on output and growth. Technical change was treated as an exogenous phenomenon to the economy a view which posed clear limitations for policy analysis. Since the 1960s, the focus of the literature has shifted to the role of economic factors in technical change (Thirtle and Ruttan, 1987). The new paradigm views technical change as an endogenous factor and that it may be induced. This view is reflected in the increased interest in the use of learning curves in technology analysis. Recently, the notion of induced technical change has been adopted in analysis of energy and environmental technologies (Criqui et al., 2000; Grubb et al., 2002).
Innovation theory and cross-technology analysis using learning curves can shed light on the characteristics and the stages of technical change process. It is also of interest to improve the process of learning and identify those technologies that are likely to achieve most progress during a given period. Further, it is important to determine whether resources allocated to promotion of a given technology are better spent on research and development (R&D) or on capacity promotion policies.
In recent years, learning curves have been applied to analysis of induced technical change in energy technologies. The most commonly used forms are single-factor learning curves that estimate the effect of cumulative capacity or production on reducing the cost of technology (learning by doing). This framework overlooks the effect of R&D as an influential factor and policy tool (learning by research). As a result, not only the effect of R&D on technical progress cannot be determined but the estimates of learning by doing can also be affected. In addition, understanding the relative importance of learning by doing and learning by research on technical progress is important for improving innovation theory and energy technology policy.
This paper presents a comparative analysis of technical change in a range of electricity generation technologies at different stages of development using extended learning curves that reflect the main tenets of innovation theory. We use simultaneous two-factor learning curves and diffusion models to estimate the effect of learning by doing and learning by research on technical progress. We then examine the relative importance of R&D and capacity deployment for different technology categories. The results generally show higher learning by research than learning by doing rates. We do not find any technological development stage where learning by doing is the dominant driver of technical change. We also compare the learning by doing results from our model with those of single factor learning by doing models. Finally, we calculate the elasticity of substitution between R&D and capacity deployment for the technologies examined. The next section reviews the relevant literature and concepts of technical change and technology learning curves. Section 3 describes the methodology and data used for the analysis in the paper. Section 4 presents the results of the analysis. Section 5 summarizes and concludes the paper.
2. INDUCED TECHNICAL CHANGE AND LEARNING CURVES
Technical change is generally conceptualized as a gradual process that involves different stages of progress. The process and its stages have been described in various ways. The most established of these is Schumpeter's invention-innovation-diffusion paradigm (Schumpeter, 1934; 1942). Briefly, within this framework, invention is viewed as the generation of new knowledge and ideas. In the innovation stage, inventions are further developed and transformed into new products. Finally, diffusion refers to widespread adoption of the new products. The relationship between the stages of technical progress is no longer thought to be linear but a non-linear process with feedbacks between its components (Stoneman, 1995). However, this process is not well understood and a coherent theory of technical change remains illusive. The concepts and characteristics of the stages and process of technical change also apply to electricity generation technologies as these generally evolve through similar stages of progress (see Jensen, 2004).
R&D and capacity deployment are the main drivers of change in energy technologies (Skytte et al., 2004; Criqui et al., 2000). R&D has a role in all stages of technical progress although the nature of it can change. There is a broad correspondence between the process of technical change and the main R&D activities--i.e. basic research, applied research, and development. Basic research is related to the invention and early stages of conception of technology. As the technology matures, applied research and development are associated with the innovation and diffusion stages of technical progress. In addition, the knowledge and learning by doing gained from manufacturing, scale of production, and utilization is an important source of technical progress.
The perceived view of the process of technical progress is that the relative importance of R&D and capacity deployment varies in different stages of development of a technology. At early stages of development, technical progress is mainly achieved through R&D and, in the absence of commercial viability, growth in capacity is limited. Gradually, diffusion of installed capacity begins to grow as cost reductions and technology support policies improve commercialization of a technology. While capacity deployment is constrained, R&D plays a leading role in achieving technical progress. As the technology matures and is adopted the effect of capacity deployment increases.
It is, therefore, important to study the relative importance of technology push and market pull and, in particular, their role in different stages of technological development (see Grubler et al., 1999). This will enhance our understanding of the process and stages of technical changes and will help in the design of more effective policies and allocation of technology promotion resources between R&D and capacity deployment. However, it takes a long time before a technology evolves from invention to innovation stage and ultimately becomes fully commercialized. The transition from invention and innovation to diffusion stage is crucial for technological progress. Theory informed policies and empirical evidence could improve the process and contribute to better allocation of technology promotion funds between R&D and capacity deployment across technologies.
2.1 R&D and Technology Policy
There is a range of electricity generation technologies at different stages of progress. Meanwhile, the notion of induced technical change implies that the process of innovation can be influenced. The logical extension of this is that policies can be devised to mitigate market failure for new technologies. A typology of such policies, consistent with the invention-innovation-diffusion paradigm, divides these into supply push and demand pull measures.
R&D activities can be subject to three types of market failure namely indivisibility, uncertainty, and externalities (Ferguson and Ferguson, 1994). The aim...
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