A two-factor learning model for private sector industrial firms

ABSTRACT

The Cobb–Douglas form of the two-factor learning curve model has been the conventional choice in empirical studies of the relative importance of R&D versus capacity expansion for cost reduction in energy technologies. Most empirical studies have focused on the role of public R&D in the context of renewable energy technologies. In this study, we provide a rationale for considering a different model formulation of the tradeoff between R&D and production capacity expansion when studying technology development in the private sector and we compare it to the conventional Cobb–Douglas model. We then apply our model formulation to a particular emerging technology case study.     

Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach

The purpose of this paper is to explain the sources of energy system lock-in. It presents a comparative analysis of the respective contributions of some features of increasing returns to adoption factors, i.e. learning-by-doing, learning-by-searching and returns to scale effects in explaining the technological change dynamics in the energy system. The paper is technically based on a critical analysis of the learning curve approach. Econometric estimation of learning and scale effects inherent to seven energy technologies were performed by the use of several learning curve specifications. These specifications permit to deal with some crucial issues related to the learning curve estimation which are associated with the problem of omitted variable bias, the endogeneity effects and the choice of learning indicators. Results show that dynamic economies from learning effects coupled with static economies from scale effects are responsible for the lock-in phenomena of the energy system. They also show that the magnitude of such effects is correlated with the technology life cycle (maturity). In particular, results point out that, 1) the emerging technologies exhibit low learning rates associated with diseconomies of scale which are argued to be symptomatic of the outset of the deployment of new technologies characterized by diffusion barriers and high level of uncertainty, 2) the evolving technologies present rather high learning rates meaning that they respond quickly to capacity expansion and R&D activities development, 3) conventional mature technologies display low learning rates but increasing returns to scale implying that they are characterized by a limited additional diffusion prospects.     

Profit-maximizing R&D in response to a random carbon tax

This paper determines a firm’s profit-maximizing R&D response to an uncertain carbon tax, for two different R&D programs: cost reduction of low carbon energy technologies and emissions reductions of currently economic technologies. We find that optimal R&D does not increase monotonically in a carbon tax. R&D into alternative technologies increases only if the firm is flexible enough; R&D into conventional technologies first increases then decreases in a carbon tax. Firms that are very flexible may increase R&D into alternative technologies when the uncertainty surrounding a carbon tax is increased; otherwise firms will generally decrease R&D investment in uncertainty.     

Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan

This work presents a novel gray-based cost efficiency (GCE) model that integrates the gray forecasting model into a two-factor cost efficiency curve model for renewable energy (RE) technologies and identifies the optimal forecasting model for power generation cost of RE technologies. The analytical framework of proposed GCE model improves short-term prediction of power generation cost, and can be applied during the early developmental stages for RE technologies. Empirical analysis is based on wind power data for Taiwan. Time lag of knowledge stock was simulated to represent the actual relationship between R&D expenditures and cost reductions in power generation by knowledge stock. Analytical results demonstrate the GCE model is a useful tool to quantify the influences of cost reductions in power generation. The implications of analytical results are that institutional policy instruments play an important role in RE technologies achieving cost reductions and market adoption. The proposed GCE model can be applied to all high-technology cases, and particularly to RE technologies. The study concludes by outlining the limitations of the proposed GCE model and directions for further research.     

Wind Power in Europe: A Simultaneous Innovation–Diffusion Model

The purpose of this paper is to provide a quantitative analysis of innovation and diffusion in the European wind power sector. We derive a simultaneous model of wind power innovation and diffusion, which combines a rational choice model of technological diffusion and a learning curve model of dynamic cost reductions. These models are estimated using pooled annual time series data for four European countries (Denmark, Germany, Spain and the United Kingdom) over the time period 1986–2000. The empirical results indicate that reductions in investment costs have been important determinants of increased diffusion of wind power, and these cost reductions can in turn be explained by learning activities and public R&D support. Feed-in tariffs also play an important role in the innovation and diffusion processes. The higher is the feed-in price the higher is, ceteris paribus, the rate of diffusion, and we present some preliminary empirical support for the notion that the impact on diffusion of a marginal increase in the feed-in tariff will differ depending on the support system used. High feed-in tariffs, though, also have a negative effect on cost reductions as they induce wind generators to choose high-cost sites and provide fewer incentives for cost cuts. This illustrates the importance of designing an efficient wind energy support system, which not only promotes diffusion but also provides continuous incentives for cost-reducing innovations.      

Technology variation vs. R&D uncertainty: What matters most for energy patent success?

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.     

Technology variation vs. R&D uncertainty: What matters most for energy patent success?

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.     

Dynamic and endogenous change of input–output structure with specific layers of technology

There are obvious gaps between long-term change in economic structure and its principal driving force—technological progress. History has shown the influence of technological progress on the economy and current insights in technological development can almost predict the technological waves of the next 50 years, but their potential impact on the economy has not yet been assessed. In this paper, we aim to simulate the evolution of economic structure as represented by input–output structure under specific technological change. A new version of a dynamic input–output model is developed in which both technological progress and deployment are endogenous. Investment in R&D drives the development of new technologies, installation of capital stock brings new technical processes into sector production, new and old technical processes within a sector exchange their relative weights in production as they are phased in or out, and sectors evolve or transform over time. Scenario analysis using this model applied to the Chinese electric power industry shows that the phasing-in of non-fossil energy technology will greatly change the structure of both the sector and the economy over the next 100 years.     

Diffusion trajectories of emerging sciences in Malaysian R&D system

In the last two decades, Malaysia has demonstrated significant economic progress and achieved an impressive growth performance in manufacturing exports as a result of its industrial development policies. In order to achieve higher-value-added sector, Research and Development (R&D) activities are necessary. Since the introduction of the first national science and technology policy (1986-1987) and Industrial Technology Development: a National Plan of Action (1990-2001), the Malaysian government has been committed to develop and building up competencies in learning to advance its R&D activities. This paper aims to analyze the trend of scientific production in Malaysia to indicate some characteristics of its R&D system. Logistic growth function is developed to model the growth trajectories of the selected sciences. A time-series of projection of selected technologies is made through logistic curves. In addition, Thailand and Singapore (a country with advanced diffusion of sciences), are included for comparison.     

A simple theory and evidence on the determinants of firm R&D

This paper derives a simple, but informative, model of firm R&D to figure out key factors that determine firm R&D effort. The model suggests a demand-pull, technology-push theory of R&D by showing that a firm's profit-maximizing R&D expenditure is determined jointly by both demand-side factors and technology-side factors. The former includes demand size (firm sales) and consumer preference over quality and price and the latter includes R&D cost structure or the production-cost effect of product R&D and firm-specific technological competence. In addition, the model shows that other things being equal, the stock of exogenous technological knowledge, including the firm's previously accumulated technological knowledge, relevant to current R&D which is negatively related with current R&D effort. An empirical analysis of firm R&D intensities and technological capabilities of more than 1600 firms in nine industries across six countries provides supportive evidence for the theory. Further, the theory implies that R&D intensity or the R&D-to-sales ratio is independent of firm size unless firm size affects technological competence and that given consumer preference and R&D cost structure facing all firms in the same industry, the distribution of firm-specific technological competence among firms determines the distribution of firm R&D intensities within the industry.     

Patent portfolios and knowledge flow(s) of photovoltaic companies

The current trend towards green energy is encouraging manufacturers to invest in photovoltaic technologies. In order to guarantee R&D and optimal operational performance, operators should be able to identify the technological advantages of their competitors for the conduct R&D and to ensure adequate technological knowledge intake. Therefore, from the perspective of patent portfolios and knowledge flow(s) of photovoltaic companies, this study adopted two approaches namely, patent counts and patent citations, to discuss the technical capability of R&D portfolios and the technological knowledge flow. Three patent indices were utilised for the integration of R&D portfolios which include: technology attractiveness, relative patent position, and revealed patent advantage. Technological knowledge flow allows construction of a patent citation network through backward citation of patents. Sources and movement directions of technological knowledge are measured by calculating the relative citation propensity. R&D portfolios and knowledge flow are complementary perspectives of each photovoltaic company.     

Converging scientific fields and new technological paradigms as main drivers of the division of scientific labour in drug discovery process: the effects on strategic management of the R&D corporate change

The thesis of this study is that the convergence of genetics, genomics and proteomics spurs new technological paradigms in medicine, which are generating a R&D corporate change: division of scientific labour of the drug discovery process by strategic alliances among firms in order to reinforce the integrative capabilities in different biomedical research fields and collective and cumulative learning between in-house R&D and external sources of innovation. This study shows, by key a case study of pharmaceutical companies, as scientific and technological paradigms in medicine are main drivers of industrial and R&D corporate change to enhance and accelerate the discovery process of ground-breaking drugs for more and more personalised healthcare.     

A model of technological breakthrough in the renewable energy sector

Models with induced technological change in the energy sector often predict a gradual expansion of renewable energies, and a substantial share of fossil fuels remaining in the energy mix through the end of our century. However, there are historical examples where new products or technologies expanded rapidly and achieved a high output in a relatively short period of time. This paper explores the possibility of a ‘technological breakthrough’ in the renewable energy sector, using a partial equilibrium model of energy generation with endogenous R&D. Our results indicate, that due to increasing returns-to-scale, a multiplicity of equilibria can arise. In the model, two stable states can coexist, one characterized by a lower and one by higher supply of renewable energy. The transition from the low-output to the high-output equilibrium is characterized by a discontinuous rise in R&D activity and capacity investments in the renewable energy sector. The transition can be triggered by a rise in world energy demand, by a drop in the supply of fossil fuels, or by policy intervention. Under market conditions, the transition occurs later than in the social optimum. Hence, we identify a market failure related to path-dependence and technological lock-in, that can justify a strong policy intervention initially. Paradoxically, well-intended energy-saving policies can actually lead to higher emissions, as they reduce the incentives to invest in renewable energies by having a cushioning effect on the energy price. Hence, these policies should be supplemented by other instruments that restore the incentives to invest in renewable energies. Finally, we discuss the influence of monopoly power in the market for innovations. We show that market power can alleviate the problem of technological lock-in, but creates a new market failure that reduces static efficiency.     

后发企业技术创新的演化动力机制研究-以华为早期(1988-1995年)研发为例

以演化经济学和系统动力学视角,综合运用文献分析和访谈等研究方法,对华为早期技术研发进行系统性分析,建立后发企业技术创新及其演化的系统动力模型。研究发现：新产品开发是资本增长的动力,技术创新是产品开发的动力,人才和研发组织是技术创新的动力,同时人才也是组织结构优化的动力,而发挥人才价值的关键是人才激励;市场既为后发企业提供不断进化的市场动力,也传递先发企业和后发企业的竞争压力,促使后发企业不断产品开发和集成创新,供应商为后发企业快速进入市场并对关键技术进行模块化创新提供动力;企业家和研发组织把外部动力和压力转化为内部动力,使内部动力及进化选择机制与外部市场竞争协调一致。     

Technological districts localized spillovers and productivity growth. The Italian evidence on technological externalities in the core regions

Technological R&D externalities are the effects on the technological capacity of each firm stemming from the complementary and interrelated activities of R&D activities of other firms that operate both in the same industry and in other industries. R&D technological externalities are specially influential at the regional level. Regional proximity enhances the circulation of information, the opportunities for external learning; the scope for capitalizing on potential complementarities among the variety of firms and the different R&D activities being carried out by each firm, and the opportunity for technological networking. The empirical evidence on core regions in Italy in the 1980s confirms that regional clustering of complementary and interrelated R&D activities facilitated the emergence of technological districts. Firms located within technological districts benefited enormously from the R&D technological externalities spilling out from the complementary and interrelated R&D activities of other firms localized in the same area. Consequently, firms localized within technological districts had fast rates of introduction of technological innovations which, in turn, made it possible for total factor productivity levels to be raised with comparatively low levels of intra-muros R&D expenses.     

Is Embodied Technology the Result of Upstream R&D? Industry-Level Evidence

This paper provides an exploratory analysis of whether data on the research and development (R&D) spending directed at particular technological/product fields can be used to measure industry-level capital-embodied technological change. Evidence from the patent literature suggests that the R&D directed at a product, as the main input into the “innovation” production function, is proportional to the value of the innovations in that product. I confirm this hypothesis by showing that the decline in the relative price of a good is positively correlated with the R&D directed at that product. The hypothesis implies that the technological change, or innovation, embodied in an industry's capital is proportional to the R&D that is done (“upstream”) by the economy as a whole on each of the capital goods that a (“downstream”) industry purchases. Using R&D data from the National Science Foundation, I construct measures of capital-embodied R&D. I find they have a strong effect on conventionally measured total-factor productivity growth, a phenomenon that seems to be due partly to the mismeasurement of quality change in the capital stock and partly to a positive correlation between embodied and disembodied technological change. Finally, I find the cross-industry variation in empirical estimates of embodied technological change accord with the cross-industry variation in embodied R&D. Journal of Economic Literature Classification Number: O3.     

Green taxes and uncertain timing of technological change

This paper concerns the role of environmental taxation in a model with endogenous technological change, where the latter implies that natural inputs become more productive. The timing of technological change is, in turn, uncertain and the likelihood of discovering the new technology is related to the amount of resources spent on R&D. The analysis is based on a dynamic general equilibrium model. One purpose of the paper is to design a policy so as to internalize the external effects arising from pollution and R&D. Another is to develop cost benefit rules for green tax reforms, when the initial equilibrium is suboptimal.     

Political construction and control of technology: wave-power renewable energy technologies

Recognizing that gouernment-sponsored research, development and demonstration (R,D&D) is a rentral factor in the construction of new technologies, this paper analyzes selected aspects of the relationships between government officials, ‘private’ businesses and technologists, within the process of technolog development. Building on precious zoork in the social construction of technology tradition for example, of Hughes, Pinch and Biker) and in technology policy and decision-making theoy (for example, of Collingridge and Morone and Woodhouse), and using the case of wave-power R&D in the UK; the paper identifies characteristics of embryonic technological artefacts and their institutional contexts which tend to bias the socio-political construction process. The key to the paper is the extent to which such distortions lead to unwise, uneconomical and/or unfair innovations or hinder learning about seemingly inflexible technologies, which could help to reduce their complexity, scale or cost.     

Ready-steady-go for emerging technologies in post catch-up countries: a longitudinal network analysis of nanotech in Korea

This paper presents a case study of government R&D nanotech networks in Korea to elaborate the characteristics of post catch-up countries on the border line between developing and developed countries. Using to longitudinal network analysis, this paper examines expectation building and learning process as key components for the emerging technology development during the post catch-up era. Findings suggest that a change of the global R&D landscape sparks the development of emerging technologies by expectation building and resource attraction for technology development. Prior to this spark, however, an internal learning mechanism is ready for the development process.     

Optimal Timing of Climate Change Policy: Interaction Between Carbon Taxes and Innovation Externalities

This paper addresses the impact of endogenous technology through research and development (R&D) on the timing of climate change policy. We develop a model with a stock pollutant (carbon dioxide) and abatement technological change through R&D, and we use the model to study the interaction between carbon taxes and innovation externalities. Our analysis shows that the timing of optimal emission reduction policy strongly depends on the set of policy instruments available. When climate-specific R&D targeting instruments are available, policy has to use these to step up early innovation. When these instruments are not available, policy has to steer innovation through creating demand for emission saving technologies. That is, carbon taxes should be high compared to the Pigouvian levels when the abatement industry is developing. Finally, we calibrate the model in order to explore the magnitude of the theoretical findings within the context of climate change policy.