R&D and aggregate fluctuations

Empirical observations raise interesting questions regarding the sources of the excessive volatility in the R&D sector as well as the nature of the relation between the sector and aggregate fluctuations. Using US data for the period 1959–2007, we identify sectoral technology and capital investment-specific shocks by employing a Vector Autoregression. The identifying assumptions are motivated by a two-sector dynamic general equilibrium model. Controlling for real and nominal factors, we find that capital investment-specific shocks explain 70 percent of fluctuations of R&D investment, while R&D technology shocks explain 30 percent of the variation of aggregate output, net of R&D investment. Technology shocks jointly explain almost all the variation of output in the R&D sector and 78 percent of the variation of output in the rest of the economy. They also constitute the main factor of the procyclicality of R&D investment.     

Impact of R&D and Technology Diffusion on Productivity Growth: Empirical Evidence for 10 OECD Countries

This paper examines the empirical evidence on the impact of performed R&D and of R&D embodied in intermediate and capital goods on productivity performance in 10 major OECD countries over the last two decades. To quantify intersectoral and international technology flows, industry-level embodied R&D variables were constructed from an input–output (IO) R&D embodiment model. The productivity variables used are discrete Divisia growth indexes of total factor productivity (TFP), which were estimated from an IO growth accounting model. The results from pooled regressions indicate that the rates of return of the R&D variables were positively significant and increasing in the 1980s. In particular, embodied R&D is an important source for TFP growth in services, indicating very high social returns of the flows of capital-embodied technology into this sector. Moreover, the information and communi-cation technology (ICT) cluster of industries played a major role in the generation and cquisition of new technologies at the international level.     

Channels of R&D spillovers: An empirical investigation of Swiss firms

Research and development (R&D) spillovers are, potentially, a major source of endogenous growth in various recent ‘new growth theory’ models. According to these models, firms engage in R&D in order to seek rents. Public aspects of this R&D then spill over to other firms, thereby creating increasing returns relating to scale and long-term growth. The actual mechanisms by which spillovers occur has, however, not been systematically studied. In order to provide empirical facts on this issue the paper investigates the effectiveness of various channels of R&D spillovers. The analysis is based on a survey conducted among 358 Swiss R&D executives representing 127 different lines of business, mainly in the manufacturing sector. The results can be summarized as follows. First, undertaking independent R&D was perceived by the R&D executives questioned as the most effective channel of R&D spillovers at the intra-industry level. This was followed by reverse engineering for product innovations and the utilization of publications and information from technical meetings for process innovations. Second, learning methods that rely on interpersonal communication were judged as moderately effective in the following order of importance: (i) publications and technical meetings; (ii) conversations with employees from innovating firms; and (iii) hiring away employees from innovating firms. The last method, especially, is not valued as effective in the Swiss context. Third, learning methods related to the patent system—licensing technology and patent disclosures in the patent office—were seen as moderately effective or not effective at all. Fourth, the effectiveness of the various channels of R&D spillovers varies from one industry to another. Fifth, the various channels of R&D spillovers can be reduced to subgroups, so that patterns of learning of competitive technology can be established.     

Spillovers,Linkages and Technical Change

Using US input–output data for the period 1958–87, I find strong evidence that industry total factor productivity (TFP) growth is significantly related to the TFP performance of the supplying sectors, with an elasticity of almost 60%. R&D intensity is also found to be a significant determinant of industry TFP growth, with an estimated return of about 10–13% and the return to embodied R&D is estimated at 43%. Direct productivity spillovers, from the technological progress made by supplying sectors, appear to be more important than spillovers from the R&D performed by suppliers. They also play a key role in explaining changes in manufacturing TFP growth over time. Changes in the contribution made by direct productivity spillovers to TFP growth account for almost half of the slowdown in TFP growth in manufacturing from 1958–67 to 1967–77, and for 20% of the TFP growth recovery in this sector from 1967–77 to 1977–87. Changes in R&D intensity and embodied R&D are relatively unimportant in explaining movements in manufacturing TFP growth over these three periods.     

How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level

Although significant progress has been made in China's basic research in recent years, there remains a wide gap between research in China and that from developed countries. How to optimize the allocative efficiency of research resources is of great importance for increasing research output. In this paper, using the fixed effect stochastic frontier model based on the translog production function, we estimate output and substitution elasticities of research and development (R&D) inputs at universities in China's provincial level during 2009–2016. We find that the R&D technical efficiency of China's universities, after a rapid growth, has tended to become relatively stable. Improvements of internationalization degree and exogenous R&D capabilities are conducive to promoting R&D technical efficiency, whereas expenditures from government grants inhibit the promotion of R&D technical efficiency; the effects of R&D capital deepening and internet penetration are not evident. The output elasticity of R&D capital is much higher than that of R&D personnel, suggesting that R&D capital is the main driving force of research output. The substitution elasticity between R&D capital and personnel has experienced a change from substitution to complementary since 2014. To realize sustained growth of research output, we should increase R&D input with positive output elasticity or reduce R&D input with negative output elasticity, making the necessary trade-offs according to the substitution relationship between the two R&D inputs.     

R&D policy in a volatile economy

The literature on R&D-based growth establishes that market equilibrium is inefficient and derives optimal R&D policy. Normative analyses of this type use the assumption of steady state, largely motivated by analytical convenience. This paper questions this steady-state approach by introducing endogenous cycles as long-run equilibria. We show that the government fails to maximize welfare if policy which is optimal in steady state is myopically applied in cyclical equilibria. More specifically, we demonstrate that (i) cycles arise in the (very) standard R&D-based model of Grossman and Helpman [1991. Innovation and Growth in the Global Economy. MIT Press, Cambridge, MA (Chapter 3)] once the model is framed in discrete time, (ii) these cycles are inefficient in the sense that they prevent welfare maximization, (iii) optimal steady-state R&D policy fails to eliminate cycles, and can even create inefficient cycles, (iv) the application of R&D subsidies leads to a trade-off between growth and macroeconomic stability, and (v) optimal R&D policy in a fluctuating economy is state-dependent, which generalizes optimal steady-state R&D policy.     

Intellectual Capital Profiles: An Examination of Investments and Returns*

ABSTRACT Using data collected from executives in 208 organizations, this study takes a configurational approach to examine how human, social, and organizational capital coexist to form distinct intellectual capital profiles across organizations. We then examine how investments in human resource management (HRM), information technology (IT), and research and development (R&D) differ across these intellectual capital profiles and investigate differences in financial returns and Tobin's q between the profiles. Results indicate that a relatively small group of superior performing organizations exhibit high levels of human, social, and organizational capital. Most firms, however, tend to focus primarily on only one form of intellectual capital, and a small group of underperforming organizations have very low levels of all three types of intellectual capital. At a general level, HRM and IT investments appear to influence intellectual capital development more than R&D investments. More specifically, HRM investments tend to be higher in firms with profiles high in human and social capital, while IT investments are stronger in firms with profiles high in social capital. Further, HRM, IT, and R&D investments are all very high in the group of superior performing organizations that have high levels of human, social, and organizational capital.     

Managing knowledge: changing ways of wealth creation

R&D will have to be at the core of enterprises which expect to prosper in the new economy. Investors now realise there is a link between R&D investment and long-term growth, so share prices are boosted for companies which know how to extract value from R&D spending. The IT and pharmaceutical industries are probably the leaders in new ways of managing knowledge more effectively.     

From R&D to Productivity Growth: Do the Institutional Settings and the Source of Funds of R&D Matter?

This paper presents estimates of the long-term impact of various sources of knowledge (R&D performed by the business sector, the public sector and foreign firms) on multifactor productivity growth of 16 countries from 1980 to 1998. The main results show that the three sources of knowledge are significant determinants of long-term productivity growth. Further evidence suggests that several factors determine the extent to which each source of knowledge contributes to productivity growth. These factors are the absorptive capability, the origin of funding, the socioeconomic objectives of government support, and the type of public institutions that perform R&D.     

Interindustry Technology Flows and Productivity in Finnish Manufacturing

The paper examines the importance of interindustry technology flows in finnish manufacturing in the 1980s and early 1990s. An attempt is made to distinguish between embodied technology flows and spillovers, so clarifying the spillover concept.Embodied technology covers intermediate goods and capital equipment. The embodied technology data that have been used are partly based on input—output analysis, while the spillover estimates presented are based on measures of technological distance based on the industry-specific distributions of R&D expenditures. Econometric analysis of the effects of the various technology inputs on total factor productivity implies that technology embodied in foreign machinery, domestic spillovers and, to some extent, the firms' own R&D have been the most important technology sources on average.     

A model of intersectoral flow of technology using technology and innovation flow matrices

This paper builds a simple general equilibrium model that sheds new light on the mechanism of intersectoral flows of technology. It explicitly models the production of technology using diverse technology components as inputs. The model shows that demand shocks do not cause innovation while technology shocks as deviations from a balanced growth path induce asymmetric productivity changes across sectors. We also conduct a simple quantitative analysis using recent Japanese R&D data, which shows that most productivity effects remain within the bounds of the sector. We find some important exceptions to this rule, however, in particular for shocks occurring in information technology and precision instruments.     

中国行业层面研究与试验发展资本存量核算

研究目标：测算中国八大行业门类研究与试验发展（Research and Development,R&D）资本存量。研究方法：基于SNA2008的GDP核算框架,厘清R&D支出的资本化核算框架;再利用美国BEA方法测算R&D资本存量。研究发现：1990~2015年,中国R&D资本存量不到美国R&D资本存量的1/4;然而,自1994年后,中国R&D资本存量年均增长率达24.79%,而美国仅为4.86%,中美两国R&D资本存量差距呈逐渐缩小趋势;制造业R&D资本存量占全国R&D资本存量的比例呈上升趋势,从1990年的17.41%增长到2015年的68.08%,这与中国加快制造业转型升级的事实相符。研究创新：给出完整的R&D支出资本化核算过程,首次测算中国八大行业门类的R&D资本存量。研究价值：为分析技术进步对经济增长的影响提供了行业层面R&D投资和资本存量数据序列。     

Intangible investment in people and productivity

Organizational activity, information and communication technology work, and research and development (R&D) can be classified as work that creates intangible capital. We measure the returns to these three types of labor input by accounting for differences in their productivity compared with other labor inputs using Finnish firm-level data from 1998 to 2008. We apply a novel idea to use hiring as one proxy for productivity and demand shocks. We find that organizational workers increase total factor productivity and improve the profitability of high-productivity firms. R&D workers account for a large share of intangible capital; however, the returns to R&D are low. Investments in organizational competence are more likely to result in more rapid productivity growth. Firms with performance-related pay or domestically owned firms with extensive foreign activities have been among the highest performers with respect to the use of organizational work.     

R&D strategy and Australian manufacturing industry: an empirical investigation of emphasis and effectivness

This research investigates the degree of emphasis placed by the Australian manufacturing industry on Research and Development (R&D) strategy as well as on other organisational strategies such as marketing, HRs (HR), technology, and operations at the functional level. The research extends further to investigate whether the emphasis on R&D strategy differs with contextual factors such as firm size, firm’s generic strategy, type of market, firm’s life cycle stage, etc. A mail survey was carried out to collect information from manufacturing firms across Australia. This was followed by an on-site interview of some of the senior managers of manufacturing firms from three Australian states: South Australia, Victoria and New South Wales.The study found that although the emphasis on R&D strategy has received a high priority in the past few years, it has not been as effective as the operations strategy and the technology strategy. The operations strategy has been accorded the highest importance in the past few years and this will continue in the future. The study also indicated that firms, which adopt a differentiation generic strategy, put more emphasis on R&D than the firms, which adopt cost leadership and focus generic strategies. The results did not indicate any evidence of a relationship between firm size and the R&D strategy although large firms are found to put more emphasis on operations and marketing strategies. Relatively higher emphasis was placed on the R&D strategy by businesses: at the growth stage; involved in the manufacture of consumer goods as opposed to industrial goods; involved in exports with a higher sales growth in export markets; and having a higher growth rate relative to their industry.     

Technology transfer, licensing and the speed of endogenous technological change

As licensing contracts usually require the licensee firm to purchase tied inputs from the seller of the technology, the licensee firm has an incentive to embark on an R&D program for development of substitutes for the tied inputs. This paper characterizes the licensee firm's optimal speed of R&D and the optimal expected date of switch-over to the substitute inputs in an infinite-horizon model under uncertainty about the timing of success of the R&D program. The effects of different government subsidies or taxes, changes in input prices and other parameters on the optimal speed of R&D and on the expected date of switchover to the substitute are also investigated. It is also shown that strategic behavior by the seller of the technology may delay the development of the substitute by the licensee firm.     

Convergence to symmetry in dynamic strategic models of R&D: The undiscounted case

Will R&D increase or decrease the asymmetry between firms over time? We examine this issue in the context of a dynamic, alternate-move duopoly model of non-cooperative R&D. The asymmetry we consider is with respect to the initial stocks of technological knowledge which provides one firm a greater potential for current and future profits in the product market. Utilizing a value loss process constructed from the Optimality Equation of dynamic programming, we show that for the undiscounted case the asymmetry between the firms disappears over time. We obtain this convergence-to-symmetry result by imparting a temporal character to R&D through the notion that R&D investment cannot be changed instantaneously, by allowing research externalities, complementarity or substitutability between own and appropriated R&D, and either increasing, decreasing or constant returns to scale in the production of technical knowledge from own and rival R&D.     

Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective—A case study of Taiwan

Government supported technological research and development can help the private sector to compete globally. A more accurate evaluation system considering multi-factor performance is highly desired. This study offers an alternative perspective and characterization of the performance of Technology Development Programs (TDPs) via a two-stage process that emphasizes research and development (R&D) and technology diffusion. This study shall employ a sequential data envelopment analysis (DEA) with a non-parametric statistical analysis to analyze differences in intellectual capital variables among various TDPs. The results reveal that R&D performance is better than technology diffusion performance for the TDPs. In addition, the “Mechanical, Mechatronic, and Transportation field” is more efficient than the other fields in both R&D and technology diffusion performance models. The findings of this study point to the importance of intellectual capital in achieving high levels of TDP efficiency. The potential applications and strengths of DEA and intellectual capital in assessing the performance of TDP are also highlighted.     

Firms' human capital,R&D and performance: a study on French and Swedish firms

This paper studies the effects of human and technological capital on productivity in a sample of large French and Swedish firms. While the role of technological capital as measured by R&D has been intensively investigated, almost no work has been done on the role of human capital as measured by firm-sponsored training and even less its interaction with technological capital. The level of intangible capital may also have a lasting effect on productivity growth, as emphasised by some endogenous growth models in a macroeconomic setting.The study uses data from two panels of large French and Swedish firms for the same period (1987–1993). It constructs measures of a firm's human capital stock, based on their past and present training expenditures. The results confirm that firm-sponsored training and R&D are significant inputs in the two countries, although to a different extent, and have high returns. However, except for managers and engineers in France, we do not find evidence of positive interactions between these two types of capital. Finally, growth effects at the firm level do not appear.     

Informal thinkering — how is it important?: Case studies of technical change processes: an industrialized–industrializing country comparison

This paper examines innovation processes or technical change-generation processes at the firm level. It emphasizes the notion that the element of technical and technological change that is a determinant of productivity growth is strongly induced by internal factors related to the firm's behavior, via investment in R&D but also, and importantly, on the basis of “informal thinkering” within plants.This research is unique in that it seeks first, to examine the pattern of technical change and its ensuing productivity growth, and second, to make a comparison between an industrialized and an industrializing country. This comparison is conducted by empirically comparing mills in the pulp and paper manufacturing sector in two countries: Canada and India. The overall analysis is based on direct observations at the mill level, face-to-face interviews and on analysis of mill documentary records.This paper highlights the importance of managing change as a continuous process. The introduction of intermittent, “step-jump” technical changes contributed little to explain the differences in performance between the mills. Much more important are (i) the continuing sequences of smaller investments in technical change, often linked to the larger step-jumps, and (ii) the existence of steady streams of changes and improvements that are unrecorded as capital expenditure.     

Resonant R&D structure for effective technology development amidst megacompetition—an empirical analysis of smart cooperative R&D structure in Japan’s transport machinery industry

Under the megacompetition in globalizing economy while facing long lasting economic stagnation, the effective utilization of potential resources for innovation has become a crucial strategy for R&D intensive industries. The construction of a smart cooperative R&D structure has thus become significant. Among Japan’s R&D intensive industries, the transport machinery industry has constructed an effective cooperative R&D structure by smartly complementing both comparatively advantageous and disadvantageous technologies by means of integrating the effective utilization of technology spillover and joint collaborative R&D.Prompted by this complementing system, this paper attempts to elucidate the mechanism enabling the transport machinery industry to construct the smart cooperative R&D structure. Resonant R&D structure is identified as a source of such R&D cooperation leading to increasing its marginal productivity of technology.