R&D and productivity growth: Evidence from the UK

Although the econometric evaluation of R&D has attracted wide interest in many countries, it has not attracted much in the UK. The main objective of this paper is to fill this void, i.e., to estimate the impact of R&D on productivity growth of the UK manufacturing sector. However, there are some additional objectives. Firstly, we estimate the impact of R&D on productivity growth of large and small firms and we discuss a number of theoretical arguments regarding the role of firm size. Secondly, given that the technological infrastructure influences the innovative capacity of a firm, we compare the impact of R&D on productivity growth of high-tech firms with the corresponding impact on productivity growth of low-tech firms. Thirdly, we investigate whether the contribution of R&D to productivity growth has changed over time.

Based on firm-level data (78 firms, 1989–2002), we find that the contribution of R&D is approximately 0.04. Although the R&D-elasticity of large firms (0.044) is higher than the corresponding elasticity of small firms (0.035), the difference is small. In contrast, the R&D-elasticity is considerably high for high-tech sectors (0.11), but statistically insignificant for low-tech sectors. Finally, the investigation of the elasticity of R&D over time revealed an interesting discontinuity showing that although until 1995 the R&D-elasticity was approximately zero, after 1995 it increased dramatically to 0.09. We investigate the potential causes of such non-linearity and we suggest a number of possible explanations.     

Assessing measurement errors in the CDM research–innovation–productivity relationships

ABSTRACT

The Crépon-Duguet-Mairesse 1998 article, known as CDM, initiated a structural econometric framework to analyze the relationships among research, innovation and productivity, which has been estimated most generally on the basis of cross-sectional innovation survey-type data. Some econometric implementations of the CDM approach have suggested that such data give useful but imprecise measures of the innovation output (share of innovative sales), and to a lesser degree of the innovation input (R&D). These ‘measurement errors’ may result in attenuation biases of the estimated R&D and innovation impact elasticities in the two basic CDM ‘roots’ relations of R&D to innovation and innovation to productivity, as well as in the extended production function à la Griliches linking directly R&D to productivity. Using a panel of three waves of the French Community Innovation Survey (CIS), we assess these biases and the magnitude of the underlying measurement errors, assuming mainly that they are ‘white noise’ errors. We do so by comparing two pairs of usual panel estimators (Total and Between) in both the cross-sectional and time dimensions of the data (Levels and Differences). We find large measurement errors on innovation output in the innovation–productivity equation, resulting in large attenuation biases in the related elasticity parameter. We also find smaller but sizeable measurement errors on R&D, with significant attenuation biases in the corresponding elasticity estimates, in the R&D–innovation equation and the extended production function. Simulations suggest that the measurement errors on innovation and R&D are unaffected by similar measurement errors on the capital variable.     

How effective are level-based R&D tax credits? Evidence from the Netherlands

This article examines the impact of the R&D fiscal incentive programme on R&D by Dutch firms. Taking a factor demand approach, we measure the elasticity of firm R&D capital accumulation to its user cost. Econometric models are estimated using a rich unbalanced panel of firm data covering the period 1996 to 2004 with firm specific R&D user costs varying with tax incentives. Using the estimated user cost elasticity, we perform a cost–benefit analysis of the R&D incentive programme. We find some evidence of additionality suggesting that the level based programme of R&D incentives in the Netherlands is effective in stimulating firms’ investment in R&D. However, the hypothesis of crowding out can be rejected only for small firms. The analysis also indicates that the level based nature of the fiscal incentive scheme leads to a substantial social deadweight loss.     

E-commerce,R&D,and productivity: Firm-level evidence from Taiwan

This study investigates the impact of e-commerce and R&D on productivity, using a unique panel dataset obtained from Taiwanese manufacturing firms for the period from 1999 to 2002. We specifically consider the network externalities of e-commerce and employ the system generalized method of moment (GMM) technique to deal with the endogenous problem of e-commerce adoption. The empirical results show that both e-commerce and R&D capital have a positive influence on productivity, while R&D exhibits a larger productivity-enhancing effect. We also find a complementary relationship between e-commerce and R&D on enhancing productivity. Crucially, the inter-industry network externality of e-commerce significantly contributes to productivity.     

Evidence on the impact of R&D and ICT investments on innovation and productivity in Italian firms

Both research and development (R&D) and information and communication technology (ICT) investment have been identified as sources of relative innovation underperformance in Europe vis-à-vis the USA. In this article, we investigate the R&D and ICT investment at the firm level in an effort to assess their relative importance and to what extent they are complements or substitutes. We use data on a large unbalanced panel data sample of Italian manufacturing firms constructed from four consecutive waves of a survey of manufacturing firms, to estimate a version of the CDM model of R&D, innovation, and productivity [Crépon–Duguet–Mairesse 1998. Research, innovation and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology 7, no. 2: 115–58] that has been modified to include ICT investment and R&D as the two main inputs into innovation and productivity. We find that R&D and ICT are both strongly associated with innovation and productivity, with R&D being more important for innovation, and ICT investment being more important for productivity. For the median firm, rates of return to both investments are so high that they suggest considerably underinvestment in both these activities. We explore the possible complementarity between R&D and ICT in innovation and production, but find none, although we do find complementarity between R&D and worker skill in innovation.     

R&;D and productivity: using UK firm-level data to inform policy

The UK’s business R&D (BERD) to GDP ratio is low compared to other leading economies, and the ratio has declined over the 1990s. This paper uses data on 719 large UK firms to analyse the link between R&D and productivity during 1989–2000. The results indicate that UK returns to R&D are similar to returns in other leading economies and have been relatively stable over the 1990s. The analysis suggests that the low BERD to GDP ratio in the UK is unlikely to be due to direct financial or human capital constraints (as these imply finding relatively high rates of return).     

INTELLIGENT MACHINE TECHNOLOGY AND PRODUCTIVITY GROWTH

This paper provides preliminary estimates of the productivity impact of intelligent machine technology (IMT) and the rate of return to IMT research and development (R&D) over the next two decades. The paper adapts economists’ traditional productivity growth model to enable the use of industrial experts’ forecasts of a few key parameters of the model to form the estimates of productivity growth and rate of return. Respondents – from a sample of firms operating in IMT development and applications in the automotive, aerospace, and capital construction industries – anticipate that IMT will generate substantial productivity growth over the next two decades, and the estimated social rates of return to IMT R&D are substantial.     

R&D spillovers and productivity: Evidence from U.S. manufacturing microdata

This paper deals with the estimation of the impact of technology spillovers on productivity at the firm level. Panel data for American manufacturing firms on sales, physical capital inputs, employment and R&D investments are linked to R&D data by industry. The latter data are used to construct four different sets of `indirect' R&D stocks, representing technology obtained through spillovers. The differences between two distinct kinds of spillovers are stressed. Cointegration analysis is introduced into production function estimation. Spillovers are found to have significant positive effects on productivity, although their magnitudes differ between high-tech, medium-tech and low-tech firms. First version received: April 1997/final version received: April 1999     

R&;D and productivity: testing sectoral peculiarities using micro data

The aim of this study is to investigate the relationship between a firm’s R&D activities and its productivity using a unique micro data panel dataset and looking at sectoral peculiarities which may emerge; more specifically, we used an unbalanced longitudinal database consisting of 532 top European R&D investors over the 6-year period 2000–2005. Our main findings can be summarised along the following lines: knowledge stock has a significant positive impact on a firm’s productivity, with an overall elasticity of about 0.104; this general result is largely consistent with previous literature in terms of the sign, the significance and the estimated magnitude of the relevant coefficient. More interestingly, the coefficient increases monotonically when we move from the low-tech to the medium-high and high-tech sectors, ranging from a minimum of 0.03/0.05 to a maximum of 0.14/0.17. This outcome suggests that firms in high-tech sectors are still far ahead in terms of the impact on productivity of their R&D investments, at least as regards top European R&D investors.     

Research and development and labour productivity: do high-tech firms exhibit labour- or capital-saving technical change?

Employment and output in the advanced technology sectors have generally exhibited above-average growth for more than two decades. While this industry accounts for a relatively small share of total employment, the majority of private sector research and development (R&D) expenditures in the US is concentrated within seven sub-sectors. However, little attention has been paid as to whether high-tech productivity exhibits Hicksian capital or labour ‘savings’ bias or tendency to displace either factor input over time. Biased technical change can occur as economies transition between growth regimes. An augmented production function is employed to analyse the additional impact of R&D activity on firm-level labour productivity. A panel data set comprised of high-tech firms located across the advanced economies, China and India from 1990 to 2013 is used in the analysis. Labour-saving technical change was present across the advanced technology sectors and most countries. The expanded models of labour productivity that used fixed effects with lagged regressors confirmed the prior results as well as finding that R&D per employee, relative R&D intensity and firm market share contribute to firm-level labour productivity growth across countries and sectors. Additional support was found for diminishing returns to scale but not for R&D spillover effects.     

The missing link between innovation and performance in Brazilian firms: a panel data approach

This study aims to verify if there is a positive relationship between innovation and productivity and/or profit in Brazil using a recursive model in line with the Crépon, Duguet and Mairesse (CDM) model. Using a rich combination of three databases, this paper considers a sample of more than 10,000 Brazilian industrial firms and the period 2001–2008. Besides using R&D expenditure as a measure of innovation input, this study also tests the technical-scientific personnel stock as a more appropriate measure of innovative effort in emerging countries. This variable considers the tacit knowledge intrinsic to the worker and corrects a possible measurement error bias in the R&D expenditure variable. The empirical strategy uses a reduced form of the CDM model in a panel data structure to provide consistent estimates as it controls for selectivity, simultaneity and endogeneity due to unobserved firm effects. There are still few studies that apply the CDM model for panel data, especially regarding developing countries. The main results suggest that technical-scientific workers positively affect the firms’ probability to innovate while R&D expenditure has no effect. The results also highlight the absence of the effect of innovation on productivity and profit, suggesting a missing link between innovative efforts and Brazilian firms’ performance.     

Firms’ productivity growth and R&D spillovers: An analysis of alternative technological proximity measures

This paper aim at assessing the impact of R&D spillovers on firms’ economic performance as measured by productivity growth. The construction of R&D spillovers is based on Jaffe's methodology (1988, 1996) which associates econometrics and data analysis. The main objective of the paper is to extend Jaffe's methodology by examining alternative methods for measuring R&D spillovers and to test their impacts in terms of the robustness of results. In particular, the method used to classify firms into technological clusters as well as the metrics implemented to appreciate firms’ technological proximities which enter the construction of spillovers are further investigated. In addition to R&D spillovers, firms’ own R&D capital, labour and physical capital are estimated by means of a Cobb–Douglas production function. The data set consists of a representative sample of 625 worldwide R&D intensive firms over the period 1987–1994.     

R&D and productivity in Danish firms: some empirical evidence

The aim of the paper is to examine the relationship between R&D capital and productivity using microdata for Danish manufacturing firms. The influence of factors such as ownership, innovative characteristics and source of funding accounted for. The return to accumulated R&D capital is estimated to be in the neighbourhood of 9–12%, whereas the short-run effect of R&D is insignificant. Furthermore, the direct influence from foreign ownership, source of funding accounted for, innovative characteristics and ownership dispersion on productivity are analysed. However, none of the factors seem to have an impact on firm productivity. The same is the case for the indirect influence coming from interaction with accumulated R&D capital.     

R&D,productivity, and exports: Plant-level evidence from Indonesia

This study examines the relation between productivity and exports in Indonesian manufacturing firms by taking account the endogenous choice of R&D. We first examine the determinants of R&D activity and find that exporting activity contributes positively to plants' R&D activity, while multinational corporate do not have a higher R&D propensity. The simultaneous estimates on the interrelation of R&D, productivity, and export show that R&D has a positive impact on both productivity and exports, suggesting the importance of R&D to Indonesian economic growth. It suggests also a two-way causality between productivity and exports, implying the coexistence of self-selection and learning-by-exporting effects in Indonesian manufacturing sector.     

Technological knowledge,spillover and productivity: evidence from Taiwanese firm level panel data

Using a panel data on Taiwanese manufacturing firms from 1990–1997, this study investigates the relationship among technological knowledge, spillover and productivity. In addition to R&D stock, we also employ patent counts to construct the output-side indicators of knowledge and spillover to explore the relationship between knowledge and productivity. We find a very significant contribution of R&D, patents and spillover stock to productivity. In addition, the magnitude of the patent stock coefficient is substantially larger than that estimated by R&D stock. Our results imply that innovative activity investment has been very productive in increasing output for Taiwanese manufacturing firms in the 1990s.     

Innovative growth: the role of market power and negative selection

ABSTRACT

This paper relies on register-based statistical data from Finland to measure broad research and development (R&D), organizational capital (OC) and information and communication technology (ICT) investments as innovation inputs in addition to formal survey-based R&D and CIS survey data on innovations. The linked panel data are appropriate for a comparison of low-market-share (small) and large-market-share (large) firms. We analyze the productivity growth and profitability of Finnish firms with varying market power. In contrast to high-market-share firms, low-market-share firms are characterized by low profit derived from new innovations. This study suggests that in addition to imitative growth, a ‘negative selection mechanism’ explains the high productivity growth relative to the low profits.     

Does R&D spending boost tangible investment? An analysis on European firms

This article investigates the relationship between firm’s R&D intensity, expressed as R&D expenditure over sales, and investment intensity in tangible assets. It is commonly acknowledged that R&D requires additional physical investment to be implemented. R&D increases a firm’s productivity and return to tangible investments, thus, providing to the firm incentives to bear high tangible capital costs and to invest more. This represents a crucial issue for a firm’s growth, particularly considering the strong interaction between physical capital accumulation and technological progress. The analysis is based on a large sample of manufacturing firms across seven European countries in the period 2007–2009. Since the sub-sample of firms performing R&D might not be random, there may potentially be an endogeneity issue. The analysis also considers that firms may decide to spend on R&D and investment in physical capital simultaneously. The questions of both endogeneity and simultaneity are dealt with by employing an instrumental variable two-step procedure. We find a positive and significant impact of R&D intensity on firms’ tangible investment intensity. The econometric results highlight the importance of financial factors, particularly with respect to firms’ internal resources. Exposure to international trade has a negative impact on investment, possibly depending on the time-span of the sample used.

Abbreviations: Technological Innovation and R&D; Investment Capital; Industry Studies; Firm Behavior; Empirical Analysis     

Is Private R & D Spending Sensitive to Its Price? Empirical Evidence on Panel Data for Italy

In this paper empirical evidence is presented on theelasticity of private R & D spending on its price. Acensored panel-data regression model with random effectsis applied to a balanced panel of 726 Italian firms overthe 1992–1997 period. Implied estimates point out thatItalian firms' response to policy measures (including taxcredits), aimed at reducing the user cost of R & D capital,is likely to be substantial (1.50–1.77). Furthermore, wealso find that the elasticity of R & D spending is higherin recession (2.01) than in expansion (0.87).     

What is the optimal rate of R&D investment to maximize productivity growth?

Governments in modern economies devote much policy attention to enhancing productivity and continue to emphasize its drivers such as investment in R&D. This paper analyzes the relationship between productivity growth and levels of R&D investments. The econometric analysis shows that more than 65 per cent of productivity growth variance is due to its dependence on gross domestic expenditure on R&D expressed as percentage of GDP (GERD). Economic analysis shows that productivity growth = f(GERD) is a concave function downwards due to diminishing returns to research investments. In addition, the research shows that the range of GERD between 2.3 per cent and 2.6 per cent maximizes the long-run impact on productivity growth and it is the key to sustained productivity and technology improvements that are becoming more and more necessary to modern economic growth.     

Technological convergence, R&D, trade and productivity growth

This paper analyses productivity growth in a panel of 14 United Kingdom manufacturing industries since 1970. Innovation and technology transfer provide two potential sources of productivity growth for a country behind the technological frontier. We examine the roles played by research and development (R&D), international trade, and human capital in stimulating each source of productivity growth. Technology transfer is statistically significant and quantitatively important. While R&D raises rates of innovation, international trade enhances the speed of technology transfer. Human capital primarily affects output through private rates of return (captured in our index of labour quality) rather than measured TFP.