Pollution abatement with disruptive R&D investment

This paper examines a model of investment in abatement where polluting firms produce output while investing in R&D. This investment, however, increases production costs, thus disrupting first-period output. We identify three equilibrium profiles where firms choose to either: (1) invest in R&D alone (thus rationalizing a common modeling assumption in the literature); (2) produce output alone; or (3) engage in both activities. We evaluate how the emergence of each result is affected by the market structure in which firms compete and by the severity of spillover effects. We then measure welfare levels in each equilibrium profile. Overall, we show that firms endogenously choose to focus on R&D only when the market is concentrated and spillover effects are small. In other type of industries, our findings indicate that firms may focus on output production or engage in both activities under relatively large conditions.     

Detecting which firm-specific characteristics impact market-oriented R&D

This paper investigates which firm-specific characteristics lead multinational enterprises (MNEs) to increase their R&D expenditure in host countries as a way of expanding their business into local markets (market-oriented R&D). The literature that addresses this topic is quite limited because of the difficulty of separating market-oriented R&D expenditure from knowledge-sourcing R&D expenditure in the data. We argue that determining the relationship between local sales and R&D expenditure is a better way to identify whether MNEs are investing market-oriented R&D than is separating the two types of R&D. For connecting firm-specific characteristics, local-market sales, and R&D expenditure together, we adapt two-stage regressions. By employing data from Taiwanese multinationals from 2003 to 2006, we found that if an MNE moves its technology toward capital-intensive products, it increases its R&D expenditure to promote its sales in the local markets in the host country.     

Identifying R&D partners for dye-sensitized solar cells: a multi-level patent portfolio-based approach

There is a great and urgent necessity for appropriate data and methods to help organisations select appropriate R&D (Research and Development) partners. In response, this study presents a structured framework for identifying and selecting the R&D partners with the greatest potential to meet a given technological need. The formal methods in the framework include systematic information collection, patent portfolio analysis, technological similarity screening, and semantic evaluation that views subject-action-object (SAO) sentence structures as problem-solution patterns. The results of an empirical evaluation, with dye-sensitive solar cells as the case, demonstrate that using this framework can reveal more appropriate R&D partners for technological needs (TNS) in less time and with less cost than methods based solely on one criterion.     

On `indirect' trade-related R&D spillovers

An influential literature argues that trade promotes knowledge flows and technology transmission between trading partners. This literature focuses on `direct' research and development (R&D) spillovers which are related to the levels of R&D produced by the trading partners. In this paper, we argue that `indirect' trade-related R&D spillovers also take place between countries, even if they do not trade with each other. These `indirect' spillovers are associated with available rather than with produced levels of R&D. Our empirical results suggest that these `indirect' trade-related spillovers are at least as important as the `direct' ones, and strengthen the view that trade does matter for the international transmission of R&D. They also suggest that, due to the existence of these `indirect' effects, bilateral trade patterns are relatively less important determinants of the level of foreign R&D spillovers acquired through trade.     

R&D spillovers and employment: evidence from European patent data

In this paper we investigate the role of patents in the relationship between R&D activity, spillovers and employment at the firm level. A reduced-form labour demand equation is estimated. Our analysis is based upon a dataset consisting of 879 R&D-intensive manufacturing firms worldwide for which information was collected for the period 2002–2010. We use data from all EU R&D investment scoreboard editions issued every year until 2011 by the JRC-IPTS (scoreboards). Since the innovation output of the industrial strategy of every firm is the number of patents, the main contribution to the existing literature is to investigate also the impact of patents/R&D ratio and patents/spillovers ratio on employment level. The empirical results suggest a significant impact of R&D spillover effects on company employment although the results differ substantially according to the spillover stock, which may considerably affect policy implications.     

Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

The aim of the present research is to provide a new systematic methodology to explore potential Research and Development (R&D) collaboration partners using patent information. The potential R&D collaboration partners are visualised as a patent assignee level map based on technological similarity between patents by using the network analysis. The proposed framework utilises two analytic methods to measure technological similarity. The first method, bibliographic coupling analysis, measures technological similarity based on the citation relationship using patent bibliographic information. Second, latent semantic analysis is utilised based on semantic similarity using patent textual information. The fuel cell membrane electrode assembly technology field is selected and applied to illustrate the proposed methodology. The proposed approach allows firms, universities, research institutes and governments to identify potential R&D collaborators as a systematic decision-making support tool.     

An Empirical Analysis of R&D Transactions: Surrogate Technological Networks and Firm Performance

This paper uses statistical data from the annual R&D survey of financial flows received or spent by the firm for R&D activities. To describe R&D networks, attention is focused on the construction of four synthetic proxies from empirical literature on social networks: variety of partners, intensity, regularity of links, and centrality in networks. On the bases of the two available innovation surveys, we explore, through a cross section study, the impact of these different variables on technological performance of firms.     

Productivity in China's high technology industry: Regional heterogeneity and R&D

This paper analyzes the impact of Research and Development (R&D) on the productivity of China's high technology industry. In order to capture important differences in the effect of R&D on output that arise from geographic and socioeconomic differences across three major regions in China, we use a novel semiparametric approach that allows us to model heterogeneities across provinces and time. Using a unique provincial level panel dataset spanning the period 2000–2007, we find that the impact of R&D on output varies substantially in terms of magnitude and significance across different regions. Results show that the eastern region benefits the most from R&D investments, however it benefits the least from technical progress, while the western region benefits the least from R&D investments, but enjoys the highest benefits from technical progress. The central region benefits from R&D investments more than the western region and benefits from technical progress more than the eastern region. Our results suggest that R&D investments would significantly increase output in both the eastern and central regions, however technical progress in the central region may further compound the effects of R&D on output within the region.     

Multinationals,R&D and Endogenous Productivity Asymmetries

We analyze the influence of endogenous productivity asymmetries between firms, in terms of competitiveness and size, on multinational activity. In the model, productivity depends on cost-reducing R&D (research and development). We show that when firms differ on commitment power in R&D, the R&D leader, independently of being a multinational or a domestic firm, tends to invest more in R&D than the R&D follower. Because of these productivity advantages, the R&D leader can more easily become multinational. Therefore, in addition to the proximity-concentration trade-off, we identify another FDI (foreign direct investment) determinant: technological competition.     

An investigation over inbound open innovation in SMEs: insights from an Italian manufacturing sample

ABSTRACT

Building on the Open Innovation (OI) framework, the purpose of this paper is to examine the R&D inbound model of small and medium enterprises (SMEs). Specifically, we focus on the impact of different horizontal R&D collaborations on product innovation and innovation performance. Hypotheses are tested using a Probit/Tobit regression on an Italian sample of 2591 manufacturing SMEs. Our analysis shows that collaborating with different horizontal R&D partners brings to different innovation outcomes. In particular, R&D collaboration with universities has a positive impact on product innovation, but not on innovation performance. Whereas, R&D collaboration with research centres and other private companies has a positive impact on both product innovation and innovation performance. Our findings provide implications for SMEs managers and entrepreneurs who have to decide between R&D partners for their innovation strategy. Avenues for future research are discussed.     

Tax policy endogeneity: evidence from R&D tax credits

This paper estimates the causal effect of research and development (R&D) tax incentives on R&D expenditures using new data on U.S. states. Identifying tax variation comes from changes in federal corporate tax laws that heterogeneously and, due to the simultaneity of state and federal corporate taxes, automatically affect state-level tax laws. Instrumental variables regressions indicate that a 1% increase in R&D tax incentives causes a statistically significant 2.8–3.8% increase in R&D. Alternatively, ordinary least squares (OLS) regressions of R&D expenditures on R&D tax incentives, which do not correct for the policy endogeneity of R&D tax incentives, indicate that a 1% increase in R&D tax incentives causes a statistically insignificant 0.4–0.7% increase in R&D. One possible explanation for these results is that tax policies are implemented before an economic downturn.     

Does partner type matter in R&D collaboration for product innovation?

Most firms tend to utilise various types of R&D collaboration partners simultaneously and partnerships between different types of partners show different properties. Thus, the effect of R&D collaboration may vary depending on partner types. This study considers four partner types: competitors, customers, suppliers and universities. It empirically examines the effect of R&D collaboration with each type of partner on product innovation, employing the Korean Innovation Survey data. Results show that R&D collaborations with customers and universities have a positive effect on product innovation, whereas R&D collaborations with suppliers and competitors have an inverted-U shape relationship with product innovation. This result can provide an explanation to the chaotic results of previous research and assist managers in selecting appropriate R&D partner.     

On the impact of innovation and inequality in economic growth

We present robust results on the empirical relationship among income inequality, innovation, and economic growth for a panel dataset of 74 countries over the period 1996–2014. We estimate pairwise causality tests to show that there is bidirectional causality between GDP per capita and R&D, while R&D causes the Gini index of income inequality, and it causes human capital. Allowing coefficients to be different across cross-sections of countries, we get in any case a pairwise bi-directionality. By dynamic panel data estimations, when regressing R&D on GDP per capita, we obtain a threshold value of 0.16% of R&D such that for values above it there is economic growth. While regressing R&D on the Gini index, we get a threshold of 0.10% of R&D above which, the income distribution begins to improve. Finally, we estimate a growth equation that depends on R&D, income inequality, and physical capital. We obtain two thresholds, one of 38.79 for the Gini (above which the economic growth decreases), and one of 0.06% for R&D such that above it, economic growth is rising.     

The productivity effects of local R&D outsourcing: the moderating role of subsidiary mandate and internal R&D

This study examined the moderating effects of two kinds of internal R&D (parent R&D and subsidiary R&D) on the relationship between local R&D outsourcing and subsidiary productivity as internal R&D challenges the collaborative relationships in the local environment. Due to the specific feature of a subsidiary mandate, we further investigate whether two distinct kinds of internal R&D have different moderating effects between exploitation-oriented subsidiaries and exploration-oriented subsidiaries, based on a sample of longitudinal data set of 263 Taiwan-based manufacturing subsidiaries over the period 2006–2009. The empirical results show that for exploitation-oriented subsidiaries parent R&D plays a significant and negative moderating role on the impact of local R&D outsourcing based on local collaboration, while a positive moderating role is observed with parent R&D and subsidiary R&D for exploration-oriented subsidiaries.     

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.     

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.     

A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies

The planning of technological research and development (R&D) is demanding in areas with many relationships between technologies. To support decision makers of a government organization with R&D planning in these areas, a methodology to make the technology impact more transparent is introduced. The method shows current technology impact and impact trends from the R&D of an organization's competitors and compares these to the technology impact and impact trends from the organization's own R&D. This way, relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs can be identified.A quantitative cross impact analysis (CIA) approach is used to estimate the impact across technologies. Our quantitative CIA approach contrasts to standard qualitative CIA approaches that estimate technology impact by means of literature surveys and expert interviews. In this paper, the impact is computed based on the R&D information regarding the respective organization on one hand, and based on patent data representative regarding R&D information of the organization's competitors on the other hand. As an illustration, the application field ‘defence’ is used, where many interrelations and interdependencies between defence-based technologies occur. Firstly, an R&D-based and patent-based Compared Cross Impact (CCI) among technologies is computed. Secondly, characteristics of the CCI are identified. Thirdly, the CCI data is presented as a network to show the overall structure and the complex relationships between the technologies. Finally, changes of the CCI are analyzed over time. The results show that the proposed methodology has the potential to generate useful insights for government organizations to help direct technology investments.     

Driving forces for research and development strategies: an empirical analysis based on firm-level panel data

This paper investigates empirically different ways to organize research and development (R&D) within Swiss firms. Based on a longitudinal data set comprising three cross-sections (1999, 2002, and 2005) of the Swiss Innovation Survey, four different types of R&D strategies could have been separated; firms combine in-house R&D with R&D co-operations (coop) or in-house R&D with external R&D (buy), or they conduct in-house R&D, external R&D, and R&D co-operations (mixed), or they exclusively rely on in-house R&D (make). It is the aim of this paper to understand what drive firms to go for different strategies. Based on econometric estimations controlling for correlations between the dependent variables and endogeneity among the independent variables, it was found that concepts related to the absorptive capacity, incoming spillovers, and appropriability, the importance of different knowledge sources, the competitive environment, costs, and skill aspects as well as technological uncertainty are essential factors to determine a firm's decision to choose a specific way to organize R&D.     

Measuring universities’ R&D performance in China’s provinces: a multistage efficiency and effectiveness perspective

ABSTRACT

Evaluating universities’ R&D performance can serve as a tool for policymakers, enabling them to identify the best R&D practitioners for benchmarking and develop strategies for enhancing performance by highlighting areas of weakness. This study divides universities’ R&D performance into two dimensions: efficiency and effectiveness. We employ a network data envelopment analysis approach to measure universities’ R&D efficiency, and separately use a multi-index comprehensive evaluation method to assess their R&D effectiveness from a multistage perspective. We calculate the comprehensive R&D performance of universities in 30 provinces in China from 2004 to 2014. An analysis of temporal variation reveals that universities’ R&D performance in some provinces improved rapidly, increasing gaps in performance as a whole. The spatial pattern for the 30 provinces shows that universities in provinces with medium or strong R&D performance are mainly in coastal provinces and central areas in China. Finally, universities’ R&D activities are considered ‘efficient and effective’ only in a small number of provinces, while in most provinces, these activities can generally be considered ‘less efficient and effective.’ Hence, universities in provinces with different R&D performance modes should implement targeted improvement strategies according to their characteristics.     

Effect Of Liquidity On Firms' R&D Spending

The effect of firms financial condition on their R&D investment is explored using a relatively long panel data set for five high-technology industries. We find that financial condition, whether measured as cash flow, the stock of liquid assets or the ratio of liquid assets to current liabilities, does affect the R&D spending of small firms. The effect persists after controlling for unobserved permanent firm effects, and the pattern of significance of lagged effects supports the interpretation of causality running from liquidity to R&D. For larger firms, there is no evidence of such an effect. Using these data, we cannot say whether the absence of an effect in larger firms results from better access to capital markets or from higher adjustment costs in R&D.