Skip to main content

Advertisement

SpringerLink
Log in

R&D spillovers in a supply chain and productivity performance in British firms

  • Published:
The Journal of Technology Transfer Aims and scope Submit manuscript

Abstract

It is well known that there are incentives for cooperation and collaboration along the supply chain, as the performance of any one firm is dependent on that of its suppliers. However, R&D by any firm or sector may affect the performance of other firms and sectors that it supplies irrespective of whether collaboration takes place or not, as reflected in endogenous growth models where positive spillovers play a major role. This paper studies the impact of R&D spillovers on productivity performance in British firms, focusing on spillovers in a supply chain. The results show that R&D spillovers along the supply chain has the largest positive and most significant impact on labour productivity, followed by own-sector spillovers, then by own-internal R&D and own purchases of external R&D. Moreover, R&D spillovers tend to stimulate firms’ R&D and innovation spending and these, in turn, increase labour productivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. WIOD provides time-series of world input–output tables for forty countries worldwide and a model for the rest-of-the-world, covering the period from 1995 to 2009. It also provides data on labour and capital inputs and pollution indicators at the industry level. (http://www.wiod.org/new_site/home.htm).

  2. Cambridge Econometrics is a leading independent consultancy specialising in applied economic modelling and data analysis techniques (http://www.camecon.com/Home.aspx).

  3. While the estimated coefficient of the downstream flow spillovers will be a mixture of impacts from both downward and upward spillovers, the situation is slightly more complex. The downstream flows are based on a weighted sum of suppliers to a buying sector, the upstream will generally be quite different, as it will be based on a weighted sum of the buyers for a supplying sector.

  4. Non-rivalry means one person’s use of knowledge does not prevent another’s use of it. Partial excludability indicates that the owner of knowledge cannot stop others to benefit from it free of charge or at a lower cost than the initial R&D investment (Cincera 2005, p. 659).

  5. ARD is a confidential dataset which is only accessible for approved researchers.

  6. Cambridge Econometrics is a leading independent consultancy specialising in applied economic modelling and data analysis techniques (http://www.camecon.com/Home.aspx).

  7. For detailed explanation of the construction of real capital stock, please refer to the article “Estimating Capital Stock at the Firm Level” in the ARD user guide (Office for National Statistics User Guide 6644, page 149).

  8. A detailed description can be found in ONS6644 user guide, page 149.

  9. Note that the reference category for the variables scieeng and othersub is the proportion of workers without a degree. scieeng and othersub are included to control the quality of labour inputs of a firm.

  10. ARD and UKIS are both confidential datasets and are only accessible subject to approval from the UK Secure Data Service.

  11. By the time of this research project was completed, WIOD data was only available up to 2009.

  12. Details of NACE Rev. 1 and other versions can be found on Eurostat at: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Statistical_classification_of_economic_activities_in_the_European_Community_%28NACE%29

  13. Agriculture, Hunting, Forestry and Fishing sector, Public Administration and Defence, Compulsory Social Security, Education, Health and Social Work sectors are not included due to their exclusion in UKIS.

  14. Minimum and maximum values are not allowed to be released by the Secure Data Service for data confidentiality reasons.

  15. A probit regression has also been conducted regressing product innovation on the other independent variables from Eq. (8). The four spillovers measures (R&D and education purchase) are not found to have any significant impact. Having more employees with a degree significantly increases the probability of product innovation. More expenditure in firms’ internal and external R&D spending and training significantly links to higher probability of product innovation. Being a process innovator is also significantly related to product innovation. Detailed results of this regression are not provided in this paper but can be provided under request.

References

  • Aldieri, L., & Cincera, M. (2009). Geographic and technological R&D spillovers within the triad: Micro evidence from US patents. Journal of Technology Transfer,34, 196–211.

    Article  Google Scholar 

  • Antonelli, C. (2006). Diffusion as a process of creative adoption. Journal of Technology Transfer,31(2), 211–226.

    Article  Google Scholar 

  • Antonelli, C., & Patrucco, P. (2016). Organizational change, technological innovation and ICTs: The case of platforms. In J. M. Bauer & M. Latzer (Eds.), Handbook on the economics of the internet (pp. 323–343). Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing.

    Google Scholar 

  • Arora, A., Fosfuri, A., & Gambardella, A. (2001). Specialised spillovers, international spillovers and investment: Evidence from the chemical industry. Journal of Development Economics,65, 31–54.

    Article  Google Scholar 

  • Asche, F., Roll, K. H., & Tveteras, R. (2007). Productivity growth in the supply chain—Another source of competitiveness for aquaculture. Marine Resource Economics.,22(3), 329–334.

    Article  Google Scholar 

  • Audretsch, D. B., Bozeman, B., Combs, K. L., Feldman, M., Link, A. N., Siegel, D. S., et al. (2002). The economics of science and technology. Journal of Technology Transfer,27, 155–203.

    Article  Google Scholar 

  • Belderbos, R., Gilsing, V., & Lokshin, B. (2012). Persistence of, and interrelation between horizontal and vertical technology alliances. Journal of Management,38(6), 1788–1811.

    Article  Google Scholar 

  • Bernstein, J. I. (1996). International R&D spillovers between industries in Canada and the United States. Canadian Journal of Economics,29(Special issue), 463–467.

    Article  Google Scholar 

  • Blalock, G., & Veloso, F. M. (2007). Imports, productivity growth, and supply chain learning. World Development,35(7), 1134–1151.

    Article  Google Scholar 

  • Bogliacino, F., & Pianta, M. (2009). The impact of innovation on labour productivity growth in European Industries: Does it depend on firms’ competitiveness strategies? IPTS working paper on corporate R&D and innovation, No. 13.

  • Bosworth, D. L., & Wilson, R. A. (1992). Technological change: The role of scientists and engineers. Belfast: Avebury Press.

    Google Scholar 

  • Bosworth, D. L. (2005a). Determinants of enterprise performance. Manchester: University of Manchester Press.

    Google Scholar 

  • Bosworth, D. L. (2005b). Determinants of enterprise performance. Manchester: Manchester U.P.

    Google Scholar 

  • Bosworth, D. L., & Li, Y. (2014). Spillovers, endogenous growth and creative destruction in Europe. In Paper Prepared for the Schumpeter Conference, 2014, Jena, Germany.

  • Bosworth, D. L., Li, Y., & Wilson, R. (2014). Estimating innovation spillovers: An International Sectoral and UK Enterprise Study. Department for Business, Innovation and Skills, Research paper, No. 178.

  • Bottazzia, L., & Peric, G. (2003). Innovation and spillovers in regions: Evidence from European patent data. European Economic Review.,47, 687–710.

    Article  Google Scholar 

  • Cameron, G., Proudman, J., & Redding, S. (2005). Technological convergence, R&D, Trade and productivity growth. European Economic Review.,49, 775–807.

    Article  Google Scholar 

  • Cincera, M. (2005). Firms productivity growth and R&D spillovers: An analysis of alternative technological proximity measures. Economics of Innovation and New Technology.,14(8), 657–682.

    Article  Google Scholar 

  • Clarke, P., Crawford, C., Steele, F. & Vignoles, A. (2010). The choice between fixed and random effects models: Some considerations for educational research. IZA Discussion Paper, No. 5287

  • Consoli, D., & Patrucco, P. P. (2008). Innovation platforms and the governance of knowledge: Evidence from Italy and the UK. Economics of Innovation and New Technology,17(7), 701–718.

    Google Scholar 

  • Czarnitzki, D., & Kraft, K. (2012). Spillovers of innovation activities and their profitability. Oxford Economic Papers,64, 302–322.

    Article  Google Scholar 

  • De La Potterie, B. V. P. (1997). Issues in assessing the effect of interindustry R&D spillovers. Economic Systems Research.,9(4), 331–356.

    Article  Google Scholar 

  • Deltas, G., & Karkalakos, S. (2013). Similarity of R&D activities, physical proximity, and R&D spillovers. Regional Studies and Urban Economics.,43, 124–131.

    Article  Google Scholar 

  • Faff, R., Ho, Y., Lin, W., & Yap, C. (2013). Diminishing marginal returns from R&D investment: Evidence from manufacturing firms. Applied Economics,45(5), 611–622.

    Article  Google Scholar 

  • Goncalves, E., Perobelli, F. S., & de Araujo Jr, I. F. (2017). Estimating intersectoral technology spillovers for Brazil. Journal of Technology Transfer,42, 1377–1406.

    Article  Google Scholar 

  • Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. Bell Journal of Economics,10(1), 92–116.

    Article  Google Scholar 

  • Griliches, Z. (1992). The search for R&D spillovers. Scandinavian Journal of Economics.,94, 29–47.

    Article  Google Scholar 

  • Griliches, Z. (1995). R&D and productivity: Econometric results and measurement issues. In P. A. Stoneman (Ed.), Handbook of the economics of innovation and technological change (pp. 52–89). Oxford: Blackwell.

    Google Scholar 

  • Ho, Y. K., Tjahjapranata, M., & Yap, C. M. (2006). Size, leverage, concentration and R&D investment in generating growth opportunities. Journal of Business,79, 851–876.

    Article  Google Scholar 

  • Ikeuchi, K., Belderbos, R., Fukao, K., Kim, Y.G., & Kwon, H.U. (2015). Buyers, suppliers, and R&D spillovers. The Research Institute of Economy, Trade and Industry, Discussion Paper Series 15-E-047.

  • Jaffe, A. (1986). Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits, and market value. American Economic Review,76(5), 984–1001.

    Google Scholar 

  • Jaffe, A. (1989). Characterizing the “Technological Position” of firms, with application to quantifying technological opportunity and research spillovers. Research Policy,18(2), 87–97.

    Article  Google Scholar 

  • Jorgenson, D. W., & Griliches, Z. (1967). The explanation of productivity change. Review of Economic Studies,34(2), 249–280.

    Article  Google Scholar 

  • Keller, W. (2004). International technology diffusion. Journal of Economic Literature,XLII, 752–782.

    Article  Google Scholar 

  • Laursen, K., Reichstein, T., & Salter, A. (2011). Exploring the effect of geographical proximity and university quality on University–Industry collaboration in the United Kingdom. Regional Studies,45(4), 507–523.

    Article  Google Scholar 

  • Lebedinski, L., & Vandenberghe, V. (2013). Assessing Education’s Contribution to Productivity Using Firm-Level Evidence. IRES Discussion Papers, 2013–17.

  • Leontief, W. (1941). Structure of the American Economy, 1919–1929: an empirical application of equilibrium analysis. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Mairesse, J., & Mohnen, P. (1995). Research and development and productivity—A survey of the econometric literature. Paris: Insee.

    Google Scholar 

  • Mairesse, J., & Sassenou, M. (1991). R&D and productivity: A Survey of econometric studies at the firm level. Science, Technology and Industry Review: OECD.,7, 265–269.

    Google Scholar 

  • Malerba, F., & Orsenigo, L. (2009). User-producer relations, innovation and the evolution of market structures under alternative contractual regimes. Structural Change and Economic Dynamics,21, 26–40.

    Article  Google Scholar 

  • Mank, D. A., & Nystrom, H. E. (2001). Decreasing returns to shareholders from R&D spending in the computer industry. Engineering Management Journal.,13(3), 3–8.

    Article  Google Scholar 

  • Matsui, Y. (2007). An empirical analysis of just-in-time production in Japanese manufacturing companies. International Journal Production Economics.,108, 153–164.

    Article  Google Scholar 

  • Nickell, S., Wadhwani, S., & Wall, M. (1992). Productivity growth in U.K. companies, 1975–1986. European Economic Review,36, 1055–1091.

    Article  Google Scholar 

  • Parham, D. (2009). Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement? In OECD/FSO (Ed.), Productivity measurement and analysis. Paris: OECD Publishing.

    Google Scholar 

  • Patrucco, P. P. (Ed.). (2014). The economics of knowledge generation and distribution: The role of interactions in the system dynamics of innovation and growth. London: Routledge.

    Google Scholar 

  • Pianta, M., & Tancioni, M. (2008). Innovations, profits and wages. Journal of Post Keynesian Economics.,31(4), 101–123.

    Article  Google Scholar 

  • Razzak, W. A., & Timmins, J. (2010). Education and labour productivity in New Zealand. Applied Economics Letters,17, 169–173.

    Article  Google Scholar 

  • Reddy, P. (2011). The evolving role of universities in economic development: The case of university–industry linkages. In B. Göransson & C. Brundenius (Eds.), Universities in transition. Insight and Innovation in international development (pp. 25–49). New York: Springer.

    Google Scholar 

  • Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Public Economics.,94, 1002–1037.

    Google Scholar 

  • Saunoris, J. W., & Payne, J. E. (2011). An empirical note on R&D growth models with regional implications. The Journal of Regional Analysis and Policy.,41(1), 16–21.

    Google Scholar 

  • Scherer, F. M. (1982). Inter-industry technology flows and productivity growth. Review of Economics and Statistics,64(4), 627–634.

    Article  Google Scholar 

  • Scherer, F. M. (1984). Using linked patent and R&D data to measure interindustry technology flows”. R&D, patents, and productivity. In NBER Conference Proceedings ed., Zvi Griliches, 417-61. Chicago and London: University of Chicago Press.

  • Schumpeter, J. 1942. Capitalism, socialism, and democracy. New York: Harper & Bros. http://cnqzu.com/library/Economics/marxian%20economics/Schumpeter,%20Joeseph-Capitalism,%20Socialism%20and%20Democracy.pdf.

  • Serrano-Domingo, G., & Cabrer-Borras, B. (2016). Direct and indirect knowledge spillovers and industrial productivity. Industry and Innovation,24(2), 165–189.

    Article  Google Scholar 

  • Solow, R. M. (1957). Technical change and the aggregate production function. Review of Economics and Statistics,39, 312–320.

    Article  Google Scholar 

  • Stiroh, K. J. (2001). What drives productivity growth? Economic Policy Review, Federal Reserve Bank of New York, issue Mar, pp. 37–59.

  • UKCES. (2015). High level STEM skills requirements in the UK Labour Market. Wath-upon-Dearne: UK Commission for Employment and Skills.

    Google Scholar 

  • Von Hippel, E. (1988). The sources of innovation. Oxford, UK: Oxford University Press.

    Google Scholar 

  • Von Hipple, E. (2005). Democratizing innovation: The evolving phenomenon of user innovation. Journal of Business Administration,55(1), 63–78.

    Google Scholar 

  • Werner, S. (2002). Recent development in international management research: A review of 20 top management Journals. Journal of Management,28(3), 277–305.

    Article  Google Scholar 

  • Zachariadis, M. (2003). R&D, innovation, and technological progress: A test of the Schumpeterian framework without scale effects. Canadian Journal of Economics,36, 566–586.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Finance, Shanghai International Studies University, 550 West Dalian Road, Shanghai, China

    Yuxin Li

  2. Institute for Employment Research, University of Warwick, Coventry, UK

    Derek Bosworth

Authors
  1. Yuxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Derek Bosworth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuxin Li.

Appendices

Appendix A: Details of the variables included in the regression

Firm level variables have been given a name using lower cases, while sector level variables are in capitalised names. (Tables 4, 5, 6).

Table 4 Variables included in the regression analysis
Full size table
Table 5 Average R&D and education purchase and their spillovers between 1995 and 2009, by sector (€ million)
Full size table
Table 6 Statistical description of key variables
Full size table

Appendix B: Regression results of region and year dummies

See Table 7.

Table 7 Estimated results for region and year dummies
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Bosworth, D. R&D spillovers in a supply chain and productivity performance in British firms. J Technol Transf 45, 177–204 (2020). https://doi.org/10.1007/s10961-018-9652-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10961-018-9652-x

Keywords

JEL Classification

Search

Navigation