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Patents for evidence-based decision-making and smart specialisation

Abstract

The article compares and contrasts different sets of patent-based indicators, traditionally used to assess countries’ technological capacities and specialisation. By doing that, we seek to determine how a chosen metric might affect the results of such an analysis, sometimes causing misleading conclusions on technological profiling. This goal is achieved with the statistical analysis of patent activity of the top-10 patenting economies. Findings indicate the need for policymakers to employ a complex of patent-related indicators when formulating technological specialisation strategies. Results also offer a taxonomy of technological capacities of the leading countries, which can further help understanding their current status and prospects for future progress. Thus, the paper might be of interest for researchers and analysts, which seek to offer methodological approaches and models to assess technological development of economies, as well as for policymakers governing the process.

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References

  • Aghion, P., Boulanger, J., & Cohen, E. (2011). Rethinking industrial policy. Bruegel Policy Brief, 2011(04), 1–8.

    Google Scholar 

  • Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities for developed and developing countries (ArCo). World Development, 32(4), 629–654.

    Google Scholar 

  • Archibugi, D., & Pianta, M. (1992). Specialisation and size of technological activities in industrial countries: The analysis of patent data. Research Policy, 21(1), 79–93.

    Google Scholar 

  • Bečić, E., & Švarc, J. (2015). Smart specialisation in Croatia: Between the cluster and technological specialisation. Journal of the Knowledge Economy, 6(2), 270–295.

    Google Scholar 

  • Boschma, R., Balland, P., & Kogler, D. (2014). Relatedness and technological change in cities: The rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010. Industrial and Corporate Change, 24(1), 223–250.

    Google Scholar 

  • Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69–87.

    Google Scholar 

  • Capello, R., & Kroll, H. (2016). From theory to practice in smart specialisation strategy: Emerging limits and possible future trajectories. European Planning Studies, 24(8), 1393–1406.

    Google Scholar 

  • Capello, R., & Lenzi, C. (2016). Relevance and utility of European Union research, technological development and innovation policies for a smart growth. Environment and Planning C: Government and Policy, 34(1), 52–72.

    Google Scholar 

  • Carayannis, E. G., Grigoroudis, E., Campbell, D. F., Meissner, D., & Stamati, D. (2018). The ecosystem as helix: An exploratory theory-building study of regional co-opetitive entrepreneurial ecosystems as Quadruple/Quintuple Helix Innovation Models. R&D Management, 48(1), 148–162.

    Google Scholar 

  • Cerulli, G., & Filippetti, A. (2012). The complementary nature of technological capabilities: Measurement and robustness issues. Technological Forecasting and Social Change, 79, 875–887.

    Google Scholar 

  • Correa, P. (2015). Public expenditure: Reviews in science, technology, and innovation. Washington: World Bank Group.

    Google Scholar 

  • Correa, P., & Güçeri I. (2016). Research and innovation for smart specialisation strategy. Policy Paper Series, Paper No. June 2006, Oxford University Centre for Business Taxation.

  • Dosi, G. (1988). Sources, procedures and microeconomic effects of innovation. Journal of Economic Literature, 26(3), 1120–1171.

    Google Scholar 

  • Fai, F., & Von Tunzelmann, N. (2001). Industry-specific competencies and converging technological systems: evidence from patents. Structural Change and Economic Dynamics, 12(2), 141–170.

    Google Scholar 

  • Filipetti, A., & Peyrache, A. (2011). The patterns of technological capabilities of countries: A dual approach using composite indicators and data analysis. World Development, 39, 1108–1121.

    Google Scholar 

  • Fischer, B., Kotsemir, M., Meissner, D., & Streltsova, E. (2018). Patents for evidence-based decision-making and smart specialization. No. WP BRP 86/STI/2018. National Research University Higher School of Economics.

  • Fleming, L., & Sorenson, O. (2001). Technology as a complex adaptive system: Evidence from patent data. Research Policy, 30(7), 1019–1039.

    Google Scholar 

  • Foray, D. (2014). From smart specialisation to smart specialisation policy. European Journal of Innovation Management, 17(4), 492–507.

    Google Scholar 

  • Foray, D., & Goenaga, X. (2013). The goals of smart specialisation. JRC Scientific and Policy Reports, Paper No S3 Policy Brief Series 01/2013, European Commission.

  • Freeman, C., & Soete, L. (2009). Developing science, technology and innovation indicators: What we can learn from the past. Research Policy, 38, 583–589.

    Google Scholar 

  • Frietsch, R., Neuhäusler, P., Jung, T., & Van Looy, B. (2014). Patent indicators for macroeconomic growth—The value of patents estimated by export volume. Technovation, 34(9), 546–558.

    Google Scholar 

  • Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31(6), 899–933.

    Google Scholar 

  • Gokhberg, L. M. (2003). Statistika nauki. [Statistics of Science]. Moscow: Teys (in Russian).

  • Granstrand, O. (1998). Towards a theory of the technology-based firm. Research Policy, 27(5), 465–489.

    Google Scholar 

  • Griliches, Z. (1990). Patent statistics as economic indicators: a survey. Journal of Economic Literature, 28(4), 1661–1707.

    Google Scholar 

  • Grillitsch, M. (2016). Institutions, smart specialisation dynamics and policy. Environment and Planning C: Government and Policy, 34(1), 22–37.

    Google Scholar 

  • Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373–1384.

    Google Scholar 

  • Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and Statistics, 81(3), 511–515.

    Google Scholar 

  • Hausmann, R., & Rodrik, D. (2003). Economic development as self-discovery. Journal of Development Economics, 72(2), 603–633.

    Google Scholar 

  • Havas, A., Schartinger, D., & Weber, M. (2010). The impact of foresight on innovation policy-making: Recent experiences and future perspectives. Research Evaluation, 19(2), 91–104.

    Google Scholar 

  • Heimeriks, G., & Balland, P. (2016). How smart is specialisation? An analysis of specialisation patterns in knowledge production. Science and Public Policy, 43(4), 562–574.

    Google Scholar 

  • Hidalgo, C., & Hausmann, R. (2009). The building blocks of economics complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575.

    Google Scholar 

  • Iacobucci, D. (2014). Designing and implementing a smart specialization strategy at regional level: Some open questions. Scienze Regionali, 13(1), 107–126.

    Google Scholar 

  • Jiang, J., Goel, R. K., & Zhang, X. (2019). Knowledge flows from business method software patents: influence of firms’ global social networks. The Journal of Technology Transfer, 44(4), 1070–1096.

    Google Scholar 

  • Khayyat, N. T., & Lee, J.-D. (2015). A measure of technological capabilities for developing countries. Technological Forecasting and Social Change, 92, 210–223.

    Google Scholar 

  • Khramova, E., Meissner, D., & Sagieva, G. (2013). Statistical patent analysis indicators as a means of determining country technological specialisation. NRU Higher School of Economics. Series WP BRP “Science, Technology and Innovation”, Paper No. 09/STI/2013.

  • Komninos, N., Musyck, B., & Iain Reid, A. (2014). Smart specialisation strategies in south Europe during crisis. European Journal of Innovation Management, 17(4), 448–471.

    Google Scholar 

  • Kopczynska, E., & Ferreira, J. J. (2018). Smart specialization as a new strategic framework: Innovative and competitive capacity in European context. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-018-0543-z.

    Article  Google Scholar 

  • Kotnik, P., & Petrin, T. (2017). Implementing a smart specialisation strategy: An evidence-based approach. International Review of Administrative Sciences, 83(1), 85–105.

    Google Scholar 

  • Krüger, J. (2008). Productivity and structural change: A review of the literature. Journal of Economic Surveys, 22(2), 330–363.

    Google Scholar 

  • Lee, K. (2013). Schumpeterian analysis of economic catch-up: Knowledge, path creation, and the middle-income trap. Cambridge: Cambridge University Press.

    Google Scholar 

  • Lopes, J., Farinha, L., Ferreira, J., & Silveira, P. (2018). Smart specialization policies: Innovative performance models from European regions. European Planning Studies, 26(11), 2114–2124.

    Google Scholar 

  • Lopes, J., Ferreira, J., & Farinha, L. (2019). Innovation strategies for smart specialization (RIS3): Past, present and future research. Growth and Change, 50(1), 38–68.

    Google Scholar 

  • Mancusi, M. (2012). National externalities and path-dependence in technological change: An empirical test. Economica, 79(314), 329–349.

    Google Scholar 

  • McCann, P., & Ortega-Argilés, R. (2015). Smart Specialisation, regional growth and applications to European Union Cohesion Policy. Regional Studies, 49(8), 1291–1302.

    Google Scholar 

  • Meissner, D., Gokhberg, L., & Saritas, O. (2019). What do emerging technologies mean for economic development? In D. Meissner, L. Gokhberg, & O. Saritas (Eds.), Emerging technologies for economic development (pp. 1–10). Berlin: Springer.

    Google Scholar 

  • Meissner, D., Polt, W., & Vonortas, N. (2017). Towards a broad understanding of innovation and its importance for innovation policy. Journal of Technology Transfer, 42(5), 1184–1211.

    Google Scholar 

  • Meissner, D., & Rudnik, P. (2017). Creating sustainable impact from foresight on STI policy. Foresight, 19(5), 457–472.

    Google Scholar 

  • Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for science, technology and innovation. Switzerland: Springer.

    Google Scholar 

  • Mokyr, J., Vickers, C., & Zierbach, N. L. (2015). The history of technological anxiety and the future of economic growth: Is this time different? Journal of Economic Perspectives, 29(3), 31–50.

    Google Scholar 

  • Mowery, D. C., & Rosenberg, N. (1995). Technology and the pursuit of economic growth. Cambridge: Cambridge University Press.

    Google Scholar 

  • Neffke, F., Henning, M., & Boschma, R. (2011). How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Economic Geography, 87(3), 237–265.

    Google Scholar 

  • OECD. (2009). Patent Statistics Manual. Paris: OECD.

    Google Scholar 

  • OECD. (2013). Innovation-driven growth in regions: the role of smart specialisation. Paris: OECD.

    Google Scholar 

  • OECD. (2018). Main Science and Technology Indicators (Vol. 1). Paris: OECD.

    Google Scholar 

  • Ozawa, T. (2009). The rise of Asia, the ‘flying geese’ theory of tandem growth and regional agglomeration. Cheltenham: Edward Elgar.

    Google Scholar 

  • Petralia, S., Balland, P., & Morrison, A. (2017). Climbing the ladder of technological development. Research Policy, 46(5), 956–969.

    Google Scholar 

  • Piirainen, K., Tanner, A., & Alkærsig, L. (2017). Regional foresight and dynamics of smart specialisation: A typology of regional diversification patterns. Technological Forecasting and Social Change, 115, 289–300.

    Google Scholar 

  • Porter, M. (1998). The competitive advantage of nations. Newyork, NY: The Free Press.

    Google Scholar 

  • Radosevic, S., & Yoruk, E. (2014). Are there global shifts in the world science base? Analysing the catching up and falling behind of world regions. Scientometrics, 101(3), 1897–1924.

    Google Scholar 

  • Radosevic, S., & Yoruk, E. (2016). Why do we need a theory and metrics of technology upgrading? Asian Journal of Technology Innovation, 24(1), 8–32.

    Google Scholar 

  • Santini, C., Marinelli, E., Boden, M., Cavicchi, A., & Haegeman, K. (2016). Reducing the distance between thinkers and doers in the entrepreneurial discovery process: An exploratory study. Journal of Business Research, 69(5), 1840–1844.

    Google Scholar 

  • Schmoch, U. (2008). Concept of a technology classification for country comparisons. Final Report to the World Intellectual Property Organization (WIPO), Fraunhofer Institute for Systems and Innovation Research, Karlsruhe.

  • Trajtenberg, M. (1990). A penny for your quotes: Patent citations and the value of innovations. The Rand Journal of Economics, 21(1), 172–187.

    Google Scholar 

  • Trappey, A. J., Trappey, C. V., Wu, C. Y., & Lin, C. W. (2012). A patent quality analysis for innovative technology and product development. Advanced Engineering Informatics, 26(1), 26–34.

    Google Scholar 

  • UNDP. (2001). Human development report 2001. Making New technologies work for human development. United Nations Development Program.. Oxford: Oxford University Press.

    Google Scholar 

  • Urraca-Ruiz, A. (2019). On the evolution of technological specialization patterns in emerging countries: Comparing Asia and Latin America. Economics of Innovation and New Technology, 28(1), 100–117.

    Google Scholar 

  • Wagner, C. S., Brahmakulam, I., Jackson, B., Wong, A., & Yoda, T. (2001). Science and technology collaboration: Building capability in developing countries (No. RAND/MR-1357.0-WB). RAND CORP SANTA MONICA CA.

  • WIPO. (2015). World intellectual property report: Breakthrough innovation and economic growth. Geneva: WIPO.

    Google Scholar 

  • Zacharakis, A. L., Shepherd, D. A., & Coombs, J. E. (2003). The development of venture-capital-backed internet companies: An ecosystem perspective. Journal of Business Venturing, 18(2), 217–231.

    Google Scholar 

Download references

Acknowledgements

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’.

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Appendix: Technological profiles of the 10 studied countries

Appendix: Technological profiles of the 10 studied countries

See Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10.

Fig. 1
figure 1

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of China in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 2
figure 2

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of the USA in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 3
figure 3

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of Japan in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 4
figure 4

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of the Republic of Korea in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 5
figure 5

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of Germany in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 6
figure 6

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of France in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 7
figure 7

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of Switzerland in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 8
figure 8

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of United Kingdom in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 9
figure 9

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of Netherlands in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

Fig. 10
figure 10

Sources: Authors’ calculations based on data from WIPO IP Statistics Data Center https://www3.wipo.int/ipstats/index.htm. We take here “total count by applicant’s origin (equivalent count)” indicator as the measure of number of patent publications for each country. All calculations are done in June 2018

Technological profile of the Russian Federation in 2012–2016. Note. In pale grey colour, we highlight technological domains that are considered as technological capabilities of a country by at least one of the four indicators. In dark grey, we colour technological domains that appear as a country technological capabilities by all the four indicators.

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Fischer, B.B., Kotsemir, M., Meissner, D. et al. Patents for evidence-based decision-making and smart specialisation. J Technol Transf 45, 1748–1774 (2020). https://doi.org/10.1007/s10961-019-09761-w

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Keywords

  • Technological development
  • Technological specialization
  • Patent statistics