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The Fruits of Intellectual Production: Economic and scientific specialisation among OECD countries

Author

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  • Keld Laursen

    (CEBR, Danish Ministry for Trade and Industry, and Department of Industrial Economics and Strategy, Copenhagen Business School)

  • Ammon Salter

    (SPRU, University of Sussex)

Abstract

This paper brings together data from 14 OECD countries on scientific publications, patents and production specialisation to explore the relationship between economic and production specialisation for 17 manufacturing sectors. Since Marx, there has been a fundamental debate in economics about the link between science and the economic system. Marx argued that the developments in the science system are strongly influenced by changes in the economic sphere, whereas Polanyi argued that developments in science are largely independent of economic sphere. Using a panel data model and econometric estimations at the sectoral-level, the paper assesses the two positions and finds considerable support for Marx's position, that is, that scientific and production specialisation are, often, tightly linked.

Suggested Citation

  • Keld Laursen & Ammon Salter, 2002. "The Fruits of Intellectual Production: Economic and scientific specialisation among OECD countries," SPRU Working Paper Series 78, SPRU - Science Policy Research Unit, University of Sussex Business School.
  • Handle: RePEc:sru:ssewps:78
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    8. Anthony Arundel & Aldo Geuna, 2001. "Does Proximity Matter for Knowledge Transfer from Public Institutes and Universities to Firms?," SPRU Working Paper Series 73, SPRU - Science Policy Research Unit, University of Sussex Business School.
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    10. Keld Laursen & Ina Drejer, 1999. "Do Inter-Sectoral Linkages Matter for International Export Specialisation?," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 8(4), pages 311-330.
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    13. Keld Laursen & Valentina Meliciani, 2000. "The importance of technology-based intersectoral linkages for market share dynamics," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 136(4), pages 702-723, December.
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    15. Hicks, Diana & Breitzman, Tony & Olivastro, Dominic & Hamilton, Kimberly, 2001. "The changing composition of innovative activity in the US -- a portrait based on patent analysis," Research Policy, Elsevier, vol. 30(4), pages 681-703, April.
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    Cited by:

    1. Maxim N. Kotsemir & Tatiana E. Kuznetsova & Elena G. Nasybulina & Anna G. Pikalova, 2015. "Empirical Analysis of Multinational S&T Collaboration Priorities –The Case of Russia," HSE Working papers WP BRP 53/STI/2015, National Research University Higher School of Economics.
    2. Jane Marceau, 2007. "Bringing science to life in Australia: the need for a new approach in human health biotechnology policy," The Journal of Technology Transfer, Springer, vol. 32(4), pages 303-327, August.
    3. Hemert, P. van & Nijkamp, P., 2009. "Knowledge investments, business R&D and innovativeness of countries. A qualitative meta-analytic comparison," Serie Research Memoranda 0041, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. Maxim Kotsemir & Tatiana Kuznetsova & Elena Nasybulina & Anna Pikalova, 2015. "Identifying Directions for Russia’s Science and Technology Cooperation," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 9(4), pages 54-72.
      • Maxim Kotsemir & Tatiana Kuznetsova & Elena Nasybulina & Anna Pikalova, 2015. "Identifying Directions for Russia’s Science and Technology Cooperation," Foresight-Russia Форсайт, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 9(4 (eng)), pages 54-72.
    5. Anthony Arundel & Aldo Geuna, 2004. "Proximity and the use of public science by innovative European firms," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(6), pages 559-580.
    6. Valentina Meliciani, 2010. "Exports of Knowledge-intensive Services and Manufactures: The Role of ICTs and Intersectoral Linkages," Chapters, in: Robert M. Solow & Jean-Philippe Touffut (ed.), The Shape of the Division of Labour, chapter 4, Edward Elgar Publishing.
    7. Krammer, Sorin M.S., 2017. "Science, technology, and innovation for economic competitiveness: The role of smart specialization in less-developed countries," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 95-107.
    8. Castellacci, Fulvio, 2006. "The interactions between national systems and sectoral patterns of innovation: a cross-country analysis of Pavitt’s taxonomy," MPRA Paper 27601, University Library of Munich, Germany.
    9. Laursen, Keld & Meliciani, Valentina, 2010. "The role of ICT knowledge flows for international market share dynamics," Research Policy, Elsevier, vol. 39(5), pages 687-697, June.
    10. Keld Laursen, 2015. "Revealed comparative advantage and the alternatives as measures of international specialization," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 5(1), pages 99-115, June.
    11. Castellacci, Fulvio, 2008. "Innovation and the competitiveness of industries: comparing the mainstream and the evolutionary approaches," MPRA Paper 27523, University Library of Munich, Germany.
    12. Alessandra Colombelli & Antonio De Marco & Emilio Paolucci & Riccardo Ricci & Giuseppe Scellato, 2021. "University technology transfer and the evolution of regional specialization: the case of Turin," The Journal of Technology Transfer, Springer, vol. 46(4), pages 933-960, August.

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    More about this item

    Keywords

    Scientific specialisation; international economic specialisation; bibliometric data;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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