Advertisement

Edge factors: scientific frontier positions of nations

  • Mikko PackalenEmail author
Article

Abstract

A key decision in scientific work is whether to build on novel or well-established ideas. Because exploiting new ideas is often harder than more conventional science, novel work can be especially dependent on interactions with colleagues, the training environment, and ready access to potential collaborators. Location may thus influence the tendency to pursue work that is close to the edge of the scientific frontier in the sense that it builds on recent ideas. We calculate for each nation its position relative to the edge of the scientific frontier by measuring its propensity to build on relatively new ideas in biomedical research. Text analysis of 20 + million publications shows that the United States and South Korea have the highest tendencies for novel science. China has become a leader in favoring newer ideas when working with basic science ideas and research tools, but is still slow to adopt new clinical ideas. Many locations remain far behind the leaders in terms of their tendency to work with novel ideas, indicating that the world is far from flat in this regard.

Keywords

Science Novelty Impact factor New ideas Idea adoption 

Notes

Acknowledgements

I thank Jay Bhattacharya, Bruce Weinberg, Partha Bhattacharyya, Richard Freeman, Horatiu Rus, Joel Blit, David Autor, Larry Smith and Peter Tu for discussions. I acknowledge financial support from the National Institute on Aging grant P01-AG039347.

Supplementary material

11192_2018_2991_MOESM1_ESM.pdf (1.4 mb)
Supplementary material 1 (PDF 1469 kb)

References

  1. Agrawal, A., & Goldfarb, A. (2008). Restructuring research: Communication costs and the democratization of university innovation. American Economic Review, 98, 1578–1590.CrossRefGoogle Scholar
  2. Alberts, B. (2013). Impact factor distortions. Science, 340, 6134.Google Scholar
  3. Besancenot, D., & Vranceanu, R. (2015). Fear of novelty: A model of strategic discovery with strategic uncertainty. Economic Inquiry, 53, 1132–1139.CrossRefGoogle Scholar
  4. Bornmann, L., Wagner, C., & Leydesdorff, L. (2017). The geography of references in elite articles: What countries contribute to the archives of knowledge, Unpublished manuscript available at https://arxiv.org/pdf/1709.06479.pdf. Accessed 1 July 2018.
  5. Boudreau, K. J., Guinan, E. C., Lakhari, K. R., & Riedl, C. (2016). looking across and looking beyond the knowledge frontier: Intellectual distance. Novelty, and Resource Allocation in Science, Management Science, 62, 2765–2783.Google Scholar
  6. Brezis, E. S., Krugman, P. R., & Tsiddon, D. (1993). Leapfrogging in international competition: A theory of cycles in national technological leadership. American Economic Review, 83, 1211–1219.Google Scholar
  7. Ding, W., Levin, S., Stephan, P., & Winkler, A. (2010). The impact of information technology on acedemic scientists’ productivity and collaboration patterns. Management Science, 56, 1439–1461.CrossRefGoogle Scholar
  8. Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80, 875–908.CrossRefGoogle Scholar
  9. Freeman, R. B. (2013). “One ring to rule them all?” Globalization of knowledge and knowledge creation. Nordic Economic Policy Review, 1, 11–44.Google Scholar
  10. Freeman, R. B., & Huang, W. (2015). China’s “Great Leap Forward” in science and engineering. In A. Geuna (Ed.), Global mobility of research scientists: The economics of who goes where and why. Amsterdam: Elsevier.Google Scholar
  11. Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 15, 108–111.CrossRefGoogle Scholar
  12. Garfield, E. (1972). Citation analyses as a tool in journal evaluation. Science, 178, 471–478.CrossRefGoogle Scholar
  13. Hidalgo, C. E., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106, 10570–10575.CrossRefGoogle Scholar
  14. Jones, B. F. (2010). Age and great invention. Review of Economics and Statistics, 92, 1–14.CrossRefGoogle Scholar
  15. Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: shifting impact. Geography, and Stratification in Science, Science, 322, 1259–1262.Google Scholar
  16. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: Chicago University Press.Google Scholar
  17. Kuhn, T. S. (1977). Objectivity, value judgment and theory choice. In T. S. Kuhn (Ed.), The essential tension (pp. 320–339). Chicago: University of Chicago Press.Google Scholar
  18. Lee, Y.-N., Walsh, J. P., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44, 684–697.CrossRefGoogle Scholar
  19. Lucas, R. E., Jr. (2004). Lectures on economic growth. Cambridge, MA: Harvard University Press.Google Scholar
  20. Lucas, R. E., Jr., & Moll, B. (2014). Knowledge growth and allocation of time. Journal of Political Economy, 122, 1–55.CrossRefGoogle Scholar
  21. Marshall, A. (1920). Principles of economics. London: Macmillan and Co.Google Scholar
  22. Mokyr, J. (1994). Cardwell’s law and the political economy of technological progress. Research Policy, 23, 561–574.CrossRefGoogle Scholar
  23. National Science Board. (2016). Science and engineering indicators. National Science Foundation.Google Scholar
  24. Osterloh, M., & Frey, B. S. (2015). Ranking games. Evaluation Review, 32, 102–129.CrossRefGoogle Scholar
  25. Packalen, M., & Bhattacharya, J. (2015). Cities and ideas, National Bureau of Economic Research, working paper no. 20921.Google Scholar
  26. Packalen, M., & Bhattacharya, J. (2016). Age and the trying out of new ideas. Journal of Human Capital, forthcoming.Google Scholar
  27. Packalen, M., & Bhattacharya, J. (2017). Neophilia ranking of scientific journals. Scientometrics, 110, 43–64.CrossRefGoogle Scholar
  28. Rzhetzky, A., Foster, J. G., Foster, I. T., & Evans, J. A. (2015). Choosing experiments to accelerate collective discovery. Proceedings of the National Academy of Sciences, 112, 14569–14574.CrossRefGoogle Scholar
  29. Usher, A. P. (1929). A history of mechanical inventions. New York: McGraw-Hill.zbMATHGoogle Scholar
  30. Wang, J., Veugelers, R., & Stephan, P. (2016). Bias against novelty in science: A cautionary tale for users of bibliometric indicators, National Bureau of Economic Research, working paper no. 22180.Google Scholar
  31. Weber, G. M. (2013). Identifying translational science within the triangle of biomedicine. Journal of Translational Medicine, 11, e126.CrossRefGoogle Scholar
  32. Yu, X., Zhang, C., & Lai, Q. (2014). China’s rise as a major contributor to science and technology. Proceedings of the National Academy of Sciences, 11, 9437–9442.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

Personalised recommendations