, Volume 90, Issue 1, pp 271–287 | Cite as

Regional development in South Korea: accounting for research area in centrality and networks

  • Matthew A. Shapiro
  • Han Woo Park


This paper provides a first-ever look at differences of centrality scores (i.e., networks) over time and across research specializations in Korea. This is a much needed development, given the variance which is effectively ignored when Science Citation Index (SCI) publications are aggregated. Three quantitative tests are provided—OLS, two sample t-tests, and unit-root tests—to establish the patterns of centrality scores across Korea over time. The unit-root test is particularly important, as it helps identify patterns of convergence in each region’s centrality scores. For all other geographic regions besides Seoul, Gyeonggi, and Daejeon, there appears to be little promise—at least in the immediate future—of being network hubs. For these top three regions, though, there is a pattern of convergence in three-quarters of all research specializations, which we attribute in part to policies in the mid- and late-1990s.


Network analysis Korean NIS Centrality Density Fragmentation 

Mathematics Subject Classification (2000)

90B18 68M10 62G07 91G70 91F99 62P25 62J05 

JEL Classification

C14 C31 C33 C65 D85 L14 O31 O32 O33 O38 R12 



This work was supported by the National Research Foundation of Korea, SSK (Social Science Korea) Grant funded by the Korean Government (NRF-2010-330-B00232). Also, the authors are grateful to Min-Ho So and Young-Long Kim for their assistance.


  1. Bernard, A. B., & Durlauf, S. N. (1996). Interpreting tests of the convergence hypothesis. Journal of Econometrics, 71(1–2), 161–173. doi: 10.1016/0304-4076(94)01699-2.CrossRefzbMATHMathSciNetGoogle Scholar
  2. Chang, P. L., & Shih, H. Y. (2005). Comparing patterns of intersectoral innovation diffusion in Taiwan and China: A network analysis. Technovation, 25(2), 155–169.CrossRefGoogle Scholar
  3. Cooke, P., & Leydesdorff, L. (2006). Regional development in the knowledge-based economy: The construction of advantage. Journal of Technology Transfer, 31(1), 5–15.CrossRefGoogle Scholar
  4. Dreher, A., & Krieger, T. (2005). Do gasoline prices converge in a unified Europe with non-harmonized tax rates? KOF Working Paper No. 114. doi: 10.2139/ssrn.617346.
  5. Etzkowitz, H. (2008). Triple helix innovation: Industry, university, and government in action. London: Routledge.CrossRefGoogle Scholar
  6. Evans, P., & Karras, G. (1996). Convergence revisited. Journal of Monetary Economics, 37(2), 249–265. doi: 10.1016/s0304-3932(96)90036-7.CrossRefGoogle Scholar
  7. Hirshman, A. O. (1958). The strategy of economic development. New Have: Yale University Press.Google Scholar
  8. Khan, G. F., & Park, H. W. (2011, forthcoming). Measuring the triple helix on the web: Longitudinal trends in the university-industry-government relationship in Korea. Journal of the American Society for Information Science and Technology.Google Scholar
  9. Kim, M.-J. (2005). Korean science and international collaboration, 1995–2000. Scientometrics, 63(2), 321–339.CrossRefGoogle Scholar
  10. Kim, I. K. (2010). Socioeconomic concentration in the Seoul metropolitan area and its implications in the urbanization process of Korea. Korean Journal of Sociology, 44(3), 111–128. (written in Korean).Google Scholar
  11. Kim, L., & Dahlman, C. J. (1992). Technology policy and industrialization: An integrative framework and Korea’s experience. Research Policy, 21(5), 437–452.CrossRefGoogle Scholar
  12. Kwon, K.-S. (2009). The co-evolution of academic research and knowledge-transfer activities of universities in catch-up countries: In the case of Korea. A paper presented to the Triple Helix 2008 conference. .
  13. Kwon, K.-S. (2011). Are scientific capacities and industrial funding critical for universities’ knowledge-transfer activities? A case study of South Korea. Journal of Contemporary Eastern Asia, 10(1), 15–23.
  14. Kwon, K.-S., Park, H. W., So, M. H., & Leydesdorff, L. (2011). Has globalization strengthened South Korea’s national research system? National and international dynamics of the triple helix of scientific co-authorship relationships in South Korea. Scientometrics. A special issue on Triple Helix and WSI (Webometrics Scientometrics Informetics) in Asia. doi: 10.1007/s11192-011-0512-9.
  15. Lee, W.-Y. (2000). The role of science and technology policy in Korea’s industrial development. In L. Kim & R. R. Nelson (Eds.), Technology learning and innovation: Experiences of newly industrializing economies. Cambridge: Cambridge University Press.Google Scholar
  16. Lee, K., Pesaran, M. H., & Smith, R. (1997). Growth and convergence in a multi-country empirical stochastic Solow model. Journal of Applied Econometrics, 12(4), 357–392. doi: 10.1002/(sici)1099-1255(199707)12:4<357::aid-jae441>;2-t.Google Scholar
  17. Levin, A. T., Lin, C.-F., & Chu, C.-S. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.CrossRefzbMATHMathSciNetGoogle Scholar
  18. Liu, S.-G., & Chen, C. (2003). Regional innovation system: Theoretical approach and empirical study of China. Chinese Geographical Science, 13(3), 193–198.CrossRefGoogle Scholar
  19. Park, H. W., Hong, H. D., & Leydesdorff, L. (2005). A comparison of the knowledge-based innovation systems in the economies of South Korea and The Netherlands using triple helix indicators. Scientometrics, 65(1), 3–27.CrossRefGoogle Scholar
  20. Park, H. W., & Leydesdorff, L. (2008). Korean journals in the Science Citation Index: What do they reveal about the intellectual structure of S&T in Korea? Scientometrics, 75(3), 439–462.CrossRefGoogle Scholar
  21. Park, H. W., & Leydesdorff, L. (2010). Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives. Research Policy, 39(5), 640–649.CrossRefGoogle Scholar
  22. Shapiro, M. A., So, M., & Park, H. W. (2010). Quantifying the national innovation system: Inter-regional collaboration networks in South Korea. Technology Analysis and Strategic Management, 22(7), 845–857.CrossRefGoogle Scholar
  23. Sohn, D.-W., & Kenney, M. (2007). Universities, clusters, and innovation systems: The case of Seoul, Korea. World Development, 35(6), 991–1004.CrossRefGoogle Scholar
  24. Surowiecki, J. (2005). The wisdom of crowds: Why the many are smarter than the few. London: Little Brown.Google Scholar
  25. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.Google Scholar
  26. Yang, C. H., Park, H. W., & Heo, J. (2010). A network analysis of interdisciplinary research relationships: The Korean government’s R&D grant program. Scientometrics, 83(1), 77–92.CrossRefGoogle Scholar
  27. Young, A. T., Higgins, M. J., & Levy, D. (2008). Sigma convergence versus beta convergence: Evidence from U.S. county-level data. Journal of Money, Credit and Banking, 40(5), 1083–1093. doi: 10.1111/j.1538-4616.2008.00148.x.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.Department of Social SciencesIllinois Institute of TechnologyChicagoUSA
  2. 2.Department of Media and CommunicationYeungNam UniversityGyongsan-siSouth Korea
  3. 3.WCU Webometrics InstituteYeungNam UniversityGyongsan-siSouth Korea

Personalised recommendations