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Asymmetry of knowledge spillovers between MNCs and host country firms

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Abstract

We use patent citation data to examine knowledge flows between foreign multinational companies (MNCs) and host country organisations in 30 countries. We find not just significant knowledge inflows from foreign MNCs to host country organisations, but also significant outflows back from the host country to foreign MNCs. In fact, in technologically advanced countries, knowledge outflows to foreign MNCs greatly outweigh knowledge inflows. Even in technologically less advanced countries, knowledge outflows are only slightly weaker than inflows. Additional analysis shows that the exact pattern varies across sectors within a country, depending on the country's expertise in individual sectors. Finally, knowledge inflows and outflows appear to track personnel flows between foreign MNCs and host country organisations.

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Figure 1

Notes

  1. Although concentration of technology has been declining somewhat since regions such as South Korea, Taiwan, Ireland and Israel have also rapidly built innovative capabilities (Mahmood and Singh, 2003; Furman and Hayes, 2004), it is still likely to remain relatively high, at least in the near future.

  2. Indeed, governments around the world continue to spend enormous resources as subsidies and tax breaks to attract MNCs. This is true not just for developing countries (such as the widely cited case of Intel in Costa Rica) but also for the more industrialised countries. For instance, the 2001 mission statement of the Industrial Development Authority of Ireland said: ‘We will win for Ireland, its people and its regions, the best in international innovation and investment so as to contribute to the continued transformation of Ireland to a world-leading society which is rich in creativity, learning and social well-being.’ Even in the US, the state of Alabama in 1994 spent $230 million, or about $150,000 per newly created job, to attract a new plant of Mercedes-Benz (Haskel et al., 2002).

  3. See Caves (1996) and Keller (2004) for surveys. As Dani Rodrik puts it, ‘Today's policy literature is filled with extravagant claims about positive spillovers from FDI, but the hard evidence is sobering’ (Rodrik, 1999: 37).

  4. It is worth emphasising that we use patent citations as a measure correlated with actual knowledge flows without assuming that citations are the actual mechanism behind these flows. Consider the analogy that a student's citation to his adviser's research paper may tell us that she built upon knowledge that her adviser created, even if most of the actual knowledge transfer happened not through her reading the paper but by working closely with the adviser.

  5. To ensure a unique definition of the parent, we define an assignee to be an MNC subsidiary when a foreign firm has a majority stake in it. For cases with a 50–50 stake, we simply broke the tie in favour of the first firm. See Mowery et al. (1996) or Gomes-Casseres et al. (2006) for a closer look at the issue of alliances.

  6. This approach of studying intra-national knowledge flows between different kinds of firms (domestic vs foreign) echoes the views expressed by Baldwin et al. (1998): ‘The connection between productivity measurement and a geographic basis for economic accounting is strong if the only important inputs are land, labour, physical capital, and possibly human capital…. If, however, technology, organisational skills, patents, or brand name are major determinants of output and productivity, the advantage of the geographic measure disappears because these types of intangible capital reside not in locations but in organisations that may span state and national borders’ (p 3).

  7. See Amemiya (1985), King and Zeng (2001) or Greene (2003) for more discussion on WESML. Sorenson and Fleming (2004) and Singh (2005) have also used WESML specifically for predicting patent citations.

  8. The marginal effect of a variable j is given by β j Λ′(). From the logit formula, it is easy to show that this equals β j Λ()[1−Λ()]. One can then substitute either the mean predicted probability or the population mean for Λ() for getting an estimate of the marginal effect. We report the former. The latter is typically slightly greater in value.

  9. Because citations are rare events, Λ() is typically several orders of magnitude smaller than 1, so the marginal effect β j Λ()[1−Λ()]≈β j Λ(). Therefore, in this setting, β j can actually be interpreted as the percentage change in the probability of citation when the corresponding dummy variable goes from 0 to 1.

  10. Note that we have not modelled the endogeneity of an MNC's decision of whether and where to locate overseas. Therefore our results represent knowledge flow patterns resulting from existing location choices by MNCs, and not necessarily what would result if MNCs faced incentives to make a different set of location choices.

  11. In order to rule out the possibility that the results are driven just by greater knowledge spillovers from domestic universities or research labs to MNC subsidiaries, analysis not reported here separated out the effect of universities and research labs. The result on asymmetry in knowledge flows still persisted.

  12. These findings could be driven by the fact that patent data include only subsidiaries active in R&D. It seems plausible that knowledge flows involving subsidiaries not doing R&D are much less symmetric.

  13. As in Jaffe et al. (1993), all citations for which either the original or the control citation involved a self-citation from a firm to itself were excluded from the sample. In addition, there were a few observations for which a match could not be found. This caused the average number of control citations to be less than five for some actual citations.

  14. If we cannot get enough nine-digit matches for some cited patent, we resort to a three-digit match. However, the main results are largely robust to an alternative approach where observations with no nine-digit match are simply excluded.

  15. To check for consistency and robustness of the aggregate results obtained from WESML in the previous section, the last row in Table 5(a) and 5(b) repeats the matching analysis for all countries aggregated together. Consistent with Table 4, we find evidence for bidirectional knowledge spillovers, with the D → M intensity again being greater than the M → D intensity. Note that the D → M and M → D intensity definitions for matching are not directly comparable in magnitude to the corresponding coefficients with the same name in WESML.

  16. Co-employment is not the only means through which strong interpersonal relationships develop. For example, a growing literature shows the importance of co-ethnicity as another mechanism promoting interpersonal ties (Kerr, 2005; Agrawal et al., 2006; Kalnins and Chung, 2006). However, data constraints prevent us from exploring these alternative sources of interpersonal ties.

  17. For example, analysis not reported here revealed that, though Japanese MNCs benefit from knowledge spillovers in the US, US MNCs enjoy similar gains when investing in Japan. We find no evidence that Japanese firms are worse at sharing knowledge, a conclusion consistent with Spencer (2000).

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Acknowledgements

An earlier version of this paper was titled ‘Multinational firms and knowledge diffusion: evidence using patent citation data’, and was awarded the 2004 William H. Newman Award by the Academy of Management. This paper is based on Chapter 2 of my PhD dissertation, and I am grateful to my advisers Tarun Khanna, Josh Lerner and Richard Caves for their guidance. I thank Bharat Anand, Wilbur Chung, Ken Corts, James Costantini, Mihir Desai, Lee Fleming, Bronwyn Hall, Elhanan Helpman, Rebecca Henderson, Adam Jaffe, Wolfgang Keller, Walter Kuemmerle, Megan MacGarvie, Anita McGahan, Marc Melitz, Jan Rivkin, Jordan Siegel, Olav Sorenson, Catherine Thomas, Peter Thompson and Manuel Trajtenberg for comments. This paper has also gained from feedback during seminars at CMU, Columbia, Emory, GWU, Harvard, HEC, IESE, INSEAD, Instituto de Empresa, LBS, Maryland, Minnesota, MIT, NBER, NUS, NYU, Rutgers, SMU, UNC, Vanderbilt, Wharton, and the 2004 meetings of the American Economic Association, Academy of International Business and Academy of Management. I also thank the JIBS Departmental Editor, J. Myles Shaver, and two anonymous referees for detailed suggestions. Finally, I am grateful to Harvard Business School and INSEAD for funding. Errors remain my own.

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Correspondence to Jasjit Singh.

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Accepted by J Myles Shaver, Departmental Editor, 22 September 2006. This paper has been with the author for one revision.

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Singh, J. Asymmetry of knowledge spillovers between MNCs and host country firms. J Int Bus Stud 38, 764–786 (2007). https://doi.org/10.1057/palgrave.jibs.8400289

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