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Seek foreign funds or technology? Relative impacts of different spillover modes on innovation

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Abstract

This paper identifies channels of influence of foreign linkages on innovative activity in nations and it compares the relative effects of aggregate innovation linkages, FDIs, high-tech imports, and ICT imports, on patenting across a large sample of nations. Whereas various drivers of the international innovative activity have been studied in the literature, our understanding of the contributions of different linkages to innovation deserves more attention. We ask: Are the different innovation linkages equally complementary to research inputs in fostering innovation? We find that a broader index of innovation linkages shows positive and significant spillovers on innovation. We also find some support for the positive link between FDIs and innovation; however, high-tech imports and ICT imports have opposite effects on innovation, with the former effect being negative. These spillovers are reinforced by the positive and expected impacts of R&D spending. In other results, greater venture capital investments boost innovation in most cases. The findings are somewhat sensitive across two alternative measures of patenting and there are some nonlinearities in the influence of FDIs and imports on innovation.

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Notes

  1. A broader survey of the literature is in Audretsch et al. (2002).

  2. See Goel and Grimpe (2013) for a related study of German scientists.

  3. According to an earlier issue of the Global Innovation Index (2016, p. 54) GII, “The Innovation linkages sub-pillar draws on both qualitative and quantitative data regarding business/university collaboration on R&D, the prevalence of well-developed and deep clusters, the level of gross R&D expenditure financed by abroad, and the number of deals on joint ventures and strategic alliances”, (https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2016-annex1.pdf).

  4. Some studies have used a narrower index of patent protection to account for some related aspects (Goel and Saunoris 2016). Also see Goel (2020).

  5. While the GII is available for more recent years, the coverage of nations (and in some cases of the variables included) varies somewhat from year to year. We employ a cross-section analysis from the 2020 GII report, in part to maximize coverage and because the index and institutional variables in the analysis do not change much from year to year.

  6. “Robust regression can be used in any situation in which you would use least squares regression. When fitting a least squares regression, we might find some outliers or high leverage data points. We have decided that these data points are not data entry errors, neither they are from a different population than most of our data. So we have no compelling reason to exclude them from the analysis. Robust regression might be a good strategy since it is a compromise between excluding these points entirely from the analysis and including all the data points and treating all them equally in OLS regression. The idea of robust regression is to weigh the observations differently based on how well behaved these observations are. Roughly speaking, it is a form of weighted and reweighted least squares regression”, https://stats.oarc.ucla.edu/r/dae/robust-regression/.

  7. Due to the three large outliers for Patent1 shown in Fig. 1, the robust regression drops the three observations when Patent1 is the dependent variable.

  8. Note that Table 3 shows a modest correlation between HighTkIMP and IctIMP, still, we include the two imports in separate models to avoid possible inherent overlaps.

    We did, however, try including FDI, HighTkIMP, and IctIMP in the same model with the respective dependent variables from Table 6. All three variables maintained their signs, with HighTkIMP negative and statistically significant (at the 10% level) with Patent1 as the dependent variable, and IctIMP was positive and significant (at the 10% level) with Patent2 as the dependent variable. Additional details are available upon request.

  9. A reader might argue that FDIs and imports might also be endogenous. To address that possibility, we reran 2SLS regressions for each dependent variable, alternatively taking FDI, HighTkIMP, and IctIMP to be endogenous, and using ISLAND and INFRAst as instruments. The three focus variables maintained their signs from Table 6, while the statistical significance of the estimated coefficients was low (additional details are available upon request). One reason for the low statistical significance might be that the measures of FDIs and imports are composite, masking characteristics of individual dimensions (example: greenfield versus established FDIs, etc.). This aspect deserves greater attention in future research, subject to the availability of appropriate data.

  10. The corresponding coefficient on FDI in Model 6a.1 is statistically insignificant and thus no elasticity is reported for that case.

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Acknowledgements

I am very grateful to Al Link for making numerous comments and suggestions that led to substantial improvements in this research.

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Authors and Affiliations

  1. Department of Economics, Illinois State University, Normal, IL, 61790-4200, USA

    Rajeev K. Goel

  2. Innovation and International Competition, Kiel Institute for the World Economy, Kiel, Germany

    Rajeev K. Goel

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Goel, R.K. Seek foreign funds or technology? Relative impacts of different spillover modes on innovation. J Technol Transf 48, 1466–1488 (2023). https://doi.org/10.1007/s10961-022-09950-0

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