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Firm performance and knowledge spillovers from academic, industrial and foreign linkages: the case of China

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Abstract

Firm performance may be enhanced by linkages with academic institutions, other firms, and foreign markets that confer knowledge spillovers as well as internal R&D that creates firm-specific knowledge. In particular, firm productivity and innovation may be enhanced by positive externalities from knowledge and technology produced by universities and research institutions (URIs) and diffused to the domestic economy. Productive contributions from such linkages might be particularly expected in China, where policy measures have explicitly supported and facilitated connections between URIs and firms to stimulate economic development and competitiveness. In this paper, we measure the performance impact of such knowledge spillovers in Chinese firms by using a variety of specifications, estimators, and robustness checks, including an “instrumental variable” specification that controls for endogeneity. We find more patent activity in Chinese firms with URI connections and enhanced firm productivity particularly from linkages with research institutions (RIs). Introduction of new products, processes, and new businesses is also positively associated with linkages with research institutions, as well as with linkages with other firms.

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Notes

  1. See Paul (1999) for a detailed review.

  2. Chen and Kenney (2007) find that for firms in some areas (Beijing) the proximity of universities and research institutions is very important but for others (Shenzhen) it is not. Beugelsdijk and Cornet (2001) find that distance does not limit spillovers from all-discipline universities but does for technological universities.

  3. For example, if g(A j ) is a Cobb-Douglas function this becomes a first-order linear-in-logarithms function.

  4. For our application we have only a cross-section of firms, so we cannot capture differences over time.

  5. To determine whether the impacts of spillovers from our targeted firm linkages have a spatial dimension (differ by city), which has been suggested in the literature, we also estimated a model with interaction effects among the dummy variables and the spillover variables. The result suggests that the impact of URI connections is not significantly different for the 18 cities in the sample. The impact of the other firm connections was significantly different (lower) at the 5% significance level for only one city. These results suggest that the estimated marginal effect of spillover variables do not change much relative to the base specification; the interaction effects do not matter much in terms of accurately teasing out the effect of spillover variables on the productivity of the firms. Similar results were found for industry differences. We therefore did not pursue these possibilities further.

  6. In preliminary investigation we also included total factor productivity (TFP) and (log of) profitability as dependent variables. However, the results for TFP were very similar to those for LP, and more than a third of our observations did not included data on profits, so we omitted these variables for the final analysis.

  7. Although our data are cross sectional, and thus most questions in the survey intend to obtain information for 2002, the input and sales variables were available for the previous 2 years for some firms in the sample. We thus also used a two-step semi-parametric technique introduced by Olley and Pakes (1996), which allows us to control for simultaneity bias when estimating a production function, to obtain consistent parameter estimates and reliable productivity estimates (Yasar et al. 2008). The results from this specification were also similar. Since this causes us to lose observations and the data we use are actually cross-section we did not pursue this further.

  8. The number of patents granted is also included in the data, but the results were not substantively different for that variable so we included only patent applications for our analysis.

  9. Both R&D variables are included because the coefficient on RDUM indicates whether firms with positive R&D have higher levels of the performance variable under consideration (P kj ) than those with no R&D expenditures, and RINT shows what happens to P kj as R&D intensity increases for the R&D performing sub-sample. Other results were robust to including both or only one of these variables.

  10. See Long (1997) for an excellent illustration of the approaches used to interpret the parameter estimates for binary choice models.

  11. See (Long 1997) for detailed information.

  12. For an excellent discussion of these data, see Lederman (2007).

  13. Although the survey was conducted for firms in both service and manufacturing industries, we dropped the firms in service industries because information on own R&D expenditures was available only for manufacturing firms. The results were not substantively affected by this omission.

  14. We also directly estimated a (translog in inputs) production function with labor, materials, and capital as inputs and the variables in Eq. 1 as shift variables, with similar results; insignificant productivity impacts of firm and university linkages and significantly positive (at the 10% significance level) impacts of connections with research institutions.

  15. This is consistent with Medda et al. (2005) and Hall et al. (2001, 2003), who concluded that firms are likely to collaborate with universities when they engage in basic research projects that require a long-run commitment, and thus the performance impact of collaborative research with universities is more likely to be realized in the long run. This is also suggested by the results of Thursby et al. (2009).

  16. See Cameron and Trivedi (2005) for more detailed information.

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Acknowledgments

We are indebted to the editor and two anonymous referees for their constructive and useful suggestions.

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Authors and Affiliations

  1. The University of Texas at Arlington, Arlington, TX, USA

    Mahmut Yaşar

  2. Emory University, Atlanta, GA, USA

    Mahmut Yaşar

  3. University of California, Davis, CA, USA

    Catherine J. Morrison Paul

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  1. Mahmut Yaşar
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  2. Catherine J. Morrison Paul
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Correspondence to Mahmut Yaşar.

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Sadly, Catherine J. Morrison Paul passed away on June 30, 2010. She was a leading economist in applied production economics and econometrics, who made significant contributions to the field of productivity analysis. She ranks amongst the top in both the quantity and quality of her published work. Her legacy will undoubtedly continue to inspire many scholars for generations to come. She will be missed greatly, but many will continue the work in her spirit.

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Yaşar, M., Paul, C.J.M. Firm performance and knowledge spillovers from academic, industrial and foreign linkages: the case of China. J Prod Anal 38, 237–253 (2012). https://doi.org/10.1007/s11123-011-0262-y

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