# Multilevel heterogeneity of R&D cooperation and innovation determinants

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## Abstract

Assessing the impact of public support to innovation on R&D collaboration may require a more complex multilevel design, that describes the likely correlation present among firms characteristics within a particular sector. Using data from the 2006 edition of the Community Innovation Survey (CIS) for the Netherlands, we propose a methodology to study the effect of firm-level characteristics on the propensity to undertake a research collaborative agreement. In particular, we show that controlling for a richer variance structure yields a different picture with respect to simpler regression frameworks adopted in the literature of R&D cooperation determinants. Moreover, such a hierarchical framework can be generalized allowing for clustering at higher levels, such as sectors or geographical areas. Besides the link between public funding and R&D collaboration, our results confirm the findings of the literature: technological spillovers, risk and cost sharing rationales, firm’s size, and type of innovative activity are related to the decision of engaging in different sorts of research alliances.

## Keywords

R&D collaboration Innovation R&D policy Bayesian analysis## JEL Classification

O30 O32 C11## References

- Almus, M., & Czarnitzki, D. (2003). The effects of public R&D subsidies on firms’ innovation activities: The case of Eastern Germany.
*Journal of Business & Economic Statistics*,*21*(2), 226–236.CrossRefGoogle Scholar - Arora, A., & Cohen, W. M. (2015). Public support for technical advance: the role of firm size.
*Industrial and Corporate Change*,*24*(4), 791–802.CrossRefGoogle Scholar - Belderbos, R., Carree, M., Diederen, B., Lokshin, B., & Veugelers, R. (2004a). Heterogeneity in R&D cooperation strategies.
*International Journal of Industrial Organization*,*22*(8–9), 1237–1263.CrossRefGoogle Scholar - Belderbos, R., Carree, M., & Lokshin, B. (2004b). Cooperative R&D and firm performance.
*Research Policy*,*33*(10), 1477–1492.CrossRefGoogle Scholar - Belderbos, R., Carree, M., & Lokshin, B. (2006). Complementarity in R&D cooperation strategies.
*Review of Industrial Organization*,*28*(4), 401–426.CrossRefGoogle Scholar - Busom, I., & Fernández-Ribas, A. (2008). The impact of firm participation in R&D programmes on R&D partnerships.
*Research Policy*,*37*(2), 240–257.CrossRefGoogle Scholar - Cassiman, B., & Veugelers, R. (2002).
*R&D cooperation and spillovers: Some empirical evidence from Belgium*. Open access publications from katholieke universiteit leuven, Katholieke Universiteit Leuven.Google Scholar - Cassiman, B., & Veugelers, R. (2006).
*In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition*. Open access publications from katholieke universiteit leuven, Katholieke Universiteit Leuven: Open access publications from katholieke universiteit leuven.Google Scholar - Catozzella, A., & Vivarelli, M. (2014). The possible adverse impact of innovation subsidies: some evidence from Italy.
*International Entrepreneurship and Management Journal*, 1–18.Google Scholar - Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation.
*Administrative Science Quarterly*,*35*(1), 128–152.CrossRefGoogle Scholar - Coull, B., & Agresti, A. (2000). Random effects modeling of multiple binomial responses using the multivariate binomial logit-normal distribution.
*Biometrics*,*56*(1), 73–80.CrossRefGoogle Scholar - Crespi, F., Ghisetti, C., & Quatraro, F. (2015). Environmental and innovation policies for the evolution of green technologies: A survey and a test.
*Eurasian Business Review*,*5*(2), 343–370.CrossRefGoogle Scholar - d’Aspremont, C., & Jacquemin, A. (1988). Cooperative and Noncooperative R&D in Duopoly with Spillovers.
*American Economic Review*,*78*(5), 1133–1137.Google Scholar - Dewar, R. D., & Dutton, J. E. (1986). The adoption of radical and incremental innovations: An empirical analysis.
*Management science*,*32*(11), 1422–1433.CrossRefGoogle Scholar - Dosi, G. (1999). Some notes on national systems of innovation and production, and their implications for economic analysis. In D. Archibugi, J. Howells, & J. Michie (Eds.),
*Innovation policy in a global economy*. Cambridge University Press.Google Scholar - Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2003).
*Bayesian data analysis*(2nd ed.). UK: Chapman and Hall.Google Scholar - Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In J. Berger, J. Bernardo, A. Dawid, & A. Smith (Eds.),
*Bayesian statistics*(pp. 169–194). Oxford: Oxford University Press.Google Scholar - Goldstein, H. (1995).
*Multilevel statistical models*(2nd ed.). New York: Halstead Press.Google Scholar - Hadfield, J., & Kruuk, L. (2010). MCMC methods for multi-response generalised linear mixed models: The MCMCglmm R package.
*Journal of Statistical Software*,*33*(2), 1–22.CrossRefGoogle Scholar - Hanley, A., Liu, W.-H., & Vaona, A. (2015). Credit depth, government intervention and innovation in China: Evidence from the provincial data.
*Eurasian Business Review*,*5*(1), 73–98.CrossRefGoogle Scholar - Heckman, J. J., Lochner, L., & Taber, C. (1998). General-equilibrium treatment effects: A study of tuition policy.
*American Economic Review*,*88*(2), 381–86.Google Scholar - Hedeker, D., & Gibbons, R. D. (1996). MIXOR: A computer program for mixed-effects ordinal regression analysis.
*Computer Methods and Programs in Biomedicine*,*49*, 157–176.CrossRefGoogle Scholar - Heidelberger, P., & Welch, P. D. (1983). Simulation run length control in the presence of an initial transient.
*Operations Research*,*31*(6), 1109–1144.CrossRefGoogle Scholar - Henderson, R. (1993). Underinvestment and incompetence as responses to radical innovation: Evidence from the photolithographic alignment equipment industry.
*RAND Journal of Economics*,*24*(2), 248–270.CrossRefGoogle Scholar - Hernán, R., Marín, P. L., & Siotis, G. (2003). An empirical evaluation of the determinants of Research Joint Venture Formation.
*Journal of Industrial Economics*,*51*(1), 75–89.CrossRefGoogle Scholar - Kaiser, U. (2002). An empirical test of models explaining research expenditures and research cooperation: Evidence for the german service sector.
*International Journal of Industrial Organization*,*20*(6), 747–774.CrossRefGoogle Scholar - Kamien, M. I., Muller, E., & Zang, I. (1992). Research joint ventures and R&D cartels.
*American Economic Review*,*82*(5), 1293–1306.Google Scholar - Katz, M. L. (1986). An analysis of cooperative research and development.
*RAND Journal of Economics*,*14*(4), 527–543.Google Scholar - Kim, J. (2014). Formal and informal governance in biotechnology alliances: Board oversight, contractual control, and repeated deals.
*Industrial and Corporate Change*,*23*(4), 903–929.CrossRefGoogle Scholar - Kirat, T., & Lung, Y. (1999). Innovation and proximity.
*European Urban and Regional Studies*,*6*(1), 27–38.CrossRefGoogle Scholar - Klette, T. J., Moen, J., & Griliches, Z. (2000). Do subsidies to commercial R&D reduce market failures? Microeconometric evaluation studies.
*Research Policy*,*29*(4–5), 471–495.CrossRefGoogle Scholar - Korotayev, A. V., & Tsirel, S. V. (2010). A spectral analysis of world GDP dynamics: Kondratieff waves, Kuznets swings, Juglar and Kitchin cycles in global economic development, and the 2008–2009 economic crisis.
*Structure and Dynamics*,*4*(1), 3–57.Google Scholar - Kultti, K., Takalo, T., & Tanayama, T. (2015). R&D spillovers and information exchange: a case study.
*Eurasian Economic Review*,*5*, 63–76.Google Scholar - Leifer, R., Gina Colarelli, O., Rice, M., & Gina Colarelli, O. (2001). Implementing radical innovation in mature firms: The role of hubs.
*The Academy of Management Executive (1993–2005)*,*15*(3):102–113.Google Scholar - Lopez, A. (2008). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms.
*International Journal of Industrial Organization*,*26*(1), 113–136.CrossRefGoogle Scholar - Mohnen, P., & Röller, L.-H. (2005). Complementarities in innovation policy.
*European Economic Review*,*49*(6), 1431–1450.CrossRefGoogle Scholar - OECD, E. (1997). Proposed guidelines for collecting and interpreting technological innovation data: Oslo manual.Google Scholar
- Piga, C. A., & Vivarelli, M. (2004). Internal and external R&D: A sample selection approach.
*Oxford Bulletin of Economics and Statistics*,*66*(4), 457–482.CrossRefGoogle Scholar - Reinganum, J. (1983). Uncertain innovation and the persistence of monopoly.
*The American Economic Review*,*73*(4), 741–748.Google Scholar - Rodríguez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses.
*J. Royal Statistical Society*,*158*(1), 73–90.Google Scholar - Schmitz, H. (1999). Collective efficiency and increasing returns.
*Cambridge Journal of Economics*,*23*(4), 465–483.CrossRefGoogle Scholar - Tether, B. (2002). Who co-operates for innovation, and why: An empirical analysis.
*Research Policy*,*31*(6), 947–967.CrossRefGoogle Scholar - Train, K. (2009).
*Discrete choice methods with simulation*(2nd Edn.). Online economics textbooks: Cambridge University Press.Google Scholar - Veugelers, R. (1997). Internal R&D expenditures and external technology sourcing.
*Research policy*,*26*(3), 303–315.CrossRefGoogle Scholar - Wang, L., & Zajac, E. (2007). Alliance or acquisition? A dyadic perspective on interfirm resource combinations.
*Strategic Management Journal*,*28*(13), 1291–1317.CrossRefGoogle Scholar - Zeger, S., & Karim, M. (1991). Generalized linear models with random effects; A Gibbs sampling approach.
*Journal of the American statistical association*,*86*(413), 79–86.CrossRefGoogle Scholar