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University–Industry collaboration in the biopharmaceuticals: the Italian case

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Abstract

We investigate the determinants of University–Industry (U–I) interactions in the biopharmaceuticals in Italy over the period 2004–2010, choosing co-publishing as a proxy of U–I partnerships. We construct a novel dataset of co-published articles, that contains measures of proximities, agglomeration, firms’ and universities’ characteristics. Following a consolidated methodology, we integrate our dataset of effective interactions with the set of all potential interactions, to estimate probabilistic models for the occurrence and the intensity of U–I interactions. Our main findings confirm and extend the predictions of the previous literature: (1) geographical proximity and prior partnership increase the probability and the intensity of co-publication; (2) the proximity of a firm to other biopharmaceutical firms and universities attenuates the relevance of geographical proximity; (3) there exists complementarity between prior partnerships and geographical proximity. A novel result is that firms’ and Universities’ size, firms’ R&D and patents expenditure and the composition of the academic staff as well as quality of academic research exert a significant impact on the intensity of co-publishing.

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Notes

  1. For a systematic analysis on the variety of channels of interactions, see D’Este and Patel (2007). Among the contributions of the last decade, see, for instance the analyses conducted for several countries: Calvert and Patel (2003), D’Este and Patel (2007), D’Este and Iammarino (2010), Laursen et al. (2011), D’Este and Perkmann (2011), D’Este et al. (2013), Hewitt-Dundas (2013) for studies on the U.K.; Balconi and Laboranti (2006), Abramo et al. (2009, 2010, 2011), Iacobucci and Micozzi (2014), Muscio and Pozzali (2013) for Italy; Ponds et al. (2007), Bekkers and Freitas (2008) for Netherlands; Grimpe and Fier (2010) and Slavtchev (2013) for Germany; Azagra-Caro et al. (2006), Azagra-Caro (2007), Barbolla and Corredera (2009) for Spain; Boardman (2009), Boardman and Ponomariov (2009), Bozeman and Gaughan (2007) for the U.S. Recently, Perkmann et al. (2013) highlighted in their review on U–I relations as this literature may suffer of limited comparability given the way measures are constructed. In fact, the construction of the dependent variable representing academic engagement with industry may vary considerably among contributions. Moreover, the comparability is further hampered by differences in industries and countries for which U–I collaborations are investigated.

  2. Ponds et al. (2007) stress how successful interaction is less correlated to geographical proximity when institutional proximity is high (as in the case of two universities) since the underlying structure of incentives and constraints is similar.

  3. Proxied by a measure of prior partnership as in D’Este et al. (2013). The underlying idea is that if a collaboration took place, it is likely that the collaborating partners are organizational compatible (proximate).

  4. Biopharmaceutical industry is usually intended as a subset of pharmaceutical industry (biopharmaceuticals are biological medical products manufactured using biotechnology), however in this study we adopt a broad definition since the progression of biopharmaceuticals implies the involvement of several industrial North American Industrial Classification System (NAICS) sectors such as biological product manufacturing, medicinal and botanical manufacturing (on this point, see Battelle 2013). Brusoni et al. (2005) highlighted as pharmaceutical firms increased the breath of their knowledge base and exhibit depth in knowledge integration in particular in the fields of biotechnology, biochemical research and neurosciences. Since chemical-biotech and pharmaceutical research are strictly related, and the present study focuses on joint research between university and industry, it is more appropriate to investigate these sectors as a single industry. Therefore, hereinafter, we will refer to biopharmaceutical industry as the chemical, biotech and pharmaceutical industry.

  5. According to Pavitt (1984) taxonomy.

  6. At the end of a long and difficult process of incubation, that requires between 12 and 14 years from the initial phase to the phase of marketing of a drug composed of new chemical entities, a large part of the products cannot even cover the effective costs, with the consequence that the sector generally counts on a small number of blockbusters drugs to recover the totality of investments sustained (Grabowski 1995).

  7. U–I partnerships increase the average number of firm’s biotech patents by 34 % and the number of newly developed products by 27 %.

  8. For a detailed description of the dataset see “Appendix A”.

  9. We examined a total of 92,985 publications over the period 2001–2010.

  10. Our dataset included 48 universities. Those universities which do not employ academic staff in the scientific areas of interest have been excluded.

  11. We consider those firms with at least one co-publication in the period 2004–2010 and that, in principle, can be considered as “active firm” in interacting with academic research. This approach is the same used by empirical analyses based on surveys, where the list of firms included in the survey is restricted to those which had at least one collaboration. Samples of this kind can be found in the empirical literature which, for instance, relies on data sets on collaborative research grants awarded by the U.K. Research Council.

  12. Proximity and cluster variables have been computed as in D’Este et al. (2013). The authors are deeply grateful to Frederick Guy and Simona Iammarino for providing Stata codes for generating these variables.

  13. In the complete dataset the average distance between U–I pairs is about 420 km, which reduces to about 240 km in the case of real U–I pairs.

  14. Latitudes and longitudes of firms and universities have been computed from their addresses. For the case of firms we referred to the address of the specific business unit (laboratory) involved in the collaboration (we do not refer to the legal address of the firm).

  15. See D’Este et al. (2013).

  16. “Area 3” and “Area 5”, respectively, in the classification system by the Italian Ministry of University and Research.

  17. We did not estimate a model containing simultaneously Cluster index and University cluster index due to the high level of correlation (0.88) among the two variables.

  18. On this point see D’Este et al. (2013).

  19. The same result holds also for those regressors measuring firm-specific and university-specific characteristic such as, for example, firm’s size or academic staff . Therefore, these kind of variables have not been included among the regressors of the logit model. On the contrary, as firm-specific and university-specific covariates vary over the range of the “number of co-publications” variable, they are included as additional regressors in the negative binomial model.

  20. The impact of Prior partnership measures to what extent having co-published before increases the probability of co-publishing today and therefore can be taken as an indicator for persistence in the U–I partnership.

  21. The likelihood ratio test revealed that the phenomenon of co-publications is over dispersed and therefore the negative binomial model is more appropriate than the Poisson model.

  22. As outlined in Sect. 2, this is particularly true for biopharmaceutical industry.

  23. As robustness check we performed the same analysis as in Tables 3 and 4 on the subsample of observations given by only real pairs (230 observations) and all our findings are confirmed.

  24. Academics at lower stages of the career.

  25. In 2012 the average age of full professors in chemical and biological sciences was 56 years; that of associate professors in chemical sciences was 52 and in biological sciences was 51. The most striking result regards the average age of assistant professors: 44 in chemical sciences and 45 in biological sciences (source: http://statistica.miur.it).

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Acknowledgments

The authors gratefully acknowledge the financial contribution of the Roma Tre University (Internationalization research grant) and the Manlio Rossi-Doria Centre for Economic and Social Research. The authors wish to thank the participants to the University–Industry workshop organized by Roma Tre University (2013 and 2014 editions), to the 55th Italian Economic Association conference and to the International Conference of Technology Transfer (University of Urbino) and, in particular, Riccardo Crescenzi, Andrea Filippetti, Frederick Guy, Simona Iammarino, Maria Luisa Mancusi, Parimal Patel, Francesco Trivieri and Silvio Vismara for their invaluable comments on a previous version of the paper. All remaining errors are the authors’ own responsibility. Fabrizio Striani provided excellent research assistance.

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Authors and Affiliations

  1. Roma Tre University, Rome, Italy

    Anna Giunta

  2. Department of the Treasury, Italy’s Ministry of Economy and Finance, Rome, Italy

    Filippo M. Pericoli

  3. University of Basilicata, Potenza, Italy

    Eleonora Pierucci

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  1. Anna Giunta
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Appendix A: the dataset

Appendix A: the dataset

We consider publications indexed in the Web of Science database, in the following categories: Biochemistry molecular biology; Chemistry medicinal; Chemistry multidisciplinary; Chemistry analytical; Chemistry applied; Pharmacology pharmacy; Biology or cell biology; Biochemical research methods; Biotechnology applied microbiology; Plant sciences or chemistry organic; Microbiology or reproductive biology, for a total of 92,985 publications in the period 2001–2010.

Data on academic employees come from the Italian Ministry of University and Research (MIUR) and refer to the following sectors: Analytical, chemistry-physics (3A), Pharmaceutical, technological and alimentary (3D); Animal biology and anthropology (5B); Biochemistry (5E); Applied biology (5F); Experimental and clinical pharmacological sciences (5G); Genetics and microbiology (5I).

The statistical source of data on firms is the AIDA—Bureau van Dijk database, that has been integrated with data from the Italian High Institute for Public Health and from the Annual Report of the Regional Council of Latium (CREL) of year 2008. We have restricted the analysis to enterprises operating in the following sectors: Cultivation of healing herbs (01.28 according to the ATECO classification system); Production of chemical products (20); Production of pharmaceutical products (21); R&D in biotechnologies (72.11); Pharmacology (72.19.09).

Data on Research quality come from the Evaluation of Research Quality (VQR 2004–2010) conducted by the Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR).

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Giunta, A., Pericoli, F.M. & Pierucci, E. University–Industry collaboration in the biopharmaceuticals: the Italian case. J Technol Transf 41, 818–840 (2016). https://doi.org/10.1007/s10961-015-9402-2

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