Abstract
The channels through which firms access and acquire new and relevant knowledge for their innovative activities is a critical issue to the geography and the management of innovation. In this regard, recent studies have suggested that the mobility of workers across firms is a primary source of new knowledge for the hiring firm and, more in general, of knowledge diffusion across firms. However, little evidence has been presented and discussed about the role and the impact of workers’ mobility on the processes of knowledge transfer across firms. The present paper, thus, aims precisely at contributing to this stream of research by making use of unique data on Italian inventors’ curriculum vitae. The results of the empirical analysis indicate that the mobility of inventors is a mechanism that spurs processes of cumulative knowledge and innovation building from the departure firm to the destination one and significantly impacts on knowledge diffusion across firms.
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Notes
In the innovation studies literature, patent citations are largely considered as ‘paper trail’ of knowledge flows, and are used as proxy in order to measure their intensity and geographical extension (Jaffe et al. 1993).
In this work the authors shortly discuss whether knowledge flows go disproportionately to the inventors’ previous employer. However, two considerations should be put forward in this regard. Firstly, they consider only inventors that patent at both the departure and the destination firms. Secondly, they use US Patent and Trademark Office (USPTO) data. The relevant literature points out that the rate of citations per patent is systematically greater in this patent system than in the EPO one (Michel and Bettels 2001).
Cespri—Centre of Research on Innovation and Internationalisation Processes—is a research centre hosted by Bocconi University, in Milan (Italy). The EP-Cespri database collects all patent applications registered at the European Patent Office since 1978.
Every patent is attributed to one or more technological classes according to the International Patent Classification (IPC) that is the technological classification adopted by the EPO. We considered only the primary class. In order to identify all the patents corresponding to the field of interest (i.e. pharmaceutical), we followed a 30 technological field classification. This is a technology-oriented classification, jointly elaborated by Fraunhofer Gesellschaft-ISI (Karlsruhe), Institut National de la Propriété Industrielle (INPI, Paris) and Observatoire des Sciences and des Techniques (OST, Paris). This classification aggregates all IPC codes into 30 technology fields.
We considered only those patents that do make citations to previous patents as Agrawal et al. (2006).
In short, the citing(originating) applicant coincides with the hiring firm; in what follows we will refer to as citing(hiring) firms.
These 302 pairs are originated from 60 different applicants firms (and 131 patents) that cite 201 different firms (and 246 patents). We excluded both firm-level self-citations as well as inventor-level self-citations. We are bound to consider only citing(hiring) firms’ patens assigned to mobile inventors’ patents because we do not have information about the mobility behaviour of the inventors of other patents of the citing(hiring) firm. In these cases, we could not assess whether citing(hiring) and cited firms are linked by an inventor’s move (i.e. whether there is match or not between cited firm and inventor’s previous employer).
Stated in other words, we detect an instance of mobility when the applicant of the patents cited by A (i.e. citing(hiring) firm) after inventor X’s move coincides with inventor X’s previous employer (i.e. firm B); in short, we compare cited applicant to inventor’s previous applicant. We argue that when citing(hiring) and cited firms are linked by an inventor’s move the knowledge inheritance that inventor X brings from firm B into firm A leads to a greater propensity of firm A to build upon (i.e. to cite) firm B’s knowledge stock.
For instance, Breschi and Lissoni (2006b) use the EP-CESPRI data on the US pharmaceutical industry and find that citing and cited patents are related by inventors’ mobility in 4.95% of the examined cases (which include also personal self-citations). Also, Agrawal et al. (2006) use the USPTO-NBER data and find that patents citing mobile inventors’ patents come from their previous location only in 5% of the examined cases. This percentage is greater compared to the one found in the present work. The greater propensity to cite in the USPTO systemas compared to the EPO one and the greater propensity to move in US compared to Europe can explain a greater frequency of the matching between cited applicant and an inventor’s previous employer compared to similar percentages computed on EPO data, especially for Italy, which is characterised by rather little level of labour mobility (Michel and Bettels 2001; European Foundation for the Improvement of Living and Working Conditions 2006). Moreover, Rosenkopf and Almeida (2003) and Corredoira and Rosenkopf (2006) find that, on average, citing and cited firms in the US semiconductor industry are linked by inventors’ mobility in 1% of the examined cases.
Despite the size of the phenomenon being quite limited, the magnitude of its effects is rather substantial. For instance, Rosenkopf and Almeida (2003) find that the mobility of an inventor from firm A to B leads to a 32% increase (up to 43%, according to the specification of the model estimated) in the expected number of citations from B to A, holding all other variables at their mean; Corredoira and Rosenkopf (2006) find as well a positive effect, though somehow smaller; in fact, in their setting, the mobility of an inventor from firm A to B leads to a 14% increase in the expected number of citations from B to A, holding all other variables at their mean.
Indeed, most of the inventors moved within the national borders (82.5% of the sample).
More precisely, we compute the number of citations made as the number of times that the citing(hiring) firm cites another with reference to different patents. For instance, if one firm cites four times a firm always with reference to the same patent, the count variable would take value 1 and not 4.
The Poisson regression model can be viewed as an extension of the Poisson distribution where the mean parameter varies across observations depending on some regressors. The dependent variable is a random variable and indicates the number of times a given event occurs. The mean parameter is the only one defining the distribution and it is supposed to be equal to the variance (equi-dispersion property). This implies the necessity of a robust estimator.
It is worth mentioning that there could be a potential risk of endogenity. In fact, it could be argued that the mobility of an inventor from one specific firm to another occurs because of the pre-existing knowledge proximity and exchanges between the two, which is a factor that in turn may result in a higher citation rate between the two and a higher probability that the citing(hiring) applicant cites the cited applicant more than once. However, in the model specification we precisely take in to account this aspect by controlling for the technological distance between citing and cited applicant. Moreover, in 90% of the examined cases there is no citation from the citing to the cited firm before hiring the new inventor. This suggests that in most of the instances inventors’ mobility behaviour is not related to previous knowledge relationships between the citing and the cited applicants.
More specifically, in Rosenkopf and Almeida (2003) the joint effect of mobility and technological distance has a positive effect on the citations rate while the pure technological distance among firms a negative one.
In particular, as far as the effect of technological distance is concerned, this result can be interpreted as follows: the mobility of an inventor from the cited to the citing(hiring) firm increases the intensity to which the citing(hiring) firm sources knowledge from the cited firm besides the firms being technologically proximate (and thus, eventually, better positioned to source knowledge one from the other).
Despite this is a very interesting research area to explore, and the impact of hirings on the research direction at the project level are likely to be even stronger than at the patent level, it is worth pointing out that this type of data are scarcely available compared to more ordinary statistics such as patents or R&D expenditures, although, dedicated surveys or in-depth interviews aimed at investigating these specific aspects might allow overcoming such data scarcity. Actually, collecting the complete time series of a firm’s research projects and studying its variation before and after hiring a new inventor would require identifying and interviewing key persons within the firm with deep knowledge of a firm’s story. However, inventors do not necessarily possess detailed information on a firm’s all current and, mostly, past research projects, especially in the case of mobile inventors. In fact, mobile inventors might have very little knowledge (almost none) of research projects developed before she joined a new firm. Therefore, in this research and in the questionnaire implemented, we did not explore these aspects but exclusively focussed on the tracing inventors’ career path.
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The author is grateful to Valerio Sterzi and Andrea Vezzulli for valuable suggestions and comments. The comments of an anonymous referee significantly helped me to finalise the paper.
Appendix
Appendix
Distribution of number of patents per inventors
Legenda
Variables | Legenda |
|---|---|
1 | Average number of patents (cited firm) |
2 | Average number of citations received (cited firm) |
3 | Average number of citations received (cited firm)—Dummy 1 |
4 | Average number of citations received (cited firm)—Dummy 2 |
5 | Average number of citations received (cited firm)—Dummy 3 |
6 | Average number of citations received (cited firm)—Dummy 4 |
7 | Average number of patents (citing firm) |
8 | Average number of citations made by citing firm patents |
9 | Average number of patents (inventor) |
10 | Technological distance |
11 | Domestic mobility |
12 | Geographical match |
13 | Mobility |
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Lenzi, C. Workers’ mobility and patterns of knowledge diffusion: evidence from Italian data. J Technol Transf 35, 651–670 (2010). https://doi.org/10.1007/s10961-009-9130-6
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DOI: https://doi.org/10.1007/s10961-009-9130-6