Measuring variation, selection and retention

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cog-nitive distance and in the pivotal role of related variety.

The latter stream of literature places special emphasis on the process of recombination of dierent bits of knowledge.

In this chapter we build on this literature, albeit focusing on variation as a potential solution to technological weaknesses.

Therefore, the observations here are ex post recombined knowl-edge, instead of single pieces of knowledge. In doing so, we can assess the selection process, its trends and what knowledge has been selectively retained in technological evolution.

3.3 Measuring variation, selection and reten-tion

3.3.1 Variation

In this section we describe the methodological framework that will allow us to investigate the properties of knowledge dynam-ics through the lens of evolutionary patterns. Our

method-ology concerns the exploration of the knowledge codied in patent data using citations among patents to build a knowl-edge network.

Patent data provide a great deal of information that can be exploited to analyse technological change. Patents are of-ten used as a proxy for invention. In this outstanding number of studies, patent applications are used to analyse inventive activities and their diusion (Lanjouw and Mody, 1996; Jae and Trajtenberg, 1999, 2002). In addition, many authors use technological classes, which specify the technological domains of each patent, to identify dierent properties of the knowl-edge base such as coherence (Nesta and Saviotti, 2005) and knowledge relatedness (Jae, 1986, 1989; Breschi et al., 2003).

Other studies use patent classications co-occurrences to measure related and unrelated variety as well as coherence and cognitive distance (Krat et al., 2011; Quatraro, 2010).

Dierent patent classications are proposed by patent

of-ces. In this work we use International Patent Classication (IPC) codes, established by the Strasbourg Agreement in 1971, through which technological specication is provided by means of hierarchical and language independent codes assigned to each patent.

As stressed before, we may think of patent classication codes as an underlying genetic structure. Therefore, from an aggregate perspective, each combination of IPC codes included in patents may represent an insight that a technological knowl-edge variety of the aggregate underlying structure has been proposed. When dierent IPC codes appear together in the knowledge space, we assume that a variation at the genotypic level occurs. Thus, we use the number of technological class combinations proposed each year as a proxy for variation.

3.3.2 Selection and retention

Another branch of the literature in innovation studies high-lights the role of citations among patents to unfold techno-logical advances and knowledge evolution (Mina et al., 2007;

Verspagen, 2007; Fontana et al., 2009; Barberá-Tomás et al., 2011; Barberá-Tomás and Consoli, 2012; Epicoco, 2013; Mar-tinelli, 2012). Indeed, citations can be employed to analyse various dimensions of technological knowledge developments synchronically and diachronically (Mina et al.,2007). Since the works of Gareld (1955); Gareld et al. (1964); de Solla Price (1965), and Hummon and Dereian (1989), citations have been extensively used to detect patterns of scientic knowledge.

The process through which citations are included in patents implies that the inventor and patent attorney place references to prior patents (and also other non-patent references) in the patent document that they are ling to the patent oce. The list of references is controlled and, in some cases, lled in by the patent examiner that adds or deletes any missing or irrelevant citations to other patents (Popp, 2005). As a result, citations dene the legal boundaries of the inventions limiting the scope of the patent property rights (OECD, 2009). Therefore, when patent A cites patent B, a technical relation exists between the two due to the knowledge included in previous patents (B) which more recent ones are built on (A).

In this work we assume that citations among patents can be used as a proxy for selection and that of all the variations that at any time are proposed to the selection environment, only a fraction are selected. Since citations are also good indicators of the quality and relevance of cited items (Popp,2002), in this work patents that are cited are then selected.

To analyse `what' is selected in the evolution of techno-logical knowledge, we apply a method proposed by Hummon and Dereian (1989) for examining connectivity incitation net-works. The authors developed three indices for identifying the

main stream of knowledge within directed networks, i.e. the Main Path analysis (Hummon and Dereian, 1989; Hummon and Doreian, 1990). In order to dene the importance of links and nodes in the network, the Search Path Count (SPC) al-gorithm (Batagelj, 1991, 2003) is implemented within Pajek, a software that enables the analysis of large networks2. After building a citation network in which each patent constitutes a node and citations among patents the arcs, we calculate the arc weights using the SPC algorithm. These weights are then used as a measure of importance of the single arcs on the whole net-work. Indeed, the algorithm builds on the idea that the more a source-sink path3 passes through an arc, the greater the impor-tance of that arc in the whole network (Batagelj et al., 2014)4. At this point a further clarication is needed to extend the theoretical framework described above. In order to identify the technological knowledge that is selected and retained in the citation network, we propose an index of knowledge reten-tion (IoKR) that calculates the share of IPC codes that are passed on from the cited to the citing patent. The IoKR is calculated as follows:

IoKRj = si,j si

where i is the cited patent and j the citing patent, whereas si represents the number of IPC codes assigned to the cited patents and si,j the number of IPC codes that are present in both the cited and the citing patents. Therefore, when all IPC codes of the previous patents are also assigned to the following citing patents, the IoKR equals 1 and 0 otherwise.

2http://vlado.fmf.uni-lj.si/pub/networks/pajek/

3The source-sink path comprises the nodes and arcs that connect each start point to any end point. A single node is both a start point and an end point.

4See Batagelj (1991, 2003); Batagelj et al. (2014) for the technicalities of this method.

The usefulness of this measure in the analysis conducted in this chapter is twofold. On the one hand, as stated before, some patents may inherit a part of the whole set of IPC codes and some patents may not share any of the IPC codes present in cited patents. This issue can be explained by the purpose of citations that may be assigned to dene prior art which new patents build on, to indicate the state of art that preceded the patent and/or to emphasise the lack of novelty of the citing patent. Therefore, this measure can be used to analyse the selection process focusing on the technological knowledge that is selectively retained among variations, excluding those links that do not imply an inheritance of technological knowledge but may simply dene the previous state of the art. For ex-ample, assuming an IoKR of 1 we can calculate the number of times a combination of IPC codes is retained by follow-ing patents, whereas usfollow-ing a value >0 and <1, we can assess dierent degrees of retention of the underlying technological knowledge structure. In this way we discern between selection of just a part of the technological knowledge included in cited patents and selection of the whole technological knowledge in a specic variety.

On the other hand, we can combine the IoKR with the main path analysis proposed before. This exercise allows us to

nd more coherent connected sub-networks of nodes in which knowledge retention is higher. Instead of using the SPC method to weight the arcs, we implement this measure to analyse the most retained part of the network.

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