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Strengthening relationships in clusters: How effective is an indirect policy measure carried out in a peripheral technology district?


Studies of the effects of specific policy measures for innovation have focused mainly on actions based on direct R&D support. However, the innovation studies literature sees innovation as an interactive process, emphasising the role of knowledge exchange for successful innovation. Furthermore, it is increasingly accepted that co-location is not sufficient for knowledge exchange to occur. Consequently, there is also a need to assess the effectiveness of policy measures to promote knowledge exchange between co-located actors. The objective of this paper is to narrow this gap by exploring the outcome of an indirect policy in stimulating coordination and networking. The paper analyses policies for increased networking in a mechatronics district located in the peripheral and less innovative region Apulia (Southern Italy). The success of the coordination and networking action is examined by adopting a longitudinal approach. In order to assess the association of the policy with the overall network structure, social network analysis is used to analyse the data. We compare characteristics of the network in the early and later phase of the district across five dimensions of knowledge exchange, identifying a large increase in the use of partnerships as the main effect of the policy.

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  1. A counterfactual analysis carried out by a group of researchers led by de Blasio and Lotti (2008) have called into question the effectiveness of public intervention—and, specifically, industrial policy—in Italy. Despite this, since the early nineties, the Italian government and regional committees promulgated various laws and policy measures aiming to support industrial districts. Several national laws, decrees and resolutions were issued by the national government between 1991 (Law 317/1991) and 2009 (Law 5/2009) with the aim to support industrial districts. These laws have progressively shifted from a territorial to a functional rationale looking at the importance of external resources, cooperation and networking for firms making up the Italian industrial districts. In this framework, the regional legislation has primarily followed the evolution of the national laws. Several interventions, policy measures and public funding opportunities were launched in the local industrial districts with the aim to support investments in machinery, internationalisation, business services, innovation, etc. Among others, regions such as Friuli Venezia Giulia and Apulia have implemented the so-called “Programme Contract”, i.e. contracts stipulated between the state administration and large companies, SME consortia or administrators of the districts with the objective to build new industrial plants, create new jobs, and foster production, services and research. Moreover, various laws promoted actions aiming to guarantee financial benefits and co-fund regional projects at the district level (Financial Laws 2006 and 2007), boost production and employment (Decree Law 5/2009), and introduce new rules to regulate actions such as Gurantee Fund, Corporate Finance Fund, Credit Guarantee Consortia (Decree Law 5/2009) and Network Contracts (Decree Law 179/2012). For a detailed overview of the legislation and policy measures carried out at the national and regional levels, see Osservatorio Nazionale dei Distretti (2014).

  2. In this paper, with the term “indirect” we refer to a policy measure aiming to foster networking and knowledge exchange among the cluster’s firms without providing them with direct financial support to R&D activities (i.e. direct action; Nishimura and Okamuro 2011).

  3. The difference between “frequency” of contracts without research content (number of contracts) and “intensity” of research contracts (value of contracts) is determined by the nature of the activities regulated by these two types of contractual agreements. Our assumption, based on the results of a previous study (Calignano and Quarta 2014), is that the importance of knowledge exchange based on research contracts is determined by the value of the research contracts through the laws of supply and demand. Moreover, from a relational viewpoint, our assumption is that bigger research projects and related contractual agreements normally involve a higher number of face-to-face meetings, phone calls, email exchanges, joint tests, etc. As consequence of these contacts, knowledge exchange is likely more intense. On the other hand, the number of recurring contracts allows researchers to determine the frequency of knowledge exchange: this aspect is particularly relevant with respect to contracts without research content due to more cyclical and standardised operations.

  4. As explained above, several types of relationships—related to social proximity (see, among others, Boschma 2005)—potentially fostering informal contacts were mentioned during the interviews (i.e. friendship, kinship, previous common work or research experiences). However, the respondents were not asked to specify on what type of relationship such informal contacts are actually established.

  5. Weak, moderate and strong classes related to ties’ strength were defined a priori and tested during the preliminary interviews carried out with the members of the MEDIS. The only exception is represented by research contracts. In this case, the various classes were based on a previous study of university-linkages in the same geographical context, which revealed that larger contracts (those worth more than 100,000 euros) contain a higher degree of knowledge exchange since they require more know-how and larger technological and infrastructural capabilities (Calignano and Quarta 2014; see also Footnote 3). Weak and moderate research contract intensity were determined accordingly.

  6. The density scores for informal contacts (in both periods) and partnerships (in the second period) imply that 1 out of every 2 potential ties is realised. This very high level of density is partly a function of the small size of the network (i.e. only 14 nodes).

  7. Simple matching coefficient is the ratio between matching attributes (or relationships, in our case study) and total number of attributes.


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The authors thank Elisa Giuliani and Pierre-Alexandre Balland for their insightful comments on an earlier version of this paper. Thanks to Patrizia Scarcella for suggesting relevant literature on the use of retrospective data. The authors are grateful to the editor, Martin Andersson, and two anonymous reviewers for their helpful comments and suggestions.

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Correspondence to Giuseppe Calignano.

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Calignano, G., Fitjar, R.D. Strengthening relationships in clusters: How effective is an indirect policy measure carried out in a peripheral technology district?. Ann Reg Sci 59, 139–169 (2017).

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JEL Classification

  • O
  • O3
  • R1
  • R10
  • R12