ADDRESSING THE DISENCHANTMENT.
UNIVERSITIES AND REGIONAL DEVELOPMENT IN PERIPHERAL REGIONS
ABSTRACT
The paper addresses the rising criticism to innovation policies that have assumed a direct and massive impact of universities in regional economies. It integrates the literature in economics of innovation, higher education, economic geography and regional studies. The paper shows why research excellence is
considered a necessary condition for regional impact, why it is not sufficient, and whether there are substitutes. The Additional Material section includes an analysis, based on original data, on research excellence in universities in European peripheral regions. The policy implications call for a new approach to the role of universities.
1. Addressing the disenchantment
Policy makers and scholars who happen to be involved into the difficult issue of designing policies for regional development disagree on several points. If there is a point, however, on which the agreement is almost perfect it is on the role of universities: starting from the early 1990s there has been virtually no publication or policy document that has not emphasized the crucial importance of universities as sources of human capital and knowledge spillovers for the regional economy. Yet the agreement seems to falter faced with recent experiences, particularly in peripheral or catching-up regions in Europe, that contribute to some disappointment. As we will see, we entered into a period of disenchantment.
In this paper we try to address this disenchantment with respect to the role of universities. The paper is a conceptual one. It offers a discussion of the state of the art of knowledge, linking evidence at individual, organizational, regional and national level, ranging from case studies to policy reports to econometric analysis, and taking stock of the literature in economics of innovation, economics of science, higher education, economic geography and regional studies. Section 2 presents the arguments for disenchantment. Next sections illustrate the main reasons why scientific excellence is a necessary condition for regional development (Section 3) but not a sufficient one (Section 4), while in some cases there are effective substitutes (Section 5). Having established the conceptual framework, in Additional material we offer evidence that universities in cohesion regions cannot be described as unitary institutions characterized by excellence. In some sense, the debate seems to assume as given an independent variable (quality of research) which is, on the contrary, missing in most cases. Section 6 tries to derive policy implications from the discussion and the evidence.
2. Knowledge and regional development: Why did we place excess expectations on universities?
Starting from the early 1990s, there have been huge expectations on the role that universities can play for regional development. Virtually any single policy document mentions universities as key regional resources (for a summary see OECD, 1997; 2007; 2008; European Commission, 2010; European Union 2011). Their role is manifold: they are a direct source of qualified employment and expenditure, create human capital, produce and disseminate knowledge in a variety of formal and informal ways, establish network relations, participate to the regional governance and policy process. Under certain circumstances they may take the leadership of the development process (Gunasekara, 2006; Arbo and Benneworth, 2007).
The economic impact of universities has also been the object of a dedicated stream of literature, with a variety of empirical methods (Varga, 1998; Goldstein and Renault, 2004; Drucker and Goldstein, 2007). From the theoretical point of view, these empirical and policy arguments have been made persuasive by the rapid advent of endogenous growth theory in economics, and of regional systems of innovation in policy studies.
Yet in recent years several authors have called for a deep rethinking. Power and Malmberg (2008) raised the question whether the role of universities is indeed a regional problem, calling the attention on the structural mismatch between the global orientation of universities in pursuing the research mission and the needs of regional actors. There is a serious risk of “exaggerated expectations” on the role of universities for regional development (Coenen, 2007, p. 803) and a danger of equating research excellence in universities with the ability of regional economies to support innovation (Goddard, Kempton and Vallance, 2013). Charles (2006) and Boucher, Conway and van der Meer (2003) developed a typology of engagement of universities in the regional context, noting that it is far from obvious. Indeed, Uyarra (2010) emphasized that the alignment between knowledge production and the demand side is found only in few fields and few locations- exactly those fields and locations that are found in success stories (Mowery and Sampat, 2005). Summarizing the debate, “in practice regions are not, nor should they be, a primary focus for universities” (Goddard, Robertson and Vallance, 2012, p. 16).
The call for a reconsideration is more urgent for peripheral regions. In these regions the importance of universities has been, if possible, emphasized even more: the very weakness of civil society and industrial base has led to formulate the expectations that only “strong and influential” universities may contribute to economic development (Marques, Caraça and Diz, 2006, p. 541). However, the application to peripheral regions of the model of interaction with universities of more developed regions is a serious conceptual mistake (Benneworth and Hospers, 2007a; 2007b: Huggins and Johnston, 2009). In these regions the low level of infrastructure and industrialization may severely limit the ability of universities to establish productive relations (OECD, 1997). Tödtling and Trippl (2006) noted that most regional innovation policies have been driven by the imitation of success stories in high tech sectors, without a careful consideration of the context. In addition, the transferability of policies from other regions is limited, due to deep qualitative differences (Smith, 2001; Benneworth and Charles, 2008), so that “exactly those conditions that explain the success of a particular region are the most difficult element to learn from” (Hospers, 2006, p.2) and “our understanding of disfunctional and less successful systems is comparatively weak” (Asheim, Lawton Smith and Oughton, 2011, p. 881).
These arguments put into question the European policy orientation, started in 2000 with the Lisbona process, according to which the largest share of Structural Funds has been poured into public support for research and innovation in peripheral regions (also called “Lisbonisation of structural policies”). This concern was initially raised in studies that address the so called “regional innovation paradox”. According to Oughton, Landabaso and Morgan (2002) those regions in Europe that are in the strongest need of innovation and growth, and that receive large additional resources from Structural Funds, are also those that lack absorptive capacity and capabilities to spend money effectively. More recently, several studies have shown a lack of impact of public R&D expenditure under the Structural Funds on the private expenditure and on growth. Rodriguez-Pose (2014) has shown that, after more than a decade of sustained public support for R&D, we still do not see any significant impact on per capita GDP growth. As Muscio, Reid and Rivera Leon (2015) summarize, “pumping more Structural Funds into research and innovation is unlikely to lead to the expected returns in terms of the stairway to excellence” (p. 16).
3. Is research excellence necessary for regional development?
Is there a need for research to be excellent (in the strict and selective academic sense) in order to be relevant for regional development? Isnt’t enough for research to be as it stands, that is, from excellent to, say, modest from a scientific point of view but perhaps useful to the regional context? An examination of the evidence in the literature of the last three decades offers a clear answer. There are several motives to argue that research excellence is a key element here (Sachwald, 2015).
First, firms prefer to collaborate with top-tier universities rather than second-tier universities (Mansfield 1991; 1995; Mansfield and Lee, 1996; Laursen et al. 2011; Mora-Valentin et al. 2004; Muscio and Nardone, 2012; Hong and Su, 2013). Reputation and prestige of the university is a powerful attractor for industry cooperation (Schartinger et al. 2002; Link and Scott, 2005; O’Shea et al. 2005; Powers and McDouglas, 2005; Fontana, Geuna and Matt, 2006). University reputation, in fact, solves a fundamental adverse selection problem: external actors are likely to engage into relation with prestigious because they believe universities make available their best technology, contrary to what private companies would do, keeping the best technology for internal development (Effelbein, 2006). Consequently, high quality of academic research benefits from positive signaling effects (Podolny and Stuart, 1995; Sine, Shine and Di Gregorio, 2003) and mobilizes additional industry funding (Bruno and Orsenigo, 2003; Muscio and Nardone, 2012).
Second, in the case of cutting-edge knowledge, firms search for top quality universities irrespective of the distance (Zucker, Darby and Brewer, 1998; Darby and Zucker, 2003; Gertler and Levitte, 2005; Broström, 2010). A plausible interpretation of this evidence is that firms with high absorptive capacity look for high quality departments, irrespective of the distance, while firms with low absorptive capacity need proximity, somewhat irrespective of, or less sensitive to, research quality (D’Este and Iammarino, 2010; Laursen et al. 2011).
Third, large companies base their R&D location decisions on the quality of the university research located in the host country and site, scanning for the best graduate programs (Malecki, 1987) and the presence of star scientists (Zucker et al. 1998; Mariani, 2002; Karlsson and Andersson, 2006; Athey et al., 2007; Thursby and Thursby, 2009). Attractiveness of talent is associated to strong local research (Beeson and Montgomery, 1993; Audretsch, Lehmann and Warning, 2005; Woodward, Figureido and Guimares, 2006), although this effect is stronger in some research fields than in others (Abramovsky, Harrison and Simpson, 2007; Bekkers and Bodas-Freitas, 2008).
Fourth, the weight of the evidence is towards a positive association between research productivity and spillovers that may contribute to regional growth, sucs as academic patenting and academic entrepreneurship (Thursby and Thursby, 2002; Breschi, Lissoni and Montobbio, 2007; Carayol, 2007; Lissoni et al., 2008; Toole and Czarnitzky, 2010; Crespi et al., 2011; van Looy et al., 2011; Lawson, 2013; Perkmann et al., 2013). Faculty quality is positively related to the number of university invention disclosures and their commercialization (Friedman and Silberman, 2003). With respect to entrepreneurship, there is large evidence that startup firms created from high quality research are more numerous, and survive and grow with higher probability (Di Gregorio and Shane, 2003; Audretsch, Lehmann and Warning, 2005; O’Shea et al. 2005; Link and Scott, 2005; Wright et al., 2008; Colombo, D’Adda and Piva, 2010; Avnimelech and Feldman, 2011; Audretsch, Hülsbeck and Lehmann, 2012; Fritsch and Aamoucke, 2013; Conceiçao, Faria and Montes, 2014). Researchers from top universities have easier access to critical resources (van Looy et al. 2011). There is also some evidence that startup firms originated from high quality research go more frequently into IPOs and receive a higher initial stock evaluation (Powers and McDougall, 2005). As Hill (2006) puts it, “universities with the greatest economic impacts are generally those with the highest quality research programs” (p. 4).
From this literature we are led to conclude that excellence is crucial for the generation of economic opportunities associated to new technologies and industries, collaboration with large companies, and academic entrepreneurship.
4. Is excellence sufficient for regional development? Or arguments for explaining missing impact Once established the importance of scientific excellence for economic processes of transformation of new knowledge, one has to be clear that excellence is necessary but not at all sufficient. The relationship between universities and regional development is complex, multi-level, probabilistic, fragile, and lagged. It is crucial to understand why.
4.1 Critical mass of research
Excellent research must have a critical mass in order to generate economic opportunities, not because of economies of scale, which are not decisive (Bonaccorsi, Daraio and Simar, 2006), but due to the need to achieve a certain level of organization and division of labour in research teams. Isolated researchers or sub-critical research teams do not have resources to interact productively with the external environment in the forms of contract research, management of IPs, or consultancy.
There are other reasons for the existence of threshold effects. Charlor, Crescenzi and Musolesi (2012) show the impact of human capital and R&D expenditure on innovation activities at regional level are not only strongly complementary, but subject to threshold effects. A similar argument may be found in the model of densification of regional techno-economic networks (Fontes and Coombs, 2001): in order to generate an autocatalytic process there must be sufficient density of interactions.
4.2 Absorptive capacity
Yet a substantive literature suggests that the spillover of knowledge from universities to industry does not only depend on the quality and critical mass of research, but also on factors that are internal to firms and on contextual factors at regional level, on which universities have no scope of control. Several studies based on CIS surveys have shown that the probability to collaborate with universities on the side of firms is positively related to firm size, level of expenditure in R&D, and industry-level variables such as high or medium-to-high tech sectors (Mohnen and Hoareau, 2003; Veugelers and Cassiman, 2005; Segarra-Blasco and Arauzo-Carod, 2008). In other words, cooperation with university requires absorptive capacity (Muscio, 2007) and is complementary to other innovation-related firm-level activities (Cassiman and Veugelers, 2002; Laursen and Salter, 2004; Veugelers and Cassiman, 2005; Miotti and Sachwald, 2003).
Thus regions in which the industrial specialization is based on low technology and small firms find it difficult to establish long term relations between university and industry. Therefore it is becoming clear that not only universities contribute to regional development, but also the reverse is true: peripheral regions are characterized by a low level of interaction between academic and non-academic actors mainly due to lack of demand from the business sector (Martinez-Sanchez and Pastor-Tejador, 1995; Sánchez-Barrioluengo, 2014). Lehmann and Menter (2015) suggest that the relation between university knowledge and regional growth is one of coevolution, not one of direct causation. Azagra Caro et al. (2006) conclude quite drastically that “knowledge exchange does not contribute to regional development in a region with low absorptive capacity” (p. 53). In the absence of absorptive capacity, firms may find it exceedingly difficult to use the kind of analytic knowledge developed in universities (Asheim and Gertler, 2005).
A general explanation, put forward by Acemoglu, Aghion and co-authors, is that R&D and human capital have a stronger growth-enhancing effect in economies that are closer to the technological frontier (Acemoglu, Aghion and Zilibotti, 2006; Vandenbussche, Aghion and Maghir, 2006). Somewhat in a similar direction, Marrocu, Paci and Usai (2013) have found that economies of diversity (or Jacobian) are only relevant for knowledge intensive services in densely populated areas of Old Europe, while low tech manufacturing activities in new Member States (mainly in Eastern Europe) are only affected by traditional Marshallian specialization economies.
Recent studies suggest a complementary explanation. Investment into research delivers an impact on regional growth only if there is sufficient accumulation of human capital. There must be people to transform knowledge into innovation and growth. Thus there is a threshold of human capital accumulation below which the impact of R&D on growth of GDP per capita is negligible (Midelfart-Knarvik and Overman, 2002; Sterlacchini, 2008; Charlot, Crescenzi and Musolesi, 2012). In the same direction, investment into human capital and R&D are strongly complementary (Rodriguez-Pose 2014).
The accumulation of human capital, in turn, depends on sustained investment into secondary and, more importanly, tertiary education. However, higher education per se is not yet sufficient. There are two main factors that may weaken the positive relation between investment in human capital and growth: migration of skilled people, and employment into low productivity sectors.
The first disturbing factor is associated to the mobility of graduates: if the local university produces highly skilled graduates, but these migrate in other regions or countries, the virtuous cycle between education, research, innovation and growth is disrupted. There is large evidence that university graduates are indeed highly mobile (Kodrzycki, 2001; Whisler et al. 2008; Faggian, McCann and Sheppard, 2007). Even more problematic, graduates from better universities exhibit greater mobility (Kodrzycki, 2001; Faggian and McCann, 2006; 2009; Whisler et al., 2008; Haapanen and Tervo, 2012). If a critical mass in the regional labour market is not in place skilled individuals migrate to richer regions in search of occupations that match to their skills (Abreu, Faggian and McCann, 2014), so that migration of skilled workers tends to reinforce the advantage of metropolitan areas (Abel and Deitz, 2012). The ability of a region to maintain its competitiveness, therefore, relies heavily on its capability not only to retain its own university graduates, but also to attract graduates from other regions (Faggian and McCann, 2009). At the end of the day, migration effects may be more important that university-industry spillovers in explaining regional learning effects (Faggian and McCann, 2006).
The second factor is linked to the possibility that graduates look for employment opportunities in low productivity sectors- most notably, the Public Sector. It has been noted that the allocation of talent between growth-enhancing and rent seeking sectors is influenced by institutional factors (Murphy, Shleifer and Vishny, 1991). Universities attract location of economic activities in traded goods, while in the case of untraded goods (e.g. medical assistance) it has been found that graduates disperse across the country, without generating growth effects (Bound et al. 2004). In an important case of laggard regions, i.e. Southern regions in Italy, Sestito (1991) found a bias towards bureaucratic skills among graduates. This may be an important explanation for an intriguing empirical finding for these regions, namely a lack of positive impact of tertiary education on productivity growth (Di Liberto, 2008).
Regions with low absorptive capacity suffer from another distinctive weakness, i.e. the private sector of Knowledge Intensive Business Services (KIBS) is poorly articulated, mainly due to lack of demand. Recent studies have shed light into this sector, which does not contribute, like universities, to the production of new knowledge, but greatly contributes to the diffusion and adaptation of productive knowledge. An intriguing possibility is that, given the relative weakness of the private KIBS sector, firms use universities as substitute providers of services. This is what Pinto, Fernandez-Esquinas and Uyarra (2015) found in the Spanish region of Andalusia: “universities are used as a form of KIBS” (p. 1886). In examining the relation
between low and medium-tech firms in Austria, Schartinger et al. (2001) found that the main request was for support services such as technical accreditation, calibration, and analysis, as opposed to collaborative R&D.
4.3 Co-specialization
Most regional policy making is premised on the notion that the knowledge produced in universities is relevant to the regional industry. This reasoning implicitly follows the myths of origin of regional policies, that is, the cases in which academic research and industry have been perfectly co-specialized, as it happened in Silicon Valley and Route 128 in USA or the Golden Triangle in UK. In these regions the industrial base was created greenfield from breakthroughs in scientific research (as in biotech) or from existing specialisation in related fields. The authors in Kenney and Mowery (2014) describe in great detail the process of co-evolution between academic research and industrial specialisation in California. Consequently, in these regions publications and patents are found in the same technological fields (Agrawal and Cockburn, 2003), so that co-specialisation helped to trigger the virtuous circle between research, innovation and growth (Feldman, 1994; Bade and Nerlinger, 2000). While the perfect co-specialization of science and industry has made the linear model plausible, it is not robust to theoretical criticism. This is by no-means the normal situation: these cases are rather the “totemic sites of the new economy” (Armstrong, 2001), they are not the rule but the exception (Lundvall, 2002) and were created out of contingencies that are difficult to replicate (Sternberg, 1996). In so called non-core, or ordinary, or non-totemic regions, the existing industrial base may be completely unrelated to the fields of academic research.
Mismatch may be a serious obstacle to the deployment of knowledge from academic research. Cognitive distance may be difficult to overcome (Noteboom et al. 2007). Empirical evidence shows that academic research has a selective impact based on disciplines (Arvanitis, Kubli and Woerter, 2008; D’Este and Iammarino, 2010; Bonaccorsi et al. 2014).
Based on the influential concept of related variety introduced by Boschma (2005), recent evidence shows that regional growth is enhanced by the diversification of industrial production based on a related basis of competence (see Frenken, van Oort and Verburg, 2007 and Boschma and Gianelle, 2014 for a survey). If the bases of knowledge are unrelated, the opportunities for innovation are much lower.
The search for a thematic matching between research and industry has been exacerbated by the reliance of regional policies on the model of clusters. Based on the literature on agglomeration in regional economics, the emphasis on Marshallian increasing returns, and the evidence about clustering in high technology and traditional industries, there has been a trend of policy making encouraging the creation of local clusters. While in some cases these policies have been effective, in many others they have badly failed. More recent theorizing suggests that in laggard regions the best policy might be not to cluster with existing firms ( buzz), but to search for distant partners which may have complementary technologies and solutions (pipeline) (Rodriguez Pose and Fitjar, 2013; see Bathelt et al. 2004; Storper and Venables, 2004). A pipeline-based policy option may be mandated under conditions of zero or weak co-specialization.
4.4 Institutional quality and intermediaries
Recent studies emphasize that the impact of regional innovation policies is contingent on the quality of the governance. Ederveen, de Groot and Nahuis (2006) found that Structural Funds are effective in increasing the rate of investment but only conditionally on institutional quality.
The issue of intermediaries is more controversial. We are dealing with Technology Transfer Offices (TTO), Industrial Liaison Offices (ILO), science and technology parks, incubators, and the like. A separate category of intermediaries is represented by joint centers, consortia, foundations, or companies dedicated to applied research and technology transfer.
In principle, there is a strong need for dedicated organizations in charge of facilitating the transformation of knowledge. Knowledge produced at universities is in an embryonic stage (Jensen and Thursby, 2001). Filling the gap between this stage and the birth of commercial applications is long, uncertain, bound with failures (Geiger and Sá, 2008) and requires downstream industrial activities, such as prototyping, testing, process engineering, production scale up, that are seldom of interest to researchers. However, it cannot be filled without the active collaboration of researchers. The simple co-location of research and industry in a physical environment, such as science parks, is not sufficient (Massey et al. 1992).
This creates a well known problem of search, incentives and coordination. Researchers do not know ex ante which firms might be interested in their discoveries, and firms do not know which researchers have developed research that fits with their problem. Search is therefore expensive (Calcagnini et al. 2015). As Hellman (2005) and Effenbein (2006) have shown, under these circumstances intermediaries have a specialization advantage, so that researchers may trustfully delegate to them time-consuming search activities, concentrating on research activities. If interactions are left only to informal channels, there might be overburden for researchers, exacerbation of trade-offs between industry engagement and academic research, and ultimately lack of effectiveness (Martinez-Sanchez and Pastor-Tejador, 1995; Mora-Valentin et al. 2004; Rondé and Husslera, 2005).
While the literature on intermediaries is scarce, there is agreement on the notion that they are important to reduce information costs (e.g. matchmaking), to carry out specialized activities not covered by either university or industry (e.g. exploratory applied research, testing) and to act as catalyzers of regional innovative dynamics (Smedlund, 2006; Howells, 2006; Bramwell and Wolfe, 2008; Goddard, Robertson and Vallance, 2012; Watkins et al. 2015). Yet the recent literature warns against the proliferation of intermediary organizations and suggests that their role and funding mechanisms should be designed carefully (Chapple et al., 2005; Antonopoulos et al., 2009; Clark, 2009; Huggins and Kitagawa, 2012).
4.5 Motivation and sustainability of external engagement of researchers
From a policy point of view it is also important to ask a question related to the long term sustainability of the interaction between universities and the regional context. Universities are not regional institutions. There are inevitable tensions, or trade-offs, between excellence and relevance, or between being active at world level in a scientific community and interact productively with the regional context. While part of the literature in the last two decades has raised the issue of potential deterioration of quality of research as a consequence of industry engagement (Henderson et al. 1998; Mowery and Ziedonis, 2002; Murray and Scott, 2007; Czarnitzki et al., 2009), the overall weight of evidence does not support a negative view.
It has been repeatedly found that among those university reserchers with the largest impact in terms of exploitation (e.g. academic entrepreneurship, patenting and licensing, contract research) we find people with excellent scientific records. More precisely, being engaged into these activities does not detract from their scientific publication activities, but generates new research ideas and attracts additional resources for research. Publications linked to a patent receive more citations and author-inventors publish at above average rates (van Looy et al. 2006; Breschi, Lissoni and Montobbio, 2007; Czarnitzki et al., 2007; Stephan et al., 2007; Azoulay, Ding and Stuart, 2009; Bonaccorsi and Thoma, 2011; Franzoni and Scellato, 2011; Larsen, 2001; Magerman, van Looy and Debackere, 2015). University-industry co-authored papers are cited more frequently (Hicks and Hamilton, 1999; Welsh et al. 2008). With respect to more downstream activities, such
as consulting, Perkmann and Walsh (2008) found that they have limited effects on the direction of academic research towards more applied themes.
Yet other studies suggest that the trade-off depends on the discipline and the level of research funding, so that in disciplines such as life sciences, and in regions in which public funding is not abundant there might be substitution between industry contract research, and/or consulting, and academic publications (Hottenrott and Tharwarth, 2011; Hottenrott and Lawson, 2014; Rentocchini et al. 2014). Bianchini et al. (2015) find that a stronger involvement into technology transfer and consulting actitivities by engineering scholars does not necessarily detract from research performance, but diminishes teaching quality.
Some authors have therefore proposed that the relation between research productivity and industry engagement is not linear, but curvilinear, suggesting that there is a maximum level beyond which the trade-offs becomes severe (Blumenthal et al. 1996; Bonaccorsi, Daraio and Simar, 2006; Fabrizio and Di Minin, 2008; Banol-Estañol, Jofie-Bonet and Lawson, 2015).
Several recent studies, however, reveal that, while good scientists would not be damaged by an active involvement into knowledge transfer, yet it is not obvious they are motivated to do so. The comprehensive state of the art carried out by Perkmann et al. (2013) offers several insights. Azagra Caro et al. (2006) find that in regions with low absorptive capacity the engagement with firms is hindered by the fear of losing academic freedom. The UK scholars addressed by the University of Cambridge and Imperial College declared their most important constraint is the lack of time to fulfill all university roles (Abreu et al. 2009). In an earlier study only 2% of UK senior managers of universities describe themselves as “community-based institutions serving the ned of the local area/region”, while one third claim they are “international research institutions seeking to provide support to the local community where it does not conflict with international research excellence” (Chatterton and Goddard, 2000, p. 477). A recent qualitative survey of UK scholars alse reveal fear that public engagement may be deleterious to academic career (Watermeyer, 2015). Therefore active regional engagement cannot be presupposed at all and is not a clear cut process (Benneworth et al. 2009).
A related tension has been revealed in other studies. Minguillo, Tijssen and Thelwall (2015) note that science parks of high reputation tend to establish cooperation with companies outside the region, raher than with local ones. Azagra Caro et al. (2006) show that researchers in low tech regions in Spain tend to look for industry cooperation outside the regional borders while Egeln, Gottschalk and Rammer (2004) find that knowledge-intensive service spinoff companies in Germany leave their parent university and go to large cities in order to find more attractive clients. Examining the spatial scope of interaction Johnston and Huggins (2015) find that research intensive universities and large Knowledge Intensive Business Services (KIBSs) engage frequently in non-localized networks. This means that while top-tier universities are able to attract remote industrial partners, second-tier universities are vulnerable to the effect of distance (Hong and Su, 2013). In general, high quality knowledge networks tend to be selective in their search for partners (Giuliani, 2007).
These findings resonate with the results of a recent survey on Spanish researchers, which argues that researchers that engage with external actors are those that perceive past user engagement activities as beneficial for their own lines of research (Olmos-Peñuela, Benneworth and Castro-Martinez, 2015) and with the finding that the propensity of biomedical scientists to engage with industry is mediated by an extraversion personality attribute (Llopis and Azagra Caro, 2015) and by other personality attributes (Azagra-Caro, 2007; Giuliani et al. 2010).
Another paradox seems at play here. For many years the literature has warned against the perils of too much involvement of academia into industry. The most recent studies suggest, on the contrary, there might
be indeed insufficient involvement. The values and practices of academia are shown to be resilient against crossing boundaries activities. This is an issue that must be elaborated in policy terms.
5. When research excellence is not necessary: the alternative path of regional universities and the non-university sector
The findings discussed above refer to the exploitation of new knowledge. A different story can be told with respect to those processes that mainly exploit existing knowledge for the benefit of the regional economy, through consulting, technology transfer and practical training, particularly with respect to small firms. Here we find much less agreement in the literature and in the practice. The dominant view is that these processes require professional intermediaries which maintain linkages with universities but stay somewhat at a distance, developing tailored solutions for users.
In the last decade a large literature has called the attention to the importance of indirect impact of universities on regional growth, or channels that do not originate directly from research. According to Laursen and Salter (2004), in fact, “the salience of universities and public research as a direct source of innovation for industrial firms appears to be limited” (p. 1207). One important channel is informal relations between universities and firms, often associated to consultancy, as opposed to research (Bönte and Keilbach, 2005). Another channel is represented by private knowledge-based service providers, such as consultants and private research organisations (Tether, 2002; Tether and Tajar, 2008), also called the second knowledge infrastructure (den Hertog, 2000).
In a different line of reasoning, focusing more on the nature of knowledge than on the type of interaction channels, some authors have raised the question whether the kind of knowledge produced by universities is what is needed by the regional industry. For example, Asheim and Coenen (2005) have discussed the distinction between analytic and synthetic knowledge and applied it to the analysis of regional systems. In a similar line, Jensen et al. (2007) and Isaksen and Karlsen (2010) have argued that the distance between the analytic, codified and abstract knowledge produced by universities and the need for contextual, problem-driven kind of knowledge needed by firms, particularly SMEs, may be too large to be covered by universities themselves or by intermediary organisations.
These arguments are likely to be more important in mature or low-tech industries. Grimpe and Sofka (2009) and Bodas-Freitas et al. (2013) show that in high tech industries firms rely on universities and/or suppliers for sourcing technological knowledge, while in low tech industries firms search mainly from customers or competitors. Maietta (2015) has found that the research quality of departments, as measured by bibliometric and research assessment indicators, is negatively associated to the intensity of product innovation of companies in the food industry they interact with. Being in the mechanical industry reduces the probability of collaboration with universities, according to Muscio (2007). These findings are not surprising given what we know since long time about the sources of innovation for small firms and for low and low-to-medium technology firms (Piergiovanni, Santarelli and Vivarelli, 1997; Conte and Vivarelli, 2005). This literature supports the argument that calls for a clear distinction between research policy and innovation policy. For example, Capello and Lenzi (2014) find that the elasticity of GDP growth to innovative activities is mich higher (and less uneven across regions) than elasticity to R&D.
These contributions are consistent with a call for differentiation of the role of higher education institutions in regional development. Several authors have argued that the burden placed on universities for regional development is “probably too heavy” (Huggins, Johnston and Steffenson, 2008, p. 336; Huggins, Jones and Upton, 2008), is a source of tensions (Wright, 2008) and “may be too much” (Breznitz and Feldman, 2012, p. 155). New actors should be engaged: among them the so called regional universities, or PhD granting
institutions that choose deliberately to focus their offering on regional needs (Andersson, Quigley and Wilhelmsson, 2009), and non-university higher education institutions, such as Universities of Applied Sciences and colleges. More research is needed on these actors and on their role in regional development, moving beyond the one-size (of university) fits-all approach.
6. Do not throw the baby, or some policy implications for the disenchantment period
From the above discussion and the empirical evidence one might draw the conclusion that relying on universities for regional development is an illusion. We do not subscribe to this view. The old adagio of avoid throwing the baby with the water is still useful here. A few implications for addressing the disenchantment period are useful.
First, what is needed is a long term view. As it has been stated, “it can take up to 10 years for an institution, and 20 years nationally, to attain a positive rate of return from an investment in research and technology transfer” (Heher, 2006, p. 403). National and regional policies must be credible over a long time horizon. If a long term view is missing, there is a danger is to reduce public funding just when the positive impact is around the corner.
Second, we need to look for complementarity between research and innovation, or (more measurably) between publications and patents, but also between research and human capital formation or training. Public investment has a higher return if all these activities are carried out in the same territory.
Third, we should thrive for achieving threshold effects. If sufficient density (not only of resources, but of links and flows among resources) is not available, try to figure out where the threshold is likely to be. Fourth, it is important to look for co-specialization between university research, technological capabilities, and industrial interest. If such a co-specialization can be found, we should invest into dedicated programs, with clearly defined goals. Probably mission-oriented organizations might help. Co-specialization can also take the form of related variety.
Fifth, If co-specializations is not found, the policy imperative becomes: decouple. Do not try to achieve collaboration between local university and local industry of there is no common interest. As Pinto, Fernandez-Esquinas and Uyarra (2015) put it: “the provision of advanced services by universities, even when they may compensate for the relative absence of KIBS firms, should be handled carefully. It might be better to provide policy support for local firms to access the best KIBS even if they are outside the region, rather than subsidise universities to provide poor services” (p. 1887). Looking for fashionable agglomeration or Marshallian effects may be pointless. It would be better to use universities as global pipelines for external resources, rather than sources of local buzz. It is also important to clearly distinguish between research and innovation at policy level. If inputs for innovation do not come from universities, look elsewhere.
Six, we should aim at a differentiation of mandates for universities. This is a politically sensitive issue, given that universities in non-dual higher education systems do not accept any legally enforced differentiation. However, a de facto differentiation can be achieved by regional governments by allocating additional funding according to clearly defined goals. Regional universities and non university HEIs may be more motivated to engage into the training and applied research needs of their respective region (Andersson, Quigley and Wilhelmsson, 2009). On the contrary, trying to get a strong regional focus from generalist Humboldtian universities is probably wasteful. In the Additional material section we show that, in European cohesion regions, few universities produce really excellent research. Moreover, they are excellent in a few fields, and these fields do not match, in general, with the regional industrial structure.
Seventh, it is crucial for policy makers to understand the organizational complexity of universities. These are not unitary or hierarchical organizations, but organized anarchies and loosely coupled systems. They do not have a central strategy. The best way to involve researchers in external engagement is to provide opportunities that fit with their own research interests. This means that cohesion policies should differentiate between two different goals: contributing to human capital and knowledge diffusion is a goal that can be achieved by universities at the overall institutional level; creating new knowledge at excellent level and generating economic opportunities is a goal that can be achieved only by research teams. Here cohesion policies face a difficult challenge, because national and regional institutions are forced to deal with institutional leaders (e.g. Rectors), whose mission is often more spreading resources across all fields than leveraging on pockets of research excellence. However, regional policies should identify clearly those goals that require the involvement of universities as unitary institutions from those that ask for discriminating internally. The recent Europen policy of “seal of excellence” is a positive step in this direction.
Universities are among the oldest and more stable institutions of modern societies. They will contribute to the economy and society of our countries for centuries to come. More modestly, there is a need to help them to contribute to the catching up of peripheral regions. After a period of illusion, we now live in a more realistic period, in which the rules of the game are complementarity, threshold effects, differentiation, and organizational complexity.
