Thinking inside the (black) box: Technological dynamics and Smart Specialisation policies

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Scuola Superiore Sant’Anna

Universitá degli Studi di Pisa

Master of Science in Economics

Master Thesis

Thinking inside the (black) box:

Technological dynamics and Smart

Specialisation policies

CANDIDATE:

Giacomo lo Conte

SUPERVISOR:

Prof. Angela Parenti

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Abstract

This work aims to provide a new empirical approach to address the impact of Smart Specialisation on productivity. Smart Specialisation is a vague concept in the economic analysis and different approaches have been suggested. Many works on the subject deal with technological dynamics without considering how they create spillovers. On the other hand, the pure technological approaches are defective on considering the policy side.

By using technological measures from microdata and the European Funds as the re-sources spent on the policy, we are going to detect the effects if Smart Specialisation on productivity and productivity growth. Results show that technological dynamics have a positive impact when they are supported by specific policies. Moreover, our results are consistent with the idea that less productive regions benefit the most by increasing their technological basis, while more productive regions should preside over the complexity frontier.

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Introduction

Smart Specialisation has been a relevant theme in the innovation policies of the

Eu-ropean Union. Since Foray and Van Ark (2007) launched this new concept, the interest

for the subject has been growing, including many different perspectives (Capello,2014).

The idea is that regions should specialise by following their inner characteristics and their historical strength points. It is necessary to design the interventions through a bottom-up approach, by including public, private, academy and civil society. The con-sequent innovative process of development it comes from has been studied from many different points of view. Geographical economists, innovation policy scholars, political scientists have contributed to the study of the subject, but its trans-disciplinary na-ture has made it difficult to find a comprehensive approach to the matter. While some

scholars focused on understanding where differences among regions lie (Boschma,2014;

McCann and O. Ortega-Argilés, 2014), others put at the centre of their inquiry the

technological dynamics (Balland, Boschma, et al., 2019). Studies and new approaches

have grown in numbers but we are still far from a satisfactory approach it integrates the study of the subject with appropriate quantitative tools.

This work aims to define an empirical approach for estimating if and how the Smart Specialisation dynamics can have an effective impact on regional economies. The vague nature of the Smart Specialisation makes it hard to investigate properly the economic issue. This often is due to the incapacity of synthesising the technological side of the problem with the policy side. The question, as we are going to see, is not trivial and requires to identify the strengths and flaws of the most popular approaches on the sub-ject. In section 1, we are going to address the issue from a theoretical and an empirical point of view: along with the review, the issues still open in literature will be faced. Section 2 will focus on the derivation of the empirical model to estimate, with the description and choice of the variables we are going to employ. In section 3 descrip-tive and inferential analyses are reported, along with the estimation methods to solve endogeneity problems. Section 4 represents the conclusions of the work, reporting the final considerations, policy implications, limitations and possible further developments of the work.

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1

Smart Specialisation: an open issue

The European Innovation Policy: from the Lisbon Agenda

to our days

Is it possible for Europe to find a new way of growth? Is the old continent able to redefine a new definition of economy, structurally based on knowledge and innovation? Is she able to do it originally, to compete with the other economic superpowers? The problem is not trivial and it raised in the middle of ’90s when European institu-tions started to face the so-called "Transatlantic productivity gap" (R. Ortega-Argilés,

2012), i.e. the phenomenon for which, after decades of catching up, US productivity

started growing faster than in Europe, nevertheless the Single Market had just been achieved. Many attempts to explain this situation have been done by economists and it is not in the intentions of this work understand the causes that made it occur. To

address this problem R. Ortega-Argilés (2012) suggested there were two effects in the

increasing of this gap, a structural effect and an intrinsic one.

The first effect derives from the composition of European economies concerning the US productive sectors since in our continent the presence of low and medium technology firms was stronger than in the United States. This element was highly relevant for explaining the initial cause of the gap: high technology sectors were quickly developing in America during the ’90s (ICT was the principal driving force with its applications) and US firms were more able than Europeans in translating R&D in productivity gains

(Van Pottelsberghe De La Potterie, 2008): this was the so-called structural effect

be-cause it depended from the different structure of American and European economies.

This is however just a part of the tale because, as Erken and Es (2007) notices,

Eu-ropean firms were lagging behind their American counterparts even when ICT started to develop on the other side of the Atlantic, while productivity was at the stake in the other sectors too. In other words, Europeans were intrinsically less able than Amer-icans in innovating, or at least in getting productivity gains from their research and their innovative activities. This effect has been then defined intrinsic effect. Consid-erations and measures of these effects have been done by economists and it appeared

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that, while in ’90s structural effect prevailed, in the first years of the new millennium

the intrinsic one was more and more relevant (Jorgenson, Ho, and Stiroh,2008),

bring-ing the European Commission to consider the necessity of adoptbring-ing new strategies for effective innovation policy.

In order to address these relevant issues, the European Union institutions started a

po-litical reflection it culminated in the Lisbon strategy or Lisbon Agenda (Council,2000).

This strategy had as its purposes the will of making the European Union:

"the most competitive and dynamic knowledge-based economy in the world capa-ble of sustainacapa-ble economic growth with more and better jobs and greater social cohesion."

Lisbon European Council 23 and 24 March, Presidency Conclusions.

The first step towards this direction was taken with the institution of the European Research Area (ERA) in 2000. The ERA was developed to enhance pan-European knowledge transmission mechanisms by fostering greater cooperation and coordination among European researchers, industries and entrepreneurs. Nevertheless, as long as the

Lisbon Agenda boots to its end, few of the planned accomplishments were achieved1_.

The failure of the Lisbon strategy made the Commission aware that coordination strate-gies were not sufficient to stimulate European productivity and new initiatives were required. A high-level group of experts known as "Knowledge for Growth" expert group (K4G) was set up in 2006 to advise the European Commissioner for Research, Janez Potocnik. The group contained many of the leading thinkers who had already been working on the transatlantic productivity gap question. The Expert Group pro-duced various documents and research papers and their final considerations have been

summed up in Expert Group Knowledge for Growth (2008). The main contribution

in the relation has been written by Dominique Foray, David, and Hall (2009), i.e. the

Vice-Chairman of K4G Dominique Foray, Paul David and Bronwyn Hall. This crucial contribution explains systematically for the first time the concept of Smart Specialisa-tion, the new idea the European innovation policy has been based on since.

They believed that regions should develop a strategy of industrial specialisation and/or diversification linked to the technological competences the single regions already had, identifying resources and key-characteristics and sharpening competitive advantages: this approach must involve entrepreneurs and firms, inasmuch they are both the hold-ers of the technological and scientific competences and the creators of this competitive advantage, and it must be oriented to an idea of social innovation (Dominique Foray,

Goddard, Goenaga Beldarrain, et al.,2012).

1_{The aims of Lisbon Agenda were not accomplished according to some economists (Tabellini and}

Wyplosz,2006) due to the imputation at national levels of the objectives. Most of these targets need of structural reforms to be achieved with the Member States reluctant to carry them on and need for greater coordination by European Institutions.

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The first attempts: Regional Innovation System from 2000 to

2013

It must be clear Smart Specialisation is not a policy neither a political strategy, but it represents the economic concept European innovation strategies have been built upon in the last ten years. These policies are defined through the Regional Innova-tion Strategy (RIS) and are based on regional acInnova-tions for regional development. It is important to underline the regional framework of these policies has been adopted by

Commission since 2000 (European Commision, 2000), before it was suggested by the

K4G group.

The first concept of Regional Innovation Strategy was introduced in the 2000 aftermath of the Lisbon strategy, but originally it was conducted as an experiment on a restricted

number of regions they participated in the program along with the RITTS2 projects

and IRE3 _{network. Between 2000 and 2002, almost one in five European regions (30}

in total) have received financing under the ERDF4 _{Innovative Actions for the}

develop-ment of a RIS/RIS+ (500.000e co-financed at 50% by the European Commission and

the regions).

The methodological principles were well-defined and they have been applied to the following RIS too:

1. strict cooperation between public and privates;

2. strategies demand-led, based on firms’ innovation needs and bottom-up; 3. orientation to the innovative actions;

4. inter-regional cooperation and bench-marking of policies and methods.

The lessons received have been further pursued under the new generation of ERDF

innovative actions (European Commision,2000). In particular, it is interesting to look

at the following relevant document of the European Commision (2006), it pretended to

be the guide for the regional political class on the innovative policies. This guide is an overlook on many regional cases with the attempt of identifying the best practices of the regions more able in exploiting innovation in all its forms. The examples reported in the document are considered as the starting point for all the other regions in the period 2007-2013. The principles expressed in the guide were general so that every region could interpret the idea of RIS based on their inner characteristics, and they were based on the observations of the experimental of the first cycle of experimentation in the biennium 2000-2002.

The European Commision (2006) distinguished in more developed regions, they can

2_{Regional Innovation Technology and Transfer Initiatives} 3_{Innovating Regions in Europe}

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adapt to globalisation, thanks to the constant demand of high-quality goods and ser-vices, and less developed regions, they were suggested to have a two-directions policy: on one hand by attracting foreign investments due to the low costs of productive factors and on the other by exploiting these opportunities for strengthening their competences. The aim of the strategy was hence the realisation of regional systems of innovation, that is a system it facilitated the identification and the development of the infrastruc-tures available and the sources of knowledge through the cooperation of the various

regional actors (public and private) (OECD, 2013). The inclusion of all the actors

indicates the idea of a bottom-up approach, according to which the policy has a mere role of coordination, and in this framework, it acquired an important role the evalua-tion of the acevalua-tions, to make them effective. For these reasons in the period 2007-2013, the great difference of the RIS for the previous programmes was that the exploratory actions during the programme and its operational side were unified under the same managerial process. This synchronisation should have facilitated dissemination of re-sults obtained and encouraged their transfer towards other sectors of activity covered by the programme. The link with the operational programme should have also given a signal of regional commitment in innovative policies as well as a long-term assurance

of resources, as European Commision (2006) suggested.

The RIS3 and its innovations

The K4G group engaged in a contest very different from the Lisbon strategy for two main reasons. Firstly, the economic crisis stroked with different intensities Eu-ropean regions and the consequences. Not all countries and regions suffered in the same way the economic recession and it depended from the regional structure of the

economy (Davies, 2011). In particular, regions specialised in manufacture industries

reacted better to the crisis than the ones specialised in services and/or constructing on average. The second big problem was linked on the imitative approach suggested even

in the guide (European Commision, 2006), according to which less developed regions

should have imported models of more developed regions. This had brought to devel-opment models they did not take into account regional inner characteristics and the inevitable differences of the various contests.

The previous scheme of RIS suffered then of some big limitations, worsened by the economic crisis, they induced the K4G to promote the concept of Smart Specialisation. The principal limitations, according to Dominique Foray, Goddard, Goenaga

Beldar-rain, et al. (2012), derived from the lack of a trans-regional perspective, the excessive

involvement of public (bringing too often to picking-a-winner strategies) and abstrac-tion from the regional contests. In the economic contest of the crisis, when financial resources were scarce both for public and privates, this strategies resulted in inefficient allocation of R&D funds and the ineffectiveness of some policy, particularly for weaker regions.

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previous ones) started with a redefinition of the methodology about the innovation systems. The starting point of this new approach was based on the concept of en-trepreneurial discovery to avoid the mistakes of the previous period.

The entrepreneurial discovery consists of the involvement of anyone who holds en-trepreneurial knowledge, then it affects not only firms and it is referred not only to the technical knowledge. Rather it is based on the whole concept of innovative capability that starts from the invention and ends in the commercialisation and the diffusion of the new product. The political system hence ought to ensure the participation in the definition of the policy of firms, universities and civil society, providing for a set of consultation and auditing tools, as technology auditing, interviews with cluster man-agement and firms, mixed working groups, setting up of observatories and monitoring organisations. This process has to bring to a definition of priorities, they cannot be identified with a top-down approach, but considering the fields where a region can realistically hope to excel.

Once the period of identification and inclusion of the best industries has concluded, the political side must set up a coherent policy mix and action plans, such that it is valuable and measurable for the goals it wants to achieve. From a political point of view evaluation of the strategy is straightforward since it descends from the aim of the policy mix the region sets up and can be compared with the actual results (Guzzini

et al., 2019). However it cannot be said the same for the economic point of view: not

only objectives of the various regions are not comparable in an economic sense (this descends from the fact regions are very different from each other), but the economic interest is focused on the effectiveness of the policy in a wider sense, it involves the

eco-nomic structure of the regions (Frank Neffke, Hartog, et al.,2018), the constructing of

a regional technological advantage (Boschma,2014) and capacity of creating spillovers.

The aims of the RIS3 have been connected to the targets of Europe2020, i.e. the President of European Commission Barroso’s strategy for the European policy in the

decade 2010-2020, after the failure of Lisbon strategy (Wyplosz,2010). Europe2020 is

finalised to smart, sustainable and inclusive growth, fixing the targets in terms of levels of occupation, R&D, climate change, instruction and struggle to poverty. It becomes evident that S3 cannot be considered just a policy of industrial innovation but it must be strongly linked with a reformulation of a system of social inclusion via technological and managerial innovations.

Squaring the circle: Smart Specialisation in economic

terms

The challenge Smart Specialisation proposes to actual economic theory is, in my opinion, how to combine the exploitation of regional technological capabilities with the development and economic growth. On one hand, we have a concept defined in a broad economic sense but difficult to translate in a formal model, on the other we face

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a practical problem of making EU regions acquire and/or improve their technological advantages and, hence, grow. I think the best way to address the issue is to bring the issue back to the economic concepts underlying the idea of Smart Specialisation.

The Smart Specialisation purposes

Smart Specialisation has been thought for the first time by Foray and Van Ark

(2007) in the Knowledge For Growth group. The basic idea is that human capital and

entrepreneurial knowledge belong to technicians and entrepreneurs, then it is "con-tained" in firms’ organisation: innovation opportunities and innovation capabilities can be fully exploited only by starting from a technological and managerial competen-cies analysis.

This concept has been further developed in Dominique Foray, David, and Hall (2009)

thanks to the idea of entrepreneurial discovery. This idea is defined as a "collective strategy formation process [. . . ] focused on the identification of science and technology areas with a distinctive market potential in the region" (Dominique Foray, Goddard,

and Beldarrain, 2012). In this learning process, entrepreneurial actors are likely to

play leading roles in discovering promising areas of future specialisation. They need to adapt to local skills, materials, environmental conditions and market access conditions and these elements are unlikely to be drawn on codified, publicly shared knowledge. Instead, they will entail gathering localised information and the formation of social capital assets.

In other words knowledge "is not in the air" as Balland, Boschma, et al. (2019) say,

but it has a precise identification inside the firms. However firms are not closed identi-ties and they have continuous relations, in the form of commercial exchange, workers’ mobility or cooperation in projects. Knowledge and innovations are likely to be trans-mitted among firms in the form of spillovers, especially if firms are close and related

(Frank Neffke and Martin Henning,2013). The proximity is a broad concept and Foray

and Van Ark (2007) referred to the Smart Specialisation considering the sectoral

dimen-sion of firms. The geographic element was included in Dominique Foray, David, and

Hall (2009) after the Barca’s report (Barca, 2009), prepared at the request of Danuta

Hübner, Commissioner for Regional Policy under Barroso’s Presidency. Barca’s report addressed a series of major problems, in particular, it suggested a clear concentration on a restricted number of objectives and greater control by the Commission. The optimal

focus of the policies was confirmed to be the regional NUTS-II level,5 _{then Smart}

Spe-cialisation concept integrated the technological approach to the geographical-regional

5_{The Nomenclature of Territorial Units for Statistics (fr. Nomenclature des Unités Territoriales}

Statistiques, NUTS) is the classification of territorial administrative units that European statistical institutes use for statistics. The cardinal number indicates the level of depth in the analysis, for example, Italian NUTS-I indicates Northern Italy, Centre and South with island, NUTS-II corresponds to regions and NUTS-III to provinces. The classification is made based on population: under the 150.000 inhabitants there is a further classification defined by LAU (local administrative units).

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framework (McCann and O. Ortega-Argilés,2014).

To sum up, the Smart Specialisation concept is based on three important pillars: a bottom-up approach for the policy design, the exploitation of spillovers from other firms and effects on regional development and competitiveness.

The policy design: when the strategy comes from the bottom

The great revolution Smart Specialisation should have brought to the European Innovation policies so far concerns the mechanism of design of this strategy. As it

emerges from Dominique Foray, Goddard, Goenaga Beldarrain, et al. (2012), the risk

of a top-down approach has been always present in the previous waves of policies in the

form of the "picking-a-winner syndrome". Dominique Foray, David, and Hall (2009)

however emphasise the centrality of entrepreneurs in this process under two aspects, the knowledge-production one and the innovation process management one. No one is more entitled in the definition of the process more than who produces the innovation

itself. As Hughes (2012) suggests, the approach becomes of choosing races and placing

bets, since the previous system tended to generate misallocation of resources and to reward not perfectly fitting sectors.

To have instruments to make the right decision, economic and business literature in the ’90s started to discuss of mechanisms of sharing the decision, thanks the work

of Etzkowitz, Leydesdorff, et al. (1995) too. They explored the relations among who

have knowledge and competences in innovation management (industry) with the ones who allocate fund (regional governments) and another important actor, that is the academia, capable to foster innovation process and form professional figures. This tri-angle has been defined by the authors the Triple Helix and it has been the cornerstone of the innovation policies in the first years of the new millennium. ERA itself is a political translation of this conception, with its aim of creating a Single Markets for

research and researchers too. As Capello (2014) argues anyway Triple Helix does not

take into account another important element of RIS3, that is the social repercussions of the strategy. For this reason, it has been adopted a variation on the theme proposed

by Carayannis and Campbell (2009) called Quadruple Helix, it involves the civil society

along with the three previous actors, to create value and to improve standards of living in the regions. The bottom-up approach then implies a political convergence on the industrial specialisation, shared and decided by all the possible actors, but oriented to few industries.

Smart Specialisation is then a non-neutral6 _{policy (Coffano and Dominique Foray,}

2014) because it decides which sectors have to be advantaged by public actions. The

decision, however, is made based on inclusive considerations. It is clear that this pro-cess is likely to generate confusion at the political level and it may easily decline in a

6_{A neutral policy is a policy that select projects according to the market signals (Trajtenberg,} 2002).

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logic of over-definition of the priorities. It is important then to define some economic criteria to investigate the political approach and on the coherence of what is decided concerning what is the best specialisation strategy for the region.

The design of the policy first must take into account the determinants of firms’ com-petitiveness, regional physical and knowledge infrastructures, the linkages within the innovation ecosystem and type of support provided by public resources (Landabaso,

2014). Most of these elements are not in the sphere of competences of privates, and

in the majority of cases, they are "given" condition they cannot change over seven years (the duration of a RIS), like the presence or the quality of a university in the region, the development of technological and scientific hubs, quality of the labour pool and education programmes. The role of the public becomes then the role of acting as an enabler and in part of sharing the risk of investments in some innovative sectors if

necessary (Landabaso,2012), anyway the process is not so linear.

According to Camagni, Capello, and Lenzi (2014) the public role must change with

respect to the situation of the single region, but there are some similarities among re-gions in the EU. Such similarities are based on the patterns of innovation these rere-gions embark and they depend on the degree of endogeneity of their innovative capabilities.

Camagni and Capello (2013) notice indeed a region can embark a path of a scientific

network, where innovation processes are self-enforcing, a path of the creative applica-tion, when the region is not on the frontier but has technological capabilities to improve innovations of others, and an imitative pattern.

Iacobucci (2014) however argues these kind of strategies are unlikely to be suddenly

adopted through a bottom-up process. The lack of vision of a process from the bot-tom may make the region choose a non-optimal path. He suggests at the beginning top-down approach to define a vision to encourage. Top-down approaches, anyway, need to be guided by analytical tools for "placing" the right "bet". The more correct instrument to achieve that is the structural coherence among the regional industries. Coherence is comparable to the degree of relatedness of the industries and implies a consideration on the variety of structural components of the economy in the region. But how can we observe the variety and relatedness of an economy? Recycling a concept

proper of business sciences, Frenken, Van Oort, and Verburg (2007) define the concept

of variety dividing it in two forms: related variety and unrelated variety. As "variety", we mean the sectorial diversification of regions in different industries. The relatedness instead indicates the level of proximity among the sectors and sub-sectors inside the geographical unit of analysis (mostly NUTS-II, but often NUTS-III too). This variety is related when sectors (or sub-sectors) are similar, i.e. they share common elements of tacit knowledge and specific competencies, and thereby is addressed as the source of knowledge spillovers. Unrelated variety is instead a residual category among those (macro)sectors that cannot use the same knowledge set of competence in their activi-ties, then it represents a measure for sectorial diversification. From the other side, we expect RIS3 to involve structural diversification for some economies and, in some cases, it is necessary a certain degree of shift of competences from one industry to one another

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(Capello, 2014). Are bottom-up policies appropriate for this perspective? A too high

level of coherence in the economy can bring to lock-in situations (Boschma,2014) when

the economy cannot move forward without public intervention. For these reasons it is necessary to understand whether structural changes happen and whether they move toward a more advanced and knowledge-based economy. Further, we need to detect how same region industries share competences among them and hence, indirectly, how many shared competencies are the basis for a specialisation strategy. A model for ex-plaining and designing Smart Specialisation needs to take into account these nuances, to predict which new sectors are likely to develop and excel in the region.

Technology, knowledge and sectorial spillovers

As we said, one of the pillars the concept of Smart Specialisation relies on is the idea of the agglomeration effects of R&D and their exploitation. Agglomeration effects have

been widely discussed in a regional framework after the work of Jacobs (1969) about

advantages and disadvantages of agglomeration for firms near cities. Jacobs (1969)

dis-cussed the reasons behind the emergence of industrial clustering, but it is not able to explain why a region specialises in a particular technological field or sector, mostly for manufacture. Industry clustering is based on two opposite "forces": a centripetal force and a centrifugal one. This conception derives from the Marshallian idea of increasing returns and it has been applied in a regional framework for the first time by Krugman

(1991).

Same industry firms have incentives in locating in the same region near each other because they can exploit others’ results in research (knowledge spillovers), lower and share costs of formation of human capital, increase and improve their relations and create scale and scope economies; these factors have a centripetal effect, bringing to the creation of clusters and leading to the specialisation of regions in industrial sectors

(Marshall, Sauvaire-Jourdan, and Bouyssy, 1906). In other terms, according to the

models inspired on the work of Krugman (1991), the many the incumbent firms are

in the clusters the greater is the benefit for the marginal firm entering in it. These forces however rise along with two kinds of costs: congestion costs and opportunity costs. The firms indeed do not face only a decrease in costs, but also face a splitting of demand of the region due to firms overcrowding. The spatial frame of the model

suggests if transportation costs are high,7 _{firms will have convenience in moving to}

another region, with a lower aggregate demand than the one with the other firms, but with higher profit margins. If transportation costs of selling in the second region for the other firms are particularly high and the opportunity costs of not producing in the

7_{In these model transportation costs are usually described in the form of iceberg costs as Samuelson}

(1954) proposed. The iceberg costs are transportation costs calculated as a fraction τ of the total quantity produced, indicating the number of goods that perished during transports. They are a useful simplification because they are a linear function of the quantity produced, fitting well in two-regions models like the most of these spatial works.

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cluster are low, centrifugal forces rise and may overcome the centripetal ones.

Krugman (1991) explained hence that when centrifugal forces are lower than the

cen-tripetal ones we will attend to clustering of a determined industry in a given region, hence there will be a specialisation path for that region. But this says nothing or little about the starting hint that makes the clusters born: is it a causal hint? From what does it depend? Are the initial causes measurable? But the major question is whether there are industries in which agglomeration is more likely? Audretsch and

Feldman (1996) found out industries more knowledge-based have a greater tendency

to agglomerate than the others. They define the intensity of knowledge through the R&D expenditure and they find there is a positive correlation between the concentra-tion of producconcentra-tion and innovaconcentra-tion, defined by a Gini index. Thus the more firms use knowledge in production the more clustering is favourable and the region considered will tend to specialisation.

This approach presents a big limitation insomuch it considers only a side of the coin,

that is the explicit knowledge produced by R&D. Jensen et al. (2007) define two modes

of producing knowledge, the knowledge derived from Science Technology and Innova-tion (STI) and the knowledge created by learning by Doing Using and Interacting process (DUI). The great distinction is in the replicability of the knowledge produced. The first mode of production allows the creation of codified and explicit knowledge, then it is replicable and shareable in the form of spillovers and knowledge externalities. The second form produces the know-how and tacit knowledge, much more difficult to

replicate and to replace. In Europe B. Asheim (2012) argued that competitiveness of

regions depends mostly on the latter, due to the longer industrial and technical

tra-dition, even if B. T. Asheim, Boschma, and Cooke (2011) underlined how the modes

of knowledge production and transmission vary from sector to sector and it is more correct to talk about of the degree of a mix between STI and DUI. The presence of DUI processes indicate also the capacity of firms of absorption of explicit knowledge and it is not perforce sectorial specific.

These considerations make rise new relevant observations to the spatial models based

on Krugman (1991), particularly on the spatial conception of proximity: on one hand,

we have spatial proximity may not be sufficient to ensure increasing returns, on the other different sectors may share similar characteristics of tacit knowledge. The close-ness of firms thus is not only spatial: geography becomes just one level of analysis for the problem, but it needs to be supported by measures of industries proximity and relatedness.

Since proximity must take into account the knowledge relations, we can define two di-mensions through which technological dimension that interferes with the creation and exploitation of spillovers: the closeness and complexity. The closeness depends on the

physical proximity and the culture relatedness (Maskell and Malmberg, 1999), in the

sense that all other things being equal they make cheaper and smoother the commu-nications among firms intra and inter-industry. The not-physical proximity is defined

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indicating the level of relatedness with the others based on competences. It is slightly different from the related variety concept: related variety is a comprehensive measure of the regional diversification structure, what we want to observe is the how any sector i present in a given region r is related to any other industry j present in r. Measures and data for this variable will be discussed in the next section with the other empirical issues.

For what it concerns knowledge complexity the question goes beyond the degree of tacit and explicit knowledge employed in the industry, but it is addressed according

to Fleming and Sorenson (2001): the complexity of technological knowledge is defined

as a function of the number of components out of which it is constructed and the

interdependence of those components. As Balland and Rigby (2017) notice the more

technology is complex and interdependence is high, the more knowledge is geograph-ically stable and the less is replicable in other regions. Knowledge complexity thus is an important tool to measure the capacity of a region of acquiring and preserving a competitive advantage in a sector in which it tends to specialise.

The empirical models of Smart Specialisation

Smart Specialisation is a complex issue to investigate from an economic point of view since quantitative methodologies are not the best used in literature. Scholars’

interests focus on the political architecture of the policy (Landabaso, 2014), on the

match between RIS and effective structures (Guzzini et al.,2019), on the best regional

systems of innovation for exploiting it (Boschma, 2014). All this kind of research

questions employs mostly qualitative approaches, even for there are few quantitative effective instruments so far.

The economics of Smart Specialisation should describe and analyse the policies tar-geted to patterns of technological development in a spatial, well-defined dimension. The empirical literature on the subjects developed a wide range of different approaches to test empirical hypotheses. Anyway approaches and methodologies are not always comprehensive of all the nuances of the problem. Empirical models might be summed

up in three main threads8, based on research questions, focus dimension and

method-ology.

The first two threads have in common the fact they are employed in literature for test-ing empirical hypotheses. In particular, the first one is defined by a macro-dimensional regional approach to the problem, while the second one focuses on a micro-dimensional sectorial approach. The third one, instead, groups very different works based on com-putational models of economic equilibrium. In this section, we are going to see each of these threads and we are going to understand which of their strength points and their main limitations.

8_{I want to clarify this is a personal made-up distinction I find useful to adopt for the sake of this}

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The regional macro-fundamentals approach

The first thread is that part of empirical literature it investigates Smart Special-isation by focusing on its regional dimension. Smart SpecialSpecial-isation in these works is investigated via the economic phenomena they produce. The main concept is that some variables enhance productivity and foster growth.

This thread derives from the literature on the endogenous growth (Crescenzi,2005;

Er-tur and Koch,2011), according to innovative activities drive development and disclose

feasible paths for persistent growth. The core-problem of this thread, then, was to

understand which are the factors fostering regional growth (Foray and Van Ark,2007)

and why regions perform so differently (Boschma, 2014). The key to understand the

problem is hence, the geographical heterogeneity among European regions. Regions are not only unity of observation, but social and institutional entities with very different

patterns of economic development (McCann and O. Ortega-Argilés, 2014). This

in-terpretation of the problem can be translated in empirical models they investigate the effects of specific determinants on macro-fundamentals at regional level

(Rodríguez-Pose and Crescenzi, 2008; Capello and Lenzi, 2014) or taking into account spatial

spillovers on growth models (Ertur and Koch, 2007; Fiaschi, Lavezzi, Parenti, et al.,

2009). Whereas the second ones are characterised a specific estimation design, the

first ones aim to identify which regional characteristic affect more productivity and/or growth, to derive proper policy tools for innovative growth.

The geographical dimension of the problem, as suggested since Krugman (1991), is

centred onto the agglomeration effects of the regional economies. As Beaudry and

Schiffauerova (2009) broadly review, the agglomeration has a positive effect on

inno-vation performances in a region. Agglomeration indeed generates (and it is generated by) external scale economies: this kind of scales is external to firms, i.e. they increase with the number of productive unities. This causes attractiveness for the region and foster firms’ birth and growth. These phenomena generate diffused entrepreneurial

competences (Parker,2005; Acs and Varga, 2005): birth rate of firms and measures of

entrepreneurship are expected then to affect positively the productivity.

Another important characteristic literature relies on for explaining growth in knowledge-based economies is the innovation inputs. One input is the expenditures in Research and Development (R&D). They represent an important driver for the innovative

pro-cess, largely employed in empirical works (Acemoglu, Aghion, and Zilibotti, 2006;

Rodríguez-Pose and Crescenzi,2008). Anyway, R&D expenditures do not have always

a clear effect, even if it is expected to be positive. They indeed are an input it does not allow to distinguish commercialised innovation from non-commercialised. Moreover, when it is taken along with innovation proxy it has ambiguous effects. R&D expen-ditures are not, however, the only input (neither the most relevant) used by empirical literature for explaining innovative capabilities. High-specialised workers represent an important factor in the economy to absorb innovative knowledge produced elsewhere.

Duranton and Puga (2000) find upper-level occupations to be more skill-intensive and

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growth - than lower-level occupations.

Finally, an important characteristic linked to the possible realisation of external scale

economies is the dimension of the regional economy. Capello and Lenzi (2014)

individ-uate the dimension of a region as leverage for attractiveness and economic relevance in terms of attraction of investments. Greater regions have more firms and are more likely to develop specialised agglomerations. Furthermore, regions seem to present

in-stitutional and cultural heterogeneity among them (Tatarkin, Kotlyarova, et al.,2013),

even if in the same country. Bigger regions, thus, allow to a pooled specialised-labour market and a more correct match between demand and supply.

In general, these works have the advantage of describing empirical models for Smart Specialisation impact on economic fundamentals. Hypotheses they test derive from the theoretical background, according to which some regional characteristics affect more than others (or in a different way than others) the regional capabilities of generating innovation and enhancing productivity. The effects on macro-fundamentals are hence the direction towards Smart Specialisation policies go and the estimates help to iden-tify their magnitude. However, the entire process of Smart Specialisation is treated as a black box. This branch of literature is not interested in how the spillovers work and how regions embrace a path of Smart Specialisation, whereas this is a fundamental is-sue for understanding the entire question. The technological dimension of the problem is treated as some non-defined dynamics and the focus is targeted on those variables affecting its development. This represents, according to me, the biggest limitation of the works of this thread in the literature.

The sectorial-technological approach

The second thread is based on sectorial analyses and their capabilities of generating diffused knowledge spillovers.

If we consider the aims cited by Dominique Foray, David, and Hall (2009) the process

should tend to strengthen the technological competencies embedded in a region: the strategies should implement a specialisation in those fields where the region has or may

have a comparative advantage (Boschma,2014) and diversify in those sectors where the

same competences can be exploited and strengthened. A part of the empiric literature on the efficacy of these strategies then has taken into account, on one hand, what are

the best competences (thus the best industries) a region should bet on (Hughes,2012),

on the other whether the changes in industrial structure is coherent and strengthening the regional economy. The key to face the question is in the relations among industries’ capabilities, conditioned to the regional environment. These relations themselves are the spillovers and they generate Marshallian scale economies.

The relevant issue this thread develops around is how to include these spillovers and the mechanisms of transmission in the empirical models. One approach suggested

by Neffke and MS Henning (2008) and Frank Neffke, Martin Henning, and Boschma

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relatedness of the fields. The idea is the one suggested by the evolutionary "school"

(Dosi, Teece, and Winter, 1992; Teece et al., 1994) firms differentiate in the sectors

nearest to their core competencies, then the number of co-occurrences should be a good proxy for relatedness. Through this measure, they have been able to map the industry spaces of Swedish regions. This approach, however, presents some important criticalities: first, co-occurrences do not define the degree of specialisation of sectors, hence it ends up being a good image but anything near to a relatedness proxy; second, firms diversification behaviour depends from the profitability of the markets too, not only from the firms’ competences. Last but not least, we observe only the firms that can diversify, not all firms in all sectors, then we miss all the observation coming from the SMEs’ world.

A different approach is proposed by Neffke and Hemmings themselves (Frank Neffke

and Martin Henning, 2013), using a new relatedness proxy based on the labour flows

among industries. The idea is indeed spillovers are conveyed through human capital and pooled labour market for specialised workers. The flows of labour among firms is hence a very relevant form of technological spillover, but what is more interesting is the relations between sectors. The labour flows are directed towards those firms who share competences and skill needs, then the inter-industries labour flows are a good measure

of the skill relatedness of sectors. Frank Neffke and Martin Henning (2013) have found

out there is a positive correlation between the skill relatedness and the diversification

behaviour of firms in other sectors. Frank Neffke, Hartog, et al. (2018) define from this

approach an actual measure for the spillover, interpreted as the industry’s match of a sector i in a region, i.e. proxy of the spillovers of the others regional sectors on i. A great limitation of this approach, however, is that skill relatedness does not cor-respond to a technological relatedness. It, indeed, might be "soiled" by the basic competencies common to all sectors and it may not reveal the flows of the very skilled labour, the real vectors of spillovers. Moreover, a pure sectorial approach might be misleading if we do not consider the technological and the pure competences side of the analysis. A more complete approach on the matter is, thus, relies on technological patents classification rather than industrial sectors. The idea is spillovers are con-veyed by human capital of firm or industry i, but if other firms/industries have no capabilities in that field spillovers are useless. This concept is the so-called absorptive

capacity (Dominique Foray, 2004) and it implies a relevant measurement problem for

industrial sectors: it is not possible to measure the absorptive capacity without con-sidering the technological competences present in the region, but those are transversal to industries. The focus of the analysis then is targeted to the degree of relatedness in terms of which technological fields are likely to be exploited together by firms in the

same region. Boschma (2015) individuates in the Revealed Technology Advantage an

indicator of technological strengths of a region: he lends from trade literature the idea of the revealed advantages, i.e. the advantages obtained as a comparison between the share of patents in that field and the average share. This has been a huge step forward because it has represented a first real quantitative instrument to detect technological

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advantages for Smart Specialisation purposes. Balland, Boschma, et al. (2019) enlarge the set of quantitative instruments by measuring the technological relatedness through patents co-occurrences for every pair of sectors. What they find is that relatedness for a technological field is positively correlated with a revealed advantage and with patent growth.

Nevertheless, relatedness is not the only dimension it matters in a technological frame-work: technology and knowledge complexity cover a huge role too. Balland, Boschma,

et al. (2019) find complexity is positively correlated with patent growth, although it

harms catching a new advantage. Complexity is thus an important discriminant for the regional capability of effectively diversify. Moreover, the empirical results on the

complexity of Fleming and Sorenson (2001) and Balland and Rigby (2017) have shown

the more complex is a sector the fewer patents are cited in regions different from the more specialised ones, i.e. complex knowledge tends to be geographically more stable and less capable to generate spillover in other regions.

Even if these works have solved the problem of black-boxing the technological dimension of Smart Specialisation, they suffer from a big limitation too. They are policy-neutral, that is they treat Smart Specialisation as it were a natural development in the regional

technological portfolio. Most of these work uses decades-old data9 _{when Smart}

Spe-cialisation was not even a concept and innovation trajectories of technology was far from the current one. Empirical works on Smart Specialisation, in my opinion, must include the policy side of the problem. Despite this limitation, I think this approach is the best quantitative tool to investigate Smart Specialisation so far developed in the literature.

The computational models approach

The third thread in the literature concerns computational models, they have been used to identify the impact of Smart Specialisation policies on long-run equilibrium. They are mostly used for evaluation of policies at national and European level, then they are far beyond the aims of this work. For the sake of completeness, I am going to examine the main three models: Regional HOlistic MOdeL (RHOMOLO) Brandsma

and Kancs (2015), Geographical Macro Regional model (GRM) (Varga and Schalk,

2004) and the MAcroecomic Sectoral Social Territorial model (MASST) (Capello,

2007).

RHOMOLO is a Spatial Computable General Equilibrium model (SCGE) developed

for the European Commission (Brandsma and Kancs,2015). An SCGE model is

formu-lated as a set of (sub-national) regions where regions are not independent but connected by linkages like transportation and migration. The short-run equilibrium of the model is reached when supply matches demand in each market in each of the regions.

How-9_{For example, Balland, Boschma, et al. (}₂₀₁₉_{) use patents data since the ’80s onward and Balland}

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ever, this does not necessarily mean that this equilibrium is stable because differences in factor prices might induce inter-regional migration. Equilibrium becomes stable in the long run when no motivation for further factor migration is present. RHOMOLO

is a six-sectors10 model where the spillovers rise due to inter-regional linkages, not

uniform distribution of physical, human and knowledge capital and increasing returns

on production. Brandsma and Kancs (2015) assume a monopolistic competition in

the labour market and the presence of durable goods in firms’ production function to fit a competence-based growth model. They model households’ behaviour and firms’ production function through CES functions, then they run a short-run and a long-run analysis. Clearly in the short run are possible positive profits, while in the long run, most profitable markets assist to higher rates of firms’ entry, then the model predicts through this rule the growing sectors and the effectiveness of specialisation policies.

The GRM model has been developed by Varga and Schalk (2004) to include in

macroe-conomic fundamentals in a general equilibrium model. Regional growth, knowledge spillovers and agglomeration effects influence macroeconomic variables but in turn, they are affected too, then the model associates an SCGE model for the regional and microeconomic dimension a DSGE model for the macroeconomic level. The idea behind the model is there are three levels: national and international, regional and sectorial. The sectoral level concerns the changes in productivity due to firms’ innovation be-haviour, computed as total factor productivity. Then through a mechanism designed by an SCGE model, it is defined the change in factors employment at the regional level and, by aggregation, at national. At national levels are modelled the macroeconomic characteristic of the model, i.e. inflation rates, interest rates, government expenditure and the aggregate endowment of productive factors as capital and labour. Macroeco-nomic policies determine the change in capital, labour and total factor productivity at the meso-economic level. At a regional level, they are defined as the level of public investment and physical and knowledge infrastructures, they affect the regional change of endowment of productive factors determined by macro fundamentals. When fac-tors change at the regional level, firms have to face a new situation with the new endowments and define a new level of factors employment, generating a change in total factor productivity. There are two directions of interactions inside the economy, the first bottom-up through an SCGE model and the other top-down through a DSGE. The model sums up again with two equilibria, one in the short run when the total amount of factors is given, and one in the long run. The equilibrium is reached when

the output of the DSGE models and the ones of the SCGE converge. Varga (2017)

underlines as more and more GRM has been developed for matching innovation policies evaluation and, in particular, as Hungary chose this model as the instrument for her Smart Specialisation policies’ evaluation.

MASST model (Capello,2007) instead is quite different from the previous two because

10_{Agriculture and forestry, mining and manufacture, construction, services, financial markets and}

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it is a partial equilibrium model since it considers the labour market, public sector budget and monetary markets as exogenous. The idea is to identify regional growth

(∆Yr) as an endogenous, competitive, bottom-up and cumulative process it starts from

the national growth (∆Yn):

∆Yr = ∆Yn+ sr

The model is divided into national and a regional models. The national

sub-model describes GDP growth (∆Yn) via national accounting fundamentals growth,

then exogenous macroeconomic factors affect national growth through the effects on the latter differentials. On the other hand, regional sub-models are divided into three blocks. The first block defines the regional structural policies for physical and knowl-edge infrastructures’ regional endowment. The second block determines the population changes in terms of population growth and migrations. Lastly, the third block defines the migration flows based on unemployment and regional differentials in growth. These three blocks are needed to define the arguments of the regional differential shift

equa-tion (sr), i.e. the equation it explains the difference between national growth rate and

regional growth rate through the regional characteristics. The difference of this model from the previous ones is that it does not reduce the regional growth issue as an ag-glomeration and a redistribution of total factor productivity, but it tries to adopt a more complete approach, much more adequate when we address such a complex as the Smart Specialisation.

The approach of this thread, however, is not adequate to analyse the Smart Special-isation issue from an empirical point of view, but it is used almost exclusively by institutions when they allocate funds or make forecasts.

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2

Dealing with the empirical problems: how to

derive an empirical model

As we have explained, Smart Specialisation is not a policy or a specific strategy, but an approach for defining a coherent regional plan of industrial specialisation or di-versification, by maintaining and/or improving the technological competences in tech-nological fields. From the economic point of view, the focus hence should be directed to the effects of this kind of policies. Firstly then, it is necessary to define which is the dimension of investigation.

The empirical literature, as we have seen, proposes two main roads. On one hand, we have the regional macro-economical dimension, by studying the effects of precise policies on the regional macro-fundamentals. On the other, we can exploit the micro-economical dimension, by observing the trajectories of industrial sectors or technolog-ical fields. We have highlighted the strengths and limitations of both approaches in the previous chapter. In this chapter, we are going to define an empirical model to investigate Smart Specialisation policies effects and identify proper proxies and data for the chosen variables.

To analyse Smart Specialisation, the first thing we have to define is the width of the focus. As we said, the choice is between the regional macro-fundamental and the sectorial-technological approach. The great limitation of the first approach is the risk of black-boxing the micro-dimension of Smart Specialisation policies. Since the effects of the policies are conveyed via knowledge and technological spillover, an empirical model of Smart Specialisation must take into account this dimension. On the other side, the sectorial-technological approach does not allow to define the policy-side of the phenomenon. Smart Specialisation intervention indeed are rarely targeted on single sectors or technological fields.

An empirical model of the first approach, according to me, is more useful to detect Smart Specialisation effects. I think the second approach presents some good instru-ments for defining which are technological or industrial sectors in the RIS. However, it is restrictive for a policy-analytical framework. The first approach, instead, allows es-timating the effects of the regional policies promoted by the Commission and financed

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by the European Union. The main questions we have to solve for defining an empirical model, then, are:

1. Which economic variables should be included in the model?

2. How can we define the policy and identify the Smart Specialisation in it? 3. How do we compensate the black-boxing of Smart Specialisation dynamics?

The empirical heuristics of Smart Specialisation

For defining an empirical model, firstly we have to decide which fundamentals have to be included and which is the equation design. The first consideration we have to make is which economic fundamental to deploy as a dependent variable. Capello and

Lenzi (2014) use as dependent variable the growth rate of GDP between 2005 and

2007. Their model design describes the effects of some regional characteristics on the growth rate. They choose, then, to identify which are the effects of regional charac-teristics directly on production and its growth. Their research question relies on the idea that Smart Specialisation can be computed as an effect on regional differentials of growth. I am sceptical on their model design for three main reasons. The first one is that they must consider a very narrow period (2005-2007) for avoiding crisis effects in European economies. Smart Specialisation policies, anyway, are likely to show their effects over a longer time. Moreover, if we adopted their approach on a longer period, we should consider that not all regions have responded to the crisis in the same

man-ner: as Davies (2011) has underlined manufacturer regions have been more resilient.

A production-design model must take into account that regions specialised in those sectors struck more strongly by the crisis note a larger reduction in GDP growth rates. This phenomenon may generate bias in estimates. Lastly, production growth may not be an indicator as good as differentials in growth rates. The latter might better reflect the differentials in regions performances and they are easy to compare over time.

Rocchetta, Ortega-Argiles, and Kogler (2019) use, instead, the growth of productivity

as the dependent variable. Productivity is intended as labour productivity, i.e. the gross value added produced by unity of labour input in a year. This framework is more invariant regarding the crisis period because it relies more than GDP on invest-ments made in the past and workers’ level of competences. Moreover, the initial intent of Smart Specialisation policies was to define a new path of growth for European re-gions productivity. The entire policy aims to create self-sustaining systems of economic growth. For these reasons, I think in an empirical design, productivity and its growth rate are the most adequate instruments for the estimates.

For the independent variables, we have to define firstly which is the hypothesis to test and how to test it. The general question we may want to translate into regression is "which are the effects of Smart Specialisation policies on productivity?", but we have seen how this may sound extremely naïf. Smart Specialisation, indeed, is not a

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policy but a set of instruments they should be innovative and coherent with regional capabilities. These instruments are hard to be tested quantitatively. On the other hand, we cannot define a rule for distinguishing regions that made Smart Specialisa-tion from those who did not. Every region must deliver to the European Commission the RIS document under penalty the exclusion from the European financing programs. However, we cannot either observe if what is written in the RIS is an effective Smart Specialisation measure. What we can observe, though, is the actual expenditure of European Funds and to what they are targeted. Thus the question becomes how to measure the targets of European Funds expenditures. By adopting again the

frame-work of Rocchetta, Ortega-Argiles, and Kogler (2019), we can use the technological

dimension and the industrial structure of regions as a proxy of Smart Specialisation dynamics. The research question to test then should be "which are the effects on the productivity of European Funds financing technologically coherent programs?". Now we have a testable hypothesis and the economic fundamental to use as a framework. We have to deal with the European Structural Funds and the meaning of "technological coherent" to derive hypotheses to test and write in mathematical terms our research question.

The last aspect is to define the controls, by choosing those variables we know they may interfere with the estimation of our research question. In particular, as said in the previous chapter, innovative activities are likely to have a huge role in the labour

productivity definition (Rodríguez-Pose and Crescenzi, 2008) as well as diffused

en-trepreneurial capabilities (Parker, 2005). The quality of employment is an important

driver of labour productivity (Capello and Lenzi,2014), whereas FDI may represent a

good proxy of regional attractiveness. Lastly, we may want to observe if scale effects are present as a proxy of diffused external economies of scale generated (and generating)

agglomeration effects (Beaudry and Schiffauerova, 2009).

The European Funds and the financing mechanisms

The efforts made by the European Union after the failure of the Lisbon strategy

have been collected in the context of Horizon2020 (European Commission,2013). The

budget for the period 2014-2020 is a nearly 280 billion e (Cohesion Data, 2020),

finalised to strengthen research and innovation activities and to enhance a knowledge-based economy. The main channel of EU funding system is the five European Structural and Investment Funds (ESIF). They are jointly managed by the European Commission and the EU countries. The purpose of these funds is to invest in job creation and a sustainable European economy. The European Structural Funds are:

1. European Regional Development Funds (ERDF);

2. European Social Funds (ESF); 3. Cohesion Funds (CF);

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4. European Agricultural Funds for Rural areas Development (EAFRD); 5. European Maritime and Fisheries Funds (EMFF).

These funds are allocated by the European Commission on a regional basis, to achieve the targets in the preamble of Treaty of Rome, when Members were:

"anxious to strengthen the unity of their economies and to ensure their har-monious development by reducing the differences existing between the various regions and the backwardness of the less-favoured regions [. . . ]"

Treaty of Rome, 25th March 1957.

The regional focus of the European Structural Funds is an important feature of the financing and it is useful for our analysis. Each of these funds is assigned to spe-cific projects. Financing a project requires the European Funds on an additional basis regarding regional or national funds, a multi-year programming plan and partnership among all possible interlocutors. The EARDF and the EMFF are targeted to projects related to the primary sector and they are not likely to structurally help Smart Special-isation objectives. Thus, we are going to see what are the first three financing channels and their aims.

ERDF action is designed to reduce economic, environmental and social problems in

ur-ban areas, with a special focus on sustainable urur-ban development (Commission,2013b).

The ERDF focuses its investments on innovation and research, digital agenda, support for small and medium-sized enterprises and a low-carbon economy. The Fund is al-located through three classes of regions based on their degree of development. More developed regions must allocate at least 80% of funds of at least two of these priorities, it becomes 60% for transition regions and finally, it goes down to 20% for the less developed ones. Furthermore, some ERDF resources must be channelled specifically towards low-carbon economy projects, such that more developed regions must invest at least 20%, transition regions 15% and less developed regions 12%.

The ESF investments cover all European regions. More than 80 billione is earmarked

for human capital investment in the Member States between 2014 and 2020, with an

extra of at least 3,2 billione allocated to the Youth Employment Initiative

(Commis-sion, 2013c).

Finally, the Cohesion Fund is aimed at Member States whose Gross National Income

(GNI) per inhabitant is less than 90 % of the European average (Commission,2013a). It

aims to reduce economic and social disparities and to promote sustainable development. For the 2014-2020 period, the Cohesion Fund concerns Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Greece, Hungary, Latvia, Lithuania, Malta, Poland, Portu-gal, Romania, Slovakia and Slovenia. The Cohesion Fund allocates a total of 63,4

billion e to activities under the following categories: trans-European transport

net-works, notably priority projects of European interest as identified by the Union, under the Connecting Europe Facility; environment, in terms of improvement of energy effi-ciency, use of renewable energy, developing rail transport, supporting inter-modality,

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strengthening public transport, etc.

The funds are allocated thanks to an equalising method, such that the less developed regions receive more in absolute terms than the more developed ones. The calculation on regional development is based on the past productivity of the region and past levels of GDP per capita, then the richest and most productive regions "pay" for developing the poorest ones. To show how the difference in allocation in absolute and relative

terms, in figure 2.1 it is reported the expenditures of the ESIF by the amount and

by share on national GVA for Italy, France, Germany, Poland, Czechia, Romania and

Hungary.1

As we can see from the first plot, Poland considerably receives the most in absolute terms, followed by Germany, Italy and Czechia. The pattern of expenditures is cyclical it increases during the programming period to reach its maximum in the last years; in the first years of the following period it restarts from higher levels than the initial ones: according to the European legislation, regions had until 2015 to spend the funds allocated for the period 2007-2013 without losing them. In the second figure, it is instead reported the expenditures of the European Funds on the national GVA. As we

can observe, despite the four biggest economies2 by themselves recollect more than 90

billione, the impact on their economies in minimal.

These figures allow us to make some considerations: the expenditures of the ESIF follows a trend over the programming period and they reach the maximum of spending in the final years of the program. Despite the largest economies receive the most substantial share of the ESIF, they are the net payers of the European budget, i.e. they structurally finance the financing system receiving very little with respect what

they give based on their GDP.3 _{Lastly, the real impact on regional development must}

take into account the dimension and the level of development of a region: a correct analysis on the ESIF requires to weight their amount on regional GDP or GVA.

Inside the black-box: technological dynamics for regional

spe-cialisation

The central point of the research question is how to target the expenditures of Eu-ropean Funds to measure of specialisation or related diversification. This consideration is central because it cannot be limited to a measure of relatedness between sectors or the degree of specialisation. For regional economies, data on the industrial structure are not available. Moreover, this kind of measure risk to black-box the sectorial and technological dynamics behind the specialisation/diversification. If we want to mea-sures without black-boxing these regional technological dynamics, we have to consider

1_{The latter is reported only in the second plot.} 2_{UK is not considered.}

3_{It is relevant to underline, however, their economies are far larger than the CEECs. To make}

a comparison, the Italian economy by herself is almost 25% bigger than all non-Eu-15 countries combined.

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108.5 109 109.5 1010 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Years ESIF (log) GEO CZ DE FR IT PL RO 0 2 4 6 2007.5 2010.0 2012.5 2015.0 2017.5 Years P ercentage of ESIF on GV A GEO CZ DE FR HU IT PL RO

Figure 2.1: ESIF by complete expenditure (log) and percentage on national GDP.

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the two dimensions of the technology (Balland, Boschma, et al.,2019): relatedness and complexity.

As we said in the previous chapter, technological/industrial relatedness means how much a technological field/industrial sector is correlated to another, i.e. how often it

is linked with another field (Neffke and MS Henning, 2008; Balland, Boschma, et al.,

2019). Anyway, relatedness cannot be extended on a regional basis. It represents the

strength of the link between a pair of fields or sectors. Even if we computed a regional average for the relatedness, it would not take into account the possibility of multilat-eral links between sectors. This consideration is viable both for a technological and for a sectorial approach. If we focus on the first one, however, a possible solution is

de-scribed by Rocchetta and Mina (2019). They use the method described by Dosi, Teece,

and Winter (1992) on regional patenting activities data. They define a dummy, Gir

which takes value of one if region r is active in the patenting field i, then Ki =

P

rGir

represents the number of regions patenting in i. In the same way Oij =

P

rGirGjr

represents the number of regions patenting at the same time in i and j. In order to

adjust the distribution of Oij they normalise it creating the variable τij:

τij =

Oij − µij

σij

where µij is the expected value of the a hyper-geometric distribution, it describes the

probability that region r patents in i and j conditioned to Ki and Kj. The values of

mean and variance of such a distribution are:

µij = KiKj K σ2_ij = µij 1 − Ki K ! K − Kj K − 1 !

where K is the total number of regions. This variable is used as weight for the weighted

relatedness average (W RAir), i.e. an index of relatedness based on the number of

patents in the technological field i in region r :

W ARir = P j6=i τijPjr P j6=i Pjr

where Pjr is the number of patent in the field j in region r. From this measure they

gain an index Cr of coherence of the region as:

Cr = X i W ARir Pir P jPjr