Research Paper

Regional policies for innovation: the case of technology districts in Italy Federica Bertaminoa, Raffaello Bronzinib, Marco De Maggioc and Davide Revellid

ABSTRACT Regional policies for innovation: the case of technology districts in Italy. Regional Studies. This paper studies a policy tool implemented in Italy in the last decade to foster local innovation activity called technology districts. First, it examines the characteristics of technology districts and those of the firms within them. Next, it assesses the performance of district firms. The South of Italy has more technology districts, but they are small, poorly sectorially diversified and far from the economic structure of the area. Firms that did join a district had previously been, on average, larger, more innovative and profitable, and show also higher leverage than the others. After the institution of a district the performance of the firms that joined it did not differ significantly from that of similar firms that did not.

KEYWORDS technology districts; innovation; patent; public policies; matching; differences-in-differences

摘要

促进创新的区域政策：意大利的科技区之案例研究。Regional Studies。本文研究过去十年间，在意大利执行的一个 名为科技区的地方创新活动促进之政策工具。本文首先检视科技区以及位于区内的企业之特徵。本文接着评估该区

企业的表现。南意大利具有更多的科技区，但它们规模小，部门间的多样化不足，并且与该区的经济结构相距甚远。

过往加入科技区的企业，平均而言较大、较具创新且更能获利，同时较其他企业显示出更高的槓杆程度。有了科技区

的制度之后，选择加入的厂商之表现与其他未加入的类似厂商相较之下，并无显着的不同。

关键词 科技区；创新；专利；公共政策；配对；差异中的差异

RÉSUMÉ Les politiques régionales en faveur de l’innovation: étude de cas des districts technologiques italiens. Regional Studies. Cet article étudie un outil politique mis en oeuvre en Italie pendant la dernière décennie dans le but d’encourager l’activité locale d’innovation, à savoir les districts dits technologiques. Dans un premier temps on examine les caractéristiques des districts technologiques et celles des entreprises qui s’y installent. Il s’ensuit une évaluation de la performance des entreprises du district. Le sud de l’Italie est mieux doté de districts technologiques, mais ils sont petits, mal diversifiés sur le plan sectoriel et sont loin de refléter la structure économique de la zone. Auparavant, les entreprises qui se sont implantées sur un district ont été en moyenne plus grandes, plus innovatrices et plus rentables, et montrent aussi un effet de levier plus important que les autres. Suite à l’établissement d’un district, la performance des entreprises qui s’y sont installées n’a pas beaucoup différé de celle des entreprises similaires qui ne s’y sont pas implantées.

MOTS-CLÉS districts technologiques; innovation; brevet; politiques publiques; appariement; approche de l’écart des différences

© 2016 Regional Studies Association

CONTACT a federica.bertamino@agenziacoesione.gov.it Agency for Territorial Cohesion, Rome, Italy. b(Corresponding author) raffaello.bronzini@bancaditalia.it Bank of Italy, Regional Economic Research Division, Rome, Italy. c marco.demaggio.esp@agenziacoesione.gov.it Agency for Territorial Cohesion, Rome, Italy. d davide.revelli@bancaditalia.it Bank of Italy, Regional Economic Research Office, Genoa, Italy.

REGIONAL STUDIES, 2017 VOL. 51, NO. 12, 1826–1839 https://doi.org/10.1080/00343404.2016.1255321

ZUSAMMENFASSUNG Regionale Politikmaßnahmen für Innovation: der Fall der Technologiebezirke in Italien. Regional Studies. In diesem Beitrag untersuchen wir ein politisches Instrument mit der Bezeichnung ‘Technologiebezirke’, das im letzten Jahrzehnt in Italien zur Förderung von lokalen Innovationsaktivitäten umgesetzt wurde. Zunächst analysieren wir die Merkmale der Technologiebezirke sowie der in ihnen vorhandenen Firmen. Anschließend bewerten wir die Leistung der Firmen des Bezirks. Der Süden Italiens verfügt über mehr Technologiebezirke, die allerdings klein ausfallen, eine schlechte Branchendiversifizierung aufweisen und weit entfernt von der Wirtschaftsstruktur des Gebiets liegen. Die Firmen, die einem Bezirk beigetreten sind, waren zuvor im Durchschnitt größer, innovativer und rentabler und verfügen zudem über mehr Einfluss als die anderen. Nach der Gründung eines Bezirks änderte sich die Leistung der beigetretenen Firmen nicht signifikant von der Leistung ähnlicher, nicht beigetretener Firmen.

SCHLÜSSELWÖRTER Technologiebezirke; Innovation; Patente; öffentliche Politikmaßnahmen; Übereinstimmung; Difference-in-Differences

RESUMEN Políticas regionales para la innovación: el caso de los distritos tecnológicos en Italia. Regional Studies. En este artículo analizamos una herramienta política denominada distrito tecnológico que se ha puesto en práctica en Italia en la última década para fomentar la actividad innovadora de ámbito local. En primer lugar, examinamos las características de los distritos tecnológicos así como de las empresas que existen dentro de ellos. A continuación, evaluamos el rendimiento de las empresas de los distritos. El sur de Italia tiene más distritos tecnológicos, pero son pequeños, mal diversificados sectorialmente y lejos de la estructura económica del área. Las empresas que se unieron a un distrito habían sido antes más grandes, innovadoras y rentables de promedio, y ejercen más influencia que el resto. Después de la creación de un distrito, no variaba de forma significativa el rendimiento de las empresas incorporadas en comparación con otras empresas similares no incorporadas.

PALABRAS CLAVES distritos tecnológicos; innovación; patente; políticas públicas; comparación; diferencias en diferencias

JEL H2, O31, R0 HISTORY Received 23 September 2015; in revised form 14 October 2016

INTRODUCTION

The economics of innovation have extensively emphasized the role of national and regional innovation systems in pro- moting the innovative capacity of firms and geographical areas (Cooke, Uranga, & Etxebarria, 1997; Lundvall, 1992; Organisation for Economic Co-operation and Development (OECD), 1997). This literature highlights, both theoretically and empirically, the importance of the interactions among firms and institutions that are able to shape the innovative process, and which are typical of each territory. Such intangible assets are crucial determi- nants of the local innovation ability that are external to the firms but internal to the area where they are located (e.g., Capello & Faggian, 2005). Inspired by these theoreti- cal frameworks, several place-based policies have been implemented in European countries to create and promote dynamic and successful clusters of technologically advanced activities concentrated in a particular area.

The paper sheds some light on one of the policy instru- ments implemented in Italy in the early 2000s, namely technology districts (TDs). Grounded in the theory of regional innovation systems and the triple-helix model (Etzkowitz & Leydesdorff, 2000), the policy aims to enhance firms’ innovation capabilities and the competitive- ness of local production systems by creating synergies among firms, universities and research centres located

within limited territorial boundaries. One feature of the policy is the role played by regional government, which proposes the creation of the districts and, together with other local public authorities, coordinates the activities within them. The policy is widespread throughout Italy, involving almost all the Italian (NUTS-2) regions and uti- lizing a significant amount of public funds.

By focusing on firms, the article maps the TDs and identifies their main features, such as size, sectorial special- ization and diversity, together with those of the districts’ firms, in terms of several balance sheet and innovation indi- cators. Next, it assesses the performance of the district firms by matching those that joined a district with similar enterprises that did not join a district, and using difference-in-difference estimates to compare the perform- ance of the two groups – measured by a number of balance sheet variables and the propensity to patent – before and after the district’s birth.

While the literature on technological clusters and regional innovation systems is quite extensive (e.g., Anto- nelli, 2000; Cooke et al., 1997; Evangelista, Iammarino, Mastrostefano, & Silvani, 2002; Patrucco, 2003; Rychen & Zimmermann, 2008), the empirical papers on local pol- icies for innovation in Italy are scant (Colombo & Delmas- tro, 2002; Liberati, Marinucci, & Tanzi, 2016; Miceli, 2010). The study contributes to this stream of research in several respects. First, unlike most of the previous literature

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which is based on case studies, it focuses on the TDs nationwide and examines the universe of the firms that belong to TDs. Second, it uses a wide set of firms’ balance sheet variables together with key measures of firm inno- vation output, such as patent applications, in order to illus- trate the features of the firms and measure their performance. Third, to evaluate the performance of the dis- trict firms it employs matching techniques combined with difference-in-difference estimates over a relatively long time period, 2001–12. This allows one to follow the firms over a relatively long time span and control, as far as possible, for unobservables that might have affected the performance of district enterprises.

As regards the last exercise, it is worth stressing that it cannot be considered a comprehensive evaluation of the effectiveness of the policy. The TD programme, which aimed to enhance local innovation systems where the dis- tricts are located, involved other actors as well as firms, e.g. universities, public and private research centres, and local government bodies. Therefore, a comprehensive evaluation of the TD policy should measure the policy’s impact on the geographical areas and all the actors affected by the pol- icy. This exercise, which is opened for future research, is challenging because of the difficulties in finding appropriate measures of the performance of the actors other than firms affected by the policy, and a suitable identification strategy to evaluate the policy’s effects. However, even with its limits this study provides a valuable contribution to the knowledge of an under-investigated, but important, innovation policy that deserves further attention in future.

The analysis shows that TDs in the Italian southern regions are more numerous, but include fewer firms than those located in the Centre–North, they are poorly diversi- fied sectorially and more distant from the economic struc- ture of the area. These characteristics might limit the synergies among firms, and hinder the economies of scale and scope that the policy would implicitly like to trigger. Overall, firms that did join a district are larger and more innovative than other firms of the same sector located in the same region; moreover they also show higher invest- ment rates and leverage. This exercise shows that, on the whole, after the birth of a TD, district firms did not outper- form similar non-district firms; only the profitability of lar- ger district firms in the North-West turned out to be higher than that of the control group after the policy.

The paper is organized as follows. The next section dis- cusses the theoretical framework of the policy and the related empirical literature. The third section presents the main characteristics of the TDs and the most important features of district firms compared with those of non- district firms in the same region. The fourth section pro- vides some evidence on the performance of district firms. The fifth section concludes.

TECHNOLOGY DISTRICTS: THEORETICAL AND EMPIRICAL BACKGROUND

The usual policies for innovation aim at increasing the level of innovative investment by reducing its costs through

grants, fiscal incentives or facilitated loans. From a theor- etical point of view, public intervention to spur innovation is justified by a market failure argument. Since knowledge is a public good, innovative firms are unable to benefit fully from the returns of an innovative investment because of knowledge spillovers, and consequently under-invest with respect to the social optimal level (Arrow, 1962). The rationale for the policy is to boost innovative invest- ment towards a level that maximizes social well-being by decreasing the cost of the investment.1

In the last few decades new polices for innovation have been implemented across several advanced countries. These policies were influenced by theories that stressed the systemic nature of the process of innovation. A number of scholars have argued that innovation and technology development depends not only on the innovative efforts of the enterprises, but also is related to the specific economic and institutional characteristics of each national or local system of innovation (Lundvall, 1992; Nelson & Rosenberg, 1993). In this con- text, a crucial role is played by various forms of agglomeration economies associated with geographical proximity, such as those emerging from research and development (R&D) col- laborations among firms, face-to-face interactions and infor- mal contacts. Moreover, it is argued that an innovative performance is promoted by the sharing of rules and values, which are characteristic of the socio-economic environment and support the exchange of tacit knowledge and learning mechanisms among the actors (Audretsch & Feldman, 2004; Capello, 1999; Capello & Faggian, 2005; Cooke, Hei- denreich, & Braczyk, 2004; Dosi, 1988).

In the late 1990s, with the triple-helix model, Etzkowitz and Leydesdorff (2000) re-elaborated the concept of national and local innovation systems in the light of the development of information and communication technologies (ICTs), and the intensification of economic globalization. This model refers to the need for a strategic integration of the three dri- vers of development – research, government and industry – that enable the activation of knowledge flows, thereby stimu- lating the innovation ability of the local system.

In this framework, the rationale for public intervention has moved from market failures to system failures: public policies are justified in order to overcome imperfections in the innovation systems because some essential elements are missing, or the linkages within them are not working well. The goal of an innovation policy is thus to create and promote the favourable conditions that enhance the functioning of innovative systems.

In Italy, the technology district is a region-oriented policy instrument implemented in the early 2000s to foster inno- vation and firms’ competitiveness, which is largely grounded on the theoretical framework of regional inno- vation systems and the triple-helix model. The aim is to act as an instrument of governance and coordination of the processes in order to streamline learning mechanisms appropriate for innovation.

2

Empirical evidence Public policies to promote and enhance local clusters of innovative activities have been implemented in many

1828 Federica Bertamino et al.

REGIONAL STUDIES

European countries and in the United States, e.g., Albert, Bernasconi, and Gaynor (2002) for the case of France, Dohse (2000) for Germany, Viladecans-Marsal and Ara- uzo-Carod (2012) for Spain, and Moretti and Wilson (2014) for the United States.

As regards the Italian case the empirical literature is scant and mainly focused on case studies. Colombo and Delmastro (2002) compare 45 new technology-based firms located in Italian incubators within science and tech- nology parks – a local innovation policy similar to TDs – with a control sample of off-incubator firms in the same industry and area. Using a firm-level survey, the study shows how on-incubator firms invested more in human capital and were more likely to adopt technological inno- vations than similar off-incubator firms. By contrast, the two groups of firms did not differ in terms of innovation indicators such as the intensity of R&D expenditure and number of patents. Liberati et al. (2016) analyze a larger sample of firms located in Italian science and technology parks, and find that firms residing in the parks did not show a substantially different business or innovative per- formance from those of similar firms outside the parks. Only a couple of papers are focused on TD policy. Miceli (2010) compares each district’s sectors of specialization with the specialization of the areas where they are located. She finds that in several regions (eight regions, principally in the South) there is no consistency between the TD’s specialization and that of its home area. However, in that paper no micro-economic information on district firms is used. Ardovino and Pennacchio (2014) study the inter- firm R&D cooperation within a sample of six TDs, out of 29, showing that the propensity to cooperate is hetero- geneous across TDs and firms of different sizes.

Unlike previous studies, this paper examines all the TDs in Italy and provides a broad overview of the charac- teristics and performance of the universe of firms within the districts using a wide set of firm-level information.

3

THE CHARACTERISTICS OF ITALIAN TECHNOLOGY DISTRICTS

The Italian TDs were defined by the 2002–04 and 2005– 07 National Programs of Research (NPR) of the Italian Ministry of Education, University and Research (MIUR). They are defined as local aggregations of high-tech activi- ties, made up of geographically concentrated universities or public research centres, firms and local governments, which aim to foster firms’ innovation capabilities and local com- petitiveness. TDs are legally constituted by an act issued by the MIUR following the proposal of the regional gov- ernment.4 A district is formally created by a legal agree- ment between the region and the ministry (Framework Agreement Programme; Accordo di programma quadro – APQ). The legal status of the entity responsible for the management of the initiatives in the district is usually that of a limited liability consortium with a majority of pub- lic shareholders, participated in by firms, universities, the region and other public entities. TDs and district firms can benefit from public funds from the European Union,

and national (or regional) funding. According to prelimi- nary information provided by the MIUR, public funds dis- bursed to the TDs, excluding regional funds, amounted approximately to €450 million by the end of 2011.5

As mentioned above, the three main subjects of the TDs are firms, public research centres or universities and local public authorities. For firms the main benefits of par- ticipating in a TD come from establishing collaborations with other firms, research centres and universities. More- over, they may benefit from public funds and the use of common laboratories, equipment and services available in the district. Universities and public research centres sup- port firms by providing services related to innovation activi- ties, carrying out basic research and coordinating the largest projects. Additionally, some are also involved in promoting spin-offs. Finally, public authorities belonging to the TD – such as regions, provinces, municipalities or chambers of commerce – participate in the government bodies of the district, provide public funding and coordinate and pro- mote the activities within the districts. The region is the link between the TD and the ministry. This study focuses on the Italian TDs created by the end of 2011; the sample used in the analysis represents almost all the TDs since only a few districts were established afterwards.

For the empirical analysis are used three main datasets. First, the dataset built by the Ministry of the Economic Development (Department for Development and Econ- omic Cohesion), which collects identifiers of enterprises belonging to the TDs existing on the 31 December 2011 (2298 firms). Unfortunately, the dataset does not include information about the year in which firms joined a district, so it is not possible to study the birth of new firms (start- ups). Second, the balance sheet dataset for all the Italian limited companies sourced by the Cerved group, which provides the financial statements of the firms together with other information such as economic activity, localiz- ation and year of birth of the enterprises. In the last year covered by the analysis (the end of 2012) there were 1236 firms in the Cerved database out of 2298 existing dis- trict firms. Presumably the missing firms are those too small to be registered in Cerved, such as general partner- ships or individual companies.6 Third, the PATSTAT dataset, which collects the information on patent appli- cations to the European Patent Office (EPO). Finally, other information on TDs, such as the year of establish- ment and the sector of specialization of the districts, is gathered from the institutional websites of the Ministry of Education, University and Research, the regions and each TD.

Structure and territorial distribution of technology districts By the end of 2011, 29 TDs had been formally recognized in Italy by the Ministry of Education, University and Research. These districts were in 18 of the 20 Italian regions, with 2298 district firms (Table 1). The vast majority of the dis- tricts (21) were created between 2003 and 2005.

There is a strong heterogeneity of the districts in terms of distribution across the regions, size and economic

Regional policies for innovation: the case of technology districts in Italy 1829

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Table 1. Characteristics of the technology districts by region.

Region (geographical area)

Number of districts

Year of establishment Activities

Number of district firms

Average number of firms by district

Number of district firms stored in

Cerved

Number of four- digit sectors by

district

Piedmont (NW) 1 2003 Information and

communication technology

(ICT)-wireless

439 439 201 14

Lombardy (NW) 3 2004

2004

2004

ICT

Biotechnologies

Advanced materials

89

154

164

136 333 7

10

10

Liguria (NW) 1 2005 Intelligent integrated systems 26 26 22 4

Trentino-Alto Adige (NE) 1 2006 Sustainable technologies for

building

171 171 109 14

Veneto (NE) 1 2004 Nanotechnologies 16 16 15 4

Friuli-Venezia Giulia (NE) 1 2004 Molecular biotechnologies 14 14 5 2

Emilia-Romagna (NE) 2 2004

n.a.a Advanced mechanics

Biomedical equipment

89

40

65 29 n.a.

n.a.

Tuscany (CE) 1 2006 Mechatronics 175 175 64 8

Umbria (CE) 1 2006 Mechatronics 59 59 28 7

Lazio (CE) 3 2004

2008

2008

Aerospace

Biosciences

Cultural activities

221

80

90

130 226 15

9

4

Abruzzo (SO) 1 2005 Agri-bio-food 25 25 17 5

Molise (SO) 1 2006 Agri-bio-food 23 23 2 1

Campania (SO) 1 2005 Polymeric materials 15 15 10 2

Puglia (SO) 4 2005

2005

2008

2005

Mechatronics

Nanotechnologies and ICT

Energy

Agri-bio-food

9

10

31

101

38 82 4

n.a.

6

9

Basilicata (SO) 1 2005 Hydrogeological and seismic

risks

4 4 2 1

Calabria (SO) 2 2005

2005

Logistics and transports

Cultural activities

26

18

22 23 7

1

Sicily (SO) 3 2005

2005

2005

Micro and nanosystems

ICT and microelectronics

Agri-bio-food

12

136

10

53 54 3

8

2

1 8 3 0

Fed erica

B ertam

in o et

al.

REG IO N A L STU

D IES

activities. There are only two regions with no districts (Marche and Valle d’Aosta), whereas in many regions (six) there is more than one district. Notice that three of them are in the South, an area traditionally under-special- ized in technological advanced sectors. Overall, 14 districts are in the South, while in the North-West, North-East and Centre there are five districts based in each area.

7

TDs differ substantially in size. For example, the largest districts are in Piedmont and Lazio with 439 and 221 firms, respectively, while in the South there are the smallest ones (fewer than 10 firms in some districts of Puglia and Basilicata). On average, north-western and central TDs are the biggest, including on average 174 and 125 firms, respectively. In north-eastern TDs there are on average 66 firms, while the smallest are in the South with an aver- age of only 34 firms. This evidence suggests how the agglomeration economies that TDs are supposed to trigger might involve the TDs of southern regions less because of their limited size.

The genesis of the southern districts is quite different from that of the other areas and this can help to explain the reason why in the South there are more districts, but those districts are also relatively smaller in terms of the number of firms. In the North, especially in the North- West, TDs were often created on existing high-tech clusters of firms. In these areas, the legal constitution of the districts acknowledges or sometimes formally ratifies the existence of local productive systems strongly special- ized in high-tech activities. In contrast, in the southern regions the creation of the districts was often driven by the regional government. In such areas the TDs were lar- gely used as an instrument to favour the innovation of small local enterprises by creating networks between them and intensive research activities carried out by other local players. In these circumstances, national and local authorities had pinpointed the strategic activities of the ter- ritory, which could be enhanced by the TDs, and guided the adhesion of the players to the districts, such as big national or multinational firms. Another important differ- ence between central-northern districts and southern dis- tricts is that the latter take more advantage of the national public funds (Fund for Underutilized Areas – FAS) and European Cohesion Funds (European Regional Development Fund – ERDF) than the former.

For the subsample of district firms present in the Cerved database it is also possible to look at the economic activity of the enterprises. Most belong to industrial or ser- vice sectors, 600 and 534 enterprises, respectively (see Table A1 in the supplemental data online). The most rep- resented branches are ICT, with 259 firms, professional activities and electronic products, with 175 and 151 enter- prises, respectively; the less represented branches are paper and publishing, and textiles and clothing.8

For descriptive purposes, the paper compares the sec- torial specialization of the districts with that of the areas in which they are located, and studies the sectorial variety within the districts. It is possible to find a strong hetero- geneity in the structure and the characteristics of the TDs across areas. In the North-West the districts are largerSa

rd in ia

(S O )

1 2 0 0 5

W ea lt h te ch n o lo g ie s

5 1

5 1

1 4

4

N o rt h -W

es t – N W

5 –

– 8 7 2

1 7 4

5 5 6

9 .0

N o rt h -E as t – N E

5 –

– 3 3 0

6 6

1 5 8

6 .7

C en

tr e – C E

5 –

– 6 2 5

1 2 5

3 1 8

8 .6

So u th

– SO

1 4

– –

4 7 1

3 4

2 0 4

4 .1

It al y

2 9

– –

2 2 9 8

7 9

1 2 3 6

6 .2

N o te s: D at a re fe r to

2 0 1 2 (t h e la te st

av ai la b le

ye ar ). D is tr ic t fi rm

s al so

in cl u d e th o se

w h o se

h ea d q u ar te rs

ar e in

an o th er

re g io n .

a Th

e ye ar

o f es ta b lis h m en

t o f th e ‘B io m ed

ic al

eq u ip m en

t’ d is tr ic t is n o t av ai la b le .

Regional policies for innovation: the case of technology districts in Italy 1831

REGIONAL STUDIES

and more sectorially diversified than in the other areas; at the same time their specialization only partially reflects the local economic structure. In the North-East the TDs are smaller in size than in the North-West, however they are rather diversified and strongly mirror the sectorial specialization of the area. In the Centre the structure of TDs is similar to those in the North-West: they are rather big, sectorially diversified and only partially reproduce the specialization of the area. In contrast, in the South there are more TDs, but they are small, poorly sectorially diver- sified and far from the economic structure of the area. For greater details see section A1, Table A2, of Appendix A in the supplemental data online.

The characteristics of district firms: evidence from balance sheet and patent data This section examines the main characteristics of the dis- trict firms by comparing their balance sheet indicators and patent applications with those of similar firms outside the districts. The comparison is carried out the year before the birth of the district to avoid the differences being due to the effects of the policy. District firms are compared with non-district firms drawn from the Cerved dataset that belong to the same four-digit sector and are localized in the same NUTS-2 region of the on-district firms. It is possible to find balance sheet data for about 900 and 62,000 district and non-district firms, respectively, the year before the establishment of the TDs (Figure 1; see also Table A3 in the supplemental data online).

9 The

analysis excludes 14 district firms and 37 non-district firms with sales of more than €1 billion because otherwise they might have driven the results of both the samples.

The first striking characteristic of district firms is that they are, on average, much larger than those of the same sector localized in the same region: the median assets, sales and added value are more than eight times bigger. Furthermore, for both variables the 10th and 90th percen- tiles are considerably higher for district enterprises than for non-district ones. In relative terms such differences are less marked in the northern districts (about six times) than in central and southern ones (up to 10 times). Moreover, in all the geographical areas, but especially in the North- East, firms inside the districts are more homogeneous in terms of size than firms outside the districts as shown by the coefficients of variation. As regards the overall profit- ability measured by the return on assets (ROA), district and non-district firms are on average rather similar. On the other hand, considering the operating profitability, measured by the gross operating margin over assets (EBITDA/assets), non-district firms seem to perform bet- ter than district firms, in particular in the South and North- East (the smallest areas in terms of number of firms).

District firms show a higher investment propensity compared with non-district firms, considering both the investment rate and the ratio between investment and sales. However, there is a large dispersion of these indi- cators as shown by the coefficients of variation. District firms appear much more leveraged, consistent with their bigger size: the difference between the two groups of

firms is more marked in the Centre and in the South. Looking at the data at single TD level, these differences between district and non-district firms become even more marked.10

The descriptive analysis is completed by examining the innovative capabilities of the firms. Innovation propensity is measured through the number of patent applications submitted by firms to the EPO, using the archive PAT- STAT. For each TD the article considers the patent appli- cations submitted to the EPO by district and non-district firms of the same region and four-digit sector, in the five years before the birth of the districts.11 As done for the sample of firms for which the balance sheet data are avail- able, those with sales of more than €1 billion are excluded.

In all the areas, district firms seem to be much more innovative than non-district firms (Figure 2; see also Table A4 in the supplemental data online). The percentage of firms that applied for at least one patent in the five years before the district’s birth is, on average, about eight times bigger for district firms. The average number of patents by firm, multiplied by 100, is almost 30 for district firms, against about four for non-district firms. For both indi- cators, the difference between the two groups of firms is remarkable in each area, but particularly in the Centre. Among the non-district firms, the northern ones show a better performance compared with those located in the other areas of the country.

After having examined the differences for each variable separately, the paper examines the correlation between the probability to participate in a district and some firm-level variables all together. Over the same sample of district and non-district firms it estimates a probit model in which the probability of participating in a district – measured by a dichotomous variable equal to 1 if the firm joins a district and zero otherwise – is regressed on firm size (proxied either by assets or added value), profitability (ROA and EBITDA/assets), leverage and patent propen- sity (a dummy equal to 1 if the firm has applied for at least one patent in the five years before the establishment of the district; 0 otherwise), with reference to the year before the establishment of the TDs.

When all the variables are used together, and control- ling for sector and regional dummies, all the regressors except EBITDA/assets are statistically significantly corre- lated with the dependent variable (Table 2). To sum up, the most profitable, innovative, leveraged and large firms are those that are more likely to join a district.

THE PERFORMANCE OF DISTRICT FIRMS

This section assesses whether, after having joined a TD, firms enjoyed a higher performance than those of non- district firms. In general, one would expect that adhesion to a district might have a positive impact on the innovative capability of firms, as a result of the establishment of closer relationships (and synergies) among the various actors of the district and thanks to public aid. Furthermore, it is arguable that the policy may lower district firms’ costs, fostering tangible and intangible investments. Therefore,

1832 Federica Bertamino et al.

REGIONAL STUDIES

district firms might eventually increase their size, profit- ability, productivity and innovative propensity compared with non-district firms.

As a result, firm performance is evaluated in terms of size (assets, sales and added value), profitability (gross oper- ating margin over assets and return on assets), accumu- lation of tangible or intangible fixed assets (investment rate), financial structure (leverage), labour productivity (added value over labour cost and sales per capita) and innovation capabilities measured by patent applications submitted to the EPO. The analysis is based on the

comparison between district firms and similar non-district firms, before and after the creation of the district, using matching methods and difference-in-difference estimates (e.g., Imbens & Wooldridge, 2009).

More detail on the procedure is the following. First, each district firm is matched with a non-district firm which is in the Cerved database is found in the same geo- graphical area of the district firm (i.e., North-West, North- East, Centre and South), belongs to the same two-digit sector, and is among those that minimize the Mahalanobis distance (Rubin, 1980). The variables used for the Maha- lanobis function are sales, added value, the ratio of gross operating margin over total assets, and the ratio of invest- ments over sales (that are proxies of the firm size, operating profitability and capital accumulation, respectively). The matching is carried out using the nearest-neighbour method after having imposed the common support; the procedure is based on data referring to the year before the birth of the districts (to exclude any potential effects of the policy on the district firms). The biggest district firms, those with more than €1 billion of sales, were excluded because it was not possible to find appropriate controls for them.12 Notice that the matching procedures allowed to match 847 non-district firms to 900 district ones. In order to obtain a more robust assessment of firm performance, firms corresponding to the observations exceeding the first and 99th percentiles for each variable are excluded. The exclusion of the outliers reduced the

Figure 1. Balance-sheet indicators. Note: Data refer to the year before the establishment of the districts. Firms with sales larger than €1 billion are excluded. Sales in thousands of euros; EBITDA/assets, investment rate and leverage in percentages.

Figure 2. Patent applications. Note: (left) Percentages of firms that applied for a patent; and (right) number of patent applications by firm multiplied by 100.

Regional policies for innovation: the case of technology districts in Italy 1833

REGIONAL STUDIES

sample to 766 district firms and 727 non-district ones. Table A5 in the supplemental data online shows that after the matching procedure, for a large set of observables, district firms and non-district firms in the control group are very similar: the mean differences are never statistically sig- nificant in the overall Italian sample, showing that the two groups of firms are highly comparable in terms of the avail- able observables.

The second step is to compare the performance of dis- trict and matched non-district firms by differences-in- differences estimates to assess whether the two groups of firms show a different path after the constitution of the dis- tricts. The differences-in-differences estimate allows one to control for the initial differences in the levels of observable and unobservable characteristics between the two groups of firms. At the same time, the methodology relies upon the hypothesis that in the absence of the TD the two groups of firms would have followed the same path (parallel trend assumption), i.e., that firms of the control group mimic the path that district firms would have followed if they had not joined the district. Therefore, the common trend assumption is tested for some of the main balance sheet variables of the two groups of firms (sales, added value, tangible and intangible investments). The T-test is run over the rate of growth of the variables over the two years before the establishment of the districts, and differ- ences are not statistically significant (results are not shown but available upon request). Moreover, in order to make the assumption less restrictive, some control vari- ables, which are also interacted with the time dummy in the empirical model, are included (see below and for another robustness exercise see section A2 in Appendix A in the supplemental data online).

For each district firm and its control the pre-policy period is represented by the year before the creation of the district. The post-policy period includes the year of the constitution of the district and four years afterwards. Therefore, the estimates are carried out over six years.

The sample only includes firms present in the dataset over the entire examined period. The estimated model is:

yit = a + b1(DISTi) + b2(POST) + b3(DISTi∗POST) +

∑

s

(g1sSECTORs) + ∑

s

(g2sSECTORs∗POST)

+ ∑

r

(g3rREGr) + ∑

t

(g4tYEARt) + eit

(1)

where yit is the outcome variable with which the firms’ per- formance is evaluated; DISTi is a dummy equal to 1 if firm i participates in a district, and 0 otherwise; t ¼ 1, 2 is a time index; POST is a dummy equal to 1 over the post-policy period, and 0 otherwise; REG, SECTOR and YEAR are dummies for the region of localization of the district, the three-digit sector of the firm and the years, in order to con- trol for common factors at regional, sectorial and time level; the interaction among sector dummies (SECTOR) and the post-policy dummy (POST) controls for potentially differ- ent time trends across sectors after the policy; and εit is a stochastic error with the usual properties. The baseline regressions are carried out considering the average of every variable of interest over the five post-policy years (the year of the creation of the district and the following four years) for the post-policy period, and taking the year before the creation of the TDs for the pre-policy period. The coefficient β3 measures to what extent the performance of district firms changed after the creation of the district with respect to that of the control group. Robust standard errors reported in the tables are clustered at the firm level.

As mentioned above, in order to measure the firm’s per- formance the following outcome variables are used: assets, sales and added value as proxies of firm size; ROA and gross operating margin over assets as indexes of total and operating firm profitability; investments and the invest- ment rate to measure the accumulation of tangible and

Table 2. Probit – dependent variable: probability of joining a district. Variable Probita Variable Probita

Assets 0.000166**

(0.00008)

Added value 0.00465***

(0.000969)

ROA 1.25***

(0.336)

ROA 1.26***

(0.334)

EBITDA/assets –0.452

(0.327)

EBITDA/assets –0.497

(0.318)

Leverage 0.0618**

(0.0243)

Leverage 0.0625**

(0.0243)

Patentsb 0.724***

(0.0697)

Patentsb 0.674***

(0.0727)

Observations 59,008 Observations 59,008

Notes: Data refer to the year before the establishment of the districts. Firms with sales of over €1 billion are excluded. All coefficients and standard errors (except ‘patents’) are multiplied by 1000 to improve readability. Robust standard errors are shown in parentheses. aProbit with four-digit sector dummies and regional dummies as controls. bPatents: dummy equal to 1 if the firm has applied for at least one patent in the five years before the establishment of the district; 0 otherwise.

1834 Federica Bertamino et al.

REGIONAL STUDIES

intangible assets and, finally, leverage to assess changes in the financial structure.13

Since firms of various sizes might have benefitted in varying degrees from participation in the district – e.g., small firms might have received a larger amount of public funds or services than large firms, or might have benefitted more from the linkages created by the policy – the results are also split between small and large firms to ascertain potential heterogeneities in the performance of firms according to their size. The former are separated from the latter according to the median value of their sales the year before the birth of the district.

Table 3 displays the results over the whole post- programme period, with all the fixed effects used as con- trols: it shows a positive and significant coefficient for the total profitability (ROA) but not for the operating profit- ability. This means that, after the creation of the district, firms that participated in the policy improved their per- formance in terms of overall profitability with respect to the control group of non-district firms, although such an effect is not driven by the operating profitability measured by EBITDA/assets, but rather by the non-operating reven- ues.14 By splitting the firm sample by size one can find that this overall effect is driven by large firms, whereas there is no effect for small ones. This suggests how the benefits of the policy might have mainly benefitted large district firms that, for example, have been able to reduce their financial costs or expand their financial revenues arguably thanks to the financial aids linked to their participation in the district. As regards the other balance sheet variables examined there are not any significant differences in the performance among district and non-district firms. The model is also estimated for each year of the post-policy period, in order to detect potential changes of the impact of the policy, but there are not any evident trends in the coefficients; they turn out to be almost never statistically significant (see Table A6 in the supplemental data online).

Next, the sample is broken down into the main four geographical areas to verify the potential heterogeneity of performance according to the location of the districts. The results are that district firms of the Centre outperform non-district firms in the same area in terms of investment rate (Table 3), thanks to the small firms (see Table A7 in the supplemental data online, where it is shown also that the positive effect on the large firms’ total profitability is due to the north-western districts). As regards the other variables the coefficients are not statistically significant. Further robustness checks are carried out, without substan- tial changes in the results. For greater details, see section A2 in Appendix A in the supplemental data online.

Productivity and innovation capabilities In this section the analysis is extended to labour pro- ductivity and innovation capabilities which are studied sep- arately from the previous ones because they come from different firms’ samples.

As regards productivity, since the number of employees is only available for a very limited subsample of larger firms, the model (1) is estimated over a reduced sample that

includes 430 district firms and 379 matched non-district firms of the control group. The results are shown in the last columns of Table 3. Any significant differences between the performance of district and non-district firms are found after the birth of the TDs, either for Italy or for the other subsamples in which the results are broken down (geographical areas and size). In order to overcome the loss of observations due to the use of an indicator scaled by the number of employees, the same analysis is also car- ried out using the added value scaled over the labour cost as proxy for productivity but the results do not change.

Next, the paper assesses whether participation in a TD has enhanced the innovation propensity of the firms, measured by the number of patent applications presented at the EPO and the probability of applying for a patent. Both are calculated over a five-year period, before or after the creation of the TD. Information on patent applications comes from the merging of the PATSTAT dataset and the AIDA balance sheet dataset at the firm level carried out by Lotti and Marin (2013).

Overall, before the creation of the districts, the patent propensity of district firms was considerably higher than that of non-district firms located in the same region and belonging to the same four-digit sector. The number of patent applications by firm, and the share of firms that applied for at least one patent were much larger in the dis- trict firms than in non-district firms (see Table A8 in the supplemental data online). However, if the three most patenting district firms are excluded, the two groups turned out to be very similar in terms of number of patent applications.

After having balanced the samples by excluding those three firms, the next step was to estimate equation (1) on the new firm samples. Now the outcome variable yit is the sum of the patent applications presented to the EPO by firm i over the five years before the start-up of the policy (pre-policy period) and the five years after (the year of the start-up included). Over the whole 10-year period, 159 dis- trict firms and 71 non-district firms have applied for a patent. Since there are several firms with zero patent appli- cations, it cannot be excluded that the error term does not have a normal distribution. Therefore, the model is esti- mated both by ordinary least squares (OLS) and by maxi- mum likelihood (ML), assuming that the error term has a Poisson distribution (a standard practice for the empirical studies on patents). As robustness checks, four-digit sector dummies are included and the regional dummies are excluded, without appreciable differences in the results (they are not shown but are available upon request).

The main findings, in line with those previously obtained, are reported in Table A8 in the supplemental data online. The differences in the number of patent appli- cations between district and non-district firms do not change after the start-up of the TDs. The estimates of the coefficient β3 are never statistically significant.

Finally, to verify whether a firm’s probability to apply for a patent could have changed after the establishment of the district for the same samples, the equation is esti- mated by using a probit model where the outcome variable

Regional policies for innovation: the case of technology districts in Italy 1835

REGIONAL STUDIES

Table 3. Differences-in-differences regression: coefficients of dist*post.

Areas Observations Total assets Sales

Added value ROA

EBITDA/ assets Investments

Investment rate Leverage

Added value*100/ labour cost

Sales per capitaa

Pre-period: t – 1; post-period: average from t to t + 4 – all fixed effectsb

Total 2482 606.4

(613.5)

45.92

(427.3)

–14.33

(153.0)

0.897**

(0.435)

0.0894

(0.550)

57.32

(102.4)

1.875

(8.501)

–1.195

(3.170)

2.338

(5.096)

–26.68

(26.88)

Small 1152 134.7

(146.5)

144.7*

(85.92)

44.61

(37.33)

0.529

(0.727)

1.282

(0.954)

–15.59

(29.03)

4.628

(16.37)

–5.003

(5.013)

1.977

(8.978)

7.984

(25.72)

Large 1330 389.0

(1263)

–987.5

(962.5)

–268.8

(308.5)

1.003*

(0.513)

–0.930

(0.683)

70.69

(205.8)

3.423

(7.069)

3.736

(4.902)

3.362

(6.703)

–36.67

(32.67)

Pre-period: t – 1; post-period: average from t to t + 4 – all fixed effectsb

North-

West

1479 353.7

(761.5)

189.3

(598.5)

89.39

(204.9)

0.906*

(0.547)

0.375

(0.700)

36.97

(151.3)

–9.010

(10.19)

1.197

(3.042)

4.557

(5.217)

–28.92

(35.90)

North-

East

214 4929

(3,312)

724.1

(769.5)

–63.20

(261.8)

0.524

(0.857)

–1.106

(1.587)

–158.1

(256.8)

–3.972

(12.05)

0.873

(5.568)

5.609

(11.49)

–3.925

(38.96)

Centre 544 255.5

(817.2)

–86.82

(725.0)

115.4

(183.6)

0.149

(0.940)

–0.665

(1.345)

105.1

(94.93)

32.72***

(11.20)

–5.235

(9.836)

–10.10

(16.88)

–138.4*

(72.91)

South 245 –1315

(2206)

–1213

(1459)

–765.0

(787.0)

3.195

(2.140)

0.400

(2.118)

219.3

(246.3)

39.18

(30.04)

–16.75

(22.80)

17.03

(26.95)

–85.40

(85.88)

Notes: Firms exceeding the first and 99th percentiles for each variable are excluded. Robust standard errors are shown in parentheses. aRegression on ‘sales per capita’ was carried out on a subsample of 809 firms for which the information on the number of employees was available. bRegression with dummies ‘year’, ‘ATECO 3 digit’, ‘(ATECO 3 digit*post)’ and ‘region’. Robust standard errors are clustered by firm. ***p < 0.01; **p < 0.05; *p < 0.1.

Investments ¼ (total fixed assetst – total fixed assetst–1) Investment rate ¼ (total fixed assetst – total fixed assetst–1)/Total fixed assetst–1

1 8 3 6

Fed erica

B ertam

in o et

al.

REG IO N A L STU

D IES

yit ¼ 1 if firm i applied for at least one patent in period t, and 0 otherwise. In line with previous results any signifi- cant changes, in the outcome variable of district firms with respect to that of the control group, are found after they had joined the district.

CONCLUSIONS

The article examines Italy’s TDs, a policy instrument implemented in order to stimulate the creation and devel- opment of local innovative systems. Its focus is on district firms’ characteristics and their performances.

The analysis shows that TDs are very heterogeneous, in terms of innovative activities, number of firms and distri- bution throughout Italy. In southern Italy there are more TDs which, however, are much smaller in size than the dis- tricts in the other geographical areas; southern TDs are also poorly diversified sectorially and more distant from the economic structure of the area. These characteristics might limit the synergies among firms and the economies of scale and scope that the policy would implicitly like to trigger. On the contrary, in the Centre and North the dis- tricts are bigger and more diversified in terms of sectors, however only in the north-eastern regions do they closely mirror the sectorial specialization of the area.

The empirical evidence shows that firms that joined a TD were, before the creation of the district, larger, more innovative and also more profitable than non-district firms belonging to the same sectors and located in the same geographical areas. Moreover, after the start-up of the district, district firms did not perform significantly bet- ter than similar non-district firms, except in terms of total profitability. The result is driven by large district firms in the North-West, plausibly also thanks to the public finan- cial aid, but it does not apply to small district firms. Small firms in the districts of the Centre outperform non-district firms – after the birth of the district – in terms of invest- ment rates. However, this result must be taken cautiously because of the limited size of that specific subsample.15

Overall, some suggestive evidence seems to emerge that a firm’s performance is weakly correlated to the participation in a district.

The research is a further step to a deeper understanding of a widespread policy instrument for innovation. However, it has some limitations that need to be borne in mind. Since the evaluation of firms’ performance is focused on enter- prises present in the dataset before the creation of the TDs, the results cannot be extended to the start-ups estab- lished at the same time as the TDs or to the smallest (non- limited) companies, for which balance sheet data are not available. It is possible that these categories of firms, i.e., younger and smaller, could have benefitted more from the policy, as found by Lach (2002) and Bronzini and Piselli (2016), among others. The investigation of start- ups or the smallest firms’ performance would be extremely interesting, but it requires information that is not available thus far. Second, to evaluate the effectiveness of the policy, consistently with the theoretical approach on which the TDs were based, it would be essential to evaluate the

performance of all the actors and geographical areas tar- geted by the policy. These are challenging avenues open for future research.

ACKNOWLEDGMENTS

The authors thank Alessio D’Ignazio, Alessandro Fabbrini, Roberto Gabriele, Simone Martelli, Silvia Magri, Diego Scalise, Alessandra Staderini, two anonymous referees, and the participants at the Bank of Italy’s workshops held in Rome (September 2012) and Perugia (December 2012), and of the annual conference of AISRE (Palermo, 2013) and ERSA (St Petersburg, 2014), for their valuable comments and suggestions. The collection of the data on technology districts’ firms was made with the important contribution of Stefano Maiolo, Alessandro De Iudicibus and Francesco Termite, who the authors thank. The views expressed herein are those of the authors and do not necessarily reflect those of the respective institutions. The usual disclaimer applies.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

SUPPLEMENTAL DATA

Supplemental data for this article can be accessed at https:// doi.org10.1080/00343404.2016.1255321

NOTES

1. For recent empirical surveys on the impact of R&D incentives, see Zúñiga-Vicente, Alonso-Borrego, Forca- dell, and Galàn (2014) and Becker (2015); and on the econometric methods, see Cerulli (2010). 2. In Italy similar policies include the science and technol- ogy parks, and the more recent poles of innovation and poles of excellence. 3. For recent assessments of different regional innovation policies in Italy, see Corsino, Gabriele, and Giunta (2012), Fantino and Cannone (2013), Bronzini and Iachini (2014) and Bronzini and Piselli (2016). 4. There are three main strategic sectors of intervention: (1) environment, energy and transport; (2) agri-food and wealth; and (3) production systems, biotechnology, new materials and nanotechnology, ICT and cultural activities. 5. This is a minor share of public funds used for TDs. The incomplete knowledge of the amount of disbursed funds is a limitation of the paper which prevents exercises like cost– benefit analysis or dose–response function. 6. It was not possible to collect the balance sheet data for the districts established in Emilia-Romagna (NE) because of some shortcomings in the information: the fiscal codes of the firms for the ‘advanced mechanics’ district were not available in the dataset provided by the ministry; the authors do not know the year of establishment for the

Regional policies for innovation: the case of technology districts in Italy 1837

REGIONAL STUDIES

‘biomedical’ district (the website of the district reports that it has not yet been recognized by the ministry). 7. The geographical areas are: North-West (Piedmont, Liguria, Valle d’Aosta, Lombardy); North-East (Veneto, Friuli-Venezia Giulia, Trentino-Alto Adige, Emilia- Romagna); Centre (Tuscany, Umbria, Marche, Lazio); South (Molise, Abruzzo, Campania, Puglia, Basilicata, Calabria, Sicily, Sardinia). 8. The activities carried out in the districts often reflect some features of the geographical areas in which they are located. Greater details on this aspect can be found in sec- tion A1 in Appendix A in the supplemental data online and in Bertamino, Bronzini, De Maggio, and Revelli (2016). 9. The authors considered just once the data of the firms belonging to more than one district. The analysis includes only the firms with positive assets and sales (or, in alterna- tive, the added value). 10. Data are not reported but are available from the authors upon request. 11. For more information on the dataset, see Bertamino et al. (2016). 12. For greater details, see Bertamino et al. (2016). 13. Investments are calculated by the Cerved dataset. 14. The exercise was repeated on each single balance sheet item of ROA components: positive effects are found, even if not statistically significant, for all the considered items. This would probably mean that the positive effect of the adhesion to a district on the total profitability is the result of the cumulative effect on each item (depreciation of fixed assets, financial revenues and costs, other revenues and costs) rather than a significant effect on a single item. 15. Excluding the outliers, in the Centre there were, respectively, 98 district firms and 70 non-district small firms.

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