Abstract
This chapter provides a general overview of the role of space in firms’ economic activity. It first addresses the different conceptualizations of space. After that, the chapter describes agglomeration economies as a source of firm and territorial competitiveness. Then, it disentangles the proximity dimensions affecting innovation. After describing the most explored agglomerated areas, that is, industrial districts and business clusters, the chapter concludes by exploring the role of SMEs in peripheral areas.
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Notes
- 1.
Parr (2002b, p. 152) defines agglomeration economy more broadly as a “decrease in the unit cost of a firm, consequent on the concentration of economic activity at a given location.” The traditional conception of agglomeration externalities views these benefits as external economies of scale consisting in decreased average costs for the individual firm resulting from an increase in industry-wide output within a given geographical area. Hence, space becomes a source of static efficiency of the firm’s production processes.
- 2.
The Marshallian industrial district denotes the existence of a local area characterised by a strong concentration of small and medium enterprises, mostly family owned and managed, each of which is specialized in one or few phases of the production process (Sforzi & Boix, 2015, p. 62).
- 3.
With regard to urbanization economies, which are external to the individual firm and to its industry, these are internal to the urban concentration and cannot therefore be referred to as external economies of scale in the usual Marshallian sense. Rather, it is argued, these may be approached in terms of “economies of scope” (Parr, 2002b).
- 4.
As an alternative to the number of employees, Rensky (2011) computes the LQ in terms of establishment counts.
- 5.
The Gini index, which is similar to the Herfindhal index, represents an alternative and largely employed indicator for capturing diversification externalities (Galliano et al., 2015).
- 6.
A relevant implication of the difference between information and tacit knowledge is that, while the marginal cost of transmitting the former across the geographic space has been rendered invariant by the ICT (Information and Communication Technologies) revolutions, the marginal cost of transmitting the latter is positively associated with spatial distance (Audretsch & Feldman, 2004).
- 7.
One of the main arguments for the emergence of knowledge spillovers is the existence of public knowledge which is “non-rival” and “non-excludable” in consumption. A good is non-rival, when the consumption by someone does not preclude the opportunity for others to consume it at the same time. A good is non-excludible in the extent to which, once it has been made, it is impossible or extremely difficult preclude the consumption by anyone who has not paid for obtaining it.
- 8.
Boschma (2005, p. 66) defines social proximity in terms of “socially embedded relations between agents at the micro-level. Relations between actors are socially embedded when they involve trust based on friendship, kinship and experience.” The concept of social proximity is strongly related to that of social embeddedness that I shall explore this more thoroughly in Sect. 4.1 of Chap. 4, especially with regard to its spatial implications (i.e., local embeddedness).
- 9.
From this perspective, the concept of cultural homogeneity is somewhat overlap** with that of “cultural proximity” consisting in the sharing of same values among local economic actors, which is at the foundation of the high level of cooperation of a given territory (Capello & Faggian, 2005).
- 10.
Camagni (Camagni, 1995) defines innovative milieu as “the set of relationships that occur within a geographical area that bring unity to production system, economic actors, and an industrial culture, that generate a localized dynamic process of collective learning and that act as an uncertainty-reducing mechanism in the innovation process.” Examples of innovative milieu were found in metropolitan specialized in advanced types of production (e.g., north-eastern part of Milan), in non-metropolitan industrialized areas (e.g., many areas in the Third Italy) old industrial areas (e.g., the Swiss Jura specialized in watch production) and poles of excellence (e.g., Silicon Valley).
- 11.
Collective learning is the territorial counterpart of learning processes occurring inside the firm. As compared to the “interactive learning,” which envisions an explicit decision of cooperate by the local firms, collective learning is related to the spontaneous exchange of knowledge, which occurs through social contact at local level (Capello, 2016).
- 12.
By drawing on survey data from a software cluster in a develo** country, Kesidou and Romijn (2008) identify six types of informal interactions that take place at local level: (1) interactions at exhibitions and conferences; (2) horizontal interaction (i.e., with competitors); (3) backward linkages (i.e., with suppliers); (4) forward linkages (i.e., with distributors and customers); (5) interaction with universities and research institutes; and (6) interaction with support institutes.
- 13.
These local conditions are attributable to: (1) a strong local institutional presence; (2) high level of interaction between local organizations; (3) a mutual awareness of being involved in a common enterprise; and (4) structures of domination and/or patterns of coalition (Coulson & Ferrario, 2007).
- 14.
The learning region identifies a socio-economic system able to develop interactive learning internally. In particular, Rutten and Boekema (2012, p. 986) define it as a region in which “regional actors engage in collaboration and coordination for mutual benefit, resulting in a process of regional learning. Regional characteristics affect the degree to which the process of regional learning leads to regional renewal.”
- 15.
Alfred Marshall (1842–1924) is also the founder of the Cambridge School of Economics and neoclassical economics.
- 16.
Using the local labor market (LLM) areas, the Italian National Institute of Statistics has identified in the ISTAT-Census (2011) 141 industrial districts. They are scattered throughout the country, with a prevalence in the northeast of the country (45 industrial districts). More than 90% of them are specialized in the production typical of Made in Italy, such as mechanical industry (27.0%), textile and clothing (22.7%), furniture (17.0%), and leather and footwear (12.1%).
- 17.
The Sforzi-ISTAT method was employed in the latest census to map the IDs in Italy. After defining local labor market (LLM) areas, a set of statistical tests based on employment concentration quotients is used to identify: the prevalence of manufacturing activities, the significant presence of SMEs, and high levels of industrial specialization. Such conditions are assumed to be consistent with the presence of an ID.
- 18.
There are various channels through which local export spillovers occur. Intra-industry linkages among firms operating in the same industry. Labor mobility of workers with market-specific information. The networks of buyer–supplier relationships. And finally, informal interactions, mainly on the occasion of social gatherings. Proximity dimensions in MIDs facilitate the transmission of this type of information (Choquette & Meinen, 2015).
- 19.
When a key anchor tenant in the district is a public or non-profit entity (e.g., a large public university, lab, and military base), the State-anchored industrial district does emerge. The local business structure is dominated by the presence of such facilities, hence resembling the satellite platform case. The location choices and economic relationships are determined by political entities rather than by the private sector. Even though facilities can operate with few or minimal connections to the regional economy, some new SMEs may emerge from technological transfer (e.g., via universities) or business services provided by (or spilling over from) the anchor institution.
- 20.
Among the EU cluster initiatives is the European Cluster Collaboration Platform (ECCP), whose ultimate goal is to ease cluster collaboration between clusters in Europe and between cluster members. ECCP reports 2.950 regional industrial clusters across Europe. In 2022, Italy consisted of 86 cluster organizations. For more information, please see https://reporting.clustercollaboration.eu/
- 21.
Eurostat defines “rural areas” as all areas outside urban clusters. “Urban clusters” are clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5000. On the basis of the share of their population in rural areas, NUTS 3 regions are classified as follows: (1) “Predominantly rural,” if the share of the population living in rural areas is higher than 50%; (2) “Intermediate,” if the share of the population living in rural areas is between 20% and 50%; (3) “Predominantly urban,” when the population living in rural areas is below 20%. The US Census Bureau defines rural areas based on similar population criteria.
- 22.
The SNAI is part of the EU territorial cohesion, included for the first time in the Lisbon Treaty, which seeks to reduce disparities between the levels of development of various regions, focusing on the least developed regions in Europe, mainly rural areas.
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Amato, S., Patuelli, A. (2023). The Spatial Dimension of Firm’s Economic Activity. In: Family Firms and Local Roots. CSR, Sustainability, Ethics & Governance. Springer, Cham. https://doi.org/10.1007/978-3-031-31793-4_3
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