ArticlePDF Available

Clusters, economic performance, and social cohesion: a system dynamics approach

  • IAE Business School
  • Indiana University Bloomington and Alpen-Adria-Universität Klagenfurt


Cluster policies pervade all the regions of the world to promote employment growth, innovation and entrepreneurship. Yet research mostly focuses on cluster economic performance but less on regional social cohesion, which is important when economic growth coexists with deprivation, poverty and inequality. This paper aims to understand both the economic and social dynamics of clusters by developing a theoretical model based on system dynamics. It shows that clusters with positive economic performance do not necessary lead to regional social cohesion. Multiple positive economic-related feedback processes can be mitigated by negative social-related feedback processes. Implications for academics and policymakers are proposed.
Forthcoming Regional Studies
Hector Rocha
IAE Business School – Austral University – Argentina
Martin Kunc*
Southampton Business School - United Kingdom
David B. Audretsch
Indiana University - USA
Cluster policies pervade all the regions of the world to promote employment growth, innovation and
entrepreneurship. Yet research mostly focuses on cluster economic performance but less on regional
social cohesion, which is important when economic growth coexists with deprivation, poverty and
inequality. This paper aims to understand both the economic and social dynamics of clusters by
developing a theoretical model based on system dynamics. It shows that clusters with positive economic
performance do not necessary lead to regional social cohesion. Multiple positive economic-related
feedback processes can be mitigated by negative social-related feedback processes. Implications for
academics and policymakers are proposed.
* Corresponding author
Keywords: Clusters, Economic Growth, Economic Development, Social Cohesion, System Dynamics
JEL Codes: R11, L26, M16, M14
Interest in the role of spatial clusters –i.e. geographically proximate groups of interconnected firms
and associated institutions in related industries-- has grown in regional studies over the last decades since
the seminal work of Porter (1998; 2003). Hundreds of cluster initiatives have been launched in all the
regions of the world (Porter, 2003; UNIDO, 2009) suggesting that, among other benefits, they promote
employment growth, innovation performance and entrepreneurship (Delgado, Porter & Stern, 2010; 2014;
Pietrobelli & Rabellotti, 2007).
The interest in clusters has been motivated by the goal of enhancing economic performance (Porter,
2003; Spencer et al. 2010) and is grounded in both theory (Romer, 1986; Lucas, 1988; Krugman, 1991;
2000; Audrestch & Feldman, 1996; Massard & Autant-Bernard, 2015) and empirical validation (Delgado,
Porter & Stern, 2010; 2014; Rocha & Sternberg, 2005; Spencer et al. 2010; Uyarra & Ramlogan, 2012).
However, as global interest in Thomas Piketty’s seminal work on Capital in the Twenty-First Century
(Piketty, 2014) and the questioning of place-neutral policies (Barca et al. 2012; Massard & Autant-
Bernard, 2015) reflect, concerns have broadened beyond economic performance to include both income
distribution and social cohesion (Atkinson, Piketty, & Saez, 2011; Novy, Swiatek, & Moulaert, 2012;
Stiglitz, 2012; Cobb, 2016).i
Despite this burgeoning focus on social cohesion, the question of how clusters affect both economic
performance and social cohesion within a region remains unexplored. This question is central to both
academics in the regional studies field and policymakers. In fact, regional studies focuses on research
spanning not only economic performance, but also the social, political and environmental dimensions
influencing subnational change. In terms of policy, the sectoral-regional nature of clusters (Martin &
Sunley, 2003; 2015; Rocha, 2004)) sheds light on the critical evaluation of place-neutral policies, which
assume the translation of sectoral policies to the territory (Barca et al., 2012; Massard & Autant-Bernard,
2015; McCann & Argiles, 2015; Belussi & Trippl, 2018) and focus on economic drivers that ignore social
community and informal institutions (Becattini, 1990; cf. Konstantynova & Wilson, 2017:458).
This paper aims to fill this important void in the literature by developing a theoretical model to
analyze how and why a cluster affects both economic performance and social cohesion within the region
in which operates.
To this aim, this paper uses system dynamics modelling (Davis, Eisenhardt, & Bingham, 2007;
Sterman, 2000) to theoretically investigate the behavior of a cluster within a region. System dynamics
was developed in the 1950s by Professor Forrester from MIT and it is based on principles of information
feedback theory. It has been successfully applied to model large complex social and economic systems
such as cities (Forrester, 1970) and societies (Forrester, 1971). System dynamics has recently captured the
attention of regional studies’ scholars, especially of those who conceptualize clusters as “complex
adaptive systems” (Martin & Sunley, 2015:1303) and propose formal models to capture the dynamic
interaction between sectors and territories (Fratesi, 2015) to inform place-based policies (cf. Massard &
Autant-Bernard, 2015: McCann & Argiles, 2015).
The novel contribution of this paper is to develop a theoretical model to understand how and why
clusters affect not only the economic performance of regions (Porter, 2003; Delgado, Porter and Stern,
2014) but also social cohesion within a region. In particular, this novel contribution spans theory,
methods, and policymaking in regional studies. As to theory, the model shows that the impact of clusters
on economic performance and social cohesion within a region depends on the degree of integration
between regional socio-economic factors and cluster dynamics to integrate different groups such as
migrant specialized workers and local workers (both, specialized and non-specialized) and embed large
firms within the region. As to methods, this paper uses a system dynamic methodology, which is
employed for the understanding of the behavior of systems with complex causality and timing (Davis et
al, 2007) such as regions and clusters (Fratesi, 2015; Martin and Sunley, 2015). Thus, this paper
contributes to the emergent interest in system dynamics in regional studies by conceptualizing clusters as
“complex adaptive systems” (Martin & Sunley, 2015:1303) and proposing a formal model at the meso
level of analysis to capture the dynamic interaction between sectors and regions (Fratesi, 2015). Finally,
as to policy, this paper shows that promoting sectoral clusters but ignoring regional social cohesion may
be doomed to failure, or at least fall short of generating the enhanced economic performance of regions.
Thus, our model sheds light on current policy debates such as the place-based vs. place-neutral policies
(Barca et al. 2012) and the Washington-Silicon Valley Consensus vs. new required consensus that takes
into account the time and geographic dimensions of trade and innovation (Piore & Schrank, 2018).
The structure of this paper follows the roadmap of Davis et al (2007) to perform simulation studies.
First, it defines the research question -i.e, how do clusters affect both economic performance and social
cohesion at the regional level and why? Second, it reviews the basic theories to define the concepts and
explain the relationship between clusters, the economic performance, and social cohesion of regions.
Third, it develops the conceptual causal model using system dynamics (Sterman, 2000) to represent the
feedback loops and key constructs described in the literature. Fourth, it runs the simulation with
hypothetical (not real) data and discusses the dynamics to answer the research question. Finally, it
summarizes contributions and lines for future research, focusing on calibrating the conceptual model with
real data. Table 1 summarizes the structure of the paper.
Insert Table 1 about here.
This section defines the key concepts and summarizes the main cluster approaches that help to explain
the relationship between the cluster and the economic and social performance of regions.
Extensive reviews of the evolution and definitions of clusters have been carried out elsewhere (Porter,
1998; Martin & Sunley, 2003; Rocha, 2004), suggesting the distinction between cluster as an economic
phenomenon, cluster policy and cluster organizationsii. In terms of conceptual definitions of the
phenomenon, Porter defined clusters in sectorial terms that is, industries related by vertical and horizontal
links in his first work on clusters (Porter, 1990). In contrast, Porter’s 1998 definition is more
comprehensive and includes three main dimensions: the sectorial, the geographical (pp.227-230), and the
socio-economic network (p.225-7).
Despite Porter’s new conceptualization of clusters and hypothesized socio-economic impact (Porter
and Kramer, 2011), his methodology remains similar to his 1990 view: it first creates an industrial cluster
template based on industrial interdependencies and, second, it applies this cluster template to different
regional levels (Porter, 2001). Therefore, “the specific causal mechanisms that link spatial and socio-
cultural factors to both the process of clustering and the generation of competitive advantage are not
included in the model” (Rocha, 2004:375). Porter continues this line of reasoning in his latest work,
where he defines clusters as “groups of closely related industries co-located in a region” (Delgado et al,
Economic performance is better understood within the context of the concept of development. The
literature suggest three main conceptualizations: economic growth, economic development and
development (Allen & Thomas, 2000; Todaro, 2000). Economic growth is “a continued increase in the
size of an economy, i.e. a sustained increase in output over a period” (Allen & Thomas, 2000: 31) and it is
generally measured as the variation in gross domestic product (GDP) per capita. Economic development
is the enhancement of the productive factors of an economy and is measured as either innovation (R&D
and/or patents) or, more specifically, total productivity factor (Todaro, 2000; Capello & Lenzi, 2015).
Finally, development is the expansion of capabilities (Sen, 1999) and is measured in terms of Human
Development Index or employment (Sen, 1999), given the human, social and economic implications of
being employed.
In this paper, we define economic performance in terms of economic development.
Researchers interested in developing theory in the field of social cohesion are confronted with a
complex body of work that involves various definitions, specialized literatures on particular dimensions,
and lines of inquiry at different levels of analysis (cf. Kearns & Forrest 2000; Friedkin, 2004; Cheong et
al. 2007; Chan, To & Chan, 2006; Novy, Swiatek, & Moulaert, 2012). For example, Novy et al. trace back
the origin of the concept to the work of Durkheim and analyze four approaches to social cohesion:
socioeconomic, which emphasizes solidarity and social inclusion; cultural, which emphasizes common
values and identity; ecological, which stresses sustainability and ecological justice; and political, with its
emphasis on citizenship and participation (Novy, Swiatek, & Moulaert, 2012).
This plurality of approaches has led some authors to focus on typologies and dimensions of social
cohesion. For example, Kearns and Forrest (2000), after an extensive review of the literature, suggest that
the core dimensions of social cohesion are “the need for a shared sense of morality and common purpose;
aspects of social control and social order; the threat to social solidarity of income and wealth inequalities
between people, groups and places; the level of social interaction within communities or families; and a
sense of belonging to place” (pp. 2128).
In this paper, we define social cohesion as the level of social integration between different groups in a
geographical area (cf. Kearns & Forrest, 2000; Novy, Swiatek, & Moulaert, 2012). These different groups
include, in the context of dynamic clusters within a region, specialised workers, non-specialised workers,
and migrant specialized workers attracted by the growth of the cluster in the region. This definition
captures the potential impact of relevant phenomena such as the capacity of foreign firms to embed
themselves in local networks (Cantwell, 2009), the possible dangers to social cohesion represented by
growing immigration flows and ethnic diversity (Cheong et al. 2007), the embeddedness of innovation
dynamics (i.e. knowledge spillovers and knowledge transfer) within a region (McCann & Argiles, 2015),
the influence of training programmes to develop new required skills and, more generally, the impact of
place-based vs. place neutral policies on “reducing persistent social exclusion” (Barca, 2009: p. VII).
This section summarizes different regional and cluster approaches to discover the different variables
and their relationships leading to the economic and social dynamics within a region (cf. Rocha, 2004).
As to the economic performance literature, two economic theories are relevant to explain the
performance of regions: endogenous growth (Romer, 1990; Lucas, 1988) and endogenous development
(Friedmann, 1979; Garofoli, 1992; Moulaert & Sekia, 2003) theories.
As to the cluster literature, two streams are relevant to explain the socio-economic dynamics within
regions: the economic stream (Porter, 1990; 1998; Krugman, 1991) and the socio-economic and
innovation stream. The economic stream of clusters focuses on external economies (Marshall, 1920),
competitiveness (Porter, 1990; 1998; 2001) and new economic geography dynamics (Krugman, 1991).
The socio-economic and innovation stream of clusters highlights the territorial, social, institutional, and
cultural factors underpinning cluster dynamics and includes sociological constructs such as embeddedness
(Granovetter, 1985), social networks (Nohria & Eccles, 1992), and untraded interdependencies (Storper,
1997). This stream encompasses several approaches: the Italian School of Industrial Districts (Becattini,
1990; Brusco, 1992); the innovative milieu school (Aydalot, 1986; Camagni; 1991), the Nordic School of
innovation and learning (Lundvall & Johnson, 1994; Malmberg & Maskell, 1997), the geography of
innovation approach (Jaffe, 1989; Feldman, 1994; Audretsch & Feldman, 1996; Asheim & Gertler, 2005;
Massard & Autant-Bernard, 2015), and the cultural-institutional approach (Powell, 1990; Saxenian, 1994;
Ingram & Roberts, 2000).
Given the scope of this paper, we focus on the content of the approaches most closely related to our
conceptual model (for a detailed review see Rocha, 2004 & Gilbert, 2017). Appendix A summarizes the
main variables explained by each approach, which will be part of the system dynamics model.
This section develops the conceptual causal model based on the set of constructs and their linkages
discussed in the literature, which are summarized in Table 2. To this end, it uses system dynamics
modeling (Davis et al, 2007; Sterman, 2000) to represent the feedback loops and key constructs at the
territorial level (Fratesi, 2015) and strives for an endogenous theory of the dynamics among clusters,
economic performance and social cohesion.
Insert Table 2 about here.
We proceed as follows. First, we employ the increasing returns to agglomeration theoretical concepts
(Krugman, 1991; Delgado et al, 2014) as our basic Economic Performance theory. Then, we investigate
how the increasing number of firms in a region leading to higher employment rates impact on social
cohesion. Third and finally, we create a theoretical computational representation of the conceptual causal
model to simulate the dynamics of the cluster given the theoretical assumptions. Theoretical simulations
are employed to explore the consequences of theoretically derived processes, e.g. the values of variables
change over time based on theoretical reasoning, even when the outcomes cannot be assessed empirically
(Harrison et al, 2007, page 1232-3). This approach has been employed in computational research (Lomi
and Larsen, 2001; Garavaglia, 2010) with the purpose of adding the temporal dimension to the causal
modelling and evaluate the dynamic performance of systems.
Economic growth in a cluster facilitates the creation of new firms and the entry of large firms, which
accumulate over time expanding the existing market, which positively affects the location of upstream
and downstream producers in the same location (Krugman, 1991; 2000). The resulting impact on demand
benefits the creation of new firms because customers located within close geographic proximity not only
increase the likelihood of sales but also minimize transportation costs. In addition, Porter (1990) stresses
the importance of the competitive environment within industrial clusters, providing lower entry and exit
barriers because of reduced uncertainty in terms of prices, costs, and way of doing business, all of which
foster the creation of firms.
In turn, the agglomeration of firms within clusters leads to economies of scale and scope that further
enhances the economic performance of the cluster, creating a positive reinforcement process which is
depicted as increasing returns to agglomeration in Figure 1 (see R1). The literature has tended to contrast
two potential types of agglomerating forces: localization (increasing returns to activities within a single
industry) and urbanization (increasing returns to diversity at the overall regional level). As to localization,
assuming that upstream and downstream producers are subject to increasing returns and barriers to trade
are reduced, backward and forward linkages tend to concentrate firms in a single location (Krugman,
2000: 55). Krugman explains the reasons for localization of industries in terms of Marshall’s sources of
external economies –that is, local pool of specialized labor, local subsidiary industries, and technological
spillovers (Krugman, 1991: 36). The existence of increasing returns to scale means that individual
producers are motivated to concentrate their production geographically in order to benefit from the
resulting economies, leading to reinforcing processes. This agglomeration leads to knowledge spillovers
and knowledge spillovers attract new firms (see R2a-b in Figure 1). As to urbanization as a catalyst of
agglomeration of economic activity, it is argued that interdependencies across complementary activities
give rise to increasing returns and, thus, to agglomeration through economies of scope. Porter and
colleagues have moved beyond the trade-off between localization vs. urbanization and found that both
are present within clusters of related industries, therefore positively affecting economic performance
(Delgado et al, 2014).
Moreover, the creation of new firms and the entry of large firms creates new knowledge, further
expanding the stock of knowledge existing in the region, which leads to spillovers and new innovation
processes. This agglomeration leads to knowledge creation, spillovers and innovation in Figure 1 (see
R2a, R3, and R4). According to endogenous growth theory, technological change or innovation explains
not only the growth of output or economic growth, but also the rate of growth or economic performance.
Technological change, in turn, is based on innovations fostered by investments in R&D (Romer, 1990),
existing knowledge and knowledge spillovers, which tend to be spatially restricted (Audretsch &
Feldman, 1996).
One important consequence of large firms in the region is their financial capacity to invest in R&D
which fosters new innovations and, in turn, augments the stock of innovations in the region leading to
even better economic performance, a process of agglomeration which leads to innovation in Figure 1 (see
R3). According to endogenous growth theory, technological change is conducive to imperfect
competition, which can enhance the incentive to invest in new technologies, given that investment is
partially excludable by intellectual property rights such as patents. In this scenario the firm will invest to
improve technology and therefore innovation is determined within the model. The innovations generated
from investments in R&D produce both an absolute increase in the stock of technology and a change in
the rate of technological progress, therefore positively affecting the rate of growth (cf. Solow, 1994) or
economic performance.
In turn, investments in R&D increase the level of knowledge and technology not only directly through
capital investment (in a broad sense including not only physical but also human and R&D capital) but
also indirectly through spillover effects on the rest of the economy, agglomeration processes that lead to
knowledge creation and knowledge spillovers in Figure 1 (see R2a and R4). Spillover effects imply that
knowledge is a non-rival good, and therefore it generates increasing returns at the aggregate level –that is,
Marshallian external economies (Romer, 1994: 14). Therefore, investments in R&D increase the level of
public knowledge fostering new innovations, adding to the accumulation of a pool of innovations which
contribute to the economic performance of the region. Figure 1 depicts the processes discussed within the
Economic Performance Model and the Social Cohesion Model we describe below.
Insert Figure 1 about here.
The social cohesion model is based on two main assumptions. First, economic performance is driven
by economic factors rooted in spatial, social and cultural characteristics reflected by local embeddedness
(Granovetter, 1985), social networks (Nohria & Eccles, 1992), informal ties, and untraded
interdependencies (Storper, 1997). Second, these factors operate mainly at the regional rather than at the
industry and sectoral levels. Therefore, the main unit of analysis is the region rather than the industry or
the region-industry as in the case of the Economic Performance Model.
Social cohesion, territorial embeddedness of agglomerated firms and inter-organizational networks
among firms and institutions reinforce each other at any given level of social cohesion (R5, R6 and R7 in
Figure 1), creating a symbiotic relationship among these factors for both new firms and existing large
In the case of new firms, people usually start businesses at the location where they were born, have
worked or already reside (Cooper & Dunkelberg, 1987), which goes some way towards explaining why
nascent entrepreneurs are very well established in their careers, lives and communities (Reynolds &
White, 1997). Also, economic activities by new firms embedded in their specific environment are shaped
by local history and institutions (Aldrich, 1999). Established relationships such as existing networks of
colleagues or relationships from previous work contexts provide the credentials to overcome the lack of
history of new businesses. In addition, links to formal institutions which enhance legitimacy and provide
access to resources, are important and result in a positive relationship between links to formal institutions
and entrepreneurial activity (Baum & Oliver, 1996). Finally, local embeddedness cements established
relationships and inter-organizational networks, facilitating the economic and non-economic resources to
start and sustain new businesses.
In the case of existing large firms, their degree of regional embeddedness and inter-organizational
networks are key factors related to social cohesion within the region. Both positive and negative examples
illustrate this point. For example, on the positive side, large local firms in the Irish medical technology
cluster (Giblin, 2011) and MNCs in Malaysia (Malecki, 1997: p.229) and Costa Rica (UNCTAD, 2004;
Oxfam, 2002) enabled the upgrading of the local industry and the participation of the relevant
stakeholders in the distribution of the value created. On the negative side, the Irish software cluster
(Giblin, 2011), the Brazilian car industry, the maquilas and blue jean cluster in Mexico, the Dominican
Republic and China (cf. Malecki, 1997; Oxfam, 2002; UNCTAD, 2004; McCann & Argiles, 2015) are
examples of lack of embeddedness and capturing of value mainly by MNCs.
Therefore, the effect of new large firms on social cohesion is a function of both the way they are
integrated in inter-organizational networks and embedded in the region. In particular, clusters of few
dominant MNCs (Chinitz, 1961; Duranton & Puga, 2001; cf. McCann & Argiles, 2015), especially those
with hierarchical governance structures combined with lack of embeddedness in the region (Rocha, 2015)
are a potential source of socio-economic divides.
Therefore, clusters including both new firms and existing large firms, especially local ones, show a
positive feedback process between embeddedness, inter-organizational networks and social cohesion at a
given initial level of social cohesion, leading to reinforcing processes (R5, R6, and R7).
Inter-organizational networks have a positive impact on economic performance both directly and
indirectly through knowledge spillovers. The direct effect is based on explanations from both the cultural-
institutional approach and the endogenous development theory. The former argues that the social,
institutional, and cultural factors underlying the interdependence of economic actors, when they are
embedded in a web of relationships among them, are conductive to local economic development (cf.
Saxenian, 1994). In fact, ties embedded in social relationships enhance collaboration, mitigate
competition, and foster information exchange. Additionally, in clusters with a strong division of labor, the
differentiation among clustered firms leads to functional complementarities that create mutualistic effects
and therefore neutralize the negative effect of sourcing from the same resource pool. For example, as
endogenous development theory suggests, inter-organizational networks such as subcontracts between
new and established large firms through quasi-vertical integration improve the economic performance of
clusters, not only through the reduction of transaction costs (Storper & Scott, 1989) but also through
innovation diffusion (Garofoli, 1992) or knowledge spillovers.
As to the indirect impact, inter-organizational networks are conductive to knowledge spillovers, which
are key processes for innovation and economic performance according to the geography of innovation
approach, given that knowledge spillovers are important to innovation and tend to be spatially localized
within close geographic proximity to the knowledge source (Jaffe, 1989; Feldman 1994;Audretsch &
Feldman, 1996.
Marshall’s sources of external economies include not only knowledge spillovers (Figure 1) but also
local pools of specialized workers, and both factors lead to a reinforcing process named agglomeration
that leads to employment and knowledge creation (see R9). Preliminary evidence suggests a positive
impact of clusters on job creation both in developed and emerging countries (cf. OECD, 2002; Porter,
2003; Spencer et al. 2010; EC, 2010; Temouri, 2012; Rocha, 2015; Delgado et al, 2014).
The pool of specialized workers in the region, which includes both local and migrant workers, also
increases the attractiveness of the region for new and large firms, processes identified as specialized
workers attract firms (R11 in Figure 1). In the medium term, the creation of firms and the growth of
existing firms attracted by the pool of specialized workers (R11) will increase the migration of specialized
workers to the region if there is a paucity of specialized local workers, not only fostering employment
(R9) but also changing the mix of non-specialized workers-specialized workers.
Economic growth generates a process of integration of existing workers in the region when there is a
common vision (Forrest & Kearns, 2001) or a sense of community (Sen, 2009) among local actors
supporting training and skills upgrading. Such processes influence economic growth along with a
common vision, which leads to social integration in Figure 1 (see R10) By contrast, economic growth
hinders integration when a common vision, especially among specialized and non-specialized workers, is
lacking, because it creates strong cohesive groups that “could be in conflict with one another and
contribute to a divided and fragmented city” (Forrest & Kearns, 2001:2128). In fact, economic growth
driven by economic activities that reshape occupational structures and opportunities could drive different
groups toward opposite economic poles (Castells, 1998), which lessens integration.
Summing up, we have described the integrated Economic Performance and Social Cohesion model,
depicted in Figure 1. Clusters are dynamically complex systems because of the many feedback loops
driving their behavior, e.g. ten reinforcing or positive feedback loops and one balancing feedback loop.
Inherently, cluster dynamics originate from strong agglomeration forces and positive returns to scale
leading to continuous economic growth and employment (loops R1 to R9). However, the impact of social
forces embedded in, and fostered by, a balancing feedback loop (B8) can curtail the strong economic
positive feedback loops. Positive economic performance generates economic growth which attracts new
firms and has a positive impact on the growth of existing firms, thereby enhancing economies of scale
and economic performance. A short term potentially divisive dynamic is generated by new firms and the
entry of firms in the regional cluster, which import technology and human resources, as they may recruit
workers from other regions. An unbalanced employment process weakens the integration of the workers
belonging to the cluster in the region, which leads to reducing social cohesion and hurting informal ties
(Figure 1, B8). Declining informal ties and inter-organizational networks can affect knowledge spillovers
and innovation, limiting future economic performance. The ultimate result can be the stagnation and
withering of the cluster.
We create a computational representation of the conceptual causal model depicted in Figure 1 based on
the constructs and operational definitions used in the literature (Table 2). This representation uses a set of
integral equations to compute the state of the different variables comprising the cluster over time (Davis
et al 2007, Sterman, 2000). The integral equations are the mathematical representation of feedback
processes where the state of the cluster at time t-1 is the basis for calculating endogenously the state of the
cluster at time t. Then, we use logical data for the simulation -e.g. values that resemble the results that can
be obtained from collecting real data- to run and calibrate the model (Lomi and Larsen, 2001; Garvaglia,
2010). Logical data is obtained from the evaluation of the empirical literature to identify a minimal set of
values that explain observations related to the state of the cluster (Boros et al, 2000). For example, a
meta-study of more than 500 clusters in US, Germany, other OECD countries, and Latin America
concludes that, on average, inter-organizational linkages are higher in clusters of developed countries
than in Latin American countries, which leads to a higher impact of clusters on economic performance in
the former countries (Rocha, 2013). Based on this empirical result, our model considers the value of the
variable 'Inter-organizational Networks" with an initial value of 4 in a scale between 1-7, and a direct
relationship with the variable 'Economic Performance', using a weight of 0.33iii. Thus, a decrease in the
value of inter-organizational networks in one unit will reduce the economic performance of the cluster by
0.33”. Figure 1 depicts the theoretical causal modelling without the temporal dimension; Figure 2 shows
the dynamics between economic growth, economic performance and social cohesion for a period of 14
years after running the simulation with hypothetical data. Appendix B includes the detailed steps we
follow to create the computational model and run the simulation; the next section discuss the dynamics
between clusters and socio-economic performance.
Insert Figures 1 and 2 about here.
Figure 2 shows that the economic performance of clusters does not necessarily lead to social cohesion
within the region where the cluster operates, which questions the assumption of an expected linear
relationship between the number of firms and employment, so that more firms will hire more people
leading to a decline in poverty and improving social cohesion. Cluster dynamics are inherently driven by
strong agglomeration forces and positive returns to scale leading to continuous economic growth and
employment. However, the impact of social forces can curtail the strong economic positive feedback
loops in the middle term. As firms expand and new firms enter the cluster, the limited number of
specialized people in the region will attract people from other regions (since firms cannot wait to train the
local workers). When non-resident employees are attracted, the ratio between local and migrant workers
changes and the social cohesion tends to decline, compensating for the initial impact on local workers
employment. When social cohesion declines, informal ties, embeddedness, social proximity and inter-
organizational networks also decline, which reduce knowledge spillovers and innovation, hurting future
economic performance. Firms subsequently stop coming to the region, leading to stagnation or even
deterioration due to the diminished social cohesion.
In sum, the final balance between economic performance and social cohesion is a function of three
factors. First, the initial levels of integration and social cohesion among specialized and non-specialized
workers within the region; second, the factors harmonizing growth in employment and social cohesion
(such as cluster governance mechanisms, the integration of economic sectors within the region, and
internal training programmes for non-specialized workers); third and finally, the degree of embeddedness
of migrant workers and new firms within the region. This conclusion is in line with previous studies in
which factors such as the embeddedness of MNC and their degree of foreign ownership, the initial level
of regional development, and the degree of diversification and integration among sectors in the region are
important for the dynamic interaction between economic performance and social cohesion in Latam
(Rocha, 2015; Birkinshaw and Hood, 2000), USA (Porter, 2003; Cobb, 2016), and Europe (Fratesi, 2015;
Konstantynova and Wilson, 2018).
Both economic and sociological approaches explain the dynamics of clusters, economic performance
and social cohesion. From an economic perspective, agglomeration exerts a countervailing force on
regional performance. In the presence of agglomeration economies, growth is increasing in the level of
economic activity (Glaeser et al, 1992), which can increase inequality across regions over time (Dumais,
Ellison & Glaeser, 2002). Also, agglomeration of economic activity leads to the clustering of economic
activity and the concentration of development in specific areas creating center-periphery patterns, which
produce regional divergence due to a process of cumulative causation and inflexibility (Arthur, 1989;
Krugman, 1991). Therefore, both a strong economic performance and a low degree of social cohesion
could potentially co-exist within a given region. From a sociological perspective, the arguments
developed for the interaction between neighborhoods and cities could be used to explain the integration
between migrant specialized workers and local workers (both, specialized and non-specialized). A
cohesive cluster could contribute to unite a divided and fragmented region because actors will have a
strong sense of place attachment and loyalty to their respective location: “thus, whether society is said to
face a crisis of social cohesion depends upon what spatial scale one is examining and the relative strength
of the countervailing forces operating at each scale.” (Forrest & Kearns, 2001:2128).
These economic and social explanations are in line with both regional studies and economic
geography literatures. First, as to regional studies, our model shows that regional change requires the
harmonization of innovation, growth in employment and social cohesion, which is in line with recent
studies in regional economic development (Fargerberg et al. 2013; Barca et al. 2012) and the geography
of innovation (Feldman, 1994; Masard & Autant-Bernard, 2015; McCann & Argiles, 2015). In this
harmonization process, initial conditions are key, given that the innovation and economic dynamics could
negatively affect less-developed regions –i.e. those with low initial conditions. Our model sheds light on
previous findings on the need to consider both technological and social capabilities (Fargerberg et al.
2013), innovation and embeddedness (McCann & Argiles, 2015), as well as historical-institutional factors
(Barca et al. 2012) for regional change. For example, research suggests that the embeddedness of
information and communication technologies across different sectors could affect the balance in
employment leading to social cohesion. In most OECD countries the “adoption, adaptation and
application of information and communication technologies across a wide range of industries appears to
have exacerbated the differences between core and none-core regions” (McCann & Argiles, 2015:1294).
Therefore, the economic performance of a region that results from innovation and new occupational
structures may negatively affect social cohesion when the diffusion of ICTs is not linked to “tailored skills
training or actor-networking related to ICTs” (McCann & Argiles, 2015:1299).
As to economic geography, which pays special attention in explaining how regional disparities arise
and why they persist over time (Armstrong & Taylor, 2000), our model shows potential threats to social
cohesion represented by growing immigration flows and innovation dynamics neither integrated nor
embedded within a region. For example, the growth of the IT cluster has been concomitant with a growth
in social cohesion while the demise of the aerospace cluster has been associated with a decline in social
cohesion in the region (Storper et al. 2015). These divergent results among sectors could be explained by
the different initial levels of integration and the embeddedness of specialized and non-specialized workers
and firms. In other words, our model highlights the relationships among immigration, innovation and
geography and the role of integration, embeddedness and inter-organizational networks or, in terms of
economic geography, “embeddedness, relatedness and connectivity” (McCann & Argiles, 2015:1296).
A rich and important literature has emerged focusing on the links between clusters and their impact on
economic performance of both firms and regions. However, the extant literature has not analyzed how
clusters affect both economic performance and social cohesion i.e. the level of social integration
between different groups in a geographical area- at the regional level. This oversight is striking, because
social cohesion has recently emerged as a priority for both scholars as well as policymakers. In addition,
even as scholars and thought leaders in public policy highlight the potential for cluster policies to enhance
economic performance, ignoring the impact of clusters on social cohesion is fraught with peril and may
ultimately undercut the economic impact of policies. Only by explicitly analyzing the impact on social
cohesion as well as economic performance can the overall impact of such policies be fully analyzed and
This paper is one of the first studies to investigate how clusters affect both economic performance and
social cohesion at the regional level. This novel contribution is particularly important to the regional
studies literature, which is concerned on the dynamics of economic, social and political factors to
understand how and why regions evolve. Clusters involve both sectoral and territorial dimensions (Martin
& Sunley, 2003; 2015; Rocha, 2004) and, therefore, understanding the co-evolution of economic
performance and social cohesion in regions in which clusters operate could contribute to understanding
how and why regions evolve.
This novel contribution spans across theory, methods, and policymaking in regional studies. As to
theory, it answers the How and Why questions: the impact of clusters on economic performance and social
cohesion within a region depends on the interaction of regional and cluster factors to integrate different
groups such as migrant specialized workers and local workers (both, specialized and non-specialized) and
embed new and large firms within the region; the reason is the differential causal dynamics and pace
between sectoral-economic-driven factors such as large firms, technology and innovation and regional-
social-driven factors such as a common vision, embeddedness and local inter-organizational networks. As
to methods, this paper uses a system dynamic methodology, thus contributing to the recent interest of
regional studies scholars that conceptualizes clusters as “complex adaptive systems” (Martin & Sunley,
2015:1303) and propose formal models at the meso level of analysis to capture the dynamic interaction
between sectors and regions (Fratesi, 2015). Finally, as to policy, by showing that promoting sectoral
clusters with the goal of convergence could not only lead to lower economic performance but also social
exclusion within regions, this paper contributes to current policy debates such as the place-based vs.
place-neutral policies debate (Barca et al. 2012) and the Washington-Silicon Valley Consensus vs. new
required consensus that takes into account the time and geographic dimensions of trade and innovation
(Piore & Schrank, 2018).
Economic development policies could lead to social exclusion at both inter and intra-regional levels
(cf. McCann & Argiles, 2015; Barca et al 2012:139) if their focus is solely on the sectoral (Massard &
Barnard-Autant, 2015; Konstantynova & Wilson, 2017), innovation or (McCann & Argiles, 2015),
technological (Fargerberg et al. 2013) dimensions and/or core agglomerations in the territory (Barca et al.
2012; Belussi and Trippl, 2018) with the goal of convergence. By contrast, our conceptual model shows
that policies creating the conditions for a common vision (Forrest & Kearns, 2001) or a sense of
community (Sen, 2009) among specialized (both migrant and local) and non-specialized workers could
foster both economic performance and social cohesion in the medium term. Therefore, our model is
aligned with policy recommendations that acknowledge the importance of places, embeddedness and
informal institutions (Uyarra and Ramlongan, 2012; Barca et al. 2012), focusing on place-based policies
(Barca et al. 2012; Massard & Autant-Barnard, 2015; McCann & Argiles, 2015; Konstantynova &
Wilson, 2017) and partnerships between different levels of governance (cf Barca et al. 2012:148) for
sustainable and social development (Barca et al. 2012).
This paper opens opportunities for further research such as to calibrate the model using real data and
evaluate it in different industries. First, the conceptual model requires calibration using real data to test
the relationships, evaluate policy impact and create more precise paths to economic performance and
social cohesion in regions. Appendix B includes the units of measurement of each variable and real data
can be collected using both statistical sources and surveys of experts as it is standard in cluster research
(cf. Porter, 2003; Pietrobelli and Rabellotti, 2007; Delgado et al. 2014; Rocha and Sternberg, 2005;
Rocha, 2013). In particular, our model could complement the much needed evaluation of the impact of
cluster policies and institutions (cf. Uyarra & Ramlogan, 2012; Fratesi, 2015; Konstantynova and Wilson,
2018; Aranguren et al. 2014).
Second, the model is general enough to evaluate the dynamic behavior of different industries using
sensitivity analysis with industry data for variables such as size of firms, availability of workers, speed of
creation of new enterprises, economies of scale and initial level of social cohesion. For example, social
imbalances seem to occur more in high-technology than in other sectoral clusters. It is possible to expect
some displacement effects when the fast growth of a cluster is not linked to the growth of other industries
and places within the same regions. Empirical studies show that, although high-growth technology
clusters contribute to the wealth of the region, they also create social divides (Keeble & Wilkinson, 2000),
as in the case of Bangalore (OECD, 2002), and Silicon Valley (Harrison, 1994; Rose, Ciechanover, &
Modi, 2015; Forbes, 2015). Porter has found that high-technology clusters only contribute to less than 3
per cent of the employment in the US economy (2001: p.25; 2003: p.564) but have an average wage
almost three times higher than that provided by local industries (2003: p.560; 564). These benefits do not
trickle down to people employed in the local industries. In effect, the correlation between high-tech
employment and local wages is only 14.4 per cent (2003: p.564), and the correlation between regional
innovation and employment growth is negative (2003: p.557). Research in Latin American countries is in
line with these results, given that clusters in natural resource-based and specialized services have a
positive impact on collective efficiency, but clusters in traditional manufacturing and complex products
do not (Pietrobelli & Rabellotti, 2007; Rocha, 2015).
These lines of future research will improve the understanding of the economic performance and social
cohesion in regions advanced in this paper.
Allen, T. & Thomas, A. 2000. Poverty and Development into the 21st Century, Oxford:Oxford University
Press. Arikan, A.T. 2009. Interfirm Knowledge Exchanges and the Knowledge Creation Capability of
Clusters, Academy of Management Review, 34: 658-676.
Aranguren, M. J., De La Maza, X., Parrilli, M. D., Vendrell-Herrero, F., & Wilson, J. R. (2014). Nested
methodological approaches for cluster policy evaluation: An application to the Basque
Country. Regional Studies, 48(9), 1547-1562.
Armstrong, H. & Taylor, J. 2000. Regional Economics and Policy, Oxford: Blackwell Publishers Ltd.
Atkinson, A. B., Piketty, T., & Saez, E. (2011). Top incomes in the long run of history. Journal of
economic literature, 49(1), 3-71.
Arthur, W.B. 1989. Competing Technologies, Increasing Returns, and Lock-in by Historical Events,
Economic Journal, 99: 116-131.
Asheim, B. T., & Gertler, M. S. (2005). The geography of innovation: regional innovation systems. In The
Oxford handbook of innovation.
Audretsch, D. B., & Feldman, M. P. 1996. R&D spillovers and the geography of innovation and
production. The American Economic Review, 86(3), 630-640.
Audretsch, D.B. & Stephan, P.E. 1996. Company-Scientist Locational Links: the Case of Biotechnology,
The American Economic Review, 86: pp. 641-652.
Aydalot, P. 1986. Milieux Innovateurs en Europe, GREMI, Paris.
Barca, F. (2009). Agenda for a Reformed Cohesion Policy. European Communities.
Barca, F., McCann, P., & Rodríguez‐Pose, A. (2012). The case for regional development intervention:
place‐based versus place‐neutral approaches. Journal of regional science, 52(1), 134-152.
Baum, J. A., & Oliver, C. 1996. Toward an institutional ecology of organizational founding, Academy of
Management Journal, 39: 1378-1427.
Becattini, G. 1990. The Marshallian Industrial District as a Socio-Economic Notion. In Pyke, F. &
Sengenberger, W. (eds.), Industrial Districts and Local Economic Regeneration, International Institute
for Labour Studies, Geneva, pp. 37-51.
Belussi, F., & Trippl, M. (2018). Industrial Districts/Clusters and Smart Specialisation Policies.
In Agglomeration and Firm Performance (pp. 283-308). Springer, Cham.
Birkinshaw, J., & Hood, N. (2000). Characteristics of foreign subsidiaries in industry clusters. Journal of
international business studies, 31(1), 141-154.
Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation
of logical analysis of data. IEEE Transactions on knowledge and Data Engineering, 12(2), 292-306.
Brusco, S. 1992. Small Firms and the Provision of Real Services. In Pyke, F. & Sengenberger, W.
Industrial Districts and Local Economic Regeneration, Geneva: International Institute for Labour
Camagni, R. 1991. 'Local Milieu', Uncertainty and Innovation Networks: Towards a New Dynamic
Theory of Economic Space, London: Belhaven Press.
Capello, R., and Lenzi, C. "Knowledge, innovation and productivity gains across European
regions." Regional Studies 49.11 (2015): 1788-1804.
Cantwell, J. (2009). Location and the multinational enterprise. Journal of International Business
Studies, 40(1), 35-41.
Cantwell, J., & Brannen, M. Y. (2011). Positioning JIBS as an interdisciplinary journal.
Castells, M. 1998. The Information Age: Economy, Society and Culture. End of Millennium. Malden, MA
and Oxford: Blackwells.
Chan, J., To, H. P., & Chan, E. (2006). Reconsidering social cohesion: Developing a definition and
analytical framework for empirical research. Social indicators research, 75(2), 273-302.
Cheong, P. H., Edwards, R., Goulbourne, H., & Solomos, J. 2007. Immigration, social cohesion and social
capital: A critical review, Critical Social Policy, 27: 24-49.
Cobb, J. A. (2016). How firms shape income inequality: Stakeholder power, executive decision making,
and the structuring of employment relationships. Academy of Management Review, 41(2), 324-348.
Coe, N.M. and Wrigley, N. 2007. Host economy impacts of transnational retail: the research agenda,
Journal of Economic Geography, 7: 341-371.
Cooper, A. C., & Dunkelberg, W. C. 1987. Entrepreneurial research: Old questions, new answers and
methodological issues. Purdue University, Krannert Graduate School of Management.
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. 2007. Developing theory through simulation methods,
Academy of Management Review, 32: 480-499.
Delgado, M., Porter, M. & Stern, S. 2010. Clusters and entrepreneurship, Journal of Economic
Geography, 10: 495-518.
Delgado, M., Porter, M. E., & Stern, S. 2014. Clusters, convergence, and economic performance,
Research Policy, 43: 1785-1799.
Dumais, G., Ellison, G., & Glaeser, E. L. 2002, Geographic concentration as a dynamic process, Review
of Economics and Statistics, 84: 193-204
ECLAC (2016), Latin America is the Most Unequal Region in the World,,
accessed 12 January 2018.
European Commission, 2018.
and-social-cohesion; accesed 25/5/2018.
Feldman, M.P., 1994. The Geography of Innovation (Vol. 2). Springer Science & Business Media.
Forrest, R., & Kearns, A. 2001. Social cohesion, social capital and the neighborhood. Urban studies,38:
Forrester, J. W. (1970). Urban dynamics. IMR; Industrial Management Review (pre-1986), 11(3), 67.
Forrester, J. W. (1971). World dynamics. Wright-Allen Press.
Fratesi, U (2015) Regional Knowledge Flows and Innovation Policy: A Dynamic Representation,
Regional Studies, 49:11, 1859-1872,
Friedkin, N. E. 2004. Social cohesion, Annual Review of Sociology, 409-425.
Friedmann, J. 1979. Basic needs, agropolitan development, and planning from below, World
Development, 7: 607-613.
Garavaglia, C. (2010). Modelling industrial dynamics with “History-friendly” simulations. Structural
Change and Economic Dynamics, 21(4), 258-275.
Garofoli, G. 1992. Endogenous Development in Southern Europe, Aldershot, Averbury.
Giblin, M. 2011. “Managing the Global–Local Dimensions of Clusters and the Role of ‘Lead’
Organizations: The Contrasting Cases of the Software and Medical Technology Clusters in the West of
Ireland.” European Planning Studies 19: 23–42.
Gilbert, B. A. (2017). Agglomeration, Industrial Districts and Industry Clusters: Trends of the 21st
Century Literature. Foundations and Trends® in Entrepreneurship, 13(1), 1-80.
Glaeser, E.L., Kallal, H.D., Scheinkman, J.A. & Shleifer, A. 1992. Growth in Cities, Journal of Political
Economy, 100: 1126-1152.
Granovetter, M. 1985. Economic action and social structure: the problem of embeddedness, American
Journal of Sociology, 481-510.
Han, U., & Kunc, M. 2015. Philosophical insights in system modelling: an application to the field of
innovation systems, International Journal of Economics and Business Research, 9: 437-453.
Harrison, A. E. (1994). Productivity, imperfect competition and trade reform: Theory and evidence.
Journal of International Economics, 36(1-2), 53-73.
Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and
management research. Academy of Management Review, 32(4), 1229-1245.
Ingram, P., & Roberts, P. W. 2000. Friendships among Competitors in the Sydney Hotel Industry,
American Journal of Sociology, 106: 387-423.
Jaffe, A.B. 1989. Characterizing the “technological position” of firms, with application to quantifying
technological opportunity and research spillovers, Research Policy, 18: 87-97.
Kearns, A., & Forrest, R. 2000. Social cohesion and multilevel urban governance. Urban Studies,37: 995.
Keeble, D. & Wilkinson, F. 2000. High-Technology Clusters, Networking and Collective Learning in
Europe, Ashgate, Aldershot, England.
Konstantynova, A., & Wilson, J. R. (2017). Cluster policies and cluster institutions: an opportunity to
bind economic and social dimensions? Economia e Politica Industriale, 44(4), 457-472.
Kunc, M., 2008. Using systems thinking to enhance strategy maps. Management Decision, 46(5): 761-
Krugman, P. 1991, Geography and Trade, Cambridge: MIT Press.
Krugman, P.R., 2000. Technology, trade and factor prices. Journal of International Economics, 50(1): 51-
Lee, T.-L. & von Tunzelmann, N. 2005. A dynamic analytic approach to national innovation systems: The
IC industry in Taiwan, Research Policy 34: 425–440
Lomi, Alessandro, and Erik R. Larsen, Dynamics of organizations: computational modeling and
organization theories. Mit Press, 2001.
Lucas, R. 1988. On the Mechanics of Economic Development, Journal of Monetary Economics, 22: 3-42.
Lundvall, B. A., & Johnson, B. 1994. The learning economy, Journal of industry studies, 1: 23-42.
McCann, Philip, and Raquel Ortega-Argilés. "Smart specialization, regional growth and applications to
European Union cohesion policy." Regional Studies 49.8 (2015): 1291-1302.
Malecki, E.J. 1997. Technology & Economic Development. The Dynamics of Local, Regional, and
National Competitiveness, Addison Wesley Longman, London and Boston.
Malmberg, A., & Maskell, P. 1997. Towards an explanation of regional specialization and industry
agglomeration. European planning studies, 5: 25-41.
Martin, R. & Sunley, P. 2003. Deconstructing Clusters: Chaotic Concept or Policy Panacea?, Journal of
Economic Geography, 3: 5-35.
Martin, R. & Peter Sunley. "Conceptualizing cluster evolution: beyond the life cycle model?." Regional
Studies 45.10 (2015): 1299-1318.
Maskell, P., & Malmberg, A. 1999. Localised learning and industrial competitiveness”, Cambridge
journal of economics, 23: 167-185.
Massard, Nadine, and Corinne Autant-Bernard. "Geography of innovation: new trends and implications
for public policy renewal." (2015): 1767-1771.
Moulaert, F., & Sekia, F. 2003. Territorial innovation models: a critical survey, Regional studies, 37: 289-
Nohria, N. & Eccles, R.G. 1992. Networks and Organizations, Cambridge: Harvard University Press.
Novy, A., Swiatek, D. C., & Moulaert, F. (2012). Social cohesion: a conceptual and political
elucidation. Urban Studies, 49(9), 1873-1889.
OECD. 1999. The Cluster Approach, OECD Proceedings. OECD Publication Service.
OECD. 2002. International Conference on Territorial Development: Local Clusters, Restructuring
Territories, and Environment-Enterprises-Districts, OECD – DATAR, Paris.
Oxfam. 2002. Rigged Rules and Double Standards. Trade, Globalisation, and the Fight against Poverty,
Oxfam Report.
Patel, P., & Pavitt, K. 1991. Large firms in the production of the world's technology: an important case of"
non-globalisation, Journal of International Business Studies, 1-21.
Pietrobelli, C. & R. Rabellotti. 2007. Upgrading to Compete. Global Value Chains, Clusters and SMEs in
Latin America. Cambridge: Harvard University Press.
Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press.
Piore, M.J. and Schrank, A. 2018. Root Cause Regulation. Cambridge, MA: Harvard University Press.
Porter, M.E. 1990. The competitive advantage of nations, London: Macmillan.
Porter, M.E. 1998. On Competition, Boston, MA: Harvard Business School Press.
Porter, M.E. 2001. Clusters of Innovation: Regional Foundations of U.S. Competitiveness, Council on
Competitiveness, Washington, DC.
Porter, M.E. 2003. The economic performance of regions. Regional studies, 37: 545-546.
Porter, M.E. & Kramer, M.R. 2011. The big idea: creating shared value. Harvard Business Review, 89: 2.
Reich, R. B. 1991. The work of nations: preparing ourselves for 21. century capitalism. Simon &
Reynolds, P. D., & White, S. B. 1997. The entrepreneurial process: Economic growth, men, women, and
minorities. Praeger Pub Text.
Rocha, H.O. & Sternberg, R. 2005. Entrepreneurship: The Role of Clusters. Theoretical Perspectives and
Empirical Evidence from Germany, Small Business Economics, 24: 267-292.
Rocha, H.O. 2004. Entrepreneurship and Development: the Role of Clusters. A Literature Revie, Small
Business Economics, 23: 363-400.
Rocha, H. O. (2013). Entrepreneurship and regional development: The role of clusters. Springer.
Rocha, H.O. 2015. Do clusters matter to firm and regional development and growth? Evidence from Latin
America”, Management Research: Journal of the Iberoamerican Academy of Management, 13: 83-123.
Rodriguez-Pose, A. & Arbix, G. 2001. Strategies of Waste: Bidding Wars in the Brazilian Automobile
Sector, International Journal of Urban and Regional Research, 25: 134-153.
Romer, P. 1986. Increasing Returns and Long-run Growth, Journal of Political Economy, 94: 1002-37.
Romer, P. 1990. Capital, labor, and productivity. Brookings papers on economic activity Microeconomics,
Romer, P. 1994. The origins of endogenous growth. The Journal of Economic Perspectives, 8(1), 3-22.
Saxenian, A. 1994. Regional Advantage. Culture and Competition in Silicon Valley and Route 128,
Cambridge, MA: Harvard University Press.
Sen, A. 1999. Development as Freedom, Oxford: Oxford University Press.
Sen, A. K. (2009). The idea of justice. Harvard University Press.
Spencer, Gregory M., Tara Vinodrai, Meric S. Gertler, and David A. Wolfe, 2009, “Do Clusters Make a
Difference? Defining and Assessing their Economic Performance,” Regional Studies, 44, 697-715.
Solow, R.M. 1994. Perspectives on growth theory, The Journal of Economic Perspectives, 45-54.2
Sterman, J. 2000. Business dynamics: Systems thinking and modeling for a complex world. New York:
Irwin McGraw-Hill.
Stiglitz, J. (2012). The price of inequality. Penguin UK.
Storper, M., Kemeny, T., Makarem, N., & Osman, T. (2015). The Rise and Decline of Great Urban
Economies: Los Angeles and San Francisco since 1970.
Storper, M. 1997. The Regional World, New York: Guilford.
Storper M, Scott A J, 1989, The geographic foundations and social regulation of flexible production
complexes, in The Power of Geography: How Territory Shapes Social Life Eds Wolch J R, Dear M J,
(Unwin Hyman, London) pp 21–40
Temouri, Y. 2012. The Cluster Scoreboard: Measuring the Performance of Local Business Clusters in the
Knowledge Economy. OECD Local Economic and Employment Development (LEED) Working Papers,
2012/13, OECD Publishing.
Todaro, M. 2000. Economic Development, Addison-Wesley, Essex, England.
UNCTAD 2004. The Least Developed Countries Report 2004, UNCTAD, Geneva, Switzerland.
Uyarra, E., & Ramlogan, R. (2012). The effects of cluster policy on innovation. Compendium of Evidence
on the Effectiveness of Innovation Policy Intervention, Manchester Institute of Innovation Research.
Figure 1. Economic Performance and Social Cohesion Causal Model
Social cohesion
embeddedness Social
Informal ties
local workers
Economic growth
New firms and
existing firms' growth
+ -
R9 -
leads to
and knowledge
R7 - Social
cohesion leads
to inter-
organizat ional
B8 - Unbalanced
leads to reducing
social cohesion
hurting informal
R5 - Social
cohesion leads
R10 - Economic
growth together
with a common
vision leads t o
R6 -
to inter-
organizat ional
R11 -
attract firms
to the cluster
Economies of
R& D
R3 -
leads to
R2a -
leads to
R4 - Agglo m
eration l eads
to kno wledg
e creation
R1 -
returns to
R2b -
attract new
Figure 2. Economic growth, employment and social cohesion
Table 1. Structure of the Paper and System Dynamics Modelling
Step Section of the paper Associated Appendix, Table
and Figure
Formulate the research question Introduction -
Review the basic theories Clusters, Economic
Performance and Social
Appendix A
Develop the causal theoretical model
using system dynamics modelling
Causal Modelling Table 2
Figure 1
Create the computational representation
of the model and run the simulation
using hypothetical (not real) data to add
the temporal dimension and evaluate the
dynamics of the model
Causal Modelling –
Representation of the
causal model and
Appendix B - Tables 3 and 4
Appendix B - Figures 3 to 6
Discuss the dynamics of the Model Discussion Figures 2
Appendix B - Figures 3 to 6
Collect real data and calibrate the model Conclusion (future
Table 2. Constructs and relationships considered in the causal model
Construct Description Refere
Verbal Description of the Equation
  !"#!" $%%
( !"#
( 1(( !"#!" (%02030(0
!"#!" 7$283
!"#!" <0((%
4(' !" 4((&'
= 0;
!"#!" =./0
7 7'B
!"#!" (;0
!"#!" %0
!" $/0;
!0"#!" 7$;
!" 7'
 $(
!" C./0%%'
Reference: [a] Delgado et al (2014); [b] Temouri (2012); [c] Porter (2001); [d] Porter (2003); [e] Forest and Kearns (2001); [f] Wennberg & Lundqvist (2010); [g] Pietrobelli&Rabellotti (2007); [h] Audretsch and
Stephan (1996); [i] Ingram and Roberts (2000) Ingram and Roberts (2000); [j] Becattini (1989); [k] Becattini (1990); [l] Saxenian (1994); [m] Aydalot (1986); [o] Keeble and Wilkinson, (2000)
i Social cohesion is defined as the level of social integration between different groups in a geographical area (cf. Kearns &
Forrest, 2000; Novy, Swiatek, & Moulaert, 2012).
iii Economic Performance is the result of Inter-organizational networks (weight 0.33), economies of scale (0.33), and
innovation (0.33). See Appendix B, Table 2.
... In this cluster, entrepreneurial education [23,[52][53][54][55][56][57][58][59][60][61][62] and performance appear because a well-formed entrepreneur has a higher performance level [6,[63][64][65], which reinforces his or her empowerment [21,66]. This cluster also includes the social entrepreneurship keyword [31,[67][68][69], an area distinguished from sustainability [24,44,52,53,70,71], although it has some common points regarding socio-economic interdependence [54,72]. ...
... In this cluster, entrepreneurial education [23,[52][53][54][55][56][57][58][59][60][61][62] and performance appear because a well-formed entrepreneur has a higher performance level [6,[63][64][65], which reinforces his or her empowerment [21,66]. This cluster also includes the social entrepreneurship keyword [31,[67][68][69], an area distinguished from sustainability [24,44,52,53,70,71], although it has some common points regarding socio-economic interdependence [54,72]. ity 2022, 14, x FOR PEER REVIEW 8 of 18 ...
... In line with the Community Psychology Paradigm [43][44][45], Entrepreneurship Education could reinforce concern for Sustainability and Social Impacts with regard to the territory, developing a sense of empowerment among citizens and Entrepreneurial Organizations, which could foster a functional attitude with a spontaneous initiative and/or through Institutional Intervention provided by the Government, which could encourage people, services and communities to adopt social functions, as represented in Figure 10. It suggests that organizational change for entrepreneurs comes from the top, via direct Statal-Institutional intervention, combined with change at the bottom. ...
Full-text available
In this review, we study the state of entrepreneurial education as it applies to business resilience. We consider records over the last 20 years about entrepreneurial resilience that consider their social impact and focus on sustainability. The aim of the study was to determine whether an enterprise that stresses social impact and sustainability rather than profits could reinforce entrepreneurial resilience. The importance of this study is that it offers a more complex description of entrepreneurial resilience by connecting social and environmental sensitivity with a profit-oriented logic. We found a mild incremental rise in, first, the years of the 2000s and a jump by 2010. We then used VosViewer to create a cluster map from the record list of WOS, creating three clusters of: “education and sustainability”, “entrepreneurship and social impact” and “innovation”, and these three clusters were related to superior entrepreneurial resilience. This approach should be adopted in real time to be able to adapt to socio-economic crises, adopting a functional approach based on cooperativeness and awareness of complexity.
... Benner (2017a ) argues that both cluster policy and smart specialization can be of considerable value for territorial institution-sensitive sectoral development that provides a set of instruments for establishing public-private dialogue platforms and prevents a dangerous lock-in situation. However, the study of Rocha et al. (2019 ) reveals that clusters with positive economic performance do not necessarily lead to regional social cohesion as in some cases they fail to integrate di erent groups, such as migrant or local workers, and embed new and large fi rms within the region. ...
Smart specialization is a key element of the EU regional policy. This chapter analyzes the experience of Galicia, a peripheral region in North-West Spain, and it addresses two main issues: to identify the main drivers and barriers in the prioritization of Galician Smart Specialization Strategy and to understand the implications of the smart specialization approach on the policy-making process and effectiveness as well as on institutional learning. This research combines a literature review on smart specialization with a case-study of the Galician experience, using in-depth semi-structured interviews and the analysis of the Galician innovation system and the innovation policies implemented so far. Our results show that the main weaknesses of the Galician Regional Innovation Smart Specialization Strategy are its limited prioritization, a poor development of the entrepreneurial discovery process, the limited coordination with innovation plans and, finally, the poor evaluation process. This research uses the Galician experience as an illustration of the implementation of the smart specialization strategy on peripheral regions, providing policy recommendations. It is worthy of study because it is a good example of formal implementation, but the results in the innovation performance and the policy design and implementation have hardly improved. The research highlights some relevant aspects for a proper definition and implementation of S3, which are regional singularities, the level of granularity on prioritization, the inclusiveness of discovery entrepreneurship process, the need of the evaluation and impact, a better coordination and governance, and finding policy synergies. It should also be considered the time required for a strategic definition and prioritization.
... Results can be applied to agile IT project management as well. Rocha et al. [10] focused their research on economic performance using cluster policies. A theoretical model based on SD principles was developed. ...
Full-text available
System dynamics, as a methodology for analyzing and understanding various types of systems, has been applied in research for several decades. We undertook a review to identify the latest application domains and map the realm of system dynamics. The systematic review was conducted according to the PRISMA methodology. We analyzed and categorized 212 articles and found that the vast majority of studies belong to the fields of business administration, health, and environmental research. Altogether, 20 groups of modeling and simulation topics can be recognized. System dynamics is occasionally supported by other modeling methodologies such as the agent-based modeling approach. There are issues related to published studies mostly associated with testing of validity and reasonability of models, leading to the development of predictions that are not grounded in verified models. This study contributes to the development of system dynamics as a methodology that can offer new ideas, highlight limitations, or provide analogies for further research in various research disciplines.
... This can attract more talent, capital, and cargo clustering to promote innovation, which would form a sustainable mechanism and enhance the competitiveness of the port city. According to the above modeling ideas and referring to Rocha et al. (2019), Fu and Jiang (2019), and Uriona and Grobbellar (2019) on the system dynamics modeling method, the specific port city model is as Figure 5. ...
Full-text available
Port cities will be the important growth poles along the Belt and Road, including coastal and inland ports. But the innovation stimulus driven by the Belt and Road Initiative on the coastal and inland port cities is imbalance. This research takes two groups of Chinese port cities as empirical objects, one has coastal port and the other has inland port, and then establishes a system dynamics model based on the theory of triple helix to analyze port cities’ innovation features. The empirical case shows that the positive stimulation of Belt and Road Initiative to innovation activities of coastal port cities is better than that of inland cities in general. If the decision makers of inland cities want to achieve innovation output like that of developed coastal areas, they need to encourage local universities and research institutes to conduct more market-oriented research on one hand. On the other hand, they need to cooperate with multinational companies; meanwhile, they need to provide more technical resources for local start-ups. In addition, stimulating local house prices and living pressure in a reasonable range can stimulate the innovation enthusiasm of existing R&D personnel, attract top talents to join in, and then drive the innovation output of the whole region.
... Therefore, designing artificial ICs by copying the" interaction "theoretically described in the models presented above, is nothing more than a "fake" of specific ICs in a certain territory. It seems more likely that the government can create economic zones promoting the high business activity, and then forming ICs will be possible if there are appropriate historical and cultural characteristics, a high level of the local population entrepreneurial activity, and the necessary resources' availability [13]. Artificial forming ICs is a complex, individually creative process that cannot be fully based on one of the existing theories. ...
Conference Paper
Annotation. Innovative clusters are considered as a dynamic environment in the competitiveness management system. It is proved that this environment creates certain competitive advantages for business entities. Based on the results of the study, the sources of innovative clusters competitiveness are identified and grouped by factors of direct and indirect impact. It is proved that some of them have a particularly significant impact on certain stages of forming and developing innovative clusters. The presented results form a theoretical basis for modeling the competitiveness management system of innovative clusters in order to determine the vectors of its improvement in the future.
Full-text available
Following the call for an assessment of recent developments and an understanding of the state-of-the-art of entrepreneurial ecosystems, this paper investigates the historical evolution of entrepreneurial ecosystems, regional clusters, and industrial districts to untangle their necessary and specific dimensions and policy implications. It aims at reducing the gap between the increasing academic and policy interest in entrepreneurial ecosystems and the theoretical grounds upon which research and policies are based. To this end, it traces back the phenomena of ecosystems, clusters, and industrial districts to their origin, using critical realism ontology and historical organization studies as research methods. This paper contributes a historical and theoretical framework that provides academic rigor for understanding entrepreneurial ecosystems and policy rationales for evaluating economic development policies.
The methodological approaches to the creation of a cluster in the system of innovation-oriented management of the construction complex development are determined. An advanced model that determines the principles, conditions of activity and the expected economic effect from the introduction of the cluster is proposed. The model is aimed at ensuring the innovative development of the construction complex and reducing the cost of construction. The directions of innovation-oriented management of the construction complex development are proposed; their implementation makes it possible to obtain combinatorial advantages from the joint activities of its participants.
The paper achieves an atypical approach to EU regional cohesion in terms of the sustainability objectives set in 2030 Agenda. The analysis focuses on 5 characteristic goals and 14 specific indicators during 2006–2020. The paper develops a cluster approach in identifying NUTS2 sustainable regional development. The proposed model in this paper represents an element of novelty in the field of sustainability assessment through its descriptive and correlative characteristics by which the NUTS2 classification can be associated unitarily with sustainable development clusters. The article stands out for its innovative character. It has an European impact by identifying five European development clusters and quantifying sustainability indicators in terms of the 2030 Agenda on the level of the made acquisitions and the efficiency of their implementation through the proposed regional sustainable development model. he whole analysis and the conclusions are supported by the latest official statistical data, pertinent tables and diagrams.
Full-text available
The purpose of the article is to highlight the conceptual understanding of the cluster as an innovative organizational and economic form of production in the system of sustainable development on the example of the agricultural sector and substantiate the scientific basis for ensuring its implementation at the regional level. Research methods. When carrying out the research, a set of general scientific methods and techniques was used; monographic - in the study of domestic and foreign publications and the above-mentioned problems; a systematic approach based on the principles of systems analysis and synthesis - to substantiate the relationship between sustainable development, the efficiency of agricultural production and the cluster model; abstract-logical - for the theoretical generalization of research results and determination of measures to ensure the implementation of the cluster model of agricultural development; economic analysis - to study the efficiency of agricultural production. Research results. The essence of the cluster in modern economic theory in the context of the concept of sustainable development has been studied and analyzed. Globalization, climate change and the aggravation of socio-economic and environmental problems in many countries of the world necessitate the search for new models of economic development. It has been established that one of the most optimal organizational and economic forms of effective agricultural production in the system of sustainable development is clusters. On the example of the Kirovograd region, a scientifically grounded regional model of the development of agro-clusters in the agro-industrial complex. Scientific novelty. Based on the synthesis of scientific theories of cluster and sustainable development, further study of theoretical and methodological approaches to the definition of the essence of clusters as a promising organizational and economic form of effective agricultural production in the system of sustainable development was obtained. Practical significance. The research results will find practical use in substantiating regional strategies for the development of agro-industrial production. They can be used in educational and educational programs, when providing consulting services for the creation of projects of agricultural clusters and in further scientific research.
Full-text available
ABSTRACT: Purpose – This paper aims to analyse the impact of clusters on development and growth at the firm and regional level in Latin America (LA). The past 20 years have witnessed an acceleration of cluster initiatives, assuming their positive impact on firm performance and regional development. However, theoretical development and empirical meta-studies in emerging countries to validate this assumed relationship are scarce. Design/methodology/approach – This paper reviews empirical evidence from a population of 123 studies and a sample of 45 empirical studies including 216 clusters in LA. Findings – It concludes that clusters contribute to both development and growth at the firm- and regional-level contingent to factors such as cluster stage of development, collective efficiency, the pattern of governance of the value chain and the sector in which the firm operates; however, clusters are also a potential source of socio-economic divides. Originality/value – Therefore, these results qualify the conclusions of studies of clusters in developed countries (Porter, 2003; Delgado et al., 2010). Article · Jun 2015 · Management Research
Industrial districts and clusters are of utmost importance for economic growth and innovation in the European Union (EU). In this chapter, we analyse how smart specialisation policies have worked in different region types, combining cluster policies with smart specialisation ideas. Our study selects a sample of EU regions that differs strongly in terms of geography, size, socioeconomic dynamics, innovation capacities, and governance settings. Two key components of the strategy development phase deserved particular attention, that is, stakeholder inclusion and policy prioritisation. The cases selected are grouped into three main region types: advanced, intermediate, and less-developed regions. The empirical results suggest that advanced regions are in the best position to develop inclusive governance forms and to benefit from smart specialisation strategies. Intermediate regions also perform quite well with respect to the development of smart specialisation strategies, coping with stakeholder involvement, planning capabilities, and the capacity to prioritise a set of clusters and sectors. In contrast, in less-developed regions, weak innovation systems, insufficient experience with regionalised innovation policies, and high levels of state centralisation have undermined smart specialisation processes.
Policies to support clusters of firms and other territorially-rooted agents are as popular today as they have ever been. While contemporary cluster policy most commonly adopts a microeconomic perspective to support business competitiveness, cluster policy practice remains strongly influenced also by the social foundations that can be found in the industrial district concept. This paper critically analyses contemporary cluster policy by incorporating an institutional perspective that seeks to enable greater understanding of the relevance of local community, systems of values, and informal institutions for cluster policy practices. Supported by a brief analysis of the diversity of cluster policies that are found in seven European regions, the paper makes a first step in highlighting the potential space for cluster policies to work more explicitly on the social embeddedness of clusters in their territories. This agenda requires more research, which could build on Giacomo Becattini’s key ideas in responding to the increasing sensitivity of both academics and policy makers to the intractability of the economic and social aspects of territorial development processes.
Today, the Bay Area is home to the most successful knowledge economy in America, while Los Angeles has fallen progressively farther behind its neighbor to the north and a number of other American metropolises. Yet, in 1970, experts would have predicted that L.A. would outpace San Francisco in population, income, economic power, and influence. The usual factors used to explain urban growth—luck, immigration, local economic policies, and the pool of skilled labor—do not account for the contrast between the two cities and their fates. So what does?The Rise and Fall of Urban Economies challenges many of the conventional notions about economic development and sheds new light on its workings. The authors argue that it is essential to understand the interactions of three major components—economic specialization, human capital formation, and institutional factors—to determine how well a regional economy will cope with new opportunities and challenges. Drawing on economics, sociology, political science, and geography, they argue that the economic development of metropolitan regions hinges on previously underexplored capacities for organizational change in firms, networks of people, and networks of leaders. By studying San Francisco and Los Angeles in unprecedented levels of depth, this book extracts lessons for the field of economic development studies and urban regions around the world. [Publisher's abstract]
In a previous issue of Foundations and Trends in Entrepreneurship, small firms were commonly acknowledged as an important topic in the 20th century literature on industry concentration. Since the turn of the 21st century, startups have become a topic of significant prominence. Despite this fact, none of the recent literature reviews on industry concentration highlights their importance. Likewise, several other factors that have been identified since Marshall's (1920) work, have received new attention with fresh perspectives in the 21st century literature. These topics have not yet been sufficiently explored in the literature. The purpose of this monograph is to review the early work of scholarship on agglomerations, industrial districts and industry clusters that has been published in the 21st century. This monograph explicates the prominent themes that emerged between 2000-2015, with the specific objective of highlighting the relationships of entrepreneurs, knowledge, networks and creative and high technology industries in industry concentration.
Entrepreneurship and Regional Development aims to make a theoretical and practical contribution meeting the need for studies on the impact of clusters on entrepreneurship and societal outcomes. This book aims to answer the following research question: Do clusters matter to entrepreneurship and entrepreneurship outcomes at the societal level?
The aim of this special issue is threefold. Firstly, it highlights major recent methodological advances to address the two key issues referred to above: improving extended KPF analyses, on the one hand, and developing strategic approaches using microeconomic data, on the other:[br/][br/] * Two papers are presented using Knowldge Production Functions (KPF). They offer new methodologies to deal with the issue of regional heterogeneity when estimating KPF at the regional level in Europe. [br/][br/] * Using more microeconomic approaches, three papers contribute to the second topic. They use micro-economic data to show how firms’ strategies may interact with the local environment and impact upon the determinants of agglomeration dynamics.[br/][br/] Secondly, this issue draws attention to interesting new results emerging from the application of these new methodologies to the analysis of innovation dynamics in European regions and shows how they can help one to revisit some main tenets of received wisdom concerning the rationale and impact of public policies on the Geography of Innovation.[br/][br/] Finally this special issue also identifies issues that still require further research, particularly in relation to the development of new methodologies for the evaluation of public policies integrating the spatial dimension and the interdependencies between public policies implemented at different regional scales, which remains no more than an emerging field in the Geography of Innovation. [br/][br/] The special issue concludes with a paper presenting a new theoretical framework for the analysis and evaluation of local innovation public policies using simulation methodology. All these papers have important policy implications.