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Regional Resilience Literature Review
Philip Cooke, Gillian Bristow, & Julie Porter
Cardiff University
ESRC ORA Project: 2011 - 2014
Introduction
Resilience is an emerging concept in the change context that confronts urban and regional
development. Contemporary society faces a pressing need to respond to a number of major
challenges resulting from environmental change and disasters, global economic uncertainty and
recessions and the onset of the era of peak oil. Understanding how social, ecological and economic
systems, communities and individuals can cope with such inter-linked and dynamic transformational
change is critical, with interest focusing on the concept of resilience (Christopherson et al, 2010; Duit
et al, 2010). Common in the ecological sciences, the concept is now being applied to social and
economic systems and is broadly understood as meaning the capabilities and capacities of
individuals, communities, and economies to withstand, adapt and respond to profound shocks and
anticipated change (Hudson, 2008). The burgeoning interest in resilience is reflected in the growing
number of university research centres and institutes emerging with an explicit focus on
understanding what makes for resilient cities, regions and places. For example, the US-based
MacArthur Foundation established a research network in 2008 entitled ‘Building Resilient Regions’.
The Center for Resilient Cities in the US, has a focus on helping cities develop the planning and
design strategies they need to meet future challenges, which provides a similar focus for the
Stockholm Resilience Centre.
Resilience is developing widespread appeal owing to the peculiarly powerful combination of
transformative pressures from below, and various catalytic, crisis-induced imperatives for change
from above or ‘the generalised contemporary sense of uncertainty and insecurity’ (Christopherson
et al, 2010; p. 3). It is a concept that by definition is highly attractive as in general to be resilient
refers to something positive, whether to be able to recover from disaster or withstand hardship and
disturbance and deal with risks in an appropriate way (Muller, 2010). As such, like competitiveness
which has an innate evolutionary or survival of the fittest metaphor, resilience has the advantage of
drawing upon the intuitive and positive connotations of ecological development for economic and
social development trajectories (Bristow, 2010). The appeal of resilience also reflects its
transdisciplinary nature – it is a concept that derives from the ecological sciences and is used in
engineering and now the social sciences. In that regard, it provides a useful means of bringing
differing perspectives to bear on understandings of what drives regional change – especially in the
context of the range of economic, social and environmental challenges confronting regions.
There is however, a growing debate around the notion of resilience with questions around what it
means, how it might be assessed or measured, and its implications for policy development (see, for
example, Dawley et al, 2010; Martin, 2011). Identifying resilience more carefully requires defining
the variables that shape the system, understanding the nature of the external shocks, and the
selection of some observable characteristic or outcome of the phenomenon. These aspects may
change depending on the temporal, social and spatial scale at which resilience is being considered as
well as the different sectors within any given local or regional economy (Carpenter et al, 2001; p.
767). In short, there is a need to understand precisely what is resilient, and to what (as Pendall et al,
2010).
While this increased interest in resilience is reasonable given the concepts successful applications in
other fields, there are a number of areas of weakness in the attempts to extend the resilience
concept, based in physics and ecology, to cities and regions. Muller (2010) identifies the need to
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develop more robust working hypotheses about resilience in urban and regional systems, taking into
account all facets of human life including environmental issues such as land use patterns and
metabolic flows, as well as social and economic dynamics and governance issues. Adaptive resilience
in its fullest sense implies that cities and regions be conceptualised as complex, multidimensional or
hybrid systems which embrace complex, coupled relationships between people, institutions and
economies and the context-specific natural resources on which they ultimately depend (Adger,
2000). According to CLES (2010), for example, resilience conceives places as a series of
interconnected systems with relationships and feedback processes between topography, the built
environment, use functions and people. This usefully focuses attention on the interaction of
different dynamics and hybrid processes in their manifestation of vulnerability, crisis and change,
and so allows for breadth of analysis of potential risks, crises that embrace natural hazards and
disasters as well as economic crises, deindustrisalistion, depopulation and demographic change
environment and society (Lang, 2010). It is nonetheless extremely challenging to develop models
which link the complex ecological, social and economic aspects of cities and regions. This challenge is
explicitly acknowledged in the work of the Resilience Alliance (2007) who distinguish between four
issues of relevance in urban areas: metabolic flows in sustaining urban functions, human well-being
and the quality of life; governance networks and the ability of society to adapt, learn and re-organise
the way they cope with urban challenges; the social dynamics of people as citizens, consumers and
users; and the built environment that defines the urban physical pattern. Panarchy simplifies in
many ways and implies that boundaries of systems can be identified. Human regions are however
notoriously slippery constructs requiring careful specification (Pendall et al, 2010).
In spite of recent contributions, there is also still a lack of understanding of the processes and factors
that may make some cities and regions vulnerable and others resilient, which reflects the limited,
but growing, body of empirical work on these issues (Lang, 2010; Martin, 2011). A number of studies
have begun to explore varied regional responses to economic shocks. In an analysis of how UK
regions have responded to the three major recessionary shocks which have occurred in the national
economy since the early 1980s, Martin (2011) finds significant spatial and temporal variations in
regional resistance to and recovery from recession. He asserts that a region’s economic structure
and in particular its dependence on production industry has a major influence on the sensitivity of
regional economies to recessionary shocks. Deep downturns in a region’s economy which lead to the
destruction of a significant proportion of its economic base appear to result in a downward
hysteretic shift in the region’s growth path. Hill et al (2008) quote two major studies in the US one of
which indicated that areas with warm climates and those close to metropolitan cities showed the
best post-shock recoveries (Feyrer, Sacerdote and Stern, 2007; cited in Hill et al, 2008). Another
study revealed that IT centres specialising in IT services performed better than those in
manufacturing after the IT bust in 2000 because of their highly educated labour force.
There are also a growing number of studies seeking to understand what shapes adaptive capacities
amongst firms and regional economies. Simmie and Martin (2010) suggest that among the key
factors for understanding regional resilience are endogenous sources of new knowledge combined
with market driven and conscious entrepreneurial decisions. These shape how places and their
relational and technological structures prevent lock-in effects by recombining knowledge in
overlapping technological fields and generating new ones. The most striking example is the pattern
of California’s Silicon Valley which has bounced back from the aftermath of the internet bubble crash
by developing biotech and cleantech as a source of continuous growth (Cooke, 2010). In other
example, Cambridge’s resilience is ascribed in part to its ability to ‘continually branch out of existing
specialised industrial sectors (Simmie and Martin, 2010). Other regions display resilience capabilities
by recombining knowledge and reorganising networks and sectors towards emerging technological
fields and new consumer paradigms. In that case, resilient processes occur when network structures
evolve in such a way that they succeed in disconnecting the regional trajectory to the cycle of
technologies, in particular when technologies decline (Suire and Vincente, 2009). Other analyses
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suggest regional resilience depends upon the existence of a large number of innovative and wellnetworked small firms with embedded regional capacities (Clark et al, 2010), whilst others
emphasise the role of particular central organisations which act as hubs in the resilience process
(Kechidi and Talbot, 2010).
Recent work has also suggested there is much to be gained from firms combining external sources of
knowledge accessed through so called ‘global pipelines’ with the ‘local buzz’ (and vibrancy) that
exists within their own geographical region (see Storper and Venables 2004, cited in Hervas-Oliver et
al, 2011). This suggests that nurturing relations between resident and other external firms (and
bodies) are important for a region’s future trajectory; where channels of communication are open
and strong links are formed for instance, a region may exhibit strong ‘adaptive capacity’ and this in
turn can contribute to industrial upgrading. Menzel and Fornahl (2007) (cited in Hervas-Oliver, 2011)
consider that adapting policies at various stages of the cluster’s life cycle - such as through selective
(small firm) start-up policies - might be useful to militate against decline and facilitate the
emergence of new development paths. In spite of this emerging corpus of work, however, there are
still gaps in our understanding of the critical driving forces influencing adaptive cycles, and what
flexibility and limits heterogeneous local firms, workers and institutions have in relation to the
adaption and transformation of development processes. Also missing is a unified framework for
understanding regional resilience, and detailed empirical evidence on the relative factors shaping it.
Finally, there are unanswered questions around the politics of resilience. The literature to date says
very little about why and how external events are perceived as being a disturbance or crisis and
about the intended state of recovery or when these crises can be considered to be over. Here,
questions of power concerning the determination of what constitutes crisis and objectives for
achievable adaptations need to be further explored (Hudson, 2010). In a case study of the Barnim
region of Germany, Rohring and Gailing (2010) observe that normative goals are associated with the
term ‘resilience’ that are the subject of social construction through regional discourses and forms of
governance. Thus in the Barnim region, there are two competing perceptions of resilience which are
a product of the different interests of different actors and the different stabilising elements of the
development path they choose to pursue. Thus, one supports and profits from suburban growth and
continues to stabilise it, and the other seeks to preserve the qualitative and ecological aspects of the
landscape region. Duit et al (2010) similarly recognise the inescapably normative dimensions of
resilience, whilst Smith and Stirling (2010) urge researchers to reflect upon what precisely it is that is
being made resilient, in the face of which specific dynamics, for whom and by what criteria this is
good or bad.
The idea of resilience resonates with the growing importance of an evolutionary perspective within
economic geography and the recognition that major shocks may exert a formative influence over
how the economic landscape evolves and changes over time (Boschma and Martin, 2010; Simmie
and Martin, 2010; Martin, 2011). The purpose of this paper is to thus explore recent debates within
economic geography and related disciplines which adopt an evolutionary approach to help develop
and understand the concept of resilience. The review also draws upon and makes reference to
broader literature sets pertaining to resilience to provide additional insights into the key debates
and questions around its meaning and application particularly in pertinent policy and institutional
arenas.
The literature review will continue as follows. The next section will focus on defining resilience
shocks and the concept of resilience through several theories including engineering resilience,
ecological resilience and more evolutionary-based options. This section will also demonstrate the
overlapping concepts that are at the core of several of these latter theories such as related variety
and innovation. After this analysis, the adaptation methods, based in evolutionary economic
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geography, that can be applied to regional resilience and recovery, will be reviewed. This will focus
on the capacity of the regions to adapt and evolve in the post-shock period. After this discussion,
there will be a section that highlights the issues relating to specialisation and diversification and
what this means for regional resilience. The final section will provide an overview of governance and
policy measures relating to regional economic resilience along with the institutional framework that
supports them.
What is regional economic resilience?
Most resilience research is rooted in post-productivist epistemology and applies systems thinking i.e.
an understanding of the field of study as a complex multidimensional or hybrid system. Reggiani et
al (2002) produced one of the first discussions on the possible application of the notion of resilience
to the dynamics of spatial economic systems, arguing that resilience could help understand how
such systems respond to shocks, disturbances and perturbations. Over the past five years, a number
of urban and regional analysts have explored the applicability of the concept to cities and regions
(for example, Rose and Liao, 2005; Foster, 2007; Hill et al, 2008; Newman et al, 2009; Christopherson
et al, 2010). Thus, Foster (2007; p. 14) defines regional resilience as ‘the ability of a region to
anticipate, prepare for, respond to, and recover from a disturbance’. Concepts of resilience are thus
used to describe the relationship between the system under observation and some externally
induced disruption, stress, disturbance or crisis (Lang, 2010).
Before explaining how the recovery can occur, the potential exogenous shocks that can impact the
system will be explained. Muller (2010) observes it is possible to distinguish between a number of
unexpected shocks or crises including natural, biomedical, social, technological and political.
Disturbances may also vary in temporal and spatial scale. Broadly speaking, regions face two main
categories of disturbance: system shocks (which usually come as discrete events such as disasters)
and slow-burn challenges (such as long-term de-industrialisation or population change) (Pendall et
al, 2010). There are three kinds of sudden, system shocks that can affect a region: those caused by
macroeconomic events such as an economic recession, those caused by industry-specific shocks
(when the industry is prominent in the region) such as movement of major firms out of the region or
increased competitiveness, and those caused by natural disasters in the region (Hill et al, 2011).
Alternatively, the slow-burn challenges, such as deindustrialisation, are likely to vary in nature and
magnitude and thus complicate assessment of regional and urban resilience (Pendall et al, 2007).
Much of the wider literature focuses on how regions can respond and recover from individual
shocks, as well as a combination of shocks, through equilibrium-based theories; however, starting
with the special issue of the Cambridge Journal of Regions, Economy & Society, the evolutionary
aspect of resilient regions has been highlighted based on more recent events such as the the current
economic crisis (2010). While the shock(s) affecting a region can be easily identified, the response of
the region can be difficult due to the economic systems in place in the region as well as the potential
outcome if resilient. This section will review the regional economic resilience literature highlighting
the types of resilience and the region’s shock response options. To narrow the scope of the
literature review, emphasis will be placed on the global ‘credit crunch’ as a macroeconomic shock
due to the accompanying increase in livelihood insecurity that has highlighted the advantages of
those local and regional economies that have greater economic ‘resilience’ by virtue of being less
dependent upon globally footloose activities, having greater economic diversity, and / or having a
determination to prioritise and effect significant structural change and reduce over-dependence on
a single economic activity (Larkin and Cooper, 2009; Ashby et al, 2009).
In narrowing to focus on the macroeconomic shock, there are still many ambiguities to consider
relating to resilience and what should be considered in a theoretical explanation and what should be
excluded. For example, can the theory take into account the system’s ability to resist the shock or
does the theory only apply in the post-shock period? To start, two main theories of resilience will be
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examined: engineering resilience and ecological resilience. Engineering resilience is based in physics
through its reliance on system elasticity but also has roots in economics (Martin, 2011, Simmie &
Martin, 2010). This theory considers resilience as the capacity of the system to return to or resume
a state of equilibrium after an external shock. The faster the system returns to a state of
equilibrium, the more resilient it is to external shocks (Simmie & Martin, 2010). When assessing
regional resilience in the US, a number of American academics use equilibrium to explain resilience
which is measured through unemployment and export statistics pre- and post- shock (see Hill et al,
Pendall ___). Ecological resilience is an extension of engineering resilience but differs due to the
system’s ability to have multiple equilibria. This model focuses on the role of shocks or disturbances
in pushing a system beyond its elasticity threshold to a new domain. This draws on the ecological
sciences and panarchy and the work of Holling (1973) where resilience is conceived as the capacity
of a system to withstand and respond to major external disturbances and shocks such as forest fires,
and to adapt and respond to these rather than simply to wither and die (see also Levin et al, 1998).
Resilience from this perspective is thus typically defined as ‘the capacity of a system to absorb
disturbance and reorganize while undergoing change, so as to still retain essentially the same
function, structure and feedbacks’ (Hopkins, 2008; p. 54; also Hudson, 2008).
Through reviewing these theories, an assumption can be made regarding their applicability to
regions at the current time as well as their applicability in addressing the long-term success of the
regions: neither of these theories focus on the evolution of the region due to their emphasis on
equilibrium. Regional resilience in an evolutionary perspective will be discussed further in its
relation to equilibrium in the next section. Using engineering resilience the system would be
considered resilient if it returned to equilibrium after experiencing a shock. This could apply to New
Orleans post-Hurricane Katrina where the emphasis was returning the region to a place where
tourists would vacation again (Christopherson et al, 2010). The ecological theory takes engineering a
step further by potentially allowing for a change of path due to the multiple equilibria. However, the
resilient region is characterised by being able to ‘absorb and accommodate extreme shocks without
significant change to form or function’ meaning path change would be a trait of a non-resilient
region and the basis of the resilient region is equilibrium (Simmie & Martin, 2010, pg. 30).
Furthermore, the origins of the supplementary path are not discussed; rather, as Swanstrom notes,
the industries can adapt, but how that adaptation is managed is unclear (2008). Despite this
oversight, the evolutionary ability of regions is important to consider in relation to resilience theory
due to the adaptive capacity of the regions in terms of industry, technology, and governance. Based
on this, evolutionary economic geography will be reviewed focusing on the potential adaptation
approaches that can be utilised to respond to a shock.
Evolutionary Economic Geography
The different definitions of resilience have different implications for understanding what precisely
constitutes resilience in regional and local economic systems. The engineering notion of resilience
focuses attention on the stability of a system near its equilibrium and raises questions concerning
the relevance of the assumption of equilibrium to regional or local economies. The evolutionary
economic geography (EEG) approach to economic change rejects neo-classical inspired notions of
adjustment mechanisms towards any notion of equilibrium. Instead they understand the economic
landscape as a complex adaptive system which can never be in equilibrium (Dawley et al, 2010). This
rejection of equilibrium in the EEG approach is based on the ability of the economy to self-transform
from within (Witt as cited in Boschma & Martin, 2010). This transformation has three specific
characteristics: dynamical, irreversible process and novelty. The first characteristic, dynamical,
refers to the constant state of change the economy is in, ie. it is not static. The second
characteristic, irreversible process, refers to the forward moving nature of the economy. In
combining the first and second characteristic where the economy needs to be changing and moving
forward, it can be assumed that equilibrium-based economic notions can be abandoned. The final
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characteristic, novelty, refers to the innovative emphasis in evolutionary economics to drive
markets.
Martin (2011) reviews this debate, between evolution and equilibrium, noting that some authors
(such as Pendall et al, 2010) are prepared to assert that regional growth rates of key economic
variables such as output, unemployment and poverty can be considered at least partly as
equilibrium phenomenon. Martin (2011) observes, however, a regional economy need never be in
equilibrium yet may still be characterised by an identifiable and relatively stable growth path. In this
regard, a region may be resilient or bounce-back to its pre-shock state regardless of whether that
position or path is deemed to be an ‘equilibrium’ state of affairs. This resonates with the ‘plucking
model’ of economic fluctuations used in economics. However, this makes no claims about the
impact of such shocks on the region’s economic structure and thus its resilience to future recessions.
Thus, how resilience is defined would depend on what aspect of regional economic performance and
stability is being focussed on whether growth rate, economic structure, or institutional
arrangements. This relates to a wider question which is what do we want to preserve or how should
the health of a socio-economic system be defined – i.e. what structures or variables in a local or
regional economy need to be preserved or exhibit resilience in a city or regional economy (Hanley,
1998)?
The ecological definition of resilience assumes that systems are characterised by multiple stability
domains and that if a shock pushes a system beyond its elasticity threshold, it may move to a
different state or path i.e. major shocks may exert a formative influence over how the economic
landscape evolves and changes over time (Boschma and Martin, 2010; Simmie and Martin, 2010;
Martin, 2011). What is not clear from this definition, is precisely what constitutes resilience i.e.
whether it is measured with reference to the size of the shock that a system can withstand before it
becomes unable to return to its former stability domain or path, or whether it refers to the ability of
a system to quickly move to a new stable configuration or path (Martin, 2011).
Much of the debate within evolutionary economic geography focuses upon the causal concepts of
adaptation – the ability to respond to an economic shock with a movement back towards a preconceived model, and adaptability – where a different kind of resilience emerges through
opportunities or a decision to leave a path that may have proven successful in the past in favour of a
new, related or alternative trajectory or niche (Dawley et al, 2010). The evolutionary economic
geography perspective suggests that the adaptive capabilities of a region’s economy are likely to
depend on the nature of the region’s pre-existing economy – in other words, adaptation is likely to
be a path-dependent process shaped by the region’s industrial legacy and the scope for reorientating skills, resources and technologies inherited from that legacy (Martin, 2011). Due to the
emphasis on recovery through adaptation, three evolution-based theories will be discussed in this
section regarding their relevance to the adaptation of resilient regions: Generalised Darwinism, Path
Dependence Theory & Complexity Theory.
Figure 1: Theories of Evolutionary Economic Geography
Generalised Darwinism- based
in modern evolutionary biology:
variety, novelty, selection,
mutation, adaptation
Complexity Theory- based in
complex adaptive systems theory:
emergence, self-organisation,
adaptation, hysteresis
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Path Dependence Theorycontingency & self-reinforcing
dynamics, lock-in, branching,
path creation
Adapted from Boschma & Martin, 2010
Although these theories have distinct differences, there is also substantial overlap between them.
For example, path creation in path dependence theory is similar to emergence in complexity theory.
In addition, variety in Darwinism is needed for branching in path dependence theory. Also,
adaptation is a theme in all of these theories, but to varying degrees. These similarities will be
highlighted in the next section.
Generalised Darwinism
This theory is based on Darwin’s theory of evolution albeit at the regional level which can be
characterised through diverse, innovative practices increasing resistance and recovery to an
exogenous shock. Of the characteristics listed in Figure 1 for this theory, the one discussed most in
relation to the resilience literature is variety. ‘Variety’ refers to the ability of firms, industries and
sectors to adapt through diversifying at the regional level. For example, if the industry in the region
was highly specialised in aeronautics, with a specialised labour force solely trained in aeronautics,
then the resistance and recovery to economic or competitive shocks would be more difficult than if
there were industries in the region that were diverse but could overlap, such as photonics and
aeronautics. The latter, diversified region would have a high-skilled labour force that could
transition between the industries. The reason for this resilience variation between the two
hypothetical situations is due to specialised regions being more open to sector-specific shocks and
having economic activity that is too narrow (Simmie & Martin, 2010). Related variety, and its
capacity to adapt a region, particularly in relation to technological relatedness will be discussed
further in later sections. However, there are other characteristics of this theory that need to be
assessed before moving on, namely novelty.
‘Novelty’ can be described as innovation which is a key aspect of evolutionary economic geography
as a whole field of study. Unlike the other theories listed in Figure 1, Darwinism highlights the need
for novelty which the literature connects with related variety (Simmie & Martin, 2010, Boschma &
Martin, 2010). Overall, this theory relies on a variety of industries, sectors or firms in a region to
resist and recover from shocks as the diversity of agents, and their behaviours, will reduce the
impact in the short-term and over time.
Path Dependence
In its truest form, path dependence focuses on the regional economy to be ‘locked in’ to a particular
path. To further explain, the region is locked-into a certain method of economic development which
is reinforced by significant returns to the local economy (David, 2005). If the regional economy, that
is locked-in to a specific path, is impacted by a shock then the resilience of the region can be gauged
by the region’s capacity to return to that path. Although some words have changed (regional
economy instead of system, lock-in instead of equilibrium), this account of path dependence theory
appears to be in line with engineering resilience (Simmie & Martin, 2010). If path dependence,
through lock-in, is related to engineering resilience through its focus on equilibrium then how does it
apply to adaptation and evolution? This question is particularly pertinent as the aforementioned
definition of evolutionary economic geography specifically discounts the use of equilibrium- based
theories (Witt as cited in Bochma & Martin, 2010). Simmie and Martin consider the relationship
between path dependence and evolution through theorising new path creation (2010). In creating
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the new path, which could be based on the old paths as they provide the skills and competences for
development, the system (regional economy) demonstrates the evolutionary side to path
dependence theory and also shows how the system can adapt or ‘break free’ from lock-in in
response to the shock (Martin & Sunley, 2006, Simmie & Martin, 2010). Cooke notes that path
dependence attempts to show that (technological) development is often important, but in
sometimes trivial ways, history-bound (see QWERTY keyboard example).
Based on this evolutionary side to path dependence theory, how are new paths created? Substantial
research has been completed in response to this question with conflicting findings that paths are
created at random or they are shaped by old paths (Martin & Sunley, 2006). Alternatively, there are
measures available to escape path lock-in. According to Martin & Sunley, possible scenarios for
escaping path lock-in include: branching, related variety and technological relatedness (2006). These
scenarios will be discussed in the next section in relation to resilience and adaptation.
Complexity Theory
Along with engineering resilience and ecological resilience, Martin considered ‘adaptive resilience’
which derives from the theory of complex adaptive systems (2011). These are systems which are
characterised by self-driven and co-evolutionary interactions among their constituent components
and elements, and an adaptive capacity which enables them to re-arrange their internal structure
spontaneously whether in response to an external shock or in reaction to some form of selforganised criticality. This views resilience as an evolutionary, dynamic process and not just a
characteristic or property, and in terms of local and regional economies, could be understood as
relating to their capacity to adapt structures so as to maintain an acceptable growth or development
path over time.
The main characteristics of complex adaptive systems are emergence, the interplay between the
micro & macro levels, open boundaries and adaptation (Martin & Sunley, 2007). Emergence is a
process in which the actors at the micro-level interact which has an effect at the macro level (selforganisation); however, what is created at the macro level by this interaction cannot be reduced
back to those individual actors at the micro level (whole is more than the sum of its parts) (Martin &
Sunley, forthcoming). As Martin & Sunley explain it, ‘the geographic forms that comprise the
landscape (cities, industrial districts, clusters, etc.) emerge as macro-features in an unplanned way
from interactions of the micro decisions and behaviours of the economic agents (firms, workers,
consumers) (forthcoming, pg. 2). One point to highlight is the flow from micro to macro which is
characteristic of complexity theory; however, there is no flow from macro to micro properties. In
addition, emergence can occur several times within a system as demonstrated in Table 1.
Table 1: Three Orders of Emergence
Order
Characteristics
First Order Emergence
The most basic class of emergent phenomena. Interaction
relationships between system components become amplified to
produce aggregate system patterns and behaviours that emerge with
ascent in scale. The same aggregate higher order properties can
emerge out of different micro-level details of system micro-level
composition and interaction, but there is no downward causation
from those higher order properties on the micro-level components.
Second Order Emergence
Self-organising emergent structure and phenomena. Micro-level
configurational particularities become amplified to determine macroconfigurational particularities which in turn further constrain or
amplify micro-level patterns and configurations. Specific recursive
and recurrent architectures paramount.
Third Order Emergence
Emergent phenomena and systems characterised by ‘memory’,
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where an amplification of higher-order influences on parts is
combined with a selective sampling of these influences which
reintroduces the parts into different realisations of the system over
time, imparting both continuity with and divergence from prior
states of the system.
Adapted from Martin & Sunley (forthcoming), pg. 6
The characteristics of first order emergence are discussed prior to the table in relation to the ‘whole is
more than a sum of its parts’ and that the macro level cannot be traced back to the individual
components that constructed it. Second order emergence highlights the self-organising feature of
complex adaptive systems where properties or phenomena are produced spontaneously without the
intervention of external modifications or interventions to a system, a feature that is often termed ‘selforganisation’. Lastly, third order emergence is characterised by varying the agents where history or the
memory of previous economic successes is accounted for to introduce combinations of agents. If
considering this last stage in terms of path dependence, this would be when the path evolves through
branching with the old paths leading to the new paths. Based on this, third order emergence is most
directly related to evolution and resilience.
One aspect of complexity theory, which is central in Martin’s article on the impact of recessionary
shocks, is hysteresis (2011). Hysteresis crops up specifically in the third order emergence
characteristics in table 1 in relation to ‘memory’ and in Martin’s account of ‘adaptive resilience’
(2011). As an economic term, hysteresis, in relation to resilience, has been defined as ‘where the
effect of ‘memory’ of the disturbance is left behind in the economy even after the disturbance or
shock has passed, also known as ‘remanence’ (Martin, 2010, pg. 11). Setterfield argues that
hysteresis is a form of path dependence where an exogenous shock can have permanent effects on
the economy; however, instead of all of the past events impacting the future paths, hysteresis
utilises ‘selective memory’ which focuses on recent extreme events to determine path dependence
(2010). In addition, this concept is used in the panarchy framework with the adaptive cycle model.
In this capacity, hysteresis is referred to as remembrance and allows previous experiences in
resilience to impact future resilience efforts. Further examination of recessionary shocks and
hysteresis will be conducted next in the section on panarchy.
Panarchy
Although Boschma & Martin clearly list three approaches to EEG illustrated in Figure 1, Simmie &
Martin highlight a fourth approach, panarchy (2010, 2010). According to Swanstrom, ‘the ecological
model of resilience reconciles the contradiction between specialisation & diversification through the
idea of panarchy that captures the ‘evolutionary nature of adaptive cycles that are nested one within
the other across space and time’ (2008, pg. 7). Three questions arise from this: (1) What is
panarchy?, (2) Does panarchy fit in with EEG given its ecological resilience origin?, and, discussed
later, (3) How can panarchy be applied to resilience?
Panarchy is a conceptual framework based in ecological resilience theory that accounts for the dual
characteristics of all complex systems – stability and change (Gunderson & Holling, 2002). It shows
how economic growth and human development depend on ecosystems and institutions, and how
they interact. It arose from observation of failed attempts to manage regional ecosystems that
often led to their degradation. Regional management efforts were generally linear in nature. The
focus on managing a single variable, usually the economic one, was often de-stabilising to the
ecosystem as a whole. Panarchy is especially good at addressing destabilisation over time or slow
burn shocks as changes triggered by attempting to sustain a particular variable often occurred slowly
(over decades or more). They often went unnoticed until they in turn triggered an abrupt change.
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For example, the forest became infested, the river became polluted, or the fish stock collapsed
(Gunderson & Holling, 2002). Hence, change and stability were seen to be multi-scalar.
Interestingly, particularly as Simmie & Martin discounted the ecological resilience theory due to its
inherent engineering resilience similarities, the adaptive cycle model based on ecological resilience is
the foundation for panarchy, an evolutionary concept (2010). Based on this conflict, how can
panarchy be considered a resilience theory within the EEG context if it’s rooted in ecological theory
that has already been categorised as non-evolutionary based on its equilibrium emphasis?
Figure 2: Ecological Studies of Resilience (Holling et al as cited in Pendall et al, 2008).
Panarchy identifies four basic ecosystem stages as represented in Figure 2. These are: exploitation,
conservation, release and reorganization (Holling, 1973).
• The exploitation stage is one of rapid expansion, as when a population finds a fertile
(strategic) niche in which to grow.
•
The conservation stage is one in which slow accumulation and storage of energy and
material is emphasized as when a ’dominant design’ reaches prominence.
•
The release occurs rapidly, as when a population (innovation) declines due to a competitor,
changed conditions or ‘creative destruction’.
•
Reorganization can also occur rapidly, as when certain members of the population are
selected for their ability to survive (‘creative destruction’) despite the competitor or changed
conditions that triggered the release. (e.g. Recombinations, new combinations, innovations)
The adaptive cycle is the process that accounts for both the stability and change in complex systems.
It periodically generates variability and novelty, either internally such as through genetic mutations
or adaptation, or by accumulating resources that change the internal dynamics of an ecosystem.
These changes are the triggers for experimentation. In the reorganization stage various experiments
are tested and resources are reorganized in new configurations, some of which enter a new
exploitation stage to repeat the cycle. Panarchy emphasises the interconnectedness of levels,
between the smallest and the largest, and the fastest and slowest. This phenomenon is illustrated in
Figure 3. The large, slow cycles set the conditions for the smaller, faster cycles to operate. But the
small, fast cycles can also have an impact on the larger, slower cycles. There are many possible
points of interconnectedness between adjacent levels; however, two specific points are of particular
interest with respect to sustainability:
“Revolt" – this occurs when fast, small events overwhelm large, slow ones, as when a small fire in a
forest spreads to the crowns of trees, then to another patch, and eventually the entire forest
(e.g. Sub-prime started in California)
11
“Remember" – this occurs when the potential accumulated and stored in the larger, slow levels
influences the reorganization. For example, after a forest fire the processes and resources
accumulated at a larger level slow the leakage of nutrients, and options for renewal draw from the
seed bank, physical structures and surrounding species that form a biotic legacy.
Figure 3: Revolt & Rememberance in the Panarchy Conceptual Framework
While complexity theory is conflicted in its use of connectedness, panarchy extends the limits of
complex adaptive systems and focuses on the relationship between connectedness and adaptation
through the aforementioned phases with each phase considering the potential of the region (skills,
competences, etc.), the connectedness of agents (firms) and the regional resilience through
innovation (of the regional innovation system) (Simmie & Martin, 2010). Potential sets the limits to
what is possible - the number and kinds of future options available (e.g. high variety provides more
future options than low variety). Connectedness determines the degree to which a system can
control its own destiny through internal controls, as distinct from being influenced by external
variables (e.g. A region with high legislative and taxation control is an example of high
connectedness). Resilience determines how vulnerable a system is to unexpected disturbances and
surprises that can exceed or break that control (e.g. Iceland or Ireland lose some ‘sovereignty’ due to
financial crisis and IMF rules). In addition, a characteristic of a resilient region, resistance is also
taken in account. Resistance is the capacity of a system to tolerate disturbances without collapsing
into a qualitatively different state. The greater the resilience is in a particular system the more it can
resist large or prolonged disturbances. If resilience is low or weakened, then smaller or briefer
disturbances can push the system into a different state, where its dynamics change. After a
disturbance, systems evolve through time as ecological niches fill in (increasing connectedness;
robust institutions), biomass accumulates (increasing potential; relatedness & variety) and more
successful species outcompete less successful species (decreasing resilience; bankruptcies/closures
versus new industries/clusters). A potential negative scenario could be: once resilience is
overwhelmed and an ecosystem enters a new state, restoration can be complex, expensive, and
sometimes even impossible. Research suggests that to restore some systems to their previous state
requires a return to environmental conditions well before the collapse. Resilience can be degraded
by a large variety of factors which largely depend on underlying, slowly changing variables such as
climate, land use, nutrient stocks, human values (e.g. Greed, herd instincts, free market ideology)
and policies (deregulation; low interest rates). When resilience is weakened it is sometimes possible
to restore it; however, variety is a key issue in restoring resilience (Holling, 1973).
As discussed in the previous section, Martin’s application of hysteresis to recessionary shocks
focuses on an economic system’s memory as the focal point for adaptation. This ‘memory’ aspect,
12
which has EEG traits, has been identified in hysteresis but has also appeared in complexity theory as
well as panarchy. Martin (2011) explores how the notion of resilience (as used in ecological
contexts) can be combined with that of ‘hysteresis’ (as used in economics), to understand how
regional economies react to the ‘system-wide’ shocks provided by major recessions. This, he argues,
is critical to understanding long-run regional growth patterns and thus the existence, persistence
and evolution of long-run regional disparities in economic prosperity. Hysteresis is here defined as a
situation where (following Romer, 2001; cited in Martin, 2011) one-time disturbances permanently
affect the path of the economy. Martin (2011) posits that several different possible ‘hysteretic’
outcomes of a recessionary shock on a region’s growth path may be identified, some negative and
some positive, with outcomes dependent upon variables such as, inter alia, the degree of
destruction of a region’s industrial base, the existence of spare capacity to expand output and jobs,
the degree of optimism in business expectations, the scope for new firm formation, and the nature
of economic and political reforms set in motion. While there is a more complete literature
specifically relating to hysteresis and economic shocks, figure 4 illustrates Martin’s findings of how
regional economies respond to recessionary shocks.
Figure 4:
The first is the notion of resistance or the differential ability of places to resist disruptive changes
such as recession conditions (Dawley et al, 2010). Attempts to understand differences between
regions in their resistance to such change have mostly focused upon the inter- and intra-sectoral
composition of regional economies (e.g. Industrial Communities Alliance, 2009). The second and
interrelated strand of resilience thinking understands it in broad terms as something which equates
to recovery, or in more populist terms, ‘bounce back-ability’ or ‘the boing factor’ (CLES, 2010). This is
summed up by the definition developed by the Centre for Local Economic Strategies (2010; p. 6):
‘resilience is about understanding the ability of a place to respond to the challenges that it faces;
what enables some areas to respond effectively from shocks, whether they be economic, social,
political or environmental, whilst other areas falter and decline’. Similarly, Hill et al (2008) argue that
in terms of economic development, a resilience perspective helps shift the focus to the long-term
structure of macroeconomic relationships and the relevant social, economic and political institutions
conditioning these structures. Economic resilience is thus defined as ‘the ability of a region [...] to
recover successfully from shocks to its economy that either throw it off its growth path of have the
potential to do so’ (Hill et al, 2008; p. 2). Martin (2011) suggests that in addition to resistance and
13
recovery, two other interrelated dimensions are relevant to understanding how regional economies
respond to recessionary or other shocks: the extent to which the region undergoes structural reorientation; and the degree of renewal or resumption of the pre-shock growth path or trajectory.
Indeed, whilst to date much work on resilience has focused on the ability of systems to absorb
shocks and return to a fully functioning state in the presence of adversity, the term has also been
used more broadly in reference to the governance of risk and the ability of systems to renew,
reorganise and develop in response to, or anticipation of, transformative change (Folke, 2006;
Bristow, 2010). Such a perspective positions resilience as central to ‘transitioning’ ecological, social
and economic systems towards sustainability (Folke et al., 2003; Duit et al, 2010). As such, it regards
resilience as a constant, multi-equilibrium process (rather than an objective steady state), in which
further policy adjustments are made as ecological/environmental, social and economic conditions
change - changes that in part are the outcome of previous interventions (see Walker and Shove,
2007). Thus instead of embracing stasis, resilience embraces the notions of change, flexibility and
rapid unpredictability (CLES, 2010). It rejects the notion that systems change in a linear way. Instead
it sees that elements in a system are in constant flux, are unpredictable and highly complex (Adger,
2000).
In examining Martin’s four phase approach in contrast to the ecological –based, adaptive cycle
model, there are undeniable similarities identified in table 2.
Table 2: Resilience: From Ecological to Economic Systems
Ecological
Economic
• Conservation – (i.e. slow accumulation,
• Resistance – (robustness or sensitivity
storage of energy and ’dominant
to system shocks; ‘connectedness’;
design’ reaches prominence
institutional rigour
• Release – (when a system declines due
• Re-orientation – (extent regional
to a competitor, changed conditions, or
economy undergoes structural
‘crisis’
adjustment; implications for regional
• Re-organisation – (actors selected for
output, jobs and incomes)
their ability to survive; recombinations,
• Recovery – (determined by the degree
innovations)
of resistance to the shock;
• Exploitation – (rapid expansion, as
‘connectedness’; innovation)
when a population finds a fertile niche
• Renewal – (resumption of the growth
in which to grow
path that characterised the regional
• ( after Folke, 2006 Glob. Env. Cha.)
economy prior to the shock)
• (after Martin, 2010)
In relying on the adaptive cycles model panarchy provides a conceptual framework that can produce
hypotheses to be tested but is not (and should not) be considered a theory itself (Simmie & Martin).
Regional Economic Resilience & Evolutionary Economic Geography: Adaptation
Regional economic resilience and evolutionary economic geography have been discussed
predominantly focusing on the recovery phase of resilient regions. This is the phase where the most
amount of change can occur in a region based on adaptation to the exogenous shock. Themes
relating to these issues will be explored in this section, namely: related variety, recombinant growth
and transversality.
Related Variety
As mentioned earlier (see figure 1), many of the components of EEG can overlap, one specific
example of this is with related variety. Related variety has been mentioned in relation to
Generalised Darwinism, Path Dependence and Complex Adaptive Systems. Simply, related variety
14
can be considered ‘industrial sectors that are related in terms of shared or complementary
competences (cognitive-based definition)’ ( Boschma & Iammarino, 2007, pg. 4). It allows for the
regional spillovers of knowledge and capability of economic actors and opens up possibilities for
novelty and innovation in response to change (Frenken and Boschma, 2007). This section will focus
on related variety and resilience in its applications to complexity theory, industrial diversification
and branching.
Cooke discussed related variety as an adaptation for a complex adaptive system exposed to an external
shock (forthcoming). It is held that innovation occurs mainly horizontally as regional path dependence
leads to path inter-dependence. This is either because of an external de-stabilisation (exogenous shock
or ‘resilience effect’) or because of an endogenous system topology that allows for path interdependence of the kind discussed by Martin & Sunley (2010). What occurs from such co-evolutionary
fusion is termed ‘revealed related variety’ in that it could never have been predicted yet results in
innovation or novelty of some kind. It is crucial to underline this non-physics-like process that is nonreductionist and non-predictable because human systems are, concerned with life and evolutionary
biology teaches that life, its mutations and speciations cannot be predicted ex ante, only understood ex
post. Indeed, this is gradually becoming understood in evolutionary economic geography as a key
characteristic of ‘emergence’ (see Martin & Sunley, 2011). In Fig. 5 a scheme is elaborated of the
complex ‘emergence’ of innovation through ‘preadaptation’ and/or the ‘adjacent possible’ in relation to
‘attractors’ and especially ‘strange attractors’ after Kauffman (2008).
Complex Adaptive Systems – Emergence
through Strange Attractors to Novelty
NOVELTY
ADAPTATION BY
IMITATION
CLUSTER
INTERACTION
COGNITIVE
REVERSAL
’EDGE OF
CHAOS’
PREADAPTATION
ADJACENT POSSIBLE
STRANGE ATTRACTORS
EMERGENCE
Fig. 5 The Nature of Emergence of Innovation: a Complexity Perspective
Adapted from Cooke, forthcoming
What occurs in Fig. 5 is that Martin & Sunley’s path inter-dependence evolves on the plane of a complex
adaptive system (2011). In analogue form, this is a regional economy that is invested with a topology.
The topological routeways (path dependencies) favour certain deviations and disfavour others. At a
given point they meet, as the meeting of socio-technical systems (STS) from another evolutionary
viewpoint, that of the co- evolutionary multi-level perspective (MLP). It occurs not only when they are
related or natural attractors but particularly when they are ‘strange attractors’. Strange attractors
display ‘revealed relatedness’ rather than obvious relatedness. While both types can facilitate
innovation, the innovation caused by ‘strange attractors’ has the possibility to be of the most radical
kind. This is because an adjacent possible that is utterly unknown is being explored. Contrariwise in Fig.5
the ‘preadaptation’ route is either moderately surprising because it involves a ‘cognitive reversal’ of an
existing innovation. Alternatively ‘preadaptation’ is incremental innovation and quite close to
15
‘imitation’ because it takes an innovation from one field and applies it to another. Innovation agencies
sometimes facilitate this by mounting innovation ‘fashion shows’ where a ‘smart textile’ in automotive
seats can be a solution to the quest for stay-clean medical uniforms in hospitals. The harder, more
rewarding innovation route comes where strange attractors merge at what complexity theorists call
‘the edge of chaos’ which is both stable and unstable with much interaction, communication and ‘buzz’
going on between, for example, clusters or, more precisely, innovation-spotting members of two or
more clusters (ex. Bayern Innovativ). A breakthrough here among say mobile telephony, internet media
and life sciences may lead to many big leaps forward in mobile diagnostics and even therapeutic
treatment delivered by ‘smartphone’.
Alternatively, Boschma & Frenken explore technological relatedness through less radical means with
regional branching. The new growth theory states that spillovers can occur between firms in a sector as
well as at the sector-to-sector level. Based on this, the sectoral variety in a region can be the source of
economic growth (Frenken, et al2010). Examining the theory further, Boschma & Frenken’s ask the
question: do firms that are not geographically proximate learn from other firms like them ie in the same
sector or from firms in other sectors? (forthcoming). There are two key themes to address in answering
this question: proximity and sectors. Returning to Jacob’s externalities which provides insights on
industry diversity but is not explained well in terms of knowledge spillover effects, the idea that
spillovers can occur simply because the sector are close to one another seems impractical (Boschma &
Freken, forthcoming). While Nooteboom makes it clear that proximity (to a certain extent) is crucial for
cognitive spillover, the issue becomes less about proximity as it is needed and more about the sectors
themselves (2000). For sectoral knowledge spillover and relatedness, the sectors in the region need a
common interest: technology. Sectors that are related through technology will be more likely to
partake in knowledge spillover practices making the technology the catalyst for regional branching. This
was demonstrated in a study conducted by Neffke et al, 2009 on technological relatedness in Swedish
regions where it was found that the more technologically related industries there were in a region, the
more likely it was for related sectors to remain in the region due to the potential benefit through
related variety.
Understanding the benefits of technological relatedness, what exactly is it? Technological
relatedness, connecting otherwise unconnected industries or sectors through technological overlap,
can be the foundation for a new path, or regional branching. If this was successful it would satisfy
the components of evolutionary economic geography using the historical relevance, particularly in
terms of Arthur’s concept of technology. As a result of this situation, where the new industries are
roughly based on combinations and innovations of the previous industries and regional branching is
occurring, what would happen to the structure that supported the original industries in the region?
Simply, these firms and networks would need to evolve as well to stay relevant in the changing
regional dynamic. Ideally, this evolution would occur and the region would possess several related
industries which would increase the chance of growth in the long-term due to the number of
potential technology-based collaborations.
This regional branching, where new industries arise from technologically related industries, is
facilitated by knowledge transfer mechanisms in the form of: firm diversification, entrepreneurship
in the form of spinoffs, labour mobility and social networking (Boschma et al, 2010). The firms
located in the region need to adapt to survive through diversification (Boschma & Frenken, 2010).
This intra-firm diversification can be difficult due to the level of risk involved and the capitalisation of
existing firm strengths; therefore, the diversification will be more closely related to their current
work or what is profitable in the regional market (Nelson & Winter, 1982). In terms of regional
spinoff activity, entrepreneurs support regional branching through establishing firms in the new
industry. The expertise of the entrepreneur may be based on the previous industry but the
technological relatedness of the industries allows for a seamless transition for high-skilled actors.
16
The growth of these spinoffs is important to the diversification effort as they provide more actors for
established firms to collaborate with as well as inherent skills acquired through the old industry that
can be adapted to the new industry. In transitioning to labour mobility, it enhances regional
branching through allowing the best candidates to be in the region. This can apply to intra-regional
and inter-regional labour mobility as the branching will allow a change in output which may broaden
the labour market allowing for applicants to flow into the region and those not interested in the
branching to flow away from the region. Finally, the social networking of the agents can support
regional branching in realigning the views of the individual workers as well as serving as a tool for
knowledge diffusion. However, if the region is highly connected, there is the potential for an
opposition to branching based on a more path dependent focus of the connected agents.
Recombinant Growth
This concept, based on Schumpeterian theory, focuses on an innovation that is based on a
combination of prior products or processes which has been aptly described in a Financial Times
article:
Lessons learned over the past century of industrial growth demonstrate that most
innovations—rather than being radically new ideas or processes—are actually
recombinations of existing ideas. Thus, Henry Ford’s revolutionary production methods
incorporated the idea of interchangeable parts (from the sewing machine industry),
continuous flow production (from the soup canning business) and the assembly line (from
slaughterhouses). More modern examples include the Reebok Pump basketball shoe, which
borrowed from medical technology to create the world’s first inflatable athletic shoe.
(Source: Kodanoff, 2003)
According to Schumpeter, the notion of innovation is intimately bound up with new combinations of
knowledge, including re-combinations of old knowledge as well as of combinations of new and old
and even, conceivably, new and new knowledge (Frenken & Boschma ____). Schumpeter goes on to
list five different kinds of recombination: (1) a new good, (2) a new method of production, (3) a new
market, (4) a new source of supply for intermediate goods, and (5) a new organisation (as cite in
Olsson & Frey, 2002). Weitzman describes recombination as hybridization with emphasis on biology
while using Edison’s invention of the lightbulb as an example; the lightbulb being a combination of
candle and electricity (1998). However, where Schumpeter started in conceptualising
recombination, Weitzman finishes by placing this concept into a growth framework (1998). As a
result, ‘recombinant growth’ using recombination to enhance economic growth in an area, was
derived. Van den Bergh, similar to Weitzman, studied the biological origins of recombination and
explained how this line of thinking where two ‘parents’ create a new innovation, as depicted with
the lightbulb example, is true but does not take into account the full capacity of recombination
(2008). Unlike biology, innovations can be based on multiple ‘parent’ technologies. An example of
this would be smartphones that are a combination of sms, email, Internet, music and camera (van
den Bergh, 2008). Furthermore, the spatial side of recombinant growth should be based in regions
with diverse industries as there is more potential for creative recombinations to develop (van den
Bergh, 2008).
Beyond the platform needed for recombinant growth to occur, Schumpeter identified those within
the system most likely to carry out recombination as a business strategy: entrepreneurs. Taking the
profile of an entrepreneur, as well as the economic growth aspect, Olsson & Frey studied the
constraints of recombinant growth and technological progress (2002). There were four main findings
from this research. First, innovation through recombination will not be successful if the
technological ideas that are combining are too similar. Second, innovation through recombination
17
of technological ideas where the knowledge is far apart may be successful but more costly. Third,
based on the latter two findings, a rational entrepreneur will combine ideas that are closer together
due to economic reasoning. Fourth, technological paradigms are themselves constraints to
recombinant growth but paradigm shifts are more likely when dealing with small technology
innovations. While these findings, and this section as a whole, can help in explaining innovation
through evolution where old ideas become new innovations, it provides little insight into the
geography of the evolution outside of a preference for diversified regions where there is a higher
change of mutations occurring.
Furthermore in terms of resilience, recombinant growth strategy can be applied to firms and
industries as a form of adaptation in responding to a shock. Van den Bergh points out that
recombination as a business strategy or a policy is much more likely to be enforced and funded than
a radical innovation due to the risk associated with the latter as well as the familiarity of the ‘parent’
concepts in the former (2008). The example he goes on to provide highlights clean technology
innovations in biofuels and renewable energy which in the short-term is more risky with both
economic and environmental benefits in the long-term. This is contrasted with solutions that are
reduce non-renewable energy usage but still rely on the fossil fuel economy bringing economic and
environmental problems in the long-term. The latter case demonstrates the position of
recombination during a period of macro-economic recession while the former case demonstrates
the position of recombination during a period of growth when risk is rewarded. Both scenarios rely
on recombination and the contrast demonstrates that it can be applied in either situation which
appeases corporations, government and researchers.
Transversality
Transversality is the active quest to stimulate regional (and other) relatedness to facilitate new
knowledge combinations and resulting innovations. There are three potential paths for regional
transversality. First, the region has evolved in inter-related path dependent ways, industrially and
institutionally. Innovation in the broad sense (e.g. cluster emergence) evolves through innovation in
the narrow sense (e.g. commercialisation of new knowledge) conducted by innovators and imitated
by entrepreneurs as new products or processes. Second, the region’s industry evolves path
dependent characteristics, with an established inter-industry division of labour. Innovation in the
broad sense (e.g. capability emergence) evolves through transversal (i.e. inter-industry) mutations
from which innovation in the narrow sense emanates. Third, the region and its industry are
beneficiaries of innovation intermediaries charged with inducing innovation either by stimulating
cluster emergence (difficult) or transversality (less difficult) among existing (or inwardly investing)
firms that may achieve innovation through induced knowledge recombination.
All three cases are assisted, but are not equally dependent upon the relatedness of the industry as a
means of escaping the negative aspects of ‘lock-in’ frequently associated with path dependence.
Relatedness assists the first category to the extent market processes are supported by institutional
means (e.g. strong trust, social capital, etc.) such that an industry may mutate and innovate mainly
through its own internal dynamics. But it will also absorb neighbourhood knowledge spillovers, as
appropriate, from related regional technology fields (Boschma & Lambooy, 1999). In the second
case, transversality is stronger inasmuch as the innovation impulse, still largely rising from market
interactions, here demands solutions that draw upon inter-industry knowledge spillovers. This
further implies higher lateral inter-industry (platform innovation) absorptive capacity for knowledge
recombination than vertical, cumulative (path dependent) intra-industry or firm knowledge
recombination. Relatedness in the sense captured sociologically by notions of ‘epistemic
communities’ or technologically by ‘communities of practice’ comes into play here, adding
institutional embeddedness levels to pure industrial and technological relatedness (e.g. the common
engineering knowledge discussed by Boschma & Lambooy, 1999 as underpinning diverse industrial
18
districts and their lateral knowledge flows in Emilia-Romagna). Finally, the third category of
transversality involves the highest intensity of both revealed and ‘induced’ relatedness in the
regional economy and its multi-level governance. Clearly, it is not impossible for the relatively lowkey relatedness of the first two categories to produce highly unpredictable forms of inter-industry
knowledge flows. But for the purposes of regional innovation and branching from path dependence
such ‘revealed related variety’ probably does not predominate.
This shows remarkable variety in the inter-industry interactions occurring typically with respect
to knowledge recombination for innovation. These are ex ante difficult if not impossible to predict
but ex post simple to understand. Thus ten years ago it would be considered unlikely from an
innovation perspective that farmers and car makers’ associations would have much to talk about.
But the rise of renewable fuels in automotives of many kinds means research interactions among
them are pronounced nowadays. Moreover, adding a second dimension to relatedness,
transversality is increasingly practised by regional innovation agencies which, in distinctive ways,
occasionally also focusing on ‘green innovation’ and associated transition strategy, induce
knowledge cross-pollination among a variety of regional industries or sectors. In some cases, this is
beginning to extend to inter-regional, inter-cluster crossfertilization efforts.
Specialisation & Diversification
The question of specialisation and diversification runs deep in economic geography and regional
science. This is because of two features, each being dimensions of proximity: first, it relates directly
to the distinction between localisation economies and urbanisation economies (e.g. Jacobs, 1969);
and second, it connects to questions regarding externalities of various kinds ranging from pecuniary
to technological that have informed analysis of industrial districts (Porter & Ketels, 2009; Becattini et
al, 2009) that are claimed as forerunners of clusters. The concept of localisation economies
originates with Marshall (1916) as elaborated by Hoover (1948). They identify a context in which
similar firms in a specific industry agglomerate in a single location. In Marshall’s case, such proximity
was the keystone to his explanation of the emergence in industrial Britain of industrial districts,
which were congregations of single industry, even single phase industry firms, in specific towns or
geographical spaces. It is well-known that agglomerative behaviour was, for Marshall, conditioned
by three factors; labour skills, technology and information – the last–named famously being
conceived of as ‘in the air’. This was a fore-runner concept nowadays re-conceived as ‘knowledge
spillovers’ which are far richer than in Marshall’s day when there were no venture capitalists,
electronic networks or knowledge transfer agencies to intermediate communication of economically
valuable knowledge. This, if anything is the core modern definition of a functioning, specialised
cluster, namely similar or complementary firms interacting upon informal knowledge spillovers and
formal knowledge and transactional exchange in geographical proximity. Thus it is the
interactiveness of members, whose numbers can be relatively small, rather than their numerical
‘critical mass’ that defines a cluster. Mainstream economic analysis of spatial concentrations is blind
to the key defining attribute of geographical proximity, which involves its relational or socially
interactive dimension.
For Hoover, scale is of central interest and the processes driving localisation economies include
demand, ease of transactions for common inputs and, once again, the emergence of specialised
labour pools for whom inter-industry transfer barriers are low. By now the spotlight had switched
dramatically from entrepreneurial to multinational firms that could increasingly be seen locating
similar production phases, including the routine assembly functions of different industries, in specific
types of, especially, low labour cost regions. On the other hand, Urbanisation economies do not, in
principle, share in the race to the bottom of the wage cost ‘pyramid’ (Prahalad, 2005) that
localisation economies seem historically to have done. Localisation thinking lies at the heart of the
19
international division of labour and the global value network control that Porter in his quest for
‘shared value’ so abhors. While urbanisation economies is a concept from the same Marshall,
Hoover stable it was Jane Jacobs who fully understood but less fully explored the innovative
operations of urbanisation upon the economy. For whereas localisation reifies a homogeneous
production space of absolute minimum living wage costs, urbanisation is a vehicle for heterogeneity,
interaction and innovation at interfaces. Accordingly, urbanisation economies are shaped by wholly
different mechanisms from localisation economies. This is despite the fact that, superficially, they
share an outcome that is spatial agglomeration. But urbanisation economies foment innovations due
to their Jacobian mutation effects from the interaction of difference and ‘otherness’ normally found
in urban or metropolitan contexts. While formal and informal knowledge spillovers are accidental
and peripheral to modern localisation economies, dominated as most are by multinational corporate
cultural ‘Babels’, knowledge spillovers and the creativity that comes from conjoining related but
distinctive ideas and knowledges is central to urbanisation economies.
Hence, we conclude this discussion of the history, lineages and trajectories of localisation and
urbanisation economies in the century since their first identification and find a curious inversion in
their contemporary condition. The localisation economies that were first identified as characterising
the homogeneous nature of small, entrepreneurial firms existing in industrial districts are to be
found powerfully propelling the ‘global factory’ context of the emerging economies. However, this
new homogeneity is that of a commonality at the bottom of the pyramid, of low-wage employment
in routine assembly work in a mix of industries with low inter-communication, low embeddedness,
and many different countries of origin. Although their lineaments began in Marshallian industrial
districts, clusters these are not; rather they are alienating agglomerations of cheap labour and tax
credits for multinationals in global value networks. Clusters and their successor industry forms, such
as the overlapping and inter-linked innovation platforms based on cross-pollination of knowledge
accompanied by entrepreneurial mutation and migration practices among distinctive but related
elements are found in Cambridge more than London, Silicon Valley more than San Francisco, and
various locations such as Cambridge in relation to Boston in Massachusetts. These best capture the
urbanisation economies once theorised as belonging principally to metropolitan contexts like New
York, London and Paris. The latter group of cities retain their specialised financial services ‘boiler
rooms’ as a modern legacy of their historic localisation specificities.
With the problems associated with specialisation in its effective sponsoring of a regional ‘race to the
bottom’, are there any positive attributes that can be identified? Foray notes a burgeoning wave of
specialisation that is largely growing through EU policy called ‘smart specialisation’. The difference
between the aforementioned specialisation and this form is that the latter focuses on ‘the
entrepreneurial process of discovery, identifying where a region can benefit from specialising in a
particular area of science and technology’ (Eurada, pg.1). The obvious advantage of this type of
specialisation is that it will lead to innovation, knowledge transfer and growth as opposed to the
unconnected, minimum wage, ‘global factory’ context mentioned above. Smart specialisation is
characterised by: focusing growth efforts on a few areas rather than spreading resources over many
areas which refers to both knowledge and geographic proximity, regional embeddedness in the
knowledge economy and policy strategies that can be developed at the regional level through
existing outlets such as regional development agencies. While there is substantial critique of the
concept (please see the policy section of this report), there are also successful examples of smart
specialisation in policy namely the Welsh Government’s Economic Renewal Strategy (July 2010) and
the Scottish Enterprise plan.
If specialisation is based on the Marshallian concept of localisation then diversification would be
based on the Jacobian concept of externalities which focuses on cities with a diversified
set of industries which are characterized by high economic growth, because local diversity
20
sparks creativity, new ideas and innovations. Regional diversification is based on the existing
structure within the region. This could apply to several regional variables such as: labour force skills,
knowledge spillover potential, entrepreneurial activity, political involvement, or industry proximity.
These variables, particularly the skills and competences of the labour force, can support the
diversification of the region. If the region’s existing industries are technologically related to the
potential industries, the latter are more likely to come to the region. The skilled labour force can
easily transition between the industries1. In addition, the proximity of the industries within the
region supports knowledge spillover; however, the technological relatedness of these industries,
regardless of their proximity, allows for enhanced knowledge spillover. Further identifying the ‘set of
industries’ originally mentioned by Jacobs while highlighting the knowledge spillover aspect,
Boschma notes that regional diversification is more likely to occur when knowledge spills over
between sectors, rather than within one sector, but only as long as the sectors are related (2009).
Therefore, related variety is a key source of regional economic diversification as the more related
industries there are in a region the more diversified it can be (Boschma, 2009). Due to the systemic
nature of innovation processes, diversified regions also require a critical mass of organizations that
meet the following conditions: (1) they have to be well connected, enabling flows of knowledge,
capital and labor; (2) these ties should not be, however, too strong, and not too focused on the
region, avoiding problems of lock-in; and (3) local organizations and institutions must be flexible and
responsive to new circumstances, overcoming inertial tendencies due to habits, routines and path
dependency. Furthermore, in considering the long-term growth of regions, through Schumpeter’s
creative destruction, the mature industries will be replaced by new industries which, in regards of
technological development, may point to using old concepts to innovate new technologies
(Schumpeter, Arthur).
Knowing what is needed in a region for diversification to occur, the question then becomes: what
happens to the ‘mature’ industries in the region after the new industry is introduced but before they
are replaced? One possibility is ‘regional branching’ which is when the industries, both mature and
new, work together to innovate another, technologically-based industry (Boschma & Frenken, 2009).
Branching was discussed in-depth in the path dependence theory section above. Through the
utilisation of knowledge transfer methods, the new industries can connect with existing industries to
support regional growth. The ‘mechanisms’ to achieve this growth that Boschma & Frenken list are
similar to the regional variables discussed at the beginning of this section: firm diversification,
spinoff activity, labour mobility and social networking (2009).
This section has thus far provided a better understanding of specialisation and diversification but
how do these concepts affect the resilience of regions? Resilience is often defined as the region’s
ability to experience positive economic success which is socially inclusive, works within
environmental limits and which can ride global economic punches (Ashby et al, 2009). As such,
resilience clearly resonates with literatures on sustainability, localization and diversification, and the
developing understanding of regions as intrinsically diverse entities with evolutionary and context
specific development trajectories (Hayter, 2004). Diversification of regions is important for regional
resilience as these regions are more resistant to shocks due to their reliance on more than one
industry, it can be used as a method of adaptation (related variety, branching) and it can be used as
a transition from specialised industries experiencing a shock (Hassink, 2010, Swanstrom, 2008,
Frenken et al, 2007). This answer also demonstrates the diversified regions ability to evolve through
its enhanced adaptation traits. In addition, ‘in an uncertain world a diverse system is more likely to
respond appropriately to external changes (shocks) than a homogenous system’ (Carroll & Hannan
2000 as cited in van den Bergh, 2008, pg. 568). Furthermore, a more diverse economic structure
1
A skilled workforce is also supposed to support recovery from an exogenous shock in a region, ie. those
regions with a higher skilled workforce will recover faster than if dealing with a low-skilled workforce (Hill et al,
2008).
21
provides greater regional resistance to shocks than does a more specialised structure since risk is
effectively spread across a region’s business portfolio, although a high degree of sectoral
interrelatedness may limit this (see Conrory, 1975; and Dissart, 2003; cited in Martin, 2011). While
this explanation of diversification makes specialisation appear problematic, there are positives
associated with this latter regional activity, in dealing with regional resilience, such as: having a
concentrated labour market in a specific region that, if using smart specialisation policy, can be
trained to specialise the region in a knowledge economy industry and through smart specialisation
focus on cluster creation and intra-regional knowledge transfer. Related variety allows for the
regional spillovers of knowledge and capability of economic actors and opens up possibilities for
novelty and innovation in response to change (Frenken and Boschma, 2007). Simmie and Martin
(2010) have sought to develop the notion of ‘adaptive cycles’ as a heuristic framework within which
to explore the resilience of key sectors within regions. This implies that regions progress through
different phases of adaptation and resilience depending on, inter alia, the patterns of connectedness
and dependency between firms. This helps explain why at some times specialisation is beneficial and
at other times it is constraining. In the same way that the diversified region can evolve, the
specialised region, particularly in the case of smart specialisation can evolve as part of the shock
recovery. This is commonplace in old industrial districts that go through a period of decline while
being affected by one or many shocks and take some of the supply chain or basic knowledge
available to them and develop the region under a new knowledge economy industry. An example of
specialisation-based adaptation can be found in Massachusetts with the transition of the declining
textile industry in the region to high-technology complex including MIT and Harvard (Harrison 1982,
as cited in Dawley et al, 2010). This transition is partly attributed to regional policy which will be
discussed further in the next section.
Understanding the characteristics of diversification and specialisation in light of resilience
approaches, is it possible for a highly specialised industry to respond to a shock through
diversification, using the existing resources available and retaining some characteristics of the
specialised industry to evolve? According to Treado and Giarratani, this transition has been
occurring, albeit incrementally, in industrial regions throughout the US for decades (2008). One
example is the city-region of Pittsburgh. When the regional steel industry was in decline in the
1970’s due to foreign competition, the region suffered with high unemployment and low absorptive
capacity. The intermediary supplier firms that worked with the industries clustered in an effort to
survive and as a result, have not only flourished, but have also contributed to the economic
resilience of the region through providing employment opportunities and serving as the platform for
related technologies to emerge. Although this example is addressing a different type of industry
(traditional) and a different type of shock (loss of main industry through competition) its relevance is
based on its successful transition to a technology-related cluster that has improved employment and
resistance to future shocks in the region. Furthermore, by discussing the fate of intermediary firms
during a regional transition of this kind, the Pittsburgh example highlights what happens at the
micro-level when macro-level events force adaptation.
The path dependence that Pittsburgh was originally a part of was heavily locked-in to the steel
industry. While lock-in has been discussed earlier in this report, the options of the industries using
the locked-in path have not been explored. According to Treado, ‘regional resilience can be
demonstrated by the ability of a region to rescue several key elements of its traditional industrial
base in order to become a global player in a technology-based industry’, which precisely describes
the evolution of the steel industry to the technology-based cluster, comprised of the former
intermediary firms, in this example (pg. 106). This type of adaptation is not to be confused with
recombination; rather, this finding extends the applications of path dependence in the evolutionary
perspective as it was only mentioned previously in relation to branching and other forms of
adaptation. While the initial change from traditional industry firms forming a technology-based
22
cluster is interesting, the next phase of the evolution, with many of the firms in the cluster further
adapting through technological relatedness is even more relevant. Through the technological
relatedness, the materials-based industry was formed which theoretically exists in a path
interdependent region where the path dependence enables several related industries to work
together (Treado, ____ Martin & Sunley, 2006). According to Toedtling & Trippl, this technological
collaboration is the difference between regional decline and regional renewal (as cited in Treado &
Giarratani, 2008). Similar transformations between industries, that still retain their specialised base,
have been found in the North East England steel industry and the auto design and engineering
cluster in Detroit (Sadler, 2004, Sturgeon et al, 2008).
Resilient Region Discourse
This section, focusing on resilience theory in action, will serve as a transition point between the
aforementioned theoretical debate and the upcoming policy discussion.
Resilience features strongly in the ‘grey’ literature spawned by thinktanks, consultancies and
environmental interest groups around the consequences of the global recession, catastrophic
climate change, and the arrival of the era of peak oil for localities and regions with all its implications
for the longevity of carbon-fuelled economies, cheap, long-distance transport and global trade. This
popularly labelled ‘triple crunch’ (New Economics Foundation, 2008) has powerfully illuminated the
potentially disastrous material consequences of the voracious growth imperative at the heart of
neoliberalism and competitiveness, both in the form of resource constraints (especially food
security) and in the inability of the current system to manage global financial and ecological
sustainability. In so doing it appears to be galvinising previously disparate, fractured debates about
the merits of the current system, and challenging public and political opinion to develop a new,
global concern with frugality, egalitarianism and localism (see, for example, New Economics
Foundation, 2008; Jackson, 2009’ CLES, 2010).
Resilience in this discourse is often is defined as the region’s ability to experience positive economic
success which is socially inclusive, works within environmental limits and which can ride global
economic punches (Ashby et al, 2009). As such, resilience clearly resonates with literatures on
sustainability, localization and diversification, and the developing understanding of regions as
intrinsically diverse entities with evolutionary and context specific development trajectories (Hayter,
2004). Adaptation is not passive, and resilience is understood to incorporate the capacity of
individuals and groups to navigate through the social, cultural, ecological and economic resources
that sustain their well-being, and their differential capacity to negotiate access to these resources
(Ungar, 2008). As such, resilience is a concept which illuminates the fundamental interdependence
of communities and social and economic systems with their ecological and physical environments
(Adger, 2000; Lawrence, 2005).
The discourse of resilience features most prominently in the Transition Towns movement. This seeks
to encourage communities to explore methods for reducing energy use in the era of peak oil, as well
as increasing their economic, political and social self-reliance through a variety of place-based
initiatives. These range from the development of local currencies to encourage the reduction of
food miles and support local businesses through the retention of wealth and spending power locally
(e.g. the Totnes pound in Devon, UK), as well as community gardens for food production, and
various initiatives supporting waste reduction, re-use and recycling. The movement has spread
quickly from its origins in Kinsale, Ireland and there are now estimated to be over 100 communities
recognized as official Transition Towns across the UK, Ireland, Canada, Australia, New Zealand, the
US, Chile and parts of Europe.
23
The discourse of resilience deployed by the Transition Towns movement places strong emphasis
upon place-based, integrated development strategies as a critical approach. Its proponents draw
upon the analogy with natural ecosystems which points to resilient communities as having certain
key features. The first of these is diversity (as opposed to uniformity) in the number of ‘species’ of
business, institutions and sources of energy, food and means of making a living. Thus, resilient
communities are those with strong indigenous sources of food and energy so that if outside supplies
are stopped from coming in, the bulk of what is needed can be provided locally (Hopkins, 2008).
Hanley (1998; p. 248) observes that ‘greater diversity equates with greater resilience in ecosystems.
If this is so, then the questions are whether diversity is also desirable in socioeconomic systems, and
what we wish to maintain as diverse’.
Secondly, resilient communities are characterised by modularity or the capacity to re-organize in the
event of a shock such that they can supply their core needs without substantial reliance on
transport. In other words, resilient communities are engaged with the wider world but from an ethic
of networking and information sharing, rather than of mutual dependence (Hopkins, 2008).
Thirdly, resilient communities are characterized by an emphasis on small-scale, localised activities
which are embedded in the capacities of the local environment, and cognisant of and adapted to its
limits. This is in place of expensive, large and sometimes predatory or invasive infrastructures,
business and bureaucracies. Entire towns or even regions can be vulnerable to the debilitating stasis
caused by over-dependence on key industrial sectors in structural decline such as coal mining, steel
and shipbuilding in previous monoculture places. Thus as well as diversity across a range of sectors,
resilience also implies these sectors should be relatively small scale such that no one sector or
company becomes locally dominant and there is always some spare capacity to adapt in the event of
structural change and industrial decline. In turn, this also implies that resilience is characterized by
dispersion rather than centralized control over systems (Gibbs et al, 2005). Finally, by virtue of
requiring mutual use of local assets, capacities and resources, and localized production, trading and
exchange, resilience also implies a healthy core or supporting economy of family, neighbourhood,
community and civil society, strong in reciprocity, co-operation, sharing and collaboration in the
delivery of essential services, care provision and caring of families (Hopkins, 2008; Simms, 2008;
Jackson, 2009).
In their work on local economic resilience, CLES (2010; p. 11) also emphasise the importance of a
networked place in creating resilience stating that ‘all places and life within them are dependent
upon connections and networks which, if too opaque, too unknown or too dependent on long and
complex connections, are very vulnerable to small disturbances’. Their study focuses on exploring
the factors making for greater resilience in certain places and establish that key ingredients are
strong relationships between the public, private and social economy sectors, governance,
institutions and environment; a strongly pro-active and co-ordinating role by local institutions of
governance which facilitates and brokers these key relationships; a symbiotic rather than parasitic
balance between local and global activities and connections; and flexible governance structures
which enable rather than constrain the formation of relationships and networks in an area. Folke et
al (2003) emphasise the ability of self-organisation and the capacity for learning and adaption.
Governance and policy
As mentioned in the Introduction, there has been extensive research conducted on resilience in
other, more ‘natural’ fields such as ecology. This is also the case for resilience policy which has been
developed in response to natural disasters such as Hurricane Katrina, to acts of terrorism such as
9/11 and to slow burn shocks such as an industry in decline (Campanella/ cite others). Policies of
this nature largely focus on community resilience through social capital and infrastructure resilience
both of which are enacted to increase resistance to future shocks. In comparison to the
aforementioned shocks, and the policies in place to deal with them, regional economic resilience,
24
particularly relating to a macroeconomic event is still in the nascent stage. There are policies that
were created to deal with regional growth and adapted for resilience purposes as a reactive measure
as well as existing policies that could be applied to resilience, particularly in the knowledge
economy, potentially as a proactive measure. Both forms of policy will be discussed in this section.
These policies will be taken from several different levels of government.
Resilience is attracting increasing interest in the thinking and policy discourse around local and
regional development not least because it appears timely in the change context that pervades place
development. As such it is a concept around which an array of different interests are coalescing (see
Bristow, 2010). In this regard, resilience has clear capacity to exhibit viral spread, not least because it
is being deployed by a range of different actors and interests (discursive sites), both state and nonstate entities, through a range of different networks, and across geographical boundaries. Indeed, it
appears to cut-across the so-called ‘grey area’ between academic, policy and practice discourse
which provides it with potentially very significant power to effect change in regional development
policy (as per Power and Malmberg, 2008). It features strongly in policy discourses around
environmental management, climate change and sustainable development (see Hudson, 2008), but
has also more recently emerged in relation to emergency and disaster planning with, for example,
local and regional ‘Resilience Teams’ established in the English regions to support and co-ordinate
civil protection activities around various emergency situations such as the threat of a swine flu
pandemic.
There is growing interest in the concept of resilience as a concept for understanding, managing and
governing complex socio-ecological systems. Understanding systemic cycles of growth,
accumulation, restructuring and renewal, and their temporal and spatial frames of reference, would
help identify the points at which a system is capable of accepting positive change and the points
where it is vulnerable (Lang, 2010). According to Lang (2010), the adaptive perspective on resilience
has parallels with parts of new institutionalist literature and approaches. New institutionalism helps
to understand relationships and processes in urban and regional development policy and opens up
particular perspectives on the formation of policy responses to socio-economic challenges. In
particular, place and time-specific institutional environments, which are created as a result of earlier
experience, in turn help structure local decision making processes. Lang (2010) posits that resilience
could be view as a systemic ‘capacity’, closely related to an institutional environment being
supportive of the constant advancement of the system. Resilience could then be seen as being linked
to a particular culture and form of institutional practice and orientation that constantly advances the
key properties (or controlling processes) of the system. However, he asserts that conceptualising
cities and regions as systems does not imply they are controllable. Rather they are at best adaptive
and self-organised.
The forgoing discussion on resilience as an evolutionary concept suggests it is a challenge to a
mechanical and linear approach to place making and shaping. Places need to be understood as an
interconnected system; the policy application of resilience is a search for qualities and attributes of
place which make it adaptable and able to thrive on change (Dawley et al, 2010; CLES, 2010).
Dawley et al (2010) point to a number of implications for local and regional development strategies.
Political leadership is clearly of paramount importance at the time of a disruption or crisis. However,
there also needs to be intelligent institutional leadership in framing and articulating the nature of
the event or crisis and constructing a discursive narrative of strategic adaptation or adaptability to
enrol key local and regional actors. A central challenge for policy prescription is to find ways to make
key interventions to support and guide the development of new pathways of growth and
development. In short, there is an enduring role for public policy activism and agency in stimulating
change and developing ‘de-locking’ mechanisms to help build resilience, particularly in peripheral
regions lacking many elements of adaptive capacity (Hervas-Oliver et al, 2011). In a study of the
25
ceramics industrial district in North Staffordshire in the UK, Hervas-Oliver et al (2011) draw attention
to the challenges for industrial policy in developing resilience in mature industrial districts. In
particular, their study highlights the need for industrial policy to be co-ordinated particularly in
drawing links between firms and institutions. In the North Staffordshire case, industrial policy has
generally tended to be ad-hoc and limited, often reacting to events rather than anticipating them.
This may be easier said than done however. Levin et al (1998; p. 228) highlight the challenges in
managing resilience in complex systems, observing that ‘policy should be concerned with more than
the immediate consequences of incremental actions. It should recognize the potential for an
accumulation of small actions, each on their own perhaps quite harmless, to destabilize important
natural and social systems. The difficulty is that, while we can predict with reasonable confidence
the immediate consequences of an incremental action, we cannot predict the consequences of an
entire sequence of actions without understanding the systems potentially being affected by them.’
They state that trust is also critical to the development of resilience in social systems noting that
‘reciprocal altruism is an important stabilizing force, and its evolution and maintenance are
enhanced by the local nature of interactions’ (p. 232).
Alternatively, there are knowledge economy policies –learning region, smart specialisation and
regional strategies- that have been considered in addressing regional resilience. According to Wolfe,
a region’s economic performance in the long-term is based on its ability of regional institutions to
manage the transition of the region during a period of change such as that experience during the
post-shock period (2010). The factors that will influence the final result this change are: ‘the ability
of regional and local governments to build on specialised regional assets, including public and
private research infrastructure as well as unique concentrations of occupational and labour market
skills, the presence or absence of ‘civic capital’ at the regional and local level and the ability of
regional networks to work within and across associational boundaries to support the formulation
and refinement of strategic management policies in response to external shocks’ (2010, pp. ___).
While Wolfe has identified specialisation, R&D infrastructure, civic capital and knowledge networks
as the key components of resilience, there is no specific plan or outline for how to enact this strategy
at the regional level. Hassink takes the variables mentioned by Wolfe a step further and considers
the learning region model to resilience policy models due to the link between resilience and
adaptability (2010). There are multiple definitions of the model with most academics considering
the learning region approach as a regional innovation strategy which focuses on connecting the key
innovation- actors in the region, characterised by (Markusen, 1999, OECD 2001):









carefully co-ordinating supply of and demand for skilled individuals
developing a framework for improving organisational learning, which is focused not only on
high-tech sectors but also on all sectors that have the potential to develop high levels of
innovative capacity
carefully identifying resources in the region that could impede economic development (lockins)
positively responding to changes from outside, particularly where this involves unlearning
developing mechanisms for co-ordinating both across departmental and governance
(regional, national and supranational) responsibilities
developing strategies to foster appropriate forms of social capital and tacit knowledge that
are positive to learning and innovation
continuously evaluating relationships between participation in individual learning,
innovation and labour market changes
fostering redundancy and variety of industries and networks
ensuring the participation of large groups of society in devising and implementing strategies.
Essentially, the main attributes of this approach are an increase in knowledge transfer, networking,
collaboration, and diversification. The emphasis on identifying the potential innovation capacity of
26
the sectors, as well as the paths that are locked-in in the region highlights the adaptive focus of the
approach but beyond providing suggestions for the actors, this is hardly a breakthrough approach to
regional resilience or even adaptability (Cooke, 2005). Beyond the ‘fuzzy’ learning region concept,
there any more concrete resilience-based policies, particularly those that specify pivotal regional
institutions.
Cooke focuses on smart specialisation at both the EU-level and the regional level as a policy
response to resilience. The rise of the notion of ‘smart specialisation’ as a regional policy tool has
been advocated particularly by the European Commission. This seems to have been adopted
uncritically because it was perceived as consistent with an elsewhere discredited neo-liberal policy
regime, in which ‘clusters’ had also been a favourite panacea for the EU. It is made clear that the
notion is set firmly in the Research, Technology and Development strand of European policy and is a
way forward for regions that wish “to stay in the game”, an aspiration thought likely to be more
difficult as the construction of a barrier-free European Research Area evolves.
Thereafter, the creation of a large research and innovation area – allowing unrestricted competition
– is an essential condition for specialisation. This expresses an interpretation of Adam Smith that the
degree of specialisation is determined by the scale of the market, although Smith meant the division
of labour (see also Stigler, 1951), a relationship inverted by Storper & Walker (1989) as ‘the extent of
the market is limited by the division of labour’. It is, accordingly, an entrepreneurial process
producing ‘pertinent specialisations’ of the region intended to be socially valuable and intended to
guide regional economic development. A small number of players thus compete to innovate
applications of ‘General Purpose Technologies’ (e.g. biotechnology or ICT) invented or discovered in
leader regions. Interestingly, this can be in one or several domains of the regional economy.
Governments will subsidise entrepreneurship because firms will only capture a negligible part of the
necessary social value for regional development. Governments will assess these contributions but
not ‘pick winners’ and evaluate projects on grounds of appropriateness.
This is necessary because the imputed unrestricted competitive research space is fragmented by
nations and regions. This hampers formation of world-class centres due to science agglomerations
evolving nationally alongside their resource flows. Moreover countries and regions tend to envisage
similar scientific futures, which is sub-optimal, one consequence of which is that European firms may
seek innovation opportunities from research conducted outside Europe. Europe lacks critical mass
among its scientific systems and is uncompetitive and inefficient in consequence. Economies of
agglomeration are dissipated, scale and spillovers not fully realised. Indeed economies of
agglomeration are themselves scarce resources, because of too much competition. Europe cannot
sustain 28 or 30 global centres of biotechnology. Economies of variety must be exploited as when
the US National Science Foundation transfers clinical researchers to medical schools to teach
experimental methods. Thus regions must ‘particularise themselves’ which is ‘smart specialisation’.
Governments will help find the right specialisations, evaluate the regional knowledge base and its
spillovers, arrange co-ordinated investment in the chosen regional specialisation, and cut failing
programmes. ‘Co-specialised assets’ connecting higher education, professional training and research
programmes will bolster regional specialisation, allowing biotechnology applications from, for
example, Cambridge to be applied to wine quality control, fishing, cheese and olive oil industries in,
for example, Braga (Portugal). Clearly, this promises the apotheosis of regional ‘lock-in’.
Through smart specialisation, the region becomes more resilient by increasing the knowledge base
and marketing itself in a specific, high-tech, high-growth, knowledge economy-based industry.
While this is an interesting strategy, the path creation or path evolution (depending on the history of
the region) may not necessarily work to plan. In addition, the region would have to have sufficient
high-skilled talent as well as entrepreneurs to form the base firms. For a region experiencing a
27
shock, the level of organisation needed at the institutional level may be overwhelming, particularly
relating to training, funding and infrastructure.
Unlike the previous examples, an article by Dawley et al (2010) highlights what components are
needed in regional economic resilience policy while identifying a British-based regional strategy as a
case study. The components needed for this type of policy to be successful were:
 intelligent institutional leadership- which identifies and frames the shock in the regional
setting while constructing a strategy for adaptation enacted at the local and regional level,
 continuous adaptive capacity of region-even during times of regional growth, the strategy
needs to foster development through renewal and branching,
 political leadership- needed for the above, to identify challenges and support to regional
adaptation at multiple-spatial levels (EU, national, local) and to focus on the often long-term
nature of adaptation and recovery,
 engagement with private organisations- this is to further enhance research capacity of
region as well as a potential source of funding for new projects.
The case study is the Strategy for Success programme, managed by One NorthEast, the regional
development agency, in the North East of England.
‘Drawing on the regions’ long history of offshore and subsea engineering skills, and its
proximity to natural and vast under-utilised former industries sites, the Strategy for Success
Programme, started in 2001, aimed to provide an enabling environment to connect
emergent technologies in the renewable energy field with potential regional strengths.
Through identifying renewable energy the not-for-profit R&D centre, the New and
Renewable Energy Centre, was created which has lead to FDI interested in research projects
particularly the world’s largest offshore wind farm, the Brittania. This case was successful
due to the long-term, evolutionary based perspective adopted by policymakers as well as
the substantial funding provided for R&D.’ (Dawley et al, 2010)
One point to be mentioned is that One North East identified five emerging technology industries
that were developed in the region and the outcome of the other four in unknown at this time.
Beyond this issue, and taking into account the basic principle that an old industrialised area
specialised in a knowledge economy industry, we seem to be discussing smart specialisation again.
Policy and governance, in terms of regional resilience, have been discussed thus far at the regional
level as well as the supra-national, EU ‘region’, level; however, this has not been in discussing
specific regional resilience but rather taking other policies and applying them to regional resilience.
The lack of governance, with the exception of the aforementioned disaster recovery plans, is both
alarming and interesting. The interest is based in the gap in the research that this provides.
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