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Pa rt I I I
I N N OVAT ION
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Chapter 13
Ec onomic E c o syst e ms
Philip E. Auerswald and Lokesh Dani
Introduction
‘The Mecca of the economist lies in economic biology rather than in economic dynamics’,
the great economist Alfred Marshall famously wrote in 1920, in the preface to the eighth
edition of Principles of Economics. ‘But biological conceptions are more complex than those
of mechanics’, he continued, ‘a volume on Foundations must therefore give a relatively large
place to mechanical analogies; and frequent use is made of the term “equilibrium,” which
suggests something of statistical analogy’ (Marshall, 1920, p. 19). Inspired by these words of
Marshall’s and the work of other foundational figures in the field of economics who similarly
perceived a fundamentally biological order in the evolution of the economy,1 economists
have for decades sought to represent the adaptive dynamics evident in economic systems.2
A second celebrated passage in Marshall’s Principles relates to the localization of economic
activity: ‘When an industry has thus chosen a locality for itself, it is likely to stay there long:
so great are the advantages which people following the same skilled trade get from near
neighbourhood to one another. The mysteries of the trade become no mysteries; but are as
it were in the air, and children learn many of them unconsciously.’ Emphasizing the central
role of invention and innovation in geographical localization, Marshall continues, ‘Good
work is rightly appreciated, inventions and improvements in machinery, in processes and
the general organization of the business have their merits promptly discussed: if one man
starts a new idea, it is taken up by others and combined with suggestions of their own; and
thus it becomes the source of further new ideas’ (Marshall, 1920, p. 225). This observation
similarly has inspired a now decades-​old literature within economics on the localization of
economic activity, in general, and of inventive activity, in particular.
Economists have only recently begun to connect these two Marshall-​inspired literatures,
studying localized systems of innovation as ‘ecosystems’ at the sub-​national or regional
level.3 This new work on regional entrepreneurial ecosystems has been prompted equally by
the advance of models and empirical methods to represent the adaptive evolution of ecosystems (Holling et al., 1995; Gavrilets, 1999, 2004; Holling, 2001), a discontinuous increase in
the volume and quality of data available to economic geographers (Rosenthal and Strange,
2001, 2004; Wallsten, 2001; Auerswald et al., 2007), and strong interest among policymakers
(Isenberg, 2010; Auerswald, 2015).
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In this chapter we review the concept of ecosystems as applied to economic geography.
We define an economic ecosystem as a dynamically stable network of interconnected firms
and institutions within bounded geographical space. We propose that concepts familiar to
economic geographers, such as ‘clusters’ (Porter, 1990) and ‘production networks’ (Piore
and Sabel, 1984), are subsystems within regional economies, and further that representing
regional economic networks as ‘ecosystems’ provides analytical structure and depth to otherwise mostly ad hoc theories of the sources of regional advantage, the role of entrepreneurs
in regional development, and the determinants of resilience in regional economic systems.
We frame regional economic change in terms of ecosystem dynamics, with reference to ecologically derived concepts of succession, speciation, diversity, resilience, and adaptation. We
seek both to provide a summary of the scholarly discussion on economic ecosystems and to
sketch directions for future research.
In the next section we summarize the conceptual origins of the term ‘ecosystem’ and the
concept of the ‘fitness landscape’ in evolutionary biology and explain their application to
the study of economic ecosystems. In the section, ‘Unit of Analysis’, we introduce the concept of the ‘production algorithm’, which is analogous in economic ecosystems to the gene in
biological ecosystems. In the fourth section, ‘Structure’, we summarize the structural characteristics of economic ecosystems, with a focus on the determination of ecosystem boundaries. In the fifth section, ‘Dynamics’, we describe the hypothesized dynamics of economic
ecosystems, with particular emphasis on the systemic processes that lead to speciation and
ecosystem-​scale life cycles. In the penultimate section, ‘Health’, we propose some potential
definitions of the health of ecosystems in terms of their resilience, adaptive capacity, diversity, and entrepreneurial dynamism. We conclude with a discussion on the future potential
and direction of economic ecosystems research.
Conceptual Origins
The Definition of ‘Ecosystem’
In an 1857 essay titled ‘Progress: its Law and Cause’, Herbert Spencer argued that systems of
all types—​natural and social—​tended to grow from simplicity to complexity through stages
of differentiation, a process he characterized as ‘an advance from homogeneity of structure
to heterogeneity of structure’ (Spencer, 1857, p. 234). Presenting an array of examples of such
progression from simplicity to complexity, Spencer endeavoured to establish that the
law of progress is the law of all progress. Whether it be in the development of the Earth, in
the development of Life upon its surface, in the development of Society, of Government, of
Manufactures, of Commerce, of Language, Literature, Science, Art, this same evolution of the
simple into the complex, through a process of continuous differentiation, holds throughout
(Spencer, 1857, p. 234).
Nearly six decades after the publication of ‘Progress: Its Law and Cause’, Sir Arthur Tansley
published a paper entitled ‘The Use and Abuse of Vegetational Concepts and Terms’ in
which he introduced the term ‘ecosystem’. Tansley’s insight was that dynamically stable networks of interconnected organisms and inorganic resources constituted their own distinct
domain of analysis. Evolutionary biologists in the 1930s were as naturally inclined to place
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‘the organism’ at the centre of their inquiry as economists in the 1930s were to place ‘the firm’
at the centre of production theory, but Tansley rebelled against the application of the term
‘complex organism’ to describe dynamically stable networks of interconnected organisms
and inorganic resources because ‘the term [“complex organism”] is already in common use
for an individual higher animal or plant, and because the biome is not an organism except in
the sense in which inorganic systems are organisms’. Accordingly, a new term was required.
Tansley proposed the word ‘ecosystem’, which he defined as follows:
It is the systems so formed which, from the point of view of the ecologist, are the basic units of
nature on the face of the earth. Our natural human prejudices force us to consider the organisms (in the sense of the biologist) as the most important parts of these systems, but certainly
the inorganic ‘factors’ are also parts—​there could be no systems without them, and there is
constant interchange of the most various kinds within each system, not only between the
organisms but between the organic and the inorganic. These ecosystems, as we may call them,
are of the most various kinds and sizes. They form one category of the multitudinous physical
systems of the universe, which range from the universe as a whole down to the atom (Tansley,
1935, p. 299).
As Tansley emphasized in defining the term, ecosystems come in a variety of sizes and scales
determined by internal linkages and external boundaries. Tansley included the interactions
between both the organic biome and the inorganic habitat in which these organisms live in
determining the scope of the ecosystem. Accordingly, biological ecosystems can be as small
as ponds, or as large as forests, and collections of ecosystems can be combined into higher-​
order systems. Nationally and globally, ecosystems are classified into a hierarchy of nested
geographies of interacting networks (Bailey, 2009).
In this chapter we elaborate on the proposition that the firm in an economic ecosystem is
analogous to the organism in a biological ecosystem. We propose that economic ecosystems
are characterized by interactions among densely interconnected firms, but that such ecosystems cannot reasonably be considered ‘complex firms’. Firms within an ecosystem are generally less tightly interconnected than subunits within a firm, but more tightly interconnected
than atomistic entities reacting anonymously to price signals in a market.
Evolution as the Solution to a Search Problem
At about the same time that Tansley was defining the ecosystem and establishing the basis
for ecology as a field of study, fellow biologist Sewall Wright was setting the stage for the
‘modern synthesis’ in evolutionary biology: the systematic integration of the ‘micro’ genetics of combination and recombination first postulated by Gregor Mendel with the ‘macro’
theory of evolution first and most famously expounded by Charles Darwin, with significant contemporaneous contributions from Herbert Spencer that described changes in the
character of populations over time. Sewall Wright’s work constituted a significant advance
over Darwinian theory and offered a bridge from evolutionary biology to other domains of
inquiry.
Wright began his 1932 paper ‘The Roles of Mutation, Inbreeding, Crossbreeding, and
Selection in Evolution’ by distinguishing between two mechanisms by which genetic novelty might be introduced into particular populations. The first was the one emphasized
by Darwin: single-​point mutation, which would constitute incremental change for an
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offspring as compared with a parent. The second was that emphasized by Mendel: sexual, or
‘bi-​parental’, reproduction, which would constitute large-​scale, combinatorial change for an
offspring as compared with parents. Wright noted the fundamentally unsatisfactory nature
of mutation as a sole explanatory factor in the evolutionary process:
The observed properties of gene mutation—​fortuitous in origin, infrequent in occurrence
and deleterious when not negligible in effect—​seem about as unfavorable as possible for an
evolutionary process. Under bi-​parental reproduction, however, a limited number of mutations which are not too injurious to be carried by the species furnish an almost indefinite field
of possible variations through which the species may work its way under natural selection
(Wright, 1932, p. 356).
Wright supported his claim by describing the astronomical number of combinations of
genes that are possible in higher organisms, in comparison with the linearly scaling number
of possible single-​point mutations. As a consequence of the enormous space of combinatorial possibilities afforded through sexual reproduction, populations of higher organisms
(including humans) demonstrate tremendous genetic diversity and thus different implications for a species’ reproductive success:
There is no reasonable chance that any two individuals have exactly the same genetic constitution in a species of millions of millions of individuals persisting over millions of generations.
There is no difficulty accounting for the probable genetic uniqueness of each human being
or other organism which is the production of bi-​parental reproduction (Wright, 1932, p. 356).
To organize inquiry regarding the way in which populations evolve over time, Wright introduced the idea of a ‘landscape’ that assigns an environmentally determined level of ‘fitness’,
or reproductive success, to each genetic combination. ‘The problem of evolution’, he states, ‘is
that of a mechanism by which the species may continually find its way from lower to higher
peaks in such a field. In order that this may occur, there must be some trial and error mechanism on a grand scale by which the species may explore the region surrounding a small portion of the field which it occupies. To evolve, the species must not be under the strict control
of [mutation-​driven] natural selection’ (Wright, 1932, p. 359).
As introduced by Wright (1932), fitness landscapes are a two-​dimensional visualization
of the relationship between a species’ reproductive success and its genotype. The organism’s genotype is one possible combination in the ‘genotype space’, itself represented as a
hypercube with vertices composed of all possible gene combinations. When plotted as two-​
dimensional contour map representing levels sets of reproductive fitness, fitness landscapes
show multiple local and global optima, as well as ‘maladaptive valleys’ (see Figure 13.1).
Although the combinatorial possibility of the genotype space is nearly boundless, not all
of the landscape is accessible to a given individual of a species. The geometric features of the
landscape play an important role in the accessibility to higher peaks of fitness for a given
species. Sexual reproduction—​which we will propose is analogous to Schumpeterian entrepreneurship and innovation in an economic ecosystem framework—​thus provides a mechanism by which the population can reach regions of the fitness landscape that lie beyond its
immediate ‘neighbourhood’ composed of adjacent genetic variants.
The presence of sexual reproduction alone is, however, not sufficient to sustain continued
evolution. Genetic diversity is also necessary. The reason for this is that, for a closed population, inbreeding among members of the population combines with natural selection to shift
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Economic Ecosystems 249
Figure 13.1 Sewall Wright’s Fitness Landscapes.
Source: Wright (1932, p. 3).
the distribution of genetic combinations ‘uphill’ on the landscape towards a fixed number
of peaks, where a ‘peak’ on the fitness landscape is defined as local maximum in terms of
favourable adaptation to the environment. After a sufficient time has elapsed such that the
population converges on such peaks, a certain stasis sets in. While mutation may continue
to introduce some variation after the population has reached a set of high points on the
fitness landscape, ‘the species will occupy a certain field of variation about a peak … The
field occupied [on the landscape] remains constant although no two individuals are ever
identical’. Under such conditions ‘further evolution can only occur by the appearance of
wholly new (instead of recurrent) mutations, and ones which happen to be favorable from
the first [instance it appears]’.4 Absent fortuitous encounters with entire new populations of
the same species, the single most effective way out of this trap is for the species to subdivide
into local subspecies that occasionally crossbreed. This allows for the regular introduction
of truly new combinations that fundamentally expand the field of variation occupied by the
species.
Wright’s primary conclusion is that evolution requires a balance among the various mechanisms for generating novelty upon which it depends: mutation, selection, inbreeding, and
crossbreeding. ‘There must be gene mutation, but an excessive rate gives an array of [maladapted] freaks, not evolution; there must be selection, but too severe a process destroys
the field of variability, and thus the basis for further advance; prevalence of local inbreeding
within a species has extremely important evolutionary consequences, but too close inbreeding leads merely to extinction’. In the short term, narrow specialization leads to economies of
scale and increased productivity; however, in the long term, narrow specialization leads to
the exhaustion of possibilities for search, and thus to evolutionary dead ends. Success for a
species depends on balancing these factors.
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As we will see, an ecosystems perspective on economic geography suggests that the same
holds for the densely interconnected firms that comprise economic ecosystems: specialization yields increased productivity, but success in the long term depends equally on the continued introduction of novelty.5
Unit of Analysis
Representing the ‘DNA’ of Firms
Markets exert a selection pressure on firms that is reflected in the dynamics of industries.
Standard theories of industrial organization suggest that firms with greater-​than-​average
productivity will grow over time within a given industry, while low-​productivity firms are
likely to shrink or exit (Viner, 1932; Jovanovic, 1982; Hopenhayn 1992; Ericson and Pakes,
1995; Foster et al., 2008). However, contrary to the predictions of Viner (1932), productivity
differences among industries in different geographies, among firms within industries, and
even among plant within firms, are large and tend to persist over time.6 Work by Bloom
and Van Reenen (2010) singled out the influence of management practices on cross-​country
variations in firm productivity. These results suggest that economically relevant knowledge
is generally firm-​specific and costly to transmit. Imperfect appropriability of the production process allows entrepreneurs to capture persistent rents (Aghion and Howitt, 1992;
Auerswald, 2010).
While surprising in the context of the knowledge-​based variants of ‘new growth’ theory
that emphasize the ostensible ubiquity of ‘knowledge spillovers’,7 these results fit comfortably within an ecosystems view of economic geography. If firms are organisms, then the
DNA of firms is the economically relevant knowledge embedded within the firm on which
the firm’s survival depends. We will term such economically relevant knowledge embedded
within the firm the firm’s ‘production algorithm’.
A notable conceptual antecedent to the production algorithm is the firm-​level ‘routine’ (Nelson and Winter, 1982), or those firm-​specific functions that relate inputs to outputs given the internal context and the external environment of the business operations.
Nelson and Winter (1982) proposed that routine plays ‘the role that genes play in biological evolutionary theory’ (Nelson and Winter, 1982, p. 17).8 Auerswald et al. (2000) refer
to the economically relevant knowledge encoded within a firm as its ‘production recipe’, which represents ‘the complete description of the underlying engineering process’.
Employing a culinary rather than biological analogy, the notion of recipes emphasizes
that a firm’s production plan extends well beyond the mapping of ingredients (inputs)
to outputs, that is the focus of standard production theory, to incorporating the specific
list order for routinization of the production plan. More important, the production algorithm consists of the ‘how’ of the production process—​the code that specifies the distinct
operations required to convert inputs into outputs.9 Invoking the Coasean notion that
firms exist to internalize externalities (Coase, 1937), it follows naturally that the production recipes or production algorithms—​two terms we will use interchangeably—​that are
mostly likely to survive under evolutionary pressure are complex and cannot be easily
imitated.
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In the ecosystems framework, therefore, production recipes/​algorithms are analogous to
genes in organisms. They are the basic units of economic recombination.
Learning by Doing and Adaptive Walks on Fitness Landscapes
In 1936, four years after Sewall Wright published his pioneering work introducing the concept of the fitness landscape to evolutionary biology, his brother, T.P. Wright, published a
paper titled ‘Factors Affecting the Cost of Airplanes’ that set the stage for a future modern
synthesis in economics, linking of systematic modifications at the scale of the production
algorithm to observed outcomes at the scale of economic ecosystem. This paper was a contribution to the engineering literature that documented the manner in which the cost of
airframes declined as experience accumulated. Yet what T.P. Wright had discovered—​the
organizational learning curve—​turned out to have fundamental significance in fields as varied as business strategy, industrial organization, macroeconomics, and economic geography.
Citing Wright (1932), Arrow (1962) proposed a growth model based on the observation
that per unit costs of production can fall even in the absence of capital accumulation and R &
D inputs. He attributed this productivity gain in the absence of increased inputs to ‘learning
by doing’. Also citing Wright (1932), Muth (1986) began to link production algorithms to the
emergence of learning curves by suggesting that higher efficiencies in the search procedure
can be achieved by breaking the design problem into smaller components and systematically
modifying the components individually. Building on this work, and employing the intuitive
conceptualization of fitness landscapes articulated by Wright to explain the emergence of
learning curves as documented by Wright (1932), and Auerswald et al. (2000) applied the
production recipes approach to learning by doing as a process of systematic search in space
of possibilities represented by adjacent production algorithms.
The specific form of a fitness landscape employed in Auerswald et al. (2000) is Kauffman
and Levin’s (1987) NK model. In the NK model, N refers to the number of traits of an organism that contribute to increasing fitness of the organism, while K refers to the number of
other traits of the organism that have a bearing on its fitness. The evolutionary pathway is
modelled as an ‘adaptive walk’ or a step-​wise optimization process. Genetic mutations happen at random but only those mutations that increase the species’ fitness are adopted and it
is through this evolutionary process that a species traverses the fitness landscape in search
of a more optimal peak. As K increases, the ruggedness of the landscape increases with the
number of peaks increasing, but the typical height of the peaks decreases to reflect that an
increase in the epistatic linkages increases the conflicting constraints of the fitness landscape
(Kauffman, 1989). More recently, Sergey Gavrilets suggested that the properties of multidimensional landscapes can differ significantly from low-​dimension landscapes, which can
have implications on how a species’ population moves from one peak to another, or crosses a
valley. Gavrilets and Janko Gravner (1997) answered this question by suggesting that a population can cross maladaptive valleys to reach a higher fitness peak by traversing along ‘ridges’
if and where available. However, these ridges are determined by how peaks cluster on that
landscape, and they may not be accessible.
In the production recipes application of the NK model introduced in Auerswald et al.
(2000), N refers to the number of individual operations in the production recipe, and K
refers to the average number of interactions among operations that have a bearing on overall
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efficiency of the production recipe. In this framework, the difficulty of the search problem
solved by the firm is determined primarily by K, ‘the richness of epistatic linkages in the
system’ (Kauffman and Johnsen, 1991). Strumsky and Lobo (2003), Siggelkow and Levinthal
(2003), and McNerney et al. (2010) have similarly employed landscape models to study how
the co-​evolutionary patterns of organizational and design subsystems can improve efficiency
of the search process while reducing costs.10 Hidalgo et al. (2007), Neffke and Henning
(2013), and Muneepeerakul et al. (2013) have further advanced work on economic ecosystems by mapping the search space of possibilities embedded in the knowledge structures of
regional production processes, industries, and occupations, respectively.
Combination and Recombination
The correspondence between genes and production algorithms in biological and economic
ecosystems, respectively, suggests that evolution as search may occur not only as a consequence of mutation and selection—​analogous to competition among firms in the presence
of learning by doing as described by Wright (1932) and Arrow (1962)—​but also as a consequence of the combination and recombination—​analogous to combination and recombination as a driver of technological advance, as emphasized by Schumpeter (1911) and Arthur
(2013), among others. Schumpeter famously wrote: ‘The carrying out of new combinations
we call “enterprise”; the individuals whose function it is to carry them out we call “entrepreneurs” ’. Echoing Wright (1932) and deriving conclusions from a combination of theoretical
first principles and insights derived from a plethora of historical cases, Arthur (2013, p. 129)
argues that novel technologies—​new technological ‘species’—​arise overwhelmingly as the
consequence of the purposive recombination of existing solutions:
We … have our answer to the key question of how novel technologies arise. The mechanism
is certainly not Darwinian: novel species in technology do not arise from the accumulation
of small changes. They arise from a process, a human and often lengthy one, of linking a
need with a principle (some generic use of an effect) that will satisfy it. This linkage stretches
from the need itself to the base phenomenon that will be harnessed to meet it, through supporting solutions and subsolutions … In the end the problem must be solved with pieces—​
components—​that already exist (or pieces that can be created from ones that already exist).
Learning by doing, analogous to mutation combined with selection, and recombination,
broadly analogous to sexual reproduction, drive the dynamics as the scale of production
algorithms that determine the evolution of economic ecosystems.
Co-​evolutionary Dynamics
Species do not just evolve, of course. They co-​evolve with other species in their environments. The preceding section talked about the evolution of a species as a search for a better fit
along a fixed fitness landscape. Yet, the adaptive progress of one species in an environment is
likely to have implications for the evolution of a different species sharing the same environment. “Anecdotally, development of a sticky tongue by the frog alters the fitness of the fly,
and what changes it must now make to increase its fitness; given the frog’s sticky tongue, the
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fly should now develop slippery feet. In this framework, adaptive moves by any partner may
deform the fitness landscapes of other partners.” (Kauffman and Johnsen, 1991, p. 468).
The study of the evolution of a species should also incorporate the co-​evolutionary patterns of other interacting species. To model this mathematically, Kauffman and Johnsen
(1991) extend the NK framework to include C, the number of traits of the other interacting
species that have a bearing on its fitness. Such ‘coupled NK fitness landscapes’ have varied
emergent properties and the co-​evolutionary process applies particular pressures on the
evolutionary pathways of different species.
Key developments in this field that further carry over to the economic ecosystem perspective is that ecosystems are not completely connected, but instead each species in the
ecosystem only interacts with a subset of other species in the ecosystem, forming a web of
interactions. Co-​evolutionary patterns of industries and technologies within economic ecosystems are driven by specific and relevant interactions that can be identified and evaluated
in the context of different search strategies.
Structure
Spatial Agglomeration and the Definition
of Ecosystem Boundaries
In economic geography the periphery is often contrasted with the core. Agglomeration
arises from the locational choices of manufacturing firms in the presence of transport
costs, thereby determining how the core and periphery grow over time (Krugman, 1991).
More generally, economic ecosystems are defined within specific geographies by internal
linkages and external boundaries. Just as biological habitats comprise ecosystems, which,
in turn, make up biomes, economic ecosystems are also nested within larger hierarchies of
regional, national, and global systems. While ecologists are able to rely on the physical and
topographical characteristics of space to define the boundaries of ecosystems (Bailey, 2009),
these methods have limited applicability when dissecting abstract strategic networks that
comprise economic ecosystems. The ability to identify internal linkages and define boundaries is thus a necessary condition to applying the concept of the ‘ecosystem’ to economics. To
specify interactions that contribute to defining ecosystem boundaries, they must be complemented with methods to map knowledge networks, strategic alliances, and other outcomes
of processes that develop social, cultural, and economic ties.11
Gunderson and Holling (2001) recommend a method of mapping ecosystems that is similar to the biological approach of the ‘controlling factors method’ (Bailey, 2009). They suggest
that ‘the complexity of living systems of people and nature emerges not from a random association of a large number of interacting factors rather from a smaller number of controlling
processes. These systems are self-​organized and a small set of critical processes create and
maintain this self-​organization’ (Holling, 2001, p. 391). The Gunderson and Holling (2001)
approach has developed alongside theoretically similar applications of evolutionary theories
in the multi-​level perspective, particularly to the study of technological transitions (Geels,
2002; Genus and Coles, 2008). In an outcome-​based approach to specifying the internal
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interactions that correlate with the overall performance of economic ecosystems, Stangler
and Bell-​Masterson (2015) recommend evaluating the overall performance of economic
ecosystems based on a set of four regional entrepreneurship-​specific indicator variables:
density; fluidity; connectivity; and diversity.
Ecosystem boundaries are characterized by an exchange of energy and information
between neighbouring ecosystems and are termed ‘transition zones’ (Banks-​Leite and Ewers,
2009). In natural ecosystems, these transition zones may be abrupt, such as the boundary
between a marine and terrestrial ecosystem, or a field and an adjoining forest. Alternately,
the transition zones may be more gradual, incorporating a series of overlapping structures,
such as in estuaries and marshes. While the width of the transition zone depends largely on
the geography of the region, these zones provide spaces through which one ecosystem influences another, what is termed an ‘edge effect’ (Murcia, 1995). These edge effects are primarily
driven by abiotic conditions and pose a strong influence on the environmental conditions
of the transition zones, creating unique habitats to which species are specifically adapted.
Accordingly, the greater the contrast between the habitats sharing an edge, the stronger will
be the edge effect.
Boundaries of economic ecosystems resemble transition zones in biological ecosystems.
In the most direct geographical sense, the political boundaries reflect sharp contrasts in the
transition zones making the difference between ecosystem structures sharply visible. For
instance, Figure 13.2 shows an aerial view of a section of the USA/​Mexico border, one of the
most controlled and frequently crossed borders in the world. The edge of the two national
ecosystems is very apparent in the image.
In another example, metropolitan statistical area (MSA) definitions in the USA are
updated every few years to reflect population changes along border counties (Office of
Management and Budget, 2013). These changes can include the reassigning of counties from
one MSA to an adjacent one based on commuting zones, or the addition or exclusion of a
Figure 13.2 Ecosystem Boundaries—​US/​Mexico Border.
Source: Google Earth satellite image.
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county from an MSA based on population change. Although MSA definitions are political
boundaries, they reflect the socio-​economic relationships between interior and boundary
regions of a common physical system.
Interdependencies
Firms in economic ecosystems are not uniformly distributed, and a firm’s location within the
ecosystem has strong implications on its productivity and evolution.
Many new economic combinations fail to survive in the market because complementary factors or vital inputs for production and commercialization may not yet be available.
Fagerberg (2005) gives the example of Leonardo da Vinci, who had presented designs of
advanced technologies, including airplanes, but he lacked the adequate materials or production processes to realize them during his time. A contemporary example is the recent
explosion in the fields of computational sciences. Although much of the mathematics
behind pattern-​recognition algorithms was well established more than a century ago, it
took the computational power of modern computers to allow researchers to apply fully
the programming methods that today are branching out new technological fields in augmented reality, artificial intelligence, and cybernetics. In this sense, it is hard to conceive
of the structure of innovation without also considering how the structure evolves over
time. When looking at a static representation of the innovation system we would be hard
pressed to identify the relevant components that have led up to the current opportunity for
commercialization.
Spencer (1857) long ago described how different components of an economy, when connected, become mutually dependent and begin to differentiate themselves by assuming different functions: ‘When roads and other means of transit become numerous and good, the
different districts begin to assume different functions, and to become mutually dependent.’
Economic complexity in this sense is a result of increased interdependence within systems
and where more complex interactions imply more complex social arrangements. These
complex interdependencies in ecosystems are multidimensional and can be measured in a
number of ways. Most often they are studied in terms of the number of parts to a technological artifact (Strumsky et al., 2012), but they may also be reflected in terms of organizational
complexity of production processes within firms (Auerswald et al., 2000), the diversity of
teams required to develop new technological innovations (Kash and Rycroft, 1999; Adams
et al., 2005), as well as the in the intricacies of buyer–​supplier networks and peer-​production
networks (Appleyard, 2003; Auerswald and Branscomb, 2008).
Dynamics
Succession
Ecosystem succession refers to the process by which the structure of the system evolves over
time. In broad terms, succession in biological ecosystems is represented by the emergence
of a new biological community following a large disturbance. These changes in biological
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composition of the ecosystem, termed ‘succession’, are analogous to the progressive development of practices within an industry or local economy. Tansley describes succession as
follows:
Succession is a continuous process of change in vegetation which can be separated into a
series of phases. When the dominating factors of change depend directly on the activities of
the plants themselves (autogenic factors) the succession is autogenic: when the dominating
factors are external to the plants (allogenic factors) it is allogenic. The successions (priseres)
which lead from bare substrata to the highest types of vegetation actually present in a climatic
region (progressive) are primarily autogenic. Those which lead away from these higher forms
of vegetation (retrogressive) are largely allogenic, though both types of factor enter into all
successions (1935, p. 306).
Just as succession can be either autogenic or allogenic, the evolution of industries in economic
ecosystems can be either endogenously driven or exogenously driven. Furthermore, just as
Tansley defines successions that lead towards greater biological complexity as progressive
change in biological systems, so we are suggesting that the evolution of the capabilities of a city
or region towards greater complexity constitutes progressive change in economic systems.
Speciation
Gavrilets (2004, pp. 399–​400) proposes that ‘speciation can be visualized as the process of
formation and subsequent divergence of clusters of organisms in genotype space accompanied by the evolution of RI [reproductive isolation] between the emerging clusters’. In developing this higher-​order complexity of fitness landscapes, Gavrilets coined the term ‘holey
adaptive landscapes’, defining them as ‘an adaptive landscape where relatively infrequent
well-​fit combinations of genes form a contiguous set that expands throughout the genotype
space’ (Gavrilets, 1997, 1999). These holey adaptive landscapes, being less contiguous and
offering fewer pathways for adaptive walks, have properties that can result in subdivision of
populations, leading to speciation.12
Ecosystem Life Cycles
Adaptive Capacity or the Resilience of the System
As Figure 13.3 illustrates, the biological adaptive cycle alternates between short periods of
systemic restructuring triggered by a disturbance (release and reorganization), followed by
longer periods of accumulation and transformation of resources (exploitation and conservation). The development of the ecosystem from the exploitation phase to the conservation
phase captures the traditional notions of ecological succession. Organisms rapidly colonize
a disturbed space to accumulate stores of energy and form complex interdependencies.
The shorter period beginning with the release is often referred to as the phase of ‘creative
destruction’ to parallel Schumpeterian entrepreneurship (Schumpeter, 1911, 1939). It occurs
when the over-​connected dependencies resulting from the conservation phase collapse
under some external disturbance, such as a fire or disease. The result is a release of a great
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Economic Ecosystems 257
4. REORGANISATION
2. CONSERVATION
Succession
Consolidation
STORED NUTRIENTS
Accessible carbon
and nutrients
1. EXPLOITATION
3. RELEASE
Disturbance:
Fire, storm, pest
Pioneer
Opportunist
CONNECTEDNESS
Figure 13.3 Succession and Reorganization of Ecosystems.
Source: Bengtsson et al. (2000).
amount of stored energy, potentially creating new opportunity for more complex reorganization with more diverse inputs.
These features are paralleled in social and economic ecosystems as well and can be
observed in the entrepreneurial activity that drives change in the structure of the ecosystem. To summarize the argument presented by Gunderson and Holling (2001) in their
book Panarchy: Understanding Transformations in Human and Natural Systems, we can
consider the exploitation phase to be crowded by entrepreneurial activity that is working and defining a new space of opportunity. Pioneers and opportunists who have preferential access to the newly released energy and resources will be the first to jump to
entrepreneurial action and the overall diversity of the cluster will increase. As the system matures into the conservation phase, consolidation across firms will establish new
system-​level standards that, in turn, enable more specialized innovative activity. The
structure of the ecosystem will have become denser and more interconnected across different scales of economic activity. As the networks become denser and more embedded,
the structure of the ecosystem becomes more rigid and, consequently, more vulnerable
to large-​scale disturbances. In certain configurations,13 a strong stochastic shock, such
as a regulatory change, can significantly disentangle many networks of the structure
and release abundant energy into the environment. Entrepreneurs will once again seek
opportunity in this disturbance and begin to establish a new order to the ecosystem in the
reorganization phase.
Figure 13.4 provides a generalized summary of the features of each of the four phases. Note
that the two axes of the diagram indicate levels of realized entrepreneurial potential and the
connectedness of the economic ecosystem. In the next subsections we discuss in more detail
the two life-​cycle loops of ‘release and reorganization’, and ‘exploitation and conservation’ in
more detail according to the processes that enable systemic phase shifts.
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4. Reorganization
– Release of stored nutrients introduces
novelty
– Phase characteristics: restructuring;
highest uncertainty; new order
Potential
2. Conservation
– ‘Climax’ or equilibrium phase
– Resource utilization shifts from growth to
system maintenance
– Phase characteristics: stability; rigidity;
strong interdependencies; vulnerability to
disturbance
Connectedness
1. Exploitation
3. Release
– ‘Creative destruction’ phase
– High competition for resources
– System collapse releasing stored nutrients
– New opportunities from increasing diversity
– Phase characteristics; uncertainty; chaos;
– Phase characteristics; pioneers; opportunity;
disturbance; de-stability
innovation; uncertainty
Figure 13.4 Phase Characteristics of the Entrepreneurial Ecosystem.
Source: Author’s adaptation.
Release to Reorganization
Release refers to the opportunity that fuels the creative destruction phase. Resulting from
some external disturbance, the tightly knit connected structures of the ecosystem come
undone and large amounts of stored capital and energy are released within the ecosystem.
This initiates the undoing of old established networks from the prior period of succession.
Networks established during the shift from exploitation to conservation mature over a long
period of time; however, the structural shift from release to reorganization occurs over a
much shorter time scale and is very disruptive. Although established networks deteriorate
and the interconnectedness of the ecosystem declines, reorganization sets the stage for a new
rapid phase of exploitation and entrepreneurial opportunity followed by a long period of
innovation and economic succession.
Exploitation to Conservation
Exploitation refers to the colonization of disturbed ecosystems where species capture easily accessible resources. It is the beginnings of establishing order to a chaotic system. The
conservation phase, however, is the ‘climax’ phase of succession where stored nutrients and
energy are at their peak and the system has achieved a high level of interconnectedness. It is
a result of a long period of growth and reorder in the system, and refers to the phase of the
adaptive cycle when the ecosystem has developed strong and complex interdependencies.
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Economic Ecosystems 259
As the ecosystem embarks on a long period of succession, it develops broader networks
and increases its connectivity. When no market niches are left unexploited, entrepreneurs
will look for new opportunities through innovations in established technologies rather
than opportunity identification or imitation. A key feature of succession is the macro-​level
stability of the system relative to significant churn at the micro-​levels of the organization.
Consequently, strong cross-​level synergies such as spin-​offs from large firms and more
active mergers and acquisitions markets develop in the ecosystems. The networks that build
on these interactions not only incentivize greater innovative activity, but also reinforce the
structures of the ecosystem.
Health
Diversity
Jane Jacobs (1961, 1969) made early and seminal advancements on the hypothesis that
increasing economic diversity is key to the vitality of cities. She described the engines of
growth for regions to be enabled by increasing connectivity to cities, as well as increasing
economic diversity within the region itself. Glaeser et al. (1992) studied a cross-​section of US
cities and found that ‘at the city-​industry level, specialization hurts, competition helps, and
city diversity helps employment growth’. Work by Feldman and Audretsch (1999) similarly
emphasized the importance of economic diversity in innovative activity.
Saxenian (1994) documented the relative success of Silicon Valley in developing its technology sector in the 1980s as compared with Boston Route 128. She argued that inter-​firm
and inter-​industry networks in Silicon Valley played a significant role in providing it with
regional technological advantage as a result of knowledge externalities leading to greater
innovative outputs.
Hidalgo et al. (2007) studied the co-​occurrence of products in the export portfolios of
countries to identify a relatedness measure across products, based on the expectation that
countries are more likely to produce goods together that require ‘similar institutions, infrastructure, physical factors, technology, or some combination thereof ’ (Hidalgo et al., 2007,
p. 484). The revealed network of products, called the ‘product space’, showed that more
sophisticated products were located in denser regions of the network, while less sophisticated products were on the periphery. Furthermore, they also found that advanced countries
tended to have more diverse and densely networked product spaces than the less developed
countries. They explain the developmental constrains on countries in terms of the connectedness of their product space and the co-​evolutionary patterns of their firms. Hidalgo et al.
(2007) apply a biological analogy similar in intent as the adaptive walks of firms along ‘holey
adaptive landscapes’:
Think of a product as a tree and the set of all products as a forest. A country is composed of a
collection of firms, i.e., of monkeys that live on different trees and exploit those products. The
process of growth implies moving from a poorer part of the forest, where trees have little fruit,
to better parts of the forest. This implies that monkeys would have to jump distances, that
is, redeploy (human, physical, and institutional) capital toward goods that are different from
those currently under production. Traditional growth theory assumes there is always a tree
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within reach; hence, the structure of this forest is unimportant. However, if this forest is heterogeneous, with some dense areas and other more-​deserted ones, and if monkeys can jump
only limited distances, then monkeys may be unable to move through the forest. If this is the
case, the structure of this space and a country’s orientation within it become of great importance to the development of countries (Hidalgo et al., 2007, p. 482).
Regions develop comparative advantage by having diverse but related economic structures.
Neffke and Henning (2013) studied the flows of labour across industries to identify an
‘industry space’. They define a skill-​relatedness measure based on the expectation that workers are more likely to move across jobs that have similar skill requirements. Consequently,
industries that have similar skill requirements should show greater cross-​industry flows
after controlling for other industry dynamics. Applying the industry space to study regional
diversification, they find that firms are 100 times more likely to diversify into industries that
are more skill-​related.
Ecosystem Resilience and Adaptive Capacity
The notion of resilience is an ecological concept that in economic geography has most often
been applied to a region’s capacity to resist and recover from disturbances, including natural
disasters (Fingleton et al., 2012). Rose (2004) discusses the behavioural response of individuals and regional markets to large-​scale disruptive events such as earthquakes within a computation general equilibrium framework. He defines resilience as ‘the inherent and adaptive
responses to hazards that enable individuals and communities to avoid some potential
losses’ (Rose, 2004, p. 41). Notably, he distinguishes the ‘inherent’ response as that which
allows for the substitution of inputs within the system for those that were affected by the
disturbance, and the ‘adaptive’ response as that which actively reconfigures the network of
relationships between suppliers and customers for better reallocation of resources. Both
these responses reflect the expectation for the region’s social and economic structure returning to a pre-​disaster equilibrium as its ability to accommodate shocks (Rose, 2004). While
initially appealing to traditional regional economists, economic geographers highlight that
as a necessity of the response to the shock the economic structure of the region also changes
and a new equilibrium state is reached through adjustment (Martin, 2012). Accordingly, an
evolutionary approach has been recommended whereby following a disturbance, the ecosystem does not return to pre-​disaster equilibrium, but instead is set on a new equilibrium
path (Holling, 2001).
In our ecosystem perspective we adopt not only a similar conception of ecological resilience set in an evolutionary framework, but also emphasize the dynamically stable growth
trajectories of regional economies (Martin and Sunley, 2007; Boschma, 2015). We consider ecosystem resilience to be the ability of the economy to return to, or continue on, its
path of long-​term development following a disturbance, and also allow for the ecosystem
to develop new pathways to development as a response to the disturbance. The trade-​off
between these two structural responses is framed in the literature in terms of ecosystem
adaptation versus adaptability (Hassnik, 2010; Pike et al., 2010; Boschma, 2015). Where
adaptation refers to a region’s ability to return to its pre-​disaster development trajectory,
adaptability refers to the region’s ability to innovate new pathways for growth (Pike et al.,
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Economic Ecosystems 261
2010) through industry speciation, for instance. In both these responses an ecosystem’s
diversity plays a critical role.
In terms of adaptation, regions with more diversified industrial sectors have been shown
to better accommodate sector-​specific shocks, provided other sectors in the economy are
not strongly coupled with the affected sector in terms of supply-​chain relations, but are similar in their skill composition (Essletbichler, 2007; Frenken et al., 2007; Neffke and Henning,
2013). These circumstances limit the diffusion of the supply or demand shock across sectors
while providing better-​matching opportunities for labour displaced by the shock in other
local skill-​related sectors.
In terms of adaptability, diversified ecosystems have improved capacity for algorithmic
recombination (Auerswald, 2008) resulting from ‘Jacobs’ externalities’ (Jacobs, 1961), thus
enabling opportunity for the region to innovate new development pathways. Industry speciation, in this case, is one potential development of ‘new pathways’ that can improve ecosystem outcomes as has been empirically identified by Neffke et al. (2011), but co-​evolutionary
patterns in institutional change also facilitate or inhibit the ability of ecosystems to respond
to large disturbances.
Key to the evolutionary perspective worth iterating here is the path-​dependent and co-​
evolutionary nature of the ecosystem, because an ecosystem may respond to a disturbance
in a maladaptive fashion that can reduce the overall welfare of the system, or set it upon a
new evolutionary path that is less socially optimal than the initial development trajectory
(Holling, 2001). Yet, in general, the potential for adaptation versus adaptability of the ecosystem is viewed as a trade-​off whereby the prior is the expected response of densely connected and interdependent structures, while adaptability is more plausible for systems that
have looser, more malleable ties.
Entrepreneurial Dynamism
Entrepreneurship is at the heart of economic change (Schumpeter, 1911), yet it takes an entire
ecosystem to help entrepreneurs bring innovations to market. Novelty in an economic ecosystem is a result of coordinated activities both within firms and with agents external to
the firm.
Entrepreneurship is a collective achievement that resides not only within the parent organization of the innovation but also in the construction of an industrial infrastructure that facilitates and constrains innovation. This infrastructure includes (1) institutional arrangements
to legitimize, regulate, and standardize a new technology; (2) public-​resource endowments of
basic scientific knowledge, financing mechanisms, and a pool of competent labor; (3) development of markets, consumer education, and demand; and (4) proprietary research and development, manufacturing, production, and distribution functions by private entrepreneurial
firms to commercialize the innovation for profit (Van de Ven et al., 1999, p. 149).
Furthermore, innovation is a dynamic, non-​linear process. Some innovations can have a
radical impact on the market, while most others are incremental (Freeman and Soete, 2009),
but it is far from a well-​defined phenomenon and requires the coming together of many factors to produce the right opportunity for commercialization. For instance, many inventions
never make it to market because complementary factors or vital inputs for production and
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commercialization may not be available yet (Fagerberg, 2005). Feldman and Zoller (2011)
found evidence that the presence of dealmakers in regional economies played an influential
role in fostering regional entrepreneurship. Dealmakers acted as brokers who functioned
to shape the network, manage structural holes, and ‘connect disparate actors to social networks’ (Feldman and Zoller, 2011, p. 27).
Such coordinated activities across various organizations within institutional frameworks
allude to multi-​level, networked, and interdependent relationships that define and enable
the innovative process. Entrepreneurial action is essential to evolution in economic ecosystems as it provides a selection mechanism that impacts structural change and is thus an
important indicator for assessing the health of economic ecosystems.
Conclusion
At the time when Alfred Marshall stated that ‘the Mecca of the economist lies in economic
biology rather than in economic dynamics’, the concept of the ecosystem had not yet been
developed; the ‘modern synthesis’ in evolutionary biology had not yet occurred; and mathematical models of evolutionary processes did not yet exist. Nearly a century later, economists
have access to powerful analytical tools developed by evolutionary biologists and ecologists
that have the potential to be directed towards the study of economic ecosystems.
In proposing that economic systems are, literally, ecosystems, and thus that they may be
fruitfully be studied as such, we are mindful of the fact that human beings do differ along
multiple dimensions from other biological entities. We expect that the path for research that
we suggest in this chapter ultimately will lead to an understanding that the study of economic ecosystems requires different tools from those developed by ecologists and evolutionary biologists. Thus, our contention in this chapter is not that humans are the same as other
biological entities, or that existing biologically inspired models ultimately will prove adequate in the study of social systems. Rather, it is that we will only understand the boundaries
for the application of biologically inspired models if we begin by taking economic systems
seriously as ecosystems, and studying their properties from that starting point.
In such a process, we are only at the beginning.
Notes
1. Herbert Spencer’s (1857) early work on evolution, specifically his seminal essay ‘Progress:
Its Law and Cause’ arguing that all structures in the universe evolved from simplicity
towards ever-​increasing complexity, had a foundational impact on developing biologically informed theories in the study of physical and social systems.
2. Thorstein Veblen, influenced by Spencer’s early work, first used the term ‘evolutionary
economics’ in his 1898 essay ‘Why is economics not an evolutionary science?’ (Veblen,
1898). More recently, pioneering work by Nelson and Winter (1982) has embraced biological analogies to advance the field of evolutionary economics.
3. Acs et al. (2014) introduced the related, but distinct, concept of a national entrepreneurial
ecosystem.
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Economic Ecosystems 263
4. Inbreeding also leads to greater risk of generically caused disease, as well as diminished
resilience of the phenotype.
5. As Lucas (1993, p. 263) finds: ‘A growth miracle sustained for a period of decades clearly
must thus involve the continual introduction of new goods, not merely continued learning on a fixed set of goods.’
6. Syverson (2011) provides a survey. Productivity is a residual measure of how well firms convert their inputs to outputs after accounting for observed characteristics, that is, a measure
for how well firms perform in given market structures (Syverson, 2004a), as well as their
ability to imitate the practices of the most productive firms (Bloom and Van Reenen, 2010).
Firm productivity is directly associated with firm-​specific attributes that, although unobserved, explain the wide dispersion in firm behaviour, even for firms in the same industry producing similar output. In the USA alone, considering manufacturing plants at the
four-​digit level of industry, plants in the ninetieth percentile of the productivity distribution made twice as much output with the same inputs as plants in the tenth percentile of the
distribution (Syverson, 2004b). Hsieh and Klenow (2009) found these productivity differences to be even larger for firms in China and India, where the ninetieth percentile made
nearly five times the output of the firms in the tenth percentile. Foster et al. (2008) account
for price changes (idiosyncratic demand shifts) that can affect the measure of productivity across firms and show that the differences in output persist even in the case of firms in
industries that produce homogenous products. How firms organize their production activities to produce output has been a core consideration of economic theory.
7. For example, as pioneered by Romer (1986, 1990, 1994). An inspiration for these theories
was Marshall’s observation, which was quoted at the outset, that ‘the mysteries of the trade
become no mysteries; but are as it were in the air’. Either Marshall was mistaken on this
point, or processes of production have become sufficiently more complex in the intervening century that his observation no longer holds.
8. The biological analogy grants these routines the evolutionary features of deterministic
behaviour, heritable characteristics across generations of routines, and selection based
on some routines being better suited to their markets than others (Nelson and Winter,
1982, pp. 51–​138).
9. Along similar lines Winter (1968) observes: ‘ “Knowing how to bake a cake” is clearly
not the same thing as “knowing how to bring together all of the ingredients for a cake.”
Knowing how to bake a cake is knowing how to execute the sequence of operations that
are specified, more or less closely, in a cake recipe.’
10. Early foundational work on innovation as search by Evenson and Kislev (1976) modelled
a simple trade-​off between the costs of search and the expected yield of success to determine that a research team will stop the search process when the costs exceed the expected
returns from success. Weitzman (1979) iteratively added to this discussion by introducing a dynamic programming model of an optimal sequential search strategy. His model
identified objective conditions under which a research team should engage in search, in
what order should they pursue multiple research options, and when should they stop the
search. In support of this observation, Kauffman et al. (1994) provided a simulation model
to show that parallel processing algorithms can increase the efficiency of search for complex designs.
11. An interesting recent development along these lines is the application of satellite imaging
and big data to study how economic systems grow or shrink over relatively short timeframes (Kearns, 2015). Such methods help delineate physical boundaries of economic
ecosystems.
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12. In his book Fitness Landscapes and the Origin of Species, Gavrilets (2004) makes a strong
case for the modern synthesis of the evolutionary approach to studying speciation across
academic disciplines outside of evolutionary biology. He outlines a series of models that
can be used to study various types and rates of speciation of populations. He developes a
series of models that can be applied to various studies beyond their immediate application
in evolutionary biology to even the social sciences.
13. Stochastic events may affect ecological niches differently in an ecosystem (Holling, 2001).
Similarly, a large disturbance to an entrepreneurial ecosystem may lead to collapse of
some entrepreneurial niches over others.
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