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Agglomeration, Trade and Spatial Development: Bringing Dynamics Back In1
Michael Storper
Sciences Po, LSE, UCLA
m.storper@lse.ac.uk
storper@ucla.edu
michael.storper@sciences-po.fr
Paper contributed to the Conference 50th anniversary of JRS,
New York, 23-24 April 2009
Revised version: September 28, 2009
Abstract
The field of spatial economics has made enormous progress in theorizing and measuring
agglomeration effects, trade costs, and urbanization. Typical models establish structural
determinants by making strong assumptions about which forces are relevant and how these
forces interact. But many of these assumptions, about firms, agents, spatial costs, and
market structures, are questionable. As a result, the field has a long way to go to establish
causality, and to be able to account for spatial economic dynamics. JEL: R11, R12, R13
0. Introduction
“What kind of a theoretical framework must we develop to understand the process of
economic change? The theory we possess is static; and while a truly dynamic theory may
be beyond our reach, we can incorporate the dimension of time as an integral part of the
analysis…..Economics is a theory of choice – so far so good. But the discipline neglects to
explore the context within which choice occurs.” (Douglass North, 2005).
Let us perform a thought experiment for regional science and economic geography. In this
experiment, we go back a half century, and imagine a hypothetical person with perfect foresight and
rationality, complete information about all the major “shocks” that will subsequently affect patterns
of regional development. Our hypothetical individual also has perfect knowledge of the initial
resource allocation. Thus, she knows about the rise and development of information technology; the
end of the post-war mass production manufacturing economy’s dominance and the 1970s
productivity slowdown; the development of road systems and air travel and other technologies that
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affect trade costs; the rise of air conditioning and the evolution of energy prices; changes in
preferences for housing and amenities; and the various price elasticities associated with all these
shocks. Let us further imagine that our hypothetical person was armed with some of the best new
tools in regional science and geographical economics, i.e. the tools that in reality did not exist in
1959. Such tools include the Dixit-Stiglitz-Krugman (DSK) model, on the one hand, and (very
differently) the “new urban economics” best represented by Glaeser (2007, 2008), as well as many of
the other models that have come from recent specialized work in regional economics and economic
geography, on topics such as agglomeration economies, trade costs and the fragmentation of
production. In spite of the great variety within this work, it shares the renewed commitment to use
economic theory to measure and interpret processes of urban and regional development.2
With these powerful tools, and with such knowledge, our lucky hypothetical person would
have been able to forecast a certain number of probable equilibrium levels of industry
agglomeration, and possibly say something interesting about changing housing and land use density
and city sizes. She might also have been able to forecast, in a general way, the development of the US
Sunbelt, the Brazilian Centre-West region, or Europe’s southern periphery. But she would have been
able to say very little about such topics as: the specific turbulence in the ranks of city-regions (which
metro areas move up and down); the spatial pattern of income distribution; the prices of many
factors in different territories; and the incomes of many groups of agents in many places, and
especially how they differ from one place to another. She would still have also probably made
significant errors about the rank-size distribution of city-regions in many parts of the world.
The main reason our perfectly-informed and theory-armed hypothetical person would not
have been good at these tasks is that the models she would have been using are good at comparative
statics, but much less effective at identifying complex, step-by-step evolutions from one allocational
equilibrium to another over geographical space. These models largely abstain from considering
directions of causality and sequences of development, endogenous changes in prices and preferences
for resources in different locations, and collective human learning and its economic expression,
innovation. They are also quite reluctant to consider many of the behavioral attributes that
contribute to price formation in a friction-filled and institutionally-differentiated geographical space.
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They have largely not attempted to identify the non-conventional rationing rules that come into
being when factor or product markets in certain places are imperfect and equilibria highly unstable,
made all the more complex by factor mobility between interconnected spaces. All these tasks would
require that we consider highly heterogeneous agents and behaviors over heterogeneous spaces, and
over time. In this paper, I will argue that geographical economics and regional science, especially
within the NEG tradition, should now devote greater effort to solving these problems.3
Notwithstanding the critical tone that will thread throughout this article, this critique would
not even be possible in the absence of the theoretical progress of the work that it reviews.
Economists have rightly reproached regional scientists and other students of the geography of
economic development for failing to have a general model of “second nature” geography, which is
necessary in order to account systematically for uneven and differentiated spatial-economic
development. The work reviewed here has taken up this challenge, and hence made it possible to
imagine a geographical economics that gets beyond the historical tendency of the field to lose itself in
accounts of endless empirical variation in space. Thus, this critique situates itself squarely on the
side of those who consider that general frameworks are necessary to scientific progress in
geographical economics.4
1. The origins of spatial concentration
The Dixit-Stiglitz-Krugman (DSK) model achieves the explanation of spatial concentration
(and lack thereof) as an endogenous outcome of the economic process. It does so by incorporating
three important, realistic features of the contemporary economy, i.e. labor mobility, product-variety,
and firm-level economies of scale. There were earlier core-periphery formulations in the field of
geography and development studies, such as Myrdal (1957), Hirschman (1958), Perroux (1955) ,
Dixon and Thirlwall (1975). Earlier approaches had to make assumptions about specifications in
order to get their results (e.g. in Myrdal about total factor productivity, in Hirschman and Perroux
about “linkages,” or about the pattern of imports and exports resulting from all this in DixonThirlwall extensions). NEG improves on previous efforts by replacing the need for endless
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measurement of linkages with more general, deductive models of trade costs. It also establishes
firm-level economies of scale and product variety as causes of concentration, rather than economywide TFP differences, and thus tries to get closer to micro-foundations of productivity differences
than the Myrdal model.
The criticisms of the DSK model from economists have been mainly technical, centering on
its analytical insolvability (Fujita and Thisse, 2009);5 the use of iceberg trade cost assumptions; the
lack of spatial interaction in competition; and the way that specifications for scale of concentration
are derived mechanically from assumptions about the elasticity of substitution of product varieties.
But a deeper problem is that the DSK model has a questionable view of the behaviors that initiate
spatial concentration. Both DSK and classical central place models represent the spatial-economy as
hierarchical market areas for goods that are differentiated by variety and scale. DSK adds labor
mobility as the dynamic force, and the size of the home market and hence of the core regions are then
endogenously determined. The behavior that motivates this determination is workers’ pursuit of
consumer utilities. The causal story has agricultural workers search for utility by following product
variety and lower prices (as a consequence of firm-level scale economies). This then leads to spatial
concentration as firms crowd in to follow them, unleashing more scale economies, followed by a
“snowball” process of mutual reinforcement. In this light, DSK fundamentally commits us to a view
of the origin of the formation of core regions as what Muth (1971) would have formulated as “jobs
going to people.” Once the snowball begins to roll, then the rest of the dynamic is modeled as twoway causality, in the form of a spatial dance of jobs and people, itself determined by complex
elasticities of trade costs and scale economies, giving rise to a number of possible stable patterns and
unstable states, where instability can be augmented by lower trade costs (Baldwin and Forslid,
1997).
Aside from the highly improbable view that spatial-economic development is a consequence
of endless knife-edge tradeoffs, the basic story on how the process gets going initially is also
improbable. Prior to the industrial revolution, home market effects and product diversity were
certainly present in urbanization, but they did not initiate the growth of the biggest centers. Urban
centers came into existence because they concentrated propulsive activities, such as political power
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(the State), trade (rivers and ports), and finance (Hall, 1998). Since the industrial revolution, such
propulsive industries have greater internal fragmentation (social and technical divisions of labor)
and hence involve increasingly elaborate intermediate linkages among the fragments. Some of these
linkages are geographically costly, thus encouraging co-location and agglomeration.
In NEG work, inter-urban/inter-regional linkages are seen as driven by trade costs (AbdelRahman, 1994), while intra-metropolitan fragmentation of production is seen as driven by land rents
and managerial supervision costs (Rossi-Hansberg, Sarte and Owens, 2009).6 DSK specifically holds
that product varieties tend to cluster together; with large home markets, more varieties are
supported, as are bigger individual production units for homogeneous goods. There are strong home
market effects in certain types of retailing today, as well as in infrastructure, logistics and
entertainment services, and certain types of business services. By contrast, it is unlikely that strong
home market effects are any longer present in much routine, large-scale manufacturing.7 Such
manufactures, both intermediate and final, have sufficiently low trade costs today (in the developed
countries) at almost any level of product variety, that they are not going to crowd into places and
create DSK-type spatial concentration. Moreover, thresholds for home market effects in most
retailing and some services are probably reached at levels a fraction of the size of most cities’
markets today in developed countries. This is probably one reason why empirical research on home
market effects has not turned up convincing results (Behrens, et al 2004; Redding, 2010; cf. Ciccone
and Hall, 1996; Head et al, 2000). It is unlikely that this problem can be fixed, as some have tried to
do, by altering the trade cost assumptions in the model (e.g. replacing iceberg costs with something
else).
Thus, the foundational model of NEG with respect to agglomeration and formation of core
regions – an indeterminate, simultaneous dance of firms, consumer-workers, and product varieties
and scales – is not very convincing. A more convincing perspective starts decisively on the
production (firms) side. In the eighteenth and nineteenth centuries, when transportation systems
were little developed, virtually all innovations were confined to a small number of possible locations.
In more recent times, agglomerations still get seeded in places by the – relatively mysterious –
geography of innovation, which brings with it complex patterns of fragmentation, costly intermediate
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linkages and local variety in outputs. Grafted onto this primary cause are secondary dynamics such
as the growth of the local population, home markets for consumption, and central place effects in the
allocation of consumption outlets. This is the basis for a more convincing dynamic, foundational story
in geographical economics, as we shall now see.
2. Trade costs, agglomeration, and urbanization
The NEG has made considerable progress in partial approaches to forces that shape spatial
development, notably in theorizing and measuring trade costs, agglomeration, and trade patterns
(Anderson and van Wincoop, 2004; Head and Mayer, 2004; Rosenthal and Strange, 2001). The field
has deepened our understanding of these areas in many ways: it has established better microfoundations of agglomeration; shown how transport and trade cost reductions may have counterintuitive effects on spatial concentration and trade patterns; identified many subtle barriers to trade;
theorized how economies of scale and product variety interact with spatial concentration; and
suggested that history can matter, in the form of potential multiple equilibria. But this work is at
risk of becoming victim of its own success, in two ways. One such risk is the accumulation of so many
partial insights that no sense can any longer be made of broader tendencies in spatial development.
Another is that, absent sensible constraints on modeling, the insights can be cobbled together to yield
essentially absurd visions of spatial development in the large.
To see both these strengths and dangers, we can begin with the most convincing synthesis of
the forces behind agglomeration. Duranton and Puga (2004) felicitously capture the major sources
of spatial concentration in their monikers of “sharing, matching and learning.” We know the most
about sharing, and especially the influence of trade costs on the degree of co-location within the
input-output system. Sharing (local trade in intermediates) seems to be the weakest source of
agglomeration in developed economies, although this conclusion is highly sensitive to the
measurement technique employed (Puga, 2010).8 In developing economies, by contrast, local inputoutput sharing remains more important than in developed world cities. This has long been observed
by students of industrialization and urbanization: territories with an immature infrastructure system
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have intermediate trade costs that are sufficiently high to motivate co-location in order to share
inputs. In developed economies, the apparently low and declining level of sharing has led many
observers to claim that agglomeration is instead caused by competitive selection. Empirical evidence
suggests that agglomeration is more powerful than selection (Combes, et al, 2009). Moreover, the
theoretical separation of the two is ambiguous. Even if less competitive firms leave an
agglomeration, it does not follow that selection accounts for the presence or the productivity levels of
the remaining firms, unless it can be shown affirmatively that such firms only remain because they
now somehow dominate the market and that this market is necessarily local. In other words, the
firms that remain could still be there because they enjoy agglomeration effects; it is just that they will
have crowded out those that would have wanted to enjoy such effects as well. Only average
productivity is affected by spatial exit, but the latter does not explain all of the productivity of the
remaining firms.
In “top of the food chain” activities – Wall Street, Silicon Valley, Hollywood – local input
sharing is probably quite elevated. This is due to high levels of product variety, specialization and
market uncertainty, which raise intermediate trade costs. Since these industries do not have very
high weight-to-value ratios, however, one can still ask what trade costs would be sufficiently high to
merit co-location. Two such sources come to mind. The first is the “Smithian” effect of greater skill
specialization in agglomerated firms (Baumgartner, 1988). Closely linked to this is the notion that
more specialized skills can only be used with the aid of greater managerial and communicational
inputs, because they are more cognitive and less routine than general skills (Levy and Murnane,
2004).9 In this case, then, we must ask : “what is being shared?” Complex divisions of labor are
coordinated through shared knowledge, conventions and “cues,” many of them tacit or at least only
partially codified, and hence principally shared within complex, specialized and – for that reason –
localized networks of actors (Granovetter, 1985). This is the substance of what is described as “high
intermediate trade costs” in these activities.
Another dimension of these networks of actors can now be signaled. They tend to exist in
industries with high levels of uncertainty about products and processes and rapid change in them: as
a result, their knowledge is difficult to codify and stabilize in blueprints and rules. In this case, firms
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cannot smooth output levels, and therefore need to avoid labor hoarding during downturns and
rapidly access highly specialized labor during upturns. Their workers need to be able to move fluidly
from job-to-job in these unstable markets, re-matching their highly specialized skills to employers.
Large pools of employers and workers are the solution to this matching-without-hoarding problem
(Jayet, 1983). In other words, sharing and labor market matching are not clearly separate. Moreover,
unless we introduce the degree of stability/instability to the employment contract, it is difficult to
explain why firms would not choose to spread themselves out and avoid spatial competition in the
labor market. Thus, any causal and dynamic analysis of matching must integrate the causes of
turnover, and those will lie in the dynamics of industrial organization, itself highly linked to
innovation, uncertainty and product variety.
Two challenges thus emerge for theory and modeling efforts. The first is to incorporate, as a
basic starting point, the heterogeneous firms and agents described above. By “heterogeneous” is
meant the positioning of firms in markets that are extremely technologically dynamic, so that at any
given moment, even the firms that are in a given product market are likely to display important
forms of difference according to their pathways of innovation and the product mix within the
complex product space that is evolving.10 The second is to incorporate an endogenous dimension to
uncertainty and variety, and part of this endogeneity is what occurs locally, as these agglomerated
actor-network systems generate innovation in product, processes and organizations on an ongoing
basis (Audretsch and Feldman 2004). We will return to this point shortly in discussing “learning” as
a source of agglomeration economies.
Aside from these top-of-the-food-chain activities, in much of the rest of the economy,
standard trade costs for manufactured intermediates have declined greatly (Glaeser and Kohlhase,
2004). As a consequence, production systems are increasingly unbundling themselves into different
territories (Baldwin, 2006). Even Silicon Valley and Wall Street are now mostly the brains of far-flung
input-output systems. But trade cost explanations of outsourcing and unbundling give us little handle
on the deeper causes of the spatial economy’s de-agglomeration processes and their dynamic of
development and change. The principal direction of causality is likely from the development of the
technology and market structure of the sector to its scale and scope attributes, to its basic internal
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trade cost structure, and hence to its geography. Older models, such as the product cycle, propose
interesting stylized facts about such attributes (Norton and Rees, 1979). Technological maturity
reverses the forces that lead to agglomeration, altering the scale-scope characteristics of the sector,
its internal division of labor, the quantity and unit costs of trade, and its geographical characteristics,
leading to de-agglomeration.
How should we theorize the dynamics of urbanization and regional development that occur
in the wake of de-agglomeration of sectors? Duranton and Puga (2001) improve on the product cycle
by linking the creation of new products to ‘nursery cities,’ where local learning is centered, while
Duranton (2007) shows that there are different temporalities of diffusion. The “where” of deagglomeration is sectorally-specific (type of product and its associated specific trade costs and
market locations). Firms seek cheaper land because they are no longer constrained by
agglomeration economies to co-locate in an innovative milieu and because the rise in scale of
production , as technologies become routinized, makes production more land-intensive, while the
lowering of intermediate and final trade costs (because of increasing scale and standardization)
allows a wider choice of locations involving cheaper land.11 This enables firms to seek cheaper, less
skilled labor, as in the successive waves of relocation from the Frostbelt to the Sunbelt in the US since
the 1930s (McLaughlin and Robock, 1949). Initially, then, jobs move to people in such deagglomeration-driven development. But subsequently, the vast majority of local population change
occurs first through rural-urban intra-regional migration, and then through inter-regional longdistance migration. As the snowball really gets rolling, people move to jobs. The directions of
causality are two-way, but do not appear to be in the same proportions. Moreover, there is a clear
direction to causality of the inter-regional income differences that are thus generated. Deagglomerating industries (for tradeables) have lower average skill levels, hence lower wages, than
agglomerated ones. Their respective regions diverge in terms of income.
Much regional economics would be skeptical about this claim about the direction of
causality. In partial approaches, it can hold something constant (such as the cost of fragmenting
operations, or scale, or skill levels) and then predict the degree of de-agglomeration using a transport
cost function; in this way, the issue of deeper causality is set aside. When a more ambitious model is
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proposed, however, industrial organization (fragmentation), land costs, labor costs and transport
costs must all be potentially elastic, such that firms can be assumed to be spatially indifferent to
location.
Hidden behind this kind of reasoning is a very strong assumption about the underlying
causes of development of the economy. It consists in assuming that technology (possible ranges of
factor proportions) is induced by the economic environment, i.e. that there is no bias to such change.
Such a view is not sustained by the evidence on the directions of major technological changes
(Mokyr, 1990; Rosenberg, 1982). If the broad lines of technological development are driven by
science and ideas, then fundamentally the evolution of product and production technologies drive
scale and scope, and hence drive the division of labor, which in turn drives the quantity of trade in
the sector, and this then interacts with the transport system to drive unit trade costs and the role of
distance in them. Thus, it is unrealistic to assume that at any given moment there are strong, and
equally symmetrical elasticities among all of them. Instead, there is a small number of possible
equilibria, all of which are determined by a strong hierarchy of causes.
There is one exception to this claim about directions of causality. Better technologies of
transport and communications do not only open up certain new choices of organization for a set of
existing outputs; they also generate new outputs. Faster and cheaper transport makes possible the
recombination of more inputs into more varieties; they also make competition more time-dependent
by reducing the time for competitors to imitate and invade a market space (Harrigan and Venables,
2006). Together, they stimulate the creation of new varieties, which then engender complex
intermediate relationships, many of which have high spatial costs. This is the “Zara/LA Apparel
effect” (differentiated goods) rather than the “Wal-Mart effect” (homogeneous goods). This indirect
effect of new spatial technologies is why every new round of supposedly distance-effacing
technological change has actually increased agglomeration forces (Storper and Venables, 2004;
Leamer and Storper, 2001; Duranton and Storper, 2008). It lies behind the recent attention to the
growth of “skilled, interactive cities” (Glaeser and Maré, 2001). But once again, there is not an
infinite range of simultaneous tradeoffs of organization, linkages and location, but a clear hierarchy
of developmental forces.
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Turning to the third angle of Duranton and Puga’s triangle, there is increasing evidence of
“learning” and localized knowledge spillovers, mostly indirectly observed through proxies in patent
data and some detailed case studies (Jaffe and Trajtenberg, 2002; Feldman, 2003). This long-standing
Marshallian theme has been imported to urban economics in agent-based models that hold that
workers congregate together in order to interact, and that this explains both the high wages of
certain places and the specific agglomeration-wage premium. The claim that skilled people cluster
together in order to raise their productivity (and wages), derives from the assumption that such
clustering enables them to generate more knowledge; but examined closely, this is all based on
working backward from the aggregate observation that skilled people do indeed cluster together. It
does not answer the chicken-and-egg question of jobs-to-people or people-to-jobs.
Moreover, we remain far from understanding how knowledgeable people use spatial
concentration in order to be more innovative, by creating or absorbing, and then using knowledge.
Storper and Venables (2004) have made a stab at explanation, based on the properties of face-to-face
contact(from knowledge economics and contracting), as have others, but my guess is that the
economic sociologists such as Powell et al (2009), who argue that the skilled build specific actornetworks and then endogenously generate norms and rules that favor innovation, may be making
more progress toward explanation. As shall become apparent shortly, in an innovation-driven
conception of economic geography, it is essential to be able to explain learning.
All in all, work on agglomeration has progressed considerably in the NEG, urban economics
and regional science. But it remains far from an adequate causal account of the dynamics of
agglomeration and de-agglomeration. A trade cost approach deepens the understanding of choice
sets, but the sources of the high trade costs that seem to underlie agglomeration appear themselves
to have extremely complex, hard-to-measure causes. Deciphering the causes of agglomeration will
ultimately require a much better understanding of the complex interdependencies between agents
that lead them to congregate together, and how some of these interdependencies may be
endogenously formed in local contexts, as well as the sequencing of jobs-people of those seeking to
enter into these local systems. Economics would certainly draw inspiration from the rich literatures
in urban history on agglomerations (eg, Hall, 1998), as well as the recent research in economic
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sociology, innovation studies, and economic geography that has been carried out on innovative
milieux and industrial districts (both high tech and low tech), as inputs into analytical models (eg,
Scott, 1993; Powell et al 2009; Maskell, 1992). Understanding de-agglomeration will require dynamic
models of relationships between industrial organization, trade costs, and factor demands.
Urbanization and Agglomeration
This consideration of the dynamic forces behind sharing, matching and learning returns us
to an issue raised earlier in this paper: do people follow jobs or jobs follow people? The view of
causality that I am defending here is that the two directions of causality are not equal. Innovation is
at the origin of cycles of economic and spatial development (Aghion and Howitt, 1992). The advent of
major new products or new basic technologies intensifies the need for sharing, matching and
learning. This in turn generates location and movement of people to jobs. The type of innovation we
are most familiar with is when a technology or leading firm gets established somewhere, dominates a
commercial breakthrough, and launches the establishment of a local input-output system. The
region in which this occurs then breaks away from the pack of competitors; this allows the snowball
of recursive effects between localization and home market effects to start rolling and it then picks up
speed.12 A second type of innovation is largely political or institutional. The economic reforms in
China created a shock to the world production cost structure. Long- distance trade costs
subsequently declined, but this was a result of the way that the increasing scale of production in
China, unleashed by these reforms, set into place a virtuous circle of improvements in
intercontinental transport. Another example of political innovation is that in 1947, the Taft-Hartley
Act created two separate labor relations systems in the USA, dramatically lowering relative labor
costs in the South, and encouraging a wave of relocations from the North. This got underway well
before the Interstate Highway System was built or air conditioning was widely available, but it
subsequently generated the demand for rapid shipment of goods, especially inputs, between South
and North.
Where will regional sharing, matching and learning occur, following a major innovation?
New technologies open up “windows of locational opportunity,” where the sharing, matching and
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learning processes of existing places may no longer provide them decisive productivity advantages
over new places. People then move to jobs, kicking off the urban development cycle (whether in new
places, or “on top” of existing cities). Some NEG models derive such centers by experimenting with
different specifications for trade costs, some of which can lead to big new centers (“black holes”). By
incorporating labor mobility, they have also developed intriguing suggestions about dynamic
possibilities, such as tipping points and instabilities in patterns of spatial-economic development.
These models are heuristically stimulating in the way that they present different ranges of trade
costs and locational outcomes, but they do not concern themselves with the initial sources of demand
for local sharing, matching and learning, The principal source is new or ongoing innovation, which
augments uncertainty and variety, and hence raises intermediate trade costs. From there, we get a
certain degree of agglomeration, which is initially not amenable to trade cost identification by its own
agents. This has to wait until technological uncertainty diminishes sufficiently to allow agents to see
beyond uncertainty and their own animal spirits, and to begin to analyze the emerging organizational
structure of the industry and its possible alternatives.
Where are these places? Silicon Valley could have emerged in many regions. There was no
particular concentration of skilled workers there prior to the decision of William Shockley to move to
Menlo Park (to be near his mother), or of the success of the founders of Fairchild in improving chip
design. These unique events are not amenable to theoretical generalizations (innovation as a general
process is, but not the precise ‘selection’ of a breakthrough technology). Once such events occur,
however, then there is a definite point of attraction for skilled workers. Initially, the people in place
in the pre-commercial innovation process create their own skills, through learning-by-doing in situ;
once the breakthroughs happen and the snowball gets rolling, skilled labor pools are created through
in-migration and through more elaborate formal and informal processes of skill creation locally and
elsewhere (Storper and Scott, 1987).
In this light, an innovation-and-growth approach to the dynamics of agglomeration and
regional development enables us to think more effectively about the role of product variety in spatial
development. Learning in agglomerations – which depends on spatial interaction -- combined with
the feedbacks from lower long-distance trade costs (enabling specialized new local products to attain
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wider markets), can allow the local economy to generate much more variety. As noted earlier, this in
turn can “seed” the process of agglomeration over and over because such continuous innovation
recreates re-generates higher intermediate trade costs (new or continuously improved products
often involve substantively complex knowledge or fluctuating markets). The snowball of this effect is
not initiated, as in the NEG, by drawing in consumers, but instead from the “nursery effect”
(Duranton and Puga, 2001). A more restricted version of the product variety theme can be found in
the “quality ladder” concept of Grossman and Helpman (1991). This latter concept allows inclusion of
a price ladder, which is useful for modeling. But product variety is more than moving up or down a
quality ladder, as it includes invention of new product varieties as well as differentiation of existing
ones. Another contribution to this theme comes from Baldwin and Martin (2004), through the
device of the spatial distribution of the “innovation sector.” However, the real dynamic problem is
not the allocation of an existing innovation sector, but its ongoing dynamic of self-creation and
regeneration, itself involving both local interactions and long-distance factor mobility and
absorption. Baldwin and Martin (2004) then operationalize endogenous growth by transforming it
into an investment (capital accumulation) function, thereby returning to the initial Myrdalian
inspiration. But merely investing is not enough to secure dynamic advantage, if the investments are
not linked to innovation and staying on top of the product quality ladder, as many older industrial
regions know only too well.
It follows that the creation of the core regions, those that “pull” the space economy forward,
cannot be reduced to home market effects or firm-level economies of scale, although home market
effects may indeed be affected by the concentration of population in a place as people follow jobs. 13
This would be equivalent to telling the story of the industrialization of North-West Europe or the rise
of the US Manufacturing Belt through the motor force of home market effects, or the decline of many
older cities simply through consumer choices to go elsewhere.
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Urban Systems and Income Hierarchies
Any putative “big picture” in the field of economic geography or regional economics revolves
around linking three parallel dynamics: the agglomeration and de-agglomeration of productive
activities; the development of city-systems (population dynamics); and the spatial income
distribution, usually considered through the optic of convergence and divergence. In addition to
their internal lacunae, the bridges between them are only partially built at this time.
A great deal of recent research has been devoted to income convergence at both continental
(eg North America, Europe) and world scales in recent years (Sala-i-Martin, 2006; Pomerantz, 2000;
Barro and Sala-i-Martin, 1995). Even in the most integrated countries and with the highest levels of
factor mobility (i.e. the USA), there seems to be an upper limit on convergence among states
(Connecticut has almost double the per capita income of Mississippi). Most importantly, all such
results are highly sensitive to the geographical units/scales used, and to whether data are
population-weighted or not, and highly constrained in any case by the lack of geographical
accounting units that make economic sense. When examined at finer scales, such as metropolitan
region or census tract, tendencies toward convergence disappear, even in the USA (Yamamoto,
2008).
In NEG, one approach suggests that spatial income patterns could be driven by regularities
in how trade costs drive the spatial unevenness of production, possibly taking the form of a “bell
curve” of inter-regional inequalities. This corresponds to the transition dynamic in the initial phases
of industrialization in most countries. But the process for developed countries is much less clear.
Even if technically some models predict that different levels of trade costs could give rise first to
concentration and then to dispersion, it is difficult to get from this to the big picture. The
development cycles for all sectors would have to be lined up, so that overall concentration and
overall dispersion would be predictable. In reality, spatial product cycles are in different phases at
any given time, from one industry (or even product) to another. The economy experiences both
concentration and dispersion, with the overall spatial income picture just an average of this
multifaceted process.
16
Innovation and agglomeration push toward divergence, within and between countries,
within the developed world, and between developed and emergent economies (Aghion and Howitt,
1992). There will, arguably, be periods of low innovation in which convergence is stronger than in
other periods, and others where innovation will drive divergence. 14 It is hence unlikely that any
level of labor or capital mobility could overcome the ongoing unevenness of the innovationagglomeration phase of economic development,15 nor the sharp distinctions among territories
according to their position in the hierarchy of innovation/product quality/skills (and hence
incomes). The contributions of Fujita and Thisse (2002) and Baldwin and Martin (2004) emphasize
the geographical concentration of skilled workers, who innovate, and whose innovations are sources
of the long-term growth dynamic of the economy, and where this primary innovation-driven
concentration is supplemented by the home market effect. Both sets of authors remark that
Krugman and Venables (1995) cannot develop an effective explanation of persistent income
divergence without a model of the renewal of advantages in high-income places, something like
Myrdal’s “backwash” effects. In any case, the dialectic of divergence-convergence is deeply inscribed
in the developmental dynamic of the economy. Recently, dynamic models of the geography of high
productivity have appeared (Melitz, 2003; Melitz and Ottaviano , 2008), based on selection effects
linked to export-propensity; dynamic models linked to geography of innovation would be the next
step.
The causes of the shift in the scale at which income convergence-divergence is played out
could obviously be numerous, ranging from intra-metropolitan production fragmentation and
residential segregation to intermetropolitan specialization and agglomeration. It remains to unify
this work with a better model of why inter-metropolitan incomes differ even within advanced
economies, even within similar structural classes of cities. This is a challenge, since labor mobility
within countries is much higher than at the international level, where its relative weakness is said to
account for persistent income divergence there, along with certain frictions to capital mobility, and
institutional differences (Helpman, 2004). Institutional differences between, say, New York and
Detroit might also be considered as dynamic sources of income divergence, especially if they interact
17
with agglomeration and specialization in a dynamic way, but they are certainly not of the same order
as institutional differences between the US and Zimbabwe.
Moving from these two dynamics to the third, the long-term evolution of the urban system,
has proved to be one of the most difficult tasks for geographical economics and regional science.
Much research starts from the notion that the Zipf law adequately describes regularities in the
evolution of urban systems; but this observation seems confined to non-primate urban systems such
as the US and Germany, but does not fit Japan, France, the UK, Argentina or Mexico. Researchers
must decide if the latter or the former are deviations from “normal.” Very probably, the big
difference is long-term history, which creates sunk costs in the urban system, and the ways these are
inscribed in rules that structure the current expansion of land supplies and the form of the transport
network. In primate systems, rules, networks and preferences (or at least propensities) direct output
and population expansion to the metropolitan fringe of the primate centers, and in non-primate
systems in greater proportion to inter-metropolitan smoothing of the hierarchy. It may also have
something to do with preferences for individual mobility, which seem to differ greatly among
countries.
A second concern in explaining long-term evolution of the urban system is whether to go
from organizations and agents upward – - firms and household behavior – - or from markets and
prices downward– land and labor prices in places. In the first approach, as pioneered by Henderson
(1974) and Black and Henderson (1999), the changing agglomeration economies of industries and
geographical fragmentation will determine the basic structure of the urban hierarchy. There is also a
long line of attempts by geographers to work from the innovation diffusion perspective, dating back
to Pred (1966, 1974, 1977, 1980), inspired by Hagerstrand (1967). Another line of geographers
emphasizes agglomeration dynamics, inspired by Perroux-Hirschman-Myrdal. These include Norton
and Rees (1979); Scott (1988); Scott and Storper (1986); Storper and Walker (1989) and Storper
(1997). The findings of historical geographers such as Meyer (2003), in his work on the US
Manufacturing Belt, provide strong back up for building from firm behavior on upward.
More recently, in the NEG world, we may cite Fujita and Krugman (1995) and Fujita,
Krugman and Mori (1999) as early attempts to deal with formation of new urban centers, but they
18
were highly limited by their use of the monocentric model. Fujita and Thisse (2002) added
endogenous growth with horizontally differentiated products (thus, they did not incorporate the key
issue of quality differences as a proxy for innovation), but they did consider the “spatial distribution
of the innovation sector,” and resulting spatial inequalities. Duranton and Puga (2001) then made a
major advance in the formalization of product life cycles and the existence of different types of local
economies, and Duranton (2007) added a time dimension to urban economic change, based on
relationship to innovation or diffusion. In all of these, the ambition could be to understand the
connections between the technological evolution of industries and their spatio-temporal dynamic,
and how this aggregates upward to patterns of agglomeration economies and their inter-urban
allocation. Dynamically, we would be able to better account for the rate at which urban units are
added, old ones expand or decline, and turbulence in the ranks of the system.
A more markets/prices-downward approach is represented by the line of work from Gabaix
(Gabaix and Ioannides, 2004) to Rossi-Hansberg and Wright (2007). Thus, cities or regions are
considered to be organizations, which belong to the intermediate level of aggregation in the
economy. Agents choose cities because of the benefits of agglomeration; but the number and size of
cities react to industry productivity shocks in a way that exhausts increasing returns at the local level
and yields constant returns in the aggregate. In this view, Phoenix and Las Vegas tame land, labor
and product prices in Boston and San Francisco. The authors claim that this mechanism yields a size
distribution of cities that closely resembles that observed in the data. In other words, they claim to
have resolved the puzzle evoked above: firms and industries generate the benefits of agglomeration
in such a way that agents distribute themselves in space in such a way that there is both a regular
spatial distribution of population and output and a regular size distribution of firms, and both are
consistent with overall linearity in production.
These are sweeping claims about the dynamic outcomes of allocational mechanisms of the
economy – in time and space. Most of the pieces of this very large puzzle have not been identified
empirically, but rather by deduction from a certain theoretical interpretation of the aggregate data.
We are not told how, on the ground, the firms and industries get from high levels of agglomeration to
lower levels, how agents arrange themselves in one pattern and then another, and how all the prices
19
in land, labor, and outputs actually come about over time, and why we can be so confident that the
aggregate distributions of population and industries are so regular, and how they generate constant
returns in the aggregate (and if so, what is the source of increasing wealth over time, in contrast to
the Solow- Schumpeter-Romer view of this phenomenon). It is now incumbent on the field to not
leave it at that, but to test alternative visions of the sources of change – both in terms of the overall
vision of causes, and by filling in the ways that all the change mechanisms would really work and
how they would sequence together.
3. What kind of economics for spatial economics?
In order to develop a regional science that seeks to understand the dynamics of change and
territorial development, what kind of economics should we use? In opening, we noted that one of the
principal reasons that geographical topics have attracted more interest in recent years is that the
DSK model made space legitimate to economics by finding a general theoretical solution to the
existence of spatial concentration and core-periphery patterns of development, reconciling these
phenomena to a general equilibrium framework. This has in turn inspired a great deal of work on
specific forces – notably in the areas reviewed in the previous section – and these can be used to
identify multiple equilibria and even branching points in spatial development. The challenge now is
to develop a spatial economics with assumptions, modeling strategies, and empirics that tackle the
main sources of change, dynamics and territorial development.
The spatio-temporal dynamics of general equilibrium: innovation creation and diffusion
To begin to understand this challenge, consider a core issue in spatial development: the
possible existence of localized externalities, usually known as “MAR externalities. 16” In the Romer
endogenous growth model (Romer, 1986, 1991), new knowledge can be endlessly re-used,
generating growth-inducing increasing returns. Non-excludability and non-rivalry, over the long-run,
20
allow such knowledge to be used widely, thus subjecting it to competition dynamics that ultimately
eliminate technological monopoly rents, which arise at the moment of innovation.
At the moment of innovation, there is frequently localized sharing and learning (Malecki,
2010). The participants in the “secrets” that are in the local “air” have a restricted entry ticket to
innovation and its benefits. This access allows them to reap localized knowledge rents, which in turn
are what fund the rapid development of innovation and the expansion dynamic in innovative
activities.17 This is the principal reason why incomes can have such high levels in certain
agglomerations in the world, and get reflected in local wages, land prices, and so on. It explains why
people (especially the skilled) follow jobs to these places.
Of course, if the story ended there, long-term economic growth would be very limited, as
most of the benefits of new knowledge would be restricted to the localized rent-extracting holders of
that knowledge. But, as is implied in a very simplified way by the product cycle, this is not what
happens. Knowledge that is invented in a restricted circle ultimately changes form. Novelty becomes
codified and standardized. As the knowledge is codified, the geographically-restricted communities
described by Marshall give way to wider geographical diffusion of the knowledge, through deagglomeration of its users (or, more aptly, both de-agglomeration and a variety of other processes
such as start-ups and imitation and other forms of innovation diffusion). 18 Moreover, this
breakdown of spatial knowledge monopoly corresponds to the economic diffusion of these
innovations into other parts of the economy (other sectors, uses, and users), in a way that could be
seen as consistent with the Romer model. The breakdown in geographical and economic exclusivity
of knowledge go hand-in-hand.
This means that the term “MAR externalities” is technically incorrect: there are localized
“MA externalities,” on one hand, and economy wide “R externalities,” on the other. The appropriate
big picture for geographical economics therefore has two, different but intimately related, elements:
the rent-driven (probably quite unsteady) states that follow major innovations; and the spatialeconomic diffusion process in which their rents are bid away and the sectors concerned tend toward
a competitive equilibrium. The geographical-economic development dynamic cannot be reduced to
describing only the second process, the arrival hall where monopolistic rents tend to decline and
21
equilibria become more stable, but must also include the journey from departure to arrival. RossiHansberg and Wright (2007) attempt to solve the MAR cycle identified above in an aggregate way,
but they do not consider how innovation diffusion from firms, and households and workers actually
generates the aggregate outcome, and where. Let us now examine this process in more detail.
The evolution of the US Urban System
The American urban system has undergone very significant change in the last 50 years.
Once upon a time, this was known as the “rise of the Sunbelt,” a view which has been complemented
in recent years by an emphasis on “bi-coastal development” and even “selective urban resurgence” in
older cities. The US is a demographically dynamic country with high levels of integration and labor
mobility, thus placing it somewhere in-between the other developed countries and emergent
economies, in terms of the rate and nature of changes in its patterns of regional development.
The approach that most effectively integrates major strands of what the new urban
economics and regional science has to say about these dynamics can be found in Glaeser (2007,
2009) and Glaeser and Gottlieb (2008). Three equilibria are said to determine incomes, population
growth and specialization of city-regions in a simultaneous way. Population is determined through
the “builder’s equilibrium,” where housing stock changes are shaped by land use regulation, which
determines population expansion, while housing prices figure in the determination of skill mix in the
workforce and thence, of specialization of the region. The “firm’s equilibrium” is determined by the
productivity effect of type of worker skills available, where skilled workers’ productivity can be
increased through local interaction. The wage effect combines worker tradeoffs of nominal wages for
unpriced amenities, and labor supply as driven by the builder’s equilibrium. The household
equilibrium is determined by the elasticity of amenities (such as sun, density and entertainment,
inspired by Roback (1982)), housing costs (from the builder’s equilibrium), and interactions with
other workers, which can raise productivity and nominal wages.
The interaction of these three processes thus yields structural determinants of growth in
general, and the positions of different types of cities according to how the three processes work out
22
in specific cases. Broadly, in the contemporary USA, there are “high wage, high skill, housing scarce”
cities, and lower wage, lower skill, housing-abundant cities (Glaeser, Gyourko, Saks, 2005). This
corresponds to the above-mentioned split between the urban northeast and West Coast, on one hand,
and the Sunbelt or interior cities on the other (price-based, quantity-constrained growth versus
quantity-based, price-constrained growth).19 Sub-classes are possible, when more detail about
certain kinds of amenities (especially climate, consumption and leisure) are added. The strength of
this model is precisely its ability to tackle change in the urban system (the rise of the Sunbelt) (with
the principal sources of change identified as worker skills and the pursuit of amenities), to account
for the diversity of local outcomes within the system, and to do so in a way that integrates these
different local outcomes into a general equilibrium. The model also has the virtue of incorporating
certain political economy features such as local land use regulation.
The model proposes that causality is simultaneous (or at least of a tatonnement type), and
multi-directional, among all the structural determinants. It implicitly rejects the notion of a
hierarchy and distinctive sequence of causes. The three-equilibrium model thus ultimately boils
down to the assumption that “jobs follow people” when it comes to household locational behavior,
and that people follow jobs, when it comes to firms’ locational behavior, and that the two are joined
at the hip through the endogenous and simultaneous determination of the housing stock as the
source of population versus wage growth . But that story is questionable when history is concretely
considered: textile factories de-agglomerated from New England to South Carolina in the 1940s, but
these jobs did not follow people who were seeking amenities. Those rural workers were not there
for the sun. The development of the Sunbelt was led by waves of industrial de-agglomeration from
the North, and by the rise of new industrial agglomerations on the West Coast.
The eschewing of history or sequence has to be inscribed in the modeling strategy, and this
is done via the “spatial indifference assumption” in the firm’s equilibrium. According to this
assumption, firms go anywhere where they can maximize productivity, in an infinite combination of
tradeoffs of such things as worker skills and wages. But this can only be made tractable by assuming
perfect divisibility of production and malleability of factor proportions according to location. In this
world, there can be no economies of scale, neither at firm level at production system level
23
(localization economies). This approach is fundamentally incompatible with “second nature
geography,” i.e. a world where spatial concentration and concomitant specialization are endemic
(Fujita and Thisse, 2002). If scale economies exist, then decisions about where to locate production
do not involve the perfect divisibility and trade-offs assumed by the firm’s equilibrium; hence, the
other tradeoffs to housing, wages, and amenities, would either no longer be fully simultaneous
because factor elasticities would be circumscribed by internal and external economies of scale.
These considerations would also enter into any attempts to flesh out the dynamics of change in the
top-down or bottom-up approaches to agglomeration and productivity shocks, which are discussed
above. Since this core theoretical commitment is assumed, rather than demonstrated, it should now
become the object of rigorous testing. The view of urban historians and geographers cited earlier,
which are so different from the views underlying this model, could be a good place from which to
identify alternatives for comparison.
The question of causality is also seen when we consider how well the model does in
accounting for outcomes. Consider the 10-county San Francisco metropolitan area and that of the 5county Los Angeles region. Both are in the general class of high-wage, high-income metropolitan
areas, where housing prices have increased at a higher rate than population in the last few decades.
Whereas their per capita incomes converged between 1945 and 1970, they have diverged since,
leading to a one-third difference today.20 The model suggests that such a difference could be
generated by a different position in the builder’s equilibrium, i.e. if SF had less housing expansion
than LA, thereby limiting population growth and driving wages up. Most empirical indices (with all
their methodological problems)21 suggest that the overall quality of life amenities are “higher” in SF
than LA, so according to the model workers should actually be prepared to take lower nominal
incomes in SF, caeteris paribus; the builder’s equilibrium would need to be a very powerful force for
income divergence. But since 1970, the two regions have had roughly proportional population
growth, and roughly proportional growth in the number and size of housing units. 22 Moreover,
wages in the Bay Area appear to be higher, even when controlled for education, ethnicity, gender and
industry, suggesting that no tradeoff for amenities is in evidence.
24
The only candidate left for explanation, then, would be in the area of the economic base, i.e.
the locational behavior of firms. During this period, SF grew to be the center of the world’s ICT
economy, and LA lost much of its previous high-tech base in aerospace. How can this be explained?
According to the model, it should be the result of being at different points in the builder’s
equilibrium, with San Francisco restricting growth and hence getting a more skilled migration
stream, attracting the information technology industries, while LA attracted low-wage light
manufacturing. SF did in fact get a somewhat more skilled migration stream, but as noted above the
builder’s equilibrium does not seem sufficiently different in the two regions to be able to explain it.
Explanation would then turn to something additional that determines worker productivity
and possibly in-migration. The three-equilibrium model holds that this could be “worker
interactions” (Glaeser, 2007). Even if we grant that high-tech workers in SF augment their
productivity through various kinds of interactions within the large regional labor pool more so than
do the workers in LA, it cannot be used to explain the origins of the divergence, since such large-scale
interaction could have only occurred once a large pool of people in the electronics industry were
already in place. This pool followed the initial development of information technology, it did not
precede it as a locational factor. Such a pool, once established, may in turn have many recursive
effects on productivity and specialization, but it has to start with some other force that is omitted
from the model. As we noted earlier in this paper, there are no good theories of precisely why an
agglomeration gets situated where it does.
What should we try to model?
As can be seen, then, the field has certainly been attempting to deal with dynamics, but it has
not yet zeroed in on the process of change Most importantly, we know little about how the economy,
over time and space, adjusts toward the observable long-term outcomes. An approach toward
causality which is different from the approach described above would substitute, for structural
determinants and simultaneous multi-directional causality, a view that change is sequential and that
what happens along the way is decisive to the shape of the outcomes and to the decisions of the
25
agents. In such a view, the phases of development that are dominated by innovation and
monopolistic competition have powerful effects on factor markets. The places and times where these
phenomena concentrate are still poorly understood, because -- as noted earlier – we have not yet
worked enough on factor supply dynamics, prices, and rationing in these local development
processes (what we called “MA” places, above). During the journey from innovation to diffusion, it is
highly probable that endowments, preferences, choices, undergo change, and that other shocks occur
(Maddison, 1982). Individuals and collective actors learn, and change their behaviors. These
processes involve phenomena whose labels won’t be found in the typical regional science article, but
sometimes are found in development studies: “discovery,” “invention,” “changes in preferences;”
“institutions, practices, norms, rules,” “moving up the ladder,” “cumulative advantage.”
Technically, we are referring to well-known debates in equilibrium modeling: the
Sonnenschein-Mantel-Debreu results, and the attempts to introduce agents who trade at out-ofequilibrium prices. Such trading has both a product price effect and a wealth effect. This leads to
instability and non-uniqueness, and to an inability to judge optimality of the outcomes. If there is a
lot of this kind of trading, endowments will change, and so the ultimate equilibrium won’t reflect the
one that would be derived from the initial endowments. The technical challenge in correctly
capturing the adjustment to innovation is therefore to develop models whose excess demand
functions are not overly restrictive, where rationing rules for these situations are specified correctly,
and we can understand how endowments change along the way and pathways to outcomes really
constructed.
A frequent attitude toward these processes is to focus on their transitory, “frictional” nature,
or to add any manner of ad hoc explanations for their existence. But all of this is just a way of saying
that anytime there is an economic phenomenon that can not easily be captured as a competitive
equilibrium, we avoid it by considering it temporary and focus on the ultimate outcome state, usually
by forward deduction (a set of initial conditions projected forward through assumptions about what
actors did with them). In this way, the relevant sequences of what really shaped the spatial
development process and caused it to arrive where it does thus need to be identified and modeled.
Along these lines, Thisse (2010) suggests that the creation and destruction of spatial equilibria can
26
be understood by identify sensible selection rules, including evolutionary and learning processes,
and heterogeneous agents. This introduces very important dimensionality problems into modeling.
But for the field to meet the challenge of explaining change and development, there is no choice but
to move forward in this direction.
Closely linked to the rationing problem is the need to achieve more behavioral realism in
considering how heterogeneous agents behave in heterogeneous space. Up to now, those who
recognize how thorny it is to try and model dynamics tend to conclude that we cannot introduce
forward-looking agents, because they need to be omniscient for the model to work. They then turn
to introducing restrictive conditions to make decisions equivalent to static, by making expectations
of the future irrelevant (Desmet and Rossi-Hansberg, 2010). This strategy leaves a yawning gap
between theory and empirical studies about how agents actually function in time and space. Work in
economic sociology suggests that social networks, limited knowledge, conventions, and other
“institutions,” all contribute to setting prices in these spatially-differentiated markets. They are
underpinned by imperfect information, discounting behavior (estimates of future productivity or
profits from current innovative knowledge), and what North (2005) calls “beliefs” in a shifting (nonergodic) world.
Finally, econometrics is best at analyzing frequent, repetitive events. The beginnings of
system change are rare events, whose effective analysis may require something other than the
standard econometric treatment. Econometric analyses of urban growth and regional change
identify structural determinants, but do so in a time-invariant manner, and have difficulty separating
fixed effects from the unique events and forces that may drive development down one fork in the
road versus another. An ideal way out of this dilemma would be to develop sufficiently
disaggregated data on potential causes, and then, once structural determinants are estimated and
compared, to be able to estimate them separately and accurately for individual city-regions.
Research would also benefit from assembling rigorous data on events and shocks, and not simply on
panels of fixed effects, structural determinants or broad controls. Data on the establishment and
lock-in of agglomerations, and other time-and space-dependent shocks, would immensely improve
our chances of explaining dynamics. All in all, an ideal empirical method would be able to blend
27
analysis of key events (beginnings of agglomerations; breakthrough technologies; sharp changes in
market conditions; sharp switches in preferences) with large-scale, large number processes.
4. Prudence about normative issues
One of the strengths of certain branches of NEG-driven regional science has been to re-open
the policy-important question of the welfare effects of spatial unevenness. Instead of focusing on
convergence, certain authors argue that aggregate welfare gains from spatial concentration are likely
to be greater than those from spatial equity (Charlot et al, 2006; Fujita and Thisse, 2002; Baldwin et
al, 2003; Martin, 2005). They also warn against conflating spatial distribution of gains and losses
with social distribution, meaning that an unequal spatial distribution of development could very well
correspond to greater equality in the social distribution of income and resources. There is also a
growing recognition that certain territorial policies can contribute to rent-seeking behavior and have
high opportunity costs for overall economic growth.
It is striking, nonetheless, that a lot of the advice now being dispensed in the name of NEG
and new urban economics goes beyond these salutary warnings. Cities with high housing costs are
told to reduce regulation of their land markets. Or, on a larger scale, the World Bank’s 2009 World
Development Report: Reshaping Economic Geography holds that only “spatially indifferent policies”
will maximize development and social welfare. In effect, the new spatial economics is mostly being
invoked to instruct localities that their preferences have to be viewed at a higher spatial scale, where
they become illegitimate because of the way they are said to reduce aggregate social welfare.
This is the spatial version of a long, complex, and unresolved debate in welfare economics.
For the “bird’s eye” normative claims of such policy advice (i.e. “we know better from a higher spatial
scale than the locals do”) to be sustained, they would have to show that a justifiable social welfare
function is maximized through the spatial arrangements they would bring about through policy.
From Robbins through Arrow, most economists are skeptical about the possibility of social choice,
and as such, they dismiss by assumption the notion that groups (such as territorial groups) could
28
successfully aggregate preferences locally. Place-based policies, for them, are attempts to impose
impossible social choices.
But this conclusion is, at best, premature. If mainstream economics wants to create a fully
general framework for normative judgments about spatial development issues, it would have to
parallel Arrow-Debreu general equilibrium theory. That theory has clear features, i.e. it is essentially
static and fundamentally based on methodological individualism (by which we mean considering
individual preferences as given and not considering the factors underlying their origins, and
considering that all relevant preferences are expressed individually). The normative claims that can
emerge from determination of a socially and spatially optimal distribution of resources depend on
how realistic the assumptions underlying such a conception of the world really are. The standard
aggregate welfare function gives each individual a weight inversely proportional to his or her
marginal utility of wealth. 23 When such a crude welfare function is employed, then the long-term
unemployed worker in Manchester can be told that an “inefficiently” supplied local job is not possible
for her because it contributes less to social welfare than an increase in the banker’s wage in London,
and that she should move if she wants to increase overall social welfare. This assumption can indeed
be relaxed, as for example by trying to integrate a Rawlsian maximin criterion (maximizing the wellbeing of the worst-off person), hence accounting for distributional inequality. 24 There are ardent
debates over the Rawlsian framework, and whether it is really socially welfare maximizing. There is
little attention to experimenting with such functions in policy-oriented spatial economics. The
integration of space would make the task especially difficult. Thus, if our unemployed worker in
Manchester is now assigned a much higher utility for additional earnings than the banker in London,
does it mean she should be encouraged to move? What if there are serious barriers to movement or
additional, difficult-to-observe costs? What if she prefers to stay in Manchester, and there is a low
probability of her getting a job in London in any case? What if she is likely to get a job in London but
end up there at the bottom of the socio-spatial ladder? In any event, if we are to experiment with
alternative weights that are more sensitive to distributional issues, the complex interaction of social
and spatial distribution, and the relevant costs of transforming one into another, need to be
identified. We also would need to know a lot more about the locals and how the local and the global
29
relate dynamically, before we can be confident about such advice. In other words, the essentially
static and aggregate nature of Arrow-Debreu theory is problematic when trying to construct a
realistic normative theory of spatial development issues.
Strict methodological individualism is also potentially problematic in normative extensions
of spatial economics. Sen (2008) argues extensively that social choice is, at least partially, possible;
we know that we value certain things. He argues that we need to guarantee the real capability to
function in society and lead a meaningful life, and that the allocation of resources is one of the
sources of this capability, not just the formal right to individual choice. Dworkin (2000) argues that
society has an obligation to provide equal opportunities to fulfill life plans, i.e. the ability to make the
choice (or not) to use our capacities. Since many of the sources of human capacity come not just from
the ability to transform oneself into a mobile factor of production, but also from a life that unfolds in
a place, through social relations and “belonging.” Spatial economics therefore has an even thornier
task in constructing an appropriate social welfare function than does economics in general.
Moreover, the practice of considering localized preferences, on the part of spatial
economists, tends to be ad hoc and inconsistent. Urban economists (especially public choice
economists) often endorse the Tiebout hypothesis, which essentially supports the “efficiency” of
places to have different collective preference functions, so long as people get to be mobile among
those places and “vote with their feet.” So why wouldn’t this be true for the spatial economy as a
whole? One could say that the Tiebout world requires free spatial entry and exit, so it only covers
public services but not anything that is restrictive of the spatial distribution of supply (for example,
of land and housing). But then this makes the Tiebout hypothesis meaningless, and a lot of what
Tiebout himself wanted to cover was the supply of amenities through patterns of land use.
If there is any question that must be tackled by spatial economics before it can have any
coherent normative position, it is: are spatial concentration and differentiation of activity and
resources fundamentally reducible to pecuniary advantages, perfectly competitive relations among
agents, sovereign and separable preferences, i.e. spatial indifference? As we have seen, this question
has not received a satisfactory theoretical and empirical response as of yet, in spite of having been
posed as a question for many decades (Scitovsky, 1954). We therefore do not know whether certain
30
economically dynamic forms of territorial development (such as agglomeration) represent social
choice and non-pecuniary externalities, or are merely the emergent result of individual choices and
preferences and pecuniary externalities. This is all the more the case in that the consequences are not
static but dynamic, i.e. they affect not only present allocations of resources but future development of
places and individual life chances. We know even less about territorially-differentiated “ways of life,”
as captured by the Tiebout framework. Until we carry out more research on these issues, we must be
very prudent about the normative implications of spatial economics.
5. Conclusion
The recent achievements of geographical economics, especially in the areas of
agglomeration economics, trade costs, and urban economics and urban systems research, are truly
impressive and have generated immensely important analytical advances and empirical insights.
The danger is that having come this far, the field will build increasingly complex but unrealistic
theory, and that its empirics will be based on better and better econometrics, but that both will avoid
the big questions about directions of causality and good sense about how the economy works as a
developmental dynamic. So we would be back to looking for the keys under lamppost, rather than
the world around it. In this paper I have attempted to argue that the way forward is to engage the
new insights that have been achieved in the recent generation of spatial economic research with a
broader framework whose basic orientation is innovation, growth, and development, fundamental
causes of change, and realistic assumptions about how agents create these outcomes.
REFERENCES
Abdel-Rahman, HM (1994). “Economies of scope in intermediate goods and a system of cities.“
Regional Science and Urban Economics 24,4: 497-524.
Aghion, Ph and Howitt, P. (1992). “A model of growth through creative destruction.” Econometrica
60: 323-351.
Anderson, J. and van Wincoop, E (2004). “Trade costs.” Journal of Economic Literature 42,3: 691-751.
Audretsch, D; Feldman, M (2004). “Knowledge spillovers and the geography of innovation.“ Ch 61 in
Thisse, J-F and Henderson, JV,eds, Handbook of Urban and Regional Economics, v4, Amsterdam:
Elsevier, 2713-2739.
31
Baldwin, R (2006). “Globalisation: the great unbundling(s).” Helsinki: Prime Minister’s Office,
Economic Council of Finland.
Baldwin, R and Forslid, R (1997). “The core-periphery model and endogenous growth: stabilizing and
destabilizing integration.” Economica 67,3: 307-324.
Baldwin, R; Forslid, R; Martin, Ph; Ottaviano,GIP; and Robert-Nicoud, F (2003). Economic Geography
and Public Policy, Princeton: Princeton University Press.
Baldwin, R and Martin, Ph (2004). “Agglomeration and regional growth.” IN: JV Henderson and J-F
Thisse, eds, Handbook of Regional and Urban Economics, v.4, Amsterdam: Elsevier: 2671-2711.
Barro, R. and Sala-I-Martin, X. (1995). Economic Growth. New York: McGraw-Hill.
Baumgartner, JR (1988). “Physicians’ services and the division of labor across local markets.”
Journal of Political Economy 96,5: 948-962.
Behrens, K; Ottaviano, GIP; Lamorgese, A; Tabuchi, T (2004). “Testing the home market effect in a
multi-country world.” London: CEPR, DP 4468 (www.cepr.org/pubs/dps/DP4468,asp)
Black, D. and Henderson, V. (1999). “A Theory of Urban Growth.” Journal of Political Economy 107:
252-284.
Boschma, R. and Martin, R (2007). “Constructing an evolutionary economic geography.” Journal of
Economic Geography 7,5: 537-548.
Caniels, MCJ and Verspagen, B (2001). “Barriers to knowledge spillovers and regional convergence in
an evolutionary model.” Journal of Evolutionary Economics 11: 307-329.
Cheshire, P and Malecki, E. (2004). “Growth, development and innovation: a look backward and
forward.” Regional Science 83: 249-267.
Charlot, S; Duranton G (2004). “Communication externalities in cities.” Journal of Urban Economics
56,3: 581-613.
Charlot, S; Gaigné, C; Robert-Nicoud, F; and Thisse, J-F (2006). “Agglomeration and welfare: the coreperiphery model in light of Bentham, Kaldor and Rawls.” Journal of Public Economics 90: 325-347.
Ciccone, A; Hall, R (1996). “Productivity and the density of Economic activity.” American Economic
Review 86,1: 54-70.
Combes, PP; Duranton, G; Gobillon, L; Puga, D; Roux, S (2009). « The productivity advantages of large
cities : distinguishing agglomeration from selection. » CEPR, DP 7191.
Crafts, N (1995). “Exogenous or endogenous growth? The Industrial Revolution reconsidered.”
Journal of Economic History 55,4: 745-772.
Desmet, K and Rossi-Hansberg, E (2010). “On Spatial Dynamics.” (in this issue)
Dixon, R. and Thirlwall, A. (1975). “A model of regional growth rate differences on Kaldorian lines.”
Oxford Economic Papers 27,2: 201-214.
Dumais, G; Ellison, G; Glaeser, E (2002). “Geographic concentration as a dynamic process.” Review of
Economics and Statistics 84: 193-204.
32
Duranton, G (2007). “Urban Evolutions: the Fast, the Slow and the Still.” American Economic Review
97: 197-221.
Duranton, G; Puga, D (2001). « Nursery cities : urban diversity, process innovation, and the life cycle
of products. » American Economic Review 91,5: 1454-1477 (December).
Duranton, G; Puga, D. (2004). “Micro-foundations of urban agglomeration economies.” IN:
Henderson, J and Thisse, J-F, eds, Handbook of regional and urban economics, v.4, Amsterdam:
Elsevier: 2063-2117.
Duranton, G and Storper, M (2008). “Rising Trade Costs? Agglomeration and trade with endogenous
transaction costs. “ Canadian Journal of Economics 41,1: 292-319.
Dworkin, R (2000). Sovereign virtue: the theory and practice of equality. Cambridge, MA: Harvard
University Press.
Feldman, M. (2003). “The locational dynamics of the US biotechnology industry: knowledge
externalities and the anchor hypothesis.” Industry and Innovation 10: 311-328.
Fujita, M; Krugman, P; Mori,T (1999). “On the evolution of hierarchical urban systems.” European
Economic Review 43 (February): 209-251.
Fujita, M; Krugman, P; and Venables AJ (1999) The spatial economy: cities, regions and international
trade. Cambridge, MA: MIT Press.
Fujita, M and Thisse, J-F (2002). “Economics of agglomeration: Cities, Industrial Location and
Regional Growth.” Cambridge: Cambridge University Press.
Fujita, M and Thisse, J-F (2009). “Paul Krugman’s New Economic Geography: Past, Present and
Future.” Mimeo : CORE-UC Louvain Belgium.
Gabaix, X ; Ioannides, Y (2004 ). « The evolution of city size distributions. » Ch 53 in Thisse, J-F and
Henderson, JV,eds, Handbook of Urban and Regional Economics, v4, Amsterdam: Elsevier, 2341-2378.
Galbraith, J and Hale, G, 2004, “Income Distribution and the Information Technology Bubble.” Austin:
University of Texas Inequality Project (UTIP), working paper 27
(www.utip.gov.texas.edu/papers/utip_27)
Glaeser, E (2007). “The Economic Approach to Cities.” Cambridge, MA: Harvard University, Dept. of
Economics (www.economics.harvard.edu/pub/hier/2008/HIER2149.pdf)
Glaeser, E (2008). Cities, Agglomeration, and Spatial Equilibrium. Oxford: Oxford University Press.
Glaeser, E; Gottlieb, J (2009). “The wealth of regions: agglomeration economies and spatial
equilibrium in the United States.” Cambridge, MA: NBER working paper 14806 (March).
Glaeser , EL; Gyourko, J; Saks, R,2005, “Why have housing prices gone up?” American Economic
Review 95,2: 329-333.
Glaeser, E and Kohlhase, J (2004). “Cities, regions and the decline in transport costs.” Papers in
Regional Science 83: 197-228.
Glaeser, E; Maré, 2001. “Cities and skills.“ Journal of Labor Economics 19: 316-342.
33
Grossman, G. and Rossi-Hansberg, E (2008). “Task trade between similar countries.” Cambridge, MA:
NBER working paper 14554.
Hagerstrand, T (1967). Innovation diffusion as a spatial process. Chicago: University of Chicago Press.
Hall, P (1998). Cities in civilization. London: Pantheon.
Harrigan, J and Venables, AJ (2006). “Timeliness and agglomeration.” Journal of Urban Economics 59:
300-316 (March).
Head, K; Mayer, Th; Ries, J (2000). “On the pervasiveness of home market effects.” London: CEPR, DP
3454 (www.cepr.org/dps/DP3454.asp)
Head, K and Mayer, Th (2004). “The empirics of agglomeration and trade.” IN Henderson, J and
Thisse, J-F, Handbook of regional and urban economics, v4. Amsterdam: Elsevier: 2609-2669.
Helpman, E. (2004). The Mystery of Economic Growth. Cambridge, MA: Harvard/Belknap.
Henderson, J.V. (1974). ‘The sizes and types of cities.“ American Economic Review 64,4: 640-656.
Hirschman, AO (1958). The Strategy of Economic Development. New Haven: Yale University Press.
Jaffe, A. and Trajtenberg, M (2002). Patents, Citations, and Innovations. Cambridge, MA: MIT Press.
Jayet, H. (1983). “Chômer plus souvent en région urbaine, plus longtemps en région rurale.”
Economie et Statistique 153 : 47-57.
Krugman, P. (1991). “Increasing returns and economic geography.” Journal of Political Economy 99:
483-499.
Krugman, P. and Venables, A. (1995). “Globalisation and the inequality of nations.” Quarterly Journal
of Economics 110,4: 857-880.
Leamer, E, and Storper, M. (2001)."The Economic Geography of the Internet Age." Journal of
International Business Studies 32,4: Fourth Quarter, 641-666.
Levy, F; Murnane, R (2004). The New Division of Labor. Princeton: Princeton University Press.
Maddison, A. (1982). Phases of Capitalist Development. New York: Oxford University Press.
Malecki, E (2009) “Everywhere? The geography of knowledge.” (in this issue).
Markusen , AR (1985). Profit cycles, oligopoly and regional development. Cambridge, MA: MIT Press.
Martin, Ph. (2005). “The geography of inequalities in Europe.” Swedish Economic Policy Review 12:
85-108.
Martin, Ph.; Ottaviano, GIP, (1999), “Growing locations: industry location in a model of endogenous
growth.” European Economic Review 43, 2: 281-302.
Martin, R (2008). “The ‘New Economic Geography:’ credible models of the economic landscape.” IN:
R Lee, A Leyshon, L. McDowell; P Sunley, eds, A Compendium of Economic Geography. London: Sage.
34
Maskell, P. (1992). “Learning in the village economy of Denmark.” IN Braczyk, H, Cooke, P and
Heidenreich, M, eds, Regional Innovation Systems. London: Taylor & Francis: 190-213.
McCombie, S. and de Ridder, JR (1984). « The Verdoorn Law Controversy : Some new empirical
findings using US data. » Oxford Economic Papers 36: 268-284.
McLaughlin, G and Robock, S (1949). Why Industry Moves South. Washington, DC: Committee of the
South, National Planning Association.
Melitz, MJ (2003). “The impact of trade on intra-industry re-allocation and aggregate industrial
productivity.” Econometrica 71,6: 1695-1725.
Melitz, M and Ottaviano, GIP (2008). “Market size and productivity.” Review of Economic Studies
75,1: 295-316.
Meyer, RD (2003). The roots of American industrialization. Baltimore: Johns Hopkins University
Press.
Mokyr, J. (1990). The Lever of Riches. New York: Oxford University Press.
Muth, R.F. (1971). “Migration: chicken or egg?” Southern Economic Journal 37: 295-306.
Myrdal, G. (1957). Economic Theory and the Underdeveloped Regions. London: Duckworth.
North, D. (2005). Understanding the Process of Economic Change. Princeton: Princeton University
Press.
Norton, RD and Rees, J. (1979). “The product cycle and the spatial decentralization of American
manufacturing.” Regional Studies 13,2: 141-151.
Ottaviano, GIP and Thisse, J-F (2001). “On economic geography in economic theory: Increasing
returns and pecuniary externalities.” Journal of Economic Geography 1: 153-179.
Perroux, F (1955). “Note sur la notion de pôle de croissance.” Economie Appliquée 7 : 307-320.
Pomeranz, K. (2000). The Great Divergence. Princeton: Princeton University Press.
Pred, AR (1966). The spatial dynamics of urban growth in the United States, 1800-1914. Cambridge,
MA: Harvard University Press.
--(1974). Urban growth and the circulation of information, 1790-1840. Cambridge, MA:
Harvard University Press.
---
(1977). City-Systems in advanced economies. London: Hutchinson.
--(1980). Urban growth and urban systems in the United States, 1840-1860. Cambridge, MA:
Harvard University Press.
Robert-Nicoud, F (2006) “Agglomeration and trade with input-output linkages and capital mobility.”
Spatial economic analysis, 101-126.
Powell, W; Packen, K; and Whittington, K, (2009). “Organizational and institutional genesis: the
emergence of high-tech clusters in the life sciences.” Ch 14 in: Padgett, J and Powell, W, eds, The
Emergence of Organizations and Markets, Stanford: Stanford University Press (forthcoming).
35
Puga, D (2010). “The nature and causes of agglomeration economies” (in this issue).
Redding, S (2010). “The empirics of the New Economic Geography” (in this issue).
Roback, J (1982). “Wages, rents and the quality of life.” Journal of Political Economy 90,6: 1257-1278.
Romer, P. (1986). “Increasing returns and long-run growth.” Journal of Political Economy 94,5: 10021037.
Romer, P. (1991). “Endogenous technological change.” Journal of Political Economy 98: S71-102.
Rosenberg, N (1982). Inside the black box: technology and economics. Cambridge: Cambridge
University Press.
Rosenthal, WW and Strange, W (2001). “The determinants of agglomeration.” Journal of Urban
Economics 50: 191-229.
Rossi-Hansberg, E; Sarte, PD; Owens III, R (2009). “Firm fragmentation and urban patterns.“
International Economic Review 50,1: 143- 186.
Rossi-Hansberg, E ;Wright, M. (2007). “Urban structure and growth.” Review of Economic Studies 74:
597-624.
Sala-I-Martin, X (2006). “The world distribution of income: falling poverty and
convergence….period.” Quarterly Journal of Economics CXXI: 351-397.
Scitovsky, T. (1954). “Two concepts of external economies.” Journal of Political Economy 62: 143151.
Scott, AJ (1988). Metropolis: from the division of labor to urban form. Berkeley and Los Angeles:
University of California Press.
Scott, AJ (1993). Technopolis: High-technology industry and regional development. Berkeley and Los
Angeles: University of California Press.
Scott, AJ (2004). “A perspective of economic geography.” Journal of Economic Geography 5: 479-499.
Scott, AJ (2008). “Production and work in the American metropolis.” Annals of Regional Science 42:
785-805.
Scott, AJ; Storper, M, eds, (1986) Production, Work, Territory: the Geographical Anatomy of Industrial
Capitalism. Boston and London: Allen & Unwin
Scott, AJ; Storper, M. (1987) "High Technology Industry and Regional Development: A Theoretical
Critique and Reconstruction." International Social Science Journal 112: 215-232, May (with AJ Scott).
Sen, A (2008). The Idea of Justice. Cambridge, MA: Belknap.
Storper, M (2010). “Why does a city grow? Specialization, human capital, institutions.” Urban
Studies 47 (forthcoming)
Storper , M. (2009) “Regional Context and Global trade.” Economic Geography 85,1: 1-21.
Storper, M and Walker, R (1989). The Capitalist Imperative: Territory, Technology and Industrial
Growth. Oxford: Basil Blackwell.
36
Storper, M (1997). The Regional World: Territorial Development in a Global Economy. New York and
London: Guilford.
Storper, M and Venables, A (2004), "Buzz: Face-to-Face Contact and the Urban Economy." Journal of
Economic Geography 4: 351-370.
Thisse, J-F (2009). “Toward a unified theory of economic geography and urban economics.” Louvain:
Université Catholique de Louvain, CORE, 9 April.
Thisse , J-F (2010) « Toward a unified theory of economic geography and urban economics » (in this
issue)
Van Niewerburgh, S and Weill, P-O, 2009, “Why Has House Price Dispersion Gone Up?” Los Angeles:
UCLA Dept Economics, http://www.econ.ucla.edu/poweill/houseprice.pdf
Yamamoto, D. (2008). « Scales of regional income disparities in the USA, 1955-2003. » Journal of
Economic Geography 8,1: 79-103.
I thank Gilles Duranton and the participants at the New York meeting for their critical comments, as
well as Tom Kemeny and Mike Manville for commenting on an earlier version of this paper. The usual
disclaimers apply. In a broadly-ranging review article such as this one, it is impossible to do full
justice to the literature of spatial economics and economic geography, which is now vast. Therefore,
by its very nature, the citations are indicative of lines of work, rather than meant to cover all the
high-quality research published on each topic.
2 This paper is going to refer to a number of separate strands of work under the term “geographical
economics.” The two main strands are research in the New Economic Geography, stemming from the
founding Dixit-Stiglitz-Krugman (DSK) model, and various attempts to link its concerns with trade
costs and agglomeration economies to a wider context of urban economics, and extending further
outward from that, to the dynamics of urban system change, which in turn is the terrain of regional
economics. The boundaries are not all clear. This issue of the JRS has several other papers focusing
on the NEG, including those by Thisse on theory and Redding on empirics.
3 Explicit interest in dynamics has indeed resurfaced periodically. Recently, a number of spatial
economists have made this point recently, among whom I would cite Baldwin (2006); Fujita and
Thisse (2002), Baldwin and Martin (2004); Martin and Ottaviano (1999), Krugman and Venables
(1995) and Rossi-Hansberg and Wright (2007). Among geographers, I would include my own
efforts (Storper and Walker, 1989; Storper, 1997), Scott ( 2004), Markusen (1985); Boschma and
Martin (2007); in addition to numerous economic historians (Crafts, 1995) and historical
geographers. In this issue, the contribution of Desmet and Rossi-Hansberg tackles some of the tasks
necessary to analyzing spatial dynamics, albeit in a somewhat different way from the present paper.
In offering the present reflection on this theme, I acknowledge that it is part of a long-standing, and
increasing, flow of ideas on this problem.
4 Another critical assessment of the NEG, from the geographers’ point of view, can be found in R.
Martin (2008).
5 However, efforts Robert-Nicoud (2005) have been able to identify the spatial equilibria analytically.
6 For a different perspective on intra-metropolitan form, based on analysis of location of firms and
firm fragmentation, see Scott (1988).
7 This was noted by P. Krugman in his Nobel Lecture. Glaeser and Kohlhase (2004) also make this
point, but not for the purpose of distinguishing the agglomerative tendencies of different sectors, but
to argue that urbanization is now propelled by the clustering behavior of skilled workers. We will
return to this point below.
8 Puga (2009), reviews the different attempts to measure the input sharing contribution to
agglomeration. The result is only significant when highly dispersed sectors are eliminated. In my
1
37
view, this simply means that input sharing is not strong enough to induce their co-location, which
should be one of the effects of input sharing (compare Melitz and Ottaviano, 2008).
9 This point is suggested by the empirical literature on communications (Charlot and Duranton,
2004) and by theoretical work on face-to-face contact (Storper and Venables, 2004).
10 There is important work on productivity heterogeneity, said to act as a selection criterion in the
face of export markets (Melitz, 2003; Melitz and Ottaviano, 2008). But I intend a different and
expanded notion of heterogeneity here.
11 This move to cheaper land can occur at either the intra-metropolitan scale (by moving to the
urban fringe) or at an inter-regional scale (e.g to the Sunbelt). Some authors have begun to consider
the relationship between the two, but it is still not well understood (Thisse, 2009, in this issue; RossiHansberg, Sarte and Owens, 2009).
12 EG: Fairchild/Shockley in Silicon Valley; or Donald Douglass in LA in the 1920s (see Storper and
Walker, 1989; Scott, 1988).
13 This is also why the abundant models in the field that try to derive the degree of agglomeration
from the elasticity of substitution of varieties are pretty much irrelevant.
14 For example, the end of the post-war growth period in the US also seems to have slowed down
processes of convergence, while the advent of the high-tech and then financial booms seems to have
accelerated divergence. This seems to correspond to, respectively, a phase in which the economy
was dominated by de-aggloemeration to one in which agglomeration increased in importance,
corresponding as well to different levels of technological maturity in the underlying propulsive
activities.
15 Galbraith and Hale (2004) show that in the 1990s, over three-quarters of the total increase in the
inter-county income dispersion in the USA was accounted for by differential income growth in just
four counties, the centers of the high-tech boom in that period, with its strong localized growth
phenomenon.
16 This is a short summary of an argument I make in greater detail in Storper (2009).
17 This raises also the technical issue of whether localized knowledge/technological externalities are
pecuniary or not. Most NEG models define them as pecuniary (Ottaviano and Thisse, 2001). A great
deal of the empirical literature in geography is not convinced that they are merely pecuniary, tending
to see them as truly shared sources of productivity, and evolving according to spatially-differentiated
learning processes (Cheshire and Malecki, 2004; Malecki, 2010). In this vein, we do not know the
extent to which local markets – for labor or products – are fully competitive in the presence of rapid
innovation in a local industry cluster. If they are really shared and not entirely pecuniary, then
solving the micro-foundations of urban agglomeration economies becomes much more difficult and
would require complex inter-temporal features for how such “rents” are bid away and what happens
subsequently, or whether there is cumulative advantage in the Myrdialian sense of that term
(Rosenthal and Strange, 2001; Duranton and Puga, 2004). Fujita and Thisse (2009) argue that local
product differentiation alleviates price competition; such differentiation could be interpreted as a
proxy for innovation in the sense used here.
18 This doesn’t mean to imply that such diffusion is friction-less, but simply that in the long-run, most
technologies ultimately do evolve and diffuse. In the meantime, barriers to their diffusion are
sources of important inter-regional factor price and income differences (Caniels and Verspagen,
2001).
This basic split has been identified, and interpreted quite differently, in Scott (2008).
Per capita income in the 5-county Los Angeles metro area was 94% that of the ten-county San
Francisco metro area in 1970, but it had declined to 66% by 2005.
21 Quality of life indices, used to instrument for “amenities,” include those of Mercer, the Readers’
Digest, and The Economist. All rank the San Francisco Bay Area far higher in quality of life than the
Los Angeles area.
22 In this model, as well as a large accompanying literature, the assumption is that regulation
determines the elasticity of expansion of the housing base and hence of the labor force (Glaeser,
Gyourko, Saks 2005). But an alternative (van Nieuwerburgh and Weill, 2009) assigns housing price
differences to productivity differences, reflected in wage dispersion, between metropolitan areas.
19
20
38
That is, marginal utility of wealth is inversely proportional to the square of lifetime wealth in such
a function.
24 Thus, the individual utilities are elevated to a power alpha, and when this is Rawlsian it would be
zero.
23