THE NEW ECONOMIC GEOGRAPHY:
WHAT TRANSPORT PLANNERS NEED TO KNOW
Hugh Wenban-Smith
Introduction
This paper is motivated by a conviction that the emerging body of work known as the
New Economic Geography (NEG for short) is highly relevant to the work of transport
planners – particularly those who claim to be interested in the interaction between
transport and land use. However, no papers with an NEG slant have been submitted
to ETC programme committees in recent years and generally there appears to be a
lack of interest in the topic among the AET community. The aim of this paper
therefore is to provide an introduction to NEG thinking, drawing attention to those
aspects that transport planners need to be aware of, and offering a guide to those
who would like to learn more.
On this last point, it may be helpful to list upfront the key works on which this paper
has relied. A good place for anyone to start is:
Fujita M, P Krugman & A J Venables (1999) The Spatial Economy: Cities,
Regions and International Trade (MIT Press, paperback edition 2001).
A more thorough text, which also tries to integrate NEG approaches with more
traditional urban and regional economics, is:
Fujita M & J-F Thisse (2002) Economics of Agglomeration: Cities, Industrial
Location and Regional Growth (Cambridge University Press).
More demanding, but important because it extends and develops the basic
Core/Periphery model in significant ways, and also gives consideration to the policy
implications of the results, is:
Baldwin R, R Forslid, P Martin, G Ottaviano & F Robert-Nicoud (2003)
Economic Geography and Public Policy (Princeton University Press).
Finally, for a number of survey and review type articles, particularly on the empirics of
NEG, the latest volume of the Handbooks of Regional and Urban Economics is
invaluable:
Henderson J V & J-F Thisse (Eds) (2004) Handbook of Regional and Urban
Economics, Vol 4: Cities and Geography (Elsevier North-Holland).
Overview of NEG: What’s it all about?
NEG has its origins in dissatisfaction with traditional trade theory and its assumptions
of perfect competition and constant returns to scale. Krugman in the 1980s developed
“New Trade Theory” which introduced increasing returns to scale and imperfect
competition (in the form of “monopolistic competition” as proposed by Dixit & Stiglitz
(1977)). The additional feature in NEG is mobility of some factors. The effects, as we
shall see, can be quite surprising.
Going back a step, the fundamental problem in spatial economics is how to explain
agglomeration, which is such a prevalent feature of the real world economic
landscape. Part of the explanation no doubt lies in differences in endowments (first
nature) but this seems inadequate to explain (for example) the existence of cities.
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What is needed is mechanisms that show how agglomeration can arise even when
endowments are equal; and this is what NEG provides.
Specifically, NEG models incorporate three kinds of mechanism, two of which induce
agglomeration and one which goes the other way. These are:
1. Market access/market size: Larger markets attract more firms, which attract
more workers, which further enlarges the market, and so on. The driving forces
here are economies of scale and savings in transport costs (in getting goods to
consumers). Then mobility of labour reinforces the effect.
2. Cost of living effects: A greater variety of goods, higher wages and more
competition lead to higher real wages, attracting more workers.
3. Market-crowding: By the same token, more competition can squeeze profits
and may lead some firms to relocate to places where competition is less
intense. This force will tend to reduce agglomeration.
The main determinant of the relative strength of these forces is transport costs (to be
interpreted broadly as the costs of trading across distance). The interplay is complex
but what the models show is that as transport costs are reduced, the forces favouring
agglomeration tend to predominate.
So much for an intuitive introduction to NEG. The next section gets a bit more
technical.
The basic Core/Periphery model
Matching the basic trade model with its 2 countries, 2 products and 2 factors, the
basic NEG model has 2 regions, 2 production sectors and 2 kinds of labour – hence
the term 2x2x2.
• One sector (call it Agriculture) employs labour which is immobile and
operates under constant returns to scale;
• The other sector (call it Manufacturing) employs mobile labour and
operates under a simple form of increasing returns to scale (each firm
incurs a fixed cost F as well as operating costs which are proportional to
output). There are a large number of firms each one producing a
differentiated product (this is the Dixit/Stiglitz set-up);
• Transport costs within each region are zero, as are transport costs for
agricultural output between regions but transport costs between regions for
manufactured products are of the “iceberg” type – that is to say, a
proportion of the product is lost in transit;
• Consumer utility is of the form:
U = C Mµ C 1A− µ
where CM is consumption of manufactured goods
and CA is consumption of agricultural goods, and
CM =
•
(∫ c
1− (1−σ )
i
di
)
1 /(1−1 / σ )
where the ci are the individual
manufactured products and σ is the (constant) elasticity of substitution
between industrial varieties;
Finally, because this is a general equilibrium model, equilibrium is achieved
when all markets clear, profits are zero and no manufacturing worker has
any incentive to move to the other region.
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As Fujita et al (1999, p.65) cheerfully remark, when the relevant equations are put
together, “This model does not look particularly tractable: eight simultaneous
nonlinear equations!” In fact some general properties can be deduced but anything
more requires numerical simulation. (This limitation is addressed in Baldwin et al
(2003).)
The general properties are summarised in what has come to be known as the
Tomahawk Diagram. A version of this is portrayed below:
Share of mfg workers (Region 1)
1.0
0.5
C
B
A
Transport costs
0
Figure 1: Core/Periphery Model - The Tomahawk Diagram
(Effect of transport costs on distribution of manufacturing workers)
So what’s going on here? We start with two regions, each equally endowed with
(immobile) agricultural workers and (mobile) manufacturing workers. When transport
costs are high (around the point A), this initial position is a stable equilibrium with
economic activity equally divided between the two regions. However, if transport
costs are reduced towards the point B a break point is reached where the equilibrium
becomes unstable; a small perturbation in either direction will precipitate complete
concentration of manufacturing in one region or the other. It is shown as going to
region 1 in the diagram but it could equally have gone the other way. What has
happened is that with lower transport costs, the forces cited above which favour
agglomeration dominate. And this new equilibrium remains stable as transport costs
fall further.
Some implications
There are a number of features of this picture that are distinctly odd. Before going
further, let me just emphasise how odd these features are:
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•
•
•
•
In moving from point A to point B, reductions in transport costs have no
effect on the location of manufacturing firms. Usually, in economics at least,
we expect changes in prices to produce at least some effect, and for this
effect to be smooth and continuous;
At point B a small change in transport costs precipitates a catastrophic
change in the location of manufacturing. Again this kind of outcome is not
common in economic models (although not unfamiliar to physicists in the
dynamics of non-linear models)
This is due to the self-reinforcing character of the market size and migration
effects, something else which is somewhat novel, although this kind of
effect is hinted at whenever we refer to virtuous or vicious circles in talking
about development;
Around the point C, there is stable equilibrium with manufacturing wholly
concentrated in one region. So here is a theory which demonstrates the
possibility of agglomeration in the absence of any substantive initial
difference between the two regions. There is no appeal to differences in
geography or endowments; it is entirely due to economic forces generated
within the model
While your credulity is being thus stretched, a gentle reminder of another surprising
theoretical result that, as it happens, is quite relevant in this context: The Spatial
Impossibility Theorem. This theorem can be formally expressed as follows:
“Consider an economy with a finite number of locations and a finite number of
consumers and firms. If space is homogeneous, transport is costly and
preferences are locally non-satiated, then there is no competitive equilibrium
involving transportation”
Ottaviano & Thisse (2004, p.2572) explain the implications: “If economic activities are
perfectly divisible, a competitive equilibrium exists and is such that each location
operates as an autarchy … Once economic activities are not perfectly divisible, they
have an address in space … Then … the transport of some goods between some
places becomes unavoidable.” This introduces non-convexities and prevents the
achievement of a competitive equilibrium. In consequence “If we want to understand
something about the spatial distribution of economic activity, especially the formation
of major agglomerations, it follows from the spatial impossibility theorem that we must
assume either that space is heterogeneous (as in the neoclassical theory of
international trade or in land use models …), or that externalities exist and are many
(as in modern urban economics), or that markets are imperfect (as in spatial
competition theory or in economic geography). In short, imperfections of one kind or
another are a necessary part of the explanation of agglomeration and we should not
be too surprised if theories built around these imperfections exhibit some unusual
properties.
Extensions: Towards greater realism
You may be feeling that while some of the mechanisms being invoked in the
Core/Periphery model have a certain plausibility, the model itself is altogether too
simplistic, with assumptions that appear to have been adopted more for reasons of
analytical convenience than realism. You would not be alone. However, before
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looking (briefly) at ways in which subsequent work has extended and improved the
basic model to try to make it more realistic, and policy relevant, I would urge you to
recognise the formidable intellectual achievement involved in devising a framework
within which the most fundamental questions of spatial economics can be
systematically explored.
There is now a large, and growing, NEG literature, much of it theoretical (concerned
with model development) but also an increasing number of empirical contributions
(seeking to test for NEG effects in the real world). The latter are taken up for
comment in the next section. Here we note some of the limitations of the basic model
and the main ways in which people have sought to improve it.
Baldwin et al (2003, pp.60-62) provide a useful review of the model’s assumptions.
They suggest:
a) The assumption of increasing returns that are internal to industrial firms is
essential. (“After all, if there is no loss to splitting up production there is really
no location choice to be made.”)
b) Some factor mobility is an essential ingredient of the model but the particular
assumptions about preferences and migration behaviour can be changed.
(“For example, if workers display sufficiently different degrees of attachment to
their original region, the economy can move from dispersion to agglomeration
in a non-catastrophic way.”)
c) Most of the other assumptions are a matter of expediency, e.g
i. The model assumes that there is only one non-industrial sector, which is
Walrasian and its output is traded costlessly. However, “The only crucial
assumption is that the non-industrial sector is intensive in its use of the
immobile factor so that inter-regional factor mobility is associated with
concentration of industry.”
ii. There can be more than one industrial sector. Krugman & Venables
(1997) show that in that case different sectors may agglomerate in
different countries.
iii. The assumption of two regions is not essential but it greatly simplifies
matters, albeit that the model cannot then be used to study issues that
arise with multiple locations, such as locational hierarchy.
iv. “Likewise, the assumption of only two factors is not crucial, although we
do need at least two, since one must be mobile to allow agglomeration
and one must be immobile to keep the model interesting.”
v. “The model makes extreme assumptions about the factor intensity of the
two sectors … [but] … what really matters is that the mobile factor is used
intensively in the increasing returns sector.”
vi. “The model also assumes a very particular form of trade costs, namely
iceberg trade cost. This assumption is very convenient in a general
equilibrium model because it allows us to avoid, for example, the issue of
who gets the rents from trade barriers, how transport services are priced
and which region’s factors are used up in overcoming the trade costs.”
Moving on to ways in which people have tried to improve the basic model, and make
it more relevant to policy, Baldwin et al (2003) again provide the most comprehensive
guide. In fact, this is most conveniently done by simply listing the titles of Chapters 3
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to 8 of their book and indicating the features of the models each chapter describes,
mostly in the authors’ own words.
Ch. 3: The Footloose Capital (FC) Model
“The logic of the FC model is most easily seen by contrasting it with the logic of the
CP model … The CP model features demand-linked circular causality since migration
leads to expenditure shifting (workers spend their incomes locally) and expenditure
shifting leads to production shifting … The CP model also features cost-linked
circularity since production shifting leads to ‘cost-shifting’ in the sense that it affects
the cost of living (local production allows local residents to avoid trade costs) and cost
shifting leads to production shifting (workers are attracted to regions with low costs of
living … The FC model cuts both demand-linked and cost-linked circular causality by
assuming that the mobile factor repatriates all of its earnings to its country of origin …
In the FC model, agglomeration stems from the home-market effect, that is, a
concentration of economic activity – and thus income and spending – in one market
creates forces that induce a more than proportionate share of industry to locate in the
bigger market. Agglomeration in the FC model, however, is not self-reinforcing.” The
resulting model is much more tractable than the CP model; because it does not rely
on labour migration, it is plausible to interpret the two regions as separate nations.
Ch. 4: The Footloose Entrepreneur (FE) Model
“The FE model … resembles the CP model in that the spatial concentration of activity
requires labour migration and this migration is driven by real wage differences …
such migration is the key to both demand-linked and cost-linked circular causality. In
the FE model it is assumed that the mobile factor [skilled labour] is used only to meet
the fixed cost of producing a manufactured variety. “ … producing a variety of the
industrial good requires one unit of human capital – that is, one entrepreneur –
regardless of the firm’s output, and thus firms move with their entrepreneur.”
Ch. 5: Linear Models
These models are fully tractable since the main expressions are linear. Compared
with the previous models, the crucial difference in the linear models is that “utility is
quasi-linear quadratic rather than Cobb-Douglas nested-CES, and trade costs are not
of the frictional [iceberg] kind.” “The linear models display all the agglomeration and
dispersion forces that are present in the [previous] models, so it is not surprising that
the fundamental logic of agglomeration and the role of trade costs is quite similar for
both families of models … [however] … since industrial firms face linear demand
curves, the optimal price-cost mark-up depends upon a whole host of factors
including the number of competitors in the local market. This opens the door to a procompetitive effect that acts as a distinct dispersion force. That is, since both per firm
sales and mark-ups are lower in the ‘crowded’ market, firms are more interested in
locating in the market with the fewest firms … than they are in the [previous] models.”
Secondly, “the quasi-linear structure of preferences implies … that per consumer
spending on industrial varieties is independent of income. As a consequence, relative
market size depends only upon the the number of consumers residing in each region.
Their income levels are irrelevant.”
Ch. 6: The constructed Capital (CC) Model
“The key to industrial relocation in the CC model is the construction and depreciation
of capital. In the CP model, various changes (trade costs, region size, etc) produced
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inter-regional factor flows. In the CC model, the same pressures lead to the
construction of capital in the attractive region and depreciation of capital in the other
region (inter-regional capital flows are assumed away). As the capital stock rises in
the attractive region and depreciates in the other, total expenditure rises in the
attractive region and declines in the other. This generates demand-linked circular
causality that can support further accumulation of capital in the attractive region and
depreciation in the other. As in the CP and FE models, the strength of this proagglomeration circular causality dominates the anti-agglomeration market-crowding
effect when trade is sufficiently free. Initially symmetric regions will, therefore,
experience a catastrophic agglomeration of industry when the degree of openness
surpasses a critical level (the break point).”
Ch. 7: Global and Local Spillovers (GS and LS) Models
To provide a framework in which policies to encourage growth in poor regions can be
evaluated, this chapter introduces two models in which the long run growth rate is
endogenous and which can be thought of as extensions of the CC model. “Due to
technological spillovers in the capital-creation sector, firms find it optimal to
continually invest in new capital. As in the FC and CC models, each unit of capital is
associated with a new variety of the industrial good, so the continual investment
produces an ever-expanding range of varieties. This in turn yields an ever-falling price
index so real output and real wages rise at a steady pace. The first model assumes
that spillovers are perfectly transmitted between firms in different regions.
Accordingly, we call it the ‘global spillovers’ (GS) model … It shows that growth could
dramatically alter economic geography in the sense that the process of accumulation
of capital could lead to catastrophic agglomeration. However, as in the CC model,
geography has no impact on the long-run growth rate of the GS model. This is not the
case in the second model, which assumes that spillovers are harder to transmit
between rather than within regions. For this reason, we call it the ‘localised spillovers’
(LS) model and show that, in this model, long-run location does affect the long-run
rate of growth.”
Ch. 8: Vertical Linkages (VL) Models
“In these models, input-output linkages in the presence of inter-sectoral, rather than
inter-regional labour mobility, support agglomeration … the main concern of the three
VL models we cover is the geographic location of industry. They assume a world of
two regions, two sectors, and either two primary factors (FCVL model) or – in contrast
to the CP and FE models – only one such factor (CPVL and FEVL models). One of
the sectors – call it ‘industry’ – is marked by increasing returns and monopolistic
competition in differentiated varieties. To make the location choice of industrial firms
interesting, the model assumes that it is costly to sell industrial goods across regional
borders. The other sector is kept as simple as possible by assuming perfect
competition, constant returns and costless trade. The factor that is intensively used in
such a Walrasian sector is assumed to be inter-sectorally mobile, while being interregionally immobile. The final main assumption concerns input-output linkages. All
firms in the industrial sector buy each other’s output as intermediate input … As in the
migration-based CP and FE models, the mechanics of the FCVL and FEVL models
are driven by two wel-known results in the theory of international trade. The first is the
‘market-access effect’ that describes the tendency of imperfectly competitive firms to
concentrate production in the big market and export to the small markets. The second
result concerns the impact of firms’ location on the production cost of other firms
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(‘cost of production effect’). Each industrial firm produces a differentiated variety, and,
because of trade costs, a variety is cheaper in the region in which it is produced.
Thus, consumers and firms in the region with more firms import a narrower range of
varieties and so avoid the trade costs more … Combining the market-access effect,
the cost of production effect and inter-sectoral mobility creates the potential for
circular causality – also known as ‘cumulative causality’ or ‘backward and forward
linkages’ … These forces are opposed by the market-crowding effect, which acts as a
dispersion force … As it turns out, the dispersion force is stronger than the
agglomeration forces when trade cost are very high, but falling trade costs weaken
the dispersion force more rapidly than they weaken the agglomeration forces.”
I hope this tour d’horizon has given you some feeling for the richness and variety of
NEG models now available. Nevertheless, it must be acknowledged that although the
working of these models is highly suggestive, their basic structure is quite simple – a
long way, it might be argued, from the complexity of real world economies and their
interactions. Hence the next section.
What evidence is there for NEG effects?
Here I must point you to a different source, viz the latest volume of the Handbooks of
Regional and Urban Economics, Henderson & Thisse (Eds) (2004), and in particular
the chapter by Head & Mayer entitled “The Empirics of Agglomeration and Trade”.
It is at this point that the attractive parsimony of the NEG models is confronted by the
messiness of the world we actually live in. Evidently, the world consists of more than
two regions; and how are products and factors to be defined? And how are trade
costs to be measured? And what observations would be more consistent with NEG
theories than other explanations of agglomeration (such as natural endowments)?
And how to formulate relationships that both capture the essence of (some aspect of)
NEG and are capable of being tested with the data actually available? Much of Head
& Mayer’s article is in fact taken up with proposing ways in which these somewhat
technical but by no means trivial questions can be tackled. As they remark: “There is
no agreed upon regression to estimate, nor even a consensus dependent variable to
explain.”
They suggest five propositions emerging from NEG models that might be tested
empirically:
1. Market potential raises local factor prices. A location whose access to
major markets and supplies is not impeded by large trade costs will tend to
reward its factors with higher wages and land rentals.
2. Market potential induces factor inflows. Capital will be drawn to areas with
good access to major markets for final goods and major suppliers of
intermediate inputs (backward linkages). Workers favour locations with
good access to suppliers of final goods (forward linkages).
3. Home market/magnification effect. Regions with large demand for
increasing returns industries account for an even larger share of of their
production. Put another way, the larger of two regions will be a net exporter
to the smaller region in industries characterised by plant-level increasing
returns.
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4. Trade induces agglomeration. In an industry featuring increasing returns
and partially mobile demand, a reduction in trade costs facilities spatial
concentration of producers and consumers.
5. Shock sensitivity. A temporary shock to economic activity in a location can
permanently alter the pattern of agglomeration.
Head & Mayer then proceed to review empirical work related to these propositions. I
will proceed direct to their summary conclusions:
“The diversity in approaches that characterises this literature probably stems in
large part from the difficulties inherent in testing theories involving circular
causation. In terms of results, our sense is that the dust has not settled yet.
One can see a number of supportive findings but there are just as many
findings that appear to undermine the new theory. The positive relation
between wages and market potential looks like a sturdy result but the response
of production to demand, while certainly positive, is not consistently greater
than one for one. Economic activity concentrates spatially but this
agglomeration cannot yet be seen as confirmation of the theories that were
constructed to explain the phenomenon. There are a number of other
explanations that are consistent with the data and not much yet that strongly
points to the explanation offered by NEG.”
Hardly a ringing endorsement, then. But how far these modest results are due to
faulty theory, inadequate data or weak specification remains unclear.
There may be an opportunity here for transport planners to join the fray. Our
transport/land use models can be seen as offering a kind of half way house between
theory and empirics. These models are immensely more complicated than the NEG
models but at the same time the relationships within them are based on observation
and measurement or calibrated against data from the real world. If the kind of
mechanisms invoked by NEG exist, and if their interaction produces the kind of
effects predicted by NEG models, then should not these effects be discernible in
projections or simulations produced using transport/land use models?
Why does it matter?
At this point you may be thinking: This stuff all seems rather difficult; the theoretical
results seem to be rather fuzzy and to depend on which model you happen to favour;
the evidence for NEG effects in the real world seems weak; so why should I bother?
By way of answer, I offer you the following example. An EU member state has
recently announced a regional development strategy aimed at boosting the growth of
a number of regional hubs rather than the capital, which is considered to have grown
too fast in recent years. The proposed means is substantial investment in improving
transport links between the capital and these hubs.
Now, if we give any credence to NEG models, it appears that the intended effect of
this policy is not the only possible outcome. There are (at least) two other possible
outcomes:
a) The investment will have no significant effect on the location of economic
activity;
b) The investment will in fact boost the growth of the capital rather than the hubs!
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I imagine that policy makers in the country concerned would quite like to find some
way of getting a better feel for which outcome will prevail. This is the positive
economics question: If such and such an action is taken, what will the effect be? It is
one of the questions NEG tries to answer.
Nor is solving that problem the end of the matter. There remains the normative
question: How to judge which outcome is preferable? If the investment has the
perverse effect of boosting the growth of the capital, would that necessarily be a bad
thing? Presumably this outcome would be due to companies in the capital finding it
profitable to expand output, taking advantage of economies of scale (an efficiency
gain) or producing more varieties (a consumption gain), and attracting more workers
into the capital where real incomes are higher (a welfare gain for the migrant
workers); however, this would be at the expense of possibly lower output and
incomes in the periphery, and certainly greater regional inequality. It would be a
matter of weighing up efficiency gains against equity losses. Although this is not an
issue limited to NEG models, Baldwin et al (2003) do include a couple of chapters on
the nature of regional welfare effects (Ch. 10) and on “Efficiency, Equity and Optimum
Agglomeration” (Ch. 11) which may help drifting policy makers towards clearer
thinking about this tricky topic.
On that note, I must end. My objective has been to stimulate interest in NEG among
transport planners. I hope I have at least whetted a few appetites!
References
Baldwin R, R Forslid, P Martin, G Ottaviano & F Robert-Nicoud (2003) Economic
Geography and Public Policy (Princeton University Press).
Dixit A K & J E Stiglitz (1977) “Monopolistic competition and optimum product
diversity” American Economic Review 67: 297-308
Fujita M, P Krugman & A J Venables (1999) The Spatial Economy: Cities, Regions
and International Trade (MIT Press, paperback edition 2001).
Fujita M & J-F Thisse (2002) Economics of Agglomeration: Cities, Industrial Location
and Regional Growth (Cambridge University Press).
Krugman P & A J Venables (1997) “Integration, specialisation and adjustment”
European Economic Review 40: 959-968
Head K & T Mayer (2004) “The Empirics of Agglomeration and Trade” in Henderson J
V & J-F Thisse (Eds) (2004) Handbook of Regional and Urban Economics, Vol 4:
Cities and Geography (Elsevier North-Holland).
Henderson J V & J-F Thisse (Eds) (2004) Handbook of Regional and Urban
Economics, Vol 4: Cities and Geography (Elsevier North-Holland).
Ottaviano G & J-F Thisse (2004) “Agglomeration and Economic Geography” in
Henderson J V & J-F Thisse (Eds) (2004) Handbook of Regional and Urban
Economics, Vol 4: Cities and Geography (Elsevier North-Holland).
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