European Regional Policies in Light of Recent Location Theories

Journal of Economic Geography 2 (2002) pp.
373–406
European regional policies in light of recent location

theories
Diego Puga*
Abstract
Despite large regional policy expenditures, regional inequalities in Europe
have not narrowed substantially over the last two decades, and by some
measures have even widened. Income differences across States have fallen,
but inequalities between regions within each State have risen. European States
have developed increasingly different production structures. European
regions have also become increasingly polarised in terms of their unemploy-
ment rates. This paper describes these trends, and discusses how recent
location theories can help us to explain them and to reconsider the role of
regional policies, especially transport infrastructure improvements, in such an
environment.
Keywords: regional policy, inequalities, transport infrastructure, location, Europe

JEL classifications: R58, R40, H54
Date submitted: 26 February 2001 Date accepted: 18 January 2002
1. Introduction
Profound regional income disparities exist in the European Union (EU). Nearly one
quarter of its citizens live in regions1 eligible to receive assistance under ‘objective 1’ of
the Structural Funds, the main instrument of EU regional policy. The criterion for
eligibility is to have a GDP per capita below 75% of the EU average. If a similar
measure was used for the US, only two states, Mississippi and West Virginia,
containing between them only 2% of the US population, would qualify (Puga,
1999).
There are also large disparities across the regions of the EU in terms of their
unemployment rates. In 1996 the 10 regions with highest unemployment rates had twice
the EU average unemployment rate. In comparison, differences in unemployment rates
across US states are minimal. Differences across European regions are not just the
reflection of differences in unemployment rates across countries. Belgium, Finland,
France, Germany, Greece, Italy, Spain, and the UK all have a difference of at least 700
basis points between the unemployment rate of their highest unemployment region and
that of their lowest unemployment region.
*Department of Economics, University of Toronto, 150 Saint George Street, Toronto, Ontario M5S 3G7,
Canada.
email <d.puga@utoronto.ca>
1 Defined at level 2 of the Nomenclature of Territorial Units for Statistics (NUTS), a hierarchical
classification with three regional levels established by Eurostat to provide comparable regional
breakdowns of the Member States of the EU. In 1996 the EU had 77 NUTS 1 regions, 206 NUTS 2
regions, and 1031 NUTS 3 regions (Eurostat, 1995).
& Oxford University Press 2002

374 x Puga
The growing concern about reducing these regional disparities has made the
financial instruments of EU regional policy the fastest growing component of the
EU budget. The Structural Funds have been allocated e195,000 million (at 1999
prices) for the period 2000–2006. They now account for over 30% of total EU
spending (twice the proportion they represented in 1988), and for about 0.4% of
total EU Gross National Product. The Cohesion Fund, introduced as a financial
instrument in 1993, is to provide another e18,000 million for structural spending in
2000–2006.
Despite this sizable intervention, regional inequalities in Europe have not narrowed
substantially, and by some measures have even widened. Over the past 15 years income
differences across member states have fallen, but inequalities between regions within
each member state have risen. Over the same period, the production structures of EU
member states have become increasingly different. European regions have also become
more unequal in terms of their unemployment rates: there are now fewer regions with
intermediate unemployment rates than a decade ago, and more regions with either high
or low rates.
Recent theories of location can help us explain these trends. Traditionally,
international and regional economics have explained income disparities on the basis
of differences between regions in their endowments of natural resources, factors of
production, infrastructure, or technology. In this context, the removal of obstacles
to the movement of goods and/or factors would by itself cause convergence of factor
returns and living standards. Yet both casual observation and empirical work in the
area show there are relevant forces missing from the traditional analysis, which can
widen regional disparities – even without large differences in underlying character-
istics – and prevent convergence. The main argument arises from the observation that
firms produce more efficiently and workers enjoy higher welfare by being close to
large markets, and that large markets are in turn those where more firms and
workers locate. This creates a cumulative causation process that tends to increase
regional differences. Mechanisms of this kind have been described by development
economists and geographers for some time. The main contribution of what has been
called ‘new economic geography’ is to bring together, in a common analytical frame-
work, both convergence and divergence forces. The advantage of modelling such
forces in a common framework is that we can relate their relative strength to
microeconomic conditions, and explicitly study the trade-off between the economic
advantages of the clustering of activity and the inequalities that it may bring.
Recent location theories can thus help us understand the evolution of regional
inequalities during a process of economic integration, and think about the role of
regional policy in such an environment. Such an analysis is the object of this
paper.
The remainder of the paper is structured as follows. The next section describes
the recent evolution of regional inequalities in the EU. Section 3 reviews location
theories developed in what has been called the ‘new economic geography’, and uses
them to explain the trends described in the previous section. Section 4 then looks at
European regional policies in light of these theories. The discussion of regional policies
is selective rather than exhaustive, reflecting the focus and scope of the new economic
geography. Since much of this work studies the effects of changes in transport costs, a
separate Section 5 focuses on transport infrastructure and its effects on regional
inequalities. Section 6 provides some concluding remarks.
European regional policies x 375
2. Regional inequalities in the European Union

Economic activity is less geographically concentrated in Europe than in the US. Nearly
one-half of EU industrial employment is concentrated in a small number (27) of NUTS
1 regions, which account for 17% of the Union’s total surface and 45% of its
population. In the US nearly one half of the country’s industrial employment is also
concentrated in a small number (14) of states, but these account for much smaller
shares of total surface (13%) and population (21%).
Not only is overall activity less concentrated in Europe than in the US, but so are
individual sectors. Midelfart-Knarvik et al. (2000) calculate for EU Member States and
for US States an index of spatial separation for individual industries,2 and divide it by
the same index for overall manufacturing. They show that, even after accounting for the
wider dispersion of overall industry in Europe, most individual sectors are also more
geographically dispersed than in the US.
Midelfart-Knarvik et al. (2000) also compare the industrial structures of EU member
states, and show that these have become increasingly different over the last two decades.
In fact, The Netherlands is the only country whose industrial structure has become
more similar to the aggregate EU structure. The industrial structures of the four largest
EU economies (France, the UK, Italy, and Germany) are relatively similar. However,
Germany and Italy are becoming increasingly different from each other as well as from
France and the UK. The four cohesion states (Greece, Ireland, Portugal, and Spain)
have also become increasingly different from each other. Spain is now more similar to
France, the UK, Italy, and Germany than to the other three cohesion states. Ireland is
more similar to Belgium, Denmark, and The Netherlands. Finland and Sweden have
remained similar to each other but have become increasingly different from the rest.
The increasing sectoral specialisation of EU Member States has followed an uneven
pace. Figure 1 (from Midelfart-Knarvik et al., 2000) plots an average index of national
specialisation relative to the EU for four groups of Member States (the initial Member
States, the 1973 entrants, the 1980s entrants, and the 1995 entrants). This shows little
change in specialisation during the 1970s, and then a phase of rising specialisation
which has been particularly acute since the early 1980s for the 1980s entrants and since
the mid-1990s for the 1995 entrants.
Given that production is less geographically concentrated in Europe than in the US,
one might expect a more even distribution of income per capita as well. In fact, the
opposite is true: differences in income across European regions are much wider than
across US states. In 1992 the ten best-off regions had a GDP per person equal to 1.6
times the Union’s average and 3.5 times that of the ten worst-off regions (at NUTS 1
level). By comparison, the ten best-off US states had a GDP per person equal to 1.2
times the US average and 1.5 times that of the ten worst-off States.
European regions experienced a clear convergence in income per capita up until the
late 1970s, when convergence came to a sudden stop (see, amongst others, Marcet,
1994; Canova and Marcet, 1995; Neven and Gouyette, 1995; Lopez-Bazo et al., 1999;
Rodrı́guez-Pose, 1999). Figure 2 (from de la Fuente and Vives, 1995) shows the clear
exhaustion of regional income convergence after the 1970s.
2 This spatial separation index is calculated as the production-weighted sum of all bilateral distances
between locations, which takes value zero if all production takes place in one place and increases as
production becomes more geographically dispersed.
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Figure 1. Specialisation for EU Member States grouped by entry data.
Figure 2. Dispersion of regional income in Europe.
Additional information can be gathered by decomposing income inequalities, as

measured by a Theil index, into inequalities across countries and inequalities across
regions within each country (Esteban, 1999; Duro, 2001). Figure 3 (from Duro, 2001)
shows that during the first half of the 1980s income inequalities across EU member
states accounted for about one half of overall regional inequalities, and inequalities
across regions within the same state for about another half. Since then income
inequalities across states have fallen by 25%, but regional inequalities within states have
risen by 10%. As a result, nowadays most regional income inequalities in Europe are
within rather than across member states.
Figure 3. Decomposition of a Theil index of income inequalities across European regions.
Looking at inequality indices provides a useful, but partial, picture of the evolution
of regional inequalities across European regions. For instance, using summary statistics
alone one cannot discriminate between a situation in which regions roughly maintain
their relative position over time, and another one in which the shape of the distribution
changes little but the relative positions of particular regions change significantly over
time. One way to get at this is by looking at geographical maps illustrating the whole
regional distribution for different points in time.
Figure 4 graphs the GDP per capita relative to the EU average of the NUTS 2
regions that made up the EU in 1987 for that year and for 1995. The map shows only
small changes in the relative income level of individual regions. This is in contrast with
the notable changes that appear when the same map is drawn for relative
unemployment rates in 1986 and in 1996, as shown in Fig. 5.3
More details can be learned by constructing transition probability matrices that track
changes over time in the relative position of regions within the distribution. This is an
exercise that a number of authors, including Quah (1999), have undertaken. The
transition probability matrix at the top of Table 1 is an update on that work, which
reports transitions between the 1987 and 1995 distributions of GDP per capita relative
to the European average (only regions that were part of the EU at the beginning of the
period are included in the calculation).4 The main diagonal of this matrix gives the
proportion of regions that were in the same range of the distribution in the mid-1990s
as in the mid-1980s. The large numbers on this diagonal show the strong persistence of
relative regional income levels. For instance, reading along the top row of the matrix,
we see that of the 30 regions that in 1987 had a GDP per capita below 0.6 times the EU
3 Note that the average unemployment rate for regions in the map was the same in 1986 and in 1996, and
that these years are separated by a full cycle. Greek regions are absent from the map due to lack of data.
4 The table also gives two additional pieces of information. The first column gives n, the number of regions
that begin their transitions in a given state. The second column gives the classes that divide up the state
space.
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Figure 4. Regional GDP per capita in Europe.

Table 1. Transition probability matrices of GDP per capita and unemployment rates relative
to EU average
n 1995 GDP per capita

1987 GDP per capita
30 [0–0.6) 0.83 0.17 0.00 0.00 0.00

19 [0.6–0.75) 0.21 0.47 0.32 0.00 0.00
50 [0.75–1) 0.00 0.18 0.68 0.14 0.00
53 [1–1.3) 0.00 0.00 0.13 0.72 0.15
18 [1.3–1) 0.00 0.00 0.00 0.17 0.83
[0–0.6) [0.6–0.75) [0.75–1) [1–1.3) [1.3–1)
n 1996 unemployment
21 [0–0.6) 0.19 0.00 0.00 0.00

1986 unemployment
0.81
23 [0.6–0.75) 0.52 0.26 0.09 0.09 0.04
42 [0.75–1) 0.24 0.29 0.26 0.21 0.00
32 [1–1.3) 0.06 0.22 0.34 0.19 0.19
32 [1.3–1) 0.00 0.00 0.16 0.22 0.62
[0–0.6) [0.6–0.75) [0.75–1) [1–1.3) [1.3–1)
Source: (Unemployment table) Overman and Puga (2002).
average, 83% remained in the same range in 1995, while 17% saw their relative income
rise to between 0.6 and 0.75 times the EU average, and none moved higher up in the
distribution than this.
Again it is insightful to compare the distributions of GDP per capita and
unemployment rates. Up until the mid-1980s differences in unemployment rates across
European regions were very stable, with changes in regional labour forces just offsetting
ongoing changes in regional employment (see chapter 6 in Layard et al., 1991). The
transition probability matrix at the bottom of Table 1 (from Overman and Puga, 2002)
shows that this stability no longer holds. It reports transitions between the 1986 and
1996 distributions of unemployment rates relative to the European average. The
contrast between changes in relative GDP per capita and changes in relative
unemployment rates can be seen most clearly by comparing the numbers on the
main diagonal of both transition matrices. This shows that for regions initially below
60% of the European average, the proportion of regions that remained in the same
range of the distribution in the mid-1990s as in the mid-1980s is as high for
unemployment (81%) as for income (83%). For regions initially above 130% of the
European average, persistence is also very high in both distributions, although it is
more marked for income than for unemployment (83% against 62%). But for regions
initially around the middle of the distribution the difference is striking: most regions
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Figure 5. Regional unemployment rates in Europe.

with intermediate income levels remained in the same range whereas most regions with
intermediate initial unemployment rates moved to a different range.5
As a result of regions with intermediate unemployment rates moving towards both
extremes of the distribution, the distribution of European regional unemployment rates
has become increasingly polarised: there are now more regions with either very high or
very low unemployment and fewer regions in between as compared with the mid-1980s.
The extent of this polarisation of unemployment rates can be measured using the
generalisation by Esteban et al. (1999) of the polarisation measure of Esteban and Ray
(1994). Between 1986 and 1996, the polarisation of the unemployment rate distribution
into a group of high and a group of low unemployment regions increased by 37%, from
0.096 to 0.131 (Overman and Puga, 2002).
Overman and Puga (2002) argue that this polarisation of regional unemployment
rates could be driven by three factors: different reforms in national labour market
institutions, a within-country polarisation of labour supply, and a within-country
polarisation of labour demand. They show that national considerations do not play the
main role in explaining regional unemployment outcomes. In fact, the unemployment
outcomes of individual regions are much closer to the outcomes of neighbouring
regions (domestic and foreign) than to the average outcomes of other regions within the
same member state. Regarding changes in labour supply, these not only have not
caused the polarisation of unemployment rates but have in fact mitigated it. They show
that it has been changes in the spatial distribution of labour demand that have mainly
caused polarisation, and that these changes have been similar across geographical
neighbours, resulting in similar unemployment outcomes.
Looking back at Fig. 5 we can see the strong geographical component in the
polarisation of unemployment rates. Comparing the maps for 1986 and 1996 we can see
clearly defined clusters of high and low unemployment emerging, and these extend not
just across regions but often also across countries. As a result national borders have
become increasingly blurred. Overman and Puga (2002) note that understanding this
neighbours effect is crucial to understanding the polarisation of labour demand and
unemployment rates. They consider different factors that might have resulted in similar
changes in labour demand across neighbouring regions. These include the skill
composition of regional labour forces, and regional specialisation patterns. They find
that there is a truly geographical component to the neighbour effect since, even after
controlling for national and regional characteristics, there is still a strong similarity in
the unemployment outcomes of neighbouring regions. Most surprisingly, this
geographical component is as strong within as across national borders. All of this
suggests that the polarisation of regional unemployment rates is the result of a spatial
reorganisation of employment over this decade of deepening European integration,
with clusters of rising or falling employment extending across neighbouring regions and
even across neighbouring countries.
5 Esteban (1999) decomposes inequalities in GDP per capita, as measured by a Theil index, into differences
in productivity, unemployment, participation rates, and demographic factors. He finds that, differences in
productivity are still the largest component of income differences. Furthermore, interregional
productivity differences are mostly common across sectors (Esteban, 2000). However, differences in
unemployment rates are becoming an increasingly important component of income differences at the
expense of other components, including productivity (Esteban, 1999).
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In order to help interpret the evolution of regional inequalities across Europe

described in this section and to better understand how it might be affected by further
integration and regional policies, we now turn to a brief review of some recent location
theories.
3. Forces driving the new economic geography

Economic theory has traditionally explained differences in production structures mainly
through differences in underlying characteristics (endowments of natural resources,
factors of production, infrastructure, or technology), which make space itself uneven.
In this framework, economic integration leads regions to specialise according to their
comparative advantage.
Comparative advantage, while relevant, provides a weak explanation for the
remarkable spatial concentration of activity. In order to explain the uneven
geographical distribution of economic activities across areas with similar endowments,
we must consider increasing returns to scale (Scotchmer and Thisse, 1992, call this the
‘folk theorem of spatial economics’). Models of trade with increasing returns and
imperfect competition provide an explanation as to why regions without significant
comparative advantage with respect to each other can develop different production
structures on the basis of their different market access.
The implications of these models for location, and the effects that reductions in trade
or transport costs have on it, are formalised by Krugman and Venables (1990). They
start by assuming that there are two regions: a large ‘core’ region and a small
‘peripheral’ region. There are two factors of production, which are mobile across
sectors but immobile across regions. The core region has larger factor endowments than
the peripheral region, although both have the same relative endowments – hence there
is no comparative advantage in the traditional sense. There are two production sectors.
One of these sectors is perfectly competitive and produces a freely tradeable
homogenous commodity under constant returns to scale. The other sector is imperfectly
competitive and has firms producing differentiated manufactures under increasing
returns to scale; they label this ‘manufacturing’.
It is hardly surprising that at equilibrium the core has a larger manufacturing sector
than the periphery. The interesting finding is that, for finite positive trade costs, the
core’s share of industry is larger than its share of endowments. It is therefore a net
exporter of manufactures. This effect is known as the ‘market access’ or ‘home market’
effect.6 Furthermore, each region’s share of industry changes non-monotonically with
trade costs. This is best seen by considering a process of gradual reduction in trade costs
between the two regions from prohibitively high to none, as depicted in Fig. 6. The
vertical axis is the share of industry in each region, and the horizontal axis plots trade
6 Davis (1998) introduces transport costs for the good produced under constant returns in a model similar
to the one described here, and shows that this results in no equilibrium trade in this good, and hence no
market access effect. Fujita et al. (1999) show that re-establishing equilibrium trade in the constant returns
good by having this being differentiated across countries re-establishes the market access effect. Davis
and Weinstein (1999) find evidence of market access effects for Japanese prefectures. For OECD countries
they do not when they focus on pure national market size, but they do when they consider a richer
measure of market access (Davis and Weinstein, 1998).
Figure 6. Trade costs and location in Krugman and Venables (1990).
costs (zero represents free trade, one represents trade costs equal to the producer price
of the product). In this example region 1 (the core) is assumed to have 60% of total
endowments of the two factors.
With high trade costs, firms sell mainly, but not only, in their local market. Then if a
region had many more firms relative to its market size than the other region, the greater
competition or crowdedness of that market would lead some local firms to exit,
reducing differences in the size of industry. As a result, each region’s share of industry is
close to its share of endowments.
Economic integration increases the share of sales that each firm makes in the other
region, thereby weakening the effect of more local competitors on each firm’s market
share. Yet increasing returns imply that the larger sales of firms producing in the core
give them higher profits. If more firms enter in response to those profits, the size of
industry in the core rises above its share of world endowments.
However, as the size of industry in the core increases so does demand for local
factors. For low trade costs, rising factor prices start driving some firms out of the
core, so further integration starts reducing its share of industry. As the two regions
approach costless trade it is increasingly factor price differences that determine
location, so differences in both nominal and real wages between them tend to
disappear, while each region’s share of industry tends to go back to its share of overall
endowments.
This type of trade model with imperfect competition highlights the fundamental
ambiguity of the effects of economic integration or reductions in transport costs on the
relative attractiveness of core and peripheral regions. However, like traditional theory,
it still explains differences in production structures through differences in underlying
characteristics – in this case in terms of exogenously given market size. The main
contribution of the so-called ‘new economic geography’ (or new geographical
economics) is the formalisation of mechanisms by which, even a priori very similar
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regions, can end up with very different production structures and income levels.7 The
simplest such mechanism arises when one introduces some mobile source of demand in
the framework just described.8
3.1. Endogenous core-periphery structures with labour mobility

Krugman (1991) shows that the interaction of labour migration across regions with
increasing returns and trade costs creates a tendency for firms and workers to cluster
together as regions integrate. If some factors are mobile between regions, then the
pressure put on those factors by the concentration of economic activities will be eased.
Factor mobility can make the supply of factors sufficiently elastic that small differences
in the size of industry across regions can build up. Even if regions are a priori identical,
they can become endogenously differentiated into an industrialised core and a
deindustrialised periphery.
Krugman (1991) considers two regions and two sectors: one monopolistically
competitive, the other perfectly competitive. There is no mobility of workers across
sectors, and only industrial workers are mobile across regions.9 Finally, the two regions
are assumed a priori identical in every respect, including their endowment of immobile
factors.
To understand the forces at work it is useful to consider the following thought
experiment. Suppose that both regions have equal shares of industry but that, for some
reason, one firm decides to move production from one region to the other. How does
this affect the profitability of firms in the region receiving the firm? The presence of one
more firm puts additional pressure on the product and labour markets, and tends to
make the firm go back to where it came from. If there was no migration, this would be
the end of the story and regions would remain identical. However, the rise in the share
of goods produced locally in the region receiving the firm (hence free from trade costs)
and the rise in local labour demand and wages tend to attract more workers. This
increases local expenditure and eases pressure in the labour market, so tends to attract
more firms. Whether the overall effect of entry is to increase the profitability of local
firms (encouraging further entry), or to lower that profitability (leading to exit),
depends on parameters of the model, and in particular on how integrated regions are.10
Figure 7 plots the share of industry in each region during a gradual reduction in trade
costs between the two regions. This shows that when trade costs fall below some critical
value, whichever of the two regions gets a slight advantage will build on it. The presence
of more industry attracts more workers due to higher wages and a lower cost of living.
7 The review of the new economic geography in this section only aims to highlight some of the main
mechanisms at work and not to survey the literature; it draws from a more comprehensive survey by
Ottaviano and Puga (1998). Ron Martin (1999) and Neary (2001) provide critical reviews of this work.
See also Fujita and Thisse (2000) for a complementary survey of how this approach relates to work based
on either technological externalities or spatial competition.
8 Alternatively, factor accumulation can play a similar role (Baldwin, 1999).
9 Introducing intersectoral mobility in Krugman’s 1991 model (but retaining some other immobile factor)
does not substantially change the results, but, by making labour supply to this sector more elastic,
strengthens the tendency of industry to agglomerate (Puga, 1998).
10 For the effects of changes in other parameters, see Ottaviano et al. (2002). They develop an alternative
specification of Krugman’s (1991) model with quadratic instead of CES utility, which allows them to
better disentangle the different effects.
Figure 7. Trade costs and location in Krugman (1991).
The presence of more workers in turn attracts more firms through a market access
effect. And this self-reinforcing mechanism leads to an endogenous differentiation of
the two a priori identical regions into an industrialised core and a deindustrialised
periphery.11
3.2. Low labour mobility in Europe

The cumulative causation mechanism modelled by Krugman (1991) relies on the
assumption that, when a region does relatively well in attracting firms, it is able to
attract more workers on the basis of higher wages and better access to a wider range of
goods. Blanchard and Katz (1992) show that in the US there is such an adjustment
process working through regional migration. This is not the case in Europe, where
adjustment to changes in regional fortunes takes place mostly through participation
decisions (Decressin and Fatàs, 1995).
Migration rates in Europe are low in comparison with those of the US (Bentivogli and
Pagano, 1999). They are also low by historical standards, especially when compared with
the much higher migration rates that characterised Europe in the 1960s.12 The Single
European Act was set to create a single market for goods and workers in the EU, yet
only 1.5% of EU citizens live in a member state different from where they were born.
Even within European countries, migration across regions remains small. There is some
disagreement as to whether this is the result of people being reluctant to move, the
incentives being insufficient, or barriers to migration being too high.
11 Since regions are a priori identical, which region becomes the industrialised ‘core’ and which the
‘periphery’ when they endogenously differentiate is not determined by endowments, but instead by
history – and, in extensions to this model (see, for instance, Ottaviano, 1999), by expectations.
12 See Braunerhjelm et al. (2000) for a careful discussion of labour mobility in Europe, and de la Fuente
(1999) for a literature review and some novel results for Spain.
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Low mobility in Europe is often blamed on language and cultural barriers, or on

Europeans being immobile per se. Yet culture cannot easily explain low migration rates
within European countries, which in most cases do not have substantial internal
language barriers. Nor can it explain the dramatic fall in migration rates since the
1960s.
Migration rates could also be low because of lack of incentives. This may seem an
unlikely explanation given that, as discussed in Section 2, there are large differences
across European regions both in terms of income and unemployment rates. Yet in
many European countries interregional wage differences for similar jobs are relatively
small, an issue we shall return to below. Nevertheless, Bentolila (1997) argues that in
the case of Spain the importance of wage convergence for the decline in migration is
likely to be small, given the almost nil elasticity of migration with respect to
interregional wage differentials (Bentolila and Dolado, 1991). Faini et al. (1997) argue
that the same is true for Italy. Nominal wage equalisation there was largely achieved at
the end of the 1960s following a union agreement to abolish regional wage differentials.
Yet the decline in migration has continued steadily.
Even with similar wages, differences in the probability of finding a job still result in
interregional differences in expected income. Empirical studies tend to find zero
responsiveness of interregional migration in Europe to unemployment differentials
(Bentivogli and Pagano, 1999). But de la Fuente (1999) finds that when one considers
more sophisticated measures of the probability of finding a job than the usual one
minus the unemployment rate, people are much more responsive to differences in this
probability. This suggests that one of the main reasons why there is not more migration
in Europe is that the high average unemployment rate makes the probability of finding
a job anywhere too low to make it worthwhile to move (Braunerhjelm et al., 2000).
Also, inefficiencies in the job-matching process may result in a much lower probability
of being hired outside one’s own region. Faini et al. (1997) cite evidence from Casavola
and Sestito (1995) showing that Italy’s unemployed rely largely on the networks of
family and friends to find a job. Such networks are much less effective outside the
region where the unemployed are located. Faini et al. (1997) find this to be a key reason
for the low propensity to migrate across Italian regions: those amongst the unemployed
in the Mezzogiorno who rely more heavily on family and friends to find a job are also
much less likely to migrate. Monfort and Ottaviano (2000) formalise the link between
the efficiency of the job matching process and agglomeration and suggests that, if
labour markets function more efficiently in areas where firms agglomerate, then
differences in unemployment rates across regions may be very persistent.
A low probability of finding a job outside one’s own region, be it due to high
unemployment rates or inefficiencies in job-matching is clearly only part of the story.
When the unemployed are asked as part of the Spanish labour force survey whether
they would take a job away from their place of residence if offered one, only 33.2%
answer yes (in 1997, down from 44.6% in 1987) (Pérez and Serrano, 1998).
Rising incomes may have made people more sensitive over time to amenities in their
place of residence (de la Fuente, 1999). Family and government support could also help
explain the decline in the propensity to migrate (Attanasio and Padoa-Schioppa, 1991).
According to this argument, young people, who typically constitute the bulk of
migrants, are less willing to move because rising family income has strengthened family
support when unemployed. While this argument makes sense, there are forces working
in the opposite direction. Migration is a costly investment and rising family income can
help finance it more easily. Faini et al. (1997) find that in Italy this second effect tends to
dominate: higher household income (proxied by the family’s employment rate)
increases rather than decreases the propensity to migrate. At the same time, distortions
introduced by certain government benefits may be important. Antolı́n and Bover (1997)
find that in Spain those amongst the unemployed receiving benefits (as proxied by being
registered unemployed) are significantly less likely to migrate.
Finally, the costs of housing transactions and the difficulties of finding a rented
accommodation can also play a prominent role in discouraging interregional mobility.
Oswald (1996) argues that high home ownership rates in Europe are linked to low
mobility and high unemployment rates. In the case of the UK, McCormick (1997)
discusses how rising housing prices in the faster growing regions tend to discourage
interregional migration.
3.3. Endogenous core-periphery structures without labour mobility

What do new economic geography models have to say about the relationship between
trade costs and regional inequalities if migration does not occur, be it due to lack of
mobility or to the incentives to move not materialising?
Venables (1996) addresses this question by arguing that firms benefit from being close
to each other not only because of linkages working through the supply of labour and
demand for goods from each others workers, but also because of direct input-output
linkages amongst themselves. He shows that vertical linkages between upstream and
downstream industries, when both of them are imperfectly competitive, can play a role
equivalent to that of labour migration in endogenously determining the size of the
market at different regions.
Krugman and Venables (1995) pick up this argument, and formalise it in a
framework more directly comparable to that in Krugman (1991). Cumulative causation
arises in a similar way in both papers. In Krugman (1991) an increase in the number of
firms in a location increases demand for the output of local firms through the
expenditure of the workers attracted from other regions. In Krugman and Venables
(1995) there is no interregional mobility, so workers must be drawn from other sectors
instead, and the higher demand comes from expenditure on intermediates by the newly
arrived firms. In addition to this demand linkage, in Krugman and Venables (1995)
there is a cost linkage arising from the saving in trade costs on a larger fraction of their
intermediate inputs by firms in the larger market (these demand and cost linkages can
be seen as a formalisation of Hirschman’s, 1958, backward and forward linkages).13
The relationship between transport costs and agglomeration, however, becomes more
complex without labour migration. If agglomeration does not increase interregional
wage gaps (which in the context of Krugman and Venables, 1995, simply requires that
at equilibrium all countries keep some constant returns to scale production), things
work as in Krugman (1991): a fall in trade costs below some critical level leads industry
to concentrate in a single region. However, if the concentration of industry in some
regions tends to make their wages relatively higher, the lack of interregional labour
mobility can make the relationship between regional integration and industrial
13 If these linkages affect the R&D sector, they can become intertemporal and increase the rate of growth
(Martin and Ottaviano, 2001).
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Figure 8. Trade costs and location in Puga (1999).
agglomeration non-monotonic, so close enough integration can trigger the industrial

take-off of less developed regions.14
This
-shaped relationship between transport costs and agglomeration is studied by
Puga (1999), with a unified framework in which both interregional migration and input-
output linkages may drive agglomeration.15 He finds that the lack of interregional
mobility introduces two main differences in the relationship between economic
integration and regional inequalities, which are illustrated in Fig. 8.
First, comparison of outcomes with and without interregional migration shows that
agglomeration gets an extra kick from the relocation of workers towards locations with
higher real wages. On the other hand, if workers do not move, wage differences persist
and act as a dispersion force by increasing production costs for firms producing in
locations with relatively many other firms. This dispersion force can moderate
agglomeration and sustain non-extreme equilibria in which all regions have industry,
even if in different proportions. Thus the lack of interregional mobility both postpones
agglomeration in a process of regional integration and weakens it when it happens.
One straightforward implication of this is that lower mobility in Europe together
with higher barriers to trade can play an important role in explaining why non-
agricultural employment is less geographically concentrated in Europe than in the US
but income disparities are wider across EU regions than across US states.
The second difference introduced by the lack of mobility is the non-monotonicity in
the relationship between trade costs and agglomeration. With zero trade costs each firm
finds no advantage in locating close to the rest of industry and locates in the region with
14 Helpman (1997) shows that the price of non-tradeable goods can similarly act as a dispersion force.
15 The framework captures Krugman (1991), Krugman and Venables (1995), and Puga (1998), as well as
some novel cases, in a single model.
lowest wages; therefore, if wages are increasing in industrial employment, for trade
costs sufficiently close to zero agglomeration in one region cannot be an equilibrium.
Krugman and Venables (1995) illustrate this with examples in which for low trade costs
some firms relocate from the industrial agglomeration to regions with lower wages, but
not to the extent of allowing full convergence between a priori identical regions. Puga
(1999) shows that, more generally, for high trade costs firms are split between the
identically endowed regions to meet final demand; for intermediate trade costs regional
disparities open up as some regions attract more industry than others (but not
necessarily to the extent of absorbing all of industry); for low trade costs agglomeration
unravels as the share of industry in regions with lower wages increases gradually (early
entrants look for lower prices of immobile factors relative to more industrialised
regions; later, as a critical mass of firms is created in some sectors, more firms move in
to exploit forward and backward linkages).
In a multi-country version of Krugman and Venables (1995) motivated by the
experience of European integration, Puga and Venables (1997) find that the formation
of a customs union has location effects that benefit the union as a whole, as firms find it
increasingly advantageous to serve the common market from inside (Baldwin and
Venables, 1995, call this a ‘production shifting’ effect). However, such gains are
unevenly distributed between the integrating countries, with early stages of integration
opening up differences in production structures and real income levels between the
integrating countries, and later stages leading to convergence.
The
-shaped relationship between integration and agglomeration of many of these
models could be interpreted to suggest that, due to low interregional labour mobility,
European integration may by itself cause regional convergence both in terms of real
wages and of production structures. However, the ability of poorer regions to catch up
in this context relies on integration going far enough (during intermediate stages of
integration the model predicts possibly large interregional real wage disparities), on
similar endowments in terms of skill and technologies, and on a flexible response of
wages to changes in industrial employment. Making sure these criteria are met is
essential to achieve convergence.
With respect to the last point it is worth noting that institutional constraints limit
interregional wage differentials within European countries. German reunification led to
a sharp reduction in regional wage differences between the eastern and western Lander.
According to Akerlof et al. (1991), wages in the East rose by 42% between the first
quarter of 1990 and October of that same year. This rise occurred while migration was
small and falling, and was largely due to strong union bargaining for wage equalisation
on the grounds of preventing large scale migration, and the perception on the part of
workers that higher unemployment in the East did not justify lower wages.16 In Italy
and Spain, wage setting at the national level limits the responsiveness of wages to
16 The equalisation of social security contributions also contributed towards rapid wage equalisation. While
the one to one conversion rate between Ostmarks and Deutschmark has also been blamed, this is not a
compelling reason. As Bean (1992) puts it, ‘[w]ould things really have been different if a conversion rate
of, say, 10 Ostmarks to the Deutschmark had been chosen? While much of the East German industry
might have been viable at that rate, it would have remained so only as long as East German workers were
willing to accept the corresponding low real wages. . . . At the end of the day the problem is that East
German workers want a West German standard of living, which East German capital is not presently
capable of delivering.’
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regional economic conditions (on this respect, see Jimeno and Bentolila, 1998, who
stress the impact on the regional structure of wages in Spain of wage floors set at the
national sectoral level). The rebates on social security contributions introduced in
southern Italy to create some labour cost differences have done so along a very coarse
geographical partition (North–South) and without improving the responsiveness to
local conditions, so have not offset rigidities in the wage setting process. They are now
being eliminated following an agreement with the European Commission.
Puga (1999) suggests that the combination of minimal interregional migration with
wage setting at the national sectoral level may help understand the rise in income
inequalities between European regions within each country over the last 15 years at the
same time as inequalities between countries have fallen. In the models discussed above,
when agglomeration does not get reflected in wage differentials, agglomeration
increases monotonically with integration. If the structure of wages in Europe reflects
differences in local conditions between countries more than differences between regions
within each country, further European integration could reinforce the current trend:
peripheral countries catching up in their average income to core countries, while poorer
regions keep falling behind (see also Faini, 1999, for a formalisation of this argument
which explicitly incorporates union behaviour). If agglomeration does not get reflected
in wage differences, it may get reflected instead in differences in unemployment rates.
Since clusters of activity may extend across several administrative units, this can result
in clusters of high and low unemployment extending across regional and even national
borders.
One stylised fact we have not related to the models discussed so far is diverging
industrial structures. However, in order to study the relationship between trade costs
and sectoral agglomeration, one needs to consider a more detailed input-output
structure.
3.4. Agglomeration and regional specialisation

Krugman and Venables (1996) consider a setup like that in Krugman and Venables
(1995), with one main difference: the two production sectors are imperfectly
competitive, and firms in each sector sell and buy a higher proportion of intermediates
to and from firms in the same sector than to and from firms in the other sector. This
introduces an important difference with respect to Krugman and Venables (1995): if one
more firm locates in a region, the beneficial cost and demand linkages affect more
intensely firms in the same sector, while the increased product and labour market
competition harms firms in both sectors equally (Henderson, 1974, uses a similar
argument to explain city specialisation). As a result, a fall in trade costs below some
critical level leads each region to become specialised in the production of one sector.
Venables (1999) extends the model in Krugman and Venables (1996) to a continuum
of imperfectly competitive sectors and a perfectly competitive sector. He then asks what
proportion of sectors will be located in each of the two regions when agglomeration
occurs. With just two sectors the answer was one industry in each region, this meaning
that both regions have the same income levels. But with many industries the division
need not be half and half. One region can have more industries than the other, this
leading to real income differences between regions. What Venables shows is that there
are bounds to sustainable regional differences, and that the maximum share of total
industry that one region can capture first increases and then decreases during a process
of regional integration. However, because within those bounds the actual division of
sectors between regions is indeterminate, there are strong incentives for each region to
try to secure the maximum possible number of sectors.
Most trade models predict that specialisation will increase with reductions in trade or
transport costs. What distinguishes new economic geography models in this respect is
the fact that even similar regions or countries can develop very different specialisation
patterns. This provides a justification for the increasing differences in production
structures across different core countries as well as across different peripheral countries
in Europe described in Section 2.
Having discussed how some recent location theories can help understand the
evolution of regional inequalities in Europe, we now turn to the policies that aim to
affect that evolution.
4. European regional policies

The main instrument of EU regional policy, the Structural Funds are articulated
around three ‘objectives’. Objective 1 is ‘promoting the development and structural
adjustment of regions whose development is lagging behind’. Objective 2 is ‘supporting
the economic and social conversion of areas facing structural difficulties’. Objective 3 is
‘supporting the adaptation and modernisation of policies and systems of education,
training and employment’. Of the e195,000 million (at 1999 prices) allocated to the
Structural Funds in the 2000–2006 budget, 69.7% is allocated to objective 1. The
NUTS 2 regions eligible under objective 1 are those with a GDP per capita below 75%
of the EU average. The Cohesion Fund is to provide another e18,000 million over the
period 2000–2006, in this case for Greece, Ireland, Portugal, and Spain.
Despite the articulation of European regional funds under different objectives, it is
not always clear what the aims are. The first and fundamental issue for a good design of
regional policies is to define the objectives clearly. Do we want a homogenisation across
space of certain aggregate measures (such as income per capita, unemployment or
employment rates, or health and education indicators)? Or is the objective one of
personal fairness, similar people having similar opportunities in different places?
The two objectives are related but do not necessarily go in the same direction.
The case of the UK provides a good example. Since the early 1980s there has been
a sharp divergence of both average real and average nominal earnings across UK
regions. However, Duranton and Monastiriotis (2002) show that individual
earnings for people with similar characteristics (such as sex, education, and
experience) have steadily converged. The fundamental reason underlying aggregate
divergence in the British case is the combination of three factors: a rise in the
premium to education, an increase in the geographical concentration of more
educated people in London and the South-East, and a convergence of the premium
to education which was initially lower in these two regions. Had the premium to
education and interregional differences in this premium remained stable, the so-
called North–South divide would have decreased rather than increased (Duranton
and Monastiriotis, 2002).
While it is important to distinguish between these two possible objectives, regional
policies have a role to play even when the main objective is reducing personal rather
than regional inequalities. This is because, as highlighted by recent location theories,
regional interactions can have important effects on individual outcomes. Furthermore,
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there are limits to the amount of personal redistribution that can be achieved by more
direct means.
Clarifying the objectives of regional policies is only a first step. One then needs to
look for optimal policies to achieve those objectives. Before considering possible
instruments, this requires deciding on the direction of intervention. Is the amount of
regional heterogeneity delivered in the absence of regional policies too high or too low?
The general presumption is that policy should aim to reduce regional inequalities by
focusing on poorer regions. However, the extent to which this should happen is far
from clear. Seeking full spatial homogeneity would obviously be absurd, but how far
should policy aim to go? Even in the absence of agglomeration effects, the same
differences in endowments, technology and geography that can help make some regions
richer may also make public investments more productive in those places. De la Fuente
(2001) studies this issue for the case of Spain and finds that the allocation of public
infrastructures across Spanish NUTS 2 regions has been too redistributive, even if one
considers a high degree of aversion to inequality.
Agglomeration effects further complicate this matter. The location theories reviewed
in the previous section highlight the potential efficiency gains from the clustering of
activity. If there is sufficient mobility, agglomeration need not be incompatible with a
convergence of income and unemployment rates. With little mobility, on the other
hand, reducing regional inequalities may involve giving up some of the gains from
agglomeration (see Martin, 1999a,b, for an elaboration of this argument). The idea that
regional policies should aim to disperse activity with respect to the market outcomes
indicates a presumption that market outcomes are characterised by too much
concentration. However, there are forces pushing in opposite directions. There is a
tendency for too much agglomeration, since when firms and workers move they do not
take into account the possible losses for those left behind. There is a tendency for too
little agglomeration, since when firms and workers move they only take into account
their own benefit and not the benefits they bring for other firms and their impact on
aggregate growth. Similar reasoning applies to the congestion that firms and workers
may impose on others in the same region. Thus, there is no general indication of the
direction in which governments should push with regional policies when seeking
efficiency. Even in terms of equity, the direction of policy is not obvious. Martin and
Ottaviano (1999) show in the context of a new economic geography model with
endogenous growth, that policies that increase agglomeration may nevertheless make
those that remain in poorer regions better off by raising the rate of growth. Combes and
Linnemer (2000) show in the context of a location model à la Hotelling that policies
that induce asymmetries in the location of production may help consumers everywhere
by intensifying competition and lowering prices.
Turning to the instruments of European regional policy, similar proportions of the
Structural Funds allocation are devoted to training, subsidies to enterprises, and
infrastructure investments. New economic geography models have little to say with
respect to training, since most of these models do not incorporate human capital
accumulation. One of the few conclusions they offer on this aspect is that, since training
facilitates innovation and knowledge diffusion, it can both increase aggregate growth
and reduce regional inequalities (Martin, 1999b). Training can also be important in the
context of increasing sectoral specialisation. Most trade models predict that
specialisation will increase with integration and reductions in transport costs. What
distinguishes new economic geography models in this respect is the observation that the
pattern of specialisation may not be driven by traditional comparative advantage

considerations but instead by self-reinforcing agglomeration. While specialisation will
increase the need for schemes designed to help workers withstand adjustment, the focus
of such training schemes is not straightforward. It can be hard to distinguish permanent
from temporary shocks on a certain sector. The skills needed to survive each type of
shock are very different. While sector-specific skills can help withstand temporary
shocks by building a regions ‘depth of comparative advantage’, it is general skills that
will help workers adapt in the face of permanent shocks.
Regarding subsidies to enterprises, new economic geography models analyse in detail
some of the externalities that arise from the location of individual firms, which affect
those in other regions. As usual in the presence of externalities, this implies that there is
a trade-off between centralisation or coordination to internalise these externalities as
well as those that might arise from competition between governments, and
decentralisation to exploit the better knowledge of local jurisdictions, and gains from
competition and diversity. The multiplicity of equilibria in new economic geography
models also highlights that the distribution of activities we observe is just one of many
possible outcomes, so that individual governments may have incentives to push for
certain outcomes that favour them but not necessarily other governments or aggregate
welfare. Also, governments may be tempted to contain industrial change. Looking back
at Fig. 1, the 1970s stand out as a period in which neither countries already in the
European Community nor new entrants experienced a clear increase in their industrial
specialisation. One possible interpretation is that specialisation was stifled by the
particularly strong industrial policy interventions that characterised Europe over that
decade.
A small number of papers have started to look at taxes and subsidies in the context of
new economic geography models (Andersson and Forslid, 1999; Kind et al., 2000;
Ludema and Wooton, 2000; Baldwin and Krugman, 2000).17 The main conclusion from
this line of work is that the benefits of agglomeration make firms less sensitive to taxes
and thus allow jurisdictions where firms cluster to tax them more heavily (Andersson and
Forslid, 1999; Kind et al., 2000). An additional implication is that tax harmonisation
may benefit neither rich nor poor regions: it can make rich regions lose tax revenue
while making it more difficult for poor regions to attract industry (Baldwin and
Krugman, 2000). This is an area where more work is needed. An important limitation
of most existing models arises from their very simple nature, which results in the kind of
extreme behaviour shown in Fig. 7: a large change in parameters (be it trade costs, taxes
or subsidies) may have no effect on location, and then a small additional change may
lead to a catastrophic transformation in which industry goes from being evenly split
across regions to being completely clustered in a single region. More realistic models
with, for instance, general equilibrium wage effects (Puga, 1999) or heterogeneous tastes
for amenities (Murata, 2001; Tabuchi and Thisse, 2002) exhibit the smoother behaviour
shown in Fig. 8. Then a government can no longer raise taxes without losing some
firms, although agglomeration will still mitigate the loss.
While new economic geography models still have little to say on training or subsidies
to enterprises, their focus on the relationship between transport costs, agglomeration,
17 See Besley and Seabright (1999); Braunerhjelm et al. (2000) for two recent studies of state aids from a
wider perspective.
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and regional inequalities makes these models particularly appropriate to study the role
of improvements in transport infrastructure.
5. Transport infrastructure as a regional policy instrument

The European Commission sees transport infrastructure improvements as playing ‘a
key role in efforts to reduce regional and social disparities in the EU and in the
strengthening of its economic and social cohesion’ (Commission of the European
Communities, 1999). The Commission is thus promoting the development of a Trans-
European Transport Network (TEN-T). This includes 14 priority projects, endorsed by
the Essen European Council of December 1994, and a large number of smaller projects.
Preliminary estimates put the total cost at over e300,000 million (at 1993 prices).
Projects developed as part of the TEN-T are eligible for substantial Community
support, particularly in the ‘Cohesion’ member states (Greece, Ireland, Portugal, and
Spain). The EU budget for 1995–1999 devoted a total of e2,300 million to the TEN-T.
In the 2001–2006 budget the figure was doubled to e4,600 million. National
governments will also devote substantial sums to transport infrastructure. For instance,
public infrastructure investments in Spain, mostly for transport, are expected to
amount to 2.7% of GDP annually over 2000–2006.
An explicit motivation behind these large investments is the view of transport
infrastructure as one more input into the production process. Thus, increasing the stock
of infrastructure in less developed regions, like increasing any other stock of capital, is
bound to help these regions grow closer to more developed ones. This view underlies a
large number of econometric exercises estimating aggregate production functions in
which public capital enters as an input. Early exercises of this sort (Aschauer, 1989)
reach the implausible result that one more unit of government capital pays for itself by
means of higher aggregate output in less than a year. In the US and in other industrialised
countries the rate of public investment in infrastructure fell after the late 1960s and the rate
of productivity growth fell shortly afterwards. However, concluding from this that the
productivity slowdown was caused by a slowdown in infrastructure investment is quite
a leap. Even if there is a causal relationship, it is not clear in which direction it runs.
Perhaps it was having a sustained period of growth that facilitated large infrastructure
investments and not the other way around. Moreover, even if infrastructure investments
did increase the rate of growth, this does not imply that further investments will,
particularly given that, when building a transport network, the most beneficial links
tend to be built first. More recent contributions in the macroeconomics literature,
exploiting cross-sectional detail and using careful econometrics find more modest
returns to infrastructure investment (see Gramlich, 1994; de la Fuente, 2000, for reviews).
Studying the effects of infrastructure investments by estimating aggregate production
functions, when done carefully, can give us an idea of the impact of an investment on
infrastructure of ‘average past quality’. While useful, this is not always a good
indication of the likely impact of future infrastructure investments. This observation is
particularly relevant for Europe where, as discussed below, the focus of infrastructure
investments is shifting from roads to high-speed rail. A more micro approach allows us
to look at the details of specific projects, and at the same time to consider that its role in
transporting goods and people across space gives transport infrastructure very different
properties relative to other forms of capital. The remainder of this section discusses
some of those properties and their consequences for regional inequalities.
What are the effects of an improvement in transport infrastructure? The first impact
of a transport project comes from the construction expenditure. Given the sums
involved, this is not negligible. A transport project also generates costs and revenues
associated with its operation. Further, it also has a direct impact on regions affected,
typically by reducing the cost and increasing the quality of transport between them; this
in turn induces changes in the total number of journeys undertaken, and in the way in
which these are split between different modes of transport. All of these effects, together
with the environmental impact, are normally considered as part of the economic
evaluation of projects.
It is therefore instructive to look at the cost–benefit analysis of some recent transport
infrastructure projects in Europe. A noteworthy aspect is the shift of emphasis in
European transport infrastructure investment from roads to high-speed rail. In 1996–
1997, both modes of transport represented similar shares of TEN-T investment, each
taking about e15,000 million. Almost two-thirds of railway investment went to high-speed
routes. The focus is now shifting towards rail – so much so that, on the whole, rail
investments within the TEN-T framework are expected to amount to e185,000 million (at
1993 prices), more than twice as much as roads, and over six times as much as airports.
This in itself reflects one of the characteristics of high-speed rail: its high sunk
infrastructure cost, as compared to conventional rail and roads. Track costs of the only
high-speed rail line built in Spain prior to the TEN-T (Madrid–Sevilla) were about e6.5
million (at 1993 prices) per kilometre. De Rus and Inglada (1997) perform an ex-post
evaluation of this line. Since the Madrid–Sevilla line started its operations in 1992, it
has taken a large fraction of passenger transport away from both car and air transport.
Yet de Rus and Inglada (1997) careful cost–benefit analysis arrives at a negative net
present value of the project of at least e2,300 million (at 1993 prices) for an
infrastructure investment of roughly the same amount. More broadly, Nash (1991)
casts a skeptical view of the overall benefits versus costs of high-speed rail proposals.
Traditionally cost-benefit analysis does not try to assess the impact of transport
projects on regional economic development. This approach of looking only at those
activities more directly related to the project can be enough to get an accurate
evaluation, provided that two conditions are met: first, that distortions and market
failures are not significant, so that private and public valuations are not too different;
second, that the changes in levels of activity induced by the project fade away fairly
rapidly as we move away from those activities more closely related to it. However, these
conditions are often not met. There has been increasing realisation throughout
economics that wide ranges of economic activities may be affected by market failure
and distortions. And the type of cumulative causation mechanisms modelled by the new
economic geography can make the effects of a project be amplified rather than
dampened as they spread through the economy.
For high-speed rail these sorts of additional effects may not be very important. High-
speed rail lines are generally not suitable for the transport of goods, and are thus
unlikely to have much effect on the location of industry. Even traditional rail accounts
for a relatively small fraction of goods transport in the EU: about 8% (in terms
of tonskilometre), as compared with about 40% in the US.18 At the same time,
18 There might nevertheless be an indirect effect if the development of high-speed rail lines with separate
tracks frees capacity for freight transport on traditional rail lines.
396 x Puga
high-speed rail may have larger effects on the location of business services and
headquarters. Duranton and Puga (2001) suggest that the resulting increase in the
ability to provide business and headquarter services to remote locations may lead to the
concentration of business services and headquarters in a few large urban centres. This
would raise costs in those centres and drive away production establishments, especially
to smaller cities and towns. As a result, there might be a shift in the main dimension
along which cities specialise, from a specialisation by sector to a specialisation by
function. Duranton and Puga (2001) provide some evidence of such a shift taking place
in the US. In France, there is some informal evidence that the construction of the Paris–
Lyon high-speed rail line led to the relocation of headquarters from Lyon to Paris. In
Spain, there are concerns that the Madrid–Barcelona high-speed rail line may reinforce
the process of headquarter relocation towards the capital (Vives, 2001).
Road infrastructure, being more heavily used to ship goods, is likely to have larger
effects on the spatial allocation of production, and hence on regional inequalities. These
sorts of effect have recently been used to justify the use of transport infrastructure
investments as one of the main instruments of regional policy. According to Europe’s
Committee of the Regions ‘transport should not be judged on strictly economic criteria
(economic viability), but considered in the context of a broader socio-economic and
environmental analysis. In this respect, it is important to highlight that if lack of or
inadequate basic services are not offset by an efficient transport network that diminishes
the adverse effects of such deficiencies by providing access for the population of isolated
or disadvantaged regions, this will serve to increase depopulation and reduce economic
activity, thus hampering returns on transport investment. The result is a vicious circle in
which the growing lack of supply generates a growing lack of demand and vice versa’
(Committee of the Regions, 2000).
One should not forget, however, that roads generally have lanes going both ways,
and high-speed train lines also have tracks going both ways. A better connection
between two regions with different development levels not only gives firms in a less
developed region better access to the inputs and markets of more developed regions. It
also makes it easier for firms in richer regions to supply poorer regions at a distance,
and can thus harm the industrialisation prospects of less developed areas.
New economic geography models not only point out this potential ambiguity in the
impact of lower transport costs on less developed regions, they also tell us that the
overall effect depends not just on characteristics of the projects, but also on certain
aspects of the economic environment. For instance, if there is little interregional
migration, and if wages do not vary much between regions – even when regions differ
widely in their attractiveness to firms – then investments in infrastructure can do little
to help poorer regions catch up, and may even widen their lag with respect to richer
regions. This is not just a theoretical possibility. Faini (1983) has convincingly argued
that the reduction in transport costs between northern and southern Italy in the 1950s
deprived firms in the Mezzogiorno from the protection they had previously enjoyed,
and accelerated the deindustrialisation process in southern Italy.
Of course, the effects of a transport project on the spatial allocation of production
depend crucially on the specific details of the project. New economic geography models
explicitly capture the role of transport infrastructure in facilitating trade, and in doing
so make it possible to discriminate between similarly sized projects facilitating different
types of trade, or interconnecting places in different ways. Martin and Rogers (1995)
were the first to focus explicitly on the role of infrastructure in a new economic
geography framework (and to take into account the costs of financing it). They
distinguish between projects that facilitate trade within a region, and those that
facilitate trade between regions. While improvements in interregional infrastructure
may harm rather than help peripheral regions – for the reasons discussed above –
improvements in local infrastructure in the peripheral regions lack such negative effects.
Another important distinction is between hub-and-spoke interconnections, in which
places are connected by routes going via a common centre or hub, and multilateral
connections in which places are connected pairwise with routes of similar quality. Puga
and Venables (1997) and Fujita and Mori (1996) study this distinction in the context of
new economic geography models. They show that hub-and-spoke networks promote
agglomeration in the hub of the network, as firms located there face lower transport
costs to spoke locations than firms in one spoke to another. Furthermore, they also
tend to trigger disparities between spoke regions.
There is evidence that the road and high-speed rail networks being built as part of the
TEN-T programme are likely to generate this kind of hub effect. Gutiérrez and Urbano
(1996) study changes in accessibility19 as a result the trans-European road network.
Their results are illustrated in Figure 9, with greater accessibility represented by lighter
shades. Much of the area of the EU gains better access to the main activity centres as a
result of the trans-European road network, and some of the biggest absolute changes in
accessibility take place in peripheral regions which start with very low levels of
transport infrastructure. However, the gap in relative accessibility between the areas
with the best and the worst initial accessibility increases as a result of the network.
In a related study, Vickerman et al. (1999) analyse how the TEN-T for high-speed rail
will change the relative locational advantage of different parts of the European
continent. The two maps in Fig. 10 (from Vickerman et al., 1999) plot daily accessibility
surfaces, drawn so that the height of the surface at a particular point of the map is
proportional to the population that can be reached from that particular point by a
return rail trip made during a working day with some minimum stay in the destination
(essentially population within 5 hours of train travel). This seems a particularly
appropriate accessibility indicator for high-speed rail travel, since this mode of
transport is specially relevant for the business service sector. The map on the left-hand
side is drawn for the existing European rail network in 1993, and the map on the right-
hand side for the European rail network planned for 2010. Two main conclusions
emerge from Fig. 10.
First, we can see once again the hub effect at work. The growth in accessibility of
cities in the European core is several times larger than that of cities at the European
periphery. Naturally, when major cities get connected through high-speed rail lines,
cities located more centrally get better access to nearly everywhere whereas in more
peripheral locations the improvement is felt mostly in the access to nearby locations.
This has led the European Commission to acknowledge that ‘[i]n transport policy,
cohesion countries stand to gain in absolute terms from trans-European networks but
not necessarily in relative terms’ (Commission of the European Communities, 1996).
Second, the introduction of new high-speed lines leads to strongly non-monotonic
changes in accessibility, and creates large differences in accessibility within small
19 They measure accessibility using the GDP-weighted sum of the impedances (quality-adjusted times of
travel) to all nodes.
398 x Puga
Figure 9. Accessibility by road, 1992 and 2002.

Figure 10. Daily accessibility by rail, 1993 and 2010.
distances. While the population that can be reached within 5 hours by train greatly
increases, differences on this respect between the core and the periphery as well as
between the main cities and smaller cities or rural areas increase. Only cities that are
nodes of the high-speed rail network gain accessibility, while the areas in between nodes
and those not on the network or at its edges do not. This highlights an important
difference between investments in high-speed rail and roads.
One of the characteristics that distinguishes high-speed rail from other forms of
transport is its strong nodal aspect. Too many stops and a high-speed train ceases being
high-speed. In many cases intermediate stops continue to be served by slower local
trains. Another distinguishing characteristic of high-speed rail is the large magnitude of
sunk infrastructure costs relative to operating costs. Both these characteristics are
important for the spatial organisation of production. It is a well known result in
location economics (see Beckmann and Thisse, 1986) that places that lie in between the
main nodes of a network are unattractive locations for production. Further, transport
technologies that exhibit increasing returns to scale, as is the case with high-speed rail,
are unlikely to promote new centres of production even on nodes of the network.20
The last two figures presented illustrate how changes in transport infrastructure lead
to changes in accessibility. To find the effects on regional inequalities requires one
further connection, linking changes in accessibility with changes in the spatial
allocation of economic activities. A few recent papers have built on new economic
geography models to study this connection. Venables and Gasiorek (1999) develop a
methodology for quantifying the general equilibrium effects of specific projects, which is
meant to complement more traditional cost benefit analysis. They calibrate a new
economic geography model with data for EU regions and use it to evaluate the impact
of several road projects financed by the Cohesion Fund. Results are presented in the
20 The Hakimi theorem (Hakimi, 1964; Guelicher, 1965) states that any location minimising total transport
costs for a producer must be a node of the network and not an intermediate point. In an extension to this
theorem, Louveaux et al. (1982) show that increasing returns to scale in transport technologies exclude
from the cost-minimising set of locations those nodes that are not already large markets for the product in
question, or important input sources in the case of resource-driven activities.
400 x Puga
form of multipliers, which give the real income effects generated relative to the direct
impact of a project. The main advantage of this approach for project evaluation is the
detailed microeconomic structure incorporated into the analysis. The main disadvan-
tage is that this structure is imposed and not subject to econometric testing. Their
analysis shows that sometimes a project in a single region can have strong welfare
effects rippling through numerous regions. This is the case of the completion of the
M-40 ring road around Madrid: because it acts as a link for some of the main Spanish
motorways it has strong spillover effects throughout the regions in Spain and Portugal.
Some other projects in a single region can instead have very localised effects. This is the
case of the Tagus crossing in Lisbon: because it improves mainly local transport costs,
its effects are small outside the Lisbon region. Even projects connecting different
regions can have relatively concentrated effects. For instance, the Rı́as Baixas
motorway, connecting Galicia with the Spanish Meseta, creates gains concentrated
mainly in Galicia.
A related exercise is carried out by Combes and Lafourcade (2001) who study the
locational effects of changes in transport costs in France. First they carefully assemble
an extremely detailed data set on changes in transport costs, and find that on average
transport costs within France declined by 38% between 1978 and 1993. Surprisingly,
infrastructure improvements only accounted for 7.5% of this reduction, the remaining
92.5% being due to changes in fuel consumption, maintenance, and drivers’ costs. Then
they estimate a new economic geography model to relate these changes in transport
costs to changes in employment, and find that reductions in transport costs have
increased the geographic concentration of employment in France.
6. Conclusions
Despite large regional policy expenditures, regional inequalities in Europe have not
narrowed substantially over the the last two decades, and by some measures have even
widened. Income differences across states have fallen, but inequalities between regions
within each state have risen. European states have developed increasingly different
production structures. European regions have also become increasingly polarised in
terms of their unemployment rates.
This paper has argued that recent theories of location can help us explain these
trends, and also reconsider the role of regional policies in such an environment. Firms
producing in locations with relatively many firms face stronger competition in the local
product and factor markets. This tends to make activities dispersed in space. However,
the combination of increasing returns to scale and trade costs encourages firms to locate
close to large markets, which in turn are those with relatively many firms. This creates
pecuniary externalities which favour the agglomeration of economic activities.
Reductions in trade or transport costs, by affecting the balance between dispersion
and agglomeration forces can decisively affect the spatial location of economic
activities. For high trade costs, the need to supply markets locally encourages firms to
locate in different regions. For intermediate values of trade costs, the incentives for self-
sufficiency weaken. Pecuniary externalities then take over, and firms and workers
cluster together. However, the price of local factors and the availability of goods tend to
increase wherever agglomeration takes place. If this is the case and there is enough
mobility, as trade costs continue to fall, rising factor prices simply give an additional
kick to agglomeration by inducing immigration. On the other hand, if there is little
mobility, for very low trade costs it may be firms that relocate in response to wage
differentials. However, the combination of minimal interregional migration with
institutional constraints on interregional (but not international) wage differentials can
lead to a rise in income inequalities between regions within each country at the same
time as inequalities between countries fall. Further, if agglomeration does not get
reflected in wage differences, it may show in unemployment rate differences. Since
clusters of activity may extend across borders, this can result in clusters of high and low
unemployment extending across regions and even across countries. Firms tend to co-
locate with firms to which they are more closely related and to avoid congestion by
moving away from firms to which they are not. This also promotes increasing
specialisation in a way which is not just be driven by traditional comparative advantage
considerations.
Whether there is too much or too little agglomeration in the absence of regional
policy interventions is not clear. The fact that firms and workers move without taking
into account the possible losses for those left behind implies there may be too much
agglomeration. On the other hand, since when firms and workers move they do not
fully take into account the benefits they bring for other firms and their impact on
aggregate growth, there may be too little agglomeration. Thus, there is no general
indication of the direction in which governments should push with regional policies
when seeking efficiency. Even in terms of equity, the direction of policy is not obvious.
Policies that increase agglomeration may nevertheless make those that remain in poorer
regions better off by increasing production efficiency and the rate of growth.
Despite these ambiguities, European regional policies have the explicit aim of
reducing regional inequalities. One of the main instruments for this is the improvement
of transport infrastructure. However, it is not obvious that lower transport costs
facilitate convergence. Roads and rail tracks can be used to travel both ways. A better
connection between two regions with different development levels not only gives firms
in a less developed region better access to the inputs and markets of more developed
regions. It also makes it easier for firms in richer regions to supply poorer regions at a
distance, and can thus harm the industrialisation prospects of less developed areas.
New economic geography models not only point out this potential ambiguity in the
impact of lower transport costs on less developed regions, they also tell us that the
overall effect depends on certain aspects of the economic environment (such as mobility
and wage rigidities) and on characteristics of the projects. In this respect, the Trans-
European Transport Network will give much of the EU better access to the main
activity centres. However, the gap in relative accessibility between core and peripheral
areas is likely to increase as a result of the new infrastructure, which reinforces the
position of core regions as transport hubs. The emphasis on high-speed rail links is also
likely to favour the main nodes of the network, and is unlikely to promote the
development of new activity centres in minor nodes or in locations in between nodes.
Acknowledgements
This paper was prepared for the conference ‘Globalization and the Location of Economic
Activities’, Sitges (Barcelona), October 2000. Thanks to Richard Arnott, Gilles Duranton, Henry
Overman, and two referees for useful comments on early drafts of this paper, and to Ginés de Rus
and Jacques Thisse for helpful discussion. The author is also affiliated with the Canadian Institute
for Advanced Research and the Centre for Economic Policy Research. Financial support from
402 x Puga
the Institut Català de Finances and the hospitality of the Instituto de Análisis Económico CSIC
are gratefully acknowledged. Figures 2, 9, and 10 are reprinted, respectively, from: de la Fuente
and Vives (1995), with permission from Blackwell Publishers; Gutiérrez and Urbano (1996), with
permission from Elsevier Science; and Vickerman, Spiekermann, and Wegener (1999), with
permission from Taylor and Francis Ltd, (http://www.tandf.co.uk/journals). Figures 1 and 3 are
reprinted from, respectively, Midelfart-Knarvik, Overman, Redding, and Venables (2000) and
Duro (2001), with permission from the authors.
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Journal of Economic Geography 2 (2002) pp.

European regional policies in light of recent location

Keywords: regional policy, inequalities, transport infrastructure, location, Europe

& Oxford University Press 2002

2. Regional inequalities in the European Union

Figure 1. Specialisation for EU Member States grouped by entry data.

Figure 2. Dispersion of regional income in Europe.

Additional information can be gathered by decomposing income inequalities, as

Figure 3. Decomposition of a Theil index of income inequalities across European regions.

Figure 4. Regional GDP per capita in Europe.

n 1995 GDP per capita

30 [0–0.6) 0.83 0.17 0.00 0.00 0.00

21 [0–0.6) 0.19 0.00 0.00 0.00

Source: (Unemployment table) Overman and Puga (2002).

Figure 5. Regional unemployment rates in Europe.

In order to help interpret the evolution of regional inequalities across Europe

3. Forces driving the new economic geography

Figure 6. Trade costs and location in Krugman and Venables (1990).

3.1. Endogenous core-periphery structures with labour mobility

Figure 7. Trade costs and location in Krugman (1991).

3.2. Low labour mobility in Europe

Low mobility in Europe is often blamed on language and cultural barriers, or on

3.3. Endogenous core-periphery structures without labour mobility

Figure 8. Trade costs and location in Puga (1999).

agglomeration non-monotonic, so close enough integration can trigger the industrial

3.4. Agglomeration and regional specialisation

4. European regional policies

pattern of specialisation may not be driven by traditional comparative advantage

5. Transport infrastructure as a regional policy instrument

Figure 9. Accessibility by road, 1992 and 2002.

Figure 10. Daily accessibility by rail, 1993 and 2010.

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