This paper analyses and compares the dynamics of agglomeration in Portuguese and Irish manufacturing industries between 1985 and 1998 implementing Dumais, Ellison and Glaeser (2002)'s methodology. Using comparable and exhaustive micro-level data sets, we find that industries tend to be subject to strong geographical mobility despite little net aggregate changes in agglomeration in both countries. When the aggregate concentration changes are decomposed into portions attributable to the different stages of the plant life cycle, we discover that births consistently play a deagglomeration role, which continues at least into the early stages of the life cycle, whereas deaths have acted to reinforce agglomeration in both countries. Nevertheless, there are some differences across countries and industries.agglomeration, path dependence, plant's life cycle, Ireland, Portugal

Barrios, Salvador

Bertinelli, Luisito

Strobl, Eric

Teixeira, Antonio Carlos

English

Research Papers in Economics

  1
 
 
 
CORE DISCUSSION PAPER 
2003/43 
 
INDUSTRY MOBILITY AND GEOGRAPHIC CONCENTRATION 
IN THE EUROPEAN UNION 
 
Salvador BARRIOS
1 and Eric STROBL 
2 
 
 
May 2003 
 
 
Abstract 
 
We study the pattern of geographic concentration of industries in EU countries and regions between 
1972 and 1995. We find that changes in concentration levels were mainly due to industry mobility 
rather than historical accidents and past levels of concentration as often argued by the New Economic 
Geography literature. 
 
Key words: Geographic concentration, industry mobility, Europe. 
 
JEL classification: F14, F15, R12 
 
 
                                                 
1CORE, Université catholique de Louvain. 
2CORE, Université catholique de Louvain. 
 
This research has benefited from financial support through the RTN research project “Specialization versus diversification: the 
microeconomics of regional development and the spatial propagation of macroeconomic shocks in Europe” of the European 
Commission (Grant No. HPRN-CT-2000-00072). Many thanks to Glenn Ellison for valuable advice and Antonio Teixeira for very 
useful suggestions. Also thanks to Marius Brülhart, Martin Hallet and Colin Webb for the generous provision of the data. This text 
presents research results of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian State, Prime 
Minister’s Office, Science Policy Programming. The scientific responsibility is assumed by the authors. 
   2
1. Introduction 
According to Krugman (1991a,b) and Fujita et al. (1999) among others, the concentration of 
industrial activities across space is primarily influenced by historical accidents together with 
agglomeration forces, such as increasing returns to scale, high final demand or strong backward-
forward linkages, which systematically drive the growth and decline of industrial centers. This 
framework has frequently been used to assess the possible implications of the European integration 
process on the location of economic activities, see, for instance, Krugman and Venables (1996) for a 
theoretical analysis and Amity (1998), Brülhart (2000), Hallet (2000) and Midelfart-Knarvik et al 
(2000) for empirical evidence. In particular, these authors try to see whether geographical 
concentration is likely to have risen due to economic integration. Empirical results remain rather 
inconclusive, however, with evidence showing a mixed-pattern depending on the geographical scale 
and the industries being considered, see Combes and Overman (2003). Importantly, however, 
Dumais et al. (2002) have recently shown for the US that industries are extremely mobile and that 
non-historical factors attributable to randomness in industries’ location play a major role in 
geographical concentration. We investigate whether such a conclusion can also be drawn for the 
European case. 
 2. Empirical Analysis 
We utilize two data sources covering the EU 15 members: (1) country-level employment data for a 
panel of 36 manufacturing industries over the period 1972-95 from the OECD’s STAN database
3, 
and (2) region-level Gross value added (GVA) data at constant prices from the European 
Commission REGIO database for a panel of 17 broader industries, namely manufacturing (9 
sectors), agriculture, construction, energy, and services (5 sectors), located in the 113 EU NUTS 2 
                                                 
3 The data for Ireland was not available in STAN and we had to instead draw on the Forfás employment survey 
which is an exhaustive employment survey of all manufacturing firms in Ireland.   3
regions
4.  While the former allows more detailed sectoral classification for manufacturing and is 
comparable to that used in most other empirical studies of industry location in Europe, the latter 
data enables us to consider the location in the context of the continuing rise in services activities 
(from 57 per cent to 67 per cent of total gross value added between 1980 and 1995) in Europe 
relative to the decline of other traditional industries like manufacturing (from 26 per cent to 21 per 
cent) and agriculture (from 4 per cent to 2 per cent). Additionally, since the kind of agglomeration 
forces generally depicted in the economic literature are mostly local in nature, it seems appropriate 
to also use regional classifications that go beneath national boundaries.  
We start from the simple geographic concentration index described by Dumais et al. (2002): 
Git = Σ c (sict – sct)
2             ( 1 )  
where sict is the share of industry i in country c and sct is the country’s share of employment in the 
average industry at time t.  Git is thus the sum of squared deviations of the industry’s country share 
of employment from the country’s share of employment in the average industry.  To derive an 
aggregate measure of Git we consider its simple mean across industries, Gt.  The time pattern of this 
variable is given in Table 1 using both STAN and REGIO data. As one would naturally expect the 
level of concentration is higher when considering countries rather than region. According to 
columns 1 and 2, and as been found by previous studies, the geographic concentration of 
manufacturing industry has increased over the whole period. In addition, the rise in the 
concentration appears to be deeper when using STAN rather than REGIO probably indicating the 
possibility that some important productivity adjustments have occurred over the period. The third 
column of table 1 shows in turn that the concentration of industries is lower and relatively more 
stable when including other non-manufacturing industries which simply translates into the fact that 
                                                 
4 NUTS refers to the standard regional classification system used by Eurostat, the European statistical office. The   4
services activities are more evenly distributed than manufacturing industries. Aggregate 
agglomeration patterns can mask considerable equilibrating forces involving large changes in 
industry location as shown by Dumais et al (2002). Accordingly, one can treat the change in the 
share of industry i in country c as a function of (a) the difference between initial country-industry 
share and the country’s average share and (b) the growth of the country’s average employment share 
in a simple regression: 
sict - sict+1 = α  + β (sict - sct) + γ (sct+1 - sct) + ε i s t         (2) 
where α , β , and γ  are estimated coefficients and ε ist is an estimated error term orthogonal to the 
regressors. Using (1) and (2)  one can easily show, see Dumais et al (2002), that the change in G over 
time can be decomposed as follows: 
 Gt+1 - Gt = (2β  + β
2) + (1/I) Σ cs ε
2
ist          ( 3 )  
where I is the total number of industries in the data.  The first term in (3), (2β  + β
2), can be thought 
of as the effect of mean reversion on changes in geographic concentration and depends on the 
extent of the gap between sict and sct.  If β  is positive then industry centers are growing, thus causing 
an increase in concentration.  If, in contrast, β  is negative then current industry centers are declining 
in importance (relative to non-centers), thus causing mean reversion.  The second term of (3), (1/I) 
Σ cs ε
2
ist, , which is always positive, captures the effect of randomness on changes in geographic 
concentration and reflects the degree of heterogeneity in changes in country-industry employment 
for countries with initial similar shares. Our results of estimating (2) and calculating (3) are given in 
Table 2. For the entire sample period β  is estimated to be 
–0.06 with the STAN data, thus suggesting that countries which in 1972 had a low share of a typical 
EU industry experienced on average a rise in their share by 6 per cent by 1995. While the fact that β  
                                                                                                                                                             
regions concerned here are all located in continental Europe only, see Hallet (2000) for more details.   5
is negative implies that mean reversion took place in the location of European industry, this 
equilibrating process, reducing agglomeration by 12.34 per cent, was not enough to counteract the 
heterogeneity across countries’ and industries’ growth since randomness on its own increased 
agglomeration by 57.63 per cent. We also divided our data into four sub-periods and conducted the 
same exercise. We observe some differences across sub-periods, especially between 1985 and 1990 
(which corresponds to the setting up of the Single Market Program) where mean reversion has had a 
larger impact than randomness. The results of our decomposition using REGIO data are shown in 
the lower two parts of Table 2. For the manufacturing industries the β  coefficient is found to be 
negative and is larger in absolute terms relative to the one found with the country-level database for 
the overall period. The results by period also show that mean reversion has been taking place 
continuously - a result rather different to the one we got when using the country-level data. This 
evolution appears to be persistent and more pronounced here, implying that agglomeration forces 
and the role of history for industry location are especially important for regions rather than 
countries. Despite this, mean reversion appears to have been offset by randomness and industry 
mobility and the overall picture thus shows a rise in concentration. Mean reversion dominates only 
during the 1985-1990 period as for the country-level data. In the bottom part of table 2 we consider 
the results including the other non-manufacturing industries. Here also the evolution of the β  term 
indicates a decrease in concentration, even more pronounced than when considering manufacturing 
industry alone. However, the decomposition into mean reversion and randomness indicates that the 
latter has been more than compensated by the former yielding to a relative stability in concentration.  
   6
Conclusion 
Our results then bring two important conclusions: (1) there has been mean reversion in 
concentration contrary to the general arguments made in the NEG literature about the possible 
impact of economic integration; (2) the observed rise in concentration of manufacturing activities is 
generally due to randomness in the distribution of countries’ and regions’ industrial growth, a feature 
which has not been yet considered by the empirical literature concerning the European case.   7
References 
Amity, M. (1999). “New Trade Theories and Industrial Location in the EU: A Survey of Evidence”, 
Oxford Review of Economic Policy, 14 (2), 45-53. 
 
Brülhart, M., (2001). Evolving geographical concentration of European manufacturing industries, 
Weltwirtschaftliches Archiv, 137(2), 215-43. 
 
Combes, P.P. and H. Overman, (2003). The Spatial Distribution of Economic Activities in the 
European Union, forthcoming in Handbook of Urban and Regional Economics, vol. 4, V. Henderson 
and J.-F. Thisse (eds.), Elsevier-North Holland, Amsterdam (2003). 
 
Dumais, G., G. Ellison and E.L. Glaeser (2002). “Geographic Concentration as a Dynamic 
Process”, Review of Economic and Statistics, 84 (2), 193-204. 
 
Fujita, M., P. Krugman and A.J. Venables (1999), The Spatial Economy. Cities, Regions and International 
Trade. Cambridge, MA: MIT Press.  
 
Hallet, M. (2000). “Regional Specialisation and Concentration in the EU”, Economic Paper 141, 
Directorate-General for Economic and Financial Affairs, European Commission. 
 
Krugman, P. (1991a). “Increasing Returns and Economic Geography”, Journal of Political Economy, 99 
(3), 483-499. 
 
Krugman, P. (1991b). “History versus expectations”, Quarterly Journal of Economics, 106, 651-67. 
 
Krugman, P. and A.J. Venables (1996). “Integration, Specialization, and Adjustment”, European 
Economic Review, 40 (3-5), 959-67. 
 
Midelfart-Knarvik, K.H., H.G. Overman, S.J. Redding and A.J. Venables (2000). The Location of 
European Industry, Report prepared for the Directorate General for Economic and Financial Affairs, 
European Commission, Brussels.   8
Tables 
 
Table 1 : Time pattern of Geographical Concentration Index* 
year  (1) Employment 
Stan data 
(2) GVA Regional data 
- manuf. only 
 
(3) GVA regional data 
- all sectors 
1972 0.0220  n.a. n.a. 
1980 0.0225  0.0048  0.0046 
1985 0.0254  0.0050  0.0047 
1990 0.0297  0.0049  0.0047 
1995 0.0320  0.0054  0.0048 
* mean values of Git, across industries, see eq.(1) 
 
 
 
Table 2- Decomposition of Geographical Concentration Index 
(1) Employment Stan data 
Period  β %  ∆Gt %  ∆mean reversion  % ∆randomness
1972-1995 -0.06 45.29  -12.34  57.63 
1972-1980 -0.04 2.17  -8.69  10.85 
1980-1985 0.02 12.92  4.31  8.61 
1985-1990 0.05 17.05  10.14  6.91 
1990-1995 0.01 7.59  1.60  5.99 
(2) GVA Regional data - manuf. only 
Period  β %  ∆Gt % ∆mean reversion  % ∆randomness
1980-1995 -0.09 10.81  -18.00  28.81 
1980-1985 -0.04 3.62  -8.22  11.83 
1985-1990 -0.04 -1.53  -8.22  6.69 
1990-1995 -0.01 8.61  -1.54  10.15 
(3) GVA Regional data - all sectors 
Period  β %  ∆Gt % ∆mean reversion  % ∆randomness
1980-1995 -0.12 4.69  -21.79  26.49 
1980-1985 -0.04 3.59  -7.53  11.11 
1985-1990 -0.05 -1.53  -9.93  8.40 
1990-1995 -0.04 2.64  -7.15  9.79 
  

Evolving geographical concentration of European manufacturing industries,

Geographic Concentration as a Dynamic Process”,

History versus expectations”,

Increasing Returns and Economic Geography”,

New Trade Theories and Industrial Location in the EU: A Survey of Evidence”,

Regional Specialisation and Concentration in the EU”, Economic Paper 141, Directorate-General for Economic and Financial Affairs, European Commission.

The Location of European Industry, Report prepared for the Directorate General for Economic and Financial Affairs, European Commission,

The Spatial Distribution of Economic Activities in the European Union, forthcoming

The Spatial Economy. Cities, Regions and International Trade.

The dynamics of Agglomeration: Evidence from Ireland and Portugal

The dynamics of Agglomeration: Evidence from Ireland and Portugal

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