Journal of Policy Modeling 33 (2011) 889–900

Available online at www.sciencedirect.com

EU regional convergence and policy: Does the conceptof region matter?

Adolfo Maza ∗, José VillaverdeUniversity of Cantabria, Department of Economics, Av. Los Castros, s/n, 39005 Santander, Spain

Received 1 November 2010; received in revised form 1 February 2011; accepted 1 March 2011Available online 1 April 2011

Abstract

Regional convergence has become a heated topic in the last decades. To address it, most papers defineregions on the base of normative/administrative criteria, although some consider that it could lead to mis-leading conclusions. In view of that, this article explores, over the period 1995–2006, the per capita incomedistribution of two sets of European regions: administrative (NUTS2) and functional (Metropolitan) regions.From a methodological point of view, a distribution dynamics approach – examining the external shape andmovements within these distributions – is applied to analyse the issue of convergence. The study does revealthe presence of a process of convergence across both types of regions; however, this seems to be more rapidwith Metropolitan than NUTS2 regions, which prompt us to proposing some relative major changes in theEU regional policy.© 2011 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

JEL classification: C14; E24; R11

Keywords: Administrative regions; Functional regions; Convergence; Distribution dynamics; Income

1. Introduction

The study of regional disparities has been at the forefront of the economic analysis in thelast two to three decades, especially in the case of the European Union, where the interest hasbeen mainly triggered by concerns about the ongoing process of economic integration. How-ever, which concept of region should be used for both research and policy-making purposes is

∗ Corresponding author. Tel.: +34 942 201652; fax: +34 942 201603.E-mail addresses: mazaaj@unican.es, adolfo.maza@unican.es (A. Maza).

0161-8938/$ – see front matter © 2011 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.doi:10.1016/j.jpolmod.2011.03.007

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but a settled issue. Although a great number of articles exist on regional convergence, the vastmajority of them just employ – mainly for statistical reasons linked to the availability of data –the normative/administrative criteria to define regions, something that, according to authors likeHall and Hay (1980), Cheshire and Carbonaro (1995), Magrini (1999) and Cövers, Hensen, andBongaerts (2009) among others, is not well justified due to the lack of coherence within thesetype of regions. For this reason, it is no wonder that the use of administrative regions may turn outto produce misleading conclusions and then give rise to a somewhat ill-advised policy-makingapproach. Hence, there has been a move towards the identification of alternative, mainly func-tional criteria to define regions (Ball, 1980; Casado-Díaz, 2000), which could provide additionaland better insights on the dynamics of regional disparities and how to deal with them. Drawingfrom this strand of literature, the paper is at variance with more traditional analyses of regionalconvergence, and is in common with Magrini (1999), as it focuses not only in the study of admin-istrative regions but also in the examination of functional regions. Accordingly, the main aim ofthis study, devoted to the European regions, is to evaluate whether using one or other conceptof region makes any difference from an analytical point of view and, in consequence, whetherEuropean regional policy should be more targeted to functional than to administrative regions.

Additionally the paper also adds to previous ones on convergence from a methodologicalperspective. As indicated, among many others, by Magrini (2009), the classical approach “failsto uncover important features of the dynamics that might characterise the convergence process”.To overcome this problem, the paper examines regional convergence by applying the distributiondynamics approach. First, it estimates univariate density functions to detect changes in the externalform of the distributions. Second, we analyse intra-distribution dynamics by estimating conditionaldensity functions. In this case, we do not use, being this the methodological contribution ofthe paper, the traditional conditional density estimator (as, for example, Mauro, 2004; Maza& Villaverde, 2009) but a relatively new one presenting at least two main advantages over thetraditional one: first, it offers better statistical properties and, second, it displays more powerfulvisualisation tools (see Hyndman, Bashtannyk, & Grunwald, 1996).

The reminder of the paper is organised as follows. After a brief description of data in Section2, the core of the paper is developed in Section 3, in which the distribution dynamic approach ispresented and applied. Finally, concluding and policy remarks are given in Section 4.

2. Data

As previously mentioned, a vast majority of papers take administrative regions as their unitof analysis; for the European Union these regions are commonly NUTS2 regions. Following thisapproach, in this paper we use annual per capita GDP (hereafter income) in Purchasing PowerStandards of 266 NUTS2 regions of the 27 members of the European Union (EU) for the period1995–2006.1 The data are directly provided by EUROSTAT.

However enlightening, this administrative delimitation of regions could well mask some keyaspects of the European regional economic reality. Therefore, the use of alternative definitions ofregions might offer additional insights on the issue at hand, and therefore provide a better guide forpolicy action. This being so, this paper not only analyses regional disparities from the standpoint

1 Due to the lack of data, two British regions (North Eastern Scotland and Highlands and Islands) are excluded fromthe analysis, while Denmark is considered as a NUTS2 region. In addition, some NUTS2 regions (Malta and Romanianregions) have been considered in the analysis although their series begin in 1998.

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of administrative NUTS2 regions but also from a functional perspective2; in particular, the papermakes use of the so-called Metropolitan regions (Metro for short).

As there is no clear consensus about the correct definition of a Metro region, following to thework by Dijkstra (2009) we opt for considering them as the NUTS3 regions or groups of NUTS3regions representing urban agglomerations of more than 250,000 inhabitants; these agglomera-tions have been identified using the Urban Audit’s Larger Urban Zones and, by definition, theyinclude the commuter belt around a city.3 The data for these Metro regions are also derived fromEUROSTAT, while the time period and variable of analysis are the same as for NUTS2 regions.Table 1 presents basic statistics about Metro regions, including pertinent comparisons with the EUaverage.4 As can be seen, three main features can be drawn from it: (1) Metro regions representnearly 58% of total EU population and their demographic change has been slightly more dynamicthan that for the EU average; (2) a similar conclusion is obtained with relation to income, the onlyrelevant difference being now that the share of Metro regions is much higher in output (around66%) than in population; (3) the table clearly shows that Metro regions are more developed thanthe EU average and that the difference has roughly remained steady over the last decade.

3. Regional convergence in the EU? A distribution dynamics approach

As mentioned in the Introduction, this study of regional convergence in per capita income inthe EU is based on a distribution dynamics approach. However, before proceeding to do it andas is usual in the literature, regional per capita income is normalised by the European average(EU = 1) in order to take out from the analysis the effect of absolute changes over time and pavethe way for making comparisons. After doing that, we began by analysing the external shape ofthe per capita income distribution. Specifically, we estimate univariate density functions for theinitial and final years of the sample period using a Gaussian kernel with fixed optimal bandwidthfollowing the rule of Silverman (1986).5 In formal terms, the kernel density estimate of a seriesY at a point y is given by the expression:

f (y) = 1

nh

n∑i=1

K

(y − Yi

h

)(1)

where n is the number of observations, whereas K(.) is the kernel function, h is the smoothing(bandwidth) parameter, Yi is the values of the independent variable and y is the value of thevariable for which one seeks an estimate. The results obtained by applying this approach to thetwo groups of regions are displayed in Fig. 1. With reference to NUTS2 regions, Fig. 1a allowsus to derive the following conclusions.

A distribution with two-peaks appears in 1995, one corresponding to low income regions andthe other to middle-income regions; the main mode is located at around 105% of the average

2 For an analysis of the dominant concept employed in defining functional regions see the references in Cövers et al.(2009).

3 A different perception about Metro regions can be found, for example, in Krätke (2007).4 The entire list of European Metro regions considered in this paper is shown in the Appendix A. The initial list consisted

of 258 Metro regions. However, lack of long enough time series (with at least 7 data) obliges us to exclude of the analysis5 Metro regions: 3 Danish, 1 Italian, and 1 British. In addition, it is convenient to note that 31 regions have been includedalthough their series do not begin in 1995.

5 For the sake of robustness, we also used a variable bandwidth by employing the adaptive two-stage kernel densityestimator proposed by Abramson (1982). The results obtained are roughly the same.

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Table 1Metropolitan regions: basic indicators.

Country Number ofMetro regions

% Population(2006)

Change in populationshare, in % points(1995–2006)

% income(2006)

Change in income share,in % points (1995–2006)

Per capitaincome (2006)EU = 100

Change in per capitaincome, in % points(1995–2006)

Belgium 5 42.3 0.00 53.8 −0.17 150.9 −13.15Bulgaria 3 31.1 2.62 47.4 11.67 55.6 15.44Czech Republic 4 51.4 −0.16 60.2 4.20 90.8 11.01Denmark 1 8.8 −0.11 7.3 −0.30 102.5 −9.70Germany 55 62.3 0.82 67.6 −0.22 125.9 −15.84Estonia 1 38.7 0.72 61.1 8.02 103.0 47.86Ireland 2 53.8 0.22 66.5 2.86 182.3 61.10Greece 2 46.1 1.17 58.5 10.11 119.5 29.28Spain 22 74.2 0.72 76.3 −1.46 107.2 10.71France 30 65.2 0.09 72.3 0.51 121.8 −5.82Italy 25 52.3 0.59 56.2 0.88 111.7 −17.29Cyprus 1 100.0 0.00 100.0 0.00 90.3 1.82Latvia 1 47.8 0.16 68.3 9.14 75.1 32.83Lithuania 2 44.8 0.41 57.6 9.58 71.3 34.53Luxembourg 1 100.0 0.00 100.0 0.00 267.4 45.60Hungary 3 41.1 0.09 55.8 5.20 86.4 24.21Malta 1 92.6 −1.14 94.1 0.18 78.4 −31.81Netherlands 14 64.5 −0.01 69.8 −0.07 141.8 8.33Austria 5 46.0 1.36 56.5 −0.61 153.0 −18.42Poland 22 59.0 −0.01 68.7 −9.40 61.1 4.47Portugal 2 38.3 0.41 48.6 −0.88 96.8 −0.94Romania 8 32.7 0.48 46.1 5.59 54.4 15.04Slovenia 2 40.9 0.27 49.5 2.67 106.3 20.71Slovakia 2 25.5 −0.09 38.3 1.43 95.6 26.88Finland 3 43.5 2.65 52.4 4.43 138.4 12.11Sweden 3 50.8 1.91 57.0 4.24 136.6 1.39United Kingdom 33 72.0 −0.65 76.0 1.43 127.1 11.12EU 253 57.7 0.45 65.8 0.44 123.3 0.09

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6a

b

3.753.503.253.002.752.502.252.001.751.501.251.000.750.500.250.00

1995 2006

NUTS2 regions

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

3.753.503.253.002.752.502.252.001.751.501.251.000.750.500.250.00

1995 2006

Metropolitan regions

Fig. 1. Density functions (EU = 1), 1995 versus 2006. The plots are densities calculated non-parametrically using aGaussian kernel density estimator with an optimal bandwidth following Silverman’s rule (Silverman, 1986).

while the second mode is about less than 40%. Additionally, a long tail emerges at the upper endof the distribution. As for its evolution, the estimated densities reveal that the external shape haschanged in a substantial way. In particular, along with a slightly decrease in the median value, themost remarkable change is that the dispersion has exhibited a large reduction between 1995 and2006 (the standard deviation has decreased by 4.5%). As can be seen the area under the densitycurve that is around the European average is greater in the final than in the initial year of thesample, this being a distinctive sign of convergence. Finally, it is worth mentioning two additionaland somewhat conflicting results: first, that in 2006 the second mode has almost collapsed, whichseems to reflect a trend of catching-up of the poorest regions and, therefore, confirm the existenceof a convergence process; and, second, that a new much smaller mode emerges at per capitaincome levels around 165% of the average while, simultaneously, the right tail of the distributionhas increased over time, these two facts implying a less symmetric distribution.

With regard to Metro regions, Fig. 1b reveals that in the initial year the distribution alsopresents two modes, these being located slightly more to the right than in the NUTS2 regions

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distribution; this is a sign that the former are indeed more developed than the latter. This resultis more evident if we consider the whole mass of probability for Metro regions, which is locatedto the right of that of NUTS2. Additionally the figure shows that the changes undergone inthe Metro regions distribution differ from those of the NUTS2. Particularly, the increase in thedegree of concentration of the distribution around the average has been slightly more intenseacross Metro than across NUTS2 regions; as is obvious, this reflects the existence of a morestriking process of convergence within Metro than NUTS2 regions. Other significant differencesbetween these two types of regions are, first, that the mode at low levels of income has per-sisted over time in Metro regions and, second, that the relative importance of the right tail ofthe distribution (the richest regions) is not only higher in NUTS2 than in Metro regions butalso has greatly increased, this meaning that disparities are larger in NUTS2 than in Metroregions.

In addition to knowing the changes in the external shape of the distribution, it is useful tounderstand the internal changes as well. To analyse this, a Markov-chain approach has beencommonly used. Although interesting, this approach suffers of an important drawback: the resultsobtained critically depend on the arbitrary number and width of the intervals considered. Toovercome this problem, the well-known stochastic kernel approach (a continuous version ofthe Markov-chain approach) has been proposed (see Durlauf & Quah, 1999; Quah, 1997). Thisapproach generates a conditional density function defined, for any discrete variables Y and X,as:

fτ(y|x) = 1

b

n∑i=1

ωi(x)K

( ||y − Yi||yb

)(2)

where

ωi(x) = K((||x − Xi||x)/a)∑nj=1K((||x − Xj||x)/a)

(3)

The norms || · ||x and || · ||y represent Euclidean distances on the spaces of X and Y, while a andb are smoothing or bandwidth parameters on the two spaces respectively. K(.) is, again, the kernelfunction. Eqs. (2) and (3) show how a conditional density function in the continuous variablesx and y can be obtained as the sum of n kernel functions in Y space weighted by the ωi(x) in Xspace.

Hyndman et al. (1996) developed a technique which presents at least two main advantagesover the traditional conditional density estimator just described.6 First, this new estimator hasbetter statistical properties; and second, it provides some powerful visualisation tools by meansof the so-called stacked conditional density and the highest conditional density region. Plottingthese measures permits an easier and more direct interpretation of the results. The estimator ofHyndman et al. (1996) is:

f ∗τ (y|x) = 1

b

n∑i=1

ωi(x)K

( ||y − Y∗i (x)||yb

)(4)

6 For a more complete revision of the differences between these two approaches see, for example, Maza, Hierro, andVillaverde (2010).

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Fig. 2. Intra-distribution dynamics. Stacked conditional density plot (EU = 1). Conditional densities of transitions between1995 and 2006. Estimates are based on a Gaussian product kernel density estimator with an optimal bandwidth based onthe rule suggested by Bashtannyk and Hyndman (2001). The plot was estimated at 50 points.

where Y∗i (x) = ei + r(x) − l(x). In this definition, r(x) is the estimator of the conditional mean

function r(x) = E[Y|X = x], ei = yi − r(xi), and l(x) is the mean of the estimated conditional densityof e|X = x.7

With this in mind, Figs. 2 and 3 display the results obtained using a Gaussian kernel with fixedand optimal bandwidths derived as suggested by Bashtannyk and Hyndman (2001). Although thestacked conditional density plots, representing conditional densities side-by-side in a perspec-tive plot, are somewhat illustrative (Fig. 2), we focus our comments on the highest conditionaldensity region plots (Fig. 3) because they are a more informative way to detect and display intra-distributional dynamics. A highest density region is defined as “the smallest region of the samplespace containing a given probability” (Hyndman et al., 1996). Thus, each vertical strip in the plotrepresents the conditional density for a per capita income in 1995. Specifically, this figure shows

7 It can be proved that given r(x) = m(x) =∑n

i=1wi(x)Yi, it follows that f ∗τ (y|x) = fτ (y|x). The mean function f ∗

τ (y|x)is less biased than the traditional kernel regression, and has a smaller integrated mean square error.

896 A. Maza, J. Villaverde / Journal of Policy Modeling 33 (2011) 889–900

Fig. 3. Intra-distribution dynamics. Highest conditional density region plot (EU = 1). From dark to light, the shadingsrepresent 25%, 50%, 75% and 90% of the total probability. Bullets indicate the mode. Estimates are based on a Gaussianproduct kernel density estimator with an optimal bandwidth based on the rule suggested by Bashtannyk and Hyndman(2001). The plot was estimated at 50 points.

the highest density regions for probabilities of 25%, 50%, 75% and 90% (as it passes from darkto less dark areas). In addition, the bullet (•) illustrates the mode for each conditional density foreach per capita income in 1995.

With reference to NUTS2 regions, the results obtained (Fig. 3a) indicate that mobility withinthe distribution has been quite small. In general terms, the mode positions indicate that regionshave not changed in a substantial way their position in the ranking between 1995 and 2006. Thisis quite clear, in particular, for regions with a per capita income below the average, as their modesare located very close to the diagonal; if we look at the mass of probability it is noticed thatfor all of these regions the areas representing probabilities of 25% and 50% touch the diagonal,this meaning both stability and persistence. Anyway, some signs of mobility show up. The areasrepresenting probabilities of 25% and, in some cases, 50% do not cross the diagonal for regionsstarting with an income of 1.25–1.75 times the European average; this is evidence of a modestmobility which, given its direction, has contributed to the income convergence process. Anyway,much clearer signs of mobility appear at the upper tail of the distribution, as regions with anincome above 175% of the average in 1995 have undergone notable increases or reductions intheir relative income. This evidence of polarisation is revealed by the placement of the modes of

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the upper tail of the distribution (which are clearly located above or below the main diagonal) andalso for the disjoint intervals of the highest density regions.

As for the comparison between these developments and those of the Metro regions, Fig. 3bshows that at least one important difference arises: regional mobility has been much higher inMetro than in NUTS2 regions. Starting with regions with a relative income below the average wesee that mobility seems to be a bit higher in the case of Metro regions (see differences in modesposition and the width of the highest density regions). However, a more remarkable differentdynamics turns up when regions with an income above average are analysed. As can be seen,Metro regions have worsened their position much more than NUTS2 regions; modes are far fromthe diagonal and the 25%, 50% (and even 75% in some cases) highest density regions do not crossit, an unmistakable sign of convergence. Finally, it is convenient to note that Fig. 3b also revealssome evidence of polarisation for Metro regions with a per capita income around 2.25 times theaverage.

4. Conclusions and policy implications

As pointed out by different academics, empirical convergence analyses usually employ avail-able data for administrative regions without considering the potential misleading implicationsthat, mainly from a policy perspective, may arise from this choice. To address this issue, andtaking the EU for the period 1995–2006 as our particular case study, this article examines theper capita income distribution of two different concepts and, then, sets of regions: administrative(NUTS2) and functional (Metropolitan) regions. To do that, it employs a distribution dynamicsapproach, which allows us to investigate not only the external shape of the distributions but alsotheir internal dynamics.

From the comparative analysis carried out in the paper, one main conclusion emerges: asthere are some significant differences in the convergence process depending on the concept ofregion being employed, it is evident that this concept matters, mainly when it comes to offeringpolicy advice on how to deal with regional disparities. More specifically, these differences can besummarised as follows: (1) regional disparities are greater at NUTS2 than at Metro regions level;(2) although the changes in the external shape of the distribution revealed a process of regionalconvergence, this has been more acute with Metro than NUTS2 regions; (3) intra-distributiondynamics analysis unveiled that mobility was much higher in the case of Metro than in NUTS2regions; in particular, and although in both cases most of the regions with an income above theEuropean average worsened their relative position over the study period, this deterioration hasbeen more intense in Metro than in NUTS2 regions.

Naturally, the above mentioned differences in the extent of disparities, the dynamics of regionalconvergence and the way this process has been brought about should have some implications froma policy viewpoint; in fact, the desirability, design, implementation and even the evaluation of theeffectiveness of European regional policy could vary according to the concept of region underscrutiny. What can be said about policy options then? Although this is a somewhat slippery point,we think that, if we initially leave aside the topical issue of the existence of a trade-off betweenequity (convergence) and efficiency,8 the EU regional policy should continue to focus on poorNUTS2 regions, simply because regional disparities are much greater at the NUTS2 level than atthe Metro level.

8 For recent references on this trade-off and its exceptions see Galbraith (2007), Walker (2007), and Paulus and Peichl(2009).

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Having said that, as it happens that Metro regions are the most dynamic – in fact they actas social and economic development engines not only for them but also for the EU as a whole–, the most plausible policy would seem to be to commit a larger part of EU regional funds tothe Metro regions located in poor NUTS2 regions, even though many of these may have a percapita income greater than the agreed 75% per capita income threshold. By implementing thischange of focus in policy-making from administrative to functional regions, and stressing theneed for Metro regions cooperation, the potential negative relationship between convergence andefficiency could be eliminated; in principle, by applying this policy the achievement of equity andefficiency should go hand in hand: equity because, as our results have revealed, Metro regionsseem to drive convergence to a larger extent than NUTS2; efficiency as Metro regions not onlyhave been more dynamic than NUTS2 but because they also have a diversified economic base andtend to concentrate creativity, innovation and expertise, which are key economic characteristics– now more even than in the past given the increasingly globalised and competitive world – topromote economic growth.

Although appealing, this policy proposal – more focused on the competitiveness of places thanon ensuring a traditional support to poor regions – naturally raises a major question: should weleave to their own destiny the underdeveloped non-Metro regions (intermediate and rural areas)?As is plain obvious that economic growth alone is not enough to reduce regional disparities9

and that “the notion that substantial inequality is a stimulus to growth is extremely question-able” (Baumol, 2007), the answer to the previous question should be a clear and categorical no.Specifically we think that, apart from continuing devoting a certain amount of structural funds tohelp these areas in exploiting their potentials (mainly by investing in human and social capital,fostering comparatives advantages, . . ., and reducing subsidies), a more active and deeper coop-eration between Metro regions and rural areas should be in place, as is happening, for instance,in Germany with the regional-planning model projects (Modellvorhaben der Raumordnung –MORO).

Appendix A. List of Metropolitan regions

Belgium (5): Bruxelles/Brussel, Antwerpen, Gent, Charleroi, Liège; Bulgaria (3): Sofia, Plov-div, Varna; Czech Republic (4): Praha, Brno, Ostrava, Plzen; Denmark (1): Odense; Germany (55):Berlin, Hamburg, München, Köln, Frankfurt am Main, Stuttgart, Leipzig, Dresden, Düsseldorf,Bremen, Hannover, Nürnberg, Wuppertal, Bielefeld, Halle an der Saale, Magdeburg, Wies-baden, Göttingen, Darmstadt, Freiburg im Breisgau, Regensburg, Schwerin, Erfurt, Augsburg,Bonn, Karlsruhe, Mönchengladbach, Mainz, Ruhrgebiet, Kiel, Saarbrücken, Koblenz, Mannheim,Münster, Chemnitz, Braunschweig, Aachen, Lübeck, Rostock, Kassel, Osnabrück, Oldenburg,Heidelberg, Paderborn, Würzburg, Wolfsburg, Bremerhaven, Heilbronn, Ulm, Pforzheim, Ingol-stadt, Reutlingen, Cottbus, Siegen, Hildesheim; Estonia (1): Tallinn; Ireland (2): Dublin,Cork; Greece (2): Athina, Thessaloniki; Spain (22): Madrid, Barcelona, Valencia, Sevilla,Zaragoza, Málaga, Murcia, Valladolid, Oviedo, Pamplona/Iruna, Santander, Bilbao, Córdoba,Alicante/Alacant, Vigo, Granada, Coruna (A), Donostia-San Sebastián, Cádiz, Las Palmas,Santa Cruz de Tenerife, Las Palmas; France (30): Paris, Lyon, Toulouse, Strasbourg, Bordeaux,Nantes, Lille, Montpellier, Saint-Etienne, Rennes, Amiens, Rouen, Nancy, Metz, Reims, Orléans,Dijon, Clermont-Ferrand, Caen, Grenoble, Toulon, Tours, Angers, Brest, Le Mans, Avignon,

9 For a recent reference on this issue see Fields (2007).

A. Maza, J. Villaverde / Journal of Policy Modeling 33 (2011) 889–900 899

Mulhouse, Marseille, Nice, Lens–Liévin; Italy (25): Roma, Milano, Napoli, Torino, Palermo,Genova, Firenze, Bari, Bologna, Catania, Venezia, Verona, Pescara, Caserta, Taranto, Padova,Brescia, Modena, Salerno, Prato, Parma, Reggio nell Emilia, Bergamo, Latina, Vicenza; Cyprus(1): Lefkosia; Latvia (1): Riga; Lithuania (2): Vilnius, Kaunas; Luxembourg (1): Luxembourg;Hungary (3): Budapest, Miskolc, Debrecen; Malta (1): Valletta; Netherlands (14): s’ Graven-hage, Amsterdam, Rotterdam, Utrecht, Eindhoven, Tilburg, Groningen, Enschede, Heerlen,Breda, Haarlem, Dordrecht, Leiden, Arnhem; Austria (5): Wien, Graz, Linz, Salzburg, Inns-bruck; Poland (22): Warszawa, Lódz, Kraków, Wroclaw, Poznan, Gdansk, Szczecin, Bydgoszcz,Lublin,Katowice-Zory, Bialystok, Kielce, Olsztyn, Rzeszów, Opole, Czestochowa, Radom,Kalisz, Bielsko-Biala, Walbrzych, Wloclawek, Tarnow; Portugal (2): Lisboa, Porto; Romania(8): Bucuresti, Cluj-Napoca, Timisoara, Craiova, Constanta, Iasi, Galati, Brasov; Slovenia (2):Ljubljana, Maribor; Slovakia (2): Bratislava, Kosice; Finland (3): Helsinki, Tampere, Turku;Sweden (3): Stockholm, Göteborg, Malmö; United Kingdom (33): London, Birmingham, Glas-gow, Liverpool, Edinburgh, Manchester, Cardiff, Sheffield, Bristol, Belfast, Newcastle upon Tyne,Leicester, Exeter, Wrexham, Portsmouth, Worcester, Coventry, Kingston-upon-Hull, Stoke-on-Trent, Nottingham, Bradford-Leeds, Sunderland, Brighton and Hove, Plymouth, Swansea, Derby,Southampton, Northampton, Luton, Swindon, Stockton-on-Tees, Bournemouth, Norwich.

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