convergence or divergence? types of regional responses to socio-economic change in western europe

CONVERGENCE OR DIVERGENCE? TYPES OFREGIONAL RESPONSES TO SOCIO-ECONOMICCHANGE IN WESTERN EUROPE

ANDREÂS RODRIÂGUEZ-POSE

Department of Geography and Environment, London School of Economics, Houghton St, London WC2A2AE, UK. E-mail: [email protected]

Received October 1998; revised May 1999

ABSTRACTTesting for convergence or divergence has recently become the kernel of the studies dealingwith regional growth in Western Europe. The contradictory results of these analyses, however,still do not allow us to determine which is the dominant trend. This paper shows that ourunderstanding of these processes depends on factors such as the way growth is measured andthe in¯uence of national growth on regional growth patterns. When these factors are taken intoaccount, the analysis of the evolution of regional disparities in Western Europe in the last twodecades uncovers the emergence of growth patterns that are closely related to the capacity ofevery space to respond to the challenges generated by the process of socio-economic restructur-ing.

Key words: Convergence, divergence, regions, growth, spatial autocorrelation, EU

INTRODUCTION

Since the beginning of the 1990s the numberof researchers engaged in the analysis of re-gional disparities in Western Europe hasincreased sharply. This concern is one of theconsequences of the revival of growth theory inrecent years. The publication of the groundbreaking works of Romer (1986, 1990) andLucas (1988), which established the bases of theendogenous growth theory, led to the develop-ment of a broad and fruitful debate on theadvantages and disadvantages of endogenousand neoclassical growth models. The conver-gence debate has since become one of the fore-most topics in economic research. Mostpublication on the topic has focused on conver-gence trends among countries across the world.However, the relative exhaustion of existingnational databases and the need to search fornew environments to test convergence or diver-gence hypotheses has led many researchers tothe ®eld of regional economic disparities (Blan-chard 1991; Barro & Sala-i-MartõÂn 1991). It is

therefore not surprising that the number ofpapers devoted to the evolution of regionaldisparities has risen markedly. Nonetheless ±and perhaps as a result of the explosion ofstudies on the topic ± no clear cut conclusionabout the issue of convergence or divergencehas emerged. As a general rule, neoclassicalstudies tend to underline a general trendtowards convergence, whereas endogenousgrowth models and other approaches empha-sise divergence.

Running parallel to this interest in conver-gence, a large theoretical and case-study-basedliterature on the regional impact of economicglobalisation and of changes in productionmethods has been developed (Storper 1997).According to this literature, recent socio-economic changes are providing regions withnew opportunities and constraints for economicdevelopment. In this context, the economicperformance of any region depends on itscapacity to grasp the new opportunities andsupersede the constraints imposed by theprocesses of socio-economic restructuring.

Tijdschrift voor Economische en Sociale Geografie ± 1999, Vol. 90, No. 4, pp. 363±378.# 1999 by the Royal Dutch Geographical Society KNAGPublished by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA

The aim of this paper is to show that, if wetake into account factors such as the impor-tance of the national effect and the way growthis measured, structural change is leading to theformation of a complex and variegated patternof regional growth behaviours which repro-duce, to a large extent, the theoretical and case-study-based geographies identi®ed by the litera-ture on socio-economic restructuring. Thispattern complements the perception of theevolution of regional disparities stemmingfrom convergence and divergence studies.

The paper is divided into three sections. Itstarts by revisiting the convergence analysis inthe European context. The second sectiondeals with the relevance of the national dimen-sion in determining growth, and the ®nalsection identi®es the main regional growthpatterns associated with the process of socio-economic restructuring.

CONVERGENCE REVISITED

The introduction in the mid 1980s of tech-nology as an endogenous variable in growthmodels was the spark that ignited the debate oneconomic convergence. Much of this debatehas focused on the evolution of regional dispa-rities in Europe, with a majority of neoclassicalanalyses consistently reporting convergencerates of around or below 2% per year (Sala-i-MartõÂn 1994, 1996) (see also de la Fuente 1997for a thorough review of neoclassical conver-gence growth models).

However, the alleged slow regional conver-gence in Europe is at odds with endogenousgrowth models (Romer 1990), with `neweconomic geography' models stressing thatdifferent outcomes are possible according tothe initial circumstances of every region(Krugman & Venables 1995), and with theempirical fact that since the 1980s manyregions in the European core have seen `theirscores rise and the majority of Europe's periph-ery ± both north and south ± exhibit static ordeclining index scores' (Champion et al. 1996,p. 34). This leaves us, as Cuadrado Roura(1994) and Molle and Boeckhout (1995) under-line, almost at the starting point: without aclear idea on the issue of regional convergencein Europe. How can these contrasts beexplained?

One possible explanation may be that certainmodels overlook factors that alter our percep-tion of the processes of convergence or diver-gence. In order to test this hypothesis, it isadvisable to revisit the empirical analysis of theevolution of regional disparities in WesternEurope. Data used in this analysis ± as in mostother convergence analyses ± stems from theREGIO database. Data refers to the evolutionof GDP per capita between 1977 and 19931 in110 regions of the European Union. Regionsused in the study try to re¯ect similar levels ofself-government, territory and population (afull list of regions and their codes appears inTable 3).

A preliminary analysis of regional growthdata seems to support the hypothesis of re-gional convergence in Western Europe. Figure1 reports the scatterplot between regionalgrowth rates measured in ECUs for the period1977±93 (on the y axis), and the regional distri-bution of GDP per capita in 1977 (on the xaxis). At ®rst sight, the ®gure reproduces theresults of the pioneering works by Barro andSala-i-MartõÂn (1991) and of other neoclassicalmodels. Regions with initial low levels of GDPper capita grow at a higher pace than advancedregions. Growth in areas such as Lisbon (154),the North of Portugal (154), or Molise (131)clearly outstrips that of more developedregions such as Brussels (88), the WesternNetherlands (79) or Berlin (72). The regressionline con®rms the existence of an evident catch-up trend.

The regression results further corroboratethe convergence hypothesis. When both re-gional GDP and growth of GDP are measuredin ECUs the annual convergence rate is equalto 1.2% (b = ±0.0120). This result almost repli-cates that achieved by Armstrong for 85regions in Western Europe for the period1975±92 (b = ±0.0103) (1995a) (Table 1).

However, certain elements perceivable in the®gure and in the model cast doubt on therobustness of the results achieved. The moststriking of these is the obvious national in¯u-ence over regional growth rates: regionsbelonging to the same country tend to clusterin well-delimited areas of Figure 1, not onlybecause they share analogous starting levels ofGDP per capita, but also because their growthrates tend to be similar across the period of

ANDREÂS RODRIÂGUEZ-POSE364

# 1999 by the Royal Dutch Geographical Society KNAG

analysis. The boxes in Figure 1 give a broadindication of where most regions belonging toa certain country cluster in the graph. Portu-guese regions, for example, are located in theupper left corner of Figure 1, as a consequenceof their low starting levels of GDP and growthrates well above (with the exception of Alen-tejo) the European average. In an analogousposition, although with higher starting points,we ®nd the Italian regions. Even greaternational concentration is evident in the case of

the UK. All the Greek and Spanish regions aresituated in the sector that holds the territorieswith lower starting levels of GDP per capitaand average growth rates.

Regions in countries with higher levels ofGDP in 1977 have tended to grow belowaverage (Figure 1). Only Luxembourg (129),the German regions of Hesse (109) and Bavaria(106), the French Midi-PyreÂneÂes (102) and theDanish regions of the West of the Great Belt(102) and the Hovedstadsregionen (100) are

Figure 1. Mean annual regional growth rates (1977±93), measured in ECUs vs GDP per capita in 1977, also measuredin ECUs.


CONVERGENCE OR DIVERGENCE? 365

placed above the mean. National clusters arealso evident here. With the exception ofCorsica and the IÃle de France, French regionscluster in a span of only 19 points. Slightlygreater dispersion is observed in the case ofGerman, Belgian and Dutch regions. Thesegreater differences are fundamentally due toinitial contrasts in GDP per capita, and not todifferent regional growth behaviours.

A more detailed analysis of the in¯uence ofthe national dimension brings the convergencehypothesis even more into question. If laggingareas were catching up on advanced regions, itcould be expected that lagging regions wouldconverge to the level of advanced regionswithin every country, and not just that laggingcountries would be catching up. Yet, the analy-sis of growth patterns within each countryshows that this is not always the case. In manystates the fastest growing regions are preciselythose that had the highest levels of GDP in1977. This is certainly the case in Portugal,where Lisbon (154), by far the wealthiestregion in Portugal, was also growing fastest. Incontrast, the growth rates of other less devel-oped areas ± and especially that of Alentejo(97) ± are below the Portuguese average (145).Belgium and, to a lesser extent, Denmarkreproduce similar situations. The capital areasin both countries grew at a faster pace thanother regions.

In other countries some of the least devel-oped regions performed relatively well, but thisdid not mean that well-off regions performedbadly. In this group we ®nd the largest coun-tries in Western Europe. Italy is one of themain examples of this type of growth behav-

iour. Molise, one of the regions of the laggingMezzogiorno, achieved the highest rate ofgrowth (131), but it was closely followed bysome of the wealthiest: Trentino-Alto Adige(129), Veneto (129) and Lazio (127). Similargrowth patterns are noticeable in the Britishcase. Among the fastest growing British regionswe ®nd the rich South East, with the agglom-eration of London (113). In contrast some ofthe regions with a relatively poor economicperformance belong to the old industrial aris-tocracy: the North (109), the West Midlands(106) and the North West (105). Spain, Franceand Germany fall within the same pattern.

Our perception of convergence may befurther biased by the unit of measurement. Asmentioned earlier, the use of GDP measured inECUs produced annual levels of convergenceof 1.2%. However, if instead of using GDPmeasured in a common currency (ECUs), weresort to the same variable measured inpurchasing power standards (PPS), the resultsachieved differ signi®cantly.

Figure 2 reproduces the same variables as inFigure 1 (GDP per capita in 1977 on the x axisand economic growth between 1977 and 1993on the y axis), but measured in PPS, instead ofECUs. In this case the rate of convergenceobserved in Figure 1 decreases. But perhapsmore meaningful than the decline of theapparent catch-up effect is the fact that the sizeof the residuals exceeds what can be consideredusual in a normal distribution. The b-coef®-cient drops from ±0.012 to ±0.009, and the t-statistic of the independent variable is notsigni®cant. The R2 declines from 0.16 to 0.04(Table 1). Hence, different ways of measuring

Table 1. Regression results for different ways of measuring GDP across European regions (1977±93).

Model b Standard error t s R2

Variables ±0.0120 0.0027 ±4.399 0.000 0.166in ECUsVariables ±0.0097 0.0050 ±1.927 0.051 0.039in ppsNationally ±0.0061 0.0032 ±1.748 0.084 0.031Weightedvariables



a similar phenomenon alter our perception ofregional convergence in Western Europe. Themodel using GDP measured in ECUs supportsthe convergence hypothesis; it is less so whenPPS are used. The greater level of convergenceobserved in the former case may thus be aresult not of a relative catch-up in the periph-ery, but of distortions linked to exchange rateswhich limit the comparability of income levelscross-nationally when measured in a singlecurrency. PPS provide in this case a more reli-

able indication of how regional growth patternshave evolved.

However, the decline of the observed conver-gence in Figure 2 does not contribute to areduction of the conspicuous national effect onregional growth rates, as shown by the boxes inFigure 2. Once again neatly de®ned nationalclusters can be identi®ed. Spanish regions, forexample, cluster in the lower left side of the®gure. Most Italian regions are grouped in theupper right corner. The majority of German

Figure 2. Mean annual regional growth rates (1977±93), measured in PPS vs GDP per capita in 1977, also measured inPPS.



regions, together with Luxembourg (the regionwith the highest growth rate in PPS) performfairly well and occupy the upper right-handsection of Figure 2, whereas in the centre ofthe ®gure we ®nd most of the French, Britishand Dutch regions, but then again in fairlyevident national clusters.

Convergence and spatial autocorrelation ±The appearance of well-delimited national clus-ters is associated to the existence of spatialautocorrelation: regional values are correlatedwith those in neighbouring regions, andspecially with those in regions in the samecountry. Spatial autocorrelation introduces animportant bias in our perception of thephenomenon of convergence, since it violatesthe basic condition of independence amongobservations and generates a bias in the esti-mates of the standard error (Robinson 1998, p.271). `As a result, inference based on t and Fstatistics will be misleading and indications of®t based on R2 will be incorrect' (Anselin 1994,p. 11).

In order to check for the presence of spatialautocorrelation, several tests for spatial depen-dence (Moran's I, the Lagrange Multiplier test,and the Kelejian and Robinson test) wereperformed using the SPACESTAT statisticalpackage. The existence of spatial autocorrela-tion is con®rmed by results (Table 2).

The I statistic, as well as the standardised z-

values, highlights the existence of a strongspatial autocorrelation bias in both models.The z-values are greater than 6 and the exactvaluation gives p-values of 0.0 (Hepple 1998, p.97).

THE NATIONAL DIMENSION ANDGROWTH

The problems of spatial autocorrelationdetected are, as Quah points out, connected tothe fact that in both models ± as in manyneoclassical growth models ± regions aretreated as islands, as if `neither their being partof a country, nor their being physically close toanother region makes any difference foreconomic performance' (Quah 1996b, p. 3).The existence of distortions associated with thenational dimension is, nevertheless, a well-known factor. Molle et al. (1980) alreadystressed the importance of this factor twodecades ago; other authors have proposedvarious ways of dealing with national distor-tions2 (Barro & Sala-i-MartõÂn 1991; Armstrong1995b; Cheshire & Carbonaro 1995; Quah1996b; LoÂpez-Bazo et al. 1997). In this paperwe resort to nationally weighting regionalgrowth and initial GDP per capita3 (RodrõÂguez-Pose 1994). This simple method enables us tocompare cross-nationally the variation in regio-nal growth rates in a determined nationalcontext.

Table 2. Tests for spatial dependence.

Test Variables measured in ECUsI/df z-value p-level

Moran's I (Cliff-Ord) 0.5991 9.0017 0.00000Lagrange multiplier 1 71.2390 0.00000Kelejian-Robinson 2 122.7734 0.00000

Variables measured in ppsI/df z-value p-level


Nationally weighted variablesI/df z-value p-level




The results stemming from the use ofnationally-weighted indicators depict a verydifferent panorama from the one resultingfrom the use of unweighted variables (Tables 1and 2 and Figure 3). First, there is a reductionof convergence rates. The b-coef®cientdecreases to levels of ±0.0061, that is, anannual convergence rate of 0.6%. This is insharp contrast with the annual convergencerates of 1.2% (using unweighted variablesmeasured in ECUs) and almost 1% (in PPS). Infact, a closer look at the evolution of regionaldisparities shows that patterns of regionalgrowth point more towards the hypothesis ofpolarisation and the emergence of convergence`clubs' supported by Chatterji (1992) and Quah(1996a), than to any universal trend towardsconvergence. The observed polarisation may belinked to the time span of the analysis. Recentempirical analyses have highlighted that the1980s marked a slowdown and even a reversalof previous convergence trends (Championet al. 1996; RodrõÂguez-Pose 1998). The changein long-term growth patterns coincided with animportant process of structural change inwhich the industrial society developed duringthe post-war decades gave way to a more ¯ex-ible and globalised post-industrial society (Piore& Sabel 1984). And these changing conditionsseem to have bene®ted greater structuraldifferentiation between the core and theperiphery, leading to a rise in income in thecore partly at the expense of the periphery(Krugman & Venables 1995; Venables 1998).Furthermore the explanatory capacity of themodel is low. The R2 score does not reachlevels of 4% and both the t-statistic and theresults of the F test indicate a general lack ofrobustness (Table 1).

Second, the spatial autocorrelation detectedin previous models fades away. When nation-ally weighted variables are used, Moran's I fallsfrom levels of around 0.6 in the two previousmodels to 0.15, and the z-values decreasesigni®cantly (Table 2). The eradication ofnational distortions is also evident in Figure 3.The national groupings identi®able in previous®gures are replaced by groups of regions withsimilar initial structural features and a similarcapacity to respond to the challenges posed bysocio-economic restructuring, regardless of theeconomic evolution of the nation-state to which

they belong. In Figure 3 the economic evol-ution of an industrial declining region such asWallonia is closer to those of other formerhavens of industry such as Lorraine, NorthRhine-Westphalia or Asturias than to those ofthe other Belgian regions. Correspondingly,growth patterns in Bavaria are no longersimilar to those of other German regions, butto those in other European dynamic areas suchas Veneto, the Southern Netherlands orAragoÂn.

REGIONAL RESPONSES TO SOCIO-ECONOMIC CHANGE

The replacement of national clusters by groupsof regions with similar structural characteristicsis perhaps the most important effect of the useof nationally weighted variables, since theissuing regional sets reproduce, to a greatextent, the regional typology identi®ed by themajority of the literature on structural changeand socio-economic restructuring.

This literature has tended to approach thequestion of regional growth in Europe from afundamentally structural and institutionalperspective, identifying sets of local factors thatcontribute to promote or hinder economicactivity in every region and analysing the re-gional development implications of thechanges related to the globalisation of theeconomy and the ¯exibilisation of productionmethods. Most of this literature regards struc-tural change as a process that is altering tradi-tional patterns of growth and provokingsigni®cant changes in regional disparities, aswell as greater diversity in the patterns ofdevelopment. Hence, as Hudson underlines,`the global capitalist economy is . . . now consti-tuted by a much more complex spatial mosaicof production processes, consumption patternsand contradictory regulatory practices thanever before . . . This has important theoreticalimplications for understanding industrialrestructuring changing geographies of produc-tion and the changing regional map of Europe(1997, p. 474±475). From this perspective, theprocess of structural change engenders a newterritorial model characterised by a dual orga-nisational pattern (Leborgne & Lipietz 1992)and the existence of winning and losing spaces.Core and some intermediate regions are



regarded as the regions bene®ting from recentchanges, whereas old industrial areas and mostperipheral regions as losing spaces.

From this point of view, the process ofglobalisation is fostering the concentration ofcapital and decision-making power in a limitednumber of core urban spaces (Harvey 1985;Cheshire & Hay 1989), and basically in the so-called `world-cities', `megalopolises' or `Euro-cities' (Hall 1993). In these areas the concen-tration of capital and information, togetherwith the endowment of a ¯exible and skilledworkforce, a dynamic (yet increasinglypolarised) service sector, and with the presenceof political power constitute the basis for thegenesis of virtuous economic cycles.

The other group of regions relishing thebene®ts of the process of socio-economicrestructuring is made up of many intermediateregions and some of the peripheral regions.Technological advances and the ¯exiblisationof production systems have allowed for thedevelopment of greater economic activity inareas formally handicapped by higherdistances, costs, and/or resource bottlenecks(Storper & Scott 1989; Storper 1997). Theuprooting of economic activities ¯eeing thecongestion of economic centres has helped toreinforce the local production fabric in manyintermediate and peripheral regions, and espe-cially in relatively `green®eld' areas in terms ofworkers' militancy. But not all intermediateand peripheral regions have been prosperouscompetitors in this `post-industrial' world. Onlythose located along important communicationaxes, or those combining a large stock ofskilled population with advanced researchcentres (Hall 1993), as well as some peripheralregions that have specialised in high demandsectors (and especially the tourist sector) havebeen capable of successfully adjusting to thenew economic conditions.

Among the spaces that have been less able toadapt to the new socio-economic circumstances,we ®nd the declining industrial areas. Thegreat majority of these regions have undergoneeconomic decline since the 1960s, due to a setof extremely rigid social and economic con-ditions (QueÂvit et al. 1991; Tomaney 1994).Similarly, most peripheral regions have notbeen able to bene®t from technologicaladvances and from structural change. In many

cases, it can be said that globalisation hascontributed to the destruction of theireconomic fabric (Hadjimichalis 1994), withoutgenerating an alternative. As Vandermotten etal. (1990) point out, they have become tooperipheral to compete with the core and toocentral to compete with the periphery. Many ofthe less dynamic intermediate regions are in acomparable position. They lack comparativeadvantages with regard to infrastructure, skillsand capacity to innovate. As with most periph-eral regions, these areas have encounteredserious dif®culties in order to take advantageof the socio-economic changes in the same wayas their more dynamic counterparts have done.

As a consequence of these dissimilarresponses to socio-economic challenges, a newterritorial growth pattern seems to be emer-ging. This pattern encompasses the four maintypes of regions identi®ed by the literature onsocio-economic change: capital regions andlarge ®nancial centres, declining industrialareas, intermediate and peripheral regions.This regional taxonomy identi®ed by the litera-ture on socio-economic restructuring haslargely been ignored by convergence models.Yet, when nationally-weighted variables areused a similar typology emerges (Figure 3). Ifwe take the initial nationally-weighted levels ofGDP and the economic structure of regions asthe main criteria for the establishment ofgroups, we ®nd that regions with similar initiallevels of GDP and comparable structuralfeatures cluster in well-delimited areas ofFigure 3. Peripheral regions occupy the leftside of the graph and are de®ned by belowaverage initial levels of GDP and by a still rela-tively large primary sector (with initial levels ofemployment in agriculture above 15% of totalemployment, for regions in countries withmore than 10% of total employment in agricul-ture, or three times the national rate ofemployment in agriculture for regions in allother countries). Capital regions and urban®nancial centres are located on the upperright-hand side of the ®gure. These regionsshare initial levels of GDP above the Europeanaverage and a high concentration of serviceactivities (initial service employment higherthan 65% in countries with more than 50% oftotal employment in the tertiary sector, orhigher than 60% in countries with less than



50%), and especially in ®nancial services (initialshare of credit and insurance services at least50% higher than the national average). Indus-trial declining regions are de®ned by their highconcentration of traditional industries. Regionsbelonging to this category had at least one-third of their total employment in the second-ary sector, with one-third of total industrialemployment in traditional industrial subsectors,such as coal, iron, steel and electricity (QueÂvitet al. 1991). Intermediate regions are those that

do not belong to any of the above-mentionedcategories and occupy the centre of the graph.The division between dynamic and lessdynamic regions is determined by their growthperformance during the period of analysis,with the European average being the cut-offpoint. The solid boxes in Figure 3 give a broadindication of where most members of thesecategories are placed.

As mentioned before, the groups of regionsestablished according to these criteria almost

Figure 3. Regional typology stemming from the plot of nationally weighted growth rates (1977±93) vs nationally weightedGDP per capita.



match the general typologies identi®ed by theliterature on socio-economic restructuring.However, some discrepancies ± especially whendealing with dynamic and less dynamic regions± can be highlighted. Some of the regionsconsidered to be the paradigm of the post-Fordist system of production such as Emilia-Romagna, Tuscany or Umbria (Piore & Sabel1984) grow below the European average.Valencia, also regarded as a haven of ¯exibleproduction (VaÂzquez Barquero 1993), equallysuffers relative economic decline. And amongthe peripheral regions, regions such as Extre-madura or Sicily perform much better thansuggested by their initial socio-economic condi-tions (Botazzi 1990; Trigilia 1992; VaÂzquezBarquero 1993). The discrepancies between theclusters resulting from the analysis and thegroups most commonly identi®ed by the litera-ture on socio-economic restructuring are repre-sented in Figure 3 by the dotted boxes.

The regional clusters distinguished in theanalysis also represent different regional capa-cities to adapt to recent changes. Economicgrowth between 1977 and 1993 is positivelycorrelated with a high initial specialisation inservices (0.447, signi®cant at the 0.01 level), asin capital and urban ®nancial centres, andnegatively correlated with the initial share ofagriculture (±0.246, at 0.05), as in peripheralless dynamic regions, and industry (±0.265, at0.01), as in industrial declining regions. Hence,the initial economic structure plays an impor-tant part in determining regional growthpatterns.

The economic trajectories of each cluster arebrie¯y discussed in the following pages.

Capital regions and the main ®nancial centrestend to diverge with respect to the rest ofEurope. Their growth levels have remainedabove the European average, with growth ratesranging between levels of 108 in Lazio and 100in Copenhagen. The only exceptions to therule are Berlin and Athens (Table 3).

Declining industrial regions cluster in thelower middle section of Figure 3. The greatmajority of these regions had start-off levelsbelow their national average and their growthtrajectory during the last decades has beenmarked by economic decline. Almost all indus-

trial areas have grown below the Europeanaverage, and in some cases there are obvioussigns of stagnation (Cantabria (88), Picardy andAsturias (both 89), Lorraine and Piedmont(both 90)). Only the regions that underwent anearly and/or sharp process of industrialrestructuring show slight signs of recovery.This is fundamentally the case of someGerman and British regions (Saarland (103),Bremen (100), Lower Saxony (100), Wales (99))(Table 3).

Intermediate dynamic regions share withindustrial declining regions similar startinglevels of GDP per capita, but they displaygreater dynamism. To a certain extent, theycan be considered the reverse of the industrialdeclining regions: whereas the former are inclear expansion, the latter adapt with enormousdif®culties to changes in the productionsystem. Regions such as Hesse (113), Trentino-Alto Adige (111), Veneto (110) or Bavaria (110)constitute success stories based on very differ-ent types of economic activity: the strength ofthe ®nancial sector, in particular, and of theservice sector, in general, in the case of Hesse;the combination of large and technologicallyadvanced industries with a dense network ofsmall and medium-sized enterprises in Bavaria,and the vitality of the industrial districts in thecases of Veneto and Trentino-Alto Adige.

Intermediate less dynamic regions occupy thecentre of Figure 3. Growth rates in these areasare close to, but below the European average.Few intermediate regions without specialcomparative advantages in terms of endowmentin infrastructure, in human resources or inindustrial fabric, show signs of economic dyna-mism. There are also few examples of inter-mediate regions with poor economic per-formance (Rhineland-Palatinate (94), Franche-ComteÂ (93) and Rioja (90)).

Peripheral dynamic regions combine a lowinitial GDP per capita with high growth rates.Many of these regions owe their privilegedeconomic performance to a thriving touristsector (the Canary Islands (115) or the GreekIslands (111)), while others, such as Molise(112) or Abruzzi (109), to the combination of alocation along an important development axis,



Table 3. Regional indicators (1977±93), according to regional topology.

Regional type Nationallyweightedgrowth(1977±93)

NationallyweightedGDP percapita (1977)

Employmentinagriculture(1980)*

Employmentin industry(1980)*

Employmentin services(1980)*

Regional and urbanfinancial centresLazio (I12) 108.07 107.07 8.98 23.86 67.15Madrid (E8) 107.82 114.34 1.51 32.90 65.59Lisbon and the TagusValley (P3) 106.43 129.87 7.93 30.94 61.13IÃle de France (F1) 105.86 141.67 0.82 30.71 68.48Brussels (F1) 105.73 151.25 0.01 16.90 83.10South East (UK5) 102.16 114.65 1.31 28.63 70.08Western Netherlands(NL4) 101.44 106.03 3.93 26.61 69.46Hamburg (D2) 100.92 156.92 1.13 28.60 70.27Hovedstadsregionen(DK1) 100.36 117.63 1.27 23.08 75.65Attica (G3) 94.66 120.29Berlin (D11) 73.17 109.92 0.34 32.72 66.93

Industrial decliningSaarland (D10) 103.04 86.74 2.04 48.19 49.77Bremen (D4) 100.38 128.56 0.84 36.13 63.03Lower Saxony (D3) 99.64 86.60 8.48 38.10 53.42Wales (UK9) 99.12 85.48 4.79 34.74 60.47Yorkshire andHumberside (UK2) 98.15 93.75 2.41 40.01 57.58North (UK2) 96.57 93.17 2.31 39.77 57.92Wallonia (B2) 96.44 83.63 3.86 33.49 62.65Liguria (I3) 95.67 123.27 7.45 33.90 58.66Champagne-Ardennes(F2) 95.22 104.09 10.44 38.10 51.47Basque Country (E4) 94.36 125.32 6.63 49.99 43.38North West (UK8) 94.32 96.07 1.13 38.04 60.83North Rhine-Westphalia(D5) 93.92 99.78 2.20 46.13 51.67Nord-Pas de Calais (F8) 93.15 86.33 5.07 41.86 53.07Upper Normandy (F4) 90.64 103.51 6.72 39.10 54.18Piedmont (I1) 90.23 125.19 10.71 37.60 51.70Lorraine (F9) 90.08 95.65 4.75 41.02 54.23Asturias (E2) 88.86 107.92 25.56 35.48 38.96Picardy (F3) 88.71 96.78 9.17 39.82 51.01Cantabria (E3) 87.59 110.18 24.73 33.87 41.40Northern Netherlands(NL1) 79.23 124.40 9.70 33.41 56.90

Intermediate dynamicHesse (D6) 112.88 108.78 4.72 40.62 54.66Trentino-Alto Adige (I5) 110.52 107.75 14.93 24.88 60.19Veneto (I6) 110.05 101.39 12.08 43.58 44.34Bavaria (D9) 109.92 94.50 9.92 43.40 46.68Southern Netherlands(NL3) 109.12 86.40 6.06 39.32 54.62



Table 3. Continued.






Midi-PyreÂneÂes (F16) 108.64 77.32 16.12 29.58 54.30Marche (I11) 106.53 93.93 14.60 40.35 45.05East Anglia (UK4) 105.42 96.17 6.70 32.29 61.02Friuli-Venezia Giulia (I7) 104.93 109.38 8.53 35.81 55.66AragoÂn (E7) 104.72 106.71 20.54 34.65 44.80Limousin (F17) 104.43 75.98 18.99 32.05 48.96Balearic Islands (E14) 103.90 121.42 14.10 26.19 59.72Catalonia (E12) 103.54 116.14 6.57 44.97 48.46South West (UK6) 103.36 91.71 4.70 30.42 64.88Lower Normandy (F6) 103.18 87.62 18.77 31.68 49.55West of the Great Belt(DK3) 102.90 91.07 11.48 30.09 58.43Brittany (F13) 101.87 82.10 18.90 27.91 52.99Alsace (F10) 101.73 99.75 3.93 41.73 54.34Scotland (UK10) 101.47 96.72 3.40 33.75 62.86Flanders (B1) 101.03 99.94 3.59 38.71 57.70Eastern Netherlands(NL2) 100.89 86.99 8.54 33.37 58.09Auvergne (F19) 100.82 81.33 15.15 35.04 49.82Baden-WuÈrttemberg (D8) 100.52 106.23 5.31 49.43 45.25Pays de la Loire (F12) 100.22 85.35 15.73 35.85 48.42Poitou-Charentes (F14) 100.08 81.36 16.56 31.56 51.89

Intermediate less dynamicEmilia-Romagna (I8) 99.77 123.19 13.94 37.13 48.94Umbria (I10) 99.71 97.14 15.50 39.21 45.29Languedoc-Roussillon(F20) 99.28 78.42 12.88 26.53 60.58RhoÃne-Alpes (F18) 99.09 99.18 6.42 40.02 53.61Tuscany (I9) 98.56 107.62 11.50 39.52 48.98East Midlands (UK3) 98.46 95.36 3.25 43.57 53.19Burgundy (F7) 98.36 88.74 11.48 36.16 52.36Aquitaine (F15) 97.69 91.36 14.67 29.35 55.98Centre (F5) 97.65 93.10 11.11 37.58 51.30Navarre (E5) 96.75 126.61 12.64 42.86 44.51Provence-Alpes-CoÃted'Azur (F21) 96.52 92.48 6.51 27.30 66.20Lombardy (I4) 96.45 132.65 4.46 49.69 45.85Schleswig-Holstein (D1) 96.05 89.34 7.50 33.43 59.07Valencia (E13) 95.90 100.19 14.36 38.56 47.07East of the Great Belt(DK2) 95.68 89.00 12.05 28.51 59.44West Midlands (UK7) 95.61 96.34 2.23 44.17 53.60Rhineland-Palatinate (D7) 93.80 89.38 7.53 42.23 50.24Franche-ComteÂ (F11) 93.42 98.38 8.24 46.22 45.54Rioja (E6) 89.69 123.43 20.74 43.41 35.87Valle d'Aosta (I2) 83.91 151.90 12.28 29.82 57.89Corsica (F22) 73.08 123.85



with the entrepreneurial capacity of their popu-lation and the development of networks ofsmall and medium-sized enterprises (Trigilia,1992). This dependency on one sector of theeconomy entails serious risks. Tourist regionshave remained successful mainly because politi-cal instability ± when not open war ± haveprevented competitors (i.e. ex-Yugoslavia, theMaghreb countries, Turkey, Egypt) fromgaining a larger share in the tourist market(RodrõÂguez-Pose 1996).

Peripheral less dynamic regions have notsucceeded in specialising in dynamic andhighly competitive sectors. As a result, growth

rates are low, especially in regions like Andalu-sia (94), Basilicata (92), the Centre of Portugal(90) or Alentejo (62). The growth pattern ofperipheral less dynamic regions is, togetherwith that of capital regions and urban ®nancialcentres, one of the main causes behind lack ofeconomic convergence in recent years.

In brief, the results of the analysis highlightthe emergence of a complex pattern of re-gional growth behaviours in Europe similar tothat reported by other empirical analyses ofgrowth in Europe, and notably by Championet al. (1996). These authors identify, usingcluster analysis, 13 regional typologies at theprovincial and district level, which, in turn, are

Table 3. Continued.






Peripheral dynamicCanary Islands (E17) 114.90 82.24 18.70 20.00 61.30Molise (I15) 111.58 67.06 33.60 24.00 42.40Aegean Islands and Crete(G4) 110.62 79.65Extremadura (E11) 110.06 63.31 35.64 20.42 43.94Abruzzi (I14) 109.01 79.54 23.13 30.84 46.04North of Portugal (P1) 106.43 83.20 28.46 42.08 29.46Sicily (I19) 105.63 65.48 20.69 25.64 53.67Northern Greece (G1) 104.20 90.15Puglia (I16) 102.18 70.37 22.67 26.41 50.92Northern Ireland (UK11) 102.10 78.09 7.71 28.94 63.36Castile-La Mancha (E10) 100.72 85.20 29.44 31.73 38.83Central Greece (G2) 100.34 92.66

Peripheral less dynamicSardinia (I20) 99.67 75.78 16.80 27.93 55.27Campania (I13) 98.76 68.03 21.88 26.53 51.59Murcia (E16) 97.71 90.96 21.66 33.21 45.13Castile and LeoÂn (E9) 95.85 98.43 31.00 28.60 40.40Calabria (I18) 95.78 61.39 30.49 23.12 46.40Algarve (P5) 95.08 90.73 33.08 23.85 43.08Andalusia (E15) 94.49 79.14 23.20 25.12 51.68Basilicata (I17) 92.23 69.90 33.96 24.53 41.51Galicia (E1) 90.93 85.17 41.53 24.60 33.86Centre of Portugal (P2) 90.00 80.07 43.13 29.80 27.07Alentejo (P4) 62.15 93.39 49.64 20.99 29.36

*1980 was chosen as the initial point for structural data, since data was available for most of the countriesincluded in the analysis. No data is available for Greece and for Corsica.Sources: Own calculations using Eurostat data.



grouped into four general groups (central,pericentral, intermediate and peripheralregions). They also highlight the in¯uence ofthe national effect on regional growth perfor-mance (1996, p. 38). Their conclusions under-line that `regional patterns across Europe arenow very different from those of 30 years ago,and so are the factors behind them' (1996, p.87). Yet, as this analysis has shown, thesedifferences only emerge when the nationaleffect (a major factor behind the continuity ofgrowth trends) is taken into account. Thesuppression of the national effect uncoverschanges in regional disparities which areconnected to the way in which every region ±and different types of regions ± have copedwith structural change and socio-economicrestructuring. Those regions enjoying thegreatest comparative advantages, with readyaccess to capital and information, and betterinfrastructure and human capital, are thosethat have prospered above the Europeanaverage, regardless of their initial developmentlevel. Conversely, regions burdened by strongrigidities and lacking adequate resources havestruggled to adapt to the new economic situa-tion. In this sense the economic structure ofevery region plays an important role in explain-ing regional growth patterns in the EU (Molle1997).

CONCLUSIONS

Despite the large number of studies on re-gional convergence in Western Europe, ourperception of the evolution of disparities isoften in¯uenced by factors such as the unit inwhich growth is measured, as well as by thesigni®cant distortions provoked by the nationaldimension. The only clear conclusion thatseems to emerge is, according to CuadradoRoura, that convergence is by no means amechanical phenomenon, which happenseverywhere and always (1996, p. 47).

This paper has tried to demonstrate that, ifwe take into account the problems related tothe unit of measure and to the national effect,the resulting regional panorama is extremelyvariegated. Growth patterns in every Europeanregion are linked to the geographical andnational contexts in which the region isinserted. The economic trajectories of Cam-

pania and Lombardy ± despite their profoundstructural differences ± are undoubtedlyconnected to that of Italy, as well as that ofBaden-WuÈrttemberg or Schleswig-Holstein tothat of Germany. However, if the distortionsassociated with the national dimension arecontemplated, regional growth patterns are alsorelated to the capacity and ability of everyregional structure to adapt and respond tochanges in the socio-economic arena. On theone hand, there are signs of decline in regionsexperiencing the end of their industrial andagricultural cycles. On the other, those regionsthat have been ¯exible enough to adapt to thenew production methods have prospered.Successful regions are not those with low initiallevels of development or vice versa, but thosethat have managed to ®nd their comparativeadvantages and to remain competitive through-out a period of serious restructuring.

In sum, empirical evidence suggests that theaggregated trends towards convergence and/ordivergence hide, at the micro level, a complexpattern of regional change; a pattern to a largeextent shaped by the structural type of eachregion, as well as by national factors andgeographical proximity.

ACKNOWLEDGEMENTS

The author is grateful to Paul C. Cheshire,Gilles Duranton, Kees Terlouw and the anony-mous referees for their comments on earlierdrafts of the paper.

Notes1. Data used in this paper refers to the evolution of

GDP per capita at market prices, both measured

in ECUs and in PPS. The regional data covers the

period 1977±93 for all the countries in the

European Union, with the exception of Greece

(1979±93), Spain and Portugal (1980±93),

Denmark (1977±91) and Austria, Finland and

Sweden (no regional information available). Data

for Corsica only covers the period 1982±93. No

information for the LaÈnder of the former German

Democratic Rebublic has been included in the

analysis. The only exception is East Berlin. Since

1991 East Berlin data is compiled together with

that of West Berlin, a factor that has resulted in a

serious downward bias of Berlin's growth rate.

There are incomplete series of data available for

the Portuguese regions of Azores and Madeira.



2. Using different methods Barro and Sala-i-MartõÂn

(1991), Armstrong (1995b), Button and Pentecost

(1995) and Quah (1996b) have tried to control for

the in¯uence of the insertion of European

regions in a certain geographical and national

context on economic performance. Barro and

Sala-i-MartõÂn's 1991 model incorporates national

`dummy' variables as surrogates for the various

`conditioning' variables used in the model.

Armstrong does it by means of introducing

national dummy variables in convergence models,

a method that dramatically reduces the detected

spatial autocorrelation (1995b, p. 61). Button and

Pentecost (1996) resort to national dummies

together with structural variables. Quah controls

for the national effect by normalising each

region's per capita income by the per capita

income in the host nation-state and also in the

surrounding, physically contiguous regions, reach-

ing the conclusion that `physical location and

geographical spillovers matter more than do

macro factors' (1996b, p. 7) in determining the

distribution of regional disparities.

3. The method conforms to the following equation:

yiyi= Ð ye (1)

yn

where:

. yÃi denotes the nationally weighted mean

annual rate of regional growth of GDP per

capita measured in ECUs between 1977 and

1993;

. yi represents the mean annual rate of regional

growth of GDP per capita measured in ECUs

between 1977 and 1993;

. yn denotes the mean annual rate of national

growth of GDP per capita measured in ECUs

between 1977 and 1993;

. ye represents the mean annual rate of growth

of GDP per capita measured in ECUs in the

European Union between 1977 and 1993.

The same equation is used in order to nationally

weight the initial GDP per capita. This method

also implies eliminating the differences between

measuring GDP in ECUs or in purchasing power

standards.

REFERENCES

ANSELIN, L. (1994), Spacestat Tutorial. Morgantown:

Regional Research Institute.

ARMSTRONG, H.W. (1995a), Convergence Among

Regions of the European Union, 1950±1990.

Papers in Regional Science 74, pp. 143±152.

ARMSTRONG, H.W. (1995b), An Appraisal of the

Evidence from Cross-Sectional Analysis of the

Regional Growth Process within the European

Union. In: H.W. ARMSTRONG & R.W. VICKERMAN,

eds., Convergence and Divergence Among European

Regions, pp. 40±65. London: Pion.

BARRO, R.J. & X.X. SALA-I-MARTIÂN (1991), Conver-

gence Across States and Regions. Brookings Papers

on Economic Activity 1, pp. 107±182.

BLANCHARD, O.J. (1991), Convergence Across States

and Regions: Comments and Discussion. Brookings

Papers on Economic Activity 1, pp. 159±174.

BOTAZZI, G. (1990), I Sud del Sud: I Divari Interni al

Mezzogiorno e il Rovesciamento delle Gerarchie

Spaziali. Meridiana 10, pp. 141±181.

BUTTON, K.J. & E.J. PENTECOST (1995), Testing for

Convergence of the EU Regional Economies.

Economic Inquiry 3, pp. 664±671.

CHATTERJI, M. (1992), Convergence Clubs and Endo-

genous Growth. Oxford Review of Economic Policy 8,

pp. 57±69.

CHAMPION, T., J. MéNNESLAND & C. VANDERMOTTEN

(1996), The New Regional Map of Europe. Progress

in Planning 46, pp. 1±89.

CHESHIRE, P.C. & G. CARBONARO (1995), Conver-

gence-Divergence in Regional Growth Rates: An

Empty Black Box? In: H.W. ARMSTRONG & R.W.

VICKERMAN, eds., Convergence and Divergence Among

European Regions, pp. 89±111. London: Pion.

CHESHIRE, P.C. & D.G. HAY (1989), Urban Problems in

Western Europe: An Economic Analysis. London:

Unwin & Hyman.

CUADRADO ROURA, J.R. (1994), Regional Disparities

and Territorial Competition in the EC. In: J.R.

CUADRADO ROURA, P. NIJKAMP & P. SALVAÂ, eds.,

Moving Frontiers: Economic Restructuring, Regional

Development and Emerging Networks, pp. 3±22. Alder-

shot: Avebury.

CUADRADO ROURA, J.R. (1996), Convergencia

Nominal y Convergencia Real: El Verdadero Reto

para EspanÄa. In: J.R. CUADRADO ROURA & T.

MANCHA, eds., EspanÄa Frente a la UnioÂn EconoÂmica y

Monetaria, p. 29±63. Madrid: Civitas.

DE LA FUENTE, A. (1997), The Empirics of Growth

and Convergence: A Selective Review. Journal of

Economic Dynamics and Control 21, pp. 23±73.

HADJIMICHALIS, C. (1994), The Fringes of Europe

and EU Integration: A View from the South.

European Urban and Regional Studies 1, pp. 19±29.



HALL, P. (1993), Forces Shaping Urban Europe.

Urban Studies 30, pp. 883±898.

HARVEY, D. (1985), The Urbanization of Capital.

Oxford: Basil Blackwell.

HEPPLE, L.W. (1998), Exact Testing for Spatial Auto-

correlation among Regression Residuals. Environ-

ment and Planning A 30, pp. 85±108.

HUDSON, R. (1997), Regional Futures: Industrial

Restructuring, New High Volume Production

Concepts and Spatial Development Strategies in

the New Europe. Regional Studies 31, pp. 467±478.

KRUGMAN, P. & A.J. VENABLES (1995), Globalization

and the Inequality of Nations. Quarterly Journal of

Economics 110, pp. 857±880.

LEBORGNE, D. & A. LIPIETZ (1992), IdeÂes Fausses et

Questions Ouvertes de l'ApreÁs-Fordisme. Espaces et

SocieÂteÂs 66-67, pp. 39±68.

LOÂ PEZ-BAZO, E., E. VAYAÂ, A.J. MORA & J. SURINÄ ACH

(1997), Regional Economic Dynamics and Conver-

gence in the European Union: Documents de

Treball de la DivisioÂn de CieÂnces JurõÂdiques, Econ-

oÂmiques i Socials, no 97/12. Barcelona: Universitat

de Barcelona.

LUCAS, R.E. JR. (1988), On the Mechanics of

Economic Development. Journal of Monetary

Economics 22, pp. 3±42.

MOLLE, W. (1997), The Regional Economic Structure

of the European Union: An Analysis of Long-

Term Developments. In: K. PESCHEL, ed., Regional

Growth and Regional Policy within the Framework of

European Integration, pp. 66±86. Heidelberg: Physica

Verlag.

MOLLE, W. & S. BOECKHOUT (1995), Economic

Disparity under Conditions of Integration: A Long

Term View of the European Case. Papers in Re-

gional Science 74, pp. 105±123.

MOLLE, W., B. VAN HOLST & H. SMIT (1980), Regional

Disparity and Economic Development in the European

Community. Westmead: Saxon House.

PIORE, M. & C. SABEL (1984), The Second Industrial

Divide. Basic Books, New York.

QUAH, D. (1996a), Empirics for Economic Growth

and Convergence. European Economic Review 40, pp.

1353±1375.

QUAH, D. (1996b), Regional Convergence Clusters

across Europe. Centre for Economic Performance,

WP 1286. London: London School of Economics.

QUEÂVIT, M., J. HOUARD, S. BODSON & A. DANGOISSE

(1991), Impact ReÂgional 1992: Les ReÂgions de Tradition

Industrielle. Brussels: De Boeck.

ROBINSON, G.M. (1998), Methods and Techniques in

Human Geography. Chichester: John Wiley & Sons.

RODRIÂGUEZ-POSE, A. (1994), Socioeconomic Restruc-

turing and Regional Change: Rethinking Growth

in the European Community. Economic Geography

70, pp. 325±343.

RODRIÂGUEZ-POSE, A. (1996), Growth and Institutional

Change: The In¯uence of the Spanish Regionali-

sation Process on Economic Perfromance. Environ-

ment and Planning C: Government and Policy 14, pp.

71±87.

RODRIÂGUEZ-POSE, A. (1998), Social Conditions and

Economic Performance: The Bond Between Social

Structure and Regional Growth in Western

Europe. International Journal of Urban and Regional

Research 22, pp. 443±459.

ROMER, P.M. (1986), Increasing Returns and Long-

Run Growth. Journal of Political Economy 94, pp.

1002±1037.

ROMER, P.M. (1990), Endogenous Technological

Change. Journal of Political Economy 98, pp. S71±

S102.

SALA-I-MARTIÂN, X.X. (1994), La Riqueza de las

Regiones: Evidencia y TeorõÂas Sobre Crecimiento

Regional y Convergencia. Moneda y CreÂdito 198, pp.

13±80.

SALA-I-MARTIÂN, X.X. (1996), Regional Cohesion:

Evidence and Theories of Regional Growth and

Convergence. European Economic Review 40, pp.

1325±1352.

STORPER, M. (1997), The Regional World: Territorial

Development in a Global Economy. New York:

Guilford.

STORPER, M. & A.J. SCOTT (1989), The Geographical

Foundations and Social Regulation of Flexible

Production Complexes. In: J. WOLCH M. DEAR,

eds., The Power of Geography. How Territory Shapes

Social Life, pp. 21±40. Boston: Unwin & Hyman.

TOMANEY, J. (1994), Alternative Approaches to

Restructuring in Traditional Industrial Regions:

The Case of the Maritime Sector. Regional Studies

28, pp. 544±549.

TRIGILIA, C. (1992), Sviluppo Senza Autonomia: Effetti

Perversi delle Politiche nel Mezzogiorno. Bologna: Il

Mulino.

VANDERMOTTEN, C., H. CAPRON, M. DECROLLY & P.

ROMUS (1990), Les ReÂgions et l'Europe. Actes du

NeuvieÁme CongreÁs des Economistes belges de

langue francËaise. Namur: CIFoP.

VAÂZQUEZ BARQUERO, A. (1993), PolõÂtica EconoÂmica

Local. Madrid: PiraÂmide.

VENABLES, A.J. (1998), The Assessment: Trade and

Location. Oxford Review of Economic Policy 14, pp. 1±

6.

