Eleonora Cutrini
email: ecutrini@eco.uninsubria.it

Specialisation and Concentration from a Twofold Geographical Perspective: Evidence from Europe

Keywords: C43 -Index Numbers and Aggregation < C4 -Econometric localisation, specialisation, concentration, European economic integration, twofold geographical analysis JEL classification: C43, F15, N60, R12 localisation, specialisation, concentration, European economic integration, twofold geographical analysis La spécialisation Normal, Line spacing: single (Default) Times New Roman, Complex Concentration / Intégration économique européenne / Analyse géographique à deux temps C43, F15, N60, R12 Spezialisierung und Konzentration aus einer zweiteiligen geografischen Perspektive: Belege aus Europa Integration, um die charakteristischen Rollen zu identifizieren, die von den verschiedenen geografischen Ebenen wahrgenommen werden. Die Evolution der Lokalisierung in Europa erwies sich Prognosen auf der Grundlage der Krugman-Hypothese. Mit Hilfe von Eurostat-Regionaldaten weise ich für den Zeitraum von 1985 bis 2001 nach, dass das Beschäftigungsniveau der produzierenden Industrie innerhalb der Regionen zwar einem Trickle-Down-Effekt unterlag, aber nach Vollendung des Europäischen Binnenmarkts eine leichte Agglomeration auftrat, allerdings nur über nationale Grenzen hinweg. Eine nationale Spezialisierung JEL classification: C43, F15, N60, R12 Lokalisierung Spezialisierung Konzentration Europäische Wirtschaftsintegration Zweiteilige geografische Analyse Localización Especialización Concentración Integración económica europea Análisis geográfico dual JEL classification: C43, F15, N60, R12

The paper investigates European location patterns during a period of economic integration, seeking to identify the distinct roles played by the different geographical levels. The evolution of localisation in Europe proved much more complicated empirically than the predictions based on the Krugman's hypothesis. Using Eurostat regional data for the period 1985-2001, the paper shows that while manufacturing employment trickled down among regions, after the completion of the European Single Market a slight agglomeration occurred, but only across national boundaries. National specialisation has emerged particularly in the EU founding member states. Moreover, there is evidence of an increasing polarisation of the North/South divide closely connected with the growing concentration of high-tech sectors.

INTRODUCTION

During the past two decades, declining trade barriers associated with the construction of the Single European Market have been supposed to engender drastic changes in the spatial distribution of economic activities, and they have become a prominent topic in political debate and in academic and research environments. The increasing clustering of high-value added economic activities in high incomes regions, together with the low-tech specialisation of lagging regions, is an example of the expected trend towards a greater inequality presumed to exacerbate the existing uneven spatial distribution of income and welfare.

Both traditional trade theories and the new trade theories envisage that countries will specialise as a consequence of international integration. According to the 'Krugman hypothesis' [START_REF] Krugman | Geography and Trade[END_REF], European integration will give rise to the coalescence of industrial activities in order to mimic the geographical concentration that previously arose in the United States. On this view, various models developed in the New Economic Geography (NEG), intentionally designed for the case of Europe, predict that when international transaction costs fall below a certain threshold, international openness will lead to the agglomeration of industrial activities within countries [START_REF] Monfort | Regional convergence and international integration[END_REF][START_REF] Paluzie | Trade policy and regional inequalities[END_REF]CROZET and KOENIG-SOUBEYRAN, 2004b;CROZET and KOENIG-SOUBEYRAN, 2004a; MONFORT and [START_REF] Monfort | Integration, regional agglomeration and international trade[END_REF]. Although inspired by the territorial changes following the Mexican liberalisation programme (see [START_REF] Hanson | Regional adjustment to trade liberalization[END_REF] the study by [START_REF] Krugman | Trade policy and the Third World metropolis[END_REF] can be adopted as a theoretical framework for study of European integration. KRUGMAN and LIVAS's model highlights the importance of congestion costs as a centrifugal force pushing the internal dispersion of economic activities. Similarly, PUGA (1999) predicted a dispersion propelled by congestion-related forces.

Apart from international integration, further economic forces may disrupt the existing patterns of localisation and foster the dispersion of economic activities. Recent theoretical studies have conceived widespread firm fragmentation as the cause of changes in withincountry economic geography, which in many countries has been characterised by the agglomeration of executive functions in urban areas, with peripheral areas becoming the sites of routine tasks.

International integration in commodity markets and fragmentation of productive processes are bringing about the progressive irrelevance of national borders. Consequently, adopting subnational economies as units of spatial analysis is fundamental for understanding the complexity of structural change dynamics at different spatial scales. Moreover, from a normative perspective, the development of rigorous methodologies to disentangle structural changes at different geographical levels of analysis is becoming important in light of the existence of overlapping institutional levels. Assessing whether the concentration of economic activities is occurring mostly within countries or at wider distances aids understanding of how and to what extent European, national and regional policy-makers must be involved in designing appropriate policies.

To date, few empirical studies have analysed specialisation as well as concentration (e.g. [START_REF] Aiginger | Industrial specialisation and geographic concentration: two sides of the same coin? not for the European Union[END_REF][START_REF] Aiginger | The Single Market and geographic concentration in Europe[END_REF][START_REF] Mulligan | A Note on Localisation and Specialization[END_REF] but none adopted a two-scale framework (within and across perspective). Therefore, the integrated analysis of overall localisation conducted by this study -with concentration on one side and specialisation on the other -combined with adoption of a twofold geographical perspective is still a novelty in the literature. Its advantage is that it enables deeper and richer assessment than do the methodologies prevalent in previous studies.

My aim in the paper is to provide clear-cut evidence on the location patterns of European manufacturing industries during the period 1985-2001, adopting a new methodology developed in a previous work [START_REF] Cutrini | The Balassa Index Meets the Theil Index: a Decomposition Methodology for Location Studies[END_REF]. Starting from a twofold geographical perspective, I show that localisation within countries does not evolve in parallel with localisation across national boundaries. However, since relative measures were adopted 1 , F o r P e e r R e v i e w O n l y 6 the equivalence between specialisation and concentration trends is maintained at the level of each single geographical scale. In particular, the results suggest that national specialisation (and agglomeration of industries across countries) have slightly increased since the enactment of the European Single Market Programme, whilst substantial regional despecialisation (the deconcentration of industries within countries) is evident during the entire period. A new core/periphery pattern besides the North/South divide is emerging whereby Northern Europe is specialised in high-tech industries, and Southern Europe in labour-intensive industries.

The remainder of the paper is organised as follows. The next section reviews the empirical evidence on regional specialisation and concentration in Europe, focusing specifically on the main methodological issue of a multilevel analysis. The third section describes the data and the methodology: the identity between aggregate concentration of industries and aggregate specialisation of regions and its geographical decomposition. The forth section presents the results. The fifth section puts forward some conjectures on interpretation of the apparently contrasting results obtained. A final section makes some concluding remarks and indicates further directions for research.

SURVEY OF THE EMPIRICAL LITERATURE

The empirical literature has usually evaluated specialisation in Europe on the basis of a single scale analysis. The slow specialisation of countries during the 1970s and 1980s was identified by several studies (BRÜLHART and TORSTENSSON,1996;[START_REF] Amiti | Specialisation patterns in Europe[END_REF]WIFO, 1999;[START_REF] Haaland | What determines the economic geography of Europe?[END_REF]MIDELFART et al., 2004 among others). At the same time, some authors suggested that, from the 1970s to the 1990s, regional specialisation decreased in Spain [START_REF] Paluzie | Regional integration and specialization patterns in Spain[END_REF], in Italy [START_REF] Rombaldoni | Localizzazione delle imprese manifatturiere e specializzazione regionale in Italia: 1970-1990[END_REF]DE ROBERTIS, 2001;[START_REF] Ciciotti | Capacità innovativa e sviluppo regionale: Alcune evidenze delle regioni italiane negli anni novanta[END_REF], and in Germany [START_REF] Suedekum | Concentration and specialization trends in Germany since reunification[END_REF]. However, if one looks at the specialisation of EU regions disregarding national borders, one finds contrasting empirical evidence. In fact, [START_REF] Midelfart-Knarvik | Integration and industrial specialisation in the European Union[END_REF] show that a majority of regions (53 per cent) became more specialised, although only to a slight extent [START_REF] Combes | The spatial distribution of economic activities in the European Union[END_REF].

The evidence is similarly mixed if we focus on the geographical concentration of sectors.

Adopting the region as unit of analysis gives rise to contrasting results on concentration trends, compared with those emerging from the more common country-based studies. If analysis relies on national borders, one finds that the pre-Single Market period was characterized by increasing international agglomeration in the majority of sectors, especially during the 1980s [START_REF] Brülhart | Regional integration, scale economies and industry location in the European Union[END_REF][START_REF] Brülhart | Trading places: industrial specialisation in the European Union[END_REF][START_REF] Amiti | Specialisation patterns in Europe[END_REF][START_REF] Haaland | What determines the economic geography of Europe?[END_REF]BRÜLHART, 2001;[START_REF] Midelfart | The Location of European industry[END_REF], while during the post-Single Market period spreading forces prevailed [START_REF] Midelfart | The Location of European industry[END_REF][START_REF] Aiginger | The Single Market and geographic concentration in Europe[END_REF].

Although a decreasing regional concentration of industries was a common result for specific countries, 2 EU-wide regional concentration analysis empirically supports the idea that the completion of the single market fostered the agglomeration of industries, allowing the better exploitation of regional localised advantages. On the basis of regional data on gross value added, [START_REF] Hallet | Regional specialisation and concentration in the EU[END_REF] suggested that concentration slightly declined during the 1980s, Assessment of straightforward evidence has been hindered until recent years, not only by the shortage of comparable regional data but also, as [START_REF] Combes | The spatial distribution of economic activities in the European Union[END_REF] claim, by the lack of an appropriate methodology with which to disentangle the geographical clustering internal to countries from cross-country location patterns. In fact, as [START_REF] Combes | The spatial distribution of economic activities in the European Union[END_REF] suggest, evaluating the regional specialisation patterns relative to a country is different from assessing the same process relative to Europe as a whole. 3 To date, different basic units of analysis (either region or country); different geographical benchmarks (either country or Europe); or different measures (either absolute or relative) 4 have been the main variations adopted. The multiplicity of methodologies makes it difficult to define an unquestionable pattern of specialisation and concentration in Europe through simple comparison among existing empirical studies. Moreover, economists and geographers have continued to assess the two side of overall localisation 5 separately, thus disregarding their mutual dynamic relationship. To the best of my knowledge, only one work adopts an integrated approach: AIGINGER and [START_REF] Aiginger | Specialization and concentration: a note on theory and evidence[END_REF], who show that specialisation of countries and geographical concentration of industries do not necessarily evolve in parallel.

Against the background of a theoretical model (ROSSI-HANSBERG, 2005), 6 the authors furnish consistent evidence for Europe and the US based on the application of absolute Gini.

Nonetheless, they also suggest that the trends over time in specialisation and concentration cannot diverge when relative measures are adopted.

Sectoral and spatial partitions of data

The analysis relies on employment data subdivided by manufacturing sectors. The data are drawn from the EUROSTAT Region-SBS (Structural Business Statistics) for the years 1985, 1993 and 2001. The sample of the 145 regions considered almost completely covers the following European countries: Belgium and Luxembourg (consolidated), Finland, France, Western Germany, Greece, Italy, Netherlands, Spain and United Kingdom. Some regions have been dropped, either because of overwhelming missing data or because they are not included at all in the database. The regional grid is mainly based on the NUTS 2 grid except for Germany, for which reference has been made to the NUTS 1 regions (Länder). As to Belgium, the data are drawn from a dataset provided by the national statistics office and based on the previous NACE 70 classification. Therefore Bruxelles, Vlaams Brabant and Brabant Wallon have been clustered as a single region (for detailed information on geographical coverage see Table A1).

The analysis is restricted to the manufacturing sector, owing to a lack of data for the services sector. Different partitions in the sectoral dimension should be considered, since agglomeration in the real world may arise from inter-industry linkages (i.e. linkages across the artificial boundaries of industrial classifications derived from the available statistical data). Therefore, I complement the analysis on concentration based on the twelve manufacturing sectors with a dichotomic classification based on the taxonomy adopted by OECD (2003). In this case (e.g. in Fig. 2), chemicals, machinery and equipment n.e.c., electrical and optical equipment, transport equipment, furniture, recycling and manufacturing n.e.c. are considered, as they form a single sector labelled high-tech industries. Similarly, food, textiles, wood, paper, rubber and plastic products, other non-metallic mineral products, basic metals and fabricated metal products belong to the category low-tech industries.

The methodology

Notation and basic definition

In this section, I briefly summarize the analytical model introduced in [START_REF] Cutrini | The Balassa Index Meets the Theil Index: a Decomposition Methodology for Location Studies[END_REF] and adapted to the purposes of this paper. [START_REF] Aiginger | Industrial specialisation and geographic concentration: two sides of the same coin? not for the European Union[END_REF] have already analysed concentration and specialization as the two sides of the same matrix by using absolute entropy measures. The present study relies on relative measures, and specifically on dissimilarity entropy indices which assess the 'distance' between two distributions. 9

Let x denote the variable of main interest (employment in the present case); subscripts i, Relative specialization of a basic unit of analysis (e.g. region j of country i) is taken to be the dissimilarity between the regional manufacturing structure (i.e. the allocation of the variable of main interest across all the manufacturing industries of the region) and the allocation of European employment across manufacturing industries. All the raw measures of concentration and specialization which constitute the background for the aggregate analytical model can be traced back to the dissimilarity Theil index [START_REF] Theil | Economics and Information Theory[END_REF][START_REF] Maasoumi | A compendium to information theory in economics and econometrics[END_REF]. [START_REF] Theil | Economics and Information Theory[END_REF] first introduced a dissimilarity version of the entropy index to evaluate the information content of an indirect message. Dissimilarity is therefore synonymous here with divergence, discrepancy in the comparison of two overlapping distributions.

F

Raw measures of relative concentration

The basic dissimilarity entropy index adopted here to measure concentration of one industry k is:

= = = m i r j ij k ijk k ijk k i x x x x x x T 1 1
) ln(

(total relative concentration of industry k)

(1)

The degree of concentration of each industry ( k T ) can be conceived as a measure of the strength of localization economies and/or the importance of industry-specific natural advantages. In fact, in the case of perfect regularity ( 0

= k T
), the location of the industry is mainly due to the advantages of being located in those regions with the highest density of the aggregate economic activity.The concentration of an industry may be explained in terms of the regional agglomeration economies that arise within countries and the national comparative advantages shaping the between-countries location pattern. Hence: ) ln( (within-country relative concentration of industry k)

(2)

evaluates within-country concentration of industry k, while:

= = m i i k ik k ijk b k x x x x x x T 1 ) ln( (between-country relative concentration of industry k) (3)
assesses the between-country concentration of industry k. 11

Raw measures of relative specialisation

Turning to the specialization side, it is possible to evaluate the dissimilarity between the economic structure of one region (composed by the n manufacturing industries) and that of a supra-regional economy.

Therefore, further raw specialisation indices are derived from the dissimilarity Theil index: When the dissimilarity logic is adopted, the national specialisation relative to Europe ( b i T )

= = n k k ij ijk ij ijk ij x x x x x x T
can be envisaged as a residual of the averaged regional specialisation relative to the same When a regional perspective is adopted, the average regional specialisation of a country relative to Europe ( i aRS ) is composed of two elements: the within-country component and the country bias. Specifically, the following relation holds:

b i w i i T aRS aRS + = (9)
The entropy index of overall localization

The entropy index with which I measure overall localization (L-index) is a weighted sum of the logarithms of location quotients where the weights are the industry-region shares of the aggregate manufacturing ( x x ijk ): In the present analytical model, the L-index represents the equivalence between geographical concentration and regional specialization.

= = = = n k m i r j ij k ijk ijk i x x x x x x L 1 1 1 ) ln( (10)
In fact, it is possible to derive the L-index as a composite index of both relative specialization measures and relative concentration ones (see [START_REF] Cutrini | The Balassa Index Meets the Theil Index: a Decomposition Methodology for Location Studies[END_REF]:

k n k k ij r j ij T x x T x x L = = = = 1 1 (11)
The evolution of overall localization within countries may depart from localization between countries (for details on the spatial decomposition of the L-index, see [START_REF] Cutrini | The Balassa Index Meets the Theil Index: a Decomposition Methodology for Location Studies[END_REF]. Here, I point out how specialization and concentration conceptually and analytically underpin each factor component of the composite index of overall localization.

The between-country component ( b L ) of overall localization is defined as:

= = = n k m i i k ik ik b x x x x x x L 1 1 ) ln( (12)
The twofold definition of the concept of overall localization still holds at the betweencountry level, since the following identity holds:

b k n k k b i m i i b T x x T x x L = = = = 1 1 (13)
The within-country component (L w ) of overall localization is:

= = = = n k m i r j i ij ik ijk ijk w i x x x x x x L 1 1 1 ) ln( (14)
Again, specialisation and concentration underpin the overall localisation pattern within countries:

F o r P e e r R e v i e w O n l y 16 w k n k k w ij r j ij w T x x T x x L = = = = 1 1 (15)
Each component defined in equations ( 13) and ( 15) assesses the average dissimilarity between the two distributions of interest. They are both the average sum of the raw indices of relative concentration and relative specialization.

This implies that the L-index will be also a valuable reference for the analysis of specialisation and concentration. In the case of specialisation patterns, it represents the weighted average of raw indices and is therefore a valuable reference with which to understand "how large is large" (see [START_REF] Mccloskey | The Standard Error of Regressions[END_REF], particularly in the absence of an upper bound on the specialisation and concentration measures.

In fact, the overall localization index (L) is a summary statistics of regional specialization indices ( °ij T ) weighted by the regional manufacturing shares ( x x ij ):

= = = + = = r j w ij ij m i b i i ij r j ij T x x T x x T x x L 1 1 1 (16)
On the concentration side, overall localization can be seen as a summary statistics of relative concentration Theil indices ( k T ) weighted by the industry shares ( x x k ):

= = = + = = n k w k k n k b k k k n k k T x x T x x T x x L 1 1 1 (17)
To conclude, both equation ( 16) and equation ( 17 explained by international and intra-national components.

However, like all measures based on aggregate regional data, the index of overall localization is affected by the modifiable areal unit problem and the checkerboard problem [START_REF] Arbia | Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems[END_REF].

Recently developed has been a line of methodological development based on spatial disproportionality measures of polarisation and concentration to deal with the checkerboard and the MAUP problems [START_REF] Bickenbach | Disproportionality measures of concentration, specialization, polarization[END_REF].

Entropy measures are suitable for statistical testing. Bootstrapping is a valuable method with which to ascertain whether the observed localization has significantly changed over time.

The bootstrap was introduced by EFRON (1979), and it has been more recently adopted in the context of inequality measures, although its implementation for the spatial distribution of economic activities has been quite rare 12 . The main issue to be addressed here is whether overall localisation, relative concentration, and relative specialisation changed significantly over the period under scrutiny. This issue can be resolved by bootstrapping the entropy measures and their components. The resampling process is repeated 10,000 times and the following hypothesis test is conducted:

0 : 0 = I H 0 : 1 I H
where I refers to each entropy measure of relative concentration, relative specialisation, or overall localisation.

LOCATION PATTERNS IN EUROPE: THE EMPIRICAL EVIDENCE

A declining trend in overall localisation: an overview

From the mid-1980s onwards manufacturing employment as a whole became less localised across European regions. Figure 1 illustrates the trend in overall localisation during the period 1985-2001. The internal geography of countries was much more differentiated than the European landscape evaluated on the basis of national borders. Put differently, the spatial organisation of manufacturing industries was driven mostly by the regional scale, and only to a minor extent it is due to the different national characteristics, e.g. comparative advantages.

On average, the latter component accounts for less than one third of the overall localisation.

As for the dynamics, the spreading forces acting within countries were stronger than the contrasting trends across countries.

[FIGURE 1 about here]

In fact, the evolution of overall localisation is explained mostly by the modification of the regional agglomeration of manufacturing industries. The sub-national component accounted for more than 80% of the total variation of the L-index (see Table A2). Internal regions of each country converged towards the manufacturing structure of the country to which they belonged. As a result, the spatial distribution of each industry became more similar to the interregional allocation of total manufacturing employment. Compared with the withincountry pattern, the international component was rather stable, with a slight decrease in the first period (-17.7%) which was partly recovered from 1993 onwards (+5.6%).

The sensitivity of the results on the evolution of overall localisation to the choice of the basic unit of analysis and to the choice of the intermediate aggregation level 13 1, where the same geographical benchmark (Europe 14 ) is used to evaluate overall localisation by adopting different spatial hierarchical structures. This comparison makes it possible to assess the robustness of the findings. The main conclusion to be drawn is that a pronounced declining localisation at the smaller scale -namely, within countries -is a robust finding irrespective of the basic unit (NUTS2 or NUTS1) and the intermediate aggregation level adopted (NUTS1, country).

[TABLE 1 about here] After the completion of the European Single Market, localisation at higher spatial aggregations -namely across national boundaries and over the North/South divide -displayed an upward trend (Table 1). The positive changes between-country and over the North-South divide are almost zero and they are not significant. Nevertheless, they may represent a relevant sign of a change for the second period of analysis. In fact, the interesting point here is the differential patterns at the lower geographical scale (within-country) compared to the international evolutions (i.e. between-country and the North-South divide). The declining polarisation at the smaller spatial scale and the contemporaneous slight localisation at larger distances are shown to be robust to different basic units and intermediate aggregations and call for differential economic forces that may have been at work internationally and locally.

Overall localisation patterns can be also viewed in terms of relative concentrations of manufacturing industries. As already shown, after 1993 the general fall in overall localisation over long distances stopped not only across countries but also between the North/South divide (Table 1). It is interesting to note that the rising overall localisation during the 1990s derived mainly from an increasing agglomeration of high-tech manufacturing activities which happened both at the local level and between the North/South divide. [FIGURE 2 about here] The evidence of the present work confirms that innovative industries are more geographically clustered than traditional industries. Moreover, during the nineties, instead of spreading across European economies, the former become more concentrated across regions and across the North-South divide (Figure 2). In other words, proximity matters particularly in the knowledge-intensive sector 15 plausibly, because of the higher intensity of knowledge spillovers 16 , and input-output linkages within the sector.

The increasing polarisation of the knowledge-intensive industries in the 1990s that favoured Northern European countries is usually associated to the wider availability of highly-skilled labour. During the nineties, structural changes in Northern Europe occurred towards greater specialisation in high-technology manufacturing industries, while Southern regions lagged behind.

In the following sections, I conduct more detailed analysis of the concentration and specialisation trends across and within countries. This may be due to the fact that these industries are usually highly represented where manufacturing employment is geographically concentrated.

[TABLE 2 about here] BRÜLHART and TRAEGER (2005) found that the relative concentration of value added increased in the majority of manufacturing industries, even though the changes were generally minimal and not significant ( 22 a widespread decline in relative concentration, and the results are highly significant in half of the industries. To be stressed is that the regional agglomeration of value added combined with the spreading of employment suggests the importance of within-industry spatial fragmentation along functional lines 17 [START_REF] Duranton | From Sectoral to Functional Urban Specialisation[END_REF]. The widespread increase in relative concentrations of value added found in a previous study by [START_REF] Brülhart | An account of geographic concentration patterns in Europe[END_REF] (Table 2, last column) was almost simultaneous with a significant decline in employment agglomeration (Table 2, third column) in the majority of manufacturing industries.

Increasing returns to scale sectors -non-metallic products, chemicals, transport equipment and paper and publishing -are characterized by consolidated regional localisation economies. In fact, not only do they emerge as highly clustered at the beginning of the period, but they also exhibit minimal changes. Relative concentration increases in textile and wearing apparel, where external economies are notably important, and, if value added is considered, the change is also significant.

Apart from the above-mentioned exceptional case, de-agglomeration is a widespread and robust result for the entire period considered. The most important feature of the overall modification is that it conceals different changes, which occurred within and between countries, respectively. The within-country evolution and the national change did not evolve in parallel. Some of the industries characterized by a substantial decrease in internal localization experienced intensifying between-country relative concentration associated with a process of the national specialisation of EU economies.

[FIGURE 3 More specifically, although diminishing polarisation within countries is common to almost all manufacturing industries (paper is the only exception), it should be stressed that the evolution of cross-border concentration is mixed. Agglomeration between countries occurred in no-metallic mineral products, chemicals, textiles and transport equipment (top panel of Figure 3), although changes were significant only for the latter two industries during the 1990s (see Table A7, appendix). Once the Internal Market was completed, the international agglomeration of textiles and wearing apparel was mainly due to the higher and increasing shares of Spain and Italy in European textiles employment with respect to their share in European manufacturing employment. As for transport equipment, the increasing concentration is the outcome of a manufacturing industry that remained highly embedded in Germany, despite the loss of industrial employment and deindustrialisation experienced by the country during the 1990s.

In a second group of industries, the falling relative concentration was driven mostly by deagglomeration within countries, with a low level of between-country concentration which remained almost unchanged. This group consists of medium to high-tech industries, namely basic metals, rubber and plastic products and electrical and optical equipment (middle panel of Figure 3). In the remaining sectors -food, wood, machinery, and miscellaneous manufacturing -the territorial organisation converged on the spatial distribution of overall manufacturing both across countries and within countries (bottom panel of Figure 3).

Internal structural changes and national patterns of specialisation

The magnitude of the change over time was remarkably higher during the entire period in the peripheral and smaller countries -namely Greece, Belgium and Luxembourg, Spain and Finland -which had been also characterised by a higher level of dissimilarity throughout the period (Table 3). For the Mediterranean Cohesion countries -namely Greece and Spain -this [TABLE 3 about here] By contrast, the larger countries -such as Great Britain, France, Italy and Western Germanydid not change much in terms of their region-based specialisation relative to Europe ( °i aRS ).

It is interesting to note that the minor falling changes experienced by these countries conceal a substantial and significant despecialisaton that occurred internally, particularly in Italy and Western Germany (Table 3). These countries are characterized by a falling regional specialisation which occurred simultaneously with an increasing specialisation of the national manufacturing structure.

Therefore, to gain better understanding of specialisation patterns in Europe, it is useful to distinguish international trends from intra-national evolutions. In fact, as explained in the methodology section, the overall trend in specialisation of an economy delimited by national boundaries is the outcome of separate, and somehow different, trends in specialisation that occur simultaneously at the regional and national levels: that is, internal regional specialisation does not go hand in hand with the specialisation of the whole country.

There is a group of countries in which national specialisation increased while internal regional specialisation was declining. This group includes Germany, Italy and, to a lesser extent, France. Their regional economies became less specialised relative to the national reference, but the national manufacturing structure increasingly differed from Europe (top panel of Figure 4). In particular, in Western Germany, national patterns were mainly the In the mid-1990s, Italy was a traditional light and labour-intensive producer, with significant specialisation in the production of machinery. During the subsequent periods, Italy constantly increased the distinctive nature of its manufacturing structure (see Table A8).

Despecialisation was only a national phenomenon in a second group composed of small European countries. Specifically, the pronounced downward trend in national specialisation was associated with mixed trends within countries. Slight regional specialisation occurred in Greece, while general internal stability characterized the cases of Netherlands and Finland.

The convergence of the Greek national manufacturing structure to Europe's is due to catching-up by the Greek economy 18 since its entry into the European Community, despite its internal core/periphery divide widened over time (middle panel of Figure 4).

[FIGURE 4 about here] An analogous development took place in Spain, which specialised in textiles and wearing apparel and in non-metallic mineral products, basic metals, partly losing its comparative advantage in the food industry, the wood industry, and miscellaneous manufacturing. Spaintogether with Belgium, Luxembourg and the UK -belongs to the group of countries in which internal development replicated the national specialisation patterns. This group of countries saw their manufacturing structures converge on the supra-regional reference: both regions

Within-and between-country evolution in the context of European economic integration

With the aim of totally abolishing the 'frontier' concept, the 1985 White Paper established the legislation to be adopted by the end of 1992 in order to achieve full elimination of physical, technical and tax frontiers. To be noted is that 90% of the legislative projects listed in the 1985 White Paper had been adopted by 1993 (EUROPEAN COMMISSION, 1996). In the following period, further progress was made in the transposition of EU legislation into national law and in its implementation -which had previously limited the full completion of the internal market by 1992. Table 4 suggests that international restructuring ( b i T ) may have been affected by the European integration process, while regional depolarisation ( w i aRS ) was a generalised trend invariable to the development of a new institutional environment. That is to say, while regional specialisation declined continuously throughout the whole period, for national trends, 1993 can represent a significant turning point. The present analysis confirms the first evaluation by [START_REF] Sapir | The Effects of Europe's Internal Market Program on Production and Trade: a First Assessment[END_REF], who suggested that the internal market programme did not produce the general increase in the specialisation of European economies envisaged by [START_REF] Krugman | Geography and Trade[END_REF] In fact, increasing specialisation can hardly be considered a stylized fact, neither within countries nor across countries. By contrast, it was a temporary exception to the rule which occurred, before enactment of the Single Market programme, within Greece and, to a lesser extent, in France. Moreover, national specialisation decreased in all the European countries in the sample between 1985 and 1993, except for Italy, which specialised throughout the entire period. It is likely that European countries, in a context of high trade barriers, protected industries not endowed with comparative advantages, and that the Single Market Programme imposed a structural change on their economies (AMITI, 1999) which gave rise to 'U-shaped' national specialisation patterns. On this view, further national specialisation may possibly be imminent as EU deepening and widening proceed further. A first possible confirmation of this conjecture is provided by some of the founding members of the European Union, namely Belgium and Luxembourg, France, and Western Germany, which according to the present analysis, experienced increasing specialisation from the post-Single Market period onwards.

The evolution of specialisation was matched by the agglomeration of industries across and within national borders. We have just seen that the construction of the Single Market was dominated by international adjustments towards the decreasing specialisation of countries (see Table 4).

[TABLE 5 The results on decreasing national specialisation are matched by the between-country variations over the period 1985-1993. Changes were generally negative, and in half of the industries they were significant. Therefore, international de-agglomeration of industries prevailed across countries, as suggested by [START_REF] Midelfart | The Location of European industry[END_REF], and seems also to be consistent with the geographical dispersion across countries of manufacturing industries between 1985[START_REF] Garofoli | Economia del territorio. Trasformazioni economiche e sviluppo regionale[END_REF][START_REF] Aiginger | The Single Market and geographic concentration in Europe[END_REF][START_REF] Aiginger | Industrial specialisation and geographic concentration: two sides of the same coin? not for the European Union[END_REF]). 19 By contrast, during the second period, agglomeration across national boundaries rose in additional industries (see Table 5). To sum up, after a temporary adjustment to the liberalisation of manufactured goods markets, from 1993 onwards founding member states (Belgium, Luxembourg, France, Italy and Western Germany) experienced increasing specialisation which reflected significant international agglomeration in two core industries (textiles and wearing apparel and transport equipment) accompanied by rising trends in the chemicals industry, metal products and non-metallic mineral products (see Table 4 andTable 5).

DISCUSSION OF RESULTS AND CONJECTURES

Whatever international localisation will come about in the future, to date most of the structural change, particularly since completion of the Single Market Programme, has occurred in the internal geography of countries (see Table 4 andTable 5). In particular, industrial regional de-agglomeration within countries throughout the period confirms, and extends to further European countries, the evidence provided by previous studies on Italy, Spain and Germany (ROMBALDONI and ZAZZARO, 1997; DE ROBERTIS, 2001; PALUZIE et al., 2001; SUEDEKUM, 2006). [START_REF] Krugman | Trade policy and the Third World metropolis[END_REF], because firms became less 'inward-looking' and the strength of congestion costs proved much more important than before. 21 The importance of congestion costs was also emphasized by the Italian literature on the development of peripherial regions in the 1970s and 1980s. Italian interregional dispersion was conceived in terms of the filtering-down theory (CRIVELLINI and PETTENATI, 1989) associated with the increasing congestion costs and disamenities of the main industrial area in the country. The change in the internal geography was also reinforced by lagging regions (the Third Italy), which subsequently grew faster than core regions, giving rise to profound changes in the previous relative positions [START_REF] Garofoli | Economia del territorio. Trasformazioni economiche e sviluppo regionale[END_REF]. In addition, national industrial policies and European Regional Policy in favour of peripherial and underdeveloped regions may have contributed to the large-scale de-polarisation experienced in Southern Europe. In fact, Italy and Spain were among the first six countries in terms of EU aid and state aid to manufacturing during the period 1994-96 (Greece, Portugal, Ireland and Denmark were the others, see [START_REF] Midelfart-Knarvik | Integration and industrial specialisation in the European Union[END_REF], p. 334). Moreover, at the same time as European integration increased, transportation and communication technology also improved, and industry-specific agglomeration economies were partly substituted by incentives for functional specialization within the same industry.

Hence, accounting for the simultaneous development in transportation infrastructure and communication technology is essential to gain better understanding of the underlying reasons for the new patterns in the spatial organisation of industries. In fact, when the costs of coordinating the value chain decreased, firms found it easier to relocate their production units, maintaining their headquarters close to metropolitan areas so that managers were still proximate to business service suppliers. In fact, if the spreading of labour that emerges is combined with the agglomeration of value added found by comparable previous studies [START_REF] Brülhart | An account of geographic concentration patterns in Europe[END_REF], it is likely that regional specialisation along functional lines is occurring within industry [START_REF] Duranton | From Sectoral to Functional Urban Specialisation[END_REF], implying in its turn the concentration of high value added functions in core regions and the specialisation of peripheral sites in routine tasks. Consequently, European economic integration should be regarded as part of the story, whilst the diffusion of 'unbounded' organisational forms may have helped forge the new inner-country economic geography. In fact, evidence of a general spreading of knowledge-intensive manufacturing industries was found in West Germany during the 1990s [START_REF] Suedekum | Concentration and specialization trends in Germany since reunification[END_REF], and in Italy throughout the 1970s and 1980s, particularly for transport equipment production [START_REF] Rombaldoni | Localizzazione delle imprese manifatturiere e specializzazione regionale in Italia: 1970-1990[END_REF]ZAZZARO, 1997, DE ROBERTIS, 2001). In Italy, the decentralisation of production tasks has continued in more recent years, because the economic crisis of the early 1990s forced Fiat to restructure its supply chain with a further relocation of routine tasks to South Italy.

A second interesting point is that, once the Single Market was almost completed, only a slight polarisation across national boundaries occurred, concomitant with the substantial fall in localisation in the internal geography of countries. Hence the drastic specialisation of European countries, implying the greater concentration of industries [START_REF] Krugman | Geography and Trade[END_REF], is far from being fully accomplished. One possible reason for the gap between the theory and the reality is the discrepancy between the assumptions of NEG models and the real European economic landscape. The conjecture of convergence by the EU to the US level of concentration was probably based on the assumption of increasing labour mobility within the European Single Market. 22 Yet, Europe and US continue to differ in terms of some

CONCLUSIONS AND FURTHER DEVELOPMENTS

The paper has investigated manufacturing location patterns in Europe during a period of trade integration. The decomposition methodology of entropy indices has allowed distinction between inner-country and cross-country localisation.

In contrast to the mixed empirical evidence provided by existing studies, the methodology adopted has identified a clear trend in European localisation which supports the idea that, in recent decades, substantial regional spreading has occurred simultaneously with less international polarisation. The paper has obtained robust results for EU-wide regional changes, providing compelling evidence on the regional de-agglomeration of manufacturing employment among regions within European countries. Instead, once the Internal Market had been completed, there began a polarisation between the supra-regional economies (i.e. countries and the macro areas defined by the North/South dichotomy).

The divergence between international patterns and domestic ones is not a contradictory finding if one considers that it has probably been driven by simultaneous dispersion and agglomeration forces acting at the different spatial scales. likely that peripheral countries have also benefited from the dismantling of trade barriers as they have gained better access to the market. However, it would be simplistic to conceive these changes as purely the outcome of the European Single Market, because regional policy for lagging regions may have played a key role.

It is usually considered that specialisation in knowledge-intensive industries is growthenhancing since innovation and technical progress are critical determinants of productivity improvements and international competitiveness. However, the increasingly uneven distribution of innovative activities across North and South Europe may, by itself, exacerbate regional disparities. Moreover, Cohesion Policy, for the period 2007-2013, included the goals of the Lisbon Strategy to foster regional growth and competitiveness through investment in innovation. These policy directions are highly important to attain higher efficiency but they may also deepen further the existing differences between Northern European industrial structure and the Mediterranean one. The former have more suitable specialisation patterns than the latter to seize the development opportunities provided by the European Regional Policy.

Although the paper does not claim to test the validity of the New Economic Geography's predictions, some final considerations may help bridge the gap between theory and evidence.

In the New Economic Geography framework a causal link is established between international integration and the location of economic activities. The empirical facts presented here show that localisation has followed an unexpected path contrary to the one suggested by the theory. This evidence raises several questions: has European economic integration not yet reached the level at which agglomeration economies should prevail? Do simultaneous overlapping changes reshape the European geography of industrial activities in a contrasting way? Are agglomeration economies within specific industries vanishing? These unresolved issues require further research. 2004) already showed that specialisation and concentration cannot diverge if relative measures are used. 2 Decreasing concentration was widespread across Spanish NUTS3 regions during the 1980s [START_REF] Paluzie | Regional integration and specialization patterns in Spain[END_REF], across Italian NUTS2 regions from the early 1970s to the late 1990s [START_REF] Rombaldoni | Localizzazione delle imprese manifatturiere e specializzazione regionale in Italia: 1970-1990[END_REF]DE ROBERTIS, 2001;[START_REF] Ciciotti | Capacità innovativa e sviluppo regionale: Alcune evidenze delle regioni italiane negli anni novanta[END_REF] and, more recently, also in Germany [START_REF] Suedekum | Concentration and specialization trends in Germany since reunification[END_REF]. 3 COMBES and OVERMAN (2004) pointed out that "the fact that Spanish regions did not change much with respect to one another does not mean that Spanish regions did not become more specialised relative to the rest of the EU". 4 See [START_REF] Bickenbach | Disproportionality measures of concentration, specialization, polarization[END_REF] for a classification of different polarisation, concentration and specialisation measures. 5 Overall localisation is conceptually and analytically composed of two economic phenomena: the specialisation of economies and the agglomeration of industries (see [START_REF] Cutrini | Using Entropy Measures to Disentangle Regional from National Localisation Patterns, tentatively accepted for Regional Science and Urban Economics DE ROBERTIS, G[END_REF]). 6 The model suggests that decreasing transport costs will lead to an increase in specialisation and a decrease in regional concentration. 7 Today, services make up the largest sector in most European economies and there are services that are of great importance for the distribution of regional income and welfare (e.g. financial services and R&D). Any full assessment of concentration and specialisation in Europe should include them. 8 The sectors manufacturing of leather and leather products (DC, division 19) and manufacture of coke, refined petroleum products and nuclear fuel (DF, division 23) have been excluded from the analysis because of the overwhelming missing and confidential data. 9 In this study I refer to the distinction between absolute and relative measures drawn by [START_REF] Bickenbach | Disproportionality measures of concentration, specialization, polarization[END_REF]. Therefore, measures based on the uniform reference are considered as absolute measures, while those based on a nonuniform reference are labelled as relative measures.

10 These forces may be related to intra-industry input-output linkages, labour-market pooling and industryspecific knowledge spillovers, but they may also indicate a high dependence on natural resources. 11 The two geographical components of the concentration index for each industry k can be easily derived by factor decomposition (see [START_REF] Cutrini | The Balassa Index Meets the Theil Index: a Decomposition Methodology for Location Studies[END_REF] for details on the formal decomposition of the localisation indices).

12 [START_REF] Brülhart | An account of geographic concentration patterns in Europe[END_REF] test for the significance of temporal changes of regional localisation by relying on a block-bootstrap, i.e. resampling observations from different countries separately.

13 I define intermediate spatial aggregation level as the level at which I disentangle the within-from the betweengroup component. Instead, the highest level of aggregation is the macroeconomic geographical benchmark (the set of European regions). 14 Throughout the present paper, 'Europe' refers to the 145 European regions taken together. 15 The sector includes chemicals, machinery and equipment n.e.c., electrical and optical equipment, transport equipment, furniture, recycling and manufacturing n.e.c. 16 AUDRETSCH and FELDMAN (1996) showed the different propensity of manufacturing industries to generate spatial knowledge spillovers. 17 On this reasoning, the functional specialisation of different localities is the aggregate outcome of a microeconomic change -induced by the decreased transportation and communication costs -in the firm's tradeoff between the benefits of vertical integration and the advantages of spreading the different functions across space. When spatial transaction costs (i.e. the cost of coordination and monitoring across fairly wide distances) decrease substantially, firms that used to perform managerial, R&D and production tasks under a single roof prefer to become multi-plant organizations. 18 From 1985 onwards, Greece was characterized by increasing specialisation in labour-intensive industries (food, textiles and wearing apparel) and in non-metallic mineral products, which is a manufacturing industry closely linked to the construction industry (see Table A8). 19 I refer to geographical dispersion in the latter two cases, since AIGINGER and PFAFFERMAYR ( 2004) and AIGINGER and DAVIES (2004) used absolute concentration measures and their results are not directly comparable with the present ones. Moreover, nominal value added is the activity indicator. 20 A crucial dispersion force in the models of [START_REF] Krugman | Trade policy and the Third World metropolis[END_REF] and PUGA (1999). 21 The model of [START_REF] Krugman | Trade policy and the Third World metropolis[END_REF] -inspired by the Mexican liberalisation programme -suggests that falling trade barriers may affect firm's location within each country. The fundamental idea is that, in a restrictive trade policy, forward and backward linkages foster the clustering of economic activity. As soon as protective c Results for the period 1980-95 are drawn from [START_REF] Brülhart | An account of geographic concentration patterns in Europe[END_REF]. http://mc.manuscriptcentral.com/cres Email: regional.studies@newcastle.ac.uk

Regional Studies

  during the first half of the 1990s. Although[START_REF] Brülhart | An account of geographic concentration patterns in Europe[END_REF] found generally mixed evidence for manufacturing industries, they obtained robust results for the EU-wide agglomeration of textile industry value added. More recently,[START_REF] Ezcurra R | The dynamics of industrial concentration in the regions of the European Union[END_REF] support the 'Krugman hypothesis', showing that, as soon as the European Single Act came into force, geographical concentration across European NUTS 2 regions increased in most manufacturing activities.

  7 Employment data are disaggregated by 12 manufacturing industries 8 according to NACE rev. 1 classification: food (DA), textiles (DB), wood (DD), paper (DE), chemicals (DG), rubber and plastic products (DH), other non-metallic mineral products (DI), basic metals and fabricated metal products (DJ), machinery and equipment n.e.c. (DK), electrical and optical equipment (DL), transport equipment (DM), manufacturing n.e.c. (DN). Since the results may be affected by the scale aggregation problem -which is an expression of the modifiable areal unit problem (MAUP) (ARBIA, 1989) -I assess overall localisation, varying the basic unit of analysis and the intermediate aggregation level to control for the alleged sensitivity of the methodology to scale aggregation and basic

  region j of country i relative to the respective country)

  specialisation of all the regions of country i relative to the (8) country's manufacturing structure)

  ) correspond to:L=L b +L w (18)The L-index and each single components are non-negative. Perfect regularity (

  (2000) already showed that, in 1990, the distribution of technological activity was highly concentrated in Europe due to substantial differences between southern and northern regions.

  the associated mixed trends in manufacturing concentration between countriesTable 2 ranks manufacturing industries according to their average values of relative concentration (reported in the third column) calculated on the basis of the 145 NUTS2 regions for the observation period.Textiles and wearing apparel emerge as an industry endowed with pronounced localization economies, for it exhibits the highest divergence from the spreading of overall manufacturing. Other resource-based industries, like wood production and non-metallic mineral products, rank among the most localised. Innovative industries, like chemicals and transport equipment, have intermediate levels of concentration or, like electrical and optical equipment and machinery, they are spreading even more similarly to total manufacturing.

  regarded as an expression of the catching-up which involved the whole national economy.

  specialisation in knowledge-intensive industries, such as chemicals, rubber and plastics products, metallurgy, electrical products, and the automobile industry.

  the national manufacturing structure and the national economy converged vis à vis Europe (bottom panel of Figure4).

  probably due to a combination of several forces which pushed towards internal de-agglomeration. Congestion costs 20 and the tertiarisation of metropolitan areas, together with the information technology revolution and advances in transportation infrastructure, may have driven the emerging trend. Moreover, falling trade barriers may have affected firms' locations, as suggested by the model of

  institutional and social features relevant for the agglomeration of economic activities: notably the low propensity of workers to migrate internationally, even though since 1985 the Shengen Treaty has established the free movement of people across national borders, and more 2007) with almost all the new member states as well. It is therefore also possible that scant cross-country polarisation has been the outcome of the low international mobility of workers among the European countries analysed.

  The emerging opposite pattern of change may be connected with advances in European integration because, between 1993 and 2001, localisation across countries slightly increased, as suggested by theoretical models. Increasing overall localisation patterns across countries are explained by the international agglomeration of textiles and wearing apparel and transport equipment. Similarly, it is accounted for by the divergence of the national manufacturing structures of European founding member states -Western Germany and Italy and, to a lesser extent, France, Luxembourg and Belgium -from that of Europe.The increasing polarisation across wider spatial scales during the period 1993-2001 may also be explained by the slight increase in specialisation in Northern Europe, and by the relative concentration of high-tech industries across the North/South divide. It is

  , the central place (usually the capital city) loses the advantage it had in a relatively closed economy, and firms, which now mainly sell to external markets, are more willing to migrate to peripheral regions, especially if relocation means better access to international market.

  relative Theil, bootstrap standard error in parentheses, based on 10,000 replications, positive changes over time are in bold

Fig. 1 .

 1 Fig. 1. Evolution of EU-wide localisation within and across countries,

  Table 1, a set of European regions belonging to different countries is used as the intermediate aggregation level (instead of the country). In this case, Northern Europe consists of all the regions of the following European countries: Belgium and Luxembourg, Finland, France, Western Germany, Netherlands, United Kingdom and some regions of Northern Italy, namely Piemonte, Valle D'Aosta, Liguria, Lombardia, Friuli Venezia Giulia. The rest of Italy, Greece, and Spain are labelled as Southern Europe.

Table

	F o r
	P
	e
	e r
	R e v
	i e
	w
	O n l
	is presented in y
	19

Table 2 ,

 2 last column). At the same time, employment data show

	F o r
	P
	e
	e r
	R e v
	i e
	w
	O n l
	y

  at least until 1992.

	F o r
	P
	e
	e r
	R e v
	i e
	w
	[TABLE 4 about here]	O n l
		y
	27

Table A1 .

 A1 Geographical coverage of the dataset

			Number of
	Country	Administrative partition	regions included
	Belgium	Provinces (NUTS2)	9
	Luxembourg		1
	Germany	Länder (NUTS1)	16
	Spain	Comunidades autónomas (NUTS2)	17
	Finland	Suuralueet (NUTS2)	3
	France	Régions (NUTS2)	22
	Greece	Development regions (NUTS2)	11
	Italy	Regioni (NUTS2)	19
	Netherland	Provincies (NUTS2)	12
	United Kingdom	Counties (NUTS2)	35
	Total		145
	Notes: Bruxelles (BE10), Vlaams Brabant (BE24) and Brabant Wallon (BE31) have been clustered as
	a single region; Ceuta y Melilla (ES63), Åland (FI2), 'Departments d'Autre Mar' (FR91, FR92, FR93,
	FR94), Voreio Aigaio (GR41) and Notio Aigaio (GR42), Trentino-Alto Adige (IT31) have been
	excluded. Regional partition of data for UK is according to NUTS 95 classification.

Table A2 .

 A2 Bootstrap results for localisation measures, absolute changes[1985][1986][1987][1988][1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001]

	Regional Studies	Page 36 of 56
	F o r P e e r w i aRS -0.062 0.023 b i T -0.069 0.025 Western Germany i aRS -0.016 0.020 w i aRS -0.035 0.012 b i T 0.018 0.012 Textiles k T 0.034 0.034 w k T -0.034 0.019 b k T 0.068 0.025 Wood k T -0.130 0.057 absolute changes by sub-period -2.73 -2.74 -0.83 -2.78 1.56 0.99 0.322 0.006 0.006 0.408 0.005 -1.81 0.071 2.72 0.007 -2.31 0.021 1985-1993 1993-2001 Obs. Boot. Obs. Boot. differ. Std. Err. z P>|z| differ. Std. Err. -i aRS -0.035 0.020 -1.71 0.088 -0.044 0.017 -2.63 0.009 z P>|z| w i aRS -0.030 0.014 -2.18 0.029 -0.008 0.007 b k T -0.080 0.039 -2.08 0.04 -0.013 0.025 -0.5 0.617 -1.17 0.243 b i k T -0.010 0.010 -1.09 0.28 -0.004 0.011 -0.33 0.74 T -0.005 0.014 -0.34 0.735 -0.036 0.015 -2.48 0.013 i aRS -0.011 0.015 -0.72 0.470 -0.068 0.038 w k T -0.004 0.008 -0.5 0.62 0.009 0.007 1.23 0.218 -1.78 0.076 w i aRS -0.008 0.003 -2.72 0.006 -0.001 0.003 -0.59 0.558 b k T -0.007 0.007 -0.94 0.35 -0.013 0.008 -1.55 0.12 0.118 Spain i aRS -0.078 0.029 -2.68 0.007 w i aRS -0.038 0.015 -2.45 0.014 b i T -0.041 0.022 -1.86 0.064 Finland i aRS -0.079 0.052 -1.52 0.128 w i aRS -0.009 0.005 -2.1 0.036 b i T -0.069 0.051 -1.35 w k T -0.037 0.021 -1.76 0.078 b k T -0.093 0.047 -1.97 0.049 Paper k T -0.014 0.018 -0.8 0.424 w k T 0.005 0.012 0.42 0.674 b k T -0.019 0.012 -1.59 0.112 L -0.028 0.005 -5.7 0 0.0 14 0.006 -2.16 b i T -0.003 0.016 -0.21 0.837 -0.066 0.038 -1.75 0.080 k T -0.008 0.011 -0.72 0.47 -0.012 0.015 -0.82 0.41 0.031 w L -0.019 0.003 -5.59 0 -0.016 0.004 -4.15 0.000 b L -0.009 0.003 -2.84 0.01 0.002 0.004 0.47 0.639 i aRS 0.004 0.015 0.24 0.810 -0.014 0.006 -2.33 0.020 w k T -0.005 0.010 -0.52 0.6 -0.024 0.009 -2.52 0.012 w i aRS 0.015 0.011 1.32 0.187 -0.024 0.005 -4.8 0.000 b i b k T -0.003 0.007 -0.4 0.69 0.012 0.012 0.99 0.321 T -0.011 0.007 -1.59 0.112 0.009 0.006 1.51 0.132 i aRS -0.018 0.034 -0.52 0.600 -0.107 0.040 Rubber and plastic products Spain Paper Finland Chemicals France Greece k T -0.037 0.016 -2.27 0.02 -0.019 0.009 -2.01 0.044 -2.69 0.007 w i aRS 0.035 0.013 2.65 0.008 -0.018 0.013 -1.36 0.173 w k T -0.045 0.016 -2.89 0 -0.016 0.008 -1.91 0.056 0.178 France i aRS -0.011 0.016 -0.66 0.512 w i aRS -0.009 0.011 -0.79 0.43 b i T -0.002 0.010 -0.19 0.846 Greece i aRS -0.125 0.035 -3.63 0 w i aRS 0.017 0.015 1.12 0.265 b i T -0.142 0.040 -3.53 0 Italy i aRS -0.015 0.021 -0.71 0.476 w i aRS -0.039 0.017 -2.31 0.021 b i T 0.024 0.012 2.08 0.037 Netherlands i aRS -0.094 0.023 -4.11 0 w i aRS -0.022 0.010 -2.23 0.026 b i T -0.072 0.022 -3.25 0.001 United Kingdom i aRS -0.061 0.015 -3.96 0 w i aRS -0.044 0.012 -3.66 F o r P e Chemicals k T -0.020 0.018 -1.12 0.264 w k T -0.029 0.016 -1.79 0.074 b k T 0.009 0.012 0.75 0.453 Rubber and plastic products k T -0.056 0.024 -2.35 0.019 w k T -0.061 0.022 -2.72 0.006 b k T 0.005 0.005 0.97 0.331 Other non-metallic mineral products k T -0.032 0.028 -1.17 0.241 w k F o r F o r b i T -0.053 0.039 -1.37 0.171 -0.089 0.046 b k T 0.008 0.005 1.82 0.07 -0.003 0.004 -0.88 0.379 -1.92 0.055 Italy i aRS -0.001 0.012 -0.12 0.906 -0.013 0.017 -0.77 0.443 Other non-metallic mineral products k T -0.020 0.014 -1.42 0.16 -0.012 0.016 -0.75 0.454 w i aRS -0.009 0.009 -1.04 0.298 -0.030 0.013 -2.29 0.022 b i T 0.008 0.007 1.05 0.296 0.017 0.011 w k T -0.025 0.010 -2.6 0.01 -0.020 0.013 -1.6 0.11 1.45 0.148 Netherlands i aRS -0.071 0.022 -3.23 0.001 -0.023 0.013 -1.73 0.083 w i aRS -0.013 0.008 -1.64 0.101 -0.009 0.007 -1.42 0.157 b i T -0.058 0.021 -2.72 0.006 -0.014 0.009 -1.51 0.131 United Kingdom i aRS -0.017 0.010 -1.74 0.083 -0.045 0.010 -4.56 0.000 w i aRS -0.012 0.009 -1.44 0.150 -0.031 0.007 -4.35 0.000 b k T 0.005 0.009 0.58 0.56 0.008 0.009 0.88 0.381 F Basic metals and fabricated metal products k T -0.057 0.010 -5.69 0 -0.026 0.008 -3.5 0 w k T -0.041 0.010 -4.18 0 -0.029 0.006 -4.9 0 o r b k T -0.017 0.010 -1.62 0.11 0.002 0.006 0.39 0.7 T -0.045 0.020 -2.31 0.021 b k T 0.013 0.015 0.82 0.411 Basic metals and fabricated metal products k T -0.083 0.014 -6.15 0 w k T -0.069 0.012 -5.85 0 P e P e b i T -0.004 0.005 -0.81 0.418 -0.013 0.006 -2.34 0.019 Machinery and equipment nec k T -0.020 0.011 -1.88 0.06 -0.004 0.010 -0.42 0.674 w k T -0.005 0.006 -0.95 0.34 -0.003 0.006 -0.51 0.61 P b k T -0.015 0.008 -1.88 0.06 -0.001 0.007 -0.22 0.826 Electrical and optical k T -0.019 0.007 -2.64 0.01 -0.027 0.008 -3.21 0.001 e 0 b i T -0.018 0.008 -2.3 0.021 e r b k T -0.014 0.011 -1.26 0.207 Machinery and equipment nec k T -0.025 0.016 -1.59 0.112 w k T -0.008 0.008 -1.06 e r e r equipment w k T -0.023 0.006 -3.88 0 -0.019 0.006 -3.24 0.001 e b k T 0.004 0.004 0.99 0.32 -0.008 0.007 -1.15 0.25 r Transport equipment k T -0.016 0.009 -1.71 0.09 0.037 0.020 1.82 0.069 0.29 1985-2001 Obs. Boot. differ. Std. Err. z P>|z| L -0.042 0.009 -4.52 0 w L -0.035 0.005 -6.66 0 b L -0.007 0.006 -1.08 0.01 Table A3. Bootstrap results for specialisation measures, absolute changes 1985-2001 1985-2001 Obs. Boot. differ. Std. Err. z P>|z| Belgium and Luxembourg i aRS -0.131 0.030 -4.37 0 R e v i e w O n l y Table A4. Bootstrap results for concentration measures, absolute changes 1985-2001 1985-2001 Obs. Boot. differ. Std. Err. z P>|z| Food k T -0.054 0.016 -3.41 w k T -0.018 0.008 -2.37 0.018 b k T -0.036 0.015 -2.38 0.017 y Table A5. Bootstrap results for localisation measures, y Western Germany i aRS -0.031 0.014 -2.28 0.023 0.015 0.009 1.64 0.102 w i aRS -0.027 0.010 -2.85 0.004 -0.008 0.005 -1.58 0.114 b i T -0.004 0.006 -0.7 0.482 0.022 0.009 2.4 0.016 y Wood k T -0.093 0.042 -2.2 0.03 -0.037 0.031 -1.19 0.235 w k -0.013 0.019 -0.69 0.49 -0.024 0.013 -1.82 0.069 T y 0.001 R e v i e w O n l b k T -0.017 0.011 -1.55 0.121 Electrical and optical k T -0.046 0.011 -4.18 0 equipment w k T -0.042 0.010 -4.41 0 b k T -0.004 0.008 -0.53 0.595 Transport equipment k T 0.021 0.023 0.95 0.344 k T -0.005 0.010 -0.47 0.635 b k T 0.026 0.023 1.13 0.256 Manufacturing nec k T -0.125 0.036 -3.46 0.001 w k T -0.041 0.018 -2.25 0.025 b k T -0.084 0.028 -2.98 O n l 1985-1993 1993-2001 Obs. Boot. Obs. Boot. differ. Std. Err. z P>|z| differ. Std. Err. z P>|z| Belgium and Luxembourg i aRS -0.111 0.025 -4.43 0.000 -0.021 0.017 -1.25 0.212 w i aRS -0.041 0.021 -1.93 0.053 -0.022 0.012 -1.77 0.077 b i T -0.070 0.024 -2.92 0.003 0.001 0.009 0.11 0.914 O n l w k -0.007 0.005 -1.2 0.23 -0.012 0.006 -1.93 0.053 b k T -0.012 0.007 -1.81 0.07 -0.024 0.012 -2.07 0.039 Textiles k T -0.002 0.017 -0.14 0.89 0.036 0.029 1.25 0.213 w k T -0.019 0.013 -1.44 0.15 -0.015 0.012 -1.22 0.223 b k T 0.017 0.013 1.26 0.21 0.051 0.021 2.43 0.015 l n O T 0.003 i e w Table A6. Bootstrap results for specialisation measures, absolute changes by sub-period i e w Table A7. Bootstrap results for concentration measures, absolute changes by sub-period 1985-1993 Obs. Boot. Obs. Boot. differ. Std. Err. z P>|z| differ. Std. Err. z P>|z| Food k T -0.019 0.008 -2.32 0.02 -0.036 0.011 -3.21 0.001 w 1993-2001 e i w R e v R e v w k T -0.002 0.008 -0.24 0.81 -0.003 0.007 -0.41 0.684 b k T -0.014 0.007 -2.11 0.04 0.040 0.022 1.85 0.064 R Manufacturing nec k T -0.054 0.017 -3.23 0 -0.071 0.027 -2.65 0.008 e w k T -0.016 0.013 -1.27 0.2 -0.025 0.012 -2.05 0.04 v b k T -0.038 0.015 -2.55 0.01 -0.047 0.021 -2.2 0.027
	37 38 39 40 41

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Table A8 .

 A8 Industry location quotients, by country

	Basic metals and metal products 1.02 WIFO (1999) Specialisation and (geographic) concentration of European manufacturing. 0.92 1.02 1.02 0.93 1.19
	Machinery 1.39 Electrical and optical Working paper 1, EC Enterprise Directorate-General, Brussels. 1.38 0.67 0.74 1.00	1.13
	equipment 1.21	1.22		1.05	1.09	0.90	0.84
	Transport equipment 1.10	1.48		1.21	0.98	0.93	0.61
	Manufacturing nec 1.22	0.69		1.07	0.95	1.08	1.29
	United Kingdom	Belgium-Lux.	Netherlands
	1985	2001		1985	2001	1985	2001
	Food 1.26	0.98		1.57	1.27	1.36	1.42
	Textiles 1.04	1.15		0.94	1.07	0.43	0.44
	Wood 1.52	0.80		2.41	0.82	0.55	0.90
	Paper 1.39	1.37		1.37	0.99	1.41	1.70
	Chemicals 0.85	1.00		1.49	1.60	1.25	1.24
	Rubber and plastic prod. 1.01	1.15		0.98	0.88	0.62	0.75
	No-metal products 0.95	0.80		1.40	1.24	0.65	0.83
	Basic metals and metal
	F o r products 0.82 Machinery 0.98 Electrical and optical equipment 1.04 Transport equipment 1.02 Manufacturing nec 0.27 F o r F o r F o r F o r F o r	0.85 0.83 1.09 0.97 1.02		0.99 0.76 0.87 0.25 0.45	1.13 0.59 0.69 0.91 0.93	2.02 0.77 0.83 0.52 0.62	0.98 0.91 0.94 0.62 1.03
	Spain		Greece		Finland
	P 1985 Food 1.71 Textiles 1.25 P P P P P	2001 1.23 1.31		1985 2.00 2.97	2001 1.92 2.26	1985 1.23 0.83	2001 0.81 0.45
	e e r Wood 1.58 1.57 Paper 0.85 0.97 Chemicals 0.79 0.84 e e e e e Rubber and plastic prod. 0.97 0.86 No-metal products 1.38 1.64 Basic metals and metal e r e r e r e r e r	0.80 0.91 0.80 0.79 1.48	0.77 1.30 0.99 0.79 1.55	2.97 3.08 0.59 0.86 1.06	2.16 2.08 0.70 0.91 0.88
	products 1.07	1.09		0.68	0.74	0.55	0.76
	Machinery 0.47 Electrical and optical equipment 0.45 Transport equipment 0.85 Manufacturing nec 1.88	R e v 0.65 0.17 0.45 0.57 0.32 0.37 0.83 0.54 0.56 1.34 0.69 0.69 R e v R e v R e v R e v R e v	1.15 0.60 0.73 0.94	1.24 1.25 0.64 0.78
					i e i e i e i e i e i e
	TABLES in the text	w w w w w w
	Germany 1985 2001 Food 0.61 0.75 Textiles 0.63 0.39 Wood 0.95 0.64 Paper 0.70 0.82 Chemicals 1.11 1.17		France 1985 2001 1.00 1.33 1.11 0.78 0.23 0.89 1.05 1.00 1.01 1.03	Italy 1985 2001 0.78 0.79 1.58 1.78 0.60 1.42 0.79 0.68 0.94 0.71 O n l O n l O n l O n l O n l O n l
	Rubber and plastic prod. 1.01 No-metal products 0.85	1.07 0.84		1.09 0.88	1.12 0.80	1.02 1.31	0.83 1.18	y y y y y y
		42 47

Table 1 .

 1 Robustness of results to the choice of the basic geographic unit of analysis, spatial aggregation, sectoral aggregation, same geographical benchmark

Table 2 .

 2 Relative concentration of manufacturing industries across EU regions

			Absolute change
		Average
	Taxonomy a	1985-2001	1985-2001 b	1980-95 c

Notes: a OECD technology classification: L= low-tech, M-L= medium to low-tech; M-H= medium to high-tech b */**/*** denotes rejection of the null hypothesis that Tk=0 at the 90%, 95% or 99% significance level.

Table 3 .

 3 Specialisation indices and components, 1985-2001

	(average values and differences)

Table 4 .

 4 Relative specialisation -A comparison of the pre-and post-Single Market

periods pre-Single Market post-Single Market Page 50 of 56 http

  Notes: Absolute changes for pre-Single Market refers to the period 1985-1993 while for post-Single Market the period considered is1993-2001, positive changes are in bold

			Regional Studies
	i T	b	aRS	w i	i T	b	aRS	w i
	Belgium and Luxembourg -0.070 ** -0.041		0.001	-0.022
	Finland -0.003	-0.008 **	-0.066	-0.001
	France -0.011	0.015		0.009	-0.024 **
	Greece -0.053	0.035 **	-0.089	* -0.018
	Italy 0.008	-0.009		0.017	-0.030	*
	Netherlands -0.058 ** -0.013		-0.014	-0.009
	Spain -0.005	-0.030 *	-0.036	* -0.008
	United Kingdom -0.004	-0.012		-0.013 ** -0.031 **
	Western Germany -0.004 F r o Overall localisation -0.009 ** -0.019 *** -0.027 **	0.022 0.002	* -0.008 -0.016 ***
	P
	e
	e r
			R e v
					i e
						w
							O n l
								y
		51

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Table 5 .

 5 Relative concentration -A comparison of the pre-and post-Single Market Notes: Absolute changes for pre-Single Market refers to the period 1985-1993 while for post-Single Market the period considered is 1993-2001, positive changes are in bold

	periods
	pre-Single Market	post-Single Market
	T	b k	T	w k	T	b k	T	w k

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FIGURES in the text

http://mc.manuscriptcentral.com/cres Email: regional.studies@newcastle.ac.ukRegional Studies

In NEG models (e.g. KRUGMAN, 1991[START_REF] Puga | The Rise and Fall of Regional Inequalities[END_REF]) labour mobility has an important role in sustaining agglomerations; in a symmetric way, labour immobility is an important dispersion force.

I thank the participants of the seminars at the University of Insubria, at the Polithecnic University of Marche and at the Kiel Institute for the World Economy for their suggestions on earlier drafts. I also thank the


MD Christiane Richter-Ehrenstein

Juliane Arndt

Ann-Christin Buckendahl

Jan Eucker

Wilko Weichert

Atsuko Kasajima

Achim Schneider

Aurelia Noske

Solid neuroendocrine carcinomas of the breast. Metastases or primary tumors?

Keywords: breast cancer, neuroendocrine tumors, GCDFP-15, mammaglobin

Purpose: Neuroendocrine breast carcinomas are rare but may represent either metastatic or primary lesions. So far, clinical and preoperative histopathological examinations do not distinguish properly between a primary or metastatic breast tumor.

Due to any possible consequences following an appropriate treatment, markers which may be helpful for such a distinguishment are needed.

Methods:

We addressed this study in order to evaluate the immunohistochemical expression of GCDFP-15 and mammaglobin in a subset of pure neuroendocrine breast carcinomas (n=9) and compared the expression profile with a cohort of non-mammary neuroendocrine tumors (n=99).

Results:

We observed in our study that solid neuroendocrine breast carcinomas are characterized by the expression of estrogen and progesterone receptors as well as GCDFP-15 and/or mammaglobin. GCDFP-15 was expressed in 6 out of 9 cases, mammaglobin was positive in 4 out of 9 tumors. In contrast, neuroendocrine tumors of the non-mammary cohort expressed neither GCDFP-15 nor mammaglobin.

Conclusions:

We conclude that mammaglobin and GCDFP-15 as markers of epithelial breast origin may work as a new and reliable diagnostic tool to distinguish primary endocrine tumors of the breast from a metastatic neuroendocrine disease. This is of utmost importance, especially for surgical management.

Introduction

Neuroendocrine tumors of the breast are rare and may represent either metastatic or primary lesions. They derive from neuroendocrine cells, which are present throughout the body, and arise most commonly in the bronchopulmonary system and gastrointestinal tract (small intestine > appendix > rectum). In 2003, the World Health Organization (WHO) classification of neuroendocrine tumors of the breast established the immunohistochemical expression of one or more markers (neuron specific enolase, chromogranin A, synaptophysin) in at least 50% of the tumor cells [START_REF] Who | World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Breast and Female Genital Organs[END_REF]. The existence of primary breast neuroendocrine tumors now accepted accounts for less than 2% of all primary breast cancers [START_REF] Who | World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Breast and Female Genital Organs[END_REF][START_REF] Lopez-Bonet | Solid neuroendocrine breast carcinomas: incidence, clinico-pathological features and immunohistochemical profiling[END_REF]. The recognition of the presumptive origin of the neuroendocrine tumor in the breast is extremely important due to the fact that different types of clinical management are present to distinguish between a primary and a metastatic tumor. Mammography is an important diagnostic tool for the evaluation of any breast mass. Neuroendocrine tumors often appear as clear-cut circumscribed masses and are sometimes mistaken for fibroadenomas or cysts [START_REF] Harrist | Breast metastasis: un unusual manifestation of a malignant carcinoid tumor[END_REF]. A diagnostic distinction between primary or metastatic neuroendocrine lesion by mammography or ultrasound is almost impossible. In histology, the neuroendocrine tumors typically consist of a uniform cell population with abundant eosinophilic cytoplasm and nuclei with stippled ("salt and pepper") chromatin. Conventional histomorphological evaluation does not allow an exact distinction between primary and metastatic lesions. Staining for estrogen receptor is not helpful because many studies have reported positive staining in both mammary and extramammary neuroendocrine tumors [START_REF] Cubilla | Primary carcinoid of the breast[END_REF][START_REF] Ni | Fine needle aspiration of mammary carcinoma with features of a carcinoid tumor[END_REF][START_REF] Rindi | Endocrine Tumors of the gut and pancreas tumor biology and classification[END_REF][START_REF] Sica | Immunohistochemical Expression of Estrogen and Progesterone Receptors in Primary Pulmonary Neuroendocrine Tumors[END_REF]. Clinically, some patients may present symptoms of a carcinoid syndrome. However, its absence does not rule out the possibility of its having an extramammary origin [START_REF] Kaltsas | Carcinoid tumours presenting as breast cancer; the utility of radionuclide imaging with 123 I-MIBG and 111 In-DTPA pentetrotide[END_REF][START_REF] Palma | Ileal carcinoid metastatic of the breast[END_REF][START_REF] Fishman | Solitary breast metastasis as first manifestation of ovarian carcinoid tumor[END_REF].

The purpose of this study was to evaluate a new diagnostic tool to distinguish primary neuroendocrine tumors of the breast from a metastatic disease of other sites. To our knowledge, this is the first time that Gross cystic disease fluid protein (GCDFP-15) and mammaglobin have been used as acceptable immunohistochemical markers for epithelia of breast origin [START_REF] Fritzsche | Co-expression and prognostic value of gross cystic disease fluid protein 15 and mammaglobin in primary breast cancer[END_REF]. Furthermore, we compared these neuroendocrine breast carcinomas with a cohort of non-mammary neuroendocrine tumors.

Material and methods

Patients

Our study included 9 female patients with breast cancer who underwent surgical treatment at the Department of Gynecology, Breast unit, University Hospital Charité Berlin, between 2003 and 2009. Formalin-fixed paraffin-embedded tissue from breast cancer patients with neuroendocrine differentiation were obtained from patients treated at the interdisciplinary breast unit of the Charité Berlin with institutional review board approval.

Histopathological diagnosis was performed at the Institute of Pathology, Charité University Hospital Berlin. Histological typing of the tumors was carried out according to the criteria of WHO 2003. Tumor stage was determined according to the guidelines of the UICC. Histological grading was performed following the criteria of Elston and Ellis [START_REF] Elston | Pathological prognostic factors in breast cancer: I. The value of histologic grade in breast cancer: experience from a large study with long-term follow-up[END_REF]. Data of the hormone receptor and Her2 status, proliferative fraction (MIB-1) as well as immunohistochemical expression of neuroendocrine markers were taken from the archival pathology reports and re-evaluated.

To compare expression profiles of neuroendocrine tumors derived from the breast and extra-mammary sites, we included in this study a cohort of 99 patients with gastroenteropancreatic tumors of the foregut (n=47) and midgut (n=52), who received surgical treatment in the Charité University Hospital between 1983 and 2007. In 70 cases, tissue specimens were from the primary lesion as well as from nodal (n=10) and distant metastases (n=19). Neuroendocrine tumors were staged and graded according to the novel consensus proposal for gastroenteropancreatic neuroendocrine tumors [START_REF] Rindi | TNM staging of foregut (neuro) endocrine tumors: a consensus proposal including a grading system[END_REF].

Immunohistochemistry

Immunohistochemical analysis was performed on paraffin sections of the archival tissue blocks. For the non-mammary tumor collective, sections of tissue microarrays were used as described by Kasajima A et al. (publication submitted). The staining procedure was carried out using Discovery XT autostainer (Ventana, Tuscon, AZ, USA) according to the manufacturer's instructions. We used monoclonal mouse antibodies against GCDFP -15 (D6, Covance, New Jersey, USA), mammaglobin (clone 304-1A5, BioPrime, USA), and TTF1 (8G7G3/1, Zytomed, Germany). Evaluation of immunohistochemical staining was done by two pathologists independently of each other (AB, AN).

Results

We retrospectively evaluated 9 invasive breast carcinomas with pure neuroendocrine differentiation defined by expression of at least one neuroendocrine marker in more than 50% of the tumor cells. The mean age of these patients was 64 years (range 49-82 years.). The tumor size varied from 0.6 to 5.0 cm in diameter whereby one case involved a bilateral tumor.

Histopathological evaluation revealed predominantly a solid growth pattern with features of an infiltrating ductal carcinoma, and in only one case an invasive lobular carcinoma.

Tumor cells were sometimes characterized by a granular eosinophilic cytoplasm, round and relatively monomorphic nuclei with "salt and pepper" chromatin. Small cell features were not found. Neuroendocrine differentiation was then confirmed by immunohistochemical staining by at least one neuroendocrine marker. In addition, in three cases an accompanying situ component was observed. All breast carcinomas except one expressed estrogen and/or progesterone receptors at generally high levels.

Her2/neu overexpression was found in only one carcinoma (1/9) and proliferative activity (MIB1) ranged from 1-40% (mean 12%). Diffuse expression of synaptophysin was observed in nearly all tumors (8/9), while chromogranin A was found in 6 out of 9 tumors. To differentiate between a primary solid neuroendocrine tumor of the breast and a metastatic neuroendocrine tumor of another site, we investigated the expression of GCDFP-15 and mammaglobin as markers of breast epithelium origin. GCDFP-15 was expressed in 6 out of 9 cases. In contrast, mammaglobin was positive in 4 out of 9 tumors. GCDFP-15 was always seen in combination with mammaglobin.

Immunohistochemical findings are summarized in Table 1. Figure 1 shows the immunohistochemical expression of GCDFP-15 and mammaglobin in a neuroendocrine breast carcinoma. In three patients, both markers were negative. In two of these patients, no visible primary tumor was shown at other sites on the CT scan. One of these patients (No. 6) died for an unknown reason last year. One woman (No. 9)which had initially been presented with bilateral breast cancerwas diagnosed as having a solid neuroendocrine carcinoma. Extensive staging revealed a neuroendocrine tumor of the midgut with multiple metastases (heart, lymph nodes, and breast).

In addition, we analyzed the expression of TTF-1 (thyroid transcription factor 1) which is known to be positive in the majority of primary adenocarcinomas and small cell neuroendocrine carcinomas of the lung. We did not observe any expression in the breast tumors. With regard to the comparison group of non-mammary neuroendocrine tumors, the mean age of patients with foregut tumors was 53 years, and with midgut tumors 58.2 years at the time of diagnosis. 51 patients (51.5%) were male; there was no difference observed in gender distribution between tumor locations.

We did not find any expression of GCDFP-15, mammaglobin, or TTF-1 in immunohistochemical evaluation of non-mammary neuroendocrine tumors but all expressed synaptophysin and chromogranin A.

Discussion

Neuroendocrine tumors of the breast are rare and may occur as metastases from a known neuroendocrine tumor, and may be considered as the first presentation of an occult metastatic neuroendocrine tumor, or as a primary breast tumor.

Our study demonstrates that solid neuroendocrine breast carcinomas are characterized by the expression of estrogen and progesterone receptors as well as GCDFP-15 and/or mammaglobin. In contrast, neuroendocrine tumors of the non-mammary cohort expressed neither GCDFP-15 nor mammaglobin.

Our data clearly support the use of these two markers: mammaglobin and GCDFP-15.

Recently, Fritzsche et al. showed that in 91.8% of breast cancer cases (n=165) at least one of these two markers is expressed. They demonstrated a strong correlation of GCDFP-15 and mammaglobin and showed that only very few primary breast cancers are completely negative for both markers [START_REF] Fritzsche | Co-expression and prognostic value of gross cystic disease fluid protein 15 and mammaglobin in primary breast cancer[END_REF]. Sapino et al. first described the use of GCDFP-15 for neuroendocrine breast tumors in 2001 [START_REF] Sapino | Expression of Apocrine Differentiation Markers in Neuroendocrine Breast Carcinomas of Aged Women[END_REF]. In our study population of neuroendocrine breast cancers, 6 out of 9 tumors expressed one or both markers. One case in our series (No. 9) is of clear metastatic origin, in two cases there was good reason to suspect tumors having a primary mammary origin (Nos. 3 and6). In our study, all neuroendocrine breast carcinomas did express hormone receptors, and this is in line with other reports in the literature [START_REF] Lopez-Bonet | Solid neuroendocrine breast carcinomas: incidence, clinico-pathological features and immunohistochemical profiling[END_REF][START_REF] Monsunjac | Primary small bowel carcinoid tumor with bilateral breast metastases. Report of 2 Cases with different clinic al presentations[END_REF][START_REF] David | Diffuse neuroendocrine differentiation in a morphologically composite mammary infiltrating ductal carcinoma: a case report and review of the literature[END_REF]. Lopez-Bonet et al. reported in 7 out of 7 cases of solid neuroendocrine breast carcinomas estrogen und progesterone receptor positivity [START_REF] Lopez-Bonet | Solid neuroendocrine breast carcinomas: incidence, clinico-pathological features and immunohistochemical profiling[END_REF]. However, the receptor status alone is not helpful to differentiate between a primary or metastatic breast tumor. Previous reports have shown that endocrine tumors of other origin may also express hormone receptors. Sica et al. showed in a cohort of 42 neuroendocrine tumors of the lung 17 tumors with immunoreactivity against estrogen and in 70% of those cases, a positive progesterone staining [START_REF] Sica | Immunohistochemical Expression of Estrogen and Progesterone Receptors in Primary Pulmonary Neuroendocrine Tumors[END_REF]. There are also unpublished data of ours confirming these results to our non-mammary cohort.

In view of the existing literature, most papers present case reports [START_REF] Cubilla | Primary carcinoid of the breast[END_REF][START_REF] Ni | Fine needle aspiration of mammary carcinoma with features of a carcinoid tumor[END_REF][START_REF] Hartgrink | A series of carcinoid tumours of the breast[END_REF][START_REF] Taxy | Carcinoid" tumor of the breast[END_REF][START_REF] Fisher | Solid and mucinous varieties of so-called mammary carcinoid tumors[END_REF]. In the review by Upalakalin et al. [START_REF] Upalakalin | Carcinoid tumors in the breast[END_REF], the authors report on 59 cases of neuroendocrine tumors in the literature. Thirty-eight of these were primary neuroendocrine tumors of the breast, 9 were presented with breast metastases for the first time as a neuroendocrine tumor, and 15 patients were presented with a history of neuroendocrine tumor prior to breast metastasis. This underlines the importance of a thorough work-up for these patients.

The diagnosis in the cases reported was made preoperatively or postoperatively, but mostly following surgical intervention including mastectomy. There appear to be no distinctive mammographic or ultrasound characteristics of invasive neuroendocrine tumors of the breast [START_REF] Adams | Radiologic characteristics and management of screen-detected metastastic carcinoid tumor of the breast: a case report[END_REF]. Therefore, prior to surgical intervention, the terms "primary carcinoma" and "metastasis" have to be differentiated. Patients' clinical history and preoperative pathological diagnosis by needle core biopsies must be reviewed carefully.

If the tumor morphology in the core biopsy is suspected to be due to neuroendocrine differentiation, further immunohistochemical stainings should be performed. Moreover, it is important to mention in the pathology report whether it is a neuroendocrine carcinoma with solid growth or a small cell carcinoma. Expression of GCDFP-15, mammaglobin and lack of TTF-1 as well as presence of an in-situ component support an origin of the breast. This is because bilateral tumor masses, lacking hormone receptors and GCDFP-15 as well as mammaglobin may favor a metastatic lesion. Notably, in very rare cases a mammography or biopsy can provoke a carcinoid crisis [START_REF] Ozgen | Carcinoid crisis provoked by mammographic compression of metastatic carcinoid tumour of the breast[END_REF].

There is a paucity of clear recommendations regarding surgical management of these rare tumors. It appears that patients with primary neuroendocrine carcinomas of the breast should be treated in a similar fashion to invasive ductal breast cancer which is appropriate for the size and stage of the lesion [START_REF] Jablon | Carcinoid tumor of the breast: treatment with breast conservation in three patients[END_REF][START_REF] Makretsov | Tissue microarray analysis of neuroendocrine differentiation and ist prognostic significance in breast cancer[END_REF]. Patients with neuroendocrine metastatic disease should undergo lumpectomy for debulking. Axillary node dissections are not needed, and mastectomies must be avoided. Fishman et al. reviewed that in 8 out of 13 patients with metastatic neuroendocrine tumors to the breast; these patients were initially considered to have primary breast carcinomas and were treated with mastectomy [START_REF] Fishman | Solitary breast metastasis as first manifestation of ovarian carcinoid tumor[END_REF].

Neuroendocrine tumors from the midgut cause functional symptoms due to the secretion of various peptides and hormones and most notably 5-hydroxytryptamine (5-HT) or serotonin. Serum measurements are non-specific for diagnosis and follow-up due to large individual variations [START_REF] O'toole | ENETS Consensus Guidelines for the Standards of Care in Neuroendocrine Tumors: Biochemical Markers[END_REF].

Patients with metastases of the breast are, on the average, 10 years younger than patients with primary neuroendocrine tumors of the breast [START_REF] Upalakalin | Carcinoid tumors in the breast[END_REF]. Overall survival or disease-free survival is slightly better among patients having primary tumors with neuroendocrine differentiation when compared to patients with other breast tumors [START_REF] Makretsov | Tissue microarray analysis of neuroendocrine differentiation and ist prognostic significance in breast cancer[END_REF].

Antihormonal therapy is also the standard adjuvant therapy in neuroendocrine breast cancers. In the past decade tremendous improvement in the diagnostic work-up of patients with neuroendocrine tumors was made by using new scintigraphy tools and PET-CT scans with different tracers which have to be used in order to discriminate a metastatic from a primary disease.

In conclusion, mammaglobin and GCDFP-15 as markers of epithelial breast origin may work as a new reliable diagnostic tool to distinguish endocrine tumors of the breast from a metastatic neuroendocrine disease. This is of utmost importance especially for surgical management. Taking into consideration the fact that this is one of the first descriptions of using mammaglobin and GCDFP-15 as markers in this context, further evaluation is warranted.

Figure 1 Solid

 1 Figure 1

Table 1

 1 Immunohistochemical expression profiles of neuroendocrine breast carcinomas

	Patient No.	Estrogen receptor	Progesterone receptor	Her2/ neu	MIB 1	Synapto-physin	Chromo-granin A	Mamma-globin	GCDFP-15	TTF-1
	1	100 %	100%	0	5-8%	pos	pos	pos	pos	x
	2	80%	80%	0	15-20%	pos	pos	pos	neg	neg
	3	90%	90%	3+	40%	pos	neg	neg	neg	neg
	4	80%	10%	0	1-2%	neg	pos	pos	pos	neg
	5	80%	60%	0	x	pos	pos	pos	pos	neg
	6	80%	50%	1+	x	pos	pos	neg	neg	neg
	7	80%	80%	0	5%	pos	neg	pos	pos	neg
	8	80%	0%	0	5%	pos	neg	pos	neg	neg
	9	0%	0%	0	x	pos	pos	neg	neg	neg

Acknowledgment

The authors of this work have not received any funding or financial support.