Lise Bourdeau-Lepage
email: lbourd@u-bourgogne.fr

Keywords: Economic integration, Central and Eastern European Countries (CEEC), Employment, Europe, High-order services, Region. JEL Classification: R12, R23, P29, P59

INTRODUCTION

The demise of communism, the ensuing opening up of the Central and Eastern European Countries (CEECs) and the liberalization of trade triggered the ongoing process of integration with the European Union (EU) and indeed with the world economy. Integration is a polysemic concept with political, institutional, social and economic facets. Here, its economic aspects alone are considered. Economic integration is generally taken to mean decreasing transaction costs. In the EU, economic integration is understood as trade liberalization [START_REF] Baldwin | The Eastern Enlargement of the European Union[END_REF], common trade policy, the single market, social and regional regulations, mutual recognition, harmonization, standardization and competition policy. In this paper integration is defined as a process by which interactions, especially complex, high-level interactions, develop among countries, regions and cities. The concept of integration is related to the formation of networks. The CEECs will be successfully integrated in an enlarged EU if they come to play a significant part in the post-industrial economy.

Either of two scenarios might occur. The first relates to complementarity; the CEECs might become EU "satellite countries" with EU incumbents relocating their labor intensive activities in the CEECs, where labor costs are lower. Under this scenario, the CEECs are not strategic decision-makers. This function is fulfilled by the EU incumbents. This would be a case of partial integration with a form of hierarchy headed by existing EU member states. The second scenario involves competition among all countries in an enlarged Europe. The CEECs would engage in economic activity just like any other EU states, that is, in the processes of decision making, creation and control. This would imply well-developed highorder services in the CEECs because it is the level of development of such services which determines a country's capacity to participate in the global network.

Accordingly, this paper examines the level of high-order services in the CEECs. These services are of growing strategic importance across the whole range of production sectors in developed economies [START_REF] Bailly | Localisation des services à la production et restructurations économiques[END_REF]. They are both the cause and the consequence of globalization, in a cumulative process. It is through high-order services that large cities, and the regions around them, become closely interconnected within global networks. These services are fundamental features both in the changing pattern of regional disparities across an enlarged EU and in the process of metropolization (Bourdeau-Lepage, 2004a[START_REF] Bourdeau-Lepage | Metropolization in Warsaw. Economic Change and Urban Growth[END_REF]and 2004). The development of high-order services will be a key factor in the successful integration of the CEECs in an enlarged EU.

One point is worth noting in respect of EU enlargement: the successful integration of the CEECs will depend on their overall performance in terms of GDP, but it will also depend on their involvement in the post-industrial production system in which high-order services play a leading part. For decades these countries operated with planned economies. Industry was predominant and the proportion of workers employed in the service sector was relatively low (e.g. 33.9% in Poland in 1992(e.g. 33.9% in Poland in compared with 58.4% in 2000)). With the deregulation of business, economic coordination has taken on a new form. Coordination is no longer achieved through central planning; the old regulations and routines are no longer operative and new economic agents have adopted western market practices and business strategies (Bourdeau-Lepage, 2004b). This has led to a rise in services in the CEECs, and especially in services related to production (producer services and finance). This reflects a change in the production structure which will determine whether or not their integration in the EU is successful.

The aim o f this paper is to appraise the potential o f CEEC regions for integration in the ' E U ' through the most prominent features o f their production structures with special emphasis on the relative importance o f high-order services.

The paper is organized as follows. First, the main features of production structures in the CEECs are described. By comparing the classification by country (section 2) and by region, national and regional effects are identified (section 3). Large disparities among the CEECs are revealed at national and regional levels. Regional inequalities may be partly explained by features specific to national settings, termed "country effects", and also by geographic, capital-city and historical factors specific to regions, termed "region effects". Second, regional production potentials are assessed by calculating (Euclidean) distances between the employment structure by sector in each region and the average employment structure of EU countries (section 4). This evaluation of production potential shows a shortfall compared with the EU average. Then, the resulting classification of regional disparities is compared with per capita GDP. The results confirm the relationship between employment structures and GDP but show that these two criteria have distinct but complementary meanings. So, there are clearly unequal potentials for EU integration explained by three interdependent factors: the location effect, the historical effect and the urbanization effect (section 5).

Differences in national structures despite a common legacy

This section aims at characterizing the production structures of the CEECs and changes therein relative to the EU average. The analysis is based on employment data for 17 NACE branches (EU nomenclature of activities, see table A1 in appendix for the list) at NUTS I level in 2000 and 1995. It covers Bulgaria (bg), the Czech Republic (cz), Estonia (ee), Hungary (hu), Latvia (lv), Lithuania (It), Poland (pi), Romania (ro) and Slovakia (sk) and also six current EU member states: Denmark, France, Greece, Ireland, Italy and Spain (Geographical entities, see table A2 in appendix for the list). For statistical reasons, other EU countries and Slovenia are excluded. High-order services are represented by branches J (financial intermediation) and K (real estate, renting and producer services). The analysis is conducted in three steps.

First, changes in the CEEC average employment structure are calculated to identify average trends (2.1). Second, the share of each economic activity in each country's employment is calculated for 1995 and 2000 and country structures are compared (2.2). Thirdly, all countries are classified on the basis of their employment structure (2.3).

2.1

The CEEC average employment structure is moving toward the EU-6 production structure but still had a long way to go in 2000

The CEEC average employment structure from 1995 to 2000 saw a 3.8% fall in the total number of jobs, with the greatest falls in Estonia, Bulgaria and Romania, respectively of 9.8%, 9.3% and 9.1%, and the lowest falls in Poland and Latvia (see table A1 in appendix).

The only exception to this trend was Hungary, with a 5.7% rise in employment, probably a sign of a better economic climate than in the other CEECs.

In this context, de-industrialization in the CEECs was noticeable, with 13.7% fewer jobs in the industry and construction sector. However, the sector still accounted for an average 29.6% of employment in the CEECs as against 26.5% in the EU-6 in 2000.

Conversely, financial and business activities expanded with respective increases of 9.2% and 22% in employment in sectors J and K from 1995 to 2000 (see table B1 in appendix). Nevertheless, in the CEECs, these activities occupied about half as many workers on average as in the EU-6 in 2000 (6.6% versus 12.8%, see table 1).

Agricultural employment declined everywhere except for Romania, where it rose by 9.3% from 1995 to 2000. So, in a context of rising unemployment, Romanian workers returned to farming. That activity seems to be a downturn activity in Romania. Thus, economic restructuring meant Romania remained distinct from the EU production structure in terms of agricultural employment.

Therefore, CEEC and EU-6 employment structures were still different in 2000 (see table 1). Agriculture was dominant in the CEECs and still employed 23.3% of workers as against 5.7% in the EU-6. The difference also relates to the level of development of high-order services (as seen previously) and other services such as hotels & catering. Thus, in the EU-6, the GHI sector accounted for 25.2% of employment compared with 21% in the CEECs. The CEEC employment structure differed too from the EU-6 structure in the weight of industry in economic activity. However, some CEECs (e.g. Latvia) are not as industrialized as the others. These differences can be apprehended from the analysis of employment structures in the CEECs. It reveals some interesting features. Sources: EUROSTAT (2003); BULSTAT (2003) and CSO (2003).

CEEC employment structures are veiy varied

Romania is atypical.

In Romania more than 40% of workers were employed in agriculture in 2000. From 1995 to 2000, the trend intensified. So whereas in 1995 the number of workers employed in agriculture was 50% higher than the CEEC average, in 2000, it was 80% higher (table 1). This was because of the poor economic situation in those years which saw the 1997-1999 crisis (Andreff, 2003). Economic recession and the land ownership statute (no. 18/1991) led to migration from urban to rural areas [START_REF] Parlog | Dezechilibre regionale in ocuparea fortei de munca[END_REF].

Poland and Bulgaria were also dominated by the primary sector which employed more than one in four of their workers. In the Czech Republic, Slovakia and Hungary, the proportion of agricultural employment in 2000 was very close to the EU-6 average of 5.7%, respectively 5.2%, 6.3% and 6.6% (see table 1).

The industry and construction sector stands in contrast to agriculture except for Bulgaria.

In the Czech Republic, Slovakia, Hungary and Estonia, this sector employed more than onethird of workers in 2000. There were also more jobs in industry and construction than in agriculture in Lithuania, Latvia, Bulgaria and Poland although the percentages were lower.

Poland is a special case where ranking varies by sector

When all sectors are included in the classification, agriculture appears to be the most discriminating factor and Poland joins class 1, as seen above. However, if the analysis is confined to branches J, K and D, Poland lies closer to the Czech Republic and Hungary than to Romania and Bulgaria. True, industry has a lower proportion of employment in Poland than in Hungary or in the Czech Republic, but this can be explained in part by the predominance of fanning in Poland. However, the strategic sectors, especially finance, are well-developed in Poland. By this classification Hungary, the Czech Republic and Slovakia appear to be in a more favorable position than Bulgaria and Romania for successfully integrating the EU. Indeed, this analysis conducted at national level reveals the framework in which regional structures are situated. Within the given framework, regional structures may differ and be variously endowed in activities which facilitate their integration. The occurrence of marked economic differences among the CEECs suggests there will be regional disparities in their potential for integration.

Regional disparities are very marked

This section draws attention to similarities and differences in potential for integration among CEEC regions as reflected by their employment structure in 2000. The same data are used as previously. So, employment data are available for 2000 for 52 NUTS II level regions of Central and Eastern Europe. In order to eliminate regional size effects in the cases of Prague, Bucharest and Bratislava, these regions are merged with a contiguous region (see table A2 in appendix for the list of regions). The sample includes 49 CEEC regions. The analysis is based primarily on the location quotient (LQ). Thus, the relative size of the main sectors in the 49 regions is evaluated by calculating their LQs. For a given sector, LQ is the ratio of the proportion of that sector in the region's employment to the average proportion of that sector in all 49 regions. LQ is independent of the country structures and so allows direct comparisons among the employment structures of the 49 regions purely in terms of their relative ability to interact in an enlarged Europe. The study is conducted in three stages. First, differences in regional specialization are identified with particular emphasis on high-order services (3.1). Second, CEEC regions are classified so as to characterize the overall regional structure of the CEECs (3.2). Third, the CEEC classification by region is compared with the classification by country to identify any "country effects" or "region effects" (3.3).

3.1

Differences in production structures are more marked between regions than between countries CEEC regions are distinguished by their level of agricultural activity, the proportion of which varied in 2000 from 1.7% in the Budapest region (huOl Kozep-Magyarorszag) to 51.2% in the South-West Romania region (ro04 Sud-est).

A spatial differentiation is apparent. In 2000, agriculture accounted for less than 12% of employment in the Czech, Slovakian and Hungarian regions whereas in the eastern regions of Romania and Poland more than 26% of jobs were in farming (see and table B2 in appendix). Overall, Romania, Poland and Bulgaria display a significant East-West split. In Poland, this is a familiar pattern, Poland A and Poland B (Bourdeau-Lepage, 2002;Chi et alii, 2003).

Farming is over-represented in eastern regions relative to the CEEC regional average, with the location quotient varying from 2.2 to 1.6 (see table B3 in appendix) in 2000.

The differences in agricultural activity within CEEC regions (Polish regions excluded) intensified from 1995 to 2000. The standard deviation rose from 12.5 to 15.9 over the period reflecting widening divergence in employment structures. The highest value in the rankings increased whereas the lowest value decreased (in 1995, the South-West Romania region 43.3% and 2.3% in the Budapest region). Thus, the proportion o f agriculture in employment grew in all Romanian and Bulgarian regions while it fell in the Czech, Slovakian and Hungarian regions. The Romanian and Bulgarian regions seem, then, to have lost some of their integration potential because they have become more farming-oriented, which is a movement away from the EU standard. Indeed, their sectoral adjustment process has increased their future economic vulnerability with regard to the post-industrial economy. This remark should be completed by observation of changes in their industrial employment patterns and the development of services.

Specialization in industry and construction (CDEF sector) is less marked than in farming. In 2000, the Hungarian, Slovakian and Czech regions (except for the Budapest and Prague regions) and the Slaskie region (Poland), the North-Central Bulgaria region and the Central Romania region were more specialized in industry and construction than the CEEC regional average, their LQs varying from 1.1 to 1.5 (see table B3 in appendix). As expected, the greatest specializations in the industry and construction sector and in agriculture varied in opposing directions. Thus, the eastern regions and the remaining regions were not specialized in the CDEF sector.

The finance and producer services sector (JK sector) is highly discriminating among CEEC regions. High-order services are principally concentrated in metropolitan regions (except for the Bucharest region). In these regions, they accounted for at least 10% of employment in 2000 as against an average of 6.6% in CEEC regions and respectively 2.2% and 2.7% in the NW Bulgaria region and the S-W Romania region. It appears that the relative specializations of the five capital regions in high-order services differ. The relative specialization in the JK sector is more marked in the Warsaw region (LQ: 1.8) and above all in the Prague and Budapest regions (LQ: 1.9), while the Bratislava and Sofia regions are less specialized than these three regions (LQ: 1.6). For the Sofia region, this could stem from the general economic characteristics of Bulgaria, which has less advanced structures than the other countries (Bourdeau-Lepage, 2004a).

To a lesser extent, five Polish regions are also specialized in the JK sector. These are the western border regions and a southern border region (Slaskie). Their LQs vary from 1.2 to 1.6, that is, their share of employees in high-order services is 60% higher than the average proportion in the 49 CEEC regions (see table B3 in appendix). Poland's east-west split is again apparent.

On the whole, the different levels of specialization in the GHI and LOP sectors are much less significant, with LQs varying respectively only from 0.6 to 1.4 and from 0.5 to 1.5 in 2000 (see table B3 in appendix). The Hungarian, Czech and Slovakian regions are relatively specialized in these activities unlike the Bulgarian and Romanian regions.

The employment structure analysis by region, conducted using location quotients, reveals intense specialization, especially in activities related to globalization such as finance and producer services, but also in agricultural activities. This presages large differences in integration potentials among the 49 CEEC regions. CEEC regions in 2000 displayed a simple pattern: an east-west split for agriculture and a center-south split for high-order services.

The 49 CEEC regions were ranked in ascending order to identify those with similar employment structures.

Four classes of region stand out clearly

The previous method of classification is used (see, 2.2) and applied to the 49 CEEC regions on the basis of their LQ to eliminate the size effect (see, table B3 in appendix for details). Table 2 gives the main characteristics of classes. In 2000, the 49 CEEC regions formed four clearly distinct and easily characterized classes.

An industrial core in a central zone o f the CEEC regions

More than a third of the 49 CEEC regions are predominantly industrial. Their industry and construction sector employs on average 37.1% of workers compared with a CEEC regional average of 29.6% and an EU-6 average of 26.5%. These 17 regions make up class 1 and comprise all of Slovakia, the Czech Republic and Hungary but for the capital regions and the Baltic states (Estonia, Lithuania and Latvia). They form the industrial core in a central zone of CEEC regions. This class is relatively more specialized than the others in the industrial and construction sector and in public services, with LQs of 1.3 for the CDEF sector and the LOP sector. These regions have well-developed consumer services although less so than the EU-6. In fact, such services employ only 24.2% of workers in class 1 as against 25.2% on average in EU-6. FIRE and producer services are not very developed compared with the EU-6 even if their level of development is close to the CEEC regional level. The proportion of high-order services in employment is half that of the EU-6.

An underdeveloped zone on the eastern border of the CEEC regions

By contrast with class 1, class 2 is composed of nine regions where on average more than 45% of jobs are in farming. The relevant regions are located along the eastern fringes of the CEECs. There are the eastern regions of Poland (Lubelskie, Podlaskie, Podkarpackie, Swietokrzyskie), all the Romanian regions (but for the central, western and Bucharest regions) and the NW Bulgaria region. In these highly specialized agricultural regions (LQ: 2), high-order services are not expanding and employed a mere 3.5% of workers in 2000. The relative under-specialization in public and consumer services completes the picture of this class (see table 2). The employment structure of class 2 is far removed from that of EU-6 and indeed from the other classes.

An intermediate zone between West and East, characterized by a balance between agriculture and industry

The following class includes the central regions of Poland (6 regions), three Romanian regions (the Bucharest, western and central regions) and the easternmost Bulgarian regions.

The balanced distribution of activities between industry and agriculture, and the low proportion of employment in high-order services are the two features of class 3. Thus, agriculture and the industry and construction sector respectively provide 27.7% and 28.5% of employment while FIRE and producer services (JK sector) account for only 5.2% of employment. The production structure of this class is still far removed from the EU-6 structure. The Bucharest region deserves a comment. This region is included in class 3 rather than class 4 because of its agricultural specialization and the underdevelopment of high-order services in the vicinity of the Romanian capital (sectors JK and ab respectively represented 3.1% and 48.5% of employment in 2000).

Urbanized regions (and to a lesser extent the Polish western borders) as high-order services centers in the CEECs

Class 4 is composed of the capital regions (except Bucharest) and five western regions of Poland. Its outstanding feature is its marked specialization in high-order services compared with the CEEC regional average. Class 4 easily ranks first for sector JK, which is largely more represented (LQ: 1.6) than the CEEC average. High-order services account for more than 10% of employment, which is close to the EU-6 average. Among the regions in this class, high-order services are expanding more in the capital regions.

This class is relatively less specialized in the GHI sector (LQ: 1.2) but still outranks the other classes. It is noteworthy that industry and construction is dominant, employing 30% of workers. This characteristic means this class still does not match the EU-6 employment structure.

Specialization of the capital regions in FIRE and producer services is hardly surprising. It is well known that these activities concentrate in large cities where they find a skilled labor force. It is through high-order services that large cities, and the surrounding regions, become closely interconnected within global networks. Thus, large cities seem to take on the same role in Central and Eastern Europe as in Western Europe.

Marked differences in economic performance among the four classes

Apart from employment structures, the four classes differ markedly in economic performance as measured by per capita GDP (on a base of 100 for EU). Thus, the average per capita GDP of class 4 (urbanized regions) is twice that of class 2 (agricultural regions) and more than 1.5 that of class 3. But it still only amounted to 50% of EU per capita GDP in 2000 (see table 2).

The regional classification by per capita GDP seems to be correlated with the level of specialization in high-order services, except for the Sofia region. Thus, the capital regions have the highest per capita GDP, 85 for the Prague region (Praha and Stredni Cechy region), 76 for the Budapest region (Kozep-magyaroszag) and respectively 59 and 57 for the Warsaw (Mazowia) and the Bratislava regions (Brastislavsky and Zapadne Slovensko). Next come some industrial regions from class 3, for example, the Jilozapad region (a Czech region) and the Polish regions of class 4 such as, the Slaskie region (see, table B5 in appendix). The case of the Bulgarian capital region, which, despite its relative specialization in FIRE and producer services, has a low per capita GDP (34), deserves fuller examination. It may be linked to the overall economic situation of Bulgaria. Perhaps there is a strong country effect. In this respect, the next section attempts to identify "country effects" and "region effects".

Some obvious "country effects" and "region effects"

Comparison of the classifications by region and by country suggests the occurrence of "country effects" and "region effects".

Regions o f the same country, as well o f countries with similar structures, are grouped in the same class

For example, all the Czech, Hungarian and Slovakian regions, except for their capital regions are in regional class 1 (specialized in industry), while these three countries have very similar national structures and together form country class 2 (see 2.3). This is also true, although to a lesser extent, of Bulgaria and Romania. As seen previously, these countries have somewhat similar employment structures and form country class 1. Their regions (if the Sofia region is excluded) do not form a single region class but are grouped with some Polish regions in region classes 2 and 3. That is probably because regional inequalities in terms of production structure within each of these countries are more marked than in the Czech Republic, Slovakia and Hungary. Nevertheless, regional differences in production structures in Romania and Bulgaria seem to be only slight to moderate since they cluster in just two classes.

There is also a country effect for Poland. Poland is less industrialized than Hungary, Slovakia and the Czech Republic (see table 1). This is reflected in the regional classification. In fact, none of the Polish regions is included in regional class 1, characterized by a dominant industrial activity and made up of Hungarian, Czech and Slovakian regions plus Lithuania, Latvia and Estonia.

Poland as an example o f region effects

The atypical situation of Poland, mentioned earlier (2.3), can be partly accounted for by regional disparities. Poland is made up of distinct regions. Four of them, located in the East of the country, are specialized in agriculture and along with eastern Romanian regions form region class 2. This is referred to here as the "eastern effect". Six of the remaining Polish regions are in region class 4. These regions enjoy a high level of industrialization and urbanization, as well as a favorable geographical location bordering the EU, and they also have well-developed services. These regions benefit from the "western effect". Between these two types of regions, the central regions are characterized by a balance between agriculture and industry and form a transitional zone in terms of geography and economics. This is the "Polish central region effect". Consequently, in Poland, three regional effects can be discerned, in addition to the metropolitan effect.

A metropolitan effect

The capital regions enjoy an advantage in terms of political, social and economic factors. These regions have a diversified and skilled labor force. They usually concentrate most of the foreign investment in the country (CEPII, 2003). Thus, between 1996 and 2000, in Poland, Hungary, Slovakia and the Czech Republic, the capital regions accumulated foreign direct investment stocks, evaluated as a percentage of GVA (growth of added value) at twice the national average (Tondl and Vuksic, 2003, pp. 14 and 32). As a whole, they benefit from the proximity of political power and economic decisions-makers (Bourdeau- Lepage and Huriot, 2003). TTieir economic performance in terms of progress in the transition process is better than that of other regions (see 3.2). This is confirmed by several studies which report that in the regions where capital cities are located, services are more extensive and growth outperforms other regions (in particular, [START_REF] Resmini | Economic integration, industry location and frontier economies in transition countries[END_REF][START_REF] Tondl | What makes regions in Eastern Europe catching-up? The role of foreign investment, human resources and geography[END_REF]. This kind of regional effect in capital regions is known as the "metropolitan effect" by reference to the concept of the metropolis.

The current results suggest that the CEEC regional pattern is made up of four very mixed classes of regions. The regions are subject to strong country effects. A region falls into one or other regional class depending on its country. Its possibilities are limited. Regional effects also occur, such as the metropolitan effect or the eastern effect. Thus, the eastern border regions, especially in Poland, seem to be disadvantaged in terms of geographical location whereas the western regions benefit from favorable factors such as proximity to the EU. The regional disparity in employment structure suggests that regional potentials for integration are unequal. In addition, the overall analysis predicts that the gap with the EU in terms of employment structures and per capita GDP will vary for the different CEEC regions.

CEEC regional structures faced with the EU

In order to confirm or invalidate the previous hypothesis and to evaluate the regional potentials for integration, CEEC regional structures of production are compared with a European reference in the form of the Euclidean distances between employment structures. The methodological basis for this choice is set out (4.1) before presenting the results (4.2).

Methodological remarks

An employment structure can be represented by the vector of the shares of the different branches in total employment. The gaps between employment structures can then be evaluated from the distance between the corresponding vectors.

Distances measured between numerical vectors, such as the Tchebychev distance, the Klafszky distance, the logarithmic distance or the angular distance, have statistical drawbacks, and in particular entail some loss of information. Furthermore, some of them give greater weights to the larger differences between vectors (see Colomi et alii, 2001 for a summary). Euclidean distance is chosen here because all the gaps between vectors are treated equally, and also because it is easy to calculate and interpret (see note 1 in appendix for technical considerations).

The differences in employment structure between each CEEC region, each regional class or each country measured by Euclidean distances, can be used to compare potential for successful integration in an enlarged EU. To facilitate the interpretation, distances are presented on a base of 100. Thus, the Euclidean distance varies from 0 between identical structures to 100 between the most widely differing structures. It must be recalled that these figures have no absolute meaning because they result from the particular distance used. But they can be compared with each other and over time. Table B5 in appendix shows the ranking of CEEC regions and the CEECs in terms of their distance to the EU average in 2000. It should be pointed out that regional data are here compared with the EU-6 average, which masks substantial disparities.

The Euclidean distance is a simple indicator of dissimilarity but it requires careful interpretation. A large distance to the EU-6 average may indicate that the region or the country is either lagging behind or alternatively very far ahead in the conversion process. For the CEECs, the first possibility is more likely but the second cannot be excluded. The results and data of the preceding sections are used and certain results are carefully set out. On the whole, they concur with the previous assessments.

4.2

The structural discrepancies between the CEECs and EU-6 are confirmed

The CEECs differ in production structures by sector

In 2000, the Euclidean distances between the CEECs and EU-6 varied from 6.0 for Estonia to 27.3 for Romania, with an average of 14.3.

Bulgaria and above all Romania are still removed from the EU-6 production structure. Over the period 1995-2000, their distances increased respectively from 15.0 and 21.9 to 16.6 and 27.3 (table B4 in appendix), especially because of the growing proportion of agriculture in their total employment whereas the EU-6 agriculture proportion decreased over the same period from 6.8% to 5.7% (see table 1).

Even if the Polish distance was similar to that of Bulgaria in 2000, Poland seems to be in a better position than both Bulgaria and Romania. Closer analysis of the employment structures provides some explanations. First, between 1995 and 2000, the Polish structural gap with the EU-6 narrowed (from 16.5 to 16.1; see table B4 in appendix). Second, finance and producer services are more extensive in Poland than in Romania or Bulgaria. Consequently, the Polish gap is more the result of an overdeveloped agricultural sector and underdeveloped public services than the effect of underdeveloped high-order services (table 1 and2.2). So, Poland's specificity is emphasized once more.

Given the small Euclidean distance and the characteristics of their employment structures (see 2.3), Estonia, Hungary, Slovakia and the Czech Republic were closer to the EU-6 average than Latvia and Lithuania in 2000. Thus, those four countries have the highest potentials for successful integration.

At regional level, the differences in the Euclidean distances are sizeable

Euclidean distances ranged from 4.7 to 34.5 in 2000. The Euclidean distance of the SW Romania region from the EU-6 average was around 7.5 times greater than that of the Prague region (see table B5 in appendix). Regions follow the same trend as their respective countries. Thus, the Czech, Hungarian and Slovakian regions are close to the EU average, whereas the Bulgarian and Romanian regions (except the Sofia region) are very far from it. The Polish regions lie at various distances from the EU average. The CEEC regional ranking of Euclidean distances from the EU-6 average falls into two groups.

The first group is composed of all the Czech, Hungarian and Slovakian regions together with the six Polish regions included in region class 4 and the Sofia region, the Euclidean distances of which are between 4.7 and 13.1. Thus, region class 4, (urbanized class) and region class 1 (industrial class) appear to be the classes that are the most advanced in the transition process, based on their Euclidean distance and the characteristics of their employment structure. Among these classes, the urbanized class is the closest to the EU-6 with an average distance of 7.5 (see table 2). Within class 4, the Warsaw region (pl07) lies at the greatest distance from the EU-6 structure. This does not reflect underdevelopment of the high-order services but is due to the considerable weight of its farming sector in the vicinity of the capital city. Thus, the Polish capital region is in a better position than is shown by the indicator of dissimilarity.

The second group in this regional ranking is not surprisingly made up of the Romanian, Polish and Bulgarian regions. Regions with a balance between industry and agriculture, that is, those of class 3, occupy the top end of the second group. On account of their greater industrial activity, these regions are closer to the EU-6 structure than the farming regions of class 2. The differences in employment structures are too sizeable for the gap to be closed in the immediate future. In fact, they varied from 15 for the Polish region of Warminsko-Mazurskie to 34.5 for the SW Romania region in 2000.

In order to assess the relationship between economic performance and the specificity of regional employment structures, the previous classification is set against regional disparities in GDP per capita.

The ranking o f regional distances is generally well correlated with that o f per capita GDP

The correlation coefficient is 0.72. However, differences do arise for particular regions (see, table B5 in appendix).

Therefore, certain regions which rank well in terms of per capita GDP occupy a low rank for distance from the European average. The Warsaw region is a good example, with its over proportioned agricultural sector, despite the presence of high-order services and of a welldeveloped industrial sector. Other examples are the Hungarian and Czech regions. The GDP ranking for most Czech regions is better than their distance ranking (e.g. Jihozapad or Severovychod). This may be because of the industrial specialization of these regions, which moves these regions away from the EU-6 average structure of employment, but at the same time moves them closer to EU economic performances. Here, additional factors are doubtless at play but they cannot be deduced from this analysis.

The reverse situation is found in the Sofia region, the vychodne Slovensko region (Sk04) and the Del-Dunantul region (Hu04) together with Estonia and Lithuania. These regions and countries are relatively close to the EU-6 employment structure compared with the other CEEC regions but come nowhere near EU per capita GDP. These differences in ranking may be due to the low productivity of the factors concerned. Other explanations can probably be suggested. The division into branches used here may conceal sizeable differences in intra branch compositions between the EU-6 and CEEC regions and countries. It is well-known that certain industrial activities are more productive than others, such as manufacturing with its high added value. Differences in intra-branch structures are therefore a plausible explanation.

Two conditions probably have to be fulfilled for two equivalent employment structures to yield the same per capita GDP. First, the chosen sectors must have the same intra-branch composition and, second, these sectors must have the same productivity. More in-depth analysis is required to understand the factors differentiating these two characteristics.

However, the results confirm the relation between production structures and GDP and show that these two factors have different but complementary meanings.

Conclusion

The evaluation of production structures through employment reveals large regional disparities in Central and Eastern Europe and a shortfall compared with the EU in terms of economic performance. Regional positions are not independent of the country effect. Indeed, the analysis brings out regional effects, such as the metropolitan effect and the Eastern effect. So, there are unequal potentials for integration in the EU. Consequently, the overall process of integration will prove more complex than the two scenarios described in the introduction. CEEC regions already differ and will differ in the ways they interact with each other and with the EU regions. Thus, it is very difficult to predict the future role of these regions in the EU. This role will depend on three series of interdependent factors resulting from the present analysis: the location effect, the historical effect and the urbanization effect.

The location effect: periphery as a handicap

The regions of classes 2 and 3, which are identified as underdeveloped, are still a long way short of the EU level of economic performance. High-order services are not very developed in these regions. These regions do not coordinate economic activities. These coordination functions are fulfilled by other regions such as the capital regions of the CEECs. Thus, the Bulgarian, Romanian and Polish regions and especially the eastern border regions have little chance of fully integrating the EU economic system. They do not display the requisite features to participate in the processes of decision-making, creation and control. Indeed, these regions do not enjoy a favorable geographical location. They have a peripheral position with respect to the EU and are located near weakly developed countries like the Ukraine.

In this context, the Eastern border regions, will probably become the "satellite regions" of the enlarged Europe. Thus, these regions will be only partially integrated in the EU-25 with reference to the first scenario of integration (see 1). So, the peripheral character of the Eastern and Southern CEEC regions will continue after enlargement if no changes in infrastructures and education appear in the immediate future. Some commentators, notably [START_REF] Tondl | What makes regions in Eastern Europe catching-up? The role of foreign investment, human resources and geography[END_REF], recommend investing in transport infrastructures to overcome the location disadvantages of these easternmost regions.

The historical effect

As expected, the Czech, Hungarian, Slovakian and Western Polish regions are in a more favorable position than the preceding classes of regions. In this respect, they form the historical industrial core of Central and Eastern Europe. In the early 19th century, the Czech Republic and the regions around the Hungarian cities of Miskolc, Gyor and Pecs were as industrialized as France was [START_REF] Bairoch | Victoires et déboires. Histoire économique et sociale du monde du XVIe siècle à nos jours[END_REF]. Thus, long-term history matters, as new economic geography claims. The past differences in development between the CEEC regions and countries seem to have affected the transition process of CEEC regions. The economic theory of agglomeration [START_REF] Krugman | The Self-Organizing Economy[END_REF][START_REF] Fujita | Economics of Agglomeration. Cities, Industrial Location and Regional Growth Cambridge[END_REF] and especially the cumulative character of economic development, could clarify the forces in play. In combination with their historical assets, these regions enjoy location advantages relative to their proximity to the EU, especially in terms of markets and investments (Tondl and Viskav, 2003). Consequently, the Hungarian, Czech, Slovakian and Western Polish regions, together with Estonia, Latvia and Lithuania, are the most advanced in the process of post-industrial development. Even these advantaged regions are highly differentiated depending on their level of urbanization.

The urbanization effect Among the Central and Western regions, the urbanized regions (class 4) and especially the capital regions have the employment structure that most resembles that of the EU-6. Services are more developed and productive, and efficient firms have developed in these regions. In addition, these regions attract foreign direct investments and are relatively specialized in highorder services. Agglomeration economies play a significant part in these regions.

Consequently, the capital regions will probably fully integrate the European Union, that is, interact with the other EU regions and take part in the decision-making processes. Their regions may well become the gateway to the global network for all CEEC regions.

These effects are mutually reinforcing and generate cumulative processes which can hardly be halted. A number of other factors which may influence the integration process have been omitted here because this paper seeks to shed light on production structures. Accordingly, further analysis will be necessary for a fuller understanding of the process of integration, in particular the role of foreign direct investments and of social and regional regulations. Regions/Branches ab c d e f g h i i k 1 m n op cdef ghi JK lop bgOl 1.6 0.3 0.8 2.7 0.6 0.6 0.9 1.0 0.4 0.3 0.8 1.1 1.0 0.8 0.9 0.7 0.3 0.9 bg02 1.2 0.4 1.3 1.0 0.6 0.9 1.2 1.1 0.4 0.4 0.7 1.1 0.8 0.8 1.1 1.0 0.4 0.9 bg03 1.4 0.3 0.8 1.0 0.7 0.9 1.6 1.2 0.5 0.5 0.7 1.2 0.9 0.8 0.8 1.1 0.5 0.9 bg04 0.6 1.4 1.0 0.9 0.9 1.1 1.5 1.4 1.2 1.7 1.1 1.1 1.0 1.3 1.0 1.2 1.6 1.1 bg05 1.3 1.7 1.1 1.1 0.7 0.8 1.6 0.9 0.4 0.5 0.6 1.1 0.8 0.6 1.0 0.9 0.5 0.8 bg06 1.4 0.7 0.9 0.8 0.7 0.9 1.7 1.3 0.5 0.5 0.7 1.0 0.8 0.8 0.8 1.1 0.5 0.9 cz01+cz02 0.1 0.3 0.9 0.8 1.7 1.2 2.0 1.5 1.8 1.9 1.8 0.9 1.1 1.8 1.0 1.4 1.9 1.3 cz03 0.3 0.4 1.5 1.1 1.6 1.0 2.2 1.1 1.1 0.8 1.8 0.8 1.0 0.8 1.4 1.2 0.9 1.0 cz04 0.2 3.2 1.2 1.4 1.7 0.9 1.7 1.5 1.0 1.0 1.9 0.9 1.2 1.1 1.4 1.2 1.0 1.2 cz05 0.3 0.4 1.6 0.6 1.4 0.9 1.6 1.1 1.0 0.9 1.6 1.0 1.0 1.1 1.5 1.0 0.9 1.1 cz06 0.3 0.4 1.4 0.9 1.6 0.9 1.4 0.9 1.0 1.0 0.1 1.1 0.8 0.9 1.1 0.5 0.9 1.1 1.0 0.9 1.0 1.1 0.9 1.0 1.0 1.0 1.0 pl03 2.0 0.3 0.5 0.6 0.6 0.7 0.4 0.7 1.0 0.5 0.7 1.0 1.1 0.5 0.6 0.7 0.6 0.9 pl04 0.7 0.2 1.1 0.9 0.9 1.2 0.7 1.1 1.3 1.2 1.1 1.0 1.3 1.0 1.0 1.1 1.3 1.1 pl05 1.3 0.5 1.0 1.0 0.7 1.0 0.6 0.7 1.2 1.0 0.8 0.9 1.2 0.8 0.9 0.9 1.0 0.9 pl06 1.4 0.8 0.8 0.6 1.0 0.9 0.9 0.7 0.9 1.0 0.7 0.9 1.1 0.9 0.8 0.9 1.0 0.9 pl07 1.0 0.0 0.7 0.7 1.0 1.2 0.7 0.9 2.0 1.8 0.9 0.9 1.0 1.7 0.8 1.1 1.8 1.0 pl08 1.2 0.3 1.0 1.1 1.1 0.9 0.7 0.8 1.1 0.9 0.8 0.9 1.3 0.7 1.0 0.9 1.0 1.0 pl09 1.9 0.5 0.8 0.7 0.7 0.7 0.4 0.6 0.8 0.6 0.7 0.8 0.9 0.5 0.7 0.7 0.7 0.8 plOa 1.8 0.1 0.6 0.7 0.7 0.8 0.5 0.7 1.1 0.6 0.8 1.0 1.2 0.7 0.6 0.8 0.7 1.0 plOb 0.6 0.1 1.1 0.9 1.1 1.2 1.2 1.2 1.4 1.3 0.9 1.1 1.3 1.1 1.0 1.2 1.3 1.1 plOc 0.5 6.7 1.0 1.3 1.2 1.2 0.7 1.0 1.1 1.3 0.7 0.9 1.2 0.9 1.3 1.1 1.3 0.9 plOd 1.9 0.5 0.7 0.9 0.8 0.8 0.4 0.7 0.8 0.5 0.6 0.8 1.0 0.6 0.7 0.7 0.6 0.8 plOe 1.1 0.1 0.9 0.9 0.9 1.0 0.8 0.9 1.2 1.0 1.0 1.1 1.3 0.9 0.9 1.0 1.0 1.1 plOf 1.0 0.6 1.1 0.7 1.0 1.1 0.6 0.8 1.2 1.1 0.7 0.9 1.0 0.9 1.0 1.0 1.1 0.9 plOg 0.6 0.2 0.9 1.2 1.1 1.3 1.4 1.3 1.3 1.2 1.0 1.0 1.3 1.1 0.9 1.3 1.2 1.1 roOl 2.2 0.6 0.8 0.9 0.6 0.6 0.4 0.5 0.3 0.4 0.3 0.8 0.7 0.5 0.8 0.6 0.4 0.6 ro02 1.9 0.6 0.9 1.0 0.8 0.6 0.6 1.0 0.4 0.5 0.4 0.7 0.7 0.6 0.9 0.7 0.5 0.6 ro03+ro08 1.4 1.0 1.0 1.2 0.8 0.8 0.6 1.0 0.8 1.1 0.6 0.7 0.7 0.8 1.0 0.9 1.0 0.7 ro04 2.2 2.4 0.7 1.4 0.7 0.6 0.5 0.6 0.3 0.4 0.4 0.7 0.6 0.4 0.8 0.6 0.4 0.6 ro05 1.5 2.7 1.0 1.2 0.7 0.8 0.7 0.7 0.5 0.6 0.5 0.7 0.8 0.6 1.0 0.8 0.6 0.7 ro06 2.0 1.1 0.9 0.8 0.6 0.6 0.5 0.7 0.5 0.5 0.4 0.8 0.7 0.5 0.9 0.6 0.5 0.6 ro07 1.4 0.9 1.3 1.1 0.6 0.8 0.7 0.8 0.5 0.5 0.4 0.8 0.7 0.5 1.2 0.8 0.5 0.6 sk01+sk02 0.1 16.8 0.1 3.8 2.9 0.2 4.1 0.4 5.0 0.9 2.4 0.9 0.6 0.0 1.1 1.3 1.6 1.2 sk03 0.3 0.3 1.4 0.7 1.2 1.3 0.9 1.2 0.9 0.9 1.2 1.6 1.3 0.6 1.2 1.2 0.9 Sources: calculated from EUROSTAT (2003); BULSTAT (2003) and CSO (2003) and [START_REF] Behrens | Produit Intérieur Brut régional des pays candidats 2000[END_REF]. * in 1996. Sources: calculated from EUROSTAT (2003); BULSTAT (2003); CSO (2003) and [START_REF] Behrens | Produit Intérieur Brut régional des pays candidats 2000[END_REF].

-

  Sources: calculated fromEUROSTAT (2003);BULSTAT (2003) andCSO (2003).

Table 1 :

 1 CEEC and EU-6 employment structures as percentages(1995 and 2000).

	Sector/country	j 1995 2000	1995	1 It 2000	ab 1995 2000 1995 2000 1995 2000 cdef ghi	lop 1995 2000	Total 1995 2000
	Romania	0.7 0.9 3.4 3.1 34.4 41.4 33.6 27.3 16.3 14.9 11.5 12.4 100 100
	Bulgaria	1.3 1.1 3.1 4.1 24.4 26.2 32.6 28.3 19.8 22.0 18.9 18.3 100 100
	Poland	1.3 2.2 4.8 5.6 27.8 26.3 30.1 26.8 19.3 20.3 17.3 18.8 100 100
	Czech Republic	1.9 2.1 5.0 5.6 6.7 5.2 42.1 39.9 23.4 24.3 21.0 22.9 100 100
	Hungary	2.3 2.2 3.6 5.4 8.1 6.6 33.0 33.8 24.7 25.8 28.2 26.3 100 100
	Estonia	1.1 1.3 4.9 6.9 10.1 7.1 34.0 33.2 25.2 27.1 24.6 24.3 100 100
	Slovakia	1.6 1.9 6.7 6.8 9.0 6.3 36.8 34.0 24.3 27.0 21.6 24.0 100 100
	Latvia	1.3 1.6 4.8 5.5 18.5 15.3 25.8 24.4 25.0 27.4 24.5 25.8 100 100
	Lithuania	1.3 1.0 3.0 3.7 23.8 19.9 28.2 26.2 19.8 22.8 24.0 26.5 100 100
	CEEC average	1.5 1.7 3.9 4.9 23.2 23.3 33.0 29.6 20.1 21.0 18.3 19.4 100 100
	EU-6 average	2.9 2.8 9.2 10.0 6.8 5.7 27.8 26.5 24.8 25.2 28.5 29.8 100 100

The development o f services is differentiated across the CEECs

	2.3	The CEECs fall into three clearly distinct classes for 2000
	Class 1 is composed of Romania, Bulgaria and Poland. It is characterized by the dominant
	position of agriculture, which employs more than 31% workers, but also, by a weak service
	sector, whether trading, finance, producer or public services (see histogram). Within this
	class, Bulgaria is closer to Poland than to Romania.
	The Czech Republic, Hungary, Estonia and Slovakia form class 2, which is characterized by
	having a higher proportion of workers in industry than in agriculture, as well as by having the
	highest proportions in finance, producer services and trade.
	Class 3 is made up of Latvia and Lithuania where more than one in four workers are
	employed in the public sector.
		Histogram: Production structures of country-classes (2000)
	Trade, hotels & catering, transport and communication (GHI sectors) were equally as well-
	developed in Latvia, Estonia, Slovakia, Hungary and the Czech Republic as in the EU-6 in
	2000. FIRE (Finance, Insurance and Real Estates) and producer services were more
	developed in Slovakia and Estonia, where they accounted for more than 8% of employment.
	Next came Poland, the Czech Republic and Hungary with more than 7.5%.
	A better understanding of these complex discrepancies can be gained by classifying countries
	on the basis of their employment structure. The method of clustering used here is the
	classification in ascendant hierarchy. Countries are progressively assigned to a class whose
	number is determined automatically in order to minimize the intra-class dissimilarities in
	relation to the inter-class dissimilarities. Then, variance tests of the chosen variables i.e. the
	sector of activity, for the different classes are tested and are statistically significant. So, the
	sectors of activity discriminate well the classes.

■ Lop ■ ghi ■ cdef ■ ab ■ K □ J Class 1: Romania, Class 2: The Czech Class 3: Latvia and Bulgaria and Poland Republic, Hungary, Lithuania Estonia and Slovakia

Table 2 : Characteristics o f region classes in 2000

 2

	Classes	Characteristics	Freq. LQ	ab cdef ghi JK Lop Total	Eucl. Dist. from EU-6*	GDP/inhab. UE-15 (SPA)=100*
	1	Industrial	17 8.4 37.1 24.2 5.9 24.3 100	10.0	41
	2	Agricultural	9 45.7 22.1 14.2 3.5 14.5 100	32.2	24
	3	Balance Ag-ind	13 27.7 28.5 19.4 5.2 16.9 100	19.4	29
	4	HO services	10 11.6 30 25.6 10.4 22.2 100	7.5	50
	CEEC regional average	-	-23.4 29.6 21 6.6 19.4 100	15.7	37
	UE-6 average	-	-	5.7 26.5 25.2 12.8 29.8 100		-
	1	Central	LQ 0.4 1.3 1.2 0.9 1.3
	2	Eastem/underdev. LQ 2.0 0.7 0.7 0.5 0.7
	3	Intermediate	LQ 1.2 1.0 0.9 0.8 0.9
	4	Western/urbanized LQ 0.5 1.0 1.2 1.6 1.1
	CEEC regional average	-	LQ 1.0 1.0 1.0 1.0 1.0
	Sources: Calculated from EUROSTAT (2003); BULSTAT (2003); CSO (2003) and
			Behrens (2003).* in 2000.

Table A2 : Geopolitical entities

 A2

	CEECs: 9 countries-NUTS I	49 Regions NUTS II bis level
	Bulgaria (bg)	6 regions
	The Czech Republic (cz)	7 regions instead of 8
		cz01+cz02: Praha + Stredni Cechy
	Estonia (ee)	1 region
	Hungary (hu)	7 regions
	Lithuania (It)	1 region
	Latvia (Iv)	1 region
	Poland (pi)	16 regions
	Romania (ro)	7 regions instead of 8
		ro03 + ro08: Sud + Bucaresti
	Slovakia (sk)	3 regions instead of 4
		sk01+sk02: Bratislavsky +
		Zapadne Slovensko
	Euroipean Union: 6 countries-NUTS I
	Denmark
	Greece
	Spain
	France
	Ireland
	Italy
	Source: recomposed from EUROSTAT (2003).

Table B l: CEEC average employment structure (1995-2000)

 B

	Branches 1995	2000		Percentage change in employment structure 1995-2000
	NACE	CEEC average	Bg*	ee	cz	hu	It	lv	Pi	ro	sk
	atop	100	100	-3.8	-9.3	-9.8 -4.6 5.7 -3.4 -0.7 -0.7 -9.1 -7.4
	ab	23.2	23.4	-3.0	-2.3 -37.0 -26.2 -14.7 -19.1 -17.8 -6.0	9.3 -35.2
	c	2.0	1.3	-35.6	-37.3 -21.7 -27.4 -43.8 -10.3 -35.5 -33.0 -43.9 -8.5
	d	23.1	20.5 -14.6	-21.3 -17.9 -9.7 9.6 -7.3 -13.1 -13.3 -26.3 -16.4
	e f	M -6.0	1.8 . . « 5.9	-8.5 -4.7	4.7 -23.1 14!6 -2.4 22.9 -16.0 15.6 -3.9 -23.7 -17.0 -15.4 4.1 -10.3 3.7 -26.3 -11.3 1.3 0.7
	g	11.8	12.7	4.0	9.6	-0.

9 -0.6 17.7 10.3 18.6 5.8 -10.3 4.9 h 1.8 1.9 3.4 11.4 15.7 1.5 14.4 44.3 11.3 8.7 -24.7 -4.0 i 6.6 6.3 -7.3 -12.9 -10.9 -2.9 -2.5 5.8 -7.2 -0.7 -24.6 0.6 j 1.5 1.7 9.2 -24.0 11.6 7.7 1.5 -25.2 18.0 21.3

					4.2	6.3
	k	3.9	4.9	22.0	18.5

28.2 8.7 56.8 17.7 13.5 50.6 -16.3 -5.8

	1	3.2	3.9	14.4	25.1	-1.9 14.5 6.4 14.9 11.2 22.5 12.3 5.4
	m	6.4	6.6	-1.3	-14.7 -17.1 -3.3 -5.2 14.2 -3.4	2.7	-3.4 6.9
	n	5.7	5.8	-1.9	-20.9 -20.4 2.6	4.4	7.7 -9.0 -3.1	2.5	4.4
	o	2.9	3.1	4.1	-17.3	1.0 3.1 -12.8 -17.4 31.1 39.2 -18.3 -12.2
	P	0.0	0.0	38.1	-	-150.0 -45.1	-	-	-	-	-
	ab	23.2	23.3	-3.0	-2.3 -37.0 -26.2 -14.7 -19.1 -17.8 -6.0	9.3 -35.2
	cdef	33.0	29.6 -13.7	-21.2 -11.9 -9.7	8.3 -10.2 -6.3 -11.6 -26.2 -14.3
	j	1.5	1.7	9.2	-24.0 11.6 7.7	1.5 -25.2 18.0 21.3	4.2	6.3
	k	3.9	4.9	22.0	18.5 28.2 8.7 56.8 17.7 13.5 50.6 -16.3 -5.8
	ghi	20.1	21.0	0.2	1.1	-3.1 -1.1 10.1 11.0 8.9	4.1 -16.6 2.9
	lop	18.3	19.4	2.2	-12.3 -10.8 4.1	-1.6 6.8	4.4	8.0	-2.4 2.9
	atop	100	100	-3.8	-9.3	-9.8 -4.6 5.7 -3.4 -0.7 -0.7 -9.1 -7.4
	Sources: EUROSTAT (2003); BULSTAT (2003) and CSO (2003). *1996-2000

Table B2 :

 B2 CEEC regional employment structures (1995CEEC regional employment structures ( , 2000) )

	Regions/	Total		ab		cdef		ghi	j		k			Lop
	branches		1995 2000 1995 2000 1995 2000 1995 2000 1995 2000 1995 2000
	UE-6 average	100	6.8	5.7 27.8 26.5 24.8 25.2 2.9 2.8 9.2 10.0 28.5 29.8
	bgOl*	100	30.6 38.6 31.2 25.2 16.1 15.7 1.0 0.7 1.2 1.5 19.9 18.3
	bg02*	100	25.3 27.2 36.1 32.5 18.5 20.4 1.0 0.7 1.5 2.1 17.6 17.1
	bg03*	100	31.8 33.3 27.2 23.4 20.1 22.2 1.1 0.8	1.7 2.5 18.0 17.9
	bg04*	100	12.6 13.3 33.2 29.1 23.8 26.0 2.1 2.0 7.0 8.5 21.3 21.1
	bg05*	100	27.4 30.5 35.1 30.5 16.5 19.5 0.9 0.7	1.8 2.3 18.3 16.4
	bg06*	100	31.3 32.8 28.9 24.1 20.7 23.3 1.0 0.9 1.6 2.2 16.4 16.7
	cz01+cz02	100	4.0	2.9 33.1 30.6 27.6 29.1 2.7 3.2 8.1 9.6 24.5 24.7
	cz03	100	10.3 7.4 41.8 42.1 23.2 24.3 2.0 2.0 3.6 4.1 19.1 20.3
	cz04	100	5.1	3.9 44.8 41.7 26.1 24.4 1.6 1.6 3.3 4.8 19.1 23.6
	cz05	100	7.8	6.1 46.4 43.8 21.8 22.0 1.8 1.8 3.6 4.2 18.6 22.1
	cz06	100	8.7	7.7 42.2 40.9 20.0 20.5 1.5 1.7 6.0 5.4 21.6 23.7
	cz07	100	8.4	6.0 46.5 45.3 20.7 21.9 1.6 1.7 2.8 3.5 20.0 21.5
	cz08	100	4.0	3.3 48.2 44.4 22.1 24.6 1.1 1.8 4.0 3.8 20.4 22.1
	huOl	100	2.3	1.7 27.9 28.0 29.3 30.1 3.5 3.4 6.6 9.3 30.3 27.4
	hu02	100	8.3	6.9 42.1 43.9 20.4 21.3 1.8 1.6 2.4 3.5 25.0 22.8
	hu03	100	7.7	6.1 42.0 41.8 21.9 23.5 2.0 1.6 2.3 3.7 24.1 23.2
	hu04	100	12.2 10.0 31.3 32.5 24.4 24.9 1.2 1.6 2.1 4.1 28.8 26.8
	hu05	100	6.7	5.6 37.6 37.2 21.3 23.0 2.1 2.0 2.8 3.7 29.5 28.5
	hu06	100	11.0 8.9 30.3 33.3 23.3 24.5 1.6 1.2 2.2 3.5 31.5 28.6
	hu07	100	17.9 14.9 29.9 31.7 24.1 24.8 1.5 1.7 1.7 2.8 24.8 24.1
	plOl	100	-	15.6	-	30.7	-	22.7	-	2.4	-	6.9	-	21.7
	pl02	100	-	24.2	-	29.4	-	20.6	-	2.0	-	4.7	-	19.2
	pl03	100	-	47.9	-	16.4	-	14.5	-	1.6	-	2.6	-	16.9
	pl04	100	-	17.0	-	29.3	-	23.8

-2.3

2 - 17.2 plOg 100 - 15.1 - 27.8 - 27.1 - 2.3 - 5.8 - 21.9 roOl 100 43.0 51.2 29.0 22.5 13.9 11.8 0.6 0.5 2.2 2.2 11.3 11.8 ro02 100 37.5 44.4 30.1 25.3 18.8 15.5 0.7 0.7 2.1 2.4 10.8 11.8

	ro03+ro08	100	25.9 32.2 36.9 29.5 17.9 17.8 0.9 1.3 6.0 5.6 12.4 13.6
	ro04	100	43.3 51.2 30.0 23.4 14.2 11.9 0.6 0.6 2.1 2.1	9.7 10.8
	ro05	100	30.1 35.9 35.0 30.7 18.7 16.6 0.8 0.8 3.3 2.9 12.2 13.1
	ro06	100	38.3 45.9 31.9 25.6 13.9 13.2 0.8 0.8 3.1 2.3 12.0 12.2
	ro07	100	29.1 34.0 40.4 34.3 15.8 16.0 0.7 0.9 2.5 2.3 11.5 12.5
	sk01+sk02	100	8.2	5.8 35.9 33.7 25.5 26.6 1.9 2.2 8.1	8.5 20.4 23.2
	sk03	100	9.8	7.3 40.2 36.9 22.6 25.8 1.4 1.6 4.7 4.2 21.4 24.2
	sk04	100	9.9	6.4 35.1 32.0 23.6 28.9 1.3 1.4 5.8 5.7 24.3 25.6
	ee	100	10.1 7.1 34.0 33.2 25.2 27.1 1.1 1.3 4.9 6.9 24.6 24.3
	It	100	23.8 19.9 28.2 26.2 19.8 22.8 1.3 1.0 3.0 3.7 24.0 26.3
	lv	100	18.5 15.3 25.8 24.4 25.0 27.4 1.3 1.6 4.8 5.5 24.5 25.8
	CEEC average	100	20.7 23.4 34.6 29.6 20.6 21.0 1.4 1.7 4.0 4.9 18.8 19.4
	Sources: calculated from EUROSTAT (2003); BULSTAT (2003) and CSO (2003). * in 1996

Table B3 :

 B3 CEEC regional location quotients in 2000 (employment structure)

Table B4 : Euclidean distance o f the CEECs and o f CEEC regions from EU-6 average structure (2000 and 1995) -Euclidean distance are presented on a base o f 100 year Eucl. distance in 1995 Eucl. distance in 2000 Base EU-6 1995 EU-6 2000 EU-6 2000 E U -61995

 B4

		Bulgaria	15.0	16.4	16.6	15.4
		Estonia	7.1	8.6	6.0	4.8
		The Czech Republic	8.4	9.8	8.4	7.1
		Hungary	6.1	7.5	6.6	5.1
		Lithuania	13.6	14.7	12.2	11.1
		Latvia	9.8	11.0	8.7	7.9
		Poland	16.5	17.7	16.1	15.0
		Romania	21.9	23.1	27.3	26.2
		Slovakia	7.1	8.6	7.3	6.1
		CEEC average	13.6	14.9	14.3	13.1
	bgOl*	North-West region	19.4	20.7	25.7	24.7
	bg02*	North Central regin	17.7	19.1	18.8	17.5
	bg03*	North-East region	20.0	21.1	21.5	20.5
	bg04*	South-West region	7.0	8.2	7.5	6.3
	bg05*	South central region	17.8	19.1	20.2	18.9
	bg06*	South-East region	19.9	21.0	21.2	20.1
	ee	Estonia	7.1	8.6	6.0	4.8
	cz01+cz02	Praha + Stredni Cechy	4.1	4.8	4.7	4.9
	cz03	Jihoz^pad	9.2	10.7	10.7	9.3
	cz04	Severozapad	9.1	10.2	8.8	7.8
	cz05	Severovychod	12.3	13.7	12.5	11.2
	cz06	Jihovychod	9.5	10.9	9.8	8.3
	cz07	Stredni Morava	13.6	14.9	13.1	11.8
	cz08	Moravskoslezko	11.2	12.3	10.7	9.7
	huOl	KOzep-Magyarorsz&g	5.7	5.6	4.9	5.4
	hu02	KOz^p-Dunantul	10.2	11.7	12.2	10.8
	hu03	Nyugat-Dun&ntul	11.2	12.5	11.9	10.6
	hu04	D&-Dun&itul	7.4	8.7	7.6	6.0
	hu05	Eszak-Magyarorsz&g	8.7	10.1	9.2	7.8
	hu06	Eszak-Alfi)ld	8.6	9.9	9.1	7.6
	hu07	Del-AlfOld	10.1	11.5	9.8	8.2
	It	Lithuania	13.6	14.7	12.2	11.1
	Iv	Latvia	9.8	11.0	8.7	7.9
	plOl	Dolnoslaskie	-	-	9.2	7.9
	pl02	Kujawsko-Pomorskie	-	-	15.1	13.8
	pl03	Lubelskie	-	-	31.9	31.0
	p!04	Lubuskie	-	-	10.3	8.9
	pl05	Lodzkie	-	-	18.6	17.5
	pl06	Malopolskie	-	-	21.3	20.4
	pl07	Mazowieckie	-	-	13.0	12.3
	pl08	Opolskie	-	-	17.4	16.2
	pl09	Podkarpackie	-	-	29.4	28.5
	plOa	Podlaskie	-	-	27.5	26.6
	plOb	Pomorskie	-	-	8.6	7.2
	plOc	Slaskie	-	-	10.1	9.1
	plOd	Swietokrzyskie	-	-	29.5	28.6
	plOe	Warminsko-Mazurskie	-	-	15.0	13.9
	plOf	Wielkopolskie	-	-	15.2	13.9
	plOg	Zachodniopomorskie	-	-	8.8	7.7
	roOl	Nord-Est	27.6	28.7	34.2	33.3
	ro02	Sud-Est	23.9	25.0	29.4	28.4
	ro03+ro08	Sud +Bucuresti	16.5	17.8	20.6	19.5
	ro04	Sud-Vest	28.2	29.2	34.5	33.6
	ro05	Vest	19.2	20.3	23.7	22.5
	ro06	Nord-Vest	24.6	25.8	30.6	29.5
	ro07	Centru	21.0	22.4	23.4	22.1
	sk01+sk02	Bratisl. + Zap. Slov.	6.1	7.4	6.4	5.3
	sk03	Stredni Slovensko	10.2	11.7	10.4	9.0
	sk04	Vychodne Slovensko	7.4	8.9	7.4	6.3
		CEEC regional average	12.3	13.7	14.3	13.1

Table B5 : Ranking o f CEEC regions in function o f their Euclidean distance and their per capita GDP in 2000

 B5

			Euclidean distance from EU-6	GDP/inhab EU-15
		Regions NUTS II Bis	average structure Rank Variation from	SPA=100 Rank Variation
				Oto 100
	cz01+cz02	Praha + Stredni Cechy	1	4,7	1	85
	huOl	Kozep-Magyarorszag	2	4,9	2	76
	ee	Estonia	3	6,0	15	40
	sk01+sk02	Bratisl. + Zap. Slov.	4	6,4	5	56
	sk04	Vychodne Slovensko	5	7,4	23	35
	bg04	South-West region	6	7,5	27	34
	hu04	Del-Dunantul	7	7,6	20	37
	plOb	Pomorskie	8	8,6	17	39
	lv	Latvia	9	8,7	32	31
	cz04	Severozapad	10	8,8	11	46
	plOg	Zachodniopomorskie	11	8,8	19	38
	hu06	Eszak-Alfold	12	9,1	30	32
	hu05	Eszak-Magyarorszag	13	9,2	31	32
	plOl	Dolnoslaskie	14	9,2	16	40
	cz06	Jihovychod	15	9,8	8	49
	hu07	Del-Alfold	16	9,8	21	36
	plOc	Slaskie	17	10,1	13	43
	pl04	Lubuskie	18	10,3	24	35
	sk03	Stredne Slovensko	19	10,4	18	39
	cz03	Jihozapad	20	10,7	6	52
	cz08	Moravskoslezko	21	10,7	10	47
	hu03	Nyugat-Dun&ntul	22	11,9	4	57
	It	Lithuania	23	12,2	22	36
	hu02	Kozep-Dunantul	24	12,2	7	50
	cz05	Severovychod	25	12,5	9	48
	pl07	Mazowieckie	26	13,0	3	59
	cz07	Stredni Morava	27	13,1	12	45
	plOe	Warminsko-Mazurskie	28	15,0	35	29
	pl02	Kuj awsko-Pomorskie	29	15,1	25	35
	plOf	Wielkopolskie	30	15,2	14	41
	pl08	Opolskie	31	17,4	29	33
	pl05	Lodzkie	32	18,6	28	34
	bg02	North Central regin	33	18,8	43	23
	bg05	South central region	34	20,2	46	21
	ro03+ro08	Sud +Bucuresti	35	20,6	33	31
	bg06	South-East region	36	21,2	39	26
	pl06	Malopolskie	37	21,3	26	35
	bg03	North-East region	38	21,5	44	23
	ro07	Centru	39	23,4	40	25
	ro05	Vest	40	23,7	41	24
	bgOl	North-West region	41	25,7	42	24
	plOa	Podlaskie	42	27,5	36	29
	ro02	Sud-Est	43	29,4	47	21
	pl09	Podkarpackie	44	29,4	37	28
	plOd	Swietokrzyskie	45	29,5	34	30
	ro06	Nord-Vest	46	30,6	45	22
	pl03	Lubelskie	47	31,9	38	27
	roOl	Nord-Est	48	34,2	49	16
	ro04	Sud-Vest	49	34,5	48	20

This study is funded by the French Ministry of Research.

Appendix Note 1: Calculating distances between employment structures

Let us consider a number of spatial units j (j = 1, ... J) in the CEECs, and let eu6 designates Europe of 6. The units set can be defined in terms of regions, groups of regions, or countries.

Let

e{ be the share of sector i employment, in the spatial unit j of the CEECs, with 0 < e j <100 and =100, i efu6 the average share of sector i employment in eu6, with the same properties.

The Euclidean distance between the employment structure of unit j and the employment structure of is given by:

The maximum value of this distance is 141. By dividing D by 1.41, we obtain an indicator that varies between 0 and 100:


Jean-Etienne Terrier

Philippe Paparel

Blandine Gadegbeku

Alain Ruffion

Lawrence C Jenkins

Amina Ndiaye

Genitourinary injuries following traffic accidents: analysis of a registry of 162, 690 victims

Background

Traffic accidents are the most frequent cause of genitourinary injuries, accounting for 48% to 66% of all kidney trauma [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF][START_REF] Bjurlin | The impact of seat belts and airbags on high grade renal injuries and nephrectomy rate in motor vehicle collisions[END_REF][START_REF] Wright | Renal and extrarenal predictors of nephrectomy from the national trauma data bank[END_REF]. Nevertheless, the epidemiology of genitourinary system trauma after traffic accidents has not been well studied. Only a few studies have reported the epidemiology of genitourinary injuries within a large database. Wessels et al reported an incidence of 1.2% kidney traumas in a large cohort of 500,000 patients hospitalized for trauma in the USA [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF]. More recently the same team reported 9,002 kidney injuries among 3,247,955 trauma injuries in the National Trauma Data Bank (NTDB) (0.3%) (4).

These studies were not specifically on traffic accidents and were focused solely on kidney injuries [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF][START_REF] Hotaling | A national study of trauma level designation and renal trauma outcomes[END_REF]. Bjurlin et al reported an incidence of 0.8% kidney trauma among 466,028 motor vehicle collisions [START_REF] Bjurlin | The impact of seat belts and airbags on high grade renal injuries and nephrectomy rate in motor vehicle collisions[END_REF]. The same author found 2.16% genitourinary injuries after 16,585 bicycle injuries [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF], the kidney was the most commonly injured (75%) followed by the bladder and urethra (15%), penis and scrotum (10%) [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF]. Hammad et al analyzed data from a Middle Eastern population, they reported a higher incidence of genitourinary injuries (1.8%) following traffic accidents [START_REF] Hammad | Genitourinary injuries following road traffic collisions: a population-based study from the Middle East[END_REF], probably explained by the lower use of seatbelts in this population.

In a previous analysis, we reported an incidence of 0.46% (199/43,056) of genitourinary injuries after traffic accidents (from 1996 to 2001), with 43% being kidney trauma and 24% testicular trauma [START_REF] Paparel | The epidemiology of trauma of the genitourinary system after traffic accidents: analysis of a register of over 43,000 victims[END_REF]. Kidney injuries following trauma have been well described. However, there exists a paucity of data on other genitourinary injuries and specifically injuries following traffic accidents.

The primary objective of this study was to analyze the frequency and type of all genitourinary injuries following traffic accidents. We used the registry of victims of traffic accidents in the French department of Rhone. The Registry collects the demographic characteristics of each road traffic casualty (RTC), the type of road user, the characteristics of the crash (time, location, and collision type) and a description of the bodily injuries sustained. The inclusion criteria are as follows: an RTC involving at least one vehicle (motorized or not) occurring in the Rhône area, requiring institutional health care activity from one of the 245 healthcare structures cooperating together, including prehospital primary care teams and forensic medicine institutes. Data collection method had been described previously [START_REF] Paparel | The epidemiology of trauma of the genitourinary system after traffic accidents: analysis of a register of over 43,000 victims[END_REF]. Beyond collecting the data, the various required tasks remained the same over time: centralization, verification and collating of information from different sources regarding the same accident and/or victim; coding of the data, storage and filing of dossiers (while complying with the recommendations for confidentiality of access made by the French committee for computing and freedom).

Method

The different populations analyzed were motorists, motorcyclists, cyclists, pedestrians, van drivers, van passengers, bus passengers and skateboard users. We assessed the genitourinary injuries presented in each of these categories.

Trauma to the adrenal glands was considered a genitourinary injury. The severity of injuries was coded with the Abbreviated Injury Scale (AIS version 90) and the Injury Severity Score (ISS) [START_REF] Baker | The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care[END_REF].

Kidney trauma was mapped within the classifications of the American Association for the Surgery of Trauma (AAST) [START_REF] Moore | Organ injury scaling: spleen, liver, and kidney[END_REF]. We converted AIS codes to AAST grade as described by Kuan et al [START_REF] Kuan | American Association for the Surgery of Trauma Organ Injury Scale for kidney injuries predicts nephrectomy, dialysis, and death in patients with blunt injury and nephrectomy for penetrating injuries[END_REF].

Using this methodology, kidney injuries were categorized by AAST grade as follows: grade I-AIS code 541612, grade II-AIS code 541622, grade III-AIS code 541624, grade IV-AIS code 541626 and grade V-AIS code 541628 [START_REF] Wright | Renal and extrarenal predictors of nephrectomy from the national trauma data bank[END_REF][START_REF] Hotaling | A national study of trauma level designation and renal trauma outcomes[END_REF][START_REF] Moore | Organ injury scaling: spleen, liver, and kidney[END_REF][START_REF] Kuan | American Association for the Surgery of Trauma Organ Injury Scale for kidney injuries predicts nephrectomy, dialysis, and death in patients with blunt injury and nephrectomy for penetrating injuries[END_REF].

Statistical analyses were performed using SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA). A pvalue of 0.05 was used to define statistical significance. Logistic regression analysis was used to explain the presence of a genitourinary injury whatever its severity according to different factors such age, sex, and crash characteristics.

Results

There were 162,690 victims available for analysis from 1996 to 2013. Of these victims, 963 presented with genitourinary injuries (0.59%). Almost half of these victims (47%) involved motorcyclists, 22% of victims were in a car, 18% in bicycles, 9% were pedestrians and 4% concerned other vehicles like trucks, buses, etc. 51% (491/963) of victims were hospitalized among those, 35% (174/491) required surgical management, the median time of hospitalization was 11 days.

Mean age of victims with genitourinary injuries was 28 years (range 1-88 years), 80% of genitourinary injuries involved males (sex ratio 4:1, 771 men and 192 women). The most commonly injured genitourinary organ was kidney (41%) followed by testicle (23%). Locations and frequencies are reported in Figure 1. The most common body zones injured included abdominal, legs, thorax, arms and head (Figure 2). There were 208 motorists with genitourinary injuries (22% of genitourinary injuries victims), kidney (70%), bladder (10%) and adrenal gland (9%) were the most frequent lesions and 58%

were hospitalized (Table 1). Among the 453 motorcyclist victims with genitourinary injuries, kidney (35%) and testicular (38%) injuries were the most frequent, 56% were hospitalized. Within this group, 62% of injuries involved external genitalia; and among these cases, 37 (8%) were penile trauma (Table 1). There were 175 cyclists with genitourinary injuries, 70% of injuries involved external genitalia; penile injuries (23%) were the most frequent (Table 1). 73.7%

(129/175) of cyclist accidents involved just the cyclists with no antagonist. 26.3% (46/175) had a collision with an external object (cars, trucks, cyclists, pedestrians), 25% of the cyclists were hospitalized. Among the 87 pedestrians with genitourinary injuries, kidney injuries were the most frequent (52%) and 68% were hospitalized (Table 1). In total, there were 395 renal injuries, most being low-grade (Table 2). Kidney injuries were assigned a renal injury grade per the AAST: grade I -59%, grade II -11%, grade III -16%, grade IV -9%, grade V -3% and 2% indeterminate. Distribution of renal injuries among motorists, motorcyclists, cyclists, and pedestrians is presented in table 2. The mean AIS was 3 and the mean ISS was 15. More than 50% of genitourinary injuries were associated with low and moderate injury severity scores (Table 3), ISS ≥25 rate was 23% and the mortality rate was 10% (Table 3).

For victims without genitourinary injuries, there were 91.9% of low and moderate injury severity scores, ISS ≥25 rate was 0.6%, and the mortality rate was 0.9%. Compared to non-GUI victims, GUI victims were more often young adult (16-25 years old) or elderly (66 years old and more) for motorists; young adult (26-35 years old) for motorcyclists; children (0-15 years old) for cyclists, and there were no differences based on age for pedestrians (Table 4). Thus, there was a strong interaction between road user type and age on the presence of GUI. Therefore, we presented one multivariate model per road user type, using the same factors in each model (Table 5).

For cyclists, only age remains a risk factor for urogenital lesions: compared to those aged 26-35 years old, a two-fold increase in risk was observed for children aged 15 years or younger (OR 2.1, 95% CI: 1.3-3.5) (Table 5).

For motorcyclists, even after adjustment, compared to women, a three-fold risk of GUI was observed for men (OR 2.8, 95% CI: 1.9-4.3). Crashes on rural roads had a higher risk of genitourinary injuries than crashes on city streets (OR 1.7, 95% CI: 1.4-2.3). No third party was a protective factor (OR 0.4, 95% CI: 0.3-0.5) compared to a crash with a car, whereas a collision with a heavy vehicle is a risk factor for genitourinary injuries (Table 5).

For motorists, the risk of having a genitourinary injury was higher for the elderly (ages 66 -75;

OR 2.6, 95% CI: 1.4-4.9) (ages 76+; OR 3.2, 95% CI: 1.6-6.5). Place and time of day were also predictors of genitourinary injuries. Crashes in the evening or in the first part of the night (8-11 pm [OR 2.6, 95% CI: 1.6 -4.0] and 0-3 am [OR 3.6, 95% CI: 2.2-6.1]) were more at risk of genitourinary injuries than crashes in the afternoon (4-7 pm; OR 2.1, 95% CI: 1.2-3.5). Crashes on rural roads had a higher risk of genitourinary injuries than crashes on city streets (OR 1.8, 95% CI:

1.3-2.6) (Table 5).

For pedestrians, crashes on highways (OR 3.8, 95% CI: 1.4-10.7) and rural roads (OR 3.2, 95% CI:

1.4-7.5) had a higher risk of genitourinary injuries than crashes on city streets (Table 5).

Discussion

The trauma system in France is based on prehospital care coordinated by the "Service d'Aide Medicale Urgente (SAMU)" and the Service Medicale d'Urgence et de Reanimation (SMUR). A medical regulator from the SAMU makes the initial assessment of trauma severity by phone, and then sends a medical team to the victims for the pre-hospital care (or pre-hospital resuscitation), victims are then transported to the most suitable trauma center. The anesthesiologist-intensivist is the center of this network, and the trauma surgeon does not exist, specialist surgeons treat the injured organ of their skillset [START_REF] Bouzat | French regional trauma network: the Rhone-Alpes example[END_REF][START_REF] Masmejean | Trauma care systems in France[END_REF]. The difference between the trauma system in France and USA has been well described by Nathens et al [START_REF] Nathens | Development of trauma systems and effect on outcomes after injury[END_REF].

In this study, we report a 0.59% overall rate of genitourinary injuries following traffic accidents.

Kidney trauma was the most frequent genitourinary injury [START_REF] Smith | Examination of the impact of airbags on renal injury using a national database[END_REF], 41% in our study, 75% in Bjurlin et al [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF], 51% in Zink et al [START_REF] Zink | Results of the West German multicenter study "Urological traumatology[END_REF] and 67% in Bariol et al [START_REF] Bariol | An analysis of urinary tract trauma in Scotland: imnpact on management and resource needs[END_REF]. In a population-based study on kidney trauma, frequency ranged from 0.3 to 3% [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF][START_REF] Hotaling | A national study of trauma level designation and renal trauma outcomes[END_REF]. We report a low overall incidence of kidney injury after traffic accidents (0.22%), quite similar to that reported by Hotaling et al in the largest population-based study of trauma published from a registry in the United States and Puerto Rico (9,002/3,247,955; 0.3% renal injuries) [START_REF] Hotaling | A national study of trauma level designation and renal trauma outcomes[END_REF]. In Hotaling's study, the distribution of kidney injury according to AAST grade was: grade I -30%, grade II -30%, grade III -19%, grade IV -15%, grade V -5% (5). These trends were reflected in our study with the large majority being low-grade kidney injuries (Grade I and II). However, the Hotaling study was not specifically on traffic accidents and they focused only on kidney injuries (4). Kuan et all reported renal injury mechanisms of motor vehicle collisions, among 115 subjects with renal traumas, 72.5% were lowgrade [START_REF] Kuan | Renal injury mechanisms of motor vehicle collisions: analysis of the crash injury research and engineering network data set[END_REF].

Most patients with genitourinary injuries were male, 80% in this analysis and the male gender predominance was also reported by Wessells and Hotaling et al with the same trends [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF][START_REF] Hotaling | A national study of trauma level designation and renal trauma outcomes[END_REF]. Male sex continued to be an independent predictor when factoring ISS and AIS grades, mechanism of injury and age [START_REF] Wessells | Renal injury and operative management in the United States: results of a population-based study[END_REF].

Motorcyclists and cyclists were most often exposed to external genitalia trauma, respectively 62% and 70% of their genitourinary injuries. In contrast, we reported 12% of external genitalia injuries among motorists. This is explained by the protection of the driver's compartment [START_REF] Meredith | Motorcycle fuel tanks and pelvic fractures: A motorcycle fuel tank syndrome[END_REF].

Bjurlin et al reported 2% of genitourinary injuries following bicycle accidents, with kidney injuries being predominant (75%) followed by bladder and urethra (15%), and penis and scrotum (10%) [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF]. Kim et al reported 2.2% of genitourinary injuries following mountain bike injuries [START_REF] Kim | Mountain biking injuries requiring trauma center admission: a 10-year regional trauma system experience[END_REF], the most frequent organ injured was the kidney (80%), followed by the ureters (8%), the scrotum (8%) and the testicles (4%). These trends are different in our study, 70% of injuries involved external genitalia among which penile trauma (23%) was the most frequent. Conversely to Bjurlin, we observed a low rate of bladder/urethral injuries among motorcyclists and cyclists, which is consistent with Kim study(5) which did not report any bladder and urethral injuries. This does not imply that this injury is rare but might be explained by a frequent misdiagnosis at the time the patient is admitted.

In our previous analysis, we reported no ureteric injuries (7), nor were there any in the Bjurlin et al study [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF]. In the current series, we found 5 (0.5%) ureteric injuries, an under-diagnosis at the time the patient is evaluated may explain this very low rate. CT of the genitourinary system with an injection of contrast enables ureteric rupture diagnosis with good sensitivity [START_REF] Paparel | The epidemiology of trauma of the genitourinary system after traffic accidents: analysis of a register of over 43,000 victims[END_REF][START_REF] Hammer | Effect of an institutional policy of nonoperative treatment of grades I to IV renal injuries[END_REF]; however, it is essential to ask the radiologist for delayed imaging in order to highlight urinary extravasation [START_REF] Paparel | The epidemiology of trauma of the genitourinary system after traffic accidents: analysis of a register of over 43,000 victims[END_REF][START_REF] Herbst | Imaging studies in the diagnosis of urogenital trauma[END_REF].

The current study showed that abdominal, leg, thorax, arm, head and vertebral trauma were the most common regions associated with GU injuries. Bjurlin et al found that patients with vertebral fracture following bicycle accidents had bladder and urethral injuries in 37.7% of cases and 22.6% had kidney injuries [START_REF] Bjurlin | Bicycle-related genitourinary injuries[END_REF]. Physicians in trauma centers must be aware of possible concomitant genitourinary injury even if the primary trauma involves the head or a vertebral fracture.

In multivariate analysis, only young age remains a risk factor for this type of lesion. These findings are consistent with the fact that GU injuries presented by cyclists are predominantly of minor gravity. Conversely, for motorists and pedestrians, we found many factors known to be associated with the severity of the accident: male sex, having an accident on a rural road, at night, or with a heavy vehicle. These factors, combined with the speed and violence of the shock, are quite consistent with the more serious GU injuries presented by these types of users, including a significant proportion with kidney damage. For motorists, being elderly was associated with an additional vulnerability for GU injuries. For motorists and motorcyclists, the highest risk of genitourinary injuries remained for men, even after considering the road user type and different characteristics of the crash. This may be explained by a higher prevalence of at-risk road behaviors, such as speeding or alcohol abuse. Not surprisingly, the crash characteristics linked to the highest probability of genitourinary injuries were night, rural roads, and collision with a heavy vehicle, which correspond to known factors of severity.

To our knowledge, this is the largest report in the literature of genitourinary injuries following traffic accidents. However, this study has two limitations. First, our sample represents a single department in France, it could introduce population bias, which may under-or overestimate genitourinary injuries. As the prevalence of genitourinary injuries is different from one user type to another, we gave most of our results according to these criteria, in order to make our results more comparable with other studies which could have a different distribution of user type amongst their victims. Second, with our population-based data, we were not able to account for the management of trauma, outcomes, and the quality of care varying between different trauma centers.

Conclusions

Genitourinary injury is an infrequent trauma following traffic accidents, with kidneys being the most commonly injured. Physicians must maintain a high awareness for external genitalia injuries in motorcyclists and cyclists.

  This study uses recorded data from the Rhone Registry. Since 1996, the Association for the Registration of Victims of Traffic Accidents (ARVAC) reports traffic accident cases to the IFFSTAR (French Institute of Science and Technology for Transport, Development and Networks). The Registry covers the Rhône Department (≈ 1.8 million inhabitants, 528/km 2 ) and has been approved by the health authorities (National Registry Committee and National Commission for Information Technology and Civil Liberties N° 999211).

Figure 1 :Figure 2 :

 12 Figure 1 : Overall distribution of genitourinary injuries after traffic accidents (n=963, Rhône road trauma registry, 1996-2013)

Table 1 : Hospitalization rate and type/ frequency of genitourinary injuries (n=963, Rhône road trauma registry, 1996-2013)

 1

		Motorists	Motorcyclists		Cyclists	Pedestrians
		n=208	n=453		n=175	n=87
		n	%	n	%	n	%	n	%
	Hospitalization	120	58%	252	56%	44	25%	59	68%
	Adrenal Gland	19	9%	31	7%	4	2%	9	10%
	Bladder	21	10%	17	4%	1	1%	13	15%
	Kidney	146	70%	157	35%	29	17%	45	52%
	Penile	2	1%	37	8%	41	23%	4	5%
	Perineum	3	1%	6	1%	6	3%	7	8%
	Scrotum	7	3%	62	14%	30	17%	3	3%
	Testicles	11	5%	170	38%	34	19%	4	5%
	Ureter	2	1%	1	0%	1	1%	1	1%
	Urethra	5	2%	7	2%	6	3%	6	7%
	Uterus	4	2%	1	0%	0	0%	1	1%
	Vagina	1	0%	4	1%	25	14%	5	6%
	Vulva	1	0%	4	1%	14	8%	0	0%
	Ovary	0	0%	0	0%	0	0%	1	1%

Table 2 : Distribution of renal injuries according to the AAST among motorists, motorcyclists, cyclists and pedestrians (n=963, Rhône road trauma registry, 1996-2013)

 2

	MAIS	n	%	Hospitalization n(%)	ISS	n	%
	MAIS=1	308	32	41(13)	1'-8	491	51%
	MAIS=2	222	23	143(64)	9'-15	139	14%
	MAIS=3	181	19	154(85)	16-24	108	11%
	MAIS=4	125	13	108(86)	≥25	225	23%
	MAIS=5	27	3	25(93)	Total	963	100%
	Death	100	10	20(20)
	Total	963	100	491(51)

Table 3 : Global injury severity according to Maximum Abbreviated Injury Scale (AIS) and the Injury Severity Score (ISS) (Rhône road trauma registry, 1996-2013)

 3

Table 4 : Comparison of Genitourinary (GUI) and non Genitourinary (non GUI) victims by road user type (n=150 144, Rhône road trauma registry, 1996-2013)

 4

			Motorist		Motorcyclist		Cyclist			Pedestrian
	Factors	% GUI % non-GUI	p % GUI % non-GUI	p % GUI % non-GUI	p	% GUI % non-GUI	p
		n=207	n=75 657	n=453	n=35 476	n=175	n=23 210		n=87	n=14 879
	Sex			˂0.001		˂0.001		0.132		0.003
	M	62.3%	48.7%	94.5%	85.2%	71.4%	76.3%		67.8%	51.9%
	F	37.7%	51.3%	5.5%	14.8%	28.6%	23.7%		32.2%	48.1%
	Age			˂ 0.001		0.004		˂0.001		0.215
	0-15 years	7.2%	5.2%	5.1%	8.5%	58.3%	36.6%		25.3%	27.3%
	16-25 years	39.6%	33.7%	47.2%	49.0%	14.3%	22.0%		17.2%	19.7%
	26-35 years	21.3%	24.7%	27.8%	20.5%	10.3%	13.8%		4.6%	11.4%
	36-45 years	6.8%	15.0%	11.7%	12.5%	7.4%	10.9%		12.6%	9.7%
	46-55 years	8.7%	10.6%	6.0%	6.9%	5.7%	8.1%		10.3%	9.0%
	56-65 years	5.3%	5.7%	1.3%	1.9%	2.9%	5.2%		6.9%	7.6%
	66-75 years	6.3%	3.2%	0.7%	0.4%	0.6%	2.5%		8.0%	7.1%
	+76 years	4.8%	2.0%	0.2%	0.2%	0.6%	0.8%		14.9%	8.3%
	Road network			˂0.001		˂0.001		0.900		˂ 0.001
	City Street	44.4%	49.1%	65.6%	58.6%	54.9%	53.3%		77.0%	78.5%
	Highway	14.5%	14.7%	3.5%	2.7%	0.0%	0.0%		4.6%	1.0%
	Rural Road	26.6%	14.8%	17.2%	9.6%	2.9%	3.9%		6.9%	2.1%
	Other	14.5%	21.4%	13.7%	29.0%	42.3%	42.9%		11.5%	18.3%
	Time of accident			˂0.001		˂0.001		0.110		0.024
	0-3 am	14.0%	5.2%	5.1%	3.1%	0.0%	1.4%		2.3%	1.7%
	4-7 am	13.5%	8.9%	6.6%	6.2%	1.7%	3.0%		11.5%	6.0%
	8-11 am	13.0%	15.8%	11.7%	12.2%	9.7%	12.9%		17.2%	19.0%
	12 am-3pm	13.0%	17.6%	21.9%	17.9%	18.9%	16.3%		21.8%	18.4%
	4-7 pm	16.4%	21.5%	32.5%	26.3%	26.3%	26.0%		31.0%	28.1%
	8-11pm	20.3%	10.7%	10.4%	10.2%	9.1%	5.3%		9.2%	6.2%
	Unknown	9.7%	20.3%	11.9%	24.1%	34.3%	35.3%		6.9%	20.5%
	Antagonist			0.001		˂0.001		0.022		˂ 0.001
	Motorist	50.2%	64.3%	60.9%	40.8%	9.1%	18.4%		64.4%	79.8%
	None	16.4%	11.7%	20.8%	45.3%	73.7%	66.5%		-	-
	Van/bus	7.7%	5.2%	2.9%	1.5%	1.7%	0.8%		12.6%	5.5%
	Fixed obstacle	18.4%	13.4%	10.8%	6.2%	6.9%	6.9%		-	-
	Other	7.2%	5.4%	4.6%	6.2%	8.6%	7.4%		23.0%	14.7%

Table 5 : Risk factors for Genitourinary injuries: odds ratios estimated from a multivariate logistic regression (n=150 144, Rhône road trauma registry, 1996-2013)

 5

				Motorist				Motorcyclist				Cyclist	Pedestrian
	Factors	OR		95% CI	P	OR		95% CI	P	OR		95% CI	P	OR	95% CI	P
	Sex					value 0.004					value ˂0.00			value	value
	M F	1.51 5 1	1.13 9	2.01 7		2.83 9 1	1.89 2	4.25 9	1			1.96 0 1	1.24 6	3.084	3
	Age					˂					0.009			˂0.00
	0-15 years	1.74	0.96	3.15	0.001	0.47	0.30	0.74		2.13	1.28	3.52	1
	16-25	3 1.18	5 0.82	0 1.72		2 0.72	1 0.57	1 0.90		1 0.86	9 0.47	3 1.59
	years 26-35	1	9	2	0		1	5	9	7		1	8	3	4
	years 36-45	0.53	0.29	0.98		0.67	0.48	0.93		0.91	0.44	1.86
	years 46-55	7 1.00	4 0.58	2 1.74		2 0.61	6 0.40	0 0.93		1 0.94	6 0.43	3 2.05
	years 56-65	8 1.17	1 0.60	8 2.27		1 0.45	1 0.19	0 1.03		8 0.73	6 0.27	7 1.99
	years 66-75	2 2.63	2 1.40	9 4.94		2 0.99	8 0.31	1 3.18		8 0.30	3 0.04	2 2.26
	years +76 years	6 3.23	4 1.60	7 6.51		7 0.90	2 0.12	9 6.66		2 0.93	0 0.12	4 7.04
	Road		7	8	5	0.002		6	3	3	˂0.00		5	4	2	˂
	network City Street	1					1				1			1	0.00
	Highway Rural	1.04 4 1.84	0.69 0 1.31	1.57 9 2.58		1.12 6 1.74	0.67 4 1.35	1.88 0 2.25				3.79 6 3.21	1.35 1 1.37	10.66 4 7.481	1
	Road Other	2 0.98	5 0.62	1 1.53		9 0.66	5 0.48	8 0.89				1 0.57	9 0.29	1.120
	Time of		1	9	1	˂0.00		3	9	9	0.040			4	4
	accident 0-3 am	3.63	2.18	6.07	1	1.43	0.91	2.25
	4-7 am	9 2.07	0 1.24	4 3.45		9 0.85	7 0.57	7 1.27
	8-11 am	4 1.10	4 0.66	6 1.83		3 0.80	3 0.58	0 1.10
	12 am-	7 0.99	7 0.59	8 1.64		0 1.01	2 0.78	0 1.31
	3pm 4-7 pm	1	2	8	5			9	7	8
	8-11pm	2.56	1.62	4.05		0.84	0.60	1.18
	Unknown	3 0.74	0 0.41	4 1.35		9 0.64	9 0.46	5 0.91
	Antagonis		8	4	2			9	2	2	˂0.00			˂
	t Motorist						1				1			1	0.00
	None Van/bus						0.36 2 1.24	0.28 3 0.70	0.46 1 2.19				-2.67	1.38	5.149	1
	Fixed						4 1.18	6 0.86	3 1.61				-	3	8
	obstacle Other						0 0.53	4 0.34	1 0.83				2.04	1.22	3.417
								4	1	6				3	2

Kidney injuries in bicyclists were less frequent in our analysis and injury grade according to AAST were typically low with 52% being grade I injuries. The low proportion of collisions (cyclist versus other vehicle/pedestrian) in our study (26%) might explain these different trends. As we saw in multivariate analysis, no third party was a protective factor for genitourinary injuries. Moreover, falls without any other user were probably of a higher speed for mountain bikes than for bicycles in transportation places, especially in urban areas.

Cyclists and motorcyclists are most often exposed to external genitalia injuries due to an upward force on the bony structures of the pelvis with impact on the bicycle/motorcycle frame. Meredith et al reported that 85% of pelvic injuries in motorcycle crashes were due to an impact with the motorcycle fuel tank during the crash [START_REF] Meredith | Motorcycle fuel tanks and pelvic fractures: A motorcycle fuel tank syndrome[END_REF]. Cyclists had a lower rate of testicular trauma than motorcyclists (19% vs 38%), which may be explained by the absence of a fuel tank.

Penile trauma occurred most frequently among cyclists, and physicians should maintain a high awareness for this type of injury because it can be very difficult to diagnose. In a meta-analysis, Amer et al reported that early surgical intervention of penile fracture was associated with significantly fewer complications than conservative management or delayed surgery [START_REF] Amer | Penile Fracture: A Meta-Analysis[END_REF]. Surgical intervention results in significantly less erectile dysfunction, curvature and painful erections than conservative management [START_REF] Amer | Penile Fracture: A Meta-Analysis[END_REF]. In cases with a concern for penile trauma, ultrasound may be used; however, this is very operator dependent. MRI is another good diagnostic tool, yet accessibility may be difficult, especially in emergencies when there are other major injuries to be treated [START_REF] Esposito | MRI of penile fracture: what should be a tailored protocol in emergency?[END_REF].

Conflicts of Interest

The authors have no conflict of interest to disclose with respect to this article.

Author contribution statement


Kim Yu

Shifted Weibull tail-distributions

We propose a new family of distributions referred to as shifted Weibull tail-distributions. It is designed to model distributions not necessarily positive and whose upper tails have an exponential behavior. The exponent can be estimated using inference procedures adapted from the Weibull tail-distributions literature.

Let us define a shifted Weibull tail-distribution through its distribution function by:

F (x) = 1 -exp -(x -µ) 1/θ ℓ(x -µ) , x > µ (1)
where ℓ is a slowly-varying function and µ is a real value. Our main result permits to link shifted Weibull tail-distributions with classical Weibull taildistributions, see Table 1 for examples, [START_REF] Yu | Mixtures of Weibull tail-distributions[END_REF][START_REF] Yu | A stability property of weibull tail-distributions[END_REF][START_REF] Yu | Negative weibull tail-distributions[END_REF][START_REF] Yu | Rescaled weibull tail-distributions[END_REF] for stability properties and [START_REF] Yu | Weibull tail-distributions: A bibliography[END_REF] for a review. A sufficient condition for subexponentiality has been established in [START_REF] Yu | On the subexponentiality of Weibull tail-distributions[END_REF], Theorem 1. A new diagnostic tool has been proposed in [START_REF] Yu | A diagnostic tool for Weibull tail-distributions[END_REF].

Theorem. Let X be a random variable from the shifted Weibull-tail model [START_REF] Asimit | Pitfalls in using Weibull tailed distributions[END_REF]. Let Y = Xµ. Then, Y follows a Weibull tail-distribution with associated Weibull tail-coefficient θ.

Proof. Let us consider for all y > 0,

P (Y ≤ y) = P (X -µ ≤ y) = P (X ≤ y + µ) = F (y + µ) = 1 -exp -y 1/θ ℓ(y) .
The conclusion follows.

On the basis of this result, the estimation of θ can be achieved by applying classical Weibull-tail index estimators to Xµ. Berred [START_REF] Berred | Record values and the estimation of the Weibull tail-coefficient[END_REF] uses record values while most of estimators are based on the largest observations of the sample: Asimit et al. [START_REF] Asimit | Pitfalls in using Weibull tailed distributions[END_REF], Beirlant et al. [START_REF] Beirlant | Semiparametric lower bounds for tail index estimation[END_REF][START_REF] Beirlant | The mean residual life function at great age: applications to tail estimation[END_REF][START_REF] Beirlant | Practical analysis of extreme values[END_REF], Broniatowski [6], Diebolt et al. [START_REF] Diebolt | Bias-reduced estimators of the Weibull tail-coefficient[END_REF][START_REF] Diebolt | Bias-reduced extreme quantiles estimators of Weibull tail-distributions[END_REF], Dierckx et al. [START_REF] Dierckx | A new estimation method for Weibull-type tails based on the mean excess function[END_REF], Gardes et al. [START_REF] Gardes | Estimating extreme quantiles of Weibull taildistributions[END_REF][START_REF] Gardes | Comparison of Weibull tail-coefficient estimators[END_REF][START_REF] Gardes | Estimation of the Weibull tail-coefficient with linear combination of upper order statistics[END_REF][START_REF] Gardes | Weibull tail-distributions revisited: a new look at some tail estimators[END_REF][START_REF] Methni | Estimation of extreme quantiles from heavy and light tailed distributions[END_REF], Girard [START_REF] Girard | A Hill type estimate of the Weibull tail-coefficient[END_REF], Goegebeur et al. [START_REF] Goegebeur | Generalized kernel estimators for the Weibull-tail coefficient[END_REF][START_REF] Goegebeur | Goodness-of-fit testing for Weibull-type behavior[END_REF] and Mercadier et al. [START_REF] Mercadier | Optimal rates of convergence in the Weibull model based on kernel-type estimators[END_REF]. Conditional shifted Weibull tail-distributions could also be considered on the basis of [START_REF] Gardes | On the estimation of the functional Weibull tail-coefficient[END_REF].