Analysis of the Main Factors of Regional Growth: An in-depth study of the best and worst performing regions

Transcription

1 Analysis of the Main Factors of Regional Growth: An in-depth study of the best and worst performing regions A final report for the European Commission, Directorate General Regional Policy 23 May 2012 Cambridge Econometrics Covent Garden Cambridge CB1 2HT UK Tel Fax js@camecon.com Web

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3 Contents Page Executive Summary ix 1 Introduction Scope of the study Structure of the work Structure of the report 2 2 An Overview of the Pattern of Growth and Competing Theories Introduction Regional growth performance Statistical evidence for the main factors of regional growth 12 3 The Economic Performance of Regional Groups Introduction The specialisation sectors Differences in GDP per head between regional groups Decomposition of GDP per head Data issues Outline of analysis Concluding remarks 42 4 Regional Structural Changes Effects on Growth, Productivity and Employment Introduction Cluster analysis Econometric analysis Conclusions 71 5 Case Study Selection Introduction Case study selection 73 6 Agricultural Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth Conclusions 98 7 Low-to-Medium Technology Manufacturing Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth 112 iii

4 7.4 Conclusions Medium-to-High Technology Manufacturing Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth Conclusions Tourist Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth Conclusions Financial & Business Services Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth Conclusions Basic Services Region Analysis Overview of regional performance Factors of growth Underlying drivers of growth Conclusions Scoring Factors of Growth on the Basis of the Case Study Evidence Introduction Case study overview Agricultural regions Low-to-medium technology manufacturing regions Medium-to-high technology manufacturing regions Tourist regions Financial & business services regions Basic services regions Conclusions Introduction Agriculture Low-to-Medium Technology Manufacturing Medium-to-High Technology Manufacturing Tourism Financial & Business Services Basic Services Bibliography 243 iv

5 Tables Table 0.1 Summary table for Agriculture regions xi Table 0.2 Summary table for Low-to-Medium Tech Manufacturing regions xii Table 0.3 Summary table for Medium-to-High Tech Manufacturing regions xiii Table 0.4 Summary table for Tourist regions xiv Table 0.5 Summary table for Financial & Business Services regions xv Table 0.6 Summary table for Basic Services regions xvii Table 2.1: Key Drivers of Growth in Broad Growth Ideology Categories 9 Table 3.1 Decomposition of GVA per head in the EU15 by regional group in Table 3.2 Decomposition of GVA per head in the EU27 by regional group in Table 3.3 Decomposition of GVA per head in the EU12 by regional group in Table 3.4 Decomposition of GVA per head in the EU15 by regional group in 2001 Ratios to national average 34 Table 3.5 Decomposition of GVA per head in the EU27 by regional group in 2001 Ratios to national average 34 Table 3.6 Decomposition of GVA per head in the EU12 by regional group in 2001 Ratios to national average 35 Table 3.7 Decomposition of GVA per head in the regional groups in 2008 and differences from Table 3.8 Contribution to annual average growth of GVA per head, Table 3.9 Growth of GVA per head and constituent factors relative to national averages, (Annual average % change) 41 Table 3.10 Number of regions in each classification Table 4.1 Manual cluster analysis: Number of regions by cluster in 2000 and Table 4.2 Manual cluster analysis: Absolute average growth rates by cluster 50 Table 4.3 Manual cluster analysis: Relative average growth rates by cluster 52 Table 4.4 Technical cluster analysis: Number of regions by cluster in 2000 and Table 4.5 Technical cluster analysis: Absolute average growth rates by cluster 56 Table 4.6 Technical cluster analysis: Absolute average growth rates by cluster 57 Table 4.7 Modelling the growth of GVA per head Table 4.8 Growth of GVA per head Table 4.9 Productivity growth Table 4.10 Productivity growth Table 4.11 Employment growth Table 4.12 Employment growth Table 5.1 Regional Specialisation shares of individual groupings of productivity, employment growth and GVA per capita 76 Table 5.2 Regional Specialisation and Combined Groupings of Productivity and Employment Growth as shares of total 77 Table 5.3 Year 3 Case Study Selection 78 Table 5.4: Year 4 Case Study Selection 79 Table 12.1 Agricultural regions in the EU Table 12.2 Agricultural regions in the EU Table 12.3 Low-to-medium tech manufacturing regions in the EU Table 12.4 Low-to-medium tech manufacturing regions in the EU Table 12.5 Medium-to-high tech manufacturing regions in the EU Table 12.6 Medium-to-high tech manufacturing regions in the EU Table 12.7 Tourist regions in the EU v

6 Table 12.8 Tourist regions in the EU Table 12.9 Financial & business service regions in the EU Table Financial & business service regions in the EU Table Basic Service regions in the EU Table Basic service regions in the EU Table 13.1 Summary table for Agriculture regions 230 Table 13.2 Summary table for Low-to-Medium Tech Manufacturing regions 232 Table 13.3 Summary table for Medium-to-High Tech Manufacturing regions 235 Table 13.4 Summary table for Tourist regions 237 Table 13.5 Summary table for Financial & Business Service regions 239 Table 13.6 Summary table for Basic Service regions 241 Figures Figure 2.1: Growth in GVA per capita, Figure 2.2: GVA per capita, Figure 2.3: Correlation between growth and the starting level of wealth in a region 6 Figure 6.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), agricultural regions 81 Figure 6.2 Relation between initial employment rates (1999) and changes in employment rates ( ), agricultural regions 83 Figure 6.3 Relation between changes in employment rates and growth in GDP per head ( ), agriculture regions 84 Figure 6.4 Relation between productivity growth and GDP per head growth ( ), agricultural regions 86 Figure 6.5 R&D employment rates in regions with a low initial level of GVA per capita 87 Figure 6.6 Business and Public R&D Expenditure in regions with a low initial level of GDP per capita 88 Figure 6.7 R&D Expenditure and Tertiary education in regions with a low initial level of GVA per capita 89 Figure 6.8 Tertiary education levels 90 Figure 6.9 Road network density 92 Figure 6.10 Gross Fixed Capital Formation 93 Figure 6.11 Investment output ratio 94 Figure 6.12 Working age population 95 Figure 7.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), low-to-medium tech manufacturing regions 103 Figure 7.2 Relation between initial employment rates (1999) and changes in employment rates ( ), low-to-medium tech manufacturing regions 104 Figure 7.3 Relation between change in employment rates and growth in GDP per head ( ), low-to-medium tech manufacturing regions 104 Figure 7.4 Relation between initial level of productivity (1995) and growth in productivity ( ), low-to-medium tech manufacturing regions 105 Figure 7.5 Relation between productivity growth and GDP per head growth ( ), low-to-medium tech manufacturing regions 106 Figure 7.6 Science & technology employment rates in regions with a high initial level of GVA per capita 107 Figure 7.7 R&D Expenditure in regions with a low initial level of GDP per capita 108 Figure 7.8 Tertiary education levels 109 Figure 7.9 Road network density 110 vi

7 Figure 7.10 Internet access 111 Figure 7.11 Gross Fixed Capital Formation 112 Figure 8.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), high tech manufacturing regions 118 Figure 8.2 Relation between initial employment rates (1999) and changes in employment rates ( ), high tech manufacturing regions 120 Figure 8.3 Relation between change in employment rates and growth in GDP per head ( ), high tech manufacturing regions 121 Figure 8.4 Relation between productivity growth and GDP per head growth ( ), high tech manufacturing regions 122 Figure 8.5 Science & technology employment rates in regions with a high initial level of GVA per capita 123 Figure 8.6 R&D Expenditure and Tertiary education in regions with a low initial level of GDP per capita 124 Figure 8.7 Public versus private R&D Expenditure in regions with a low initial level of GDP per capita 124 Figure 8.8 Tertiary education levels 126 Figure 8.9 Tertiary education in regions with a high initial level of GVA per capita 126 Figure 8.10 Internet access 127 Figure 8.11 Road network density 128 Figure 8.12 Gross Fixed Capital Formation 129 Figure 8.13 Investment output ratio 130 Figure 8.14 Working age population 131 Figure 8.15 Economically active population 132 Figure 9.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), Tourist regions 143 Figure 9.2 Relation between initial employment rates (1999) and changes in employment rates ( ), Tourist regions 144 Figure 9.3 Relation between change in employment rates and growth in GDP per head ( ), tourist regions 145 Figure 9.4 Relation between productivity growth and GDP per head growth ( ), tourism regions 146 Figure 9.5 Relation between initial level of productivity (1995) and growth in productivity ( ), tourism regions 146 Figure 9.6 Science & technology employment rates in regions with a high initial level of GVA per capita 147 Figure 9.7 R&D Expenditure in regions with a low initial level of GDP per capita 148 Figure 9.8 R&D Expenditure in regions with a low initial level of GDP per capita 149 Figure 9.9 Tertiary education levels 150 Figure 9.10 Road network density 151 Figure 9.11 Gross Fixed Capital Formation 152 Figure 9.12 Investment output ratio 153 Figure 9.13 Working age population 154 Figure 9.14 Economically active population 154 Figure 10.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), financial and business service regions 162 Figure 10.2 Relation between change in employment rates and growth in GDP per head ( ), financial and business service regions 163 vii

8 Figure 10.3 Relation between initial employment rates (1999) and changes in employment rates ( ), financial and business service regions 164 Figure 10.4 Relation between initial level of productivity (1995) and growth in productivity ( ), financial and business service regions 167 Figure 10.5 Relation between productivity growth and GDP per head growth ( ), financial and business service regions 168 Figure 10.6 Science & technology employment rates in regions with a high initial level of GVA per capita 170 Figure 10.7 Science & technology employment rates in regions with a low initial level of GVA per capita 171 Figure 10.8 Tertiary education levels 172 Figure 10.9 Gross Fixed Capital Formation 174 Figure Investment output ratio 175 Figure Population & working age population 176 Figure 11.1 Relation between initial level of GDP per head (1995) and growth in GDP per head ( ), basic services regions 190 Figure 11.2 Relation between initial employment rates (1999) and changes in employment rates ( ), basic services regions 191 Figure 11.3 Relation between change in employment rates and growth in GDP per head ( ), basic services regions 192 Figure 11.4 Relation between productivity growth and GDP per head growth ( ), basic services regions 193 Figure 11.5 R&D Expenditure in basic services regions with a high initial level of GDP per capita 195 Figure 11.7 Tertiary education levels 198 Figure 11.8 Road network density 200 Figure 11.9 Internet access 201 Figure Investment output ratio 202 Figure Gross Fixed Capital Formation 203 Figure Working age population 204 Figure Economically active population 204 viii

9 Executive Summary Introduction Conclusions and policy recommendations The aim of this four-year study has been to deepen understanding of the underlying factors influencing economic growth and their interaction at regional level. The focus is on NUTS2 regions across Europe and their experience of economic development over , the period for which data are available across all the regions. The study draws on in-depth regional case studies to augment published statistics with the aim of identifying the main reasons why some regions succeeded in growing faster than others. The final policy recommendations present a set of prioritised recommendations which can be used to help assess the coherence of future regional policy, including the use of Cohesion funds. This final report includes an overview of the trends in regional growth over the period of the study, an analysis of structural differences in regional economies and their impacts on growth, an assessment of the nature of transition in regional economies over the period, an in-depth analysis of regional development across six typologies of region, a factor-scoring exercise that summarises the lessons from the detailed case studies and a final set of conclusions and policy recommendations. This executive summary places those conclusions as the starting point for the discussion, and then summarises the supporting evidence. The analysis has shown that it is fruitful to consider regional performance in the light of differences in economic structure, as reflected in six typologies, defined by specialisation as measured by employment. These are; Agricultural regions Low-Medium-Low Tech Manufacturing regions High-Medium-High Tech Manufacturing regions Tourist regions Financial & Business Services regions Basic Services regions When we analyse the factors that influence growth, we find that the regions belonging to each typology are affected by the various identified growth factors to different degrees, which suggests that they need to be addressed by different policy emphases. But cutting across these typologies is a further pattern: the level of economic development that the region has reached. To reflect this, the analysis is further broken down to consider geographical location (EU15 and EU12) and the growth path over (fast-growing and slow-growing regions, defined by a comparison of GDP per capita growth with the national average for the country in which the region lies). Some policy recommendations concern actions to help regions perform better while remaining, in broad terms, the same type of region, while others seek to help regions change more fundamentally the type of economy that they possess. The tables below summarise the factors that have contributed to or limited growth within each type of region studied, and draw from these a list of policy objectives to be pursued, ranked in order of importance. Drivers of growth are defined as those economic factors that have played a significant role in driving economic growth in the regions in question, while constraints on growth are those factors that (whether ix

10 Agricultural regions through their absence or the poor application of them) have held back economic growth. There are two categories of policy recommendations for each region: overarching policies, relating to drivers that are relevant to growth in all types of region and which we are highlighted because they play a particularly significant role in economic development within that particular type of region; and region-specific policies, where the growth factor (and therefore policy recommendation) are relevant to that type of region and would not form part of a wider set of economic growth policies. Agricultural regions tend to have lower productivity and employment rates than other regions. They generally have poor access to and development of technology, lowerthan-average skill levels amongst the workforce and poor infrastructure and connectivity. As such, their economic development is severely hamstrung. Geographically they often have peripheral locations. Galicia and Extremadura were the most successful of the Agricultural regions selected for close study. In these regions, policy focused on improving connectivity, primarily through the development of transport infrastructure. Policy also sought to bring about increases in the educational attainment levels of the working-age population and in investment in transport infrastructure in order to facilitate restructuring away from agriculture. In the second half of the study period, more effort was placed on improving the competitiveness of existing manufacturing sectors, and this bore some fruit as clusters developed and productivity and employment both increased. Dytiki Ellada saw relatively low employment growth, but did benefit from productivity increases there was no specifically regional policy here, although the region did benefit from EU funding as an Objective 1 region. This funding targeted reducing regional inequality and unemployment through a mixture of direct (improving internal connectivity, raising the skills of the workforce) and indirect (encouraging FDI and R&D capacity) action. In the EU12, regional policy was much less developed, although it has been more widely adopted recently. Much of the policy was therefore shaped at a national level, and concentrated upon improving connectivity and skill levels; however this was concentrated on urban centres and the spillover effects were very limited. x

11 Table 0.1 Summary table for Agriculture regions Drivers of growth Constraints on growth Overarching policies relevant Region-specific policies EU15 regions Internal/external connectivity Human capital Human capital Human capital (1) RTDI (2) Clustering (3) Specialisation (4) RTDI Clustering EU12 regions Social capital Internal/external connectivity Internal/external connectivity (1) Specialisation (2) RTDI (4) RTDI Specialisation Human capital (3) Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority The policy recommendations reflect the fact that these regions typically have poor endowment of factors that are key to facilitating growth; specifically human capital and connectivity. Efforts to improve these should be prioritised, with secondary consideration given to actions designed to increase the economic base of the regions (particularly into higher value-added activities). Low-to-Medium Tech Manufacturing Regions Regions that specialise in low and medium-low tech manufacturing are typically those which have an industrial heritage that they have been unable to fully move away from. Economic performance is typically worse in the EU12 regions (where GDP per capita and employment rates are below the EU12 average) than in the EU15 (where these indicators are similar to the EU15 average), perhaps because restructuring has proceeded further in the EU15 so that the remaining parts of the sector are concentrated in activities that can be undertaken competitively. Accessibility is highlighted as a problem, with many regions lacking adequate transport infrastructure, while growth in the regions has also suffered as a result of intra-regional disparities. The successful EU15 regions have made good use of funding to undertake significant reform programmes, while also attempting to maintain the current industrial base. In Länsi-Suomi, for example, only some sub-regions received structural funds; this ensured that the more prosperous areas largely continued on their existing path while the weaknesses of deprived areas were addressed. Subsequent regional policy focused on bolstering innovation and human capital. In the EU12, regional policy has been much less developed; more recent policy has concentrated on improving connectivity (i.e. transport infrastructure) and attempting to encourage FDI. The case studies suggest that the latter in particularly has been largely ineffective, with FDI rates remaining low. Policy recommendations in these regions are concentrated on both improving the factors viewed as pre-conditions for growth (human capital and connectivity), a weakness in these regions in both the EU15 and the EU12, and also promoting R&D xi

12 activities in order to facilitate a move into higher value-added activities (i.e. Mediumto-High Tech Manufacturing and services), both within the current value chains and in new ones. Table 0.2 Summary table for Low-to-Medium Tech Manufacturing regions Drivers of growth Constraints on growth Overarching policies relevant Region specific policies EU15 regions Clustering Specialisation Specialisation Human capital (1) External RTDI (3) Clustering (4) RTDI Social capital connectivity (2) EU12 regions Internal/external connectivity Specialisation Internal/external connectivity (1) Specialisation (2) FDI (3) RTDI (4) FDI RTDI Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority Medium-to-High Tech Manufacturing Regions Regions specialising in high and medium-high tech manufacturing are dependent upon their ability to connect with global supply chains they therefore typically have strong transport links with other regions (both neighbouring regions and further afield). This connectivity is related in some cases to their favourable geographical location, but in the vast majority of regions it is related to their favourable physical features and in all cases it involves strong transport links. Many of these regions have adapted their productive capacity to move away from the low-tech manufacturing undertaken earlier in the 20th century. These regions have a higher-than-average concentration of high-tech activities as such, technology has a relatively large direct role in their economies. Similarly, these activities require a highly-skilled workforce to operate successfully. xii

13 The case studies show that there are several factors that have clearly been important to regions at different stages of their economic development. The most successful regions in the EU15, such as Bayern, adapted to improve their RTDI capacity and achieved employment growth through the increased specialisation brought about by the development of clusters, giving a roadmap for development to aspiring regions such as Alsace, where poorly targeted policy has hindered their ability to develop RTDI. Among the most successful EU12 regions, where initial GDP per capita levels were relatively low, but growth was rapid, such as Közép-Dunántúl, one of the key developments was the ability to attract FDI, fostered by specific regional policy and a favourable location. The challenge for these regions now is to develop RTDI capacity to ultimately become innovation leaders rather than followers, similar to their EU15 counterparts. Regions with a similar growth in GDP per capita, but with weaker employment growth, such as Śląskie, would probably have performed better if they had experienced increased specialisation (achieved through policy aimed at fostering clusters and RTDI). Policy recommendations in these regions are primarily aimed at facilitating a transition into higher value-added service activities. The key factor in encouraging Table 0.3 Summary table for Medium-to-High Tech Manufacturing regions Drivers of growth Constraints on growth Overarching policies relevant Region specific policies Clustering Specialisation FDI RTDI Internal/external connectivity Human capital Specialisation Human capital Internal connectivity EU15 regions Human capital (1) Specialisation (2) EU12 regions Clustering (3) RTDI (4) FDI Specialisation Human capital (2) Specialisation (1) Specialisation Internal connectivity RTDI (3) Internal/external connectivity Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority this development is the provision of a high-quality workforce, which should be among the primary policy priorities. Some regions may aim at improving the productivity of their manufacturing activities (which is likely to evolve organically if the key elements such as good human capital are present); however, those that aim to move into a services specialisation will require significant policy assistance. Transference to higher (and more specialised) manufacturing and services activities will require the ability to undertake R&D; and so policies aimed at fostering this should be undertaken once the other factors are in place. xiii

14 Tourist Regions Tourist regions are dependent largely on their ability to attract visitors; it is thus no surprise that these regions are often endowed with abundant natural resources and particular physical features which set them apart from other regions. Similarly, the more successful regions typically have strong transport infrastructure. GDP per capita and employment rates for these regions are generally higher than in Agricultural regions but lower than in High Tech Manufacturing and Financial & Business Services regions. While they are (relative to the national employment structure) specialised in Tourism, the industry mix of the rest of the economy can vary depending upon location and urban environment. The case studies give a clear illustration of the differing types of regional policy adopted by regions at different stages of development. Amongst the most successful, such as Salzburg, policy concentrated on tackling a lack of RTDI capacity and clustering to promote high value-added sectors outside of tourism, and the policy was relatively effective. The less well-performing regions, such as Algarve, used public funds to undertake more extensive development, which included schemes to promote diversification, development of human capital and infrastructure. Our analysis suggests that these factors are key to facilitating economic growth, but these regions were hamstrung by a lack of sufficient business investment both domestically and from overseas. Table 0.4 Summary table for Tourist regions Drivers of growth Constraints on growth Overarching policies relevant Region specific policies EU15 regions Internal/external connectivity Specialisation Intra-regional disparities Human capital (3) Clustering (1) Social capital (2) RTDI (4) Human capital RTDI Clustering Social capital EU12 regions FDI Human capital Intra-regional disparities RTDI Internal/external connectivity (2) Intra-regional disparities (1) Clustering (3) External connectivity Specialisation (4) Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority The case studies from the EU12 present a similar set of policy responses, but with different outcomes. Because of a lack of clear regional policy, in the first half of the study period short-term decisions were made, often with unforeseen negative impacts, while the overall regional performance was dominated by macroeconomic factors. However, when regional policy, largely funded by the EU, was introduced in 2000, in both Małopolskie and Severoiztochen it was very broad and in many ways overambitious, setting out to improve business support, build new transport infrastructure xiv

15 Financial & Business Services Regions and raise the skills levels of the workforce. While this was generally successful in Severoiztochen, Małopolskie saw very little impact from the policy. The policy recommendations for these regions differ between the EU15 and the EU12. However, some common themes emerge. Policy in these regions should be aimed at widening the economic base, both spatially and in terms of sector activity. While policy recommendations for the EU12 are concentrated on the fundamentals (reducing intra-regional disparities and facilitating growth through the development of higherquality human capital), in the EU15, where there is less need to concentrate on the fundamentals, policy aims should instead concentrate on facilitating wider economic development through clusters and developing high-quality social capital. Many regions specialised in financial & business services contain capital cities, or at the very least regional capitals. As such, they typically have at least one (very) large urban centre, and a very strong transport infrastructure including at least one airport. This urban concentration has implications for the industrial structure of the region; as well as a developed services sector (urban centres typically require more in the way of basic services due to the concentration of population) there is often limited scope for the large-scale development of manufacturing facilities. The case studies undertaken in this subset of regions have highlighted some of the key advantages that these regions have. The presence of large cities often means that they have strong regional identities and also strong regional policy. While a high skill level is required for many jobs in the Financial & Business Services sector, wages are also higher than in other types of region, meaning that they have been able to attract Table 0.5 Summary table for Financial & Business Services regions Drivers of growth Constraints on growth Overarching policies relevant Region specific policies Internal/external connectivity Human capital Clustering RTDI Specialisation Internal/external connectivity Human capital FDI EU15 regions Human capital (1) External connectivity suitably-skilled workers, often from across the globe. However, the rapid growth of the sector has created in some cases significant intra-regional inequality in these urban areas, and policy is now starting to tackle these. In the EU12, where economic development has been more rapid, the difference between these regions and the rest of the country is particularly marked, exacerbating (4) EU12 regions Human capital (1) Internal/external connectivity (2) RTDI (2) Clustering (3) RTDI (3) Specialisation Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority xv

16 Basic Services Regions many of the effects mentioned above. There is a shortage of suitably skilled workers and so more of an effort has been made to increase skill levels amongst the resident population. In Financial & business services regions policy should be largely aimed at consolidating and advancing the competitive advantages already enjoyed. This includes further strengthening of the fundamentals such as human capital and infrastructure (to improve connectivity still further) and the development of R&D capacity in order to ensure that these regions remain at the forefront of economic development. Basic services regions broadly fall into one of two categories; on the one hand there are regions with a history of low-tech manufacturing which have failed to sufficiently develop into higher value-added activities in manufacturing or business services; and on the other hand there are regions which have failed to turn an agricultural heritage into a specialisation in tourism or business services. The former are often held back by their industrial heritage in heavy industry, while the latter often have significant potential, as yet not fully realised, linked to their natural resources. Regions from both backgrounds lack significant urban centres, and as a result have failed to develop a significant industrial or financial & business services specialisation; the services that do exist in the region are accommodated to the local population they serve (such as public services, retail and distribution). GDP per capita in these regions is typically below average, while employment rates are also low, with only regions specialising in Agriculture having a lower average. Over the period of the study, growth rates in these variables have also been lower than average. Basic services regions have largely lacked strong regional policy, and growth has been weak even in regions of the EU15, such as Campania. Where regional policy has been present, it has been generally reactive rather than proactive, dealing with weaknesses in the regional economy and poorly targeted; for example in Campania it aimed to promote High-Tech Manufacturing rather than Tourism to which the region was better suited. There are exceptions to this, however, for example Norra Mellansverige, where the severe downturn in heavy manufacturing was quickly met with strong policy measures targeting the development of RTDI capacity to promote manufacturing. xvi

17 In the EU12, regional policy has been much less developed, with funding typically sourced from the EU and policies decided at a national level, but there are some success stories in spite of the unwieldy governance; for example in Východné Table 0.6 Summary table for Basic Services regions Drivers of growth Constraints on growth Overarching policies relevant Region specific policies EU15 regions RTDI External connectivity Clustering Human capital FDI RTDI Human capital (1) RTDI (2) Specialisation (3) Social capital (4) Specialisation Social capital Clustering Specialisation Social capital EU12 regions Internal/external Human capital (1) RTDI (2) connectivity Human capital FDI RTDI Specialisation Internal/external connectivity (3) Numbers in brackets represent order of priority attached to policies, where 1 is the highest priority Identifying the regional groupings Slovensko effective policy measures were introduced to encourage business development and promote FDI. Future growth will depend more upon strong leadership and the ability to attract/maintain new businesses in the face of increasing competition. The primary policy aim for Basic services regions is to improve the quality of the workforce, through higher education institutions and improved training schemes. This is one of the key facilitators of growth identified in the study, and poor-quality resources in this area will be a major drag on growth. Efforts to develop economic specialisation should be focused on the development of R&D capacity, which would encourage high-tech firms into these regions. Where infrastructure is inadequate (primarily a problem identified in the EU12), policy should attempt to address this so as to improve connectivity both with other regions and internally to maximise the impacts of any development in the economy. Regional development is affected by many factors. Chapter 3 of the report discusses and analyses these, and provides a framework for the remaining analysis. Development paths tend to reflect the underlying characteristics of regions, some of which are inherent in their geography and geo-physical features, some of which are inherited from the past. These characteristics, some of which have been formed as a result of past patterns of economic development rather than simply given by physical geography, have a major influence on the economic activities in which regions have a comparative advantage and, accordingly, on their areas of specialisation. The activities in which a region specialises affect its potential for growth as well as the economic development path it follows. As such, these areas of specialisation need to xvii

18 Productivity differences drive differences in GDP per head across the typologies Agricultural regions, especially in the EU12, saw rapid growth helped by an improved employment rate be explicitly taken into account when assessing growth performance; they will also affect the development policies best suited to stimulating or supporting growth. In the analysis, the regions are split into six typologies according to the sector in which the share of employment is furthest above the national average or, where there is no obvious such sector, they are assigned to the Basic Services group. GDP per head is broken down into four constituent parts; productivity, employment rate, working-age population (as a percentage of total population) and employment rate of the working-age population; and the data are adjusted for commuting effects. Taking 2001 as a benchmark year in the middle of our period of study, there is a clear hierarchy amongst the regional typologies; Financial & Business Services regions had, on average, GDP per head (PPS) 21% higher than in Medium-to-High Tech Manufacturing regions and 77% higher than Agricultural regions. While all four constituent parts of GDP per head (productivity, employment rate, working-age population and employment rate of the working-age population) are higher in Financial & Business Services regions than in Medium-to-High Tech Manufacturing or Agricultural regions, the most significant effect is due to a large differential in productivity. When the EU12 and the EU15 are considered separately the relationships still hold. When we remove differences that can be attributed to country effects (because some types of region are more common in high-income than in lowincome countries), the relationships still broadly hold although the differences are smaller; for example GDP per head is almost 60% higher in Financial & Business Services than in Agricultural regions. These gaps were larger in the EU12 than the EU15, primarily due to large differences between EU12 regions in terms of productivity. The findings are similar when using data for 2008, although Financial & Business Services pulled further ahead of the other typologies (due to an increase in the employment rate). Examining GDP per head over the period , growth was fastest in the Agricultural regions, followed by Tourist regions with Financial & Business Service regions in third place. However, it emerges that this is due to the distribution of regions between the EU12 (where growth was faster) and the EU15 (where it was slower); in both areas Financial & Business Service regions grew faster than other regions. However, Agricultural regions did experience rapid growth (but when Lithuania and Latvia are removed from the calculations for the EU12, this subset of regions had slower growth than Medium-to-High Tech Manufacturing and Tourist regions). Across the majority of typologies the contributions of employment rate growth and productivity were roughly equal, the exceptions being Agricultural regions (where employment rate gains, from a very low base, were particularly rapid) and Financial & Business Services (where productivity growth outstripped employment rate growth). If country effects are included, Financial & Business Services growth in GDP per head is marginally faster than the national average, along with Agricultural and Basic Service regions. Within the EU15, gaps between the typologies were generally small, and determined largely by changes (a mixture of increases and decreases) in the employment rate, while in the EU12 gaps were much larger and were driven by productivity differences. xviii

19 Productivity differences are reinforced by employment rate differences There is some evidence of divergence in GDP per head in both the EU15 and the EU12 Regional Transition Structural change raises productivity, but may or may not raise employment rates The analysis indicates that there are significant differences in levels of GDP per head between the regional groups, that these are much wider in the EU12 countries than in the EU15, but that the rank order of the groups in terms of GDP per head is similar as well as being relatively consistent over time. It also shows that the employment and demographic factors which are proximate determinants of GDP per head tend to reinforce the effect of productivity (or GDP per person employed) by pushing up GDP per head further or pushing it down. Within the EU15, differences between the groups of regions in productivity growth over were relatively small. Differences between the groups of regions in the extent of changes in the employment rate had a considerable effect on the differences in productivity growth over the period, with agriculture having the largest decreases in employment rates. In the EU12, there was divergence rather than convergence in regional GDP per head, as the main urban centres saw the most rapid changes. This divergence is reason enough for distinguishing between regions in terms of their areas of specialisation, especially since much of the focus of regional development policy is on regions in the EU12. Even in the EU15, where there seem to be fewer systematic differences in growth performance between the groups of regions, the difference in levels of GDP per head between the regional groups suggests differences in performance over the very long term. Moreover, over the limited nine-year period examined, it was still the case that the rate of growth experienced in the Financial & Business Services regions in the three EU15 Cohesion countries (Spain, Greece and Portugal) exceeded that in other regions, albeit only slightly. The sectoral specialisation of the regions is not necessarily stable over time, as certain sectors decline and new sectors emerge due to competition, economic policy, and other macroeconomic changes. These structural shifts alter employment structures, levels and also incomes. Generally, it is expected that structural changes take the form of a transition from low value-added toward high value-added production, resulting in higher productivity and more rapid growth in the regions undergoing this transition. Whether or not the result is higher income per head depends on whether jobs in the new sectors are created at a sufficiently high rate to offset losses elsewhere so that the employment rate also rises. The analysis in Chapter 4 identified regions that underwent a structural change over and evaluated the potential consequences of this on the regions performance as measured by the growth of GDP, productivity and employment. Cluster analysis provides some evidence that structural changes in the EU regions are correlated with differences in GDP per head, productivity and employment growth. In many regions the change from one specialisation group to another was associated with higher growth rates of GDP and productivity and, to a limited degree, of employment also, compared to the regions that did not undergo the same kind of transition. Econometric analysis provides clear evidence that increasing specialisation was associated with higher productivity (on average across sectors for the region), almost independent of what kind of sectoral shift was involved. But increasing specialisation was not generally associated with higher employment rates (again, on average across sectors in the region). xix

20 Distinguishing two causes of increased specialisation Factors of growth in Agricultural regions The betterperforming regions benefited from improved connectivity The conclusions to be drawn depend on whether the observed changes in specialisation were caused directly by faster growth in a given sector or indirectly as a result of a decline in other sectors. Where the relationship between increased specialisation and improved productivity and GDP growth is associated with a direct increase in specialisation, there is an argument for strengthening the regions existing comparative advantages and thus for policies that target the sectors in which the regions are already specialised. But where specialisation changes were caused indirectly, policies aiming at diversification are more appropriate. The econometric results suggested that the direct case was more common. Chapter 6 discusses the findings of the case studies of Agricultural regions. The case studies covered regions at different stages of development and for which the factors influencing growth therefore tend to differ. In Lubelskie, the one case study region in the EU12, the region s relative decline was mainly due to a failure to restructure away from agriculture and subsistence farming. During the decade the agriculture sector still accounted for the major part of employment and capital, despite its very low level of productivity. The industrial structure outside of agriculture was not modernised, new activity was not attracted and the economy was not diversified. The poor employment prospects in other sectors led not only to high involvement in subsistence farming but also to high levels of outward migration which further diminished the potential for growth. Underdeveloped infrastructure was an obstacle to diversification. The case studies on regions in the south of the EU15 - Galicia, Extremadura and Dytiki Ellada - show that the key factors that contributed to growth during the period were the development of human capital and investment in infrastructure. The starting point of the catching-up process in Galicia and Extremadura was mainly an improvement in transport infrastructure. Better connections both within the regions and with other regions increased market accessibility. The development of the regions was also supported by an increase in the educational attainment levels of the workingage population and comparatively high public investment, which facilitated restructuring away from agriculture. On the other hand, GDP per head in Dytiki Ellada continued to fall further below the national average during the years Agriculture remains the backbone of the region s economy despite its low productivity. Improvements in transport infrastructure and an increased inflow of capital because of the 2004 Olympic Games contributed to the development of the construction sector, in particular. Although there was some diversification of the economy, it was not enough to sustain growth in the long term. The permanent underinvestment in education in particular affected the growth potential of the region which was aggravated by the outward migration of the highly-educated. In Galicia and Extremadura, although there was some diversification away from agriculture into manufacturing and services, restructuring away from low tech activities was hindered by relatively low levels of R&D expenditure and innovation capacity in the business sector. xx

21 In higher income countries, innovation and human capital development were important factors For agricultural regions in the rest of the EU15, innovation capacity seems to play an important role in their growth. The case studies on Languedoc-Roussillon, Cumbria and Niedersachsen show that the key factors were technology and R&D, human capital and support services. Despite the generally low growth of these regions, they give some indication of the development path which might be followed by less developed agricultural regions. For many years the policy focus in Languedoc-Roussillon has been on higher education. Several universities and research centres were created which helped attract inward migration. However, there have not been enough employment opportunities because of the industrial base which remains relatively narrow, without many large enterprises to stimulate development. Inward migration, therefore, was accompanied to some extent by higher unemployment. The lack of large companies which are potential hubs of R&D, innovation and technological diffusion, was seen as one of the major constraints on the growth of the region during the period. Although in recent years there have been significant increases in public investment to support R&D and the development of high-tech activities, the effect on the region s growth performance over the period was not noticeable. In practice, this is not too surprising. The goal of the policy is to have a long-term impact on regional development which means increasing competitiveness through structural change. This policy even if successful takes time - often decades - to affect growth and employment rates. Both domestic and foreign direct investment are often lower relative to GDP in agricultural regions than at national level. Under-investment in both the public and private sector in Niedersachsen, contributed to its relatively low growth over the period and to the disparities in development between urban areas, where most part of the business services and R&D activities are located, and rural areas which remain largely agricultural. The region s economic centres were not large enough to produce spill-over effects which might have helped to improve the structure of the rural economy by encouraging a shift away from low-tech and low-growth activities in manufacturing. In this regard, it is worth noting that the relatively low endowment of human capital in the region is due to a lack of demand by employers rather than a shortage of university graduates, who exceed the number that can be absorbed by the local economy. The low growth rates achieved by Cumbria are similarly due in part to the overrepresentation of low value-added activities (agriculture, tourism) and declining industries (shipbuilding, nuclear power) and the underdevelopment of high valueadded growth sectors. The low level of R&D and innovative capacity is seen as a major inhibiting factor. This is illustrated by the nuclear industry located in the region. While Sellafield in Cumbria was the first civil nuclear plant in the world and therefore represented cutting-edge technology at the time it was built, the expertise was located in a neighbouring region, at Manchester University and this link is still important. The relatively low education attainment level of the work force did not facilitate the development of new, more competitive activities with higher technological content. In the strongly-performing EU15 regions, policy has concentrated on improving connectivity both within the region and with other regions. The aim of this has been to increase economic activity in sectors outside of the specialisation; to broaden the economy into other, higher value-added, sectors. In the poorly-performing regions, policy has been weak, too often backward looking rather than responding to current or future drivers of growth. Regions in the EU12 have had very little regionally- xxi

22 Connectivity problems need to be addressed, but human capital and innovation matter Factors of growth in Low-to-Medium Technology Manufacturing Regions EU15 regions have to respond to lowcost foreign competition Some EU12 regions have benefited from inward investment, but spillover effects have been weak Clusters have been an important factor in competitiveness, but have so far proved difficult to stimulate in EU12 regions influenced policy, and policy has instead been decided at Member State level, resulting in action thoroughly unsuited to the region. Looking forward, in the successful regions of the EU15 it is expected that human capital will increase in importance; that is, as the economy continues to shift away from agricultural activities, the labour requirements of the economy will change, and these regions must ensure that they have a suitably skilled workforce to take advantage of this transition. In the poorly performing regions the expectation is that connectivity will remain a significant challenge, and this is clearly one that must be addressed by any successful regional policy, regardless of whether the ultimate aim of that policy is to promote structural change or simply to maximise the benefits of the existing factor endowments. In the EU12 regions connectivity is also expected to be the primary concern, and improving access to markets, both domestically and abroad, must be a first step to economic development. Chapter 7 considers the case studies undertaken in regions which had a sectoral specialisation in Low-to-Medium Tech Manufacturing. The case studies (two of them in the EU15 and four in the EU12) suggest that, despite some overlap in the basic issues (like skill endowments, technology, clustering and networking), there is a difference in the fundamental conditions between the EU15 and EU12 regions. In the case of the EU15 LTM regions we are dealing with countries enjoying relatively high wages and high levels of GDP per capita, where firms historically are closely tied to their regions (and vice versa). Here, the major challenge is the impact of increasing global competition, because the regions are specialised in sectors of activity that are highly vulnerable. The main problem for those regions, according to the case studies, is to keep their industries competitive while promoting a shift towards higher valueadded activities. As far as the EU12 LTM regions are concerned, these are in countries of relatively low wages and low levels of GDP per capita (at least in a European context), and thus they have some advantages over competing regions in the EU15. At the same time many of the economically important firms in the EU12 were established fairly recently in their regions as a consequence of major FDI inflows. On the one hand, those inflows of foreign capital were an important factor in the restructuring process and one of the main sources for new jobs, income and more generally for economic growth. On the other hand, one can reasonably assume that these foreign firms are more footloose, especially when funded by investors seeking efficiency gains, and were basically exploiting the cost advantage of the EU12. One result is that, at least in the early stages, they did not create links or spillovers to the host regions economies. Thus the problems the EU12 regions face are, first, how to attract foreign firms and then how to bind them more closely to the local economies. Despite the differences in fundamental conditions between the EU15 and the EU12 regions, some of the possible solutions might be common to both groups of regions. Both EU15 case studies highlighted the importance of firm clustering, co-operation and networking and argued that these types of activities tend to raise the competitiveness of the individual firms in the networks or clusters. However, this insight is not new and as a matter of fact has inspired EU12 regions to create their own clusters. In contrast to the EU15 regions, these relatively newly created clusters or networks are seldom seen as a positive factor for growth, except on the basis of theory rather than observed facts. In part, this may have to do with the time necessary to xxii

23 R&D has played a key role in high income regions, and innovation opportunities are much broader than R&D activity establish and stabilise links between firms to facilitate some cooperation among competitors. It is important, in this regard, to observe that the development of clusters and networks in Länsi-Suomi and Veneto took place over a long period of time and mostly between local firms. In the case of the EU12 regions the impression is that there is a belief that providing a free piece of land with infrastructure is enough to create a cluster, while in reality it might take much more to make firms co-operate; especially if firms are foreign-owned or belong to a multinational value-chain and are located in the region only for cost advantages. Thus, establishing links between foreign-owned and domestic companies in the EU12 regions might be a difficult task. In fact, it might depend on the characteristics of each individual foreign firm whether domestic links are feasible and to what degree. It certainly requires that the domestic firms be able to adapt to the needs of foreign companies in terms of technology, reliability and quality. However, if such links could be established, this would tie the more footloose foreign companies a little more firmly to their host regions. Another factor that is very prominent in the case studies is technology and R&D. Länsi-Suomi is the best example of a successful strategy in this respect. Not only is R&D expenditure high in this region but also the links between companies and public institutions seem to be well developed and fruitful. Therefore, following such a strategy certainly has its merits, yet it might be difficult to replicate it in other regions, especially as far as R&D expenditures are concerned. In general those expenditures tend to be much lower in the EU12 regions and certainly also in a number of EU15 regions. However, it is worth noting that R&D is not the only source of innovation. As Verspagen et al. (2008) point out in an analysis of innovation behaviour of firms, there is a significant heterogeneity in the firms innovation strategies, which to a large extent are independent of the sector of economic activity as well as the country they are operating in. The authors identify three important types of innovation strategies. The classical type of firm engages in R&D activities to innovate, in which case enterprise co-operation is also a key element in the innovation process. However, firms with less (or even no) R&D activities innovate through: user related activities that are geared toward product effects, and involve innovation activities aimed at improving design and a smooth introduction of new products on the market. Notably this requires some sensitivity to signals from clients, consumers and firms. external activities that exploit opportunities for innovation from diffusion of technology embodied in new capital goods and products, as well as the acquisition of existing technology from other firms, e.g. through purchasing of rights to use patents, licences or software. An important element is the high importance attached to the various sources of information that are required in order to benefit from the knowledge and innovation created by other firms. Major channels of transmission are the firms suppliers as well as events like trade exhibitions. Hence, R&D expenditures are not necessarily a comprehensive indicator for the innovative potential of the regions. At the same time this analysis opens up some ways for regional policy to create strategies that are suited to the different types of innovation behaviour of firms. Importantly, strengthening education (not only school or university education but also constant learning throughout working life) increases the ability of individuals and, on aggregate, of regions to exploit such innovation potentials. xxiii

24 In the EU12, connectivity is important for attracting inward investment, but can also expose uncompetitive activities to foreign competition In the EU12, successful development has tended to accentuate regional inequality Social capital has been more important in EU15 than in EU12 regions Policy in the EU12 has aimed to improve the provision of physical capital Another factor in the R&D and technology activities of the LTM regions is the importance of the co-operation between public research institutions and private companies. This is a strategy followed in the EU12 regions. Especially for small and medium-sized enterprises, which generally tend to conduct less R&D than big firms, this opens up another route to innovation. Nevertheless, these strategies are not free of problems. One case study, for example, reports that public (university) research is financed by big companies and the danger is that any innovations coming out of this partnership accrue only to the investing company, while smaller enterprises do not have access to it (even if they were nurtured in a publicly-funded organisation). The case studies for the EU12 LTM regions have pointed to the importance of accessibility, especially with respect to the inflow of FDI. Thus it is shown that even a geographically quite central location such as Severozápad can be quite unattractive to foreign firms and presumably also to local firms if its transport networks hamper accessibility. Thus, infrastructure is a pre-requisite for economic and firm development. There are, however, some caveats. Increasing the accessibility of a region might make it more attractive for firms to locate there, yet it also makes it easier to supply the region from outside, thus increasing the competition for local firms. In the successful EU12 regions, growth has benefited from the presence of urban centres, and policy measures have been introduced to promote the development of clusters (which have also had a positive impact on economic growth); this has resulted in a degree of intra-regional inequality, which is perceived as having had a negative impact on growth. The case studies suggest increased economic activity in urban centres has come at the cost of declining incomes and depopulation in the peripheral parts of these regions. Despite the widening disparities, little policy effort has been made to tackle this issue. The experiences of the EU15 regions suggest that this may well be a trade-off worth making; certainly in the case of Veneto, these disparities have had only a slight negative impact on growth, and thus have not been the focus of policy. While social capital is not reported to have played a significant role in regions in the EU12, it has had a positive impact in the successful EU15 regions in promoting the development of economic clusters and in changing the structure of the regional economies. Economic policy in the EU12 regions seems to be mimicking that in the disadvantaged EU15 regions, in concentrating on improving the stock of physical capital, through measures to promote FDI, RTDI and clustering. This has involved a combination of national and regional policy, with the regional aspects often focusing on developing links between business and academic institutions within a region and ensuring land is available for development. It is expected that the importance of these factors will increase in the majority of regions. This is a further reason for believing that such policies are well targeted, and that they have (particularly in the areas of RTDI and clustering) been effective in promoting regional development. More generally, those factors that are expected to increase in importance for to the regional economy have been the objects of an appropriate degree of policy, indicating that regional governance has been effective in identifying the work required to bolster economic development. xxiv

25 Factors of growth in Medium-to- High Tech Manufacturing Regions The successful EU12 regions have depended heavily on inward investment EU15 regions have sought to promote diversification and innovation supported by the availability of a skilled workforce Chapter 8 discussed the range of case studies from the regions specialising in Medium-to-High Tech Manufacturing. Amongst the EU12 regions, which have typically seen fast growth in GDP per capita from a low (relative to the EU average) starting position, one of the key drivers of economic development in regions specialising in high tech manufacturing has been the influx of FDI that took place throughout the 1990s and the early 2000s in the run-up to their accession to the EU in This was largely driven by the lower cost base in these countries and the industrial heritage of the regions (including a workforce with the required skills). However, government policy, at a macro rather than regional level, was set up to encourage this investment. This was perhaps most successful in regions which opened themselves up to market forces most fully; some regions, such as Śląskie, which tried to protect existing state industries, found that these policies limited growth. This influx of FDI has had the disadvantage, however, of reducing the ability of these regions to diversify; more recent policy has been aimed at attempting to encourage business formation and improve skill levels. The high GDP per capita regions, which include the majority of EU15 regions classified as specialising in high tech manufacturing, have concentrated much of their policy efforts on trying to diversify their economies, often in two directions at the same time; both up the value chain within the same industry (typically moving into R&D activities) and also into other industries, across the high-tech manufacturing and service sectors. Over there was a general expansion in the size of the service sector across all regions, however in the regions which experienced strong GDP per capita growth this development was primarily in financial & business services rather than the lower value-added end of market services or non-market services. Many regions have adopted regional innovation policies focused around the development of business clusters, although in the poorly performing regions the impact of this is limited due to the lack of skilled workers available. The development of many of the regions within this group has built upon diversification particularly towards the most productive manufacturing activities (research & development) and business services; however, what sets the successful regions apart from the rest is that they have a highly skilled workforce to take up the jobs created. Similarly, clusters and innovation networks rely upon strong links with local educational institutions; such links help both to increase the skill levels of the resident population and to foster growth in high-value-added activities which draw investment and skilled migrants into the region. Amongst the most successful regions in the EU15, there has been a clear trend towards policy concentrating on developing productive capacity, specifically on improving human capital, developing RTDI facilities, promoting clustering and improving specialisation in high value-added activities, policies which appear to be well targeted, as these factors have played a large role in determining growth performance over the period. The regions which have experienced slower growth have typically been targeting the same drivers of growth, a combination of weak governance and poorly-targeted policy has not resulted in the same gains. xxv

26 Factors of growth in Tourist Regions Good infrastructure is a pre-condition of success Some tourist regions have succeeded in promoting diversification But some tourist regions have a low-skilled workforce which hinders diversification Looking forward, Västsverige can greatly inform regional policy decisions in those regions which wish to alter their industry mix, as an example of a region which successfully made the transition from a specialisation in High-Tech Manufacturing to Financial & Business Services. This region undertook a similar mix of policy initiatives as other successful High-Tech Manufacturing regions within the EU15, and was successful as a result of a highly-skilled workforce and strong infrastructure, suggesting that these policies can help maximise growth both within the existing sectoral structure and by altering it. In the strongly-performing EU12 regions, there is evidence of an awareness of the increasing role to be played by RTDI and clustering and any policy must be aimed at tackling deficiencies in this area. Chapter 9 considers the findings from case studies of regions specialising in Tourism. The case studies suggest that the most important factor for the development of the tourist regions is infrastructure. This relates, obviously, to transport systems but also to other types of infrastructure. The tourism potential of a region can only be exploited if it is easy of access. Algarve is a good example of this. Even though it is rather distant from the main European transport corridors, it is visited by tourists from all over Europe, in sharp contrast to some tourist regions in the EU12. Among other types of infrastructure, water and waste water treatment, energy supply as well as telecommunications also affect the attractiveness of a tourist region. Severoiztochen shows the disadvantages that flow from deficiencies in these areas. As far as other factors are concerned, one has to differentiate between regions that are highly specialised in tourism (like Algarve or some of the Greek regions) and regions that are more diversified. As far as the more diversified regions are concerned (for example, the case study regions Małopolskie and Severoiztochen) they have other specialisations, mostly in manufacturing industries. The factors that determine development in these sectors are similar to those in the regions specialising in low or high-technology manufacturing. Thus, R&D and innovation, clustering and networking, education, support services and, for the EU12 regions especially, also FDI are key determinants for their development. The discussion and findings of the thematic chapters devoted to such types of manufacturing also apply to the more diversified Tourism regions. As far as the regions that are highly specialised in tourism are concerned, the example of Algarve shows that such regions might have difficulties in diversifying their activities. It may be an open question whether there such regions need to diversify, but, should they wish to, they are encumbered by some structural drawbacks. A low level of educational attainment is the most important of these drawbacks. The example of Algarve indicates that the labour supply in highly touristic regions tends to adjust to the demand, which is mostly not for the highly educated, not only in tourism but also in all the activities depending on tourism such as wholesale and retail and catering. Thus the skill base of the regions tends to be quite low, which not only discourages investment by foreign companies, but also prevents the application of more sophisticated techniques in existing manufacturing industries or also in agriculture. Other major drawbacks include low levels of R&D and lack of innovation potential as well as the absence of clustering and networking between enterprises (outside the tourism sector). However, given the economic structure of these regions, these drawbacks are hardly surprising. xxvi

27 Factors of growth in Financial & Business Services Regions A long history of serving as a major urban centre has typically accumulated the factors that drive comparative advantage But political changes have opened up new opportunities in some cities Growth in Tourism regions in the EU15 has been largely driven by attempts to broaden their industrial base. Policy in these areas has been largely concentrated on improving the available human capital through programmes designed to up skill the workforce and policies to encourage the development of clusters of businesses in non- Tourism sectors such as High-Tech Manufacturing. An overriding theme in the regions of the EU12 has been a lack of developed regional policy; what policy there has been has tended to be very broad, and as such has not been effective. For example policy in Małopolskie aimed at improving the connectivity of the region was recorded as being almost entirely ineffective. Across the regions of the EU12 improvements in human capital and FDI have been significant; however, the primary difference between the successful and unsuccessful regions has been in the ability to promote new business activities; in the successful regions business clusters have developed and grown, while in the poorly-performing regions the economy remains largely dependent upon traditional cottage industry alongside tourism. Looking forward, the policy aims of both EU15 and EU12 regions are broadly similar; to encourage economic diversity and a shift in the industrial base. Despite that, the detail is different; in the poorly-performing EU12 regions policy needs to reduce the dependence upon historic industries and maximise the output from the existing tourism industry, while in the strongly-performing EU12 regions and the EU15 policy is aimed at developing high-value-added service sectors to sit alongside tourism activities. Chapter 10 discusses the case studies from Financial & Business Service regions. Regions containing national capitals or the capitals of German Länder have most of the resources (location, physical infrastructure, human and intellectual resources, substantial inherited economic sectors) necessary for becoming and remaining oriented to financial & business services. Within the 34 regions specialising in financial & business services there are also a few urban areas with similar advantages to capital cities (Leipzig, Zuid-Holland and Utrecht) and some regions that share in the locational and infrastructural resources of their immediately adjoining capitals and, consequently, are an integral part of the economic activities of the capitals (two Belgian regions immediately adjoining Brussels and four UK regions surrounding Inner London). The location within the country largely explains how the regions came to be or to contain the capital city or a major urban centre. Location within flows of international trade in goods and services helps to explain why some capital city regions prosper more than others. Sometimes a region can overcome disadvantages of location or acquire new advantages as a result of developments outside its control. The prominent recent example is found in the consequences of the fall of the Iron Curtain and the opening of central and eastern Europe to free markets and western economies, leading to membership of the EU. This changed the balance of economic advantage between western and eastern regions of Austria to the benefit of the eastern regions and Vienna. It also ended the isolation of capitals in central and eastern Europe. The xxvii

28 Cities that saw rapid growth benefited from major upgrading of substandard infrastructure Highly skilled labour migrates to the major urban centres, supporting their specialisation Successful regions are typically associated with high-technology activities effects in eastern Europe were not felt evenly. Prague and Budapest are located closer to western markets than Warsaw, Bucharest or Sofia. Budapest, Bratislava and Vienna form an axis of capitals between which foreign investment has created modern industry in small towns in north-west Hungary and in the region around Bratislava. The full benefits of Bratislava s proximity to Vienna (the two cities are only 60 km apart) were not seen within the reference period for the present study, but the future course of the relationship between these two cities, which had been a very close one in the 19th century, may be among the more interesting developments within Europe in the near future. The location of capital city regions encourages the growth of comprehensive transport networks within the country and beyond. Because of the nature of modern businesses, regions with better international connections by air and with more modern telecommunications structures and access to information networks are in a strong position. These advantages spread to neighbouring regions. The infrastructure has continuously to be updated, improved and expanded in capacity. One of the reasons why some capitals in former communist countries had reached higher level of GDP per capita by 1995 and continued to grow more rapidly afterwards than others lies in the better quality of their transport and communications infrastructure: Prague, Budapest and Bratislava on the one hand, contrasted with Warsaw, Sofia and Bucharest, on the other. Capital cities tend to concentrate the human, educational, scientific and research capabilities of their country. They attract students to their institutions of higher education; the question is whether they can keep them after they graduate. In smaller countries in central and eastern Europe the capital is normally the dominant urban centre and contains by far the predominant concentration of the country s human and intellectual resources. In the east, only Warsaw is an exception. In western countries the human and intellectual resources are more evenly spread, but still with the largest concentrations in and around the capital. In the more successful regions, financial & business services have grown in close relationship with a wider and more diverse economy, both sustaining it and being sustained by it, within the immediate region and its neighbours and within the country as a whole. There is no one type of successful diverse economy, but within our group of regions the more successful are focused on a range of high-technology activities. The leading examples in western Europe are: Inner London, at the centre of corridors of high technology radiating to Cambridge and Oxford and of high technology alongside the research and headquarters functions of international businesses along the Thames Valley Ile-de-France, with Paris at the centre of two rings of high technology, scientific research and defence industries in the new towns built around Paris since the 1960s Oberbayern, with the major high-technology hub around Munich high-technology centred on telecommunications equipment and services in Stockholm and the region of Helsinki trade services and physical distribution at Rotterdam (along with petrochemicals), at Hamburg (along with aerospace construction), at Amsterdam in Noord-Holland, and, on a more national rather than international scale, at Utrecht (along with headquarters functions and R&D) xxviii

29 In the EU12, the transition to a market economy underpinned rapid growth in financial and business services The presence of four west German cities (ignoring Berlin at present) among the sixteen high-level regions is partly a result of the federal structure of the country, but also an indication of the diversity of the German economy, in which manufacturing for export plays a prominent role. In the capitals of the former communist countries it was necessary first to reduce and close large parts of the antiquated industrial structure with which they entered the 1990s and to create institutions for financial and business services of a type to which they had been almost entirely unaccustomed under the old regime. Prague, Budapest and Warsaw made good progress in this direction in the early years. In Prague the reputation, physical beauty and convenient location of the city attracted western investment in finance, property and related services. In Budapest the recapitalisation of the banking sector attracted western investment. Warsaw was able to take advantage of the size of Poland, the early shock treatment of the Polish economy and its central location in a wider Europe to create and build up the major stock exchange in central Europe. At the same time foreign investment was needed to build up the broad economy in the country as a whole that could justify and make use of a growing financial and business services sector. Such investment, particularly in high-technology making products for the newly-opened eastern markets within and beyond the EU often flowed to parts of the country outside the capital (as in Hungary, the Czech Republic and Poland), but it stimulated the growth of related services in the capitals. Investment could also be concentrated near the capital (VW s large investments near Bratislava). The range of products favoured by foreign investors was wide (including white goods, lighting, and electronics); but automobiles were a favoured sector, especially in Slovakia, Poland and Hungary. The delays of several years before the economies were restructured and reforms were introduced in Romania and Bulgaria explains why Bucharest and Sofia started and ended the period with levels of GDP per capita far below those of all other regions in the group of 34. The (partial) opening of western labour markets to eastern countries before and immediately after their accession to the EU encouraged people to emigrate in search of work at a time when restructuring was destroying jobs but had not yet begun to create enough new ones. Hence, in most capitals of former communist countries (as well as Leipzig), the decline in the population contributed, along with GDP growth, to impressive growth in GDP per capita. The two cities from the former East Germany (Leipzig and the [eastern half of] Berlin) had the advantage of direct financial transfers from west Germany and they also had west Germany as a relatively open labour market in which to find work. It took Leipzig several years to build up again the physical resources of its transport networks, and its international fairground and to create new links with the global economy so as to regain something of its centuries-old tradition of being a major centre for international trade. The major investment in automobile manufacture also came in the second half of the reference period. The economic benefits of all this restructuring and investment have been seen since the reference period for this study. Berlin, with its economy artificially subsidised in the western part and artificially planned and shaped in the eastern part, is an anomaly. Eventually the restructuring was successful, but for the years of this study its many universities produced more xxix

30 Although these regions are specialised in financial and business services, their success is typically also based on other high value-added activities graduates than the economy could create jobs for. Only in very recent years (from 2010/2011 onwards) have there been signs that the volume of scientific research and university education in Berlin may at last be on the point of giving the city a high place as a centre of innovation. The federal structure of Germany prevents Berlin as the national capital from developing the same preponderance as a centre for administration, finance and business services as other capitals enjoy in small and large countries. The most successful policy interventions have succeeded by creating the conditions in which the region could take advantage of its location, its existing skills and its traditions. This involves maintaining and improving the physical and intellectual resources, broadening the economy to sustain and justify the financial & business services sector and to generate more wealth, and identifying new markets. The importance of the physical infrastructure is seen in the contrast between the more rapid progress made among new members by Prague, Budapest and Bratislava, in contrast to Warsaw, Sofia and Bucharest. It is seen also in the importance of the transport and communications infrastructure in the leading regions of western Europe. The attraction of students to institutions of tertiary education and of researchers to scientific and R&D institutes is another key to success, even if there is some lapse of time before the economy of the immediate region is able to provide enough jobs to retain a majority of such people. The contrast between the relatively narrow basis of the economy in such regions as Zuid-Holland or Lisbon and the broader-based economies of Inner London with its surrounding regions, of Ile-de-France and of the more successful regions among the former communist countries shows the importance of a broader economy, but shows also that there is no one type of broader economy or set of activities that are essential to success, except for a focus on high technology. Attraction of foreign investment is of great importance, particularly in former communist countries that needed to acquire more modern expertise and techniques of business, but also in Ireland. More generally, foreign investment encourages greater international flows of talent and gives the opportunity of learning from a wider range of approaches. The successful EU15 regions recorded growth as being driven by strong human capital and FDI development, allied to strong RTDI activity and increased levels of clustering and specialisation. This growth was also facilitated by a strong transport infrastructure, allowing free movement of capital both internally and into the region from elsewhere. These growth factors match the areas where regional policy has been strongest and also accord with the reported views on which factors will increase in importance in the future. This matching indicates that the regional policy undertaken in these regions has been well targeted. A major focus of regional policy in the poorly-performing EU15 regions has been to combat shortcomings not often present in regions specialising in Financial & Business Services, such as developing transport infrastructure to overcome their relatively inconvenient location and maximising the benefits to be gained from favourable macroeconomic conditions. However more recent policy has seen something of a shift, with the encouragement of RTDI facilities, suggesting that policy in the region is becoming more mature. xxx

31 So far, in the EU12, inward investment has been the key factor in growth, with human capital development much less important than in the EU15 Factors of growth in Basic Services Regions Lack of specialisation in sectors appropriate for future development has hindered growth A historical legacy of disadvantages has to be tackled The results from the poorly-performing EU15 regions contrast sharply with this. Their growth has been driven by FDI flows (an area in which there was no regional policy) and rather than by human capital. This suggests that the economic development of these regions is structurally very different to that of the successful regions. However, this conclusion should be treated with considerable caution, since much of our evidence is based on just one region, Berlin, where the development of business services was driven largely by the decision to re-establish the city as the capital of Germany, a process that was completed in As a result, a major focus of regional policy was to remedy shortcomings that are not often present in regions specialising in Financial & Business Services, such as poor transport infrastructure links with prosperous western Germany and the physical and industrial legacy of the different artificial supports to the economies in former West and East Berlin. In the strongly-growing EU12 regions, human capital has not received the same level of policy attention as in the EU15 regions. The main reason for this is that jobs in these EU12 regions are normally much better paid than in other regions in the same country, with the consequence that market forces often ensure that skill requirements are met. However, as economic development continues apace in the EU12, this will change, and these regions must start to develop means of sustaining their resources of human capital. Similarly, little attention has been paid to encouraging specialisation or clustering, as the prosperity brought about by membership of the EU and the emergence from Communism were sufficient factors to encourage the development of more modern sectors. In the future, however, it will not be sensible to continue with such benign neglect. Chapter 11 discusses the case studies of Basic Services regions (in effect, those which did not show a clear sectoral specialisation). Many factors influenced the development of the basic services regions during the period , and they operated in diverse ways, related to the different backgrounds and contexts of operation. Differences in income levels are of course one underlying explanation of this diversity, but equally important are the differences in the degree of deindustrialisation and the consequent shrinking of the productive base. These are related to the development path followed in the past and the historical background. Another cause of diversity is the influence and degree of development of surrounding areas. Basic services regions are regions that either did not develop any particular specialisation (e.g. Alentejo) or which have lost specialisation in their main activities, often in heavy industry dominated by a few big companies (e.g. Hainaut, Campania and Kent). The failure to restructure because of underinvestment, lack of entrepreneurship, limited innovation capacity and low attractiveness led to a decline in the productive base and consequently to high unemployment. The process was often reinforced by responsive (rather than proactive ) development policies aimed at maintaining employment in declining industries and creating jobs in public services in response to high unemployment. There was a cumulative process of decline insofar as the erosion of the productive base led to a structural deficiency of job opportunities so that unemployment in some of the regions turned into an inter-generational problem leading to poverty and a waste of human capital. This in turn affected the image of the regions and reduced even more their attractiveness as business locations (already dented by the environmental damage caused by heavy industry) particularly for high value-added and knowledge- xxxi

32 Investment in infrastructure, human and social capital and improved governance have been applied to try to break the cycle of relative decline intensive activities. The process was reinforced by permanent under-investment in human capital and outward migration of the high skilled, which impaired even more the attractiveness of these regions as business locations. The activities that were able to develop tended to require few qualifications and skills, and were mainly basic and non-market services. The process described above was observed in most basic services regions covered by the case studies both in the EU15 and in the EU12; but there were significant variations in the extent of decline experienced and/or the capacity to create new activity. The process was evident in Hainaut, Campania and to a lesser extent Kent. Although Norra Mellansverige also suffered from industrial decline, industry had played a slightly less dominant role there than in the other regions studied. Moreover, the policies put in place in this region to support human capital development and investment in traditional as well as new activities (e.g. ICT) helped to maintain employment in manufacturing. In Východné Slovensko, although there was a steep decline in industry after the Soviet era, industry is still important and even expanded over the period despite the fact that FDI went primarily to the more attractive Západné Slovensko. In Kent, the relative decline in industry is clearly linked to the more competitive surrounding areas to which several strategically important large companies which used to be in the region have relocated. The story is different in Alentejo where the primary sector still plays an important role. The development of basic services in this region is more the result of limited comparative advantages for industrial development in a region where industry has never been important. With the exception of Východné Slovensko, there was a relative decline in productivity over the period in all basic services regions covered by the case studies. Growth in employment was only significant in Alentejo. The main factors underlying development over the period considered were FDI, transport infrastructure, human capital development, social capital and governance. Improvements in these are seen as factors contributing to growth, deficiencies in them as factors inhibiting growth. In Východné Slovensko growth of output per person employed over the period was largely influenced by the presence of foreign-owned companies which increased productivity in some manufacturing sectors. Overall, FDI seems to have had a positive influence on the development of Hainaut as well. On the other hand, in Campania, the sharp reduction in FDI and the relocation of companies led to a decline in many manufacturing sectors. As in Hainaut, development policy over the period tended to focus excessively on supporting declining activities mainly to prevent job losses. This to some extent deterred investment in new activities. The main factors sustaining growth in Norra Mellansverige over were the strong policy support for upgrading technology in traditional activities and for promoting technology and the substantial investment in education. Good regional governance also played a fundamental role in encouraging the networking and clustering that proved successful in developing new activities with higher technology and knowledge content (e.g. ICT). In Východné Slovensko and in Alentejo deficiencies in transport infrastructure are seen as a factor that constrained the development of the regions. In Campania, the recent improvements in the transport system had no noticeable effect on growth xxxii

33 Inward investment in large plants offers some attractions, but also significant risks It is unlikely that the private sector will generate sufficient R&D activity although it is arguable that, without these, decline would have been even steeper than it actually was. Deficiencies in human capital and under-investment in its development are seen as important factors inhibiting growth in most basic services regions because these constrain the development of higher value-added activities. They also limit the possibility of carrying out R&D and accessing technologies developed elsewhere. The over-representation of the public sector in services activities with limited knowledge requirements is seen in these regions as a disincentive for investment in education. The attraction of FDI might be a relevant policy option in so far as it is likely to give access to technology with potentially beneficial spill-over effects on the productive system. In practice, however, FDI might be difficult to attract because of the relative under-endowment of human capital, the low R&D capacity, the environmental damage left by heavy industry or the presence of more attractive neighbouring regions (Kent near to London; and Východné Slovensko overshadowed by Západné Slovensko). In this context, even if the region were to be successful in attracting FDI, the productive system might not be able to absorb and exploit the potential spill-overs associated with FDI. It also needs to be emphasised that FDI tends to be foot-loose, in the sense that it can relocate when public incentives are reduced. In these regions, although there has been a decline in manufacturing, it still seems that growth in productivity and in GDP per head is driven by industry. In other words, specialisation in basic services is not conducive to growth either in terms of aggregate productivity or in terms of GDP per head. This observation suggests that, even in regions with no specific profile of sector specialisation, policy needs to support the development of the economic base. Basic services regions have lost competitiveness in their main industrial activities because they were not able to innovate to make production more efficient and more competitive or to develop new products which would have opened up new markets. Given the limited capacity of the private sector to carry out R&D, it is justifiable that public R&D should play a more important role in these regions. The focus should mainly be on supporting effective transfer of commercially mature innovations to the business sector. At the same time there is a need to strengthen the capacity of companies to absorb technology. This could be achieved by providing adequate training, encouraging cooperation and networking between companies inside the region and by facilitating their integration into global research networks. It is in principle difficult to see how policy could counter the pull of strong neighbouring regions which are more attractive and which tend to drain the region s factors and engines of growth (as has happened in Kent and Východné Slovensko). Instead of trying to compete in the same sectors of specialisation, the aim should be to find market niches where the region can develop activities which complement those in the stronger neighbouring one. This requires a strategic approach and a careful analysis of potential complementarities. By focusing support on these, policy can help to better capture spill-over effects. xxxiii

34 Lack of a base of entrepreneurial talent has been exacerbated by dependence on public sector employment Improved connectivity is key to future development in these regions The loss of competitiveness that led to the decline of the main industrial activities was mainly due to the lack of capacity to adapt to change. On the other hand the failure to generate new activity was due to a lack of strong entrepreneurship. Because of the weakness of the business sector, the public sector has been a major employer for many decades in these regions; and this has tended to diminish collective entrepreneurial spirit even more. This has limited the take-up of new opportunities and might explain why obvious potential areas of comparative advantage such as tourism in Campania and Alentejo are not developed. Policy has a role to play in this regard. The above considerations suggest that, given the diversity of factors that inhibit development and the interaction between these, interventions in narrowly defined areas are not likely to produce significant effects. Instead, integrated and systemic policies are required that tackle simultaneously the various factors and difficulties that constrain development. The main elements of these are the strengthening of human capital, the development of technology absorption capacity, the stimulation of the entrepreneurial spirit, and improvement of the environment with direct support targeted on carefully identified market niches. Within the EU15, all of the regions studied achieved below-national-average GDP per capita growth over the period of the study; however the analysis reveals that, at least in some regions, there were positive factors encouraging growth. In these regions there was a conscious effort to improve connectivity, through the provision of increased transport infrastructure, in order to reduce isolation from the wider economy. The case studies suggest that, although growth in GDP per capita has been relatively strong in regions in the EU12, this is more a result of the transition to a market economy and of spillovers from other strongly-performing regions, than a consequence of factors directly attributable to the regional economy. A lack of FDI and RTDI capacity in the regions has restricted growth, but some policy initiatives have been taken with a view to filling these gaps. It is also clear that the historic legacy of these regions constrains growth, with poor locations often exacerbated by poor connectivity and infrastructure. On the whole, however, regional policy (where present) has been well-matched to these shortcomings. The challenge for these regions is, on the one hand, to maximise their existing capabilities (in the sense of improving infrastructure to take full benefit from the continued catch-up growth of the EU12 and spillovers from other regions) and, on the other, to engender some degree of specialisation, with a view to encouraging high value-added activities. xxxiv

35 1 Introduction This report sets out the findings and conclusions from the project Analysis of the Main Factors of Regional Growth: An in-depth study of the best and worst performing European regions, commissioned by DG Regio. The work has been carried out by a consortium led by Cambridge Econometrics, also including Applica and WiiW. 1.1 Scope of the study The remit of the study was to deepen the understanding of economic development in the regions of the EU27 to identify those (NUTS2-level) regions that have performed well and those that have performed poorly to identify and analyse the underlying factors of this diverse (regional) performance. The study is intended to provide evidence and insight with which to better structure regional development programmes, such as those funded by the EU through the Cohesion Funds. This study has been conducted over a four-year period, being conceived and started in advance of the global downturn of mid-2008 and the subsequent sustained period of economic uncertainty and challenge. It does not include much by way of analysis of the period since This is in part because the economic crisis developed while the work was under way, and statistical evidence on the performance of regions through this period is only just becoming available; but also, the study is considering the importance of underlying factors of growth; something that is not thought likely to have changed drastically because of the economic crisis. There is some tentative evidence indicating that the strongest regions going into the period of crisis have also been those that have been relatively successful since then. That this study is reporting now is timely. Europe 2020, the growth strategy for Europe, is well established, and within this the recognition that regions need to develop Smart Specialisation Strategies (so-called S3 ). This concept stresses the need for dynamic and entrepreneurial-led growth policies, rather than top-down policies and recognises that successful S3 policies need to be place-based, that it is not a case that one size fits all. This thinking is in line with the findings of this study, which illustrates how regions have been able to make effective use of the resources they have, and also to develop these resources further to enhance their growth potential. 1.2 Structure of the work The study combines detailed quantitative analysis with substantial qualitative analysis using case studies: 46 in-depth regional case studies were carried out in all. The work has benefited from the guidance of a steering committee of academic experts comprising Professors Ron Martin (University of Cambridge), Bernard Fingleton (University of Strathclyde) and Harry Garretsen (University of Groningen) and ongoing dialogue with DG Regio. The papers produced by these authors during the period of the study are included as an Annex to this report. 1

36 The study first reviewed the existing literature about factors influencing growth and verified through detailed statistical analysis the broad factors of influence. However, this information is of partial use to policy makers, as it does not help tackle more detailed issues, such as the relative importance of different factors whether the relative importance varies for different types of regions, or for regions at different stages of development whether one factor can compensate for or reinforce the influence of other factors In order to begin to address these issues it was necessary to deepen the quantitative analysis, to examine the evidence for different groups if regions and to weigh this evidence with the qualitative evidence from the detailed case studies. An innovation in this study is that we have asked through the case studies for a local assessment of the relative importance of the key factors determining growth. This has proved particularly illuminating. 1.3 Structure of the report The remainder of the report is structured as follows: Chapter 2 provides a brief summary of the existing literature on competing theories of economic growth, draws out the various factors they identify as being influential, and goes on to summarise the econometric analysis carried out in the present study to verify their influence in EU regions. A more detailed discussion of this given in the First Annual Report, and a link to this is included in the Bibliography at the end of this report. Chapter 3 provides an analysis of structural differences between the regions, and explains the rationale behind the subsequent breakdown of regions into groups based upon employment specialisation. Chapter 4 takes this analysis further, looking at the nature of structural change in the regions, and the degree to which there was transition between the thematic groups over the period of the study. Chapter 5 provides an explanation of the process behind the selection of case study regions. Chapters 6-11 provide a summary of the findings from the case studies, one for each thematic group of regions. Chapter 12 provides an analysis of the findings of the case studies, using numerical scoring of growth factors provided alongside the case studies. Chapter 13 provides policy conclusions for the different groups of regions. The detailed case studies are provided as annexes to this report. 2

37 2 An Overview of the Pattern of Growth and Competing Theories 2.1 Introduction This study investigates the factors influencing long-term growth across Europe. A major issue for the analysis is that over the last years Europe has experienced many substantial dislocations to its economic system. For example, the late 1980s saw the fall of communism and the adoption of market economies in those countries together with the reunification of Germany; the euro was adopted in 2002 and the European Union was enlarged in 2004 and more recently in 2007 to cover 27 countries. Such changes make it difficult to identify prolonged periods during which to consider the underlying factors of economic growth, without the additional impact of institutional change and the resulting economic adjustment. That said, the institutional arrangements should be seen themselves as one factor influencing the growth. At the same time, analysis is limited by the evidence available. Consistent data on the economic performance of the regions is only available from At the time this project began data were only available to 2005, and for this reason the period used for the majority of the statistical analysis is At the time of writing, data for regional GDP per capita were only available to 2008, so it is still the case that there are few data available on which to identify the relative impact of the Great Recession on the regional economies (or indeed the on-going turmoil surrounding the Eurozone sovereign and banking debt crises). The remainder of this chapter sets out briefly the pattern of regional economic development over for which we are seeking to identify the underlying factors. It goes on to summarise the different theoretical views explaining growth and the evidence from statistical analysis about the important underlying factors 1. This summarises the work undertaken and reported in Year 1 of the project and does not represent an exhaustive review of material. 2.2 Regional growth performance Figures 2.1 and 2.2 show the pattern of growth in GDP per capita across Europe over and the level of GDP per capita at the start of the period. Figure 2.3 shows the correlation between the two indicators. An initial view from the maps is that the strongest growth has occurred in the peripheries, which is where the majority of the newer Member States (particularly those in the east) are located. These newer Member States, and those more established Member States still in receipt of cohesion funds, are also typically among the less well-off in terms of the level of GDP per capita when compared on a PPS basis. Also, the economies of eastern Europe clearly received a strong boost with the restructuring of their economies and society from the early 1990s following the collapse of communist rule. 1 Full details of the growth theories and the econometric analysis of the determining factors are provided in Analysis of the Main Factors of Regional Growth: An in-depth study of the best and worst performing European regions, Annual Report Year 1. 3

38 Figure 2.1: Growth in GVA per capita, GROWTH IN GDP PER CAPITA, GDP per capita 2000, % pa, Above 3 2 to 3 1 to 2 0 to 1 Below 0 There is a broad correlation between low starting levels of GDP per capita and the magnitude of growth. However, it is equally clear that there is considerable variation in the growth performance of regions with similar starting levels of GDP per capita. There has been a general tendency towards convergence in levels of GDP per capita over the period (regions with below-average levels of GDP per capita have seen stronger- than-average growth, and those with above-average stating levels have seen weaker-than-average growth). However, as Figure 2.3 demonstrates, a substantial number of regions have either fallen further behind the EU average (i.e. had belowaverage levels of and growth in GDP per capita) or pulled away (had above-average levels of and growth in GDP per capita). The macroeconomic context has some influence on the chart, but does not explain the variations: even within individual Member States there is clear evidence of some regions performing well while others lag behind. Different Member States also have different patterns of regional spread: 4

39 Figure 2.2: GVA per capita, 1995 GDP PER CAPITA, 1995 GDP per capita, PPS, EU27=100, 1995 Above to to to 100 Below 75 Some countries have regions with a broadly similar starting level of GDP per capita and broadly similar growth rates e.g. Bulgaria, Denmark, Spain, France (excluding the Paris region), the Netherlands, Portugal, Slovakia, and Sweden. Some countries have regions with a broadly similar starting level of GDP per capita but very different growth rates, e.g. the Czech Republic (except for the Prague region 2 ), Greece, Hungary, Poland and Romania. Some countries have regions with quite different starting levels of GDP per capita but broadly similar growth rates, e.g. Austria, Finland and Ireland. Some countries show a heterogeneous regional mix of different levels and growth rates of GDP per capita, e.g. Belgium, Germany, Italy and the UK. 2 Often in the new Member States the capital city regions behave differently because, over the period being analysed, they have been acting as a growth pole for development. 5

40 GDP per capita, , 2000 %pa Analysis of the main factors of regional growth: An in-depth study of the best and worst performing regions Figure 2.3: Correlation between growth and the starting level of wealth in a region 9 CORRELATION BETWEEN GROWTH AND STARTING LEVEL OF WEALTH GDP per capita, 1995, PPS, EU27=100 Any investigation of the factors influencing growth needs to explain these differences. Decomposing growth Influence of sector specialisation Simple correlations between growth in GDP per capita and its components (productivity, employment rate and dependency rate) show that the strongest association is between growth and productivity. The findings suggest that, firstly, productivity is the main driver behind GDP per capita growth, so regions which perform poorly on GDP per capita growth do so typically because of poor productivity growth. The employment rate and the dependency rate have a role to play, but the story (particularly when it comes to changes over time) is mostly about productivity The pattern of growth in GDP per capita could simply reflect the sectoral specialisation of the regional economies. For example, those regions with a high representation of sectors with high rates of productivity growth may be expected to see the fastest growth in GDP per capita (productivity). Also, regions with a high representation of sectors which saw strong growth nationally would be expected to achieve strong growth. Our analysis did not find such a simplistic link between representation of high productivity sectors and productivity growth: sector specialisation, at least at the broad five-sector level used in the analysis 3 did not in itself explain strong growth. However, as subsequent analysis shows it is necessary to consider a region s specialisation in understanding its relative growth performance. 3 The five broad sectors used are agriculture, tourism, medium-high technology manufacturing, low medium technology manufacturing, financial & business services and basic services. 6

41 Different views on the determinants of economic growth Classical theory: key factors are investment in capital and trade Neo-classical theory: refines explanation of trade determining growth New economic growth theory: endogenous growth the importance of knowledge Six broad theories about growth are summarised in Table In the main, these theories focus on performance at the national level. Some of them are relevant to performance at the regional level; others are less relevant. The factors highlighted by the literature review influenced much of the subsequent analysis, and provided a template of factors against which all subsequent work (including the econometric analysis and the case studies) were measured. In classical economic theory, growth is the result of the division of labour and trade (Smith, 1776). Specialisation provides for economies of scale and differences in productivity between locations (countries). Investment in capital (improved machinery) and trade (increasing the size of the market) facilitates specialisation and raises productivity and output growth. Moreover, growth itself can be reinforcing, since increasing output permits further division of labour and hence further growth. Key implications are that differences in technology between nations and across industries provide the basis for international trade (Ricardo, 1817); and even if wages are lower in the foreign industry, this does not imply the demise of domestic production under free trade as labour is not (assumed to be) internationally mobile. Thus, the key factors driving growth are investment in capital (which enhances specialisation and, hence, raises productivity) trade Classical models assume that technological differences that exist across countries are the basis for trade and subsequently growth. In contrast, standard neo-classical theory states that differences in economic activity may arise due to an initial advantage (due to factor endowments, or the state of institutions etc.) but this will be arbitraged away as capital flows to places where labour is cheaper and as new technologies are transferred (Barro and Martin, 1990). The explanation of trade as a driver of growth is refined in the neo-classical theory, with comparative advantages derived from differences in the relative abundance of factors of production (factor endowments). It was recognised that the standard (classical and neo-classical) theory had limited relevance as an explanation of sustained growth as it linked long-term per capita growth to an exogenous rate of technological progress. The contribution of endogenous growth models is to determine long-term growth within the model (Barro and Martin, 1995). In these types of models, knowledge spillovers prevent diminishing returns to capital (which also includes human capital) and per capita growth continues indefinitely. Thus, endogenous growth theory purports to provide a theory of economic history, in the sense that it tries to explain why some economies have succeeded and others have failed. However, a key assumption of endogenous growth theory is that accumulation of knowledge generates increasing returns as knowledge and know-how are not disseminated instantly among nations, regions, sectors or companies, but need to be acquired (Badinger and Tondl, 2002). Another important contribution of endogenous growth theory is the formalisation of the importance of human capital (Badinger and Tondl, 2002). Highly skilled workers 4 A full discussion is presented in the Year 1 report of this project. 7

42 Keynesian view of export-based theories of growth tend to be more productive and innovative and are therefore of importance to both companies and economies. In the view of new economic growth theory, key drivers of growth are R&D and innovation investment in human capital (education, health, training) effective dissemination of knowledge. Keynesian theory is essentially a theory of the short-run dynamics of aggregate demand and employment in the economy, based on expectations, as these influence investment and consumption behaviour. Aggregate output is taken as the sum of consumption, investment, government spending, and net exports. The drivers of the system are the consumption function and the investment accelerator, together with export demand. The latter gives rise to an export multiplier, in which aggregate output can be expressed as a derived function of export demand. In these export-led growth models a region s output growth is assumed to be driven by export demand which is dependent on growth in world demand (as well as other factors). Increased output leads to increased productivity as expansion of output is argued to induce technological change within and across firms in a region, both through the opportunities for increased task specialisation within firms, and through the accumulation of specific types of capital within which technological advances and innovations are embodied. In a regional context, inflows of labour into a high-growth region are likely to be of the more skilled and enterprising workers, thus adding to the general quality of the region s stock of human capital and its productivity. In addition, as empirical evidence attests, technological spillovers appear to be geographically localised so that once a region acquires a relative advantage in terms of innovation and technological advance, it is likely to be sustained over long periods of time. Under this framework, state intervention is necessary to ensure that the positive spread effects emanating from expanding regions such as technological progress are stronger than the negative backwash effects. In these export-based theories of growth key drivers of growth are capital intensity and investment government policy to encourage positive technology spillovers (e.g. public-sector investment, business taxes/subsidies) New trade theories: acquiring a comparative advantage Traditional trade theory (classical and neo-classical) does not explain why trade takes place between similar countries (or regions) and, by extension, why different production structures should occur in similar regions. New trade theories offer an explanation for this based on scale economies, product differentiation and imperfect competition, with increasing returns a motive for specialisation and trade even when comparative advantage is of negligible importance (Barro and Martin, 2004). These theories can also be seen in terms of a switch in emphasis from exchange efficiency to productive efficiency, where the latter is influenced by factors such as labour force skills, the level of technology, increasing returns to scale, agglomeration economies, and strategic actions of economic agents in 8

43 Table 2.1: Key Drivers of Growth in Broad Growth Ideology Categories KEY DRIVERS OF GROWTH IN BROAD GROWTH IDEOLOGY CATEGORIES Drivers of economic growth Classical theory - Investment in capital (i.e. improved technology) enhancing the division of labour (specialisation) and, hence, raises productivity. Neoclassical theory - Trade (move from autarky to free trade) provides an engine for growth. Endogenous growth theory - R&D expenditure - Innovativeness (patents) - Education level Keynesian - Capital intensity - Spending on investment in human capital (schooling, training) - Effective dissemination of knowledge (knowledge centres) - Factors influencing first mover advantage, e.g. (skilled labour, specialised infrastructure, networks of suppliers, localised technologies) - Investment - Government spending, such as investment in the public domain and subsidies/tax cuts for enterprises Spatial growth models - Productive efficiency - labour force skills - R&D - agglomeration economies - technological and institutional innovations Regional/urban growth models - Firm rivalry - Favourable demand conditions - Supply network - Factor supply conditions technological and institutional innovations. They suggest that a comparative advantage can be acquired as opposed to being natural or endowed. In new trade theories, key factors influencing growth through achieving first mover advantage include skilled labour infrastructure supplier networks localised technologies Spatial growth models Centre-periphery models Spatial growth models build upon the two main approaches: neo-classical and Keynesian, by explicitly incorporating spatial elements into the framework. Centre-periphery models provide an explanation for the fact that international and inter-regional differences in development may persist and even widen over time, thereby addressing the perceived empirical shortcoming of classical economic theory which predicts convergence in due time. 9

44 Technology gap approach New Economic Geography For example, locations with good market access will inevitably become more attractive to firms, and this will push up wages. Skilled workers will be attracted to this expanding network which will further increase market size and facilitate innovative activity through knowledge spillovers (Venables, 2006). From the production side, firms producing intermediate goods also relocate to the centre to be closer to their customers. Clusters of industrial and economic activity thus form as a result of this reinforcing feedback. That the firm s location decision is determined by proximity to complementary activities is the underlying premise of the centreperiphery model. Key determinants of growth in centre-periphery models are: transport costs supplier networks The technology gap approach makes the assumption that while technological differences introduce the possibility of countries at a lower level of economic and technological development catching up by imitating more productive technologies of the leading country, the potential for such growth is vitally dependent on the ability of the less-developed countries to exploit existing technologies successfully (Greunz, 2003). In other words, the social capabilities of the less-developed country must be sufficiently developed to understand, integrate and exploit the new technologies developed by countries at the technological frontier. In this context, imitating foreign technologies is less costly than employing resources for innovation, thus opening up the potential for catching up. Key determinants of growth in technology gap approach to development include: investment (technology transfer) skills New economic geography models addresses the shortcomings of the more traditional ideologies of growth by offering an integrated theory of location, capable of explaining divergence as well as convergence in economic performance (Martin and Sunley, 1998). The key principle behind this framework which is quite similar to the centre-periphery framework is that proximity is good for productivity (Venables, 2006) and that dense networks of activity will be more productive than fragmented pockets of activity. Mobile factors of production will relocate to take advantage of higher productivity, thereby creating a positive feedback. Firms and workers will both gravitate towards high-productivity areas and the competition created will raise productivity further. Thus, a virtuous cycle is created that will ultimately result in an uneven distribution of activity and spatial income disparities (Venables, 2006). The key implications of the new geography framework are that proximity to economic agents is conducive to increased productivity by means of increased competition, increased innovation, faster diffusion of technology etc. Also, large spatial income disparities are an equilibrium outcome in this framework and, over time, convergence may not always result. Thus, new economic geography is a micro-based approach that offers a location-based explanation of the emergence of disparities between regions and the persistence of such disparities. It also suggests that globalisation could lead to a dispersion in activities and, while some countries will grow rapidly, others may be left behind. 10

45 Regional and urban growth models Adaptive growth models Economic development is expected to occur in an uneven pattern with some countries growing faster as they move from one convergence club to another. Which countries go first depends on a variety of factors that include initial endowments, policy environment and institutional efficiency. Key factors are: factor endowments policy institutional efficiency While the growth models summarised above all are critical to aid understanding of the drivers of economic growth at any spatial level, their roots primarily lie in macroeconomic analysis. Recently increased interest has been paid to models specifically explaining regional (and urban) growth. While such research is in its early stages, some critical strands of literature have emerged that provide interesting directions of thinking on this subject. Perhaps the best-known of these is Michael Porter s cluster theory of competitiveness (Martin and Sunley, 2003). These models assume that for regions to be competitive they must have one or more successful clusters of specialized, export-orientated activity as these promote innovation (high R&D and high patenting rates) which in turn feeds through into higher productivity, and thence overall levels of prosperity and living standards. Further, the latter will attract skilled labour and capital, which in their turn help to ensure favourable factor supply conditions. Key growth factors are seen to be : clusters of activity, stimulating R&D innovation More recent work has focused on the evolutionary theory of adaptive growth, which is the result of economic transformation in an age where rapid structural change is brought about by new technological developments, globalisation, political transitions and economic liberalisation (Metcalfe et al, 2005). These theories take into account the quantitative aspects of growth, but also explicitly factor in qualitative changes. They emphasise dynamic increasing returns to knowledge as the key element in understanding the relationship between innovation and growth (similar to endogenous growth theories), but also posit that macroeconomic explanations of economic growth are consistent with micro-level evidence on the processes leading to innovation. As economic systems expand, they transform the way they organise themselves. This suggests that the concept of growth also encompasses the problem of adaptation and of changing the allocation of resources and demand patterns in response to the changed environment brought about by the growth of knowledge. Thus, enterprisedriven adaptive development is the primary growth mechanism, while aggregate growth is a secondary outcome. Key growth factors are seen to be: firm-level innovation reinvention 11

46 Data availability Methodologies used in statistical analysis Bayesian model averaging 2.3 Statistical evidence for the main factors of regional growth The previous section shows that the literature identifies a wide range of factors that could be important in determining growth performance, and highlights those that are most promising (they often have a role in more than one theory). In this study we have used a series of econometric methods to assess the (statistical) significance of various factors. The number of different factors that can be assessed in this way is clearly limited by the availability of appropriate indicators. Some factors identified in the literature could not be considered due to limitations in data availability. The following primary data sources were identified; Eurostat s Regio database the ESPON database Cambridge Econometrics European regional database other (typically national) sources However, there were clear limitations to the data availability, both in terms of variables available (for example a distinct lack of data on policy, governance and social/cultural issues) and time period (the ESPON database provides only a snapshot of 2001). As well as severely limiting the analysis in some topic areas it also meant that no time-dependent analysis could be undertaken for some variables (particularly infrastructure variables largely drawn from ESPON). As indicated above, the literature review gives a rather large number of potentially important variables derived from theoretical models. A key task in Year 1 of the project was to distil from this long list of variables a smaller set which, empirically, appeared to be the most important influences on regional growth. The work relied on three background studies 5 to reduce the list of variables from an initial set of about 60 explanatory variables grouped into six broader categories: Growth of GDP per capita as the dependent variable, Human capital endowment structures, Patents and high technology variables, Access to internet and telecommunications, Infrastructure variables, Regional typologies and other variables as explanatory variables. The section below summarises the methodologies behind the relevant approaches; for a fuller description, and detailed results, please see the studies themselves. The first method was a Bayesian approach to model uncertainty based on the background study within this project conducted by Crespo Cuaresma, Doppelhofer and Feldkircher (2009). The analysis generalises the approach taken by Barro and Sala-i-Martin (2004) in various respects and in addition focuses on spatial issues (following LeSage and Parent, 2006). The issue of model uncertainty is addressed in various dimensions, i.e. the number of variables included in the specifications, the identity of the variables and the existence/relevance of spatial effects. Bayesian Model Averaging (BMA) is a standard Bayesian solution to model uncertainty, and consists of basing prediction and inference on a weighted average of all the models considered, rather than on one single regression model. Bayesian econometrics in general and BMA in particular are becoming more and more popular, and there are numerous examples of these techniques being applied in economics. 5 These background studies are: Crespo Cuaresma J., G. Doppelhofer and M. Feldkircher (2009); Crespo Cuaresma, J., N. Foster, and R. Stehrer (2009); Schneider, U. and M. Wagner (2009). 12

47 Given data for a dependent variable, Y, a number of observations, N, and a set of candidate regressors X x1,..., xz the variable selection problem is to find the best model, or the most appropriate subset of regressors x 1,..., xz out of the total set of candidate regressors. In what follows we sketch out the basic intuition behind BMA methods. 6 We begin by denoting M 1,..., M m the set of all models considered, (m) where each model represents a subset of the candidate regressors, x. Model M m has the form, y ( m) x i ( m) (m) where x 1 is a subset of X, is a vector of regression coefficients to be (m) estimated and is the standard iid error term. We denote by m, the vector of parameters of this model M m. Bayesian inference about the parameter attached to x, a variable in X, is, j pr ( m) M Y Pr Y, M Pr M Y j m 1 which is the average of the posterior distributions under each model weighted by the corresponding posterior model probabilities. This is what is termed Bayesian Model Averaging (BMA). The posterior probability of model M is where Pr( Y M Pr M m ) m Y Pr( Y j i m m Pr Y M m Pr M m Pr Y M l Pr M l M y l 1, M )Pr( M ) d m is the integrated likelihood of model M m, Pr( M m m ) is the prior density of m under model M m, Pr( Y m, M m ) is the likelihood, and Pr( M m ) is the prior probability that M m is the true model (assuming that one of the models considered is true). The posterior model probabilities can thus be obtained as the normalised product of the marginal likelihood for each model, Pr( Y M m ), and the prior probability of the model Pr( M m ). Notice that for the simple case M 2 the posterior odds for a model against the other can be readily written as the product of the Bayes factor and the prior odds. Further assuming equal priors across models, the posterior odds are equal to the Bayes factor. m The posterior mean and variance of a regression coefficient, j, are then given by, Var ˆ m E M ( m) j Y ˆ j Pr M m Y, m 1 M 2 ( m) Y Var Y, M ˆ Pr M Y E Y j m 1 j m ( ) where j is the posterior mean of j under model M m, and is equal to zero if x j is not included in M. The posterior mean is therefore the weighted average of the m j m m m m m j 2 6 This section follows closely the description in Raftery (1995) and Raftery et al (1997) who provide a fuller description of BMA techniques. 13

48 Adaptive least absolute shrinkage and selection operator (LASSO) model-specific posterior means, where the weights are equal to the models posterior probabilities. The posterior variance reflects both the weighted average of the withinmodel posterior variances, and the between-model variation of the model-specific posterior means. In addition to the posterior means and standard deviations, BMA provides the posterior inclusion probability of a candidate regressor, Pr j 0Y, by summing the posterior model probabilities across those models that include the regressor. As mentioned above, the number of potential models given even a relatively small number of candidate regressors can be huge, and it is often not feasible to consider all possible models as no closed form solution of the posterior density is available. A number of approaches have been developed to help deal with this problem, one of them being the Markov Chain Monte Carlo Model Composition algorithm (MC 3 algorithm) which is also widely used in the literature. We will not present further details on this algorithm here. A further problem with the BMA approach is that it requires the specification of the prior distributions of all parameters in all models and maybe the model size. Usually the amount of prior information on the parameters is limited and the effort needed to specify it in terms of a probability distribution is large. Again a number of approaches have been proposed to deal with this issue. Ley and Steel (2008) discuss this issue of the effect of prior assumptions in BMA. Following Fernandez et al. (2001) for the parameters in the model one often adopts a combination of non-informative improper priors (i.e. imposing only few restrictions) for the intercept and the scale and the so-called g-prior (Zellner, 1986) for the regression coefficients. Further the number of variables to be included in a model is also to be specified. As this number will not be known, one again specifies a priori distributions about the model size (which can take various forms). For a detailed discussion see Ley and Steel (2008) and information provided in the Year 1 report. The application of Bayesian Model Averaging is not without problems as results depend on priors and assumptions about the parameters in the specified hyper-priors. These, for example, affect posterior model weights and the posterior distributions of parameters. Additionally, the computational costs for Bayesian model averaging are relatively high. As already mentioned in the literature review, apart from this Bayesian approach to model reduction or averaging, other approaches to variable selection are available and have been further developed recently. An approach which proved to be useful is the adaptive least absolute shrinkage and selection operator (LASSO) approach and one of its variants is the so called adaptive LASSO (for further details see the background study Schneider and Wagner, 2009). Ordinary least squares (OLS) minimises the sum of squared residuals. The idea, that shrinking and setting coefficients to zero might potentially improve prediction accuracy by trading off a little bias with lower variance was investigated in Tibshirani (1996). Tibshirani introduced the least absolute shrinkage and selection operator (LASSO) to combine features of ridge regression and subset selection. Two asymptotic regimes with respect to model selection can be distinguished: conservative or consistent model selection. Conservative model selection means that the correct zero coefficients are recovered asymptotically with probability less than one and consistent model selection means that the correct zero coefficients are recovered asymptotically with probability equal to one. For the LASSO estimator 14 j j c

49 consistent model selection is only possible under certain additional assumptions on the regressor matrix, whereas it is always possible for the adaptive LASSO estimator as proposed in Zou (2006). Technically, the LASSO estimator can be seen as minimising the residual sum of squares subject to a constraint or as minimising a penalised sum of squares residuals. Using the latter interpretation the LASSO estimator is given by the solution to the minimisation problem min 2 y X pen( ), N where y is the vector of the dependent variable, x j, j = 1, p, is the vector of variable j, denotes the corresponding vector of coefficients and N is the tuning parameter mentioned above. As one can see, for N going to infinity all coefficients tend towards zero; for N equal to 0 the solution coincides with the OLS solution in case the latter is well-defined. For the adaptive LASSO the minimisation problems introduces a different data dependent penalty function. The minimisation problem then becomes 2 k ~ min y X / N j 1 where ~ OLS is the vector of OLS coefficients. 7 The adaptive LASSO estimator, AL is defined as the solution of this minimisation problem. The asymptotic behaviour of AL is discussed in Zou (2006). In particular the adaptive LASSO estimator performs as well as the OLS estimator for the unknown correct model, i.e. as if the true restrictions were known. That s why it is also referred to as having the oracle property. More details can be found in the background study by Schneider and Wagner (2008). Solutions to the minimization problem above can be computed very efficiently exploiting the specific structure of the problem. It can be shown that the solutions to the corresponding optimization problems are piecewise linear in the tuning parameter N see for example Rosset and Zhu (2007). Exploiting this property, the estimator can easily be computed for all tuning parameters N [ 0, ), leading to so-called solution paths for each variable. These solution paths are initiated at N equal to infinity where all coefficients are equal to zero and ensued to N equal to zero corresponding to the OLS estimator (in case it is uniquely defined). In each step along this sequence, one variable is either included or removed from the current active subset, i.e. the set containing the coefficients that are not equal to zero in that step. To provide a final subset of variables together with a corresponding estimate of the parameter vector different approaches to select the tuning parameter are available. One of them is given by minimizing a BIC-type information criterion, see for example Wang and Leng (2007). Doing so can be shown to result in consistent model selection. Another widely used procedure is given by (k-fold) cross-validation as suggested by Leng et al. (2006). This latter procedure may result in conservative model selection, i.e. it may lead to the inclusion of some variables whose true coefficients are equal to zero. The results reported in this study are based on cross-validation, given the favourable results obtained with this approach in a variety of experiments. As just mentioned, since cross-validation is potentially conservative some potentially irrelevant variables might be included. In the results presented in the study, however, rather small models are chosen by this procedure so that potential conservativeness does not appear to be a major concern. j j 7 More generally, ~ has to be an N -consistent initial estimator of. 15

50 Quantile regression analysis A third background study (Crespo Cuaresma, Foster and Stehrer, 2009) addresses the issue of the determinants of regional growth using quantile regression analysis combined with Bayesian model averaging. The aim of standard Ordinary Least Squares (OLS) is to determine the conditional mean of a random variable, y, given some explanatory variables, x. Quantile regression models move beyond this, enabling one to determine the conditional quantile of y at any quantile on the conditional distribution of the growth distribution. There are a number of advantages of quantile regressions but the most important for this purpose is that it allows one to consider differences in growth determinants for under and over-achieving growth regions, thus allowing us to address one of the main issues of the project: what are the main causes of regional growth in the best and worst performing regions (i.e. over- and underachieving regions)? Quantile regressions were introduced by Koenker and Bassett (1978), though the history of the Least Absolute Deviations (LAD) model from which quantile methods are derived predates OLS. 8 Quantile regression analysis has recently received a great deal of attention with extensions to the existing literature that deal with the practical problem of estimating the covariance matrix, that consider the performance of the various estimators in small samples, as well as methods to deal with endogeneity, panel data and heteroscedasticity. Moreover, a growing literature applies such methods to a wide range of economic issues. Some papers including Mello and Perelli (2003), Barreto and Hughes (2004), Canarella and Pollard (2004), Osborne (2006) and Foster (2008) use quantile regression methods to consider crosscountry growth. This contribution addresses the issue of the growth determinants in two ways. Firstly, it is considered to what extent the coefficients on a robust set of growth determinants differ along the conditional growth distribution (i.e. how does the coefficient on a particular variable, say investment, differ in low and high growth regions). The set of robust variables is taken from the BMA procedures reported above. Secondly, it is examined whether the set of robust growth determinants differs along the conditional growth distribution. To do this BMA has been applied on different quantiles of the conditional growth distribution, which allows one to examine whether the set of robust growth determinants differs in over- and underachieving growth regions. Most regression analysis relies on the least squares methodology, which models the conditional mean function, describing how the mean of the dependent variable changes with the vector of independent regressors. Quantile regression models seek to model the conditional quantile functions, in which the quantiles of the conditional distribution of the dependent variable are expressed as functions of observed covariates. The main advantage of quantile regressions is that potentially different solutions at distinct quantiles may be interpreted as differences in the response of the dependent variable to changes in the regressors at various points in the conditional distribution of the dependent variable. The quantile estimator solves the following optimisation problem, min n i 1 y i x' i s where is the absolute value function that yields the th sample quantile as its solution. In general, the linear model for the th quantile 0 1 solves, 8 Useful surveys of quantile regression methods include Buchinsky (1998) and Koenker and Hallock (2001). 16

51 1 min n i y x ' i i : yi xi ' i: yi xi ' 1 y i xi As one keeps increasing from zero to one, one can trace the entire conditional distribution of output growth, conditional on the set of regressors. The resulting minimisation problem can be solved using linear programming methods. The coefficient for a regressor j can be interpreted as the marginal change in the th conditional quantile of y due to a marginal change in j. The asymptotic theory of quantile regression is provided by Koenker and Bassett (1978). One can use procedures to estimate the asymptotic standard error of the estimators, or alternatively one can use a bootstrap procedure. The use of quantile regressions has a number of benefits. The major benefit being that the entire conditional distribution of the dependent variable can be characterised by using different values of. A further benefit relates to the fact that median regression methods can be more efficient than mean regression estimators in the presence of heteroscedasticity. Quantile regressions are also robust with regard to outlying observations in the dependent variable. The quantile regression objective function is a weighted sum of absolute deviations, which gives a robust measure of location, so that the estimated coefficient vector is not sensitive to outlier observations on the dependent variable. Finally, when the error term is non-normal, quantile regression estimators may be more efficient than least squares estimators. Key findings There is a tendency for convergence Human capital is associated with stronger growth as is investment by business and in transport infrastructure From this relatively complete list of potential explanatory variables, only a small number of variables turned out to be robust determinants of economic growth in the statistical sense. The main findings can be summarised as follows: Conditional income convergence appears as a very robust driving force of income across European regions during However, the finding is strongly affected by the growth experience of Central and Eastern European countries: the convergence process between regions is dominated by the catching up process of regions in Central and Eastern European (CEE), whereas convergence within countries is mostly a characteristic of regions in some old EU member states. Human capital, measured as the share of highly educated workers in the (working age) population, has a robust positive association with regional economic growth. The estimates imply that an increase of 10% in the share of the highly educated in working age population increase GDP per capita growth on average by 0.6%. Within countries, the share of gross fixed capital formation in total output was found to be a robust determinant of growth. The finding was not robust when considering all regions in the EU as a whole. Infrastructure plays an important role in some cases, in particular infrastructure related to air transport. The effect of infrastructure is weaker if we take into account the differential effect of infrastructure in CEE countries compared to western Europe. This reflects the substantial restructuring of their economies and investment in infrastructure over the period considered. 17

52 There is evidence of some positive agglomeration effects linked to size and function Policy implications Limitations of statistical analysis On average, the growth rate of income per capita in regions with capital cities is over one percentage point higher than in non-capital city regions, after controlling for all other factors. Densely populated regions in western Europe tended to have a weaker growth performance, but those with high employment (or output) density have stronger growth. Population and employment density are fairly closely correlated, which illustrates the competing pressures from agglomeration. There is some evidence of positive spatial spillovers or growth clusters in EU regions, but not much evidence of differential effects between old and new EU member states. The statistical analysis therefore identifies two main factors on which policy can act to improve regional economic growth: Human capital endowment Gross fixed capital formation including infrastructure Although, from a policy perspective the results are in line with other empirical studies in the growth literature, notably with Barro & Sala-i-Martin (2004), the list of robust growth factors might seem relatively short. While the quantitative analysis reveals some (statistically) robust findings on the key factors supporting regional economic growth, the analysis does have a number of limitations that need to be considered when drawing conclusions from the results. As with all quantitative analysis of this type, there are issues surrounding the quality of the data: the variables for which data are available can be imprecise proxies for variables identified in the literature (particularly in relation to qualitative factors such as connectivity and environmental conditions); other variables will be omitted from the analysis altogether because they, or appropriate proxy variables, cannot be identified. Data availability greatly limits the scope of regional analysis that be carried out across the EU compared with what is possible for, say, the US. However, the analysis has other limitations that are more relevant from a policy perspective. The statistical analysis identifies robust and important determinants of growth on the basis of all regions for one period of time ( ). However, different factors may be important for different groups of regions, perhaps on the basis of their stage of development (e.g. as indicated either by their level of wealth, or their relative specialisation). The results from carrying out equivalent statistical analysis on different groups of regions identified by per capita levels of GDP provide some evidence to support this. There may be threshold effects, where certain factors (or the way in which they are used) are important until a particular level of development has been reached after which point their importance for future growth is less (or needs to be used in a different way e.g. infrastructure spending moving from roads to telecommunications etc.) The data available for the analysis meant that the analysis was restricted to consider factors influencing growth over the ten-year period However, this is still a relatively short period of time to identify the key factors determining long-term 18

53 are overcome through detailed case study analysis growth and separate them from other cyclical factors or other external shocks (there were substantial political and institutional changes in this period). While the econometric analysis provides important evidence regarding the potential factors determining regional growth, it needs to be supplemented by in-depth case study analysis of particular regions to arrive at a more complete picture of the underlying determinants of economic performance. This is particularly necessary if we are to understand how the key factors have contributed to growth, and if we are to identify lessons that could be transferred to other similar regions in the future and, we hope, improve the efficiency in the design and implementation of development policy. 19

54 3 The Economic Performance of Regional Groups Why specialisation matters 3.1 Introduction This chapter provides an analysis of structural differences between the regions, and explains the rationale behind the breakdown of regions into groups based upon specialisation. There is no unique development path that regions tend to follow, except in very general terms (regions progressing from agricultural economies to industrial ones and ultimately to service economies, though even this does not seem to fit all cases up to now at least). Instead, development paths tend to reflect the underlying characteristics of regions, some of which are inherent in their geography and geo-physical features, some of which are inherited from the past. The former will more or less permanently affect their development path (a region on the periphery of Europe will always remain so and a mountainous region will always remain a mountainous region), the latter less so but in many cases inherited factors remain important for a long period of time. Experience suggests, for example, that the legacy left by old heavy industries, such as coal mining or steelworks, which tends to deter new economic development, can be reduced in importance but only over several decades, the Ruhr in Germany being a prominent example, while the pattern of urban development is similarly difficult to change quickly. These characteristics tend to have a major influence on the economic activities in which regions have a comparative advantage and, accordingly, on their areas of specialisation. Indeed, they can be a determining factor at some points even if their importance diminishes over time, as, for example, the supply of raw materials (iron ore or coal, for example) and geo-physical features (such as a fast-flowing river) are becoming less important as locational factors as technology advances. Nevertheless, while economic activities are tending to become more foot-loose from this perspective, it is apparent that some features of particular locations remain important, such as having large centres of population, and that it is difficult for regions that do not possess these features to compete effectively in certain activities. Since the activities in which a region specialises affect its potential for growth at any moment in time if not the actual growth achieved, since this is influenced by a wide range of other factors as well as the economic development path it follows, these areas of specialisation need to be explicitly taken into account when assessing growth performance. They will also affect the development policies best suited to stimulating or supporting growth. Indeed, it is well understood that such policies need to take account of the characteristics of regions if they are to be sustainable, as well as effective, which means that it is not possible to apply a general policy across all regions but that the approach followed and the measures implemented need to be tailored to individual circumstances. Two points need to be made, however, with regard to areas of specialisation in the present context. First, as noted above, although the activities in which a region specialises might affect its growth potential, they will not necessarily be the determining influence on the actual growth achieved over any particular period, 20

55 especially over one which is comparatively short a decade or so. Accordingly, the influence of such specialisation would not be expected to show up clearly in the growth performance over the period for which reasonably consistent data are available of the kind that have been taken here as the basis for analysis (from 1995 onwards). While specialisation might affect the potential level of GDP per head which regions can expect to achieve in the very long run, it is unlikely to influence the observed rate of growth over a short period, except in the unlikely case that GDP per head is close to its potential level. Secondly, identifying areas of regional specialisation is not altogether straightforward. Not only might regions specialise in activities which cut across sectors as usually defined (in, for example, labour-intensive parts of the production process), but the extent to which specialisation is evident in the distribution of value-added or employment between sectors is also likely to be different at different stages of economic development. In particular, it tends to diminish as economies develop and communal activities such as education or healthcare increase in importance. The activities which regions have in common, retailing, public administration and basic services of one kind or other as well as education and healthcare activities which are for the most part non-tradable, or have, in the past, tended to be so - come to account for a growing share of employment and, to a lesser extent, value-added. Accordingly, the sectors of activity in which regions specialise, tradable goods or services, may account for only a small proportion of employment. In many cases, however, they account for more of the overall value-added generated by the regional economy, since the tradable sectors concerned typically have a higher level of productivity than the non-tradable ones, reflecting the importance of being competitive in both national and international markets. Indeed, while productivity can be critical in tradable sectors, it tends to be much less important in the rest of the economy where competitiveness in relation to producers elsewhere is less of an issue. Even within the tradable goods and services sector, however, the activities in which a region specialises may themselves be the direct source of relatively few jobs, but the income they generate is crucial for growth and for supporting employment in other parts of the economy. There is a strong argument, therefore, for defining specialisation in terms of valueadded rather than employment, since what is of key importance is the income generated rather than the jobs for which the area of specialisation is directly responsible. Unfortunately, however, data on value-added at the sectoral detail required are not available at regional level across the EU. There is, consequently no real alternative to relying on employment to identify sectors of specialisation. For some regions, therefore, defining specialisation in terms of employment might well give a misleading indication of the sector of specialisation and, perhaps more importantly, of the way that this has tended to change over time or not to change. For example, a region which at the beginning of the period is identified as one which is specialised in medium-to-high tech manufacturing in terms of the share of employment accounted for by the sector might be observed to shift over time to being specialised in medium-to-low tech manufacturing or, perhaps more typically to basic services, as productivity grows faster in the sector of specialisation than in others. To take some account of this, sectors of specialisation are defined in terms of the share of employment relative to the average national share rather than relative to the EU or some other cross-country average. Accordingly, if productivity growth in the different 21

56 sectors is the same across regions in a country, then the regions identified as being specialised in a particular sector at the beginning of the period would still be identified as such at the end. Moreover, defining specialisation in terms of national averages is preferable to defining them in terms of an EU-wide average not only for this reason but because the scale of employment in sectors of comparative advantage tends to be related to the level of economic development, which varies markedly across the EU. Regions specialised in agriculture, for example, tend to have a much higher level of employment in the sector if they are located in countries with GDP per head well below the EU average than if they are located in those where it is above average. 3.2 The specialisation sectors The sectors, or sector groups, chosen to define areas of specialisation and to group regions accordingly are: Business & financial services s [NACE 9 sections (J) Financial intermediation and (K) Real estate, renting and business activities] High, medium-high tech manufacturing [NACE sectors - High tech: (D) Office machinery and computers (30), Radio, television and communication equipment and apparatus (31), Medical, precision and optical instruments, watches and clocks (33); Medium-high tech: (D) Chemicals and chemical products (24), Machinery and equipment n.e.c. (29), Electrical machinery and apparatus n.e.c. (31), Motor vehicles (34), Other transport equipment (35)] Low, medium-low tech manufacturing [NACE sectors - Low tech: (D) Food products and beverages (NACE 15), Tobacco products (16), Textiles (17), Wearing Apparel; dressing and dyeing of fur (18), Leather and footwear (19), Wood and wood products (20), Pulp, paper and paper products (21), Publishing, printing and reproduction of recorded media (22), Furniture, manufacturing n.e.c. (36), Recycling (37); Medium-low tech: (D) Coke, refined petroleum products and nuclear fuel (23), Rubber and plastic products (25), Other non-metallic mineral products (26), Basic metals (27), Metal products (28)] Tourism [NACE section - (H) Hotels and restaurants] Agriculture [NACE sections (A) Agriculture, hunting and forestry and (B) Fishing] Basic services (which essentially means that there is no evident sector of specialisation) [NACE sections (L) Public administration and defence; compulsory social security, (M) Education, (N) Health and social work, (O) Other community, social and personal service activities, (P) Activities of households] Formally, the index of specialisation (Sij) can be expressed as follows: 9 NACE, Rev.1. 22

57 Sij n E j 1 ij E ij m E ij i 1 n m j 1 i 1 E ij where i refers to the region, j to the sector and E stands for employment 10. A higher employment concentration in sector j than on average at the national level yields an index higher than one. The regional specialisation types were determined by the sector aggregate with the highest index 11. At this stage it is worth noting that there is no unique way of determining sector groups. There are two main arguments behind the grouping used in this study. First, in order to make the study manageable, specialisation needed to be defined in terms which enable a limited number of regional groups to be identified, especially since initial levels of economic development also have to be taken into account. Secondly, areas of sectoral specialisation tend to reflect more deep-seated characteristics of regions which are sometimes difficult to capture directly in any systematic way. Those specialising in agriculture are, therefore, inevitably predominantly rural in nature. Equally, most regions specialising in financial and business services are highly urbanised and in many cases capital city regions. It also seemed rational to distinguish between regions specialising in medium-to-high tech manufacturing and in mediumto-low tech manufacturing because these activities have very different technology intensities and knowledge content. Tourist regions as well have specific features which tend to set them apart from others. The sixth group, basic services regions, however, comprises those with no particular sectoral specialisation, so that the distribution of employment between sectors tends to be similar to the national average. 3.3 Differences in GDP per head between regional groups While the concern of this study is with regional growth over the long term and the factors which determine this, the initial focus here is on the level of GDP per head and the main components of this. This is because of the lack of a long time series for GDP which would allow long-term growth to be satisfactorily analysed. A reasonably coherent time series at (NUTS 2) regional level for GDP which spans the EU exists only from 1995 and goes up only to 2008, which is too short a period to enable longterm trends to be reliably identified. Even then the data available are not complete or are subject to major breaks in the series which mean that it is not possible to reliably measure changes over the 13-year period as a whole. Because of these limitations the analysis of growth of GDP per head presented below covers the relatively short period Employment data come from the LFS and refer to year It is important to keep in mind that a higher sector employment share at regional than at national level does not imply that this activity is predominant in absolute terms. To illustrate this, in several agricultural regions in the EU15 as for instance in Drenthe and Flevoland (the Netherlands), in Province of Luxembourg (Belgium), in Mecklenburg and Lüneburg (Germany) or in Cumbria and Lincolnshire (UK) the employment share in agriculture does not exceed 6%. Although this is lower than their employment shares in manufacturing or in services, they are nevertheless considered to be specialised in agriculture because their employment share in this sector is three times higher than at national level. Despite being specialised in agriculture, other activities are important; and this aspect needs to be kept in mind particularly for the thematic chapters later in the report and the discussion of policy implications. 23

58 The cross-sectional data for particular years within this period are, therefore, taken as a basis for analysis, not only because of the innate interest in assessing the proximate factors underlying the differences which exist between regions across the EU, but also because differences in levels of GDP per head between regions reflect differences in growth rates over the very long term. A region with a level of GDP per head above average can therefore be assumed to have experienced a higher growth rate than other regions at some point in its history. Growth rates within the period are examined subsequently to see whether there were any marked differences in performance between the regions not only in the rate achieved in itself but equally importantly in the significance of the factors underlying this rate. The commuting effect 3.4 Decomposition of GDP per head The initial aim is to break down the GDP per head of a region into its main proximate determinants and to examine the way that these vary across regions in order to understand better the factors which determine the growth of regional GDP since they operate through their effect on one or more of these proximate determinants. These determinants are: GDP per person employed (or labour productivity), the employment rate (the proportion of working-age population resident in the region, 15-64, who are employed), the share of total resident population that are of working-age (15-64), the relative number of people above working-age who are employed (and who effectively add to the employment rate). These in combination are identically equal to GDP per head: GDP/pop=GDP/emp x emp /WAP x emp 65+ /WAP x WAP/pop where pop=total population resident in a region; emp=the total number employed in the region, WAP is working-age population, and the subscripts refer to the age group of those in employment. There is an additional determinant, commuting, either into or out from the region, which also needs to be taken into account since it increases or reduces the GDP per head of a region, as measured. It needs, however, to be separated from the other proximate determinants since its effect on GDP per head is, in a direct sense at least, artificial in that it increases or reduces the GDP of a region but leaves the number of heads included in the measure of GDP per head unchanged. In addition, while part of the GDP generated by commuting may remain within the region, adding to its income, part of it will leave the region in the form of the earnings of the commuters concerned and benefit instead, or at least to a large extent, the regions in which the commuters live. This in itself is not necessarily a reason for distinguishing the effect of commuting from other determinants of regional GDP, since there are other factors which may result in GDP not remaining in a region, such as government taxes or companies transferring profits elsewhere. But these factors do not have the same artificial effect as commuting. While they mean that the GDP generated in a region is not necessarily the same as the income which residents have access to, they do not affect the generation of GDP itself. 24

59 Estimating the commuting effect Commuting not only tends to distort comparisons of GDP per head between regions but changes in its scale can also distort comparisons of the growth of GDP per head over time. To give an example, if between two points of time people who were previously living and working in a region move out of the region but continue to work there perhaps as part of a movement out of cities to the surrounding areas what will be observed is an increase in GDP per head solely because of this. Conversely, if people are persuaded to move from outside a region to inside to reduce the extent of commuting, then the opposite will be observed GDP per head will decline as more of the people responsible for generating it are included in the measurement. While it can be argued that an increase in inward commuting reflects the growing economic strength of a region and that, therefore, it is not so important if the distorting effect is not explicitly taken into account, the above examples demonstrate that such an increase or indeed decrease may have little or nothing to do with the changing economic strength of a region. In practice, commuting represents a problem for the analysis of regional GDP growth largely because the regions being analysed are not economic, or functional, regions as such but artificial constructs which in many cases have little to do with regions in the sense that economists use the term. Regional boundaries as defined under the NUTS system which is used to identify regions in the EU can, therefore, cut through functional regions and divide natural labour markets, separating settlements from places of work, giving rise, as an inevitable consequence, to commuting between regions so defined. In principle, analysis of regional growth should really be conducted on the basis of functional rather than statistical regions. In practice, this is not possible because the data available are compiled for the latter rather the former. Nevertheless, it is at least possible to take account of the commuting effect and to make an explicit adjustment for it, as described below. At the same time, it should be noted that there are other factors which can push up GDP in a region more or less artificially in the sense that they are largely independent of the workforce in the region or the workforce it can call upon and the capital stock and they may also give rise to big difference between GDP and regional income. In particular, regions in which oil or gas reserves are located, or where such reserves are brought to land, tend to have a high level of GDP for this reason. The output in question, however, may simply be piped elsewhere, adding little to the income in the regions concerned. Groningen, in the Netherlands, is an extreme example of this, with a GDP per head well above the national average, and indeed well above the EU average but a level of income per head which is the lowest in the Netherlands. Financial services, which also tend to generate high value-added, at least as measured, might also be regarded in the same light, though their effect on GDP tends to be less extreme. There are problems of taking explicit account of commuting because of the limited availability of data on the flows involved. Although the European Labour Force Survey (LFS) contains data on the regions in which people live as well as where they work, the data are incomplete and in a number of cases are of uncertain reliability because of the relatively small size of the sample on which the estimates are based. To overcome this problem, an indirect estimate of the commuting effect can be made by breaking down GDP per head into its proximate determinants as set out above. The four components listed which make up GDP per head - GDP per person employed, the employment rate, the share of working-age population in the total and the relative 25

60 number of people above working-age in employment - can, in other words, be combined to produce an estimate of GDP per head in a given region which explicitly leaves commuting out of account. It does so effectively by restricting the generation of GDP to those employed and living in the region (which is what the employment rate, adjusted for those above working age, measures) and by assuming that the GDP they generate is the same as the GDP per person employed as measured (which includes those employed in the region but living outside). Comparing the resulting figure with GDP per head as conventionally measured then gives an estimate of the commuting effect. Moreover, it does so without relying on the LFS data on country of work and country of residence. Since it effectively strips out the effect of commuting, the estimate produced by combining the four components (or proximate determinants) listed above is arguably the most relevant figure to focus on when comparing both levels of GDP per head and their growth rates and assessing the relative importance of the contributing factors. It also enables the four components to be examined separately. 3.5 Data issues Unfortunately, breaking down GDP per head in the way described above does not completely avoid data problems. These arise because of a difference in a number of countries between the data on employment recorded by the regional accounts, which are the source of the data on regional GDP, and those reported by the LFS, which extends beyond the fact that the former relate to those working in the region and the latter to those living there. This difference between the two sources results in large measure from the way the two different sets of data are collected in many countries. In a number of countries, the source of the regional accounts data is the LFS (such as in many of the EU12 countries), which means in this case there is no difference between the two. In others, however, the regional accounts are based on surveys of employers rather than the LFS, which is based on surveys of households. While, therefore, the LFS records individuals, the regional accounts report jobs, and individuals might at the same time work for more than one employer. To this extent, the regional accounts will tend to report more employment than the LFS. In most countries which base the regional accounts employment data on employer surveys, the number employed according to these data is higher than that reported by the LFS, though the extent of the difference tends not to be large (typically around 2-5%, which is similar to the proportion of people reported by the LFS to have more than one job across the EU). In a few countries, on the other hand, the regional accounts data for employment are lower than those reported by the LFS because people working very short hours are not included in the former (this is the case in the Netherlands where a significant proportion of people work less than 12 hours a week) or because they work in informal jobs which are not captured by employer surveys. In the case of Italy, where the regional accounts data exceed the LFS data by more than elsewhere (by 7-8%), it seems that an adjustment has been made to the former to allow for employment in the informal economy (which in principle ought to be picked up by the LFS, but in practice those working informally may be reluctant to reveal the fact to a survey). The analysis here takes account of the difference between the two data sources so far as possible, at least at the national level. This difference is then assumed to be the 26

61 same across regions when estimating the commuting effect on GDP in different regions. This, of course, may well not be the case, but except in a few countries the Netherlands and Italy (as noted above) as well as Bulgaria (where the regional accounts data exceed the LFS data by between 14% and 18% over the period covered), in particular the adjustment is so small that it ought not to have a significant effect on the results of the analysis. This procedure, however, cannot be applied in all countries in all years from 1995 on, mainly because either the relevant national accounts data or, more frequently, the LFS data, are incomplete. This is the case, in particular, for the EU12 countries which entered the EU in 2004 and In addition, there is a discontinuity in the series for employment in a number of countries. This is the case for Bulgaria in 2005 and Romania in 2002, and in both of these cases the data for the years before this cannot be readily compared with the data for the years after. It is also the case for the UK, where from 2002 on, employment in the regions is based on LFS data which relate to those employed and resident in the region rather than those employed in the region as such, irrespective of whether they are resident in the region or outside. There is, therefore, an inconsistency in the regional accounts published by Eurostat between the data for employment and the data for GDP in this particular case. Accordingly, employment data have to be used from another source for the years from 2002 on in order to be consistent both with the employment data for the years before then and with the data for regional GDP 12. Moreover, for Greece, there is a pronounced discontinuity in the data for regional GDP, so that the data for the years before 2000 cannot be compared with those for the years after. Estimates are, therefore, made for the Greek regions for 1999 so that they can be included in the analysis of GDP per head growth rates presented below. 3.6 Outline of analysis Because of these data issues, the initial analysis is focused on levels of GDP per head in 2001, when the data seem least problematic for most of the regions. A comparison is then made with levels in 2005 and 2008 (the last year for which regional accounts data are at present available) to see how far the results differ between the three years. In each case, GDP per head and the component parts which make it up, as described above, are aggregated for the six regional groups and compared between them. The concern is to see, first, the extent of the difference which exists in the level of GDP per head between the groups and, secondly, how the four different elements which determine it differ. The results of carrying out the same analysis for 2005 and 2008 give an indication of the stability, or otherwise, of the results over time. The second part of the analysis focuses on the growth of GDP per head over the nine years , which is the longest period for which reasonably reliable and consistent data from the different sources are available, even though data exist for earlier years. 12 In practice, data have been provided by Cambridge Econometrics for the UK regions for these years. These seem to be consistent with the Eurostat data for the years before

62 Differences in GDP per head between the regional groups in 2001 In the EU27 taken as a whole, the Business & financial services region group had, on average, a level of GDP per head as conventionally measured, and expressed in PPS terms, which in 2001 was some 30% higher than that the Medium-to-high tech manufacturing group, which had the next highest level in these terms, and around 95% higher than the Agricultural regions, which had the lowest level (Table 3.1) 13. Adjusted for commuting, these differences are reduced, to 21% in the first case and 77% in the second. Commuting, therefore, makes a substantial difference to relative levels of GDP per head, especially in respect of Business & financial services regions, which include many capital cities where inward commuting is especially important (it adds, for example, around 80% to the GDP per head in both the Brussels and Inner London regions). At the same time, it also reduces GDP per head as measured in many other regions where net outward commuting is a feature - by some 3% on average in Agricultural regions, which, of course, tend to be rural in nature and, therefore, desirable locations for people to live in. Indeed, there are many more regions in which net outward commuting is significant than the reverse, reflecting the more dispersed nature of human settlements than centres of economic activity. The most notable cases among Agricultural regions are those neighbouring large cities, such as Flevoland in the Netherlands close to Amsterdam (where commuting reduces GDP per head by 25%) and Lüneburg near Hamburg in Germany (where it reduces it by around 20%). Commuting also has a marked effect on GDP per head in Brandenburg, near Berlin, (reducing it again by around 25%) and Burgenland close to Vienna (reducing it by 15%). Both are Basic services regions and, like Flevoland in the past, both have received large amounts of aid from the EU Structural Funds over the years because of their low levels of GDP per head. The major part of the difference in GDP per head between the regional groups comes from differences in GDP per person employed, or (approximately) labour productivity. This level of this in the Business & financial services regions, therefore, was some 18% higher than in the Medium-to-high tech manufacturing ones and around 47% higher than in the Agricultural regions. Accordingly, the other component factors which make up GDP per head, mainly the employment rate and population of working age as a share of total population, add to the difference between the Business & financial services regions and the other regions but do so to a much greater extent in respect of the difference with the Agricultural regions. All of the factors, however, work in the same direction. The employment rate in Business & financial services regions is higher than in any of the other groups - some 17% higher than in Agricultural regions - and similarly for the share of working-age population in the total, the latter reflecting the tendency for Business & financial services s regions to attract inward flows of migrant workers in addition to commuters. Although the effect of the difference in the latter factor was smaller than for the difference in employment rates, it still contributed a difference of 4% between GDP per head in Business & financial services regions and that in Agricultural ones. It is also evident that the ranking of the regional groups is similar for each of the main components which make up GDP per head, so that they tend to reinforce each other. The main exception is Basic services regions, which had a higher level of GDP per 13 It should be noted that the average for the regions included in the group, as for other groups, is population weighted to take account of the very different sizes of the NUTS 2 regions concerned, 28

63 person employed than Low-to-medium tech manufacturing or Tourism regions but a lower employment rate and a smaller share of working-age population in the total. As shown below, however, the higher level of GDP per person employed is a reflection of the location of the regions concerned, which tend to be more concentrated in the Member States with higher levels of GDP per head than those in the other regional groups. The two adjustment factors, the addition to the employment rate resulting from some of those aged 65 and over being in work and the allowance for the difference between regional accounts employment and LFS employment, had a similar effect on GDP per head in 2001 in the different regional groups, the latter also tending to increase it slightly, largely because the number of jobs exceeded the number of people. Considering the EU15 regions separately from those in the EU12 (i.e. those in the countries which have entered the EU since 2004), a similar pattern emerges in terms of the differences between the regional groups. It should be noted, however, that in both cases, the Basic services regions have a lower level of both GDP per head and GDP per person employed than the Low-to-medium tech manufacturing ones or Tourism regions (Tables 3.2 and 3.3). 29

64 Table 3.2 Decomposition of GVA per head in the EU27 by regional group in 2001 Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) 30 Popn % total popn Empl adj. for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Tourism Table 3.1 Decomposition of GVA per head in the EU15 by regional group in 2001 Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) Popn % total popn Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Tourism

65 Table 3.3 Decomposition of GVA per head in the EU12 by regional group in 2001 Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) Popn % total popn Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Tourism

66 It is evident that the difference in GDP per head between the regional groups was much larger in the EU12 than the EU15, even after making an explicit allowance for commuting. In the EU12, therefore, Business & financial services regions had, on average, a level of GDP per head which was 45% above that in the Medium-to-high tech manufacturing regions, which had the second highest level (as against just 14% in the EU15), and 121% above that in the Agricultural regions with the lowest level (as against 58% - half as much in the EU15). This suggests that the extent of the difference between the regional groups tends to diminish as development takes place. A large part of the difference between the regions in the EU12, however, is attributable to the high GDP per head in the capital city regions (included as Business & financial services regions here), but even if these regions are excluded, a substantial gap remains and one which is much wider than in the EU15. In the EU12, therefore, GDP per head adjusted for commuting was around 60% higher in the Medium-to-high tech manufacturing regions than the Agricultural ones (and 47% higher than in the Basic services ones), whereas in the EU15, the difference was only 15% (13% in respect of the Basic services regions). The further pronounced difference between the EU15 and EU12 is in the contribution of productivity differences or in GDP per person employed to the difference in GDP per head between the groups in relation to that of the employment rate. In the EU12, therefore, by far the greater part of the difference in GDP per head in 2001 was due to differences in productivity (the level of which was more than two times higher in the Business & financial services regions than in the Agricultural ones as opposed to 30% higher in the EU15), whereas in the EU15, a significant part was accounted for by the employment rate (which was 18% higher in the Business & financial services regions than the Agricultural ones as against just over 7% in the EU12). In addition, the share of working-age population in the total was 5% larger in the Business & financial services regions than in the Agricultural ones in the EU15 as against 1.5 times larger in the EU12. In the EU15, both the employment rate and the share of working-age population in the total varied between the regional groups broadly in line with GDP per person employed, so reinforcing the effect of differences in the latter on GDP per head, but this was not the case in the EU12. This partly reflects the relatively large number of people employed in subsistence agriculture in these countries, most especially in Romania and Poland, a figure which tends to vary with the overall state of the economy and the availability of jobs. In a situation where there are insufficient jobs in the market part of the economy, therefore, agriculture tends to act as an employer of last resort, in that people can at least subsist by growing their own food. Agriculture, accordingly, provides a measure of support and in large measure represents a substitute for the social protection system which is the primary means of support in higher income countries. In the EU12 countries too, employment rates are also relatively high in Basic services regions, though this might be somewhat misleading since there are only three regions classified in this group. This compares with 26 regions in the EU15, of which only four are in the three Cohesion countries (Greece, Spain and Portugal), which may reflect a tendency not necessarily for sectoral specialisation to diminish as economies become more advanced but for it to become more difficult to identify in the data on employment shares. In both Basic services regions and Agricultural regions in the 32

67 Differences in GDP per head between regions allowing for country effects EU12, there was a relatively large number of people in employment aged 65 and over, which tends to push up GDP per head relative to the level in other regions. As illustrated by the Basic services regions, which tend to be located in the higher GDP per head countries of the EU15, it is still the case that the economic performance of regions is very much influenced by the state of the economy and the circumstances in the country in which they are situated. Regions in countries which are growing only slowly are unlikely to be able to grow rapidly, while those in countries which are growing fast will tend to have their growth rate pushed up by the favourable national economic context in which producers are operating. Since the regional groups distinguished here are not made up of an even number of regions from individual Member States, the results of the analysis presented above are liable to be affected by differences in the country composition of regions. These differences can be explicitly allowed for by relating the values of each of the proximate determinants of GDP per head (i.e. GDP per employed, the employment rate and so on) in each region to the national average. The results of doing this and aggregating regions into the same groups as above are shown below. Countries in which there is only a single NUTS 2 region (Denmark, the Baltic States, Luxembourg, Cyprus, Malta and Slovenia) are excluded from the analysis (and the following tables). The aim is to examine the extent to which the above findings in terms of the ordering of regional groups in respect of the determinants of GDP per head, and the relative importance of these determinants, remain valid. Allowing for country effects, GDP per head in the EU27 in 2001 remains on average much higher in the Business & financial services regions than in any of the other groups, while the level in Medium-to-high tech regions also remains higher than in the other regional groups (Table 3.4). The level in Tourist regions, however, is now slightly higher than that in Low-to-medium tech manufacturing ones, which in turn is above that in both Basic services and Agricultural regions. At the same time, the differences between the regional groups are reduced once the country effects are excluded. GDP per head, adjusted for commuting, is therefore, only 57% higher in the Business & financial services regions than in the Agricultural ones, as opposed to 77% higher if these effects are not allowed for. This reflects the fact that regions in the groups with the higher levels of GDP per head tend to be more numerous in the countries with higher levels of GDP per head levels. The level of productivity still contributes most to the differences in GDP per head, the employment rate continuing to make a much smaller, though still significant contribution. Roughly half of the narrowing in the GDP per head differences is due to a narrowing of the productivity difference, half to a narrowing of the employment differences. There are now, however, no significant differences at all between the regional groups in the contribution made by those employed aged 65 and over, indicating that there is no marked difference in these across regions within countries, though it remains the case that the share of working-age population in the total is larger in the higher GDP per head regions than in those with lower levels. 33

68 Table 3.5 Decomposition of GVA per head in the EU27 by regional group in 2001 Ratios to national average Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) Popn % total popn Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Table Tourism 3.4 Decomposition 0.95 of GVA per head 1.03 in the EU by regional group 1.03 in 2001 Ratios 0.99 to national 1.00 average Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) Popn % total popn Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Tourism

69 Table 3.6 Decomposition of GVA per head in the EU12 by regional group in 2001 Ratios to national average Regional groups GVA per employed (PPS) Employ rate, (%) Increase for employed 65+ (ratio) Adjusted empl rate (%) Popn % total popn Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) Agriculture Basic services Business & financial services Medium-tohigh tech manuf Low-tomedium tech manuf Tourism

70 Differences in GDP per head between the regional groups in 2008 The ordering of the regional groups in terms of both the employment rate and the share of working-age population in the total is still much the same as that for productivity, but the extent of the difference between the groups is narrower in respect of the employment rate, which on average is much the same in Medium-to-high tech manufacturing regions as in Business & financial services regions, while the rate in Agricultural regions is slightly higher than in Basic services ones. In broad terms, therefore, the effect of differences in the employment factors tends to reinforce the effect on GDP per head of differences in GDP per person employed. Much the same pattern of differences between the regional groups is evident if the EU15 and EU12 are separately distinguished (Table 3.5 and 3.6). The main difference is that in the EU15, it is no longer the case that the employment rate in Business & financial services regions is higher, on average, than that in Medium-to-high tech manufacturing ones, though it remains above that in the other regional groups, if only slightly. The major result, however, of allowing explicitly for country effects is to narrow the differences between the regional groups in both sets of countries. Nevertheless, these differences remain significant, especially in the EU12 countries, indicating clearly that the characteristics of regions, as reflected in their sectors of specialisation, have an important effect on their relative levels of GDP, even if the effect tends to diminish as economic development takes place. The question arises as to how far the differences between regional groups in GDP per head and the proximate factors which underlie these differences tend to change over time. Unfortunately, as noted above, it is only possible to examine this question over a relatively short period of time given the data available. Nevertheless, it is still of some interest to see whether and to what extent the differences observed above also hold for the other years over the period for which reasonably reliable data can be compiled. In practice, the differences in the relative levels of GDP per head and the component parts between the regional groups changed very little over the period concerned, as illustrated by examining the differences in The same features as described above in respect of 2001, therefore, are equally evident in 2008, in the sense that the rank order of the regional groups in terms of GDP per head is the same, the extent of the difference between the groups is similar, the relative importance of the components parts making up GDP per head is also similar and in the EU15 in particular, differences in the employment rate and the share of working-age population in the total tend to reinforce the effect of differences in productivity on GDP per head (Table 3.7). The differences between the two years are more marked in the EU12, reflecting the much larger changes in GDP per head and productivity in particular than in the EU15 but also suggesting more volatility in the performance of the different groups. The widening gap in GDP per head between the Business & financial services regions and the others is especially marked, implying faster growth of GDP per head between 2001 and Interestingly, however, the widening gap seems to be attributable almost entirely to a greater rise in the employment rate, and to a lesser extent, in the share of working-age population in GDP. In 2008, in contrast to 2001, therefore, the employment rate was significantly higher in Business & financial services regions than in the other groups and, the Agricultural regions apart, there was a closer 36

71 relationship between differences in productivity and differences in the employment rate. At the same time, the relative level of GDP per head in the Basic services regions especially was lower in 2008 than in 2001, due in this case to declines both in productivity as compared with other regions and in the employment rate. Growth of GDP per head in the regional groups, While a comparison between the position in 2008 and the position in 2001 in the different regional groups gives an indication of the differences in the pace of change between the two years in both GDP per head and the proximate factors determining this, it is perhaps more instructive to examine directly the growth performance of the different groups concerned. This is done below. As emphasised at the outset, it is only possible to examine the economic growth of regions across the EU in any detail i.e. following the approach adopted above over a comparatively short period of time given the data available. In practice, with some estimation of data for some countries for one or two years, a reasonably consistent set of data for all (or nearly all) NUTS 2 regions in the EU can be compiled for the years 1999 to 2008 (see Box 3.1). Box 3.1: The estimation of real GVA growth in NUTS 2 regions The data analysed in this section are based primarily on the Eurostat regional accounts data for GVA growth in real terms in NUTS 2 regions in the EU. These data take account of the composition of GVA when estimating price changes in order to generate the inflation-adjusted series. These series are available for the period for most regions. For some regions, however, the data are missing for particular years. These include, in particular, data for the Polish regions before 2004 and for the Greek and Spanish regions for 1999, while, for the German regions, data on real growth are available only at NUTS 1 level. For the German regions, estimates have been made for real growth at NUTS 2 level by assuming that the same implicit price index used for obtaining real growth at NUTS 1 level applies also to the NUTS 2 regions within the NUTS 1 boundaries. For Polish regions before 2004, the implied GVA deflator at national level has been applied to the regional GVA data in current prices to estimate an inflation-adjusted series. The same was done for Spanish regions for For Greek regions, a complete break in the regional accounts series in 2000 means that it is not possible to estimate GVA in the different regions in 1999 in a form that is consistent with the figures for subsequent years. In this case, the national growth rate in GVA between 1999 and 2000 has been assumed for all regions in order not to exclude them from the analysis. 37

72 Table 3.7 Decomposition of GVA per head in the regional groups in 2008 and differences from 2001 Commuting-adj Regional groups GVA per employed (PPS) Employment rate, (%) Popn % of total popn GVA per head (PPS) GVA per head as measured (PPS) Commuting effect (ratio) EU27 Ratios to national averages, 2008 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU15 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU12 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU27 Differences 2001 to 2008 (ratios of 2008 to 2001 ratios) Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU15 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU12 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism

73 The results show that at the overall EU level, growth of GDP, adjusted for changes in commuting (commuting tends to push up growth rates in the Business & financial services regions relative to the others) over the nine-year period was fastest in the Agricultural regions, followed by the Tourist regions, with Business & financial services regions only third (Table 3.8). A disaggregation between the EU15 and EU12 regions, however, indicates that this outcome is a result of the different distribution of regions between the two, because a relatively large number of Agricultural and Tourist regions are located in EU12 countries where growth tended to be higher over the period, whereas most Business & financial services regions are in EU15 countries where growth tended to be lower. Within the EU15 and EU12, therefore, the highest growth in GDP per head was in the Business & financial services regions, but in the EU15, this was matched by the pace of growth in the Agricultural regions. In the EU12 the Agricultural regions achieved the secondhighest rate of growth. However, the high growth rate of the Agricultural regions in the EU12 is a consequence, to a considerable extent, of the inclusion of Lithuania and Latvia. If these two countries, which are both single NUTS 2 regions, are excluded from the calculation, the growth rate of GDP per head comes down to 4.2% p.a., below Medium-to-high tech manufacturing regions and Tourist regions. The breakdown of GDP per head growth into the contributions from the main constituent factors (Table 3.8) indicates that in the EU15 in most of the regional groups, a rise in the employment rate made much the same contribution to growth of GDP per head as the improvement in productivity (i.e. GDP per person employed). Table 3.8 Contribution to annual average growth of GVA per head, GVA per employed (real terms)) Employment rate, (%) Popn % total popn Total empl rel to empl Empl adj for nat ac/lfs diff (ratio) Commuter-adj GVA per head (real terms) EU27 Agriculture Basic services Business&financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU15 Agriculture Basic services Business&financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU12 Agriculture Basic services Business&financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism

74 Differences in GDP per head growth between regions allowing for country effects There are two exceptions. The first is the Agricultural regions, where growth in GDP per head was due mainly to a rise in the employment rate. This was, on average, much lower than in other groups of regions at the beginning of the period (and was still much lower in 2008 despite the relative increase). Consequently, there was a comparatively large potential for raising the rate. The second exception is the Business & financial services group, where productivity growth was the main driver of growth in GDP per head, in part perhaps because of the more limited scope than elsewhere for an increase in the employment rate. In the EU12 countries, productivity growth was the main factor contributing to the growth of GDP per head in all regions. There was relatively high growth of GDP per head in the Business & financial services regions but also in the Agricultural regions, partly due to the inclusion of Latvia and Lithuania, as noted above. The difference between these two groups of regions in growth of GDP per head is attributable to the greater rise in the employment rate in the Business & financial services regions. Indeed, the considerable rise in the employment rate in the Business & financial services regions is a major reason why GDP per head grew more strongly in this group than in all others except for Tourist regions over the period examined. When comparing growth rates of GDP per head across the regional groups, it is important to take explicit account of the different national contexts. Although many regions significantly out-performed the national economy over this period, the extent to which it was possible to do so was inevitably limited because of the influence of the situation in neighbouring regions and of the rate at which their markets as well as those elsewhere in the country expanded. To take account of the national context we measure the change in GDP per head in the various regions, and in the proximate factors contributing to this, in relation the national average change so as to isolate their performance from that of the country in which they are situated. Again, NUTS 2 regions which cover whole countries have to be excluded. Expressing the changes over the period in these terms indicates that growth of GDP per head in the Business & financial services regions in the EU as a whole was marginally higher than in other regions once the national effect is stripped out, but this was also true of Agricultural and Basic services regions (Table 3.9). The experience over this period, however, varies markedly between EU15 and EU12 regions. In the EU15, differences in performance between regional groups were small over those nine years, the main feature being a relative growth of Agricultural regions, and to a lesser extent Basic services ones, coupled with a relative decline of Tourist and Medium-to-high tech manufacturing regions. The Tourist regions apart, the main underlying factor contributing to these differences was an increase, or reduction, in the employment rate coupled with less of a fall in the share of working-age population in the total population. In the Business & financial services regions GDP per head grew slightly more slowly than in other types of region because a relative decline in the employment rate and in the share of working-age population in the total more than offset the growth of productivity that was higher than in other regions. In the Basic services regions, faster growth of productivity was reinforced by a relative increase in the employment rate and in the working-age population as a share of the total. 40

75 Table 3.9 Growth of GVA per head and constituent factors relative to national averages, (Annual average % change) EU27 GVA per employed (real terms)) Employment rate, (%) Popn % total popn Total empl rel to empl Empl adj for nat ac/lfs diff (ratio) Commuteradj GVA per head (real Agriculture Basic services Business&financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism EU15 Agriculture Basic services Business&financial services High-med tech manuf Low-tech manuf Tourism EU12 Agriculture Basic services Business&financial services High-med tech manuf Low-tech manuf Tourism terms) In the EU12, the relative performance of the regional groups was very different. The Business & financial services regions achieved by far the highest rate of growth of GDP per head over the period (just over 1% higher than in other regions after allowing for the country effect). The Tourist regions and the Medium-to-high tech manufacturing regions, in contrast to the experience in the EU15, also had a higher growth rate than other regions, while Basic services and Agricultural ones as well as Low-to-medium manufacturing regions had lower growth rates than elsewhere. In each case, the main contributing factor was a relatively high, or low, growth of productivity. Indeed, only in the Business & financial services regions was the effect of this on GDP per head growth reinforced by a greater rise in the employment rate than elsewhere. In both the Basic services and Agricultural regions, especially the former, a relative decline in the employment rate had the effect of reinforcing the relative low rate of productivity growth, so depressing the growth of GDP per head further below that in other types of region. In the Tourist regions, a decline in population of working age relative to the total combined with a reduction in the number of people of 65 and over, partly offset a relatively high rate of productivity growth. 41

76 In the EU12, therefore, differences in the growth performance between the regional groups were not only larger than in the EU15 but very much in line with the differences in the level of GDP per head examined above (see Tables 6 and 7). 3.7 Concluding remarks The foregoing analysis indicates that there are significant differences in levels of GDP per head between the regional groups, that these are much wider in the EU12 countries than in the EU15, but that the rank order of the groups in terms of GDP per head is similar as well as being relatively consistent over time. It also shows that the employment and demographic factors which are proximate determinants of GDP per head tend to reinforce the effect of productivity (or GDP per person employed) by pushing up GDP per head further or pushing it down. The analysis of growth over the period shows that differences in growth performance of the regional groups were relatively small in the EU15: there was not much sign of either convergence of divergence between more and less prosperous groups. The analysis also shows that differences in the extent of changes in the employment rate between the regional groups made a significant contribution to the differences in growth performance over the period. Again, this is not too surprising given the wide differences that still exist in the rates between the groups and, accordingly, the large scope for increases in employment in the regions where rates are lowest. In the EU12, the relative growth performance of the regional groups over showed a widening divergence: the groups with higher GDP per head saw faster growth. To judge from the experience in the EU15, this difference in speed of growth would not be expected to persist for long, but it may be a feature of the differences in growth potential in less advanced economies (namely ones which have in general a much lower level of GDP per head than in the EU15), where the scope for increases in GDP per person employed in particular are substantial and where growth has typically been fastest in more advanced regions (especially capital city regions) This difference in growth between types of region is in itself reason enough for distinguishing between regions in terms of their areas of specialisation, especially since much of the focus of regional development policy is on regions in the EU12. Even in the EU15, however, where there seem to be less systematic differences in growth performance between regions grouped in this way, the difference in levels of GDP per head between the regional groups, though much smaller than in the EU12, suggests differences in performance over the very long term. Moreover, even over the nine-year period examined, it was still the case that the rate of growth experienced in the Business & financial services regions in the three EU15 Cohesion countries (Spain, Greece and Portugal) exceeded that in other regions, if only slightly 14. Quite apart from differences in performance, as indicated at the outset, the differences in areas of specialisation are likely to reflect differing regional features and other characteristics which themselves will tend to affect the development path followed and, accordingly, the appropriate development policy to adopt. Since identification of the main factors of growth on which policy should focus is the central aim of the 14 Relative growth in these regions was 0.4% a year higher than in other regions after adjusting for national effects. 42

77 present study, this reinforces the relevance of distinguishing between regions in the way we have done. Table 3.10 Number of regions in each classification 2008 EU27 EU15 EU12 Agriculture Basic services Business & financial services Medium-to-high tech manuf Low-to-medium tech manuf Tourism Total

78 4 Regional Structural Changes Effects on Growth, Productivity and Employment Categorising regions according to their economic specialisation Defining transition as a change in specialisation over time 4.1 Introduction This chapter examines the nature of structural change in the regions, and in particular the extent to which we can observe transition from lower value-added to higher valueadded specialisation in the different groups over the period One way to view the economies of the EU regions is that they consist of various sectors of economic activity that contribute to the regions total GDP and employment. These sectors may be divided into two groups. The first group of sectors is defined as those sectors that are more or less common to all regions, and produce goods and services that are produced for the local market only, i.e. the first group consists mainly of services sectors. The size of these sectors depends to a considerable extent on the second group of sectors that produce exportable goods and services (including tourism) and thereby generate income that to a certain extent is spent on the output of the first group of sectors. Hence, the bigger the income generated from the exporting sectors, the more is spent also on the common, local sectors, thereby increasing their output and employment. At the same time, the size and structure of the exporting sectors depend on the region s capacity to support certain economic activities. Hence, it is the comparative advantages (quite broadly defined) of each region that determine which exports sectors are located in the individual regions. Natural endowments, endowments of various production factors, the degree of urbanisation, the geographic location and the region s history amongst other factors determine whether a region s productive environment supports or does not support the location of certain industries. For example, depending on their comparative advantages, certain regions are more suited to the production of lower value-added industries, like agriculture and low-technology manufacturing industries, while other regions are preferred locations for higher valueadded industries such as high-technology industries or business services. In practice, these sectors of specialisation might be relatively small in output and employment terms when compared to the sectors that are common to all regions. But nevertheless, even if they are small, the (export) sectors of specialisation are those that characterise the regions and distinguish them from others. Analysis shows that the type of regional specialisation correlates with levels of income per head. That is, regions that are specialised in higher value-added activities tend to have higher levels of GDP per head than regions specialised in low-technology manufacturing or tourism while regions specialising in agriculture usually have the lowest income levels. The areas of regional specialisation are, however, not necessarily stable over time, as certain industries decline and new industries emerge under the pressure of such forces as to EU-wide and global competition, deliberate economic policies and changes in the economic paradigm. These changes bring with them changes in employment structures and volumes and in income levels. Generally, it is to be expected that structural changes or the transition from low value-added production towards a specialisation in high value-added production lead to higher levels of income and faster GDP growth. 44

79 Three methods to identify transition regions Two methods use cluster analysis Identifying and analysing regions that managed a transition from lower value-added to higher value-added specialisation might reveal important insights. First, we seek to determine whether such structural change was accompanied by a rise in the income levels and faster growth than in initially similar regions that did not undergo such transition. Secondly, we seek to understand the reasons behind these structural changes in order to provide insights for economic and regional policy. At the same time, there is no unique and thus no unambiguously correct methodology for defining such transition regions. Certainly, any measure of regional specialisation and change has to incorporate information on the sectoral structure of economic activity. Usually, this is done by using each economic sector s share in total gross value-added, or, as in this analysis, the sector s share in total employment. However, when we compare these shares across regions, the following questions arise: whether to use absolute shares in GDP or employment by sector as absolute measures, or relative measures, e.g. relative to the national average shares of the sectors whether to group regions according to their pattern of specialisation whether this grouping should be done mechanically or manually (using expert judgement) To take account of these questions, our analysis builds on three more or less different methods to identify regions that underwent a structural change recently and to evaluate the potential consequences of this change for performance in terms of the growth of GDP, productivity and employment. The first two methods are cluster analyses, which group the EU27 regions according to their main field of specialisation for the years 2000 and Both analyses construct six groups of specialisation (following those used in the analysis described in Chapter 3): agricultural regions low-to-medium technology manufacturing regions medium-to-high technology manufacturing regions tourist regions financial & business services regions basic services regions that are not specialised in one of the above areas. We then identify those regions that changed their group between 2000 and 2008 as transition regions and compare the GDP, productivity and employment growth performance of these transition regions and with the performance of the regions that did not change their group. There are two key differences between the two cluster methods. The first difference relates to the way regional specialisation is defined. The first method defines regional specialisation relative to the respective country average specialisation and therefore links to the analysis of specialisation groups in the previous chapter. Thus, a region is considered to be specialised in a sector if its employment share in this sector is higher than the country average employment share. The second method defines specialisation relative to the average specialisation of a group of countries. For this, three country groups are defined, namely the EU15, excluding Greece, Ireland, Portugal and Spain, the EU12 countries (including the 45

80 Länder of eastern Germany) except for Cyprus and Malta; and the third group contains Greece, Ireland, Portugal and Spain from the EU15, along with Cyprus and Malta from the EU12. These differences in the definitions of specialisation make a considerable difference to the way that regions are categorised. Broadly speaking, countries tend to show quite different patterns of specialisation. For example the Czech Republic is more specialised in manufacturing industries than Poland, which in turn has a higher share of agriculture. The same is true for Germany, which focuses on manufacturing industries more than any other country in Europe. To a large extent this is also reflected in the pattern of specialisation in the regions of the respective country. Hence, on average Czech regions tend to have a higher employment share in manufacturing than Polish regions, which in turn have a higher share in agriculture. Therefore, estimating regional clusters of specialisation using the average employment shares of a group of countries as benchmark will yield different results than estimating such clusters by using the average employment shares of the respective country as benchmarks. To illustrate, in the first case (comparing regions to the average for a group of countries), it is likely that a higher proportion of Czech than Polish regions will be classified as manufacturing regions (whether high or low-tech). By contrast, in the second case (comparing regions to the national average), the allocation of Czech and Polish regions to the manufacturing group will be more evenly balanced, even though the share of manufacturing industry employment is much lower in the average Polish region than in the average Czech region. Out of the total 260 regions in our sample only 116 (i.e. 44% of all regions) are assigned by both methods to the same cluster in 2000 and 2008 whereas for the other regions the two methods result in different cluster assignments, The second key difference between the two clustering methods is their procedure for cluster analysis. The first method uses a manual assignment of the regions to the individual clusters, while the second relies on statistical procedures. Experience shows that the first method tends to provide quite sensible results and allows for the experts own specific knowledge of the regions to flow into the analysis. At the same time, this method requires sometimes a good deal of expert judgement, especially in cases where regions are not easily assigned to one or the other group. Thus, it might well be that for certain regions different experts would come up with different cluster solutions, so that the results of the manual cluster analysis are not necessarily reproducible. The second method rests on clearly defined statistical routines. This makes cluster selection reproducible and testable. However, experience shows that such cluster estimations are highly sensitive to the way these routines are applied, and this procedure does not necessarily produce useful results. Under the second method also, much expert knowledge is required to make the technical analysis plausible. Overall, however, experience shows that these differences in cluster methodology do not much affect the results as, in general, they tend to produce similar cluster results. Hence, the differences in the results that are observed are mainly due to the differences in the definition of regional specialisation. Manual assignment 4.2 Cluster analysis The cluster analysis seeks to group the EU regions according to their comparative advantages, as revealed in the each region s structure of economic activity. This links 46

81 Taking account of the influence of broad differences in each country s level of economic development The clustering procedure the analysis to other parts of the project where it has been consistently shown that, though no EU region is identical in terms of its production structure or comparative advantages to any other EU region, there exist groups of regions that are sufficiently similar in their structure to justify analysing them jointly and comparing them to other groups. The clustering is carried out for the years 2000 and 2008, in order to identify those regions which moved from one specialisation group to another over this period. Such regions are potential candidate regions for a deeper analysis of the causes and effects of structural change in the EU regions. Each region s structure of specialisation is measured by its sectoral employment structure. That is, regions that have a higher employment share than the average region in the EU (or the respective country) in agriculture are considered to be specialised in this sector and are expected to have certain (though not precisely defined) comparative advantages in this sector that are the causes of their specialisation. A main point here is that for the cluster analysis (as in other parts of the project) regional specialisation and indirectly the comparative advantages of the regions are derived in a specific way. Given the fact that, with rising economic development, there is a general structural change from the primary and secondary sector to the tertiary sector, the level of economic development has to be taken into account in the analysis. Thus, highly developed regions in the EU15 tend to have much higher employment shares in services than regions in the EU12. Likewise, the EU12 regions tend to have higher shares in agriculture and industry than the EU15 regions. If we take no account of these broad differences in the level of economic development, we would conclude that virtually all EU15 regions have their main field of specialisation or comparative advantages in the services sectors while the EU12 regions have their comparative advantage in agriculture and industry. To correct for differences in economic development we evaluate the specialisation patterns in relative terms, looking at the regions sectoral structure in relation to the respective structure of the country the regions are located in. Since we are using detailed LFS data for the years 1999 to 2008 and in these data total employment is split into 62 sectors of economic activity, we do not consider all these sectors separately, but aggregate them to six main sectors: agriculture, medium-to-high technology manufacturing, low-to-medium technology manufacturing, financial & business services, tourism and basic services 15. Energy and mining as well as construction are not considered. These six sectors also define the six groups of regions that are produced by the cluster analysis. The actual clusters are derived by calculating the ratio of the individual region s shares to the respective country s average shares (and multiplying it by 100), to get a measure of relative specialisation by each region and sector. Thus, if a region s relative share in one sector is above 100, it, compared to the country, can be considered specialised and thus be assumed to be specialised and hence have some comparative advantage in that sector. This calculation is carried out for each region and each aggregated sector. For countries that consist of only one NUTS-2 region no comparison to the country average is made, rather the absolute shares are taken to determine the country s cluster. 15 The grouping is based on Eurostat: Aggregations of manufacturing based on NACE Rev 1.1, especially with respect to manufacturing and partly for the services sector. 47

82 Just under a quarter of all regions were identified as transition regions These data are then used to allocate the regions manually to one of the six clusters. In most cases the decision to put one region in one or the other cluster is made by looking at the sector with the highest specialisation index in that region, i.e. the highest relative share. In cases, where two sectors in a region have a similar high relative share we use expert judgement to assign the region to a specific cluster. For example, in some cases financial & business services and tourism were almost equally high on the specialisation index. In those cases, financial & business services was assumed to be more important and so such regions were put into the financial & business services cluster. Moreover the relative shares are also compared between regions, so that only regions with a particular high relative specialisation are put in the respective cluster, while regions with low relative specialisation (i.e. the relative share being close to 100 the national average) are put in different clusters, according to their specialisation in the other sectors. The manual cluster analysis indicates that in the year 2000, 56 out of the total of 262 EU27 NUTS-2 regions were classified as medium-high technology manufacturing regions, 68 regions specialised in low-medium technology manufacturing, 34 regions in financial & business services, 19 in tourism, 55 in agriculture and 30 in basic services (i.e. they had no distinctive specialisation in the other five sectors). In the period , 201 regions kept their initial specialisation, while it changed for 61 regions (slightly less than one-quarter of all regions). These 61 regions are thus considered to be transition regions and they are identifiable as the off-diagonal elements in Table 4.1. The highest number of transition regions is found amongst the regions initially classified as low tech industry regions: 26 of the 68 regions (38%) changed their specialisation, mostly to agriculture or basic services indicating a certain downgrading of the regions productive structure. In relative terms most changes occurred in the basic services regions, as around 40% (12 in total) of those regions switched from the initial to another cluster and mostly to agriculture. A relatively high number of changing regions is also found amongst the medium-to-high tech manufacturing regions. Here 15 regions changed their initial specialisation, overwhelmingly to low-to-medium tech manufacturing or agriculture. No regions changed joined or left the Tourist regions cluster, and almost none did so for the Financial & business services cluster. 48

83 Table 4.1 Manual cluster analysis: Number of regions by cluster in 2000 and 2008 Cluster 2000 medium Medium-tohigh tech manuf. Low-tomedium tech manuf. Financial & business services tech manuf. Mediumto-high Low-to- tech manuf Cluster 2008 Financial & business services Tourism Agriculture Basic services Total Tourism Agriculture Basic services Total Source: EU LFS, own calculation Results for stable regions Results for transition regions Table 4.2 shows the growth rates of GDP per head, productivity and employment of each group of regions identified in Table 4.1 (for transition regions we only calculate growth rates for cells where there are four or more observations). Amongst the stable regions, the financial & business services regions tended to grow fastest in terms of GDP per head and productivity 16. In employment terms, tourist regions had the highest average growth, followed by financial & business services and agricultural regions. As far as the transition regions are concerned, the analysis indicates that shifts in specialisation are generally associated with higher growth (on all indicators) for regions that changed to agriculture than for regions that stayed in the same specialisation (switching to agriculture from medium-to-high and low-to-medium tech manufacturing as well as from basic services). An exception is that the basic services regions that shifted to agriculture had much slower employment growth than the stable basic services regions. By contrast, regions changing in specialisation from mediumto-high tech to low-to-medium tech manufacturing achieved slower growth in terms of GDP per head than pure medium-to-high tech manufacturing regions as did regions switching from low-to-medium tech manufacturing to basic services. A similar broad pattern is seen with respect to productivity and employment growth. 16 This is not precisely the same as the results presented in chapter 3, mostly because in chapter 3 we were using commuting-adjusted GVA per head, whereas here we are using non-adjusted GVA per head. Moreover, we also use unweighted averages in chapter 4, whereas in chapter 3 we used population-weighted averages. The main reason for using population weighted averages is that we did not want the results for transition regions to be governed by the high population regions. 49

84 Table 4.2 Manual cluster analysis: Absolute average growth rates by cluster Cluster 2000 Medium-to-high Medium-tohigh tech manuf. GVA per head Low-tomedium tech manuf Cluster 2008 Financial & business services Tourism Agriculture tech manuf Low-to-medium Basic services tech manuf Financial & business services 2.7 Tourism 2.3 Agriculture Basic services Medium-to-high Productivity tech manuf Low-to-medium tech manuf Financial & business services 2.1 Tourism 1.6 Agriculture 1.6 Basic services Medium-to-high Employment tech manuf Low-to-medium tech manuf Financial & business services 1.2 Tourism 1.5 Agriculture 1.1 Basic services Source: EU LFS, own calculation 50

85 At first sight some of the results look surprising, especially those where a change to agricultural specialisation is associated with higher growth in GDP per head and productivity. However, closer inspection of the data reveals that almost all regions that changed either from a medium-to-high and low-to-medium tech manufacturing or a basic services specialisation to an agricultural specialisation are borderline regions. This is because these regions have both a high share of agricultural employment and a high share of employment in their sector of specialisation in That is, all the regions could have been easily classified as agricultural regions in Knowing this, the performance of these regions is, at least terms of GDP per head and productivity, not too different from that of pure agricultural regions. Thus, the relatively good growth performance might not be so much an expression of the effects of structural changes but rather a genuine structural feature of agricultural regions. Adjusting for country-specific effects The growth rates in Table 4.2 are absolute growth rates, which, although we only considered the average growth rates for sufficiently large groups of regions, might reflect country-specific factors that raise or lower the growth rates of all regions in the respective country. To take such country effects into account, regional growth rates were expressed as a ratio of the respective country average growth (Table 4.3). Hence, relative growth rates above 1 indicate that the regions grow faster than their country and vice versa for relative growth rates below 1. In terms of relative growth rates, growth in GDP per head in the regions that stayed in the same cluster of specialisation was highest in tourism and agricultural regions and lowest in both types of manufacturing regions. Again, a similar pattern holds for productivity growth and, with some exceptions (basic services regions), also for employment growth. As far as the transition regions are concerned, the numbers indicate that shifts in specialisation tend to correlate with faster growth in GDP per head and productivity, except for low-to-medium tech manufacturing regions that turned into agricultural regions. As far as employment growth is concerned, a shift away from specialisation in manufacturing to agriculture was positively associated with higher relative employment growth, while basic services regions that became agricultural regions recorded a significantly weaker employment performance. 51

86 Table 4.3 Manual cluster analysis: Relative average growth rates by cluster Cluster 2000 Medium-to-high Medium-tohigh tech manuf. GVA per head Low-tomedium tech manuf Cluster 2008 Financial & business services Tourism Agriculture tech manuf Low-to-medium Basic services tech manuf Financial & business services 1.0 Tourism 1.1 Agriculture 1.1 Basic services Medium-to-high Productivity tech manuf Low-to-medium tech manuf Financial & business services 1.0 Tourism 1.0 Agriculture 1.0 Basic services Medium-to-high Employment tech manuf Low-to-medium tech manuf Financial & business services 0.9 Tourism 1.2 Agriculture 1.1 Basic services Source: EU LFS, own calculation Technical cluster analysis The technical cluster analysis is built on the same principles as the manual cluster analysis. The most important implication of this is that the analysis takes into account differences in economic development across the regions and the countries. However, to correct for these differences we evaluate the regions specialisation or comparative advantages in relative terms, taking as reference not the country averages but an allocation of regions to three groups. The first group contains regions in the EU15, but excluding Greece, Ireland, Portugal and Spain, i.e. it consists of regions in 52

87 Clustering method countries with the highest level of economic development countries. The second group consists of the EU12 countries (including the Länder of eastern Germany) except for Cyprus and Malta, and the third group contains Greece, Ireland, Portugal and Spain from the EU15, along with Cyprus and Malta from the EU12. For each group we calculate the population-weighted average share of the economic sectors in total employment. We use the same aggregated sectors as in the manual cluster analysis. To link the cluster analysis to the analysis in the rest of the project we predefine the number of clusters to six: medium-to-high technology manufacturing; low-to-medium technology manufacturing; agricultural; financial & business services; tourism; and basic services (no particular specialisation). Since we know the number and types of clusters that we want, we use a non-hierarchical cluster analysis that exactly fits to this task. The most demanding task in a non-hierarchical cluster analysis is the definition of cluster seed points, or initial starting values for each potential cluster, around which the regions are grouped. Once these seed points are defined, the clustering process allocates the regions to the group with the closest centre. From this basis, the mean of the regions sector shares is computed to form a new centre point. This process is repeated until all regions remain in the same group from the previous iteration. Notably, the distance to the cluster seed points and the subsequent centres is calculated as Euclidean distance. The initial six cluster seed points are derived in the following way. For each seed point we sort only those regions that have an above-average relative share in the respective sector, i.e. a value above 100. We then split this sorting in half, so that the first half has the highly specialised regions in the respective sector and the second half the less, but still, specialised regions. From that, we calculate for the highly specialised regions the mean relative share in the respective sector, but also for the other sectors, to get the seed point for the sector in question. To illustrate, all regions specialised in agriculture are sorted by the size of their specialisation in agriculture, i.e. according to the relative share. From this we take the 50% of regions that have the highest relative share in agriculture in the EU and calculate the mean of these relative shares. Additionally, for the regions highly specialised in agriculture we also calculate the means of the relative shares for the other sectors, so that we can define our agricultural seed point. Basically, this point represents the relative employment structure of the average region highly specialised in agriculture. We repeat this procedure for the other cluster seed points, except for the seed point of the basic services regions. For this seed point, we simply set all relative shares to zero. Once the seed points are derived, we perform the cluster analysis routine 17. The visual inspection of this round of cluster analysis shows that it works well for those regions that are specialised primarily in one sector of economic activity. However, there are some problems with regions with more than one specialisation as they are frequently put in the other or basic services cluster. Since some of these regions still have some distinct specialisation we perform a second iteration, this time only for those regions that have be put in the other cluster in the first iteration. The seed points are derived in a similar manner to that used in the first round, except that we form the means of the top third of specialised regions rather than of the top 50%. Given this, we apply the cluster routine to this sample and add the results to the results of the first round 17 Using the kmeans command in Stata

88 Just over a quarter of regions were identified as transition regions Characteristics of transition regions cluster analysis. This gives the final results of the cluster analysis. This procedure is done for the years 2000 and 2008 to identify those regions that changed their cluster assignment over that period of time. In contrast to the manual analysis, the sample of regions was extended to 267 (a further five regions), because of additional data for Danish and Slovenian regions (these two countries were reorganised into five and two regions respectively in 2009). In 2000 the highest number of regions were allocated to the agricultural group (64) followed by low-to-medium tech (54), medium-to-high tech (46) and financial & business services regions (45). There were also 31 basic services and 27 tourist regions. Thus, the different methodology leads to a somewhat different allocation of regions to the clusters of specialisation. Compared to the manual clustering, there are more agricultural, tourist and business services regions and fewer high and low tech industry regions. Out of the 267 regions, 192 (72%) did not change their cluster between 2000 and 2008 while 75 (28%) did. The extent to which the regions shift from one cluster to another is fairly similar to the shift shown by the manual clustering, as far as the percentage of regions and the main sectors between which these shifts occur are concerned (Table 4.4). The highest number of changes (in absolute and relative terms) are observed in the low-to-medium tech manufacturing regions, followed (in absolute terms) by the medium-to-high tech manufacturing and the basic services regions. In contrast to the manual clustering method, the technical clustering (given the different definition of reference values) exhibits a higher number of changes in the financial & business services and basic services regions, and especially in the tourist regions. Yet in contrast to the manual clustering, the pattern of transition regions is much more dispersed and also quite different. Moreover, given the differences between the manual and the technical cluster analysis in the way the regions are allocated to specialisation groups, there is only a small overlap in the particular regions that are identified as transition regions. Thus, the differences in the reference group, i.e. the national average in the manual method and the country group average in the technical method, have a significant effect on the results. Slightly larger numbers of medium-to- high tech manufacturing regions became lowto-medium tech manufacturing or the basic services regions than switched to other groups. Of the regions that switched from being low-to-medium tech manufacturing regions to other specialisations, almost two-thirds became basic services regions. Most of the regions that ceased to be tourist regions became agricultural or basic services regions, while regions that ceased to be basic services regions turned into tourism or financial & business services regions. 54

89 Table 4.4 Technical cluster analysis: Number of regions by cluster in 2000 and 2008 Cluster 2000 Cluster 2008 Mediumto-higmedium Low-to- Financial & Basic Tourism Agriculture tech tech business services Total manuf. manuf services Mediumto-high tech manuf. Low-tomedium tech manuf. Financial & business services Tourism Agriculture Basic services Total Source: EU LFS, own calculation Growth performance Among the stable regions in these groups (Table 4.5) the financial & business services regions grow fastest (in terms of GDP per head and productivity), though the difference compared with other regions is smaller than in the groups created by the manual clustering method. With respect to employment, both types of manufacturing region as well as financial & business services and tourist regions tend on average to have a similar growth performance, while in agriculture and basic services regions employment growth is slightly slower. Among the transition regions, the results indicate that a change in specialisation tends to be correlated with higher growth in productivity and GDP per head than the rates seen in regions that did not change their specialisation, exceptions being lower growth in GDP per head in the medium-to-high tech manufacturing regions that became basic services regions and in productivity in regions shifting from tourism to basic services. As far as employment growth is concerned, regions that change their specialisation away from either type of manufacturing or from financial & business services tend to perform worse than the regions that do not change from these specialisations. The reverse is generally the case for regions that cease to specialise in tourism or basic services; these tend to have higher employment growth than the regions that maintain either specialisation. 55

90 Table 4.5 Technical cluster analysis: Absolute average growth rates by cluster Cluster 2000 Medium-to-high tech manuf. Low-to-medium tech manuf. Financial & business services Medium-tohigh tech manuf. GVA per head Low-tomedium tech manuf Cluster 2008 Financial & business services Tourism Agriculture Basic services Tourism Agriculture 2.1 Basic services Medium-to-high tech manuf. Low-to-medium tech manuf. Financial & business services Productivity Tourism Agriculture 1.8 Basic services Medium-to-high tech manuf. Low-to-medium tech manuf. Financial & business services Employment Tourism Agriculture 0.7 Basic services Source: EU LFS, own calculation Relative growth performance As we did with the manual cluster analysis, we also calculated relative growth rates, but taking as the reference base not the country to which each type of region belongs but to the relevant one of the three groups (namely, EU15 excluding IE, GR, PT and ES; EU12 excluding CY and MT; and IE, GR, PT, ES, CY and MT). These groups recorded large differences in growth performance since 2000 (Table 4.6). With few exceptions, the relative growth rates are in line with the absolute growth rates. Thus, in most cases it pays off in terms of growth of GDP per head to shift from 56