Territorial Indicators of Socioeconomic Patterns and Dynamics 1

DT/TDPC(2002)23TERRITORIAL INDICATORS OF SOCIO-ECONOMIC PATTERNS AND DYNAMICS11. Context and backgroundThe OECD activity on Territorial Statistics and Indicators (TSI) is undertaking pioneer work to establish an international statistical database on comparable sub-national territories. This allows calculating sets of territorial development indicators revealing the huge variety of demographic, economic, social and environmental conditions and trends usually hidden behind national average figures. Disaggregated sub-national data and indicators enable meaningful comparisons, improve analytical capacities and insights, and thereby facilitate the design, implementation and evaluation of policies. Figure II.3.1 lists the key concerns and topics for which territorial data and indicators shall be generated in order to contribute to monitoring progress towards integrated sustainable development among and within OECD countries.This paper summarises findings and conclusions of a series of exploratory case studies, which aimed at identifying territorial indicators and analysing preliminary results concerning socio-economic patterns and dynamics across sub-national territories. A Steering Group composed of national experts provided valuable input to these analyses. Each case study focused on a specific topic. They sometimes cover only a limited number of Member countries, those for which first data sets had been made available. Even if some of the data sets refer to the 1980s and the early 1990s, and are thus outdated to describe present situations, they are still useful to explore methodological and analytical issues and to demonstrate the usefulness of territorial indicators work. They provide important policy relevant insights and help underpinning the fact that “territory matters”. While the interpretation of indicator results is primarily driven by research and policy concerns related to rural development, their relevance reaches beyond rural areas. The indicator set can be used also for analysing any other type of sub-national territorial cluster.OECD work on sub-national territorial statistics and indicators was launched in 1991 as part of the OECD Rural Development Programme. With the creation of the Territorial Development Service (TDS) in 1994, this activity has been reinforced and its scope broadened to respond also to other analytical and policy challenges of territorial development (e.g. urban affairs, regional policy, and local labour markets). The first OECD report on territorial indicators (Creating rural indicators for shaping territorial policies, Paris 1994), described the basic conceptual framework, the definitions and typologies adopted, and provided a general overview on territorial development conditions and trends in OECD countries. Subsequent indicator analyses focused more specifically on issues related to employment creation. The second territorial indicators report revealed, in particular, significant variations in performance of different types of local labour markets (Territorial indicators of employment, Paris 1996).One of the main conclusions from these initial territorial indicator analyses was that regional development performance differentials were to a significant degree due to differences in the endowment with social and natural capital and the way in which those were actually mobilised and managed. In order to deepen these analyses and to further refine the preliminary OECD set of territorial indicators, work was launched on income and social indicators, as well as on environment and amenity indicators. The following sections summarise key results of the project on Rural Income and Social Indicators (RISI),1 DT/TDPC(2002)23 which collected and processed sub-national social statistics concerning income, employment, education, demographics, health, and safety from a maximum of OECD Member countries.Figure II.3.1. Territorial indicators – concerns and topicsTerritorial Indicators shall provide information on:ECONOMICECONOMIC SOCIAL STRUCTURESTRUCTURES & & PERFORMANCE PERFORMANCE WELL-BEING & COHESIONProductivity Income and Poverty Growth Labourand Employment Investment Education and Skills Innovation Health and Safety Sectoral mix Housing and CommunityDEMOGRAPHIC ENVIRONMENTAL PATTERNS & MIGRATION QUALITY & AMENITYDensity Topography and Climate Natural balance Soils, Water, Species Youth andAgeing Habitats and Landscapes Migration Settlement and Equipment Households Cultural Heritage facilitating integrated assessment of sub-national development conditions and trendsSelected data sets were analysed with the aim of assessing the practical utility of different socio-economic indicators in describing territorial dynamics and disparities and the potential of these indicators to facilitate better understanding of complex interactions and relationships between economic performance and socio-economic characteristics. However, before presenting some of the main findings and conclusions of this work, it is indispensable to briefly outline the territorial scheme and the typologies used for generating, aggregating and interpreting sub-national territorial statistics.2. Territorial units and typologiesThe OECD activity on territorial statistics and indicators (TSI) has established a territorial scheme for empirical analysis of territorial development conditions and trends (Figure II.3.2): It · covers the entire territory of OECD Member countries; · distinguishes different hierarchical levels of geographic detail (e.g., about 2 200 regions and 70 000 local communities); · applies practical definitions based on simple and intuitive criteria for creating area typologies, appropriate for analyses of sub-national development conditions and trends in a multi-national context.2 DT/TDPC(2002)23Figure II.3.2. The OECD territorial schemeTerritorial Scheme and a typology by type of settlementOECD covers the entire National OECD territory Member countries ( 30 ) distinguishes Predominantly Intermediate Predominantly different Rural Urban Regional levels Regions ( 2 200 ) (regional & local) allows for various Local typologies ( 70 000 ) Local communities (e.g. urban / rural) Rural Urban Territorial UnitsRural Local Communities : Population density (below 150 inhabitants per sq.km)Settlement type of Region : Share of rural population (below 15, 15-50, above 50 percent)The distinction of different hierarchical levels for territorial detail, and their context specific combination, is essential for understanding the conceptual and methodological approach of the OECD indicators work on territorial development: At the local level, the territorial grid consists of basic administrative or statistical units (approximately 70 000). Most territorial analyses are, however, undertaken for a set of more aggregated sub-national regions (some 2 200). They are chosen to best reflect functional regions such as labour market areas or commuting zones.Depending on the analytical purpose these territorial units can be characterised and clustered according to various typologies. For example, for rural-urban analyses at the local level, small territories have been classified as being either rural or urban. OECD identifies these rural areas as communities with population densities below 150 inhabitants per square kilometre. However, development options and opportunities for these local rural communities, depend crucially on their relationship with urban centres, in particular those within their own region. Consequently most TSI analyses are undertaken for the more aggregated sub-national regions (approximately 2 200). These functional labour market areas or commuting zones can be characterised as being more or less rural / urban depending on the share of regional population living in rural / urban local communities. To facilitate the analysis and presentation of indicator results the multitude of individual territorial units is generally clustered into different types, e.g., Predominantly Rural (PR) regions: over 50 per cent; Intermediate regions (IM): 15 to 50 per cent; Predominantly Urbanised (PU) regions: below 15 per cent. Thus each of the three types of region contains some rural and some urban communities although to a different degree.3 DT/TDPC(2002)23Figure II.3.3. The OECD regional typologyRegional typology for rural / urban analysesPredominantly Three types of Predominantly Urbanised Intermediate regions region Rural regions regions 40 % 32 % 28 % % Population4 % 10 % % Area86 % OECD Total = 100 %The spatial organisation of OECD Member countries is characterised by a great diversity of territorial patterns. Overall, about a quarter of the OECD population dwell in predominantly rural, often remote regions with a majority of people in sparsely populated rural communities. At the other extreme, about 40 per cent of the OECD population is concentrated on less than 4 per cent of the territory in predominantly urbanised regions. The remaining third of the population lives in intermediate regions (Figure II.3.3). Whereas for some countries -- for example most Nordic countries -- the population shares are descending from predominantly rural, to intermediate, to predominantly urbanised regions, shares are ascending in others -- such as Belgium, the United Kingdom, or Germany. Other countries are characterised by a dual structure with large proportions of the population at both extremes, predominantly rural and predominantly urbanised (e.g., Ireland, Greece, Portugal), while in France, Spain and Italy the largest share falls in the intermediate category of regions (Figure II.3.4).4 DT/TDPC(2002)23Figure II.3.4. The OECD population by type of region09040 2032 70 n I 28 a 40 n t b e r CZE r How to read the diagram: U m e.g. 50 50 e Regional population share d Rural = 28 % ESP i ITA FRA a Intermediate = 32 % AUT t Urban = 40 % 60 e DNK SWE USA FIN NOR EU JAP OECD 32DEU PRT NZL TUR UK CHE AUS GRC 80 CAN BEL IRLNLD 10ISL100 %20 28 40 60 80 100 % Rural3. Productivity and incomeInternational disparitiesGross Domestic Product (GDP) per capita is one of the most frequently used indicators for measuring economic performance and comparing the state of development of different areas. It can be interpreted as an indicator of productivity and income generation. Analysing disparities in GDP per capita can contribute to a better understanding of the strengths and weaknesses of national or regional economies. Table II.3.1 provides information on the level of GDP per capita in OECD countries, on changes in their relative position, as well as on its sub-national variation. Member countries are ranked according to their average national GDP per inhabitant in 1990. International differences in Per-capita GDP are huge: figures range from less than 50 to over 125 per cent of the OECD average. For the top five countries it is at least twice as high as for the bottom five countries.5 DT/TDPC(2002)23Table II.3.1.GDP per capita – Inter-national and intra-national disparitiesIn US$ at current prices and PPP’sa, 1990-1999GDP per capita, 1999 National Regional Countryb Level Change Change US$ and PPPs Disparityd OECD = 100 1990-99c 1990-1999e Luxembourg 43 066 184 34 .. .. United States 33 725 144 2 34 -2f Switzerland 28 778 123 -10 .. .. Canada 26 444 113 -2 23 -7 Japan 24 934 106 -6 20 -4 Sweden 23 477 100 -8 11 3 Norway 29 025 124 16 24 -1f Iceland 27 695 118 10 .. .. France 23 068 98 -8 29 0 Denmark 28 030 120 14 25 6 Austria 25 697 110 5 25 -3 Belgium 24 672 105 1 37 13 Australia 25 619 109 6 16 3 Netherlands 26 488 113 11 17 4 Germany 24 601 105 4 27 .. Finland 23 413 100 -1 22 8 Italy 23 937 102 1 24 -1g United Kingdom 23 286 99 0 38 0g New Zealand 19 360 83 -1 .. .. Spain 19 045 81 5 23 0 Ireland 25 878 110 38 22 0 Czech Republic 13 550 58 -13 34 8g Portugal 17 064 73 12 31 -1 Greece 15 799 67 10 15 1 Korea 13 647 58 13 19 4 Hungary 11 505 49 29 5g Slovak Republic 11 148 48 41 2g Mexico 8 351 36 -1 46 0 Poland 9 008 38 8 45 9g Turkey 5 966 25 -3 .. .. TOTAL OECD 23 435 100 0 Total OECD Europe 20 823 89 0 European Union (15) 23 163 99 2 a) In order to correct for distortions caused by US$ exchange rates that do not reflect international differences in purchasing power, figures are calculated on the basis of GDP data at current prices and Purchasing Power Parities (PPP). b) OECD Member countries ranked by their GDP per capita in 1990. c) Change in percentage points of OECD average =100. d) Regional variation coefficient, Standard deviation of regional GDP/inhabitant in percent of national mean). e) Change of regional variation co-efficient in percentage points. f) 1995 regional disparity instead of 1999. g) 1995 – 1999 regional change. Source: OECD, Territorial Statistics and Indicators.During the 1990s, international disparities in GDP per capita did not increase among OECD Member countries. Improvement and deterioration of national positions is evenly spread over the range of countries, from the top to the bottom. Variation coefficients, expressing the standard deviation of national GDP per capita figures in per cent of the OECD average, remained fairly stable. From 1990 to 1999 five of the top ten countries experienced a relative decline in their position, whereas seven of the bottom ten6 DT/TDPC(2002)23 countries were able to improve their position. Only 2 of the 13 countries that had below average GDP per capita in 1980 lost ground in relative terms.International comparisons of this kind are typical examples of standard macro-economic analyses regularly provided on the basis of OECD statistics. Yet, it appears reasonable to argue that it can not be the final word in international economic analysis to simply compare the per capita distribution of the US$17 billion GDP of Luxembourg with that of the US$7 000 billion GDP of the United States. Any serious attempt to really understand and reveal the strength and weaknesses, the opportunities and threats of the economies in the OECD area must aim at comparing comparable economic entities.Intra-national, territorial disparitiesIf international differences are an important subject of economic analysis, how can intra-national territorial differences be neglected? National average figures often hide more than they reveal. Frequently, they do not reveal important economic and social development features that are at the centre of Member countries concern in shaping their respective economic and social policies. Territorially disaggregated, but coherent, indicator sets on economic, social and environmental conditions and trends are promising to provide a more reliable information base for comprehensive analysis and policy recommendation, implementation and evaluation.On average, in most OECD countries regional GDP per capita differs by 15 to 25 per cent from the national mean. Regional variation coefficients, calculated as standard deviation of regional GDP per capita in percent of national mean, provide a simple measure of territorial disparity within OECD countries (Table II.3.1). It should be noted that in six of the bottom ten (low-income) countries, average regional variation exceeds 25 per cent, while this is the case in only one of the top ten (high-income) countries. Furthermore, while overall international disparities remained unchanged, it is evident that in many OECD countries internal, territorial disparities among sub-national regions increased. In particular, this has been the case for the low-income countries that were already characterised by more pronounced intra-national, internal disparities. In turn, regional disparities tended to diminish in high-income OECD countries.These changes in sub-national regional disparity patterns indicate that the dynamics of economic development are not evenly spread over national territories. Analysing the driving forces for and the barriers to successful economic development in different parts of OECD Member countries is thus not only a matter of social equity. It should also lead to a better understanding of the preconditions and circumstances that are essential for triggering economic development and for improving economic efficiency and competitiveness. Where is it that economic opportunities and jobs are created? Where is it, that growth is generated? Under which economic, social and environmental settings does economic activity perform best?Territorial disparities by type of regionThe case study on GDP per capita used sub-national territorial data for France to exemplify how territorial indicators can contribute to a better understanding of the diversity of economic settings within OECD Member countries. Figure II.3.5a reveals the wide variation in regional GDP per capita. For metropolitan agglomerations like the capital region around Paris (Ile-de-France) or Lyon GDP per capita far exceeds the national average (by more than 50% and 20% respectively). In turn, a large number of regions do not even attain 80 per cent of the national average.7 DT/TDPC(2002)23Figure II.3.5. Disparities in GDP per capita in France a. GDP per capita by type of region, 1999 0 01 160= e 150 g a r e 140 v a l 130 a n o i 120 t a N, 110 a t i p 100 a c r 90 e pP 80 DG 7060 0 10 20 30 40 50 60 70 80 90 100 % of Rurality Urban Intermediate Rural regions regions regions b. GDP per capita level change, 1990-1999160 0 0 1 I II = 150 e g a 140 r e v a130 l a n o i 120 t a N, 110 a t i p 100 a c r e 90 pPD 80 G70 III IV 60 -20 -15 -10 -5 0 5 10 15 20Change in GDP per capita, 1990-99 Regional % points differences to NationalSource: OECD Territorial Statistics and Indicators.Figure II.3.5a also demonstrates, how problematic it can be to focus attention exclusively on average conditions and trends in the three types of region: urban, intermediate and rural. In France, as in all OECD countries, regional GDP per capita is positively correlated with urbanisation or, which is the same, negatively correlated with the degree of rurality of territories. Yet, as the graph shows, there is a8 DT/TDPC(2002)23 wide range of variation in GDP per capita for regions with similar settlement pattern. For example, regions with a share of rural population of about 75 per cent, GDP per capita values range from 65 to 85 per cent of the national average. For urbanised regions, although on average above national, variation is even higher. While on average intermediate regions have GDP per capita values close to the national average, for some intermediate regions income values are clearly below even the rural average.Regional GDP per capita - level and growthFigure II.3.5b provides further prove that sweeping statements on increasing or decreasing regional disparities are inappropriate and can actually lead to questionable policy conclusions. As in Figure II.3.5a, on the vertical axis (y), all French regions are plotted according to their relative position in GDP per capita level. On the horizontal axis (x) regions are plotted according to their growth performance (expressed as difference in percentage points to the national average growth rate in GDP per capita). If disparities were increasing, regions would concentrate in the upper right and the lower left quadrants (II and III) of the graph. In turn, if regions concentrate in the upper left and the lower right quadrant (I and IV), disparities decrease. The graph clearly shows that regions with above average GDP per capita levels experienced growth rates higher as well as lower (I and II) than the national average. The same was true for low-income regions (III and IV). Many low-income regions succeeded to grow faster than the national average, thus reducing their regional disparity gap in income and productivity. Majorities of the later are predominately rural and their growth rates were higher than those for the main French agglomerations (Paris and Lyon) were.Regional competitiveness - GDP, employment and productivityBy combining information on change in regional GDP and in regional employment it is possible to analyse if production growth was accompanied by employment growth and if regional labour productivity improved compared to the national average. Figure II.3.6, provides a highly condensed graphical presentation of territorial development performance for the three types of regions in France. The dots indicate the extent to which production and / or employment growth was higher or lower than the national average growth rate during the 1980s (the differential being expressed in percentage points). The arrows indicate if the position has improved or deteriorated over the 1990s. Regions, which have been successful in improving their competitive position, will be located in the upper right quadrant (indicating above average growth in both GDP and employment). If regions have also managed to increase labour productivity by more than the nation rate, they are located in the darker triangle.The graph shows that during the 1990s both rural and urban regions in France experienced a relative deterioration of their respective positions, while the relative positioning of the intermediate regions improved. In particular with respect to employment growth, they did much better than rural and urban regions. However, GDP growth remained slightly below the national average and their labour productivity position did not improve. Similar graphs can be produced for other OECD countries. They reveal significant differences in the patterns of sub-national territorial development performance. The graphical presentation also allows to position individual regions in the broader context of territorial development within the national economy.9 DT/TDPC(2002)23Figure II.3.6. Territorial development performance by type of region in France, 1980-1999Labour - _ productivity + 8 % +Employment IntermediateDifferential to national growth (%) 0 Urban 1980-991980-90 (Jobs) RuralGrowth - 8 % 0 + 8 % differential (GDP) Production4. Employment and unemploymentDifferential performance in job creationPrevious territorial indicator analyses have demonstrated a huge variability of sub-national labour market conditions and trends. They found that even a distinction into three types of region risks to mask important territorial dynamics and may lead to problematic perceptions of problems and perspectives in policy formulation and implementation. Figure II.3.7 reveals the wide variation in employment creation performance of sub-national labour market areas in Austria. Instead of focussing on disparities between rural and urban regions, more attention should be paid to differential performance among rural regions. Lagging rural regions can probably learn more from the experiences of successful, leading rural regions than from urban regions with totally different locational and settlement conditions.Territorial disparities in unemploymentIn spite of the general portrayal of unemployment and exclusion as largely urban issues, for most OECD countries, unemployment rates tend to be higher in predominantly rural regions than in more urbanised areas. Moreover, rural unemployment is an important component of overall unemployment in most OECD countries. On average, between a quarter and a third of all unemployed persons in the OECD live and search for work in predominantly rural regions. Thus, to effectively combat unemployment, regional development strategies need to be sensitive to the specific problems and settings of the areas and communities most affected. In this context it should be mentioned, that unemployment rate alone can be a problematic indicator for assessing local labour market conditions. The rates are heavily dependent on other labour market variables, which also vary according to the type of region. Participation rates, for example, tend to be lower in rural areas and this arguably results in significant “hidden” unemployment and underemployment in rural areas, particularly among women.10 DT/TDPC(2002)23Figure II.3.7. Differential performance in regional employment growth in Austria1980-199020 Employment change (%)15 = RegionsDynamic 10 5 0 -5 Lagging-10 Pred. Intermediate Predominantly Urban Rural -15 % Rural population 0 50 100Persistence of regional unemploymentOver the past two decades, there has been no clear trend in the evolution of regional disparities in unemployment. Time series data suggest that the dispersion of regional unemployment rates has changed inversely with cyclical movements in the national level of unemployment. Over the 1990s, most European countries, experienced a decline and then resurgence or stabilisation in regional disparities as national unemployment rates initially moved upwards (cyclically) and later stabilised or declined.While regional disparities in unemployment are a feature in all OECD countries, differences can be observed in the degree to which inter-regional unemployment patterns persist. Figure II.3.8 reveals such differences by plotting regional unemployment rates for 1990 and 1999. Between 1980 and 1990 the degree of turbulence in regional unemployment rates had been significantly higher in the US than in the other countries. For France, the UK and Japan the correlation of unemployment rates was much stronger than for the US. (R² above 0.8 compared to 0.4). Between 1990 and 1999, however, also the US did not see a major change in territorial unemployment patterns. (Also for the US the 1990-1999 R² is now close to 0.9). Regions with high unemployment overwhelmingly remained that way, and those with low unemployment remained low. In other words, there was little re-ordering, little adjustment.11 DT/TDPC(2002)23From the analysis of territorial data on employment and unemployment the following findings and conclusions emerge in particular with respect to rural development:· There has been no OECD-wide trend in the evolution of regional disparities over the past two decades. Convergence appears to be cyclical in most cases rather than sustained over time.· Cyclical and structural unemployment need to be distinguished. Analyses suggest that when national unemployment is rising, all regions are affected, but when economic conditions improve, regions where unemployment is largely cyclical recover quickest and their rate of unemployment drops. Other regions, by contrast, those where unemployment has a larger structural component, recover more slowly -- thus, disparities increase.· In spite of the general portrayal of unemployment and exclusion as largely urban issues, rates of unemployment tend to be higher in rural areas than in predominantly urban areas in most OECD countries. The level of unemployment in rural areas is below the national average in only a few OECD countries.· The level of turbulence/stability in regional unemployment rates differs between the US and other countries; i.e., high unemployment regions tend to remain high unemployment regions in Europe and Japan, whereas in the US some re-ordering takes place.12 DT/TDPC(2002)23Figure II.3.8. Turbulence in regional unemployment rates, 1980-90 and 1990-2000United States25 25 R 2 = 0.68 R 2 = 0.77 e 20 e g 20 g 0 0 a a r 9 r 0 e 9 e 0 v v 1 2A A n n i i s 15 s 15 e e t t a a r r t t n n e 10 e m m 10 y y o o l l p p m m e e n 5 n 5 U Average U Average0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 Unemployment rates in 1980 Unemployment rates in 1990Japan10 10 R 2 = 0.98 e e g 0 8 0 8 g a 9 0 r a 9 r 0 e 1 2 e v v A n n i A i s s e 6 e 6 2 t t a a R = 0.93 r r t t n n e e m m y 4 y 4 o o l l Average p p m m e e n n U 2 Average U 20 0 0 2 4 6 8 10 0 2 4 6 8 10 Unemployment rates in 1980 Unemployment rates in 1990France20 202 e 2 eR = 0.92 g R = 0.92 g a a r 0 0 r e 9 0 e v 9 v 0 1 A 2 A15 15 n n i i s s e e t t a a r r t t n n e 10 e 10 m m y y o o l l p Average p Average m m e e n n U 5 U 50 0 0 5 10 15 0 5 10 15Unemployment rates in 1980 Unemployment rates in 1990Source: OECD Territorial Statistics and Indicators.13 DT/TDPC(2002)235. Sectoral mix and workforce skillsStructural change in rural economiesIn OECD countries, rural economies have become service economies. More than half the regional product is generated in the service sector. While there is considerable variation both across and within countries, even in the predominantly rural regions, agriculture contributes less than 15 per cent to the total production and income generated. Moreover, this percentage has been dropping, and will continue to drop further. Agriculture is no longer the backbone of the rural economies and their viability depends on more than food and fibre. For the national economy of almost all OECD countries, manufacturing has lost in relative importance, whereas the service sector has gained. Yet, this overall shift in the national sectoral mix is accompanied by important changes in sub-national structural patterns, in particular with respect to manufacturing. Although historically an urban activity, manufacturing has been shifting out of urban regions to become a major source of rural employment. b) Manufacturing - a rural businessIn general, the more important the services sector is in an OECD country, the greater the extent to which its rural areas have specialised in manufacturing -- the greater the extent to which the proportion employed in manufacturing is higher in rural than urban regions (Figure II.3.9). This general shift in manufacturing from urban to rural areas continued at least into the 1980s. In almost all the OECD countries for which data are available over time, the share of country industry employment located in rural regions was greater at the end of the decade than at the beginning. In the 1990s, however, the rural manufacturing niche has been threatened by two broad changes -- globalisation and technological change. Globalisation has meant increasing competition from newly industrialising countries with much lower wages. Thus, manufacturers who once moved to rural areas of industrialised countries in search of lower labour and land costs may now be shifting to other countries, and manufacturers already located in rural regions may find it increasingly difficult to compete on the basis of low labour costs as globalisation expands.With the development of new production and communications technologies and new forms of work organisation it is no longer promising to rely primarily on a low wage, low skill, competition strategy. Regional development performance is increasingly determined by the capacity to innovate and to provide high quality goods for specialised markets. This requires a well-educated regional workforce. Globalisation and the adoption of new competitive strategies and technologies have been accelerating over the past ten years. Changes in the locations of manufacturing growth and decline between the 1990s and earlier decades provide a clue as to the continued viability of manufacturing in rural and low skill areas.14 DT/TDPC(2002)23Figure II.3.9. Manufacturing shifting to rural regions10 NOR DEU SWE USA AUT CHE DNK FRA IRL FIN NZL CAN 0 UK t JPN AUS n CZE e GRC m ESP y n HUN o a l b p BEL -10r m U PRT e s f u o n i s e m r l a a SLV h r s u -20 y R r t s u d n I -30 40 50 60 70 80Services as percent of national employmentNo future for low-skill strategiesTo analyse these links, a case study using territorial data for the US focused on two dimensions of regional manufacturing competitiveness: the territorial dimension (degree of rurality/urbanisation) and the skills dimension (educational attainment of young adults aged 25-44). The analysis of employment change data provided no evidence that rurality itself has hampered manufacturing competitiveness. In the 1990s, as in the previous two decades, manufacturing has been decentralising from urban areas to rural, with the most sparsely settled rural regions having the fastest rates of growth in manufacturing jobs.The picture is markedly different, however, with regard to regional education levels. In the 1970s, the regions with the lowest education levels had the most rapid rate of growth in manufacturing jobs. In the 1980s, these regions were the only ones with a net gain in manufacturing jobs. But, in 1990-96, the low education regions lost jobs at a higher rate than any other group, and only the highest education region jobs had a net gain in manufacturing jobs.Bringing the territorial and the skills dimensions together Table II.3.2 shows shifts in manufacturing jobs for both periods:· No matter what education category, jobs have been shifting towards more rural areas. This shift even accelerated during the 1990s.· No matter the urban-rural category, growth due to job shifts was greatest during the 1980s, in low education regions, and during the 1990s, in high education regions.· In the 1990s, jobs were still shifting to rural low education regions, but at a much lower rate than in the previous decade.15 DT/TDPC(2002)23A continuation of this trend will have major implications for regional inequality, as manufacturing has historically been a source of new jobs in less developed regions.Table II.3.2.Education and manufacturing shifting in USA, 1979-1996Manufacturing jobs 1979-89 1990-96 Education Rural Rural quarter Urban Urban Dense Sparse Dense Sparse In % of total manufacturing jobs In % of total manufacturing jobs Top -3.0 7.8 9.3 0.9 12.4 32.2 3rd -5.0 -2.8 8.6 -2.9 2.1 17.7 2nd -4.7 6.2 5.2 -3.4 5.0 7.2 Bottom 9.1 13.9 10.4 -15.3 6.5 8.0 Total -3.2 5.5 8.6 -4.0 5.7 18.0 Note: Regions are ordered by percent of young adults, aged 25-44, who had completed Upper secondary high school. Source: United States Department of Agriculture, Economic Research Service.6. Education and employmentHuman capital and development performanceRural regions show a wide range of change in development indicators. For example, there is a wide range of outcomes, regardless of the degree of rurality, for the indicator measuring the change in employment. Some rural regions have employment growth that is larger than the fastest growing urban regions. Numerous national studies have investigated the factors associated with rural employment growth. These factors include amongst others the level of infrastructure, the ability to develop and diffuse technologies, the type of industrial base, the degree of foreign investment and the human capital of the people. The current process of increasing globalisation, due to rapid technological changes in the communication and information sectors, emphasises the role of human capital, as regional competitiveness becomes more and more dependent on the capacity of regions to deal with new technologies. This innovative capability is related to knowledge, skills and competence of the various actors, like private enterprises and public institutions, and is achieved by both formal and informal learning. Hence strategies to augment human capacity are an important development investment.Using territorial statistics from the OECD data-base, the education and employment case study focused on the relationship between local levels of human capacity, as proxied by attainment of formal schooling, and the rate of employment growth in rural regions. Generally the level of education in rural regions was expected to be lower than in urban regions for two reasons: 1) there are fewer educational facilities available in rural regions due to the lower population density in rural regions; and 2) there are more job opportunities for individuals with higher education in more diversified economies. Young people who want a higher education usually have to attend schools and universities in urban regions and they frequently tend to stay there after finishing their studies because they cannot find a job there or due to lack of jobs in rural regions for people with a higher education. The tested hypotheses are in particular: 1) in rural regions, the educational level of the population is lower than in urban regions; and rural regions with a higher educational profile are more likely to have a “dynamic” employment growth. However, the causal relationships underlying these hypotheses are not clear. Even if positive associations of employment growth with a high level of educational attainment can be identified, the chicken and egg question remains: which comes first?16 DT/TDPC(2002)23Educational attainment - territorial differencesNational differences in the education attainment among countries exist due to cultural and other country specific characteristics. So in 1990/91 about half of the population in Canada and the USA had a third level education, whereas this was only a quarter or less in Japan and European countries. For those countries for which also data for the education of the 25-44 year population are available, it appears that relative to the education level of the 25-64 year population, the share of the younger population with a lower secondary level of educational attainment is about 10 percentage points lower, whereas the younger group has a higher share with upper secondary and third level of educational attainment.With respect to sub-national territorial differences, rural regions have a lower level of educational attainment than urban regions. In all countries under review, in the early 1990s, only about a quarter of the population in predominantly rural regions had attained third level education, against more than a third in intermediate regions, and about 40 per cent in predominantly urban regions. On the other hand, predominantly rural regions had a higher share of population with lower secondary educational attainment (Figure II.3.10).Since the early 1980s the education level of the population has improved. In all countries, the share of population with lower secondary educational attainment decreased and those of upper secondary and third level of educational attainment increased. However, in most countries the gap in the share of population with third level of educational attainment between predominantly rural and predominantly urban regions remained rather stable. Rural regions were able to increase the education level of their population, but they were not able to narrow the gap in education level relatively to urban regions.Education level and employment growthIn order to examine whether dynamic rural regions had a higher education profile, the case study compared educational attainment in regions, which were leading in employment growth to those lagging behind. On average, dynamic rural regions, those regions with an above-average rate of employment growth during the 1980s, had a somewhat higher share of population with a third level of educational attainment. Their share of population with a lower and upper secondary level of educational attainment was a bit lower. Although it appears that dynamic rural regions usually have a higher education profile relatively to lagging rural regions, the differences are not very large. Thus, based on this observation it cannot be stated that there is a clear causal relationship between the education level of the population and regional employment growth.Several reasons can be put forward to explain why no clear positive relationship between the level of educational attainment and employment growth was found. First, the case study only considered formal education. Informal training and learning on the job and participation in networks with innovative actors also affects productive capacities. Second, the increase in jobs in high-tech, high-skill sectors is often accompanied by a loss of low skilled and unskilled jobs, which may erode the net employment gain.17 DT/TDPC(2002)23Figure II.3.10. Territorial differences in educational attainment, 1990/91 a. Educational attainment of population aged 25-4460 4 4 - Average of selected OECD countries 5 250 d g a n40o i t a l u p30o p f o t20n e c r e10P0 Lower secondary or less Upper secondary Third levelPredominantly rural Intermediate Predominantly urban regions regions regions b. Population with third level education70 Percent of population aged 25-44 Percent of total population6050403020100 Austria Canada Finland Japan Norway USA Czech France GermanyNetherlands Republic * Predominantly rural Intermediate Predominantly urban regions regions regions18 DT/TDPC(2002)237. Women and participationFeminisation of the labour forceWomen play a major role in the restructuring of the economies of OECD Member countries. In most countries the last three decades saw a remarkable growth in female labour-force participation, and for some time women have been the primary source of labour for new and growing economic sectors and labour-market segments, which they have been entering in ever greater quantities and at successively higher levels of education and competence. However, women’s role in the modernisation of national economies, and its impacts on gender-relations and the welfare of women, have varied due to international and regional unevenness of change and differing cultural, political and institutional contexts.The overall trend in all OECD regions was one of rising female participation rates and falling male participation rates. Generally the OECD regions that recorded the highest rates of employment growth, like North America and Oceania, also experienced the highest rates of growth in female participation. The contrasting trends of female and male participation were particularly pronounced in the EU. In many countries women provided most of the additional supply of labour and female employment would sometimes continue to grow in spite of a general fall in the total number of employed.One tendency, which is reflecting several important aspects of relevance to the life-courses and life-situations of women, is the shrinking of the average household. At the regional level the average size of households (and the share of one-person households) is strongly correlated with the female labour-force participation rate, especially in the middle age group (25-54). Several interrelated processes are associated with this development, working in different ways and with different relative impacts from period to period over the OECD territory. Variation in processes of economic restructuring, in historical and cultural factors, in social and economic policies, and in profound socio-demographic changes, like the increase in acquisition of educational qualifications and altered patterns of marriage, fertility and household formation, produced regionally varied and locally embedded changes in female activity patterns and gender differences in participation.Female participation - national patternsTable II.3.3 provides a rough picture of the relative “female-participation-status” of those Member countries for which specific age- and gender participation rates could be calculated. For each age group of women the countries are classified according to whether they belong to the third with the highest, medium or lowest average participation rates.Table II.3.3.Female labour force participationCountries are grouped by age classesParticipation rates: ratio Labour force/Population Aged 15-24 Aged 25-54 Aged 55-64 Lowest MEX BEL LUX MEX NLD LUX MEX NLD LUX FRA GRC IRL GRC AUT BEL Medium NLD JPN IRL BEL AUT PRT PRT GRC AUS FIN PRT SWE AUS JPN CHE FRA NZL IRL Highest AUT NZL AUS FRA NZL FIN JPN CHE FIN UKD CHE NOR UKD SWE NOR UKD SWE NOR Source: NIBR, Norway.19 DT/TDPC(2002)23In the core working ages (25-54) the Scandinavian countries and France, United Kingdom and New Zealand recorded the highest rates, while Mexico, Greece, Ireland, Luxembourg and the Netherlands recorded the lowest rates. Within each third of countries the countries differ, however, with respect to their relative status regarding the other two age groups of women. Differences in participation rates in these age-groups are sensitive to differences in inter alia educational practices and retirement regulation as well as in general and cyclical labour-market conditions.Female participation - regional variationComparative international studies have addressed some evidently universal features of labour force feminisation, among OECD Member countries, often focusing on persistent gender differences in participation. Systematic international comparisons of sub-national patterns of variation and change are more scarce, however. The OECD’s territorial data and indicators work can help to shed some light on certain internationally comparable quantitative aspects of the sub-national dimension of the phenomenon of labour-force feminisation, with a particular view to rural-urban differences. It should be kept in mind, however, that simple territorial indicators such as female labour-force participation rate, or female-male participation rate ratio, are rather crude and superficial representations of female labour-market attachment and gender differences in employment. Comparing participation rates tells little about differences in the extent and quality of employment. Systematic gender disparities are wide-spread, e.g. with respect to duration and types of employment contracts, in average working hours and wages, and other indicators regarding degree and type of labour-force attachment and employment benefits.Figure II.3.11 shows the relation between the national average level of female labour-force participation and the degree of regional variation in female participation rates for the core working ages of women (25-54). Regional variation is indicated by the regional variation coefficient, and lines are drawn to suggest the association (fit-line) and to show the cut-points for three equal groups on each variable. There seems to be a clear negative association between the level of participation and the degree of regional variation in participation for females of the core working ages among the 17 Member countries. A similar but somewhat more varied picture is found for the other two age groups. However, the figure does not necessarily support a hypothesis of common (or related) causation, predicting that rising female participation automatically will reduce regional variation; but neither has such a development become less probable.In all countries, except Sweden and (especially urban) Finland, and seemingly regardless of region type, a tendency of relative concentration of the female labour-force to the core working ages appears. The variations in national and regional profiles indicate, however, that quite different constellations of driving forces are at work over the territory. Underlying general tendencies of i) increasing rates, duration and levels of education, of ii) policy and socio-demographic adjustments in order to ease compatibility of motherhood and formal employment, and of iii) changing retirement regulation and practices; probably vary in their specific courses and in their relative timing and strength. The relative outcome in each country and region, in terms of changes in levels and profiles of female participation, in its turn is modified by equally unparallel trends and fluctuations in economic conditions and employment opportunities.20 DT/TDPC(2002)23Figure II.3.11. Female labour force participation and territorial variations90 FIN e NOR80t a FRA r n NZL UKD AUT o70i CZR t AUS POR a JPN p i BEL c60i t NDL r a p 50 e l IRL GRC a m40e F30 MEX 20 0 5 10 15 20 25 30 35Regional variation coefficent8. Health and safetyRural life - healthy and safe?Rural areas have traditionally been regarded as a healthy and safe living environment, living habits, associated with healthy food and clean air and water and an unhurried rhythm. But rural life is also associated with slow adoption of new ideas, rigid social norms and poor access to services. Towns have been associated with a rapid rhythm and an exhausting living environment, rootlessness and crime. At the same time, young people have always come to towns to seek work, better living conditions and services.Nowadays this picture seems oversimplified. Occupational diversification has created a more unified culture and has enhanced pluralism in the countryside. Wide variations have been found between different types of rural and urban areas. Environmental damage is threatening rural and urban areas alike. Growing unemployment and cuts in services are liable to hit all types of areas. Improvements to information technology and communications -- for example, telework -- have lately opened up rural areas and stimulated their development. But in practice, computers and the like are used far more often in towns than in the countryside.Health and safety studies have also revealed differences between rural and urbanised areas. They have shown, for example, that most types of cancer morbidity are more frequent in towns than in the countryside; that suicide is less common in rural areas; and that crime and drug problems are aggravated in larger towns. The case study on health and safety had access to only a limited set of comparable data on access to medical care, diseases and mortality, accidents and crime. As for all social indicators work, data comparability is a serious problem owing to discrepancies in national statistical definitions. The results of the case study presented here must therefore be interpreted with caution. Owing to the wide differences in21 DT/TDPC(2002)23 the statistical definitions used by the various countries, absolute figures were not compared in this case study.Access to medical careTwo indicators have been calculated to assess territorial differences in access to medical care: the number of medical practitioners and of hospital beds per inhabitant (Table II.3.4). In all countries for which data were made available, the number of medical practitioners per inhabitant is highest in predominantly urbanised (PU) regions and lowest in predominantly rural (PR) regions. In the PU regions of Norway, the density of practitioners is even twice as high as the national average. In Canada and Finland the ratio for PR regions reaches only 50 to 60% of the country average. The PU/PR ratio of medical practitioners is highest in Finland, Norway and Canada, where in PU regions the density is more than 2.5 times that in PR regions. In Japan and Germany, by contrast, differences in access are comparatively small (Table II.3.4a).Per capita, hospital beds are more evenly distributed than medical practitioners are. Only in Norway and the Czech Republic are hospital beds clearly more numerous per population in PU regions. In Norway, their number is more than two times greater in PU regions than in PR regions. Whereas for Japan PR regions have per capita about 30 per cent more hospital bed capacity than PU regions. From high PU/PR ratios, it should not be concluded that medical care inadequately organised. The indicator is rather a measure for the challenges a country faces in delivering proper health care (Table II.3.4b).Table II.3.4.Territorial differences in access to medical carePredominantly Intermediate regions Predominantly Rural PU / PR Urbanised regions (PU) (IN) regions (PR) ratio National average = 100 a. Medical practitioners per capita Australia 121 88 62 2.0 Canada 139 91 54 2.6 Czech Republic 131 90 80 1.6 Finland 162 115 57 2.8 France 126 94 84 1.5 Germany 103 94 97 1.1 Japan 100 103 96 1.0 Netherlands 103 85 - - Norway 224 88 82 2.7 Spain 115 95 81 1.4 United States 126 111 67 1.9 b. Hospital beds per capita Czech Republic 123 91 92 1.3 Finland 99 114 89 1.1 France 96 100 104 0.9 Germany 93 114 108 0.9 Japan 87 102 122 0.7 Netherlands 103 82 - - Norway 207 83 90 2.3 Spain 105 100 90 1.2 United States 95 97 107 0.9 Note: Most recent data available during the ‘90s. Source: RISI report, 1999.22 DT/TDPC(2002)23The structure of hospital services varies widely from country to country. This is particularly so in the case of long-term treatment of the aged, which is given either in hospitals in connection with normal hospital treatment, or in old age homes, whose beds are not usually included in the total number of hospital beds. In Finland the total number of hospital beds has decreased since the middle of the 1980s. Decreases have occurred in central and specialist hospitals, because the length of treatment times has fallen due to more effective methods of treatment, and to policlinic examination and treatment. For example, short post-treatment surgery has been developed rapidly. On the other hand, the number of hospital beds in rural health centres has increased, because such centres provide much of the long-term treatment for the growing population of the aged. Beds for specialist treatment in central hospitals will continue to decrease and the amount of open care will grow correspondingly. In Finland, as in many other sparsely populated countries, specialist treatment is concentrated in regional centres. The main function of rural hospitals is to give general treatment. For all these reasons, numbers of hospital beds per capita risk to give a somewhat distorted picture of regional differences in health care.Diseases and mortalityInstead of measuring inputs, social indicators should rather focus on outcomes. Since health conditions are difficult to measure and compare directly, mortality rates are often used as indicators for diseases and health status (Table II.3.5)Infant mortality is a commonly used indicator in international comparisons. However, for the few OECD countries that made sub-national data available no significant sub-national disparities could be identified (Table II.3.5a). Regional differences vary from country to country, and they are small. Owing to the very low infant mortality in developed countries, the variations in this material are random, and no conclusions can be drawn on differences between leading and lagging regions. On the basis of the time series of five countries (Finland, Germany, Japan, Norway and the UK), infant mortality appears to be clearly decreasing in all regions of these countries. But no general conclusions can be drawn on changes in regional trends. The overall decrease of infant mortality no longer relates to the standard of living and its regional differences, but reflects a high level of medical care in all these countries. This applies equally to urban and rural areas.As with infant mortality, it is impossible to draw conclusions on regional differences and trends in cancer mortality based on the statistics available so far in the OECD territorial database (Table II.3.5b). Since cancer mortality is much higher in older age groups, and given the considerable differences in the age structures of urbanised and rural areas, it should be measured by age group (e.g., by 5-year age classes) or using age-standardised figures for longer-interval age groups that eliminate the effects of differences in age structures. Furthermore, rapid internal migration hampers the analysis of regional differences, because the causal factors of cancer often have to be sought from more than 20 years earlier.Age-adjusted results for Finland show consistently that both the incidence and the mortality of cancer are higher than average in the predominantly urbanised Helsinki region, especially among women. This is particularly evident in smoking-related cancers, which are also quite common in the northern parts of the country. Many of the most important types of cancer are less common among farmers than in the rest of the population.Heart-disease mortality, too, should be measured per age group or be age-standardised because heart diseases, like cancer, are much more common in older ages, and the proportion of older age groups varies between urbanised and rural areas (Table II.3.5c). Gender differences are also wide, so men and women should preferably be analysed separately. Although cross-national analysis is problematic, some age-standardised results are available from national studies. Regarding Finland, these results show that23 DT/TDPC(2002)23 heart-disease mortality was considerably higher in rural regions than in urbanised areas during the 1950s and 1960s, when the mortality was increasing. But since the decline in mortality had begun, the rural-urban difference faded away during the 1970s.Table II.3.5.Territorial differences in diseases and mortalityPredominantly Intermediate regions Predominantly Rural PU / PR Urbanised regions (PU) (IN) regions (PR) ratio National average = 100 a. Infant mortality Finland 103 93 105 1.0 Germany 100 101 101 1.0 Japan 97 104 100 1.0 Norway 112 81 113 1.0 Spain 102 101 93 1.1 United States 103 91 98 1.0 b. Cancer mortality Czech Republic 109 97 96 1.1 Finland 90 96 109 0.8 Japan 92 103 112 0.8 Netherlands 100 100 - - Norway 120 93 100 1.2 Spain 95 101 111 0.9 United Kingdom 99 102 109 1.0 United States 97 96 106 0.9 c. Heart disease mortality Czech Republic 98 102 98 1.0 Finland 73 94 118 0.6 Japan 86 108 115 0.8 Netherlands 98 114 - - Norway 106 87 109 1 United Kingdom 99 103 113 0.9 United States 101 88 110 0.9 Note: Most recent data available during the ‘90s. Source: RISI report, 1999.Accidents and crimeRoad accident deaths are often used as an indicator of rural/urban disparity. Fatal road-accidents are clearly more frequent in predominantly rural regions and, to some extent, in intermediate regions. For the UK and Germany, PR figures are particularly high (80% and even 120% higher than national). The frequency of road-accident fatalities is almost six times higher in PR than in PU regions of Austria, and three times higher in Finland and Norway (Table II.3.6a).However, road traffic fatalities are a problematic indicator for assessing rural/urban disparities in social well being and of equity since the victims of traffic fatalities in rural areas are not necessarily rural residents. The figures refer to the number of fatal accidents in an area, not to the traffic safety of the area's residents. Higher-category roads, which run mainly across rural areas between cities, have the greatest traffic exposure, resulting in more accidents than occur on lower-category roads. Also the greater speeds on higher-category roads usually increase the seriousness of accidents and the fatality toll in rural regions. The large volume of intersecting traffic in urbanised areas means that the number of accidents is higher than in rural areas, but the consequences of such accidents are usually relatively mild thanks to lower24 DT/TDPC(2002)23 speed. All these factors, taken together, hinder or even prevent comparison of traffic fatalities between urbanised and rural regions as possible indicators of lifestyle or equity.Table II.3.6.Territorial differences in accidents and crimePredominantly Intermediate regions Predominantly Rural PU / PR Urbanised regions (PU) (IN) regions (PR) ratio National average = 100 a. Road-accident fatalities Austria 22 114 123 0.2 Finland 38 89 139 0.3 Germany (western) 83 130 178 0.5 Japan 74 120 119 0.6 Norway 42 83 125 0.3 United kingdom 84 136 223 0.4 b. Criminal offences Austria 176 88 77 2.3 Czech Republic 158 83 59 2.7 Finland 122 100 89 1.4 Germany (western) 107 88 67 1.5 Japan 126 82 78 1.6 Norway 184 90 90 2.1 Sweden 144 112 75 1.9 c. Drug offences Austria 174 62 91 1.9 Finland 226 69 67 3.4 Japan 108 106 75 1.4 Norway 288 87 69 4.1 Sweden 140 110 78 1.8 Note: Most recent data available during the ‘90s. Source: RISI report, 1999.Crime rates are also indicators that show significant territorial variations. Criminal offences per capita are manifestly more frequent in predominantly urbanised regions. Compared to the country averages, they occur clearly more often in PU regions of Norway, Austria and the Czech Republic. Crime in the Czech Republic is almost three times more frequent, and in Norway and Sweden two times more frequent in predominantly urbanised than in predominantly rural regions. Moreover, during the early 1990s, criminal offences per capita increased and these increases have been greatest in urban regions. Recently, however, there are indications from some countries, that rural areas are “catching-up” also on this front (Table II.3.6b).According to in-depth surveys conducted in Finland, crime was most prevalent in predominantly urbanised Helsinki (the national capital), where the five-year rate of victimisation in the case of all the crimes studied was 57 per cent. The corresponding figure was lowest (36%) in the predominantly rural province of Lapland. Fear of crime is an important social cost factor, also increasing the monetary costs of security. In 1996, 15 per cent of Helsinki residents thought it likely that their home would be burglarised during the coming year. This risk was assessed at its lowest (3.5%) in the predominantly rural province of North Karelia. The fear of street violence was greatest in Helsinki, where 60 per cent of women and 19 per cent of men felt unsafe when going out alone near their home, or avoided certain places at night.25 DT/TDPC(2002)23International comparison of crime rates is problematic, since the term 'criminal offences' does not cover the same crimes in all countries, so the use of this term for. There are also wide inter-country variations in the legal definitions of crimes and in the extent to which they are reported to the police and registered. This hampers comparability of data more than is generally imagined. The 1992 International Crime Victimisation Survey of 1992 indicates that crimes are reported to the police less frequently in Finland, Italy and Norway (40%, 41%, and 43%) than in Western Europe on the average (49%). Reporting is clearly more frequent in France (61%) and Sweden (59%).Drug offences per capita are concentrated in predominantly urbanised regions and, to some extent, in intermediate regions. Compared to the country averages, they are most frequent in PU and least frequent in PR regions of Norway and Finland. Compared to drug offences in predominantly rural regions, those in predominantly urbanised regions are four times more frequent in Norway, three times more frequent in Finland, and twice as frequent in Austria and Sweden. (Table II.3.6c)9. Dependency and ageingDemography and territoryOver the past century, OECD countries experienced major socio-demographic changes with long lasting implications for their societies and economies. Since the middle of the nineteenth century, the “vital revolution”; caused a major and lasting shift from high to low mortality and fertility that was most pronounced in the nations of Europe, North America, Japan, Australia and New Zealand. This demographic transition took place parallel to economic and social changes of fundamental modernisation and rapid development brought about as societies entered the period of growing industrialism and urbanisation. Increments in human longevity culminated in an unparalleled rise in life expectancy during the first sixty years of the twentieth century.In western industrial nations individuals could expect to live about twenty years longer if they were born in 1960 rather than in 1900. Fertility declined dramatically; on the order of 50 per cent between 1870 and 1940. While the world economic crises in the 1930s and World War II caused major discontinuities, the general tendencies persisted. The post war “baby boom”, and its sudden end in the early 1960s, is still an important determinant for today’s demographic patterns. Since the mid-1960s fertility levels dropped far below replacement in country after country.The share of dependent young and old people differs considerably between regions. Figure II.3.12 presents the case of Spain where dependency ratios (population aged below 15 and above 64 to population aged 15-64) are strongly correlated with the degree of rurality or urbanisation of regions. To be efficient, policies designed for these age groups thus need to be sensitive to such territorial differences. The most cost effective solutions and ways of delivering public infrastructure and services, be it schools for children or health care for the elderly, and will differ depending the spatial setting. What is appropriate for urban settlements may be highly inefficient in a rural setting.26 DT/TDPC(2002)23Figure II.3.12. Dependency and rurality in Spain, 19960,70 4 6 r e 0,65 v Regions o d 4 n 6- 0,60 a5 4 11 - d 0 e 0,55 g d a e g n a o i t n 0,50 Spain total a o l i t u a p l o u p 0,45 p o o t p f o 0,40 o i t a R 0,35 0 10 20 30 40 50 60 70 80 90 100 % of rural populationAgeing not uniform across territoriesOECD Member countries, with exception of Turkey, Mexico and Korea, have reached an advanced stage of population ageing with persons aged 65 and older constituting more than 10 per cent of the population. Regional coefficients of variation, calculated for each country, indicate that population ageing is far from being a uniform process within countries. Considerable territorial differences exist. In countries, like Canada, Portugal, USA, France, Spain, Mexico and Australia, the regional share of persons aged 65 and above was on average 25 per cent higher or lower than the national average. This implies that rather different demographic dynamics are at work in different parts of the territory, or that the “demographic momentum” of earlier demographic history is strong enough to shadow the effects of more recent demographic trends in certain territories.However, comparisons of regional coefficients of variation for 1980 and 1990 also indicate that regional variation reduced in most countries during the decade. In a few countries, notably France and the Czech Republic, the reduction is substantial. Regional differences in timing of demographic change, especially of the most recent decline in fertility, is a probable explanatory factor, in which case an hypothesis of regional “catching up” on demographic trends seems reasonable.27 DT/TDPC(2002)23Ageing more advanced in rural regionsThe case study work on ageing showed that i) in most countries ageing seems to be more advanced in the rural region population than in the national population on average, ii) the share of children (0-14 years) is usually higher in the rural populations than in the national populations, and iii) considerable international differences exist between rural regions in patterns of relative representation of children and elderly in the population. In four countries, namely Spain, France, Portugal and Japan, the share of elderly (65+ years) in the rural region population is more than 20 per cent higher than the national average. In only three countries (Germany, Austria and the Czech Republic) are the elderly “under-represented” in the rural region populations, compared to national averages.Figure II.3.13 provides a more detailed picture of rural region age structures at two points in time, as compared to national average age structures. It underlines the point that no single pattern of rural deviation from national age distributions, or direction of change in such deviations, seem to exist. Relative rural age profiles differ considerably between groups of member countries:· “Top heavy” rural deviations, with the elderly being most “over-represented” age segments, are especially pronounced in Spain and Japan, (but also in France, Finland, and United Kingdom).· “ Bottom heavy” rural deviations, with children and young adults being the most “over-represented” age segments, are typical of Germany and Belgium, as well as of Switzerland Austria and the Czech Republic.· Countries like Canada and the USA, but also Australia, New Zealand and to some degree Sweden and Ireland, are characterised by some “over-representation” both at the top and the bottom part of the age pyramid.28 DT/TDPC(2002)23Figure II.3.13. Rural population profilesAge structure relative to national averageSpain 1980 1990 Japan 1980 1990 +65 +6555-64 55-6425-54 25-5415-24 15-245-14 5-140-4 0-4-20 -10 0 10 20 30 -20 -10 0 10 20 30Canada 1980 1990 United States 1980 1990 +65 +6555-64 55-6425-54 25-5415-24 15-245-14 5-140-4 0-4-10 -5 0 5 10 15 -10 -5 0 5 10 15Germany 1980 1990 Belgium 1980 1990 +65 +6555-64 55-6425-54 25-5415-24 15-245-14 5-140-4 0-4-10 -5 0 5 10 -10 0 10 2029 DT/TDPC(2002)23NOTES30 REFERENCES FOR CHAPTER II.3.OECD (1994), Creating rural indicators for shaping territorial policies, OECD Publications, Paris.OECD (1996), Territorial indicators of employment, OECD Publications, Paris.OECD (1999), RISI report on " Territorial Indicators of Socio-Economic Dynamics", document [DT/TDPC/TI(99)1/REV1] prepared by the Territorial Development Service, Territorial Statistics and Indicators Unit, Paris.31 1 . This chapter summarises the key findings from case studies produced under the OECD RISI Project on rural income and social indicators. The main case study authors are for : Section 3, Heino von Meyer, Pro Rural, Germany and Cécile Hochet; Section 4, Andrew Davies and Cécile Hochet, OECD Secretariat; Section 5, David McGranaham, US Department of Agriculture, Economic Research Service, United States; Section 6, Ray Bollman, Statistics Canada, Canada, and Ida Terluin and Jaap Post, LEI, Netherlands; Section 7, Olaf Foss, NIBR, Norway; Section 8, Kari Gröhn and Mika Honkaneen, Ministry of Interior, Finland; Section 9, Olaf Foss, NIBR, Norway. This synthesis was completed by Heino von Meyer and Cécile Hochet, OECD Secretariat. The EU Commission (DG VI) has granted financial assistance, which facilitated this work with a particular focus on developing territorial rural development indicators.

Territorial Indicators of Socioeconomic Patterns and Dynamics 1

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