CHAPTER II



TERRITORIAL INDICATORS OF SOCIO-ECONOMIC PATTERNS AND DYNAMICS[i]

1. Context and background

The 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 Even if some of the data sets refer to the 1980s and the early 1990s, and are thus outdated to describe present situations, they are still useful to explore methodological and analytical issues and to demonstrate the usefulness of territorial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), 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 topics

[pic]

Selected 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 typologies

The 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.

Figure II.3.2. The OECD territorial scheme

[pic]

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.

Figure II.3.3. The OECD regional typology

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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).

Figure II.3.4. The OECD population by type of region

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3. Productivity and income

International disparities

Gross 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.

Table II.3.1. GDP per capita – Inter-national and intra-national disparities

In US$ at current prices and PPP’sa, 1990-1999

|Countryb |GDP per capita, 1999 |National |Regional |

| |US$ and PPPs |Level |Change |Disparityd |Change |

| | |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 ten 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 disparities

If 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 region

The 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.

Figure II.3.5. Disparities in GDP per capita in France

a. GDP per capita by type of region, 1999

[pic]

b. GDP per capita level change, 1990-1999

[pic]

Source: 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 a 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 growth

Figure 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 productivity

By 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.

Figure II.3.6. Territorial development performance by type of region in France, 1980-1999

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4. Employment and unemployment

Differential performance in job creation

Previous 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 unemployment

In 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.

Figure II.3.7. Differential performance in regional employment growth in Austria

1.

1980-1990

[pic]

Persistence of regional unemployment

Over 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.

From 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.

Figure II.3.8. Turbulence in regional unemployment rates, 1980-90 and 1990-2000

[pic]

Source: OECD Territorial Statistics and Indicators.

5. Sectoral mix and workforce skills

Structural change in rural economies

In 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 business

In 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.

Figure II.3.9. Manufacturing shifting to rural regions

[pic]

No future for low-skill strategies

To 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.

A 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, 1979-1996

|Education quarter |Manufacturing jobs |

| |1979-89 |1990-96 |

| |Urban |Rural |Urban |Rural |

| | |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 |

| |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.

In 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 variation

Comparative 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.

Figure II.3.11. Female labour force participation and territorial variations

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8. Health and safety

Rural 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 in the statistical definitions used by the various countries, absolute figures were not compared in this case study.

Access to medical care

Two 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 care

| |Predominantly Urbanised |Intermediate regions (IN) |Predominantly Rural regions |PU / PR |

| |regions (PU) | |(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.

The 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 mortality

Instead 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 that 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 mortality

| |Predominantly Urbanised |Intermediate regions (IN) |Predominantly Rural regions |PU / PR |

| |regions (PU) | |(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 crime

Road 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 lower 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 crime

| |Predominantly Urbanised |Intermediate regions (IN) |Predominantly Rural regions |PU / PR |

| |regions (PU) | |(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.

International 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 ageing

Demography and territory

Over 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.

Figure II.3.12. Dependency and rurality in Spain, 1996

[pic]

Ageing not uniform across territories

OECD 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.

Ageing more advanced in rural regions

The 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.

Figure II.3.13. Rural population profiles

Age structure relative to national average

[pic]

NOTES

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.

-----------------------

[i]. 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.

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