DT/TDPC(2002)23
TERRITORIAL INDICATORS OF SOCIO-ECONOMIC PATTERNS AND DYNAMICS1
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Even if some of the data sets refer to the 1980s and the early 1990s, and are thus outdated to describe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),
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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 topics
Territorial Indicators
shall provide information on:
SOCIAL
ECONOMIC
ECONOMIC
STRUCTURE
STRUCTURES
&&
PERFORMANCE
PERFORMANCE
WELL-BEING & COHESION
Productivity
Growth
Investment
Innovation
Sectoral mix
Income and Poverty
Labour and Employment
Education and Skills
Health and Safety
Housing and Community
DEMOGRAPHIC
ENVIRONMENTAL
PATTERNS & MIGRATION
QUALITY & AMENITY
Topography and Climate
Soils, Water, Species
Habitats and Landscapes
Settlement and Equipment
Cultural Heritage
Density
Natural balance
Youth andAgeing
Migration
Households
facilitating integrated assessment of
sub-national development conditions and trends
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.
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Figure II.3.2.
Territorial Scheme
The OECD territorial scheme
and a typology by type of settlement
OECD
covers the
entire
OECD territory
distinguishes
different
levels
Member countries
Predominantly
Rural
(regional & local)
allows for
various
typologies
National
( 30 )
Predominantly
Urban
Intermediate
Regional
( 2 200 )
Regions
Local
( 70 000 )
Local communities
(e.g. urban / rural)
Rural
Urban
Territorial
Units
Rural 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.
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Figure II.3.3.
The OECD regional typology
Regional typology
for rural / urban analyses
Predominantly
Predominantly
Intermediate
Rural
regions
Three types of
region
Urbanised
regions
regions
40 %
28 %
32 %
%
Population
4%
10 %
%
86 %
Area
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).
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Figure II.3.4.
The OECD population by type of region
0
90
40
20
32
28
40
t
In
er
CZE
50
50
ESP
ITA
FRA
AUT
60
DNK
EU
JAP OECD
DEU
UK
How to read the diagram:
e.g.
Regional population share
Rural
= 28 %
Intermediate = 32 %
Urban
= 40 %
e
iat
ed
m
Ur
ba
n
70
USA
NZL
PRT
CHE
AUS
CAN
80
FIN SWE NOR
32
TUR
GRC
IRL
BEL
NLD
10
ISL
100 %
20
28
40
60
80
100 %
Rural
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.
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Table II.3.1.GDP per capita – Inter-national and intra-national disparities
In US$ at current prices and PPP’sa, 1990-1999
GDP per capita, 1999
Countryb
US$ and PPPs
National
Level
OECD = 100
Regional
Change
1990-99c
Disparityd
Change
1990-1999e
Luxembourg
43 066
184
34
..
..
34
-2f
United States
33 725
144
2
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
24
-1f
Norway
29 025
124
16
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
-1g
Italy
23 937
102
1
24
0g
United Kingdom
23 286
99
0
38
New Zealand
19 360
83
-1
..
..
Spain
19 045
81
5
23
0
Ireland
25 878
110
38
22
0
8g
Czech Republic
13 550
58
-13
34
Portugal
17 064
73
12
31
-1
Greece
15 799
67
10
15
1
Korea
13 647
58
13
19
4
5g
Hungary
11 505
49
29
Slovak Republic
11 148
48
41
2g
Mexico
8 351
36
-1
46
0
9g
Poland
9 008
38
8
45
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
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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.
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Figure II.3.5.
Disparities in GDP per capita in France
a. GDP per capita by type of region, 1999
160
GDP per capita, National average = 100
150
140
130
120
110
100
90
80
70
60
0
10
20
30
Urban
regions
40
50
60
70
80
Rural
regions
Intermediate
regions
90
100
% of Rurality
b. GDP per capita level change, 1990-1999
160
I
GDP per capita, National average = 100
150
II
140
130
120
110
100
90
80
70
III
IV
60
-20
-15
-10
-5
0
5
10
15
20
Change in GDP per capita, 1990-99
Regional % points differences to National
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
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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.
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Figure II.3.6.
Territorial development performance by type of region in France, 1980-1999
Labour -
_
productivity
+8%
+
Employment
Intermediate
Differential
to national
growth (%)
0
Urban
1980-99
1980-90
(Jobs)
Rural
-8%
Growth
differential
+8%
0
(GDP)
Production
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.
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Figure II.3.7.
Differential performance in regional employment growth in Austria
Figure 1.
1980-1990
20
Employment
change
(%)
15
Dynamic
= Regions
10
5
0
-5
Lagging
-10
Pred.
Urban
Intermediate
Predominantly
Rural
%
Rural
population
-15
0
50
100
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.
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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.
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Figure II.3.8.
Turbulence in regional unemployment rates, 1980-90 and 1990-2000
United States
25
25
R 2 = 0.68
Average
R 2 = 0.77
Average
20
Unemployment rates in 2000
Unemployment rates in 1990
20
15
10
5
Average
15
10
5
Average
0
0
0
5
10
15
20
25
0
30
5
10
15
20
25
Unemployment rates in 1990
Unemployment rates in 1980
Japan
10
10
2
6
4
Average
2
Average
8
Unemployment rates in 2000
Average
Unemployment rates in 1990
R = 0.98
8
6
2
R = 0.93
4
Average
2
0
0
0
2
4
6
8
10
0
2
Unemployment rates in 1980
4
6
8
10
Unemployment rates in 1990
France
20
20
Average
R = 0.92
Unemployment rates in 2000
Unemployment rates in 1990
15
2
Average
2
R = 0.92
10
Average
5
15
10
Average
5
0
0
0
5
10
15
Unemployment rates in 1980
0
5
10
Unemployment rates in 1990
Source: OECD Territorial Statistics and Indicators.
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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.
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Figure II.3.9.
Manufacturing shifting to rural regions
10
NOR
Industry shares of employment
Rural minus Urban
DEU
SWE
CHE DNK USA
AUT
FRA
IRL
FIN
NZL
CAN
UK
JPN
AUS
0
CZE
GRC
HUN
ESP
BEL
-10
PRT
SLV
-20
-30
40
50
60
70
80
Services as percent of national employment
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.
15
DT/TDPC(2002)23
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
Manufacturing jobs
Education
quarter
1979-89
1990-96
Rural
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.
Urban
Rural
Source: United States Department of Agriculture, Economic Research Service.
6. Education and employment
Human capital and development performance
Rural 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)23
Educational attainment - territorial differences
National 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 growth
In 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)23
Figure II.3.10. Territorial differences in educational attainment, 1990/91
a. Educational attainment of population aged 25-44
60
Average of selected OECD countries
Percent of population agd 25-44
50
40
30
20
10
0
Upper secondary
Lower secondary or less
Third level
Intermediate
regions
Predominantly rural
regions
Predominantly urban
regions
b. Population with third level education
70
Percent of population aged 25-44
Percent of total population
60
50
40
30
20
10
0
Austria
Canada
Finland
Predominantly rural
regions
Japan
Norway
USA
Intermediate
regions
18
France
Germany Netherlands
Czech
Republic
*
Predominantly urban
regions
DT/TDPC(2002)23
7. Women and participation
Feminisation of the labour force
Women 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 patterns
Table 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 participation
Countries are grouped by age classes
Lowest
MEX
FRA
Medium
NLD
FIN
Highest
AUT
UKD
Source: NIBR, Norway.
Participation rates: ratio Labour force/Population
Aged 15-24
Aged 25-54
Aged 55-64
BEL
LUX
MEX
NLD
LUX
MEX
NLD
LUX
GRC
IRL
GRC
AUT
BEL
JPN
IRL
BEL
AUT
PRT
PRT
GRC
AUS
PRT
SWE
AUS
JPN
CHE
FRA
NZL
IRL
NZL
AUS
FRA
NZL
FIN
JPN
CHE
FIN
CHE
NOR
UKD
SWE
NOR
UKD
SWE
NOR
19
DT/TDPC(2002)23
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.
20
DT/TDPC(2002)23
Figure II.3.11. Female labour force participation and territorial variations
Female participation rate
90
FIN
NOR
80
FRA
AUT
NZL UKD
CZR
POR
AUS
JPN
BEL
NDL
70
60
50
IRL
GRC
40
30
MEX
20
0
5
10
15
20
25
30
35
Regional variation coefficent
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
21
DT/TDPC(2002)23
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 regions (PU)
Intermediate regions
Predominantly Rural
(IN)
regions (PR)
National average = 100
a. Medical practitioners per capita
Australia
121
88
62
Canada
139
91
54
Czech Republic
131
90
80
Finland
162
115
57
France
126
94
84
Germany
103
94
97
Japan
100
103
96
Netherlands
103
85
Norway
224
88
82
Spain
115
95
81
United States
126
111
67
b. Hospital beds per capita
Czech Republic
123
91
92
Finland
99
114
89
France
96
100
104
Germany
93
114
108
Japan
87
102
122
Netherlands
103
82
Norway
207
83
90
Spain
105
100
90
United States
95
97
107
Note: Most recent data available during the ‘90s.
Source: RISI report, 1999.
22
PU / PR
ratio
2.0
2.6
1.6
2.8
1.5
1.1
1.0
2.7
1.4
1.9
1.3
1.1
0.9
0.9
0.7
2.3
1.2
0.9
DT/TDPC(2002)23
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
23
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 mortality
Predominantly
Urbanised regions (PU)
Intermediate regions
Predominantly Rural
(IN)
regions (PR)
National average = 100
a. Infant mortality
Finland
103
93
105
Germany
100
101
101
Japan
97
104
100
Norway
112
81
113
Spain
102
101
93
United States
103
91
98
b. Cancer mortality
Czech Republic
109
97
96
Finland
90
96
109
Japan
92
103
112
Netherlands
100
100
Norway
120
93
100
Spain
95
101
111
United Kingdom
99
102
109
United States
97
96
106
c. Heart disease mortality
Czech Republic
98
102
98
Finland
73
94
118
Japan
86
108
115
Netherlands
98
114
Norway
106
87
109
United Kingdom
99
103
113
United States
101
88
110
Note: Most recent data available during the ‘90s.
PU / PR
ratio
1.0
1.0
1.0
1.0
1.1
1.0
1.1
0.8
0.8
1.2
0.9
1.0
0.9
1.0
0.6
0.8
1
0.9
0.9
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
24
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 crime
Predominantly
Urbanised regions (PU)
Intermediate regions
Predominantly Rural
(IN)
regions (PR)
National average = 100
a. Road-accident fatalities
Austria
22
114
123
Finland
38
89
139
Germany (western)
83
130
178
Japan
74
120
119
Norway
42
83
125
United kingdom
84
136
223
b. Criminal offences
Austria
176
88
77
Czech Republic
158
83
59
Finland
122
100
89
Germany (western)
107
88
67
Japan
126
82
78
Norway
184
90
90
Sweden
144
112
75
c. Drug offences
Austria
174
62
91
Finland
226
69
67
Japan
108
106
75
Norway
288
87
69
Sweden
140
110
78
Note: Most recent data available during the ‘90s.
PU / PR
ratio
0.2
0.3
0.5
0.6
0.3
0.4
2.3
2.7
1.4
1.5
1.6
2.1
1.9
1.9
3.4
1.4
4.1
1.8
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)23
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.
26
DT/TDPC(2002)23
Figure II.3.12. Dependency and rurality in Spain, 1996
Ratio of population aged 0-14 and over 64
to population aged 15-64
0,70
0,65
Regions
0,60
0,55
Spain total
0,50
0,45
0,40
0,35
0
10
20
30
40
50
60
70
80
90
100
% of rural population
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.
27
DT/TDPC(2002)23
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.
28
DT/TDPC(2002)23
Figure II.3.13. Rural population profiles
Age structure relative to national average
1980
Spain
1990
+65
+65
55-64
55-64
25-54
25-54
15-24
15-24
5-14
5-14
0-4
0-4
-20
-10
0
10
20
1980
Canada
30
-20
1990
+65
55-64
55-64
25-54
25-54
15-24
15-24
5-14
5-14
0-4
0-4
-5
0
5
10
1980
Germany
15
-10
1990
+65
55-64
55-64
25-54
25-54
15-24
15-24
5-14
5-14
0-4
0-4
-5
0
5
0
-5
0
10
10
-10
29
20
1980
5
10
1980
Belgium
+65
-10
-10
United States
+65
-10
1980
Japan
0
10
1990
30
1990
15
1990
20
DT/TDPC(2002)23
NOTES
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.
30
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