2004 EDITION
Population and social conditions 3/2004/F/no 4
Study of low
fertility in the regions
of the European Union:
places, periods and causes
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Luxembourg: Office for Official Publications of the
European Communities, 2004
ISBN 92-894-7077-1
ISSN 1725-065X
Cat. No. KS-CC-04-005-EN-N
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Luxembourg: Office for Official Publications of the European Communities, 2004
ISSN 1725-065X
ISBN 92-894-7077-1
© European Communities, 2004
Population and social conditions 3/2004/F/n° 4
Study of low fertility in the regions of the
European Union: places, periods and
causes
J. Duchêne, A. Gabadinho, M. Willems, P. Wanner
Institut de démographie, Université Catholique de
Louvain
The views expressed in this document are the author’s and do not necessarily reflect the opinion
of the European Commission
Copyright: European Commission 2004
STUDY OF LOW FERTILITY IN THE REGIONS OF THE
EUROPEAN UNION:
PLACES, PERIODS AND CAUSES
PCA of fertility “timetables” in the 603 regions-periods
1,0
,5
tftr16
tftr17
tftr18
tftr19 tftr15
tftr14
tftr20
tftr13
tftr21
tftr22
tftr46
tftr47
tftr48
tftr49
tftr12
tftr23
Component 2
0,0
tftr24
tftr44
tftr43 tftr41
tftr45 tftr42
tftr40
tftr39
tftr38
tftr37
tftr36
tftr35
tftr34
tftr33
tftr32
tftr31
tftr25
-,5
tftr30
tftr26
tftr29
tftr27
tftr28
-1,0
-1,0
-,5
0,0
Component 1
Final report
Josianne Duchêne
Alexis Gabadinho
Michel Willems
with the assistance of
Philippe Wanner
Institut de démographie
Université catholique de Louvain
Louvain-la-Neuve
November 2003
,5
1,0
Study of low fertility in the regions of the European Union: places, timetable and causes
TABLE OF CONTENTS
TABLE OF CONTENTS ................................................................................................................................5
INTRODUCTION...........................................................................................................................................7
I.
REVIEW OF THE LITERATURE ON SPATIAL VARIATIONS OF FERTILITY IN EUROPE.....9
1. DESCRIPTION OF REGIONAL VARIATIONS IN THE EU MEMBER STATES ..........................................................9
1.1 Germany .............................................................................................................................................9
1.2 England ..............................................................................................................................................9
1.3 Austria ..............................................................................................................................................10
1.4 Belgium ............................................................................................................................................10
1.5 Spain ................................................................................................................................................10
1.6 France ..............................................................................................................................................11
1.7 Italy ..................................................................................................................................................11
1.8 Czech Republic .................................................................................................................................12
2. TRENDS IN SPATIAL VARIATIONS ...............................................................................................................13
3. METHODS AND EXPLANATORY FACTORS ...................................................................................................13
3.1 Indicators used and measurement of differences ................................................................................13
3.1.1 Indicators................................................................................................................................................... 13
3.1.2 Measurement of differences........................................................................................................................ 14
3.2 Factors of variation...........................................................................................................................15
3.2.1 Socio-economic variables ........................................................................................................................... 15
3.2.2 Migration................................................................................................................................................... 17
3.2.3 “Contextual” variables................................................................................................................................ 18
3.2.4 Interaction between “context” and “individual”........................................................................................... 21
4. CONCLUSION ...........................................................................................................................................21
II.
DESCRIPTIVE ANALYSIS OF REGIONAL FERTILITY IN THE EUROPEAN UNION .........23
2.1. DATA, METHODS AND INDICATORS .........................................................................................................23
2.2. DESCRIPTION OF REGIONAL FERTILITY DISPARITIES: INTENSITY AND TIMETABLE .....................................24
Table 1 – Regional fertility disparities in the European Union, 1991-1999...............................................25
Graph 1 – Total fertility rate in the European Union, 1991-1993 and 1997-1999 ....................................26
Graph 2 – Trend (as %) in the total fertility rate in the European Union, between 1991-93 and 1994-96,
and between 1994-96 and 1997-99..........................................................................................................26
Graph 3 – Trend in mean age at childbirth in the European Union, 1991-1999........................................27
Graph 4 – Trend in the standard deviation of age at childbirth in the European Union, 1991-1999 .........28
2.3 STRUCTURE OF REGIONAL FERTILITY DIFFERENCES ..................................................................................29
Graph 5 – Projection of the three fertility indicators in the first factorial design, after rotation................29
Table 2 – Composition and characteristics of the seven clusters obtained by classification in respect of
three principal components summarising TFR, AC and SD for the three sub-periods ...............................30
Table 3 – Composition and characteristics of the eight clusters obtained by classification in respect of the
standardised fertility rates of the 603 regions-periods..............................................................................32
III.
LOW FERTILITY REGIONS IN THE EUROPEAN UNION .......................................................35
3.1. IN WHICH REGIONS IS FERTILITY CURRENTLY LOWER THAN THE EUROPEAN MEAN ?..................................35
Map 1 – The fertility of the European regions, 1991-1993 .......................................................................36
Map 2 – The fertility of the European regions, 1994-1996 .......................................................................37
Map 3 – The fertility of the European regions, 1997-1999 .......................................................................38
Table 4 – List of regions showing fertility lower than the “European mean”, 1997-99 ...........................39
Table 4 – List of regions showing fertility lower than the “European mean”, 1997-99 (continued)..........40
Map 4 – Mean age at childbirth in the European regions, 1991-1993 ......................................................41
Map 5 – Mean age at childbirth in the European regions, 1994-1996 ......................................................42
Map 6 – Mean age at childbirth in the European regions, 1997-1999 ......................................................43
3.2. SINCE WHEN HAS FERTILITY IN THESE REGIONS BEEN LOWER THAN THE EUROPEAN MEAN ?......................44
3.2.1 National fertility.............................................................................................................................44
5
Study of low fertility in the regions of the European Union: places, timetable and causes
Graph 6 – Trend in the TFR in the six countries showing fertility lower than the “European mean”, 19602000 .......................................................................................................................................................44
3.2.2. Spain.............................................................................................................................................45
Graph 7 – Trend in the TFR of some Spanish regions in comparison with the EU, 1975-2000 ..................46
3.2.3 Italy ...............................................................................................................................................46
Graph 8 – Trend in the TFR of some Italian regions in comparison with the EU, 1959-2000....................46
3.2.4. Austria ..........................................................................................................................................47
Graph 9 – Trend in the TFR of some Austrian regions in comparison with the EU, 1970-1998................47
3.3. HOW CAN THESE LOW AND VERY LOW FERTILITY LEVELS BE EXPLAINED?................................................48
CONCLUSIONS .............................................................................................................................................51
BIBLIOGRAPHICAL SOURCES................................................................................................................53
ANNEXES .....................................................................................................................................................57
Annex 1 – List of NUTS2 regions of the European Union.........................................................................57
Annex 2a – Availability of fertility rates calculated on an annual basis, 1990 - 2000...............................61
Annex 2b – Availability of fertility rates calculated for three-year periods ...............................................61
Annex 3 – Main fertility indicators of the European regions (NUTS2 level), 1991 – 93, 1994 – 96 and 1997
– 99 ........................................................................................................................................................62
Annex 3 – Main fertility indicators of the European regions (NUTS2 level), 1991 – 93, 1994 – 96 and 1997
– 99 (continued)......................................................................................................................................63
Annex 3 – Main fertility indicators of the European regions (NUTS2 level), 1991 – 93, 1994 – 96 and 1997
– 99 (continued)......................................................................................................................................64
Annex 3 – Main fertility indicators of the European regions (NUTS2 level), 1991 – 93, 1994 – 96 and 1997
– 99 (continued)......................................................................................................................................65
Annex 4 – List of tables, maps and graphs in the text ...............................................................................66
ANNEX REPORT : DATA COLLECTION AND EVALUATION.............................................................67
INTRODUCTION ............................................................................................................................................69
1. GEOGRAPHICAL DIVISION AND SOURCES OF DATA .....................................................................................69
2. DATA AVAILABILITY ................................................................................................................................70
Table 1 – Problems of fertility data availability .......................................................................................72
3. DATA EVALUATION ..................................................................................................................................73
Table 2 – Internal coherence as regards regional fertility........................................................................74
4. DEFINITION PROBLEMS .............................................................................................................................75
CONCLUSION ...............................................................................................................................................77
ADDITIONAL SOURCES .................................................................................................................................79
COPYRIGHT .................................................................................................................................................79
ANNEXES ....................................................................................................................................................80
Annex 1 – List of reporting units included in the analysis.........................................................................80
Annex 2a – Summary of the availability of data on population numbers by gender and year of age in the
Regio domain of New Cronos (on 1 January) ..........................................................................................84
Annex 2b – Summary of the availability of data on births by year of age of the mother in the Regio domain
of New Cronos ........................................................................................................................................84
Annex 3 – Formulae for the transformation of a distribution of births by age reached in the year into a
distribution in completed ages.................................................................................................................85
6
Study of low fertility in the regions of the European Union: places, timetable and causes
Introduction
In recent years, fertility has fallen to very low levels never recorded before in the Member States of
the European Union. While a minimum of 1.42 children per woman was recorded in the Union as a
whole in 1995 (Laihonen and Everaers, 1998), in the Member States, Italy and Spain showed a record
decline, with 1.18 children per woman in 1995 and 1.16 children per woman in 1998 respectively. The
total fertility rate (TFR) has slightly increased in these countries since then. In 2001, the lowest level
of fertility was in Spain with a TFR of 1.24 (1.25 in Italy and 1.29 in Greece, Germany and Austria)
and the highest level in the Republic of Ireland with a TFR of 1.98 (1.90 in France) (Eurostat, 2002b).
Fertility often varies greatly between regions within the same country and figures below the national
minima may be recorded. In Germany (Hank, 2001) and in Italy (Golini, 1999), fertility levels of less
than 1 child per woman have therefore been recorded.
These very low levels of fertility in Europe are part and parcel of a wider trend (the baby bust) which
is affecting an increasingly large proportion of the world population and which has attracted the
attention of researchers (Golini, 1998; Lesthaeghe and Willems, 1999; Kohler et al, 2002) and
international institutions. Following on from the Population Division of the Department of Economic
and Social Affairs of the United Nations (Population Division, 2000) and the International Union for
the Scientific Study of Population (IUSSP, 2001) in particular, the Statistical Office of the European
Communities (Eurostat) therefore commissioned a study of regions with low fertility in the European
Union. The purpose of this study is to answer three questions:
- where are low regional fertility levels in Europe?
- how long have they been low?
- what are the causes?
It also has two objectives:
- to provide an international analysis of the reproductive behaviour of women in those regions
(NUTS 2 level) whose fertility levels are below the Community mean;
- to help to develop and improve methods of analysing and extrapolating fertility models and trends.
For this purpose, it was proposed to use Eurostat’s regional databases, and to evaluate their usefulness
and quality, before envisaging the collection of supplementary data from national statistical offices
and other international organisations.
The analysis of the fertility data collected for this study was largely geared towards answering the
three key questions listed above: places, timetable and causes of low fertility in the European regions.
In accordance with the scheduled work plan, before answering the questions, we undertook a detailed
review of the literature analysing regional fertility differences (Chapter 1). We also felt that it was
important to provide a descriptive analysis of the fertility rates of all the regions of the European
Union. This preliminary analysis is divided into three parts: a presentation of the indicators and
calculation methods used (2.1), an analysis of the dispersion and trend of regional fertility rates in
terms both of intensity (TFR) and timetable (mean age and standard deviation of age) (2.2) and an
exploratory analysis (PCA and classification) of regional fertility differences (2.3)1. Lastly, answers to
the key questions of this study are given in Chapter III: in which regions is fertility now below the
European mean? (3.1), since when have these regions been in this situation? (3.2) and how can these
situations of low or even very low fertility be explained? (3.3). The conclusion draws some lessons for
data collection and further research in this field and attempts to find out what can be done in the area
of forecasting and extrapolation of current fertility trends.
1
In order not to make this report too cumbersome, we decided to draw up an annex to the report providing a
detailed picture of the collection and evaluation of data. We felt that this was important because it was necessary
to provide a detailed picture of the state of the fertility statistics available in Eurostat’s regional database, bearing
in mind that it should be a reference source for further research in this field.
7
Study of low fertility in the regions of the European Union: places, timetable and causes
The authors would like to thank Eurostat and the managers of Units E3 and E4 who provided us with
the results of work in progress. They would also like to thank the national and regional statistical
offices, in particular those of the German Länder and the German central office, who agreed to pass on
data not available in Eurostat’s regional database. Messrs Daniel Devolder (Centre d’Estudis
Demografics of the Universitat Autonoma de Barcelona) and Jean-Pierre Grimmeau (Institut de
gestion de l’environnement et d’aménagement du territoire, Université libre de Bruxelles) were kind
enough to alter their holiday plans to attend the meeting to evaluate the work undertaken and provided
additional information on Spain.
8
Study of low fertility in the regions of the European Union: places, timetable and causes
I. REVIEW OF THE LITERATURE ON SPATIAL
VARIATIONS OF FERTILITY IN EUROPE
1. Description of regional variations in the EU Member States
Although regional fertility rates in Europe are available for periods dating back in most cases to the
middle of the 19 th century, few analyses of all the European regions have been conducted up to now.
Coale and Treadway (1986) nevertheless drew up and discussed regional marital and total fertility
rates for a period from the middle or end of the 19th century to 1960. In particular, they provided
graphs summarising the variability of the various fertility rates within each country and its trend over
time. For the more recent period (1960-1990), Decroly and Grimmeau (1996) have drawn up an
analysis using the total fertility rates (TFR) of 621 regions. As regards the variability of fertility in
Europe at a regional level, this study shows that although the amplitude of the TFR values fell
substantially (from 5.7 in 1960 to 2.8 in 1990) in parallel with the decline in fertility, the relative
dispersion (coefficient of variation) fell only slightly, from 24.1% to 21.4%.
There have been other regional studies which do not, however, all relate to the NUTS 2 regions and in
some cases refer to more detailed geographical divisions, but which have the advantage that they show
the amplitude of regional fertility differences and their trend in some European countries. Our starting
point is to review these studies, giving brief details for each country of the sources used and the
regional differences observed. Some of these studies are fairly old, but they are included as they
describe factors of variation which may still have an impact on differential fertility at regional level.
The many studies of fertility differences between central and peripheral areas are not, however, taken
into account in this study.
1.1 Germany
Hank (2001) gives a review of the literature, as well as descriptive results, concerning regional fertility
differences in West Germany for the period 1995-97. The analysis draws on the total fertility rates
calculated at a detailed level of aggregation (the Kreise, i.e. the districts). The author provides a mean
of these rates for each administrative region (Bundesland), with a further distinction between rural and
urban districts (Kreise).
Two areas of “high” fertility, in comparison with the country as a whole, are pinpointed: one in the
north-west, chiefly along the border with the Netherlands, and the other in the south of the country.
The author notes that there are low divorce rates, a young population, low levels of childcare facilities,
high unemployment and a large proportion of social welfare beneficiaries in these areas.
Any structural differences (social infrastructure, population composition and economic structure)
between the districts of high (TFR>1.55), average (TFR between 1.29 et 1.54, i.e. the mean value –
1.41 ± a standard deviation – 0.13) and low fertility (TFR <1.28) are also analysed. Hank shows in
particular that there is an inverse relationship between the availability of childcare facilities for young
children and the fertility level of the district: higher numbers of places are available in districts with
low fertility. The level of education of women completing education is also lower in districts with high
fertility and higher in districts with low fertility.
1.2 England
Armitage (1987) analysed the total fertility rates for eight English regions for the period 1975-85. In
1985, the TFR varied between 1.70 children per woman in the South-West region and 1.88 children
per woman in the North-West region, with a national mean of 1.78. During this period, the Northern
9
Study of low fertility in the regions of the European Union: places, timetable and causes
regions and the Midlands generally recorded higher fertility than the Southern regions, but Armitage
stresses that the differences between the regional levels and the national mean were never more than
8%. Between 1975 and 1985, the TFR increased in most of the regions with a high urban population
(in the case of the sub-region of Greater London, the TFR increased from 1.64 in 1975 to 1.78 in
1985) and decreased in the more rural regions.
Although the regional variations of the TFR are relatively low, analysis of the timetable (age-specific
fertility rates) shows more significant differences: in both of the periods 1974-77 and 1982-85, fertility
rates were higher in the North and Midlands than in the South among young women and not as high
among older women. In comparison with the national mean (base=1.0), fertility rates in the 15-19 age
group varied from 1.3 in the North-West region to 0.7 in the South-East region (excluding Greater
London). In the 35-44 age group, the variations were even more marked: from 1.4 for Greater London
to less than 0.8 in the North region.
Among the economic and social factors that may explain the variations observed, Armitage cites the
large number of job opportunities in the South-East (especially in London and its area), encouraging
women to put off childbearing. Regional differences could also be accentuated by migratory flows
from north to south involving a substantial proportion of single people or childless couples. The author
also notes that the relatively large proportions of people in social classes I and II (the highest) in the
southern regions probably have an impact on variations in the fertility timetable.
1.3 Austria
In an article on regional aspects of family formation and non-marital fertility in Austria, Prioux
(1993a) briefly reviews the completed fertility by region of the 1926-1930 generations, calculated
from the 1981 census data. Completed fertility was much lower in Wien (1.41 children per woman)
than in the remainder of the country, where the values ranged from 2.23 in Niederösterreich to the east
to 2.66 in the central region of Kärnten. The national mean for the generations in question was 2.19
children per woman.
The author also highlights the continuation over time of areas of high and low illegitimacy: “the map
of illegitimacy such as could be drawn up at the end of the 19th century and the current map are almost
completely identical”. There is, however, no link between the fertility rate and the illegitimacy rate,
the lowest proportions of children born outside marriage (in the 1926-1920 generations) appearing at
either end of the range of Austrian regional fertility rates (Vorarlberg to the west and Wien to the
east), whereas the central regions with high illegitimacy (Kärnten and Steiermark in particular)
showed average levels of fertility.
1.4 Belgium
Damas and Wattelar (1989) give a regional analysis of fertility in Belgium for three dates focusing on
the 1960, 1971 and 1981 censuses. In 1981, fertility varied between 1.54 children per woman in the
centre of the country and 2.03 children per woman in the Ardennes. Fertility in towns and their
surrounding areas was in all cases below the mean (1.68). The authors also note that regional trends
tended to become uniform between 1961 and 1981, at a time when the fertility of the country as a
whole fell from 2.62 to 1.70 children per woman. This standardisation was particularly significant
between 1961 and 1971.
1.5 Spain
Regional differences in fertility in Spain are described by Gozalvez Pérez (1989) for the period from
1976 to 1984. The total fertility rate is calculated for the different provinces from data on natural
changes in population. The striking finding is the north-south divide: the Madrid parallel divides the
10
Study of low fertility in the regions of the European Union: places, timetable and causes
country in two: in the south, fertility is generally much higher than the national mean, with maximum
figures in the extreme south, while the rates in the north are generally below the national mean.
In 1976, as a result of internal migration, some highly urbanised provinces of the northern half of the
country had a relatively high fertility rate. In 1981, however, the effect of this migration had
disappeared and the difference between the north and south of the country was more clear-cut. The
national mean of 2.03 children per woman was exceeded in all the southern provinces, with a
maximum of 2.84 in Cadiz. The absolute difference between the extreme values fell, however,
between 1976 and 1981. In 1984, all the rates in the south of the country exceeded the national mean
of 1.71 children per woman, whereas no northern province showed a TFR higher than the mean.
Particularly low fertility rates have been recorded in some Spanish regions since the early 1990s. In
the País Vasco, the TFR was below 1.0 children per woman from 1989 and fell to 0.9 in 1995 (Golini,
1998). In Catalonia, it was 1.1 in 1994.
1.6 France
Blanchet (1981) provides monthly regional series for the total fertility rate for the period from 1960 to
1979 in France. He notes a reduced dispersion of the regional rates during this period, both in absolute
terms (standard deviation falling from 0.37 to 0.13) and in relative terms (the coefficient of variation
falling from 13.5% to 7.5%). In addition to this standardisation of fertility behaviour, which
nevertheless took place at different speeds in different regions, examination of the monthly series
shows that the fluctuations took place at the same time: “Although […] the regional features are
reflected by reductions and increases of different amplitude, these do not affect the dates on which
these reductions and increases began and, if there was any ‘contagion’ or propagation of changes in
fertility behaviour, these were instantaneous”.
Legrand (1992) looks at the fertility situation in France by region and department in 1989-90 and
compares it with the situation in 1981-82. The author notes the continuing decline in fertility in the
“fertile crescent”, an area of traditionally high fertility extending from the north-west to the south-east
of the country. In 1989-90, the TFR by region varied from 2.00 in Nord-Pas de Calais to 1.44 in
Limousin. At departmental level, the highest fertility was in Seine-Saint-Denis, with a TFR of 2.05,
“as a result of the high proportion of foreign population”.
Like Legrand (1992), Dumont (1996) concludes that behaviour is standardising to a low level, since,
between the censuses in 1982 and 1990, the drop in fertility was higher in regions of the former fertile
crescent: -0.20 in Bretagne, -0.21 in Nord-Pas de Calais, -0.22 in Franche-Comté and –0.23 in Pays de
la Loire in comparison with a national figure of –0.10.
Fertility rates by department and region from 1975 to 1994 are given in Lincot and Lutinier (1998). In
1994, when the TFR was 1.65 for metropolitan France, the highest values were in the north of the
country, with over 1.8 children per woman in the departments of Seine-Saint-Denis, Aisne, Nord,
Yvelines and Pas-de-Calais. Fertility was lower in the centre and south-west, with a TFR lower than or
equal to 1.4 in Dordogne, Creuse, Puy-de-Dôme, Corrèze, Cantal and Haute-Vienne.
1.7 Italy
In 1983, J.L. Rallu drew up a table of fertility for the Italian regions from 1950 and stressed the extent
and ongoing nature of regional differences, particularly between the north and south. Following an
analysis by year (from 1952 to 1978) and by generation (1932 to 1948), he provides a breakdown by
birth order, noting that, in the later period, the drop in fertility affected all the birth orders, except in
the south where birth order 2 fertility had held up well.
11
Study of low fertility in the regions of the European Union: places, timetable and causes
Brunetta and Rotondi (1989) also analyse regional differences in fertility in Italy and their trends
between 1951, 1961, 1971 and 1981. The authors calculated the marital fertility rate for each of the
censuses. Although fertility fell sharply in Italy during the period in question, substantial regional
differences continued or even became more accentuated. Italy had, moreover, “within the countries of
the Council of Europe, both the highest and the lowest regional general fertility rate (GFR): 2.17 in
Campania and 1.05 in Liguria” (Santini, 1986; cited by the authors). The authors conclude that two
areas could be clearly differentiated in 1981: the centre and north with relatively low fertility and the
south and islands with much higher fertility.
Terra Abrami and Sorvillo (1993) reconstructed the fertility of the Italian regions from 1952 to 1988
enabling a combination of transverse and longitudinal analyses. The picture of the recent history of
Italian fertility which they present is at odds with the official picture, with the key word again being
diversity. They conclude, moreover, that “any reference to ‘Italian’ fertility continues to be an
abstraction”. They highlight substantial differences in levels as well as trends, which are particularly
evident from a comparison between two southern regions (Campania and Calabria) and two northern
regions (Liguria and Piemonte). Longitudinal analysis and breakdown by birth order confirm the
structural nature of these regional differences which are connected with “reproductive behaviour
which is historically and intrinsically different”. The drop in fertility which became more accentuated
after 1974, even though it affected all the Italian regions, did little to bring the models closer together:
in the north, the predominant model being that of the single child and the proportion of women with
more than two children becoming marginal; in the south, families with two or more children
continuing to account for the majority and higher numbers of children (four or more) being eroded.
In 1994, inter-regional differences were even more marked, although the TFR was lower than the
generation replacement threshold for all 20 regions and 94 provinces (Golini, 1999). On that date, the
minimum was recorded in Liguria, a region of northern Italy, with 0.93 children per woman, and the
maximum in Campania, a region of southern Italy, with 1.61 children per woman. The ratio between
the extreme regional figures was therefore 1.7, whereas it was 1.9 in 1977. Similarly, 24 of the 94
Italian provinces had a TFR of less than 1.0 child per woman, the minimum being recorded in the
province of Ferrara (in Emilia Romagna) with 0.79 children per woman.
In their analysis of Italian fertility since 1960, Livi Bacci and Salvini (2000) note that “the divide
between the north and south-east is not only demographic but also social (education is more advanced
and more women are in the labour market in the north) and economic (income is higher and
consumption is more significant)”. The authors stress, among the reasons for the low level of fertility,
that decisions to have a child depend on a stable situation (permanent job, housing, partner) which is
achieved later in life. Births outside marriage are rare (9% in 1998, the lowest level in Europe),
whereas the marriage rate is falling and marriage is often postponed. Therefore, “it would seem that
the postponement and definitive decline in marriage is more marked for women in the northern
regions of the country”.
1.8 Czech Republic
Between 1992 and 1996, the total fertility rate fell from 1.73 to 1.21 children per woman, but the
spatial distribution (administrative division into 76 then 77 districts) remained more or less unchanged
(Rychtarikova, 2000). In 1992, the regional values of the rate varied between 1.95 (rural districts of
Semily and Svitavy) and 1.48 (Plzen, capital of Western Bohemia). In 1996, the minimum value was
recorded in Prague (1.05) and the maximum in the district of Nachod, with 1.39 children per woman.
The relative variation (coefficient of variation) nevertheless remained stable dropping from 5.4% en
1992 to 5.3 % in 1996. Regions of high fertility were located in Eastern Bohemia and Western
Moravia, and fertility was generally low in towns. The author also analyses spatial variations in the
percentage of illegitimate births, the number of abortions per 100 live births and the total fertility rates
by birth order.
12
Study of low fertility in the regions of the European Union: places, timetable and causes
2. Trends in spatial variations
Various authors (see in particular Decroly and Grimmeau, 1996; Watkins, 1990) tackle the question of
whether fertility levels are converging within and between Member States. “Despite their
disagreement about the future development of fertility differentials between European countries, many
authors agree that differences within countries will diminish continuously as a consequence of
increasing social integration. Nevertheless, substantial regional variations in fertility levels persist and
can still be observed in western European low-fertility societies” (Hank, 2001).
Historical studies show that the main trends in fertility in Europe took place in line with a model of
dissemination from certain regions which were precursors of innovative behaviour (Coale and
Treadway, 1986; Lesthaeghe and Neels, 2002). In this case, the amplitude of the variations may be
higher at the beginning and during the main periods of change. However, studies of more recent
periods, for France (Blanchet, 1981) and for Europe as a whole (Decroly and Grimmeau, 1996), show
that trends in fertility in the regions were very simultaneous in nature. In their conclusion, Decroly and
Grimmeau (1996) note: “Recent trends in European fertility clearly show the end of any mechanism of
dissemination; synchronism is now the rule”.
3. Methods and explanatory factors
3.1 Indicators used and measurement of differences
The first stage in studying spatial variations in fertility is to construct appropriate indicators and to
highlight variations. Various methodological questions relating to the choice of indicators and tools for
measuring differences as well as the scale chosen for analysis are examined in this section.
3.1.1 Indicators
Most studies of spatial variations in fertility use the total fertility rate (TFR) for comparisons. Decroly
and Grimmeau (1996) discuss the pertinence of transverse analysis in comparison with longitudinal
analysis which is more difficult to apply at a regional level, bearing in mind the data available.
However, a model in which the TFR can be corrected to take account of the effect of timetable
variations is proposed by Bongaarts and Feeney (1998) and applied by Lesthaeghe and Willems
(1999) to three EU Member States (Italy, Belgium and France) and by Livi Bacci and Salvini (2000)
to Italy. This method, which requires fertility rates by age of the mother and birth order and mean ages
at childbirth by birth order, makes it possible to obtain results similar to the completed fertility
recorded for the generations and more suited to “the interpretation of trends over time”.
Some studies propose an approach by both period and generation, in particular Terra Abrami and
Sorvillo (1993) in their analysis of fertility in the regions of Italy. Etchelecou (2000) drew up, for the
French départements, the completed fertility of the 1889 to 1949 generations using the age-specific
fertility rates calculated from censuses. By using this approach, he was able to determine the fertility
profile for each département and to pinpoint fertile areas as “areas where comparable behaviour has
continued in a stable way” (see also 3.1.2).
Analysis of the components of fertility (curve of age-specific fertility rates, birth orders, marital
fertility and non-marital fertility) provides information on specific regional behaviour and on ongoing
trends. It makes it possible in particular to find out whether there is, nationally, any dissemination of
behaviour from “pioneer” areas or whether different geographical areas are evolving in accordance
with particular trends.
As Festy (1981) stresses, “identical levels do not necessarily go together with an identical structure of
fertility”. Damas and Wattelar (1989) therefore show that, for Belgium, “the trend in the fertility
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Study of low fertility in the regions of the European Union: places, timetable and causes
curves has not been the same in the north and south of the country, although the mean number of
children per woman is now similar”. Similarly, Terra Abrami and Sorvillo (1993) use a breakdown by
birth order enabling them to study “the dichotomic nature of the fertility transition in the north and
south of Italy”. In Liguria (in the north), where 16% of women born in 1920 had more than two
children and over half had no children or a single child, the main feature of the transition was an even
more marked concentration on births of order 1. In Campania (in the south), where close on 60% of
women from the 1920 generation had three or more children, the feature of the transition was a
significant decline in births of order 4 or more. Rychtarikova (2000) also looks at the breakdown of
fertility by birth order for the Czech Republic. The results of these studies breaking down fertility rates
by birth order clearly show that an approach of this kind is needed to understand the factors causing
current differences in fertility. It is necessary to assess to what extent the data needed for such a
breakdown are available for the recent period at the level of the NUTS 2 regions.
The distinction between marital and non-marital fertility also provides information on any cultural or
historic differences between regions (Prioux, 1993b; Munoz-Perez, 1991 and Rychtarikova, 2000). For
Spain, Gozalvez Perez (1989) analyses the distribution of pre-nuptial conceptions which “sheds light
on regional differences as regards early fertility and the practice of effective contraception”.
3.1.2 Measurement of differences
As regards the simple measurement of the dispersion of fertility levels within a country or a wider
area, the most appropriate indicator is probably the coefficient of variation (standard deviation/mean)
as this measurement is independent of the absolute fertility rate. This indicator is particularly useful
when it is wished to monitor changes in dispersion within the same geographical area between two
dates, when the overall fertility level has itself varied (see, for instance, Blanchet, 1981; Decroly and
Grimmeau, 1996).
Geographical division and level of analysis
The choice of the geographical division to be used for the analysis obviously had an impact on the
amplitude of the variations observed. In France, for instance, the TFR was 1.71 for the Ile-de-France
region in 1994, but within this region, fertility was 1.51 in Paris, which is a separate département, and
1.89 in Seine-Saint-Denis, which is a neighbouring département with a high proportion of foreign
population. In Italy, in 1994, the maximum and minimum values of the TFR were 0.93 and 1.61 at
regional level, whereas the amplitude was greater at provincial level, with 0.79 and 1.69 respectively
(Golini, 1999). A more detailed division obviously tends to highlight differences within geographical
areas which include both large towns and cities and less densely populated areas (see, for instance,
Hank, 2001).
When simultaneously analysing regional data from a number of countries, it is also necessary to take
account of any “artificial” variations resulting from the way in which data are collected, the way in
which variables are defined and the spatial networks that are used (Decroly and Grasland, 1992). The
fact that a region is part of a state may be a key parameter in explaining any differences observed.
Decroly and Grasland (1992) therefore show that a substantial proportion of the variations of the total
fertility rate of 724 European regions can be attributed to the fact that they belong to a particular state:
variance analysis shows that in 1980 and 1988, 51.3 and 53.8% respectively of the variance observed
can be explained by this “state effect”.
Spatial auto-correlation
Spatial auto-correlation means that two geographically close regions tend to show similar behaviour.
For France, Blanchet (1981) calculates an index of geographical coherence, proposed by Cliff and Ord
(1973), making it possible to test spatial auto-correlation (Blanchet describes the calculation method in
the annex to his study). The test starts from the hypothesis that, if there is no correlation, the values of
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Study of low fertility in the regions of the European Union: places, timetable and causes
the index in question (in this case the total fertility rate) will be distributed at random throughout the
territory as a whole. The results obtained show that geographical coherence increased fairly regularly
over the period studied (1960-1979) in particular as a result, according to the author, of more similar
behaviour in the Paris and neighbouring regions.
Pinpointing comparable areas of behaviour
The analysis also makes it possible to distinguish comparable areas of behaviour containing several
basic geographical units. Using the completed fertility of the 1889 to 1949 generations by
département, Etchelecou (2000) distinguishes 31 geographical areas forming 17 fertility areas. The
criteria used are spatialisation, comparability and sustainability. The author suggests a basic outline for
the geographical model: “an area of fertility may be defined by a pole which is highly comparable
over time and an area of influence whose heterogeneity grows as it approaches other fertility areas”.
Decroly and Grasland (1992) discuss “levels of organisation of behaviour”. Differences in behaviour
may in practice be explained by the fact that a geographical unit belongs to a state, a set of states or to
an ethnic, linguistic or cultural structure. The authors stress the interest of studies of frontier areas in
this respect.
3.2 Factors of variation
The causes of regional variations of fertility are numerous and difficult to pinpoint. In many works, the
authors merely relate fertility rates to certain characteristics of the areas considered. Two main types
of factor are used: the socio-economic structure of the population (breakdown by socio-occupational
class, level of education, nationality, etc.) and “contextual” factors of the place of residence (cultural
features, availability of infrastructure, housing market situation, etc.). Analyses which also use
individual characteristics are few and far between. The main factors used in the works consulted are
reviewed in this section.
3.2.1 Socio-economic variables
The distribution of the population in terms of certain socio-economic variables is often used to
describe regional differences. In general, the variables used are those which, at an individual level, are
connected with the differences recorded in completed fertility, in particular the level of education,
occupation and sector of activity of women, as well as nationality.
Level of education
After ranking the districts of West Germany by fertility level, Hank (2001) pinpoints significant
differences in the structure of the female population by level of education: in high fertility districts,
close on one third of women left school without qualifications or with a low level of education and one
fifth with an upper secondary certificate, whereas 36% of women living in low fertility areas possess
this latter qualification. The author also pinpoints particularly low fertility in the urban districts
(“kreisfreie Städte”) of the Bundesländer of Baden-Württemberg and Bayern (southern Germany).
These are towns of 50 000 to 200 000 inhabitants having a major university and therefore including a
high proportions of students. The author, citing Nauck (1993), stresses that the level of education of
women in university towns is higher, while the proportion of married women is lower than elsewhere.
In the case of Spain, Gozalvez Perez (1989) shows that there is a significant correlation between the
map of the percentage of women aged 20 to 34 stating in the census that they are “illiterate” or
“uneducated” and the maps of fertility in 1981 and 1984. High fertility provinces, to the south of the
Madrid parallel, are also those in which low levels of education are to be found. The author also finds
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Study of low fertility in the regions of the European Union: places, timetable and causes
a correlation between fertility and the level of education of 18-24-year-olds. He notes that low school
attendance levels at these ages are more frequent in the south of the country and generally coincide
with high fertility, a higher ideal number of children and less frequent use of modern contraceptive
methods.
Participation rate and composition of the working population
The local structure of the working population, especially among women, is also felt to be an important
parameter. Hank (2001) shows that high fertility districts generally have a higher proportion of
employees in the primary sector and a lower proportion of employees in the tertiary sector than low
fertility districts. The author, citing Blossfeld (1987), notes that career opportunities are better in the
tertiary sector and therefore that the “opportunity costs” of childbearing are felt to be higher in areas
where this kind of job predominates.
Composition by nationality
International migration causes an influx of populations, often of high fertility, into areas whose
fertility is generally lower than the national mean (urban areas, see also Section 3.2.2). Over a period
that varies in length, the behaviour of these immigrant populations may modify the level of fertility.
The composition of the population by nationality or the proportion of foreigners may therefore explain
some specific local features of fertility levels and trends.
In his description of fertility trends in the French départements between 1981-82 and 1989-90,
Legrand (1990) notes that the very small decrease in the fertility level of the Ile-de-France region
(-0.5%) is due to the high fertility of foreign women (2.77 children per woman in comparison with
1.69 per woman of French nationality) who accounted for 21% of all births. This region also includes
the most fertile French département (Seine-Saint-Denis, with 2.05 children per woman) as a result of
the high proportion of foreign women (Legrand,1992).
Religious belief and practice
The intensity of religious practice is also used as a factor to explain variations in fertility. Belonging to
the Catholic religion, in particular, is associated with relatively high fertility as “the Catholic doctrine
on procreation and birth control is more inflexible than most other religions” (Garcia Ballesteros et al,
1998). Secularisation and the loosening of the grip of religion over behaviour go together with a drop
in fertility (Lesthaeghe and Wilson, 1982).
Noting that the widespread membership of the Catholic religion among the population is among the
factors traditionally put forward to explain Spain’s high fertility up to the 1970s, Garcia Ballesteros et
al (1998) study the relationship between declining fertility and the weakening of religious practice
using regional survey data. As regards the proportion of the population stating that it is Catholic, there
is no obvious relationship with the level of fertility: “whereas in Andalusia, Extremadura, Castilla-La
Mancha and, to a lesser extent, the Canaries, high fertility goes with a high percentage of Catholics, in
Navarra, Aragón, La Rioja and Castilla y León, in contrast, fertility is low despite a high percentage of
Catholics”. The findings are also ambiguous as regards religious practice. Most of the regions in
which a high percentage of Catholics state that they are non-practising (Madrid, País Vasco, Cataluña
and Asturias; 1999 CIRES survey) also have a fertility below the national mean. However, this
relationship is not to be found in other regions (Comunidad de Valencia and Balearics), whereas some
regions with a high proportion of practising Catholics have a low fertility (Cantabria, La Rioja and
Castilla y León). The authors therefore conclude that religious practice has a relative impact on any
decline in fertility.
In Italy, Brunetta and Rotondi (1989) include in their analysis the election results of the Christian
Democrats as an indicator of the importance of Catholic culture in the province and conclude that
there is a significant relationship between fertility and the political parties’ results (see also 3.2.3).
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3.2.2 Migration
When socio-economic variables are used to explain regional variations in fertility, migration is an
important parameter. Migratory flows may have an impact on regional fertility levels by modifying the
composition of the population by including people possessing particular characteristics (nationality,
level of education) and therefore having particular reproductive practices. Migration may also have an
impact on the fertility of regions of origin. As the effect of international migration has already been
discussed in Section 3.2.1 (Composition by nationality), we shall look here at the effects of domestic
migration.
Migration to urban areas
During periods of major migration to urban centres, the influx of people from traditionally more fertile
areas increases the general level of fertility in the host region, which may lead to a levelling off of the
differences between migrants’ regions of origin and host regions. Brunetta and Rotondi (1989) note,
for Italy in 1961, an increase in fertility in industrial areas and the large cities, as areas of immigration,
and a parallel decline in the provinces of the south and the islands, as areas of emigration.
In Spain, Gozalvez Perez (1989) shows a correlation between low levels of education and high fertility
(see above) and notes that the map of the percentage of women aged between 20 and 34 stating that
they are “illiterate” or “uneducated” reflects the massive influx of women migrants from the south to
Madrid and Barcelona “where the proportion of women with little education is twice as high as in the
rest of Catalonia”.
In Belgium, the regional indicators calculated from the 1981 census, which allows a distinction
between Belgian and foreign women, show that the differences between towns and urban regions and
rural regions are more marked only among the Belgian population. The higher fertility of foreign
women in urban areas therefore helps to level off the differences with rural areas.
This effect may, however, wear off as time passes as the demographic behaviour of immigrants may
gradually and over a varying period start to mirror that of the host population (hypothesis of
adaptation). Gozalvez-Perez (1989) shows, for instance, for Spain that the relatively high fertility
levels on the Mediterranean coast and in Madrid in 1975 “are explained by the arrival in urban areas,
between 1960 and 1975, of large cohorts of young immigrants from high fertility regions”. In 1981,
however, when migratory flows had declined, the author notes that the urban provinces to which there
had been large-scale immigration between 1960 and 1975 also recorded a sharp decline in fertility
after 1975.
“Selective” migration or migration linked to fertility behaviour
Some authors also put forward the idea that migration may strengthen regional differences. Brunetta
and Rotondi (1989) therefore put forward the view that “it may be […] that emigration [to the regions
of northern Italy], by removing a human component which is probably more receptive to social
change, has helped to perpetuate traditional behavioural models, thereby causing some delay in the
decline in fertility”. This is also one of the factors put forward by Armitage (1987) to explain regional
fertility variations in England: “Migration, consisting of disproportionately large numbers of young
single persons and couples as yet childless, produces net flows from north to south which also
accentuate the gradients in regional fertility differentials”.
It may also be that a region with a high fertility in comparison with the mean may be attractive to
couples themselves keen to have a large number of children as it offers an environment and
infrastructure better suited to their education and socialisation. This migration thus “deprives” the
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Study of low fertility in the regions of the European Union: places, timetable and causes
region of origin, of low fertility, of people potentially of high fertility, thus helping to accentuate the
differences.
The findings of the study by Michielin (2002) of the city of Turin seem to bear out the hypothesis that
emigration may be linked to family plans. In urban centres, access to resources, especially housing,
may make it difficult to put these plans into practice: “fertility seems to be particularly conditioned by
the educational level of the woman, which determines more the resources for facing new births than
the rising opportunity costs of children [...]. The same covariate is then important also for outmigration, reinforcing the idea that staying and therefore having children in Turin municipality is a
matter of possibility”. However, the proportion of migration from the geographical unit in question
needs to be determined in this migration from urban centres. In practice, the migration of couples to
suburban areas does not necessarily modify fertility at a regional level, whereas the effects may be
more evident in more detailed studies (German districts; Hank, 2001) where towns are the unit of
analysis.
3.2.3 “Contextual” variables
Carrying out solely a descriptive analysis drawing on structural factors is implicitly to consider that
the environment in which people live has no influence on their behaviour, and that spatial variations
are due only to differences in the composition of the population. It is supposed, for instance, that the
fertility difference between highly educated women and women educated only to primary level is
independent from their place of residence.
In their discussion of methods of highlighting “levels of spatial organisation” of behaviour, Decroly
and Grasland (1992) propose the following general hypothesis as a starting point: “the social attributes
– for instance the fertility level – of any geographical unit are determined by various structures ranging
from the personality of the people making them up to the political structures of the state or super-state
system to which they belong”. Among these structures, “geographers tend to give priority to those
which have a clearly defined geographical scope – for instance political structures (the municipality,
region, state or set of states) – or a marked geographical scope whose limits are, however, hazy and
overlap – for instance linguistic, ethnic or cultural structures”.
Taking two examples (the linguistic boundary in Belgium between the Walloon and Flemish regions is
a significant spatial limit as regards men’s life expectancy and, in France, the mortality rates due to
alcohol vary depending on the region of residence (Picheral, 1990)), the authors conclude that “there is
therefore an infra-national level of organisation to which a regional effect – to be defined – is attached
and is likely to modify the behaviour linked to this or that social status”.
Following on from Hank (2001), Schwarz (1983) concludes from simulations that the socio-economic
composition of the population makes it possible to explain only part of the regional fertility rate
differences in West Germany. Brunetta and Rotondi (1989) also note that, in the case of Italy,
although there is generally an inverse relationship between socio-economic level and fertility: “with
the same level of education, fertility declines when moving from municipalities of secondary
importance to capitals”. Festy (1981), again as regards Italy, notes that “in Piedmont there are 1.7
births per woman who has attended secondary education and 4.4 among illiterates; in the Marches,
however, the range is only from 2.1 to 3.3 for the same groups”.
Isolating or measuring regional and cultural factors and understanding they way in which they work is
not, however, an easy task. Anderson (1986), in particular, discusses hypotheses concerning
relationships between cultural or regional variables and behaviour.
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Study of low fertility in the regions of the European Union: places, timetable and causes
Differences between urban and rural areas
Fertility differences between urban and rural areas are highlighted in many studies. The degree of
urbanisation (measured for instance by the proportion of the population living in towns) or possibly
population density are therefore parameters making it possible in many cases to differentiate areas of
high and low fertility. Generally speaking, fertility is higher in rural areas although, as mentioned
above, these differences may be attenuated by significant migratory flows leading to the arrival in
urban centres of populations whose fertility is often higher (see 3.2.1).
The problem is one of pinpointing what is due to the structure of the population (qualification levels)
or the employment market (fewer employees in the services sector and more employees in the primary
sector) and what constitutes the other characteristics of rural or urban areas. Some specific features of
urban areas may well explain the lower fertility generally recorded in major cities.
One of the particular features of urban areas could well be the higher cost of some resources
(especially housing) which restrict access to them and place an obstacle in the way of the fertility
plans of some couples (Michielin, 2002). Hank (2001) notes that, according to Strohmeier (1989),
people choosing to live in rural areas in Germany have a more traditional perception of the family.
Moreover, areas of low urbanisation may be seen as better places to bring up children, and may attract
those couples that are potentially the most fertile from urban areas (see 3.2.2).
Attitudes, social and cultural environment
Specific surveys are needed to pinpoint particular social standards and cultural traditions, or the
existence of a “regional lifestyle”. Schwarz (1979, cited by Hank, 2001) points out that regional
differences in attitudes towards the family and children may go a long way towards explaining the
fertility variations observed.
Drawing on a 1985 study of fertility, Gozalvez Perez (1989) finds, as regards ideal family size,
regional differences in keeping with the fertility levels recorded. 50% of Spanish women consider two
children to be the ideal family, with one third putting the number at three. In Castilla-La Mancha and
the region of Murcia, however, a higher proportion of women consider that the ideal size is three
children (40 and 49% respectively). A family of four children is considered to be the ideal size by
close on 20% of women in Extremadura, Castilla-La Mancha and the Canaries.
Brunetta and Rotondi (1989) carry out a correlation analysis incorporating cultural and political
indicators (electoral results of the Italian Communist Party and Christian Democrats, percentages of
votes for and against the repeal of the laws on abortion and divorce), based on the works of
Lesthaeghe and Wilson (1982) and Coale and Watkins (1986). Brunetta and Rotondi start “from the
hypothesis that the election results of the Christian Democrats are a good indicator of the importance
of Catholic culture in a province and the results of the Community party are an indicator of lay culture.
Similarly, the proportion of people voting for divorce and abortion is a good indicator of the extent of
attachment to traditional perceptions of the family and marriage and, therefore, to some extent, of the
population’s psychological attitude to procreation”. Among their findings, the authors stress “the
highly significant correlation between fertility and the percentages of the votes obtained by the
political parties” in the north and centre of Italy.
Economic circumstances and employment market
In France, the two least fertile regions are in the Massif central (Limousin, with 1.44 children per
woman and Auvergne with 1.53). The Auvergne experienced the highest drop in fertility between
1982 and 1990 (-12.6%). According to Legrand (1992), this trend has to do with the economic
problems (agriculture and in particular stockbreeding) faced by the rural world and by local industries.
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Study of low fertility in the regions of the European Union: places, timetable and causes
If women’s participation rates are high, there may also be more job opportunities for women. Hank
(2002) also stresses that a women’s propensity to enter the labour market may well be influenced by
the general level of women’s participation. A region with a high female participation rate therefore
tends to encourage women to work and helps such work to become accepted.
Childcare facilities
The availability of childcare facilities for infants (or other non-institutional arrangements) and the cost
of such services may have an impact on the number of women in the labour market. It may well be
that these factors have an impact on childbearing decisions, although there are few studies in this area
(Hank and Kreyenfeld, 2001).
Kravdal (1996) showed that an increase in the public provision of places for children aged 0 to 3 had
an effect on the probability of Norwegian women having a third child. However, this effect is
weakened when account is taken of the overall participation rate of women and disappears when the
level of coverage (places available) exceeds 10%.
Hank (2001) notes that in West Germany, the areas in which the number of places available is high are
areas of low fertility. In a further study, Hank and Kreyenfeld (2001) use individual data from the
German socio-economic panel (GSOEP), cross referenced with regional data on the numbers of places
available in crèches (children aged 0 to 3), to analyse the relationship between the first birth and the
opportunities for care for young children. The authors start from the hypothesis that it is not the cost
but the availability of childcare solutions which has an influence on fertility. In West Germany,
institutional solutions are financed by municipalities and are not expensive for parents. Moreover,
“private” solutions (nannies, etc.) are not widespread. Given that there are relatively few places in
public facilities, social networks (chiefly grandparents), representing free solutions, also play a
significant role in childcare for infants.
The parameters taken into account were therefore: at district level, the number of places available in
public care facilities above the median figure (17 per 1000), and, at an individual level, whether the
woman’s parents lived in the same town. These variables did not ultimately have a significant impact
on the probability of women giving birth to their first child in any of the models tested. In conclusion,
despite regional variations in the number of places available in public care facilities, the level is low
and probably not enough to be a key factor in the decision to have a child. The solutions available,
moreover, do not really allow women to reconcile work and motherhood, largely because of unsuitable
opening times.
Housing market
The housing market situation may have an effect on couples’ procreation decisions (Schwarz, 1979,
cited by Hank, 2001). Problems in finding housing of an appropriate size to accommodate a growing
family may delay the decision or cause people to migrate. It is assumed that this situation is
particularly true in towns, as would seem to be borne out, for instance, by the results obtained by
Michielin for the city of Turin (2002) (see 3.2.2).
Structure of the population
The structure of the population may itself be a regional “context”: although the total fertility rate is
independent of the age structure of the population since it is the total of the age-specific fertility rates,
Legrand (1992) and Dumont (1996) therefore note that, in France, areas where the age composition is
“older” generally show low fertility, below 1.6 children per woman (Aquitaine, Auvergne, Limousin
and Midi-Pyrénées). Legrand assumes that this is a problem of “loss of confidence in the future”
among young people and that “the uncertain future of agriculture and stockbreeding, and of other
activities, undoubtedly explains behaviour in these regions”.
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3.2.4 Interaction between “context” and “individual”
Although, at an aggregate level, links have been highlighted between regional characteristics and
fertility levels, the way in which these characteristics act on individuals has not, however, been
elucidated. Ultimately, decisions on fertility are taken individually. Various aspects of this problem are
discussed by Hank (2002). Brunetta and Rotondi (1989) also draw attention to the methodological
problems raised by ecological regressions, in which the statistical units are geographical sub-divisions,
the dependent variables are fertility rates and the independent variables are indicators of the socioeconomic environment.
Transposing links between regional indicators and fertility indicators to an individual level is
dangerous and may lead to what is called the “ecological fallacy” (see, for instance, Courgeau and
Baccaïni, 1997, and Anderson, 1986). To take up the example given by Courgeau and Baccaïni (1997)
for migration, and transposing it to fertility, the ecological fallacy would be to conclude, after
observing that fertility is high in areas with high unemployment levels, that unemployed people have a
particularly high fertility. As Hank (2001) stresses: “Many studies refer to local labour market
characteristics, spatial mobility or regional lifestyles as causes for the striking regional fertility
differences discussed in this paper. However, on the basis of ecological correlation alone, nothing can
be said about the underlying mechanisms that link these contextual characteristics to the individual's
reproductive behaviour”.
To remedy this problem, the solution is to use individual data which are related to “contextual” data.
Individual characteristics are then controlled and any local environmental effects may be highlighted.
Techniques for such analyses are provided in particular by Courgeau and Baccaïni (1997). Hank
(2002) applied this kind of analysis to data from the GSOEP (German Socio-Economic Panel Study, a
longitudinal study of 7000 households and 14 000 individuals). Individual data were cross-referenced
with regional indicators by means of the place of residence of respondents to each wave. The author
concludes that the analysis, covering the period 1984-1995, does not make it possible to highlight
autonomous effects of the geographical area in which people are resident. The majority of regional
differences are in practice explained by the spatial distribution of individual characteristics,
particularly in the case of the first birth. Some signs of the influence of the regional social context are
nevertheless detected as regards the birth of the second child.
4. Conclusion
All the studies conducted nationally highlight regional variations in fertility. There is only rarely a
clear-cut tendency towards a standardisation of levels between regions. In many countries, including
Italy which has been studied in detail, disparities are ongoing and are often long-standing.
The few studies simultaneously using regional data for several countries would tend to show, however,
that the state to which a region belongs is an important parameter in explaining the level of its fertility:
variations within states are less substantial than variations between states. In other words, at European
level, a large part of the variance of regional fertility indicators is “inter-national” rather than intranational.
Many factors undoubtedly play a part in explaining the variations; studies that make it possible to
pinpoint some of these in a conclusive way are few and far between. Analyses which merely relate, at
an aggregate level, fertility rates and certain socio-economic or contextual variables come up against
the problem of the ecological fallacy. The few studies that use multi-level analysis to take account
both of individual characteristics and contextual variables do not provide conclusive results either.
Individual characteristics seem much more important in terms of explanation than the characteristics
of the place of residence. However, few data are currently available for such analyses.
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Study of low fertility in the regions of the European Union: places, timetable and causes
II. DESCRIPTIVE ANALYSIS OF REGIONAL FERTILITY IN
THE EUROPEAN UNION
This chapter reviews and provides a descriptive analysis of the fertility rates that it has been possible
to calculate for all the level 2 regions of the Nomenclature of Territorial Units for Statistics (NUTS2)
of the 15 Member States of the European Union. It precedes an identification of the regions whose
fertility is below the “European mean” and is intended to provide a picture of regional fertility in
Europe from the 1990s. A brief review of the data collected, and the methods and indicators that we
decided to use, is followed by a two-stage descriptive analysis of regional fertility disparities: a study
of dispersion using the conventional tools of the mean, the variance, the standard deviation and graphs,
and an exploratory analysis based on principal component analysis and hierarchical classification.
2.1. Data, methods and indicators
On completion of the collection and evaluation operations which took place in parallel with
preparations for the analysis work, we had two main sets of data for all the NUTS2 regions2. The first
contained the distributions of live births by year of age of the mother, from 12 to 49 3, for the years
from 1990 to 2000. The second contained the numbers of women of reproductive age, by year of age
(from 12 to 49) from 1 January 1990 to 1 January 2001. There were still some gaps in the tables,
chiefly for Belgium (1998), the Federal Republic of Germany (12 of the 16 Länder, in 1990 and 2000),
Greece (2000), Portugal (1990) and the United Kingdom (1990 and 1991; incomplete in 2000). Four
French regions (the overseas Départements) were not included as a result of substantial fertility
differences in comparison with metropolitan France. As a result of the non-availability of NUTS2
data, NUTS1 data had to be used for three areas: the Land of Rheinland-Pfalz, the Republic of Ireland
and Scotland. 201 regions and 11 years of observation were therefore taken into account.
Most of these data came from the Regio domain of New Cronos, which is therefore the main source
for this study. Various national (Germany, Belgium, Spain, France, Italy) and regional (German
Länder) statistical offices nevertheless provided us either with parallel statistical series enabling us to
evaluate external coherence, or data missing from the European regional database. Germany was an
important case in this latter respect: it was possible almost totally to fill a complete vacuum as regards
fertility. We also called upon some resource persons and research institutions, including the Centre
d'Estudis Demografics of the Universitat Autonoma of Barcelona, and drew on various official
statistical publications. These additional sources were used only when they filled a gap and when they
made it possible to correct an obvious error. When there was a “disagreement” between these sources
and our primary data, we always used the latter, while noting the differences observed in the data
collection and evaluation report.
While female population structures, at specific dates, raised few problems for the calculation of
fertility rates, the same was not true of the distributions of births which raised the question of the
definition of the age of the mother at birth. Depending on the definition used – in completed years (age
at last birthday) or in year differences (age reached during the year) – the mean populations and the
mean age used in the calculation of the fertility rates differed. A long identification procedure was
then undertaken at the end of which it was concluded that five Member States (Germany4, France,
Netherlands, Finland and Sweden) used the age reached during the year, while the ten others used
2
The collection and evaluation procedures used, and their findings, are reviewed in detail in the annexed report
devoted specifically to these matters.
3
Births occurring at later ages, when registered, were aggregated with those at the age of 49 with the result that
this was an open age group (“49 and over”). When there were many such births, they were redistributed in the
last ages used: 49 and the preceding ages, so as not to falsify the end of the distribution. We also found no
registrations of births prior to the age of 12.
4
There are still doubts about Germany as not all the Länder forwarded us their definition of the age of the
mother. Where necessary, an age in year difference was always used.
23
Study of low fertility in the regions of the European Union: places, timetable and causes
completed age. A transformation was therefore applied to the regional distributions of births of these
five Member States in order to obtain a distribution by completed age of the mother, from a
distribution of the age reached during the year. This transformation was carried out assuming a
uniform distribution of births at each age reached, except at the extreme ages of fertility. It was
preceded, for all the European regions, by an initial transformation of the distributions intended to
include, by proportional distribution, births for which the age of the mother was unknown and any
error. The term “error” is taken to mean the difference often recorded between the total births
registered and the sum of the births registered at each age5.
As regards numbers of women of reproductive age, the structures available made it possible to
calculate mean numbers of women per year of age, which were obtained as the arithmetic means of the
numbers of women of the same age on January 1 at each end of each year of observation.
At the end of these transformations and calculations, the data, in comparable form, made it possible to
calculate the following indicators: the age-specific fertility rates, the total fertility rate (TFR, defined
as the sum of the fertility rates by year of age of the mother, from 12 to 49), the mean age at childbirth
(ratio of the standard deviation to the mean age) and the profile of fertility by age of the mother (ratio
of the fertility rate for each age to the TFR). We decided to calculate these indicators by two different
time divisions. First, by calendar year, for each year available from 1990 to 2000; then for three subperiods of three years each. In order to reduce the fluctuation effects due to the small numbers that
births might represent in some regions at certain ages, the numbers of births were placed in three subperiods (1991-1993, 1994-1996 and 1997-1999) in order to calculate the mean annual distributions of
births focusing on 1992, 1995 and 1998 6. The mean numbers of women per year of age in these three
years were then used to calculate the rates. This division of the observation period, also used in the
study of regions with high life expectancy in Europe, had the advantage of mirroring the division used
by Eurostat to analyse mortality by causes of death. It also enabled a detailed analysis of fertility by
age of the mother. While giving priority to this “three-yearly” division in this analysis, we also use the
results by calendar year at various times, in particular to date the transition of the various European
regions into relative under-fertility.
Annexes 2a and 2b give an overview of the fertility rates available for each of the time divisions,
while Annex 3 summarises the main rates obtained in the “three-yearly” analysis.
2.2. Description of regional fertility disparities: intensity and timetable
Regional disparities in the intensity and timetable of fertility within the 201 regions studied are
summarised in the following table7. The following comments can be made:
- the total fertility rate on average shows a slight decrease (from 1.55 to 1.48 children per woman on
average), caused by the disappearance of the highest fertility rates, and its dispersion also
decreases;
- the mean age at childbirth increases, on average, by close on one year, increasing from 28.2 to
29.0, while its dispersion changes little;
- the standard deviation of age at childbirth increases slightly on average and in terms of dispersion
during the period studied.
5
The annex report contains details of these transformations.
In the case of the Belgian regions, the distribution of births in 1998 was not available. For the last sub-period,
the mean annual numbers were therefore drawn up from the two years available (1997 and 1999). However, the
mean numbers of the female population were those of 1998.
7
Annex 3 details all these results. Four regions were withdrawn from the analysis (FR9) and six regions were
merged to form three higher level units.
6
24
Study of low fertility in the regions of the European Union: places, timetable and causes
Table 1 – Regional fertility disparities in the European Union, 1991-1999
Indicator
TFR
Mean age
Standard
deviation of
age at
childbirth
Characteristic
Mean*
Standard deviation
Minimum
Maximum
Mean*
Standard deviation
Minimum
Maximum
Mean*
Standard deviation
Minimum
Maximum
1991-1993
1.55
0.302
0.83
2.18
28.2
1.122
25.0
30.5
5.14
0.345
4.22
6.20
1994-1996
1.47
0.280
0.82
2.07
28.6
1.073
26.1
31.2
5.15
0.371
4.31
6.16
1997-1999
1.48
0.258
0.81
2.04
29.0
1.087
26.8
31.9
5.21
0.400
4.30
6.38
* It should be borne in mind that that a non-weighted mean is used in this table, which may differ from the mean
obtained by monitoring the effects of numbers and structure.
At the end of the 1990s (1997-99), European regional fertility varied between 0.81 children per
woman (the minimum being recorded in the Principado de Asturias, in Spain) and 2.04 children per
woman (the maximum being recorded in the region of Pohjois-Suomi, in Finland). This dispersion can
be seen in Graph 1 (following page), which also details the trend in the TFR of the 201 regions
between 1991-93 and 1997-99. Only 56 of these regions (in Germany and the Netherlands in
particular) experienced no drop in the TFR during the period, including Hainaut and Denmark where
fertility remained practically unchanged. All the other regions (145) experienced a substantial relative
drop ranging from –0.1 to -30.2%. Sweden is a particular case – all the regions experienced a drop in
the TFR of more than 23% – as is Germany whose northern regions (formerly East Germany)
experienced an increase in the TFR ranging from 20.1 to 32.5%8. It is important to note that the trend
in the TFR was not constant in the period examined (Graph 2). Between 1991-93 and 1994-96, only
nine regions experienced an increase in the TFR, whereas 121 regions experienced an increase
between 1994-96 and 1997-99. The drop in the TFR during the first sub-period was in most cases
(119) followed by an increase in the second sub-period, giving the fertility trend a cyclical nature.
Only two regions (DE4 Brandenburg and NL11 Groningen) experienced an increase over the two subperiods. Seven other regions combined an increase during the first sub-period with a decrease during
the second. The remaining regions (73) experienced a drop in fertility throughout the decade. Again,
the Swedish regions stand out as a result of a significant drop in the TFR during each of the two subperiods, whereas the regions of East Germany combined a drop in the TFR during the first sub-period
with a very substantial increase during the second sub-period.
8
These comments on the fertility trend are obviously shaped by the situation at the beginning of the observation
period (imposed by the availability of data): particularly high in Sweden (new family policy measures) and
particularly low in East Germany (following reunification).
25
Study of low fertility in the regions of the European Union: places, timetable and causes
Graph 1 – Total fertility rate in the European Union,
1991-1993 and 1997-1999
2,25
Pohjois-Suomi
2,00
Belgium
Ceuta y Melila
Denmark
Germany
Açores
Greece
1,75
Spain
France
TFR 1997-99
Ireland
Italy
1,50
Luxembourg
Netherlands
Austria
1,25
Portugal
Finland
Sueden
United Kingdom
1,00
Bisector
EU15
Asturias
0,75
0,75
1,00
1,25
1,50
1,75
2,00
2,25
TFR 1991-93
Graph 2 – Trend (as %) in the total fertility rate in the European Union,
between 1991-93 and 1994-96, and between 1994-96 and 1997-99
35
Mecklenburg-Vorpommern
n = 119
n=2
30
25
Belgium
Denmark
20
Germany
Relative increase 1995-98
15
Greece
Spain
France
10
Ireland
Italy
5
Luxembourg
Netherlands
0
Austria
Portugal
Valle D'Aosta
-5
Finland
Sueden
-10
United Kingdom
-15
n = 73
n=7
-20
-25
-20
-15
-10
-5
Relative increase 1992-95
26
0
5
10
Study of low fertility in the regions of the European Union: places, timetable and causes
A variance analysis makes it possible to show that the differences between countries explain the
majority of the variability of fertility (TFR) in the European Union. This proportion increased from
66% in 1991-93, to 68% in 1994-96 and 71% in 1997-99. A breakdown finer than the NUTS2 level
would increase the proportion of internal variability within total variability. When “atypical” regions,
such as Ceuta y Melilla in Spain and the Açores in Portugal, are excluded from the analysis, the results
are substantially modified and the share of total variability that can be attributed to the “country”
component increases further: from 70% in 1991-93, to 72% in 1994-96 and 76% in 1997-999. Decroly
and Grasland (1992) had already pointed to the importance of a “state effect” in the variation of
fertility levels between regions (see Chapter 1, p. 9 of this report).
At the end of the 1990s, the mean age at childbirth in the European regions 10 varied from 26.8 (Dessau
and Halle, in Germany) to 31.9 (País Vasco, in Spain). This range, although moving upwards,
contracted slightly during the decade, since in 1991-93 it ranged between 25.0 (Dessau and Halle) and
30.5 (Republic of Ireland) (see Maps 4 to 6, in Chapter III of this report). Graph 3 shows that over this
ten-year period, the mean age at childbirth increased everywhere except in the Republic of Ireland
where it remained stable. In most regions, this increase was less than one year. A particularly notable
increase was recorded in the regions of East Germany (two years or more in all cases) but these
regions had a mean age at childbirth much lower than that of the other regions at the beginning of the
period. Most of the East German and Spanish regions, and some Greek regions, recorded an increase
in age at childbirth of between one and two years, but the Spanish regions already had a mean age at
childbirth that was among the highest at the beginning of the period, while the German and Greek
regions were among the regions with the earliest fertility.
Graph 3 – Trend in mean age at childbirth in the European Union, 1991-1999
32
Pais Vasco
Belgium
Denmark
30
Germany
Greece
Spain
Mean age 1997-99
France
Ireland
Italy
28
Luxembourg
Netherlands
Austria
Portugal
Finland
Halle
Sueden
United Kingdom
26
Bisector
+ 1 year
+ 2 years
24
24
26
28
30
32
Mean age 1991-93
9
The question of the particular nature of these two regions in their respective countries has to be examined.
From a statistical point of view, they can be considered as outliers since they are over 1.5 interquartile interval of
the third quartile of the national distribution of which they are part (Tukey, 1977).
10
As it was not possible to calculate the mean age at first birth, we used the mean age at childbirth which,
although “mechanically” influenced by the fertility level, increases while the TFR decreases.
27
Study of low fertility in the regions of the European Union: places, timetable and causes
It can also be seen that a proportion of the low fertility regions (Italy and Spain) and regions of higher
fertility (Netherlands) had mean ages at childbirth which were similar and high (over 30), and that a
low mean age at childbirth (under 28) was also to be found in “low” fertility regions (the East German
and Greek regions) and “high” fertility regions (the regions of the United Kingdom)11. An analysis of
the variance of the mean age at childbirth shows that the proportion of the dispersion due to
differences between countries increases from 60% in 1991-93 to 71% in 1997-99. As in the case of
intensity, the importance of the “state effect” and the increased comparability of situations within
countries can therefore be seen.
In 1997-99, the standard deviation of age at childbirth varied from 4.30 (West-Vlanderen, in Belgium)
to 6.38 (Inner London). On average, it increased slightly during the period from 5.14 to 5.21 and its
dispersion also increased (from 0.34 to 0.40). Graph 4 shows that most European regions experienced
an increase in the dispersion of ages at childbirth but that, with the exception of the United Kingdom
where there was a substantial change, there was little change in the other regions. The most
concentrated fertility timetables are at one extreme of the distribution: the Flemish regions of Belgium
and the regions of the North of the Netherlands, as well as several regions of northern Spain; and less
concentrated timetables are at the other end, chiefly the regions of the United Kingdom. The particular
position of Ceuta y Melilla in Spain and the Açores and Madeira in Portugal can again be seen.
Graph 4 – Trend in the standard deviation of age at childbirth in the European Union,
1991-1999
6,5
Inner London
Madère
Belgium
6,0
Ceuta-y-Melila
Açores
Denmark
Germany
Outer London
Greece
Standard deviation 1997-99
Spain
5,5
France
Ireland
Italy
Luxembourg
Netherlands
5,0
Austria
Portugal
Finland
Sueden
4,5
United Kingdom
Bisector
West-Vlanderen
4,0
4,0
4,5
5,0
5,5
6,0
6,5
Standard deviation 1991-93
At the end of this initial descriptive approach to the intensity and timetable of fertility in the European
regions in the 1990s, it would seem that the intensity and timetable of fertility are not closely linked,
although not all the combinations are recorded. The current very low fertility of the Spanish, Italian
and Greek regions may therefore go together with particularly high mean ages at childbirth as in Spain
and Italy, and also with the lowest mean ages at childbirth as in Greece. Inversely, although the
Netherlands and the United Kingdom are similar in terms of the intensity of their fertility, their mean
11
The terms “high” and “low” have to be understood in a relative sense, as all the fertility levels considered are
among the lowest ever recorded and in any case are all lower than the level which, from a longitudinal point of
view, would ensure the replacement of the generations!
28
Study of low fertility in the regions of the European Union: places, timetable and causes
ages at childbirth differ: particularly high in the Dutch regions and much lower in the regions of the
United Kingdom. In the first case, a mean age at childbirth close to and often higher than 30 goes
together with a very limited dispersion of ages at childbirth, whereas in the second case, a mean age at
childbirth of between 27 and 28 goes together with a much greater dispersion of ages at childbirth.
Before pinpointing the regions which currently have a fertility level below the “European mean”, the
use of principal component analysis and hierarchical classification should make it possible better to
pinpoint the structure of regional fertility differences in today’s European Union.
2.3 Structure of regional fertility differences
In order to find relations between the intensity of fertility and its timetable, a principal component
analysis was carried out with the TFR, the mean age at childbirth and the standard deviation of age at
childbirth for each of the three sub-periods. After rotation of the first factorial design, it would seem
that the TFRs and the standard deviations are positively correlated with the first factor, while the mean
ages are in a positive relationship with the second factor (see Graph 5). The first factorial design
(which accounts for 78.0% of the total variance) makes it possible to obtain a representation which can
be readily interpreted as it has only two dimensions.
Graph 5 – Projection of the three fertility indicators in the first factorial design, after rotation
1.0
act92
act98
act95
Component 2
.5
tfrt92
0.0
tfrt98
sdt92
-.5
tfrt95
sdt98
sdt95
-1.0
-1.0
-.5
0.0
.5
1.0
Component 1
A second principal component analysis was carried out with the regional fertility timetables (agespecific fertility rates divided by the TFR) for the three sub-periods taken together (603 regionsperiods in place of 201 regions) in order to identify the ages of reproduction characterising the
29
Study of low fertility in the regions of the European Union: places, timetable and causes
different timetable profiles. The projection of these “relative”12 fertility rates led to a surprising figure
(a heart: see title page) which opposed, as regards the first component, the rates of 18 to 25 to the rates
of 29 to 40 and highlighted, as regards the second component, the rates at 27 and 28. This structure
highlights the opposition between the earlier timetables (mean age lower than 27: regions of East
Germany and Greece) and the later timetables (mean age over 30: Spain and Italy).
Following these two principal component analyses of the main fertility indicators, various hierarchical
classifications (Ward’s method) were carried out on the principal components used for these analyses.
The classification carried out on the first three components of the first PCA (TFR, AC and SD for the
three sub-periods; 97% of the variance summarised by these components) led to eight clusters ranked
by increasing mean TFRs (Table 2).
Table 2 – Composition and characteristics of the seven clusters obtained by classification
in respect of three principal components summarising TFR, AC and SD for the three subperiods
Cluster
1
2
3
4
5
6
7
8
Parameter
N
TFR mean
AC mean
SD mean
N
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
1991-93
9
0.88
25.2
4.80
23
1.15
29.6
4.89
24
1.45
29.0
5.25
55
1.46
27.6
5.19
16
1.66
29.0
4.49
30
1.81
27.4
5.47
30
1.87
28.4
5.03
14
1.86
29.0
5.63
1994-96
9
0.86
26.4
4.78
23
1.07
30.3
4.83
24
1.34
29.5
5.22
55
1.39
28.0
5.20
16
1.60
29.2
4.50
30
1.72
27.8
5.60
30
1.73
28.7
5.01
14
1.77
29.3
5.64
1997-99
9
1.10
27.2
4.80
23
1.10
30.8
4.88
24
1.34
29.9
5.28
55
1.40
28.4
5.24
16
1.66
29.4
4.51
30
1.72
27.9
5.75
30
1.70
29.0
5.05
14
1.75
29.5
5.72
The first cluster contains nine regions of the East of Germany whose fertility became very low in the
middle of the decade, before rising during the last sub-period. Only this first cluster, comparable from
the point of view of its composition, experienced an increase in the mean TFR during the decade. In
all the other clusters, the TFR fell slightly (-0.05 to –0.17) or remained stable (cluster 5). It should also
12
These fertility rates are termed “relative” to indicate that the initial rates have been divided by the sum of the
age-specific fertility rates.
30
Study of low fertility in the regions of the European Union: places, timetable and causes
be noted that the reduction was in most cases higher between 1991-93 and 1994-96 than between
1994-96 and 1997-99.
The mean age in cluster 1, while remaining the lowest in Europe, increased by two years (from 25.2 to
27.2) over the decade. In the six other clusters, the increase in the mean age at childbirth was more
limited (between 0.4 and 1.2 years).
The standard deviation of age at childbirth changed very little, except in cluster 6 where it increased
by 0.3 years.
The second cluster contains the regions of very low fertility solely of Spain and Italy whose mean age
at childbirth was among the highest and increased by one year during the decade. The third cluster is
the most heterogeneous both from the point of view of fertility and the point of view of geography as
it contains regions from seven different countries. The fourth cluster, with the largest number of
regions (55), was much more comparable geographically: 25 German regions, all the Greek regions
except Attiki (GR3), all the Austrian regions, four French regions with low relative fertility, an Italian
region (Sicilia) and all the Portuguese regions except the south (Algarve, Açores and Madeira). The
fifth cluster included seven Belgian regions and seven Dutch regions together with two French
regions. The sixth cluster contained 28 regions of the United Kingdom, together with two Portuguese
regions (Algarve, Açores). The seventh cluster included 14 French regions and the whole of Sweden
except Stockholm, together with three Belgian regions, Denmark and Luxembourg, a Dutch region
and three Finnish regions. The eighth and final cluster included the capital regions (Bruxelles, Île de
France, Stockholm and London) whose fertility remains high, together with a Spanish region (Ceuta y
Melilla), the Republic of Ireland, a Portuguese region (Madeira), two Finnish regions and four regions
of the United Kingdom, including Northern Ireland.
A second classification was carried out on the three standardised fertility indicators (TFR, AC and
SD), this time on the 603 regions-periods. The use of standardisation was dictated by the wish to give
each of the fertility indicators an equivalent weight in the calculation of the distances between regions.
The results of this classification bear out those of the preceding classification, thus showing the robust
nature of the structure obtained. Eight clusters made it possible to take account of 77% of the total
variability between the regions-periods (Table 3).
The nine regions of East Germany were again isolated in cluster 1 throughout the three periods, with a
very low fertility (mean TFR of less than 1 child/women) and a mean age at childbirth which was also
low (26.3). Cluster 2 showed a fertility which was not much higher (1.08) but a much higher mean age
at childbirth (30.3). The advance of low fertility to the south of Spain and Italy stands out in this
cluster which increased from 21 to 26 regions, all Spanish and Italian. The third cluster again
contained low fertility regions (mean TFR of 1.40) with a much lower mean age at childbirth than
cluster 2: 28.1. It contained German and Greek regions (the whole of Greece except for Attiki), and
Spanish, Italian and Austrian regions (the whole of Austria except for Vorarlberg), together with a
Belgian region and four or five Portuguese regions (mainland Portugal) depending on the period. The
fourth cluster, increasing from nine regions in 1991-93 to 31 regions in 1997-1999, included regions
of intermediate fertility (mean TFR of 1.45). Its numbers increased substantially as a result of the
reduction in fertility in some regions and the increase in the mean age at childbirth in others.
Composed initially of four Dutch regions, two Italian regions, a German region, a Spanish region and
French region, it was joined over time by German (3), Greek (1), Spanish (4), French (4) and Italian
(2) regions and by the whole of Sweden (in the third sub-period). The fifth cluster included,
throughout the three periods, the east of the Netherlands and the Flemish provinces of Belgium
together with Brabant wallon in the last two sub-periods. It showed an intermediate mean fertility
(1.59 children/woman) and a late (29.4) but very close timetable (standard deviation of 4.43 years; the
lowest). The sixth cluster was formed chiefly of United Kingdom regions (including Inner and Outer
London and Northern Ireland). Brussels, Ceuta y Melilla, the Republic of Ireland, Madeira and
Pohjois-Suomi were also in this cluster. Its mean fertility was relatively high (1.75 children/woman),
but the mean age at childbirth was intermediate (28.8). This cluster showed the highest dispersion of
31
Study of low fertility in the regions of the European Union: places, timetable and causes
age at childbirth, both because of the inclusion of two sub-populations with different fertility
timetables (the capital regions) and because of a relatively high fertility (greater than or equal to two
children per woman in Pohjois-Suomi). The seventh and eighth clusters consisted of regions of
relatively high fertility (1.77 and 1.78 children/woman respectively) but differing in terms of timetable
indicators (different mean age at childbirth and standard deviation of age at childbirth). Over the
decade, the Swedish regions moved from the seventh to the fourth cluster and the United Kingdom
regions to the sixth cluster. At the end of the decade, it still contained 30 regions: the Walloon regions
of Belgium and Antwerpen, Denmark, the Grand-Duchy of Luxembourg, the majority of the French
regions, two Dutch coastal regions and Finland (apart from Pohjois-Suomi). The eighth and final
cluster was composed chiefly of United Kingdom regions (24 in the first sub-period and 17 in the
third), together, in an ongoing way, with the Açores region. With a relatively high fertility (1.78), it
had a relatively low mean age at childbirth (27.4, i.e. 1.4 years less than the seventh cluster) and a
relatively high dispersion of age at childbirth (close to that of the sixth cluster).
Table 3 – Composition and characteristics of the eight clusters obtained by classification in
respect of the standardised fertility rates of the 603 regions-periods
Cluster
1
2
3
4
5
6
7
8
Parameter
N
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
n
TFR mean
AC mean
SD mean
1991-93
9
0.88
25.2
4.80
21
1.13
29.7
4.88
66
1.44
27.8
5.23
9
1.47
29.5
5.07
10
1.60
29.2
4.39
10
1.86
28.8
5.76
49
1.82
28.5
5.03
27
1.81
27.4
5.49
1994-96
9
0.86
26.4
4.78
25
1.06
30.3
4.87
62
1.38
28.1
5.24
15
1.41
29.7
5.08
11
1.56
29.4
4.44
24
1.74
28.7
5.62
39
1.72
28.8
4.99
16
1.75
27.4
5.67
1997-99
9
1.10
27.2
4.80
26
1.08
30.8
4.94
53
1.38
28.4
5.30
31
1.46
29.7
5.11
11
1.62
29.6
4.45
23
1.72
28.9
5.78
30
1.75
29.1
5.01
18
1.75
27.5
5.77
1991-99
27
0.95
26.3
4.79
72
1.09
30.3
4.90
181
1.40
28.09
5.25
55
1.45
29.7
5.09
32
1.59
29.4
4.43
57
1.75
28.8
5.71
118
1.77
28.8
5.01
61
1.78
27.4
5.62
At the end of these exploratory analyses, it can be seen that that regional fertility differences are
structured in four separate fertility levels (relatively low, low intermediate, high intermediate and
relatively high) each associated with two different timetables (relatively early and relatively late). The
first two associations are the most different since relatively low fertility levels (lower than or equal to
1.10 children/woman) are associated with the lowest (26.3 in the east of Germany) and highest (30.3
in Spain and Italy) mean ages. A low intermediate fertility level (1.4 children/women) is associated
with a relatively early timetable (28.1). At high intermediate fertility levels (between 1.5 and 1.6
32
Study of low fertility in the regions of the European Union: places, timetable and causes
children per woman), the timetables differ in particular as a result of a difference in dispersion of ages
at childbirth of 0.7 years: the highest mean age (29.4) corresponds to the tightest timetable (4.4 years
for Flanders and the East of the Netherlands which form most of the fifth cluster). The highest mean
fertility levels (above 1.7) again correspond to two fairly dissimilar timetables: 1.4 years’ difference in
the mean age (which separates the sixth and seventh clusters from the eighth cluster) and 0.7 years for
the standard deviation which separates the seventh cluster from the other two.
This provides the descriptive information needed to tackle the issue of the location of fertility rates
lower than the “European mean”.
33
Study of low fertility in the regions of the European Union: places, timetable and causes
III. LOW FERTILITY REGIONS IN THE EUROPEAN UNION
3.1. In which regions is fertility currently lower than the European mean?
The reply to this first question can now be provided on the basis of the total fertility rates (TFRs)
calculated for three-year periods, for the 201 European regions for which data are available. The
ranking by increasing order of TFR preceded the calculation of the “European mean”. To pinpoint this
mean, it was decided to take the TFR “recorded” for all 15 of the Member States of the EU (in its
current form) and for the median year of the last sub-period (1998): 1.45 children per woman
(Eurostat, 2002b). This choice was preferred to a simple arithmetical mean (as used in Table 1) or a
mean, weighted by the female population numbers aged from 12 to 49, of the 201 regional TFRs
calculated here.
On the basis of this European fertility in 1998, it would seem that:
1.
in the period 1991-93, 65 regions had a low fertility understood as less than 1.45 children per
woman (Map 1; Table 4). These regions included:
- all the Spanish regions, apart from six: ES42, ES43, ES53, ES61, ES62 and ES63 (the south of the
country);
- all the Italian regions, apart from four: IT8, IT91, IT93 and ITA (in the south of Italy);
- three regions of eastern Austria: AT11, AT13 and AT22;
- all the German regions, apart from 14 (DE11, DE14, DE22, DE23, DE26, DE27, DE93, DE94,
DEA3, DEA4, DEA5, DEB1, DEB2 and DEB3);
- six Greek regions: GR12, GR13, GR21, GR24, GR25 and GR3;
- a French region: Limousin (FR63);
- a Dutch region: Groningen (NL11).
2.
in the period 1994-96, 90 regions had a fertility lower than 1.45 children per woman (Map 2;
Table 4). These were:
- all the Spanish regions, apart from one: ES63 (Ceuta y Melilla);
- all the Italian regions, apart from two: ITA and IT8 (in the south of Italy);
- all the Greek regions, apart from four: GR11, GR41, GR42 and GR43;
- four Austrian regions: AT11, AT13, AT21 and AT22;
- all the German regions, apart from five (DE11, DE27, DE93, DE94 and DEA4);
- all the Portuguese regions, apart from three: PT15, PT2, and PT3;
- two French regions: Limousin and Auvergne (FR63 and FR72);
- two Dutch regions: Groningen and Limburg (NL11 and NL42);
- a Belgian region: Limburg (BE22).
3.
in the period 1997-99, 84 regions had a fertility lower than 1.45 children/woman (Map 3;
Table 4). These were:
- all the Spanish regions, apart from one: ES63 (Ceuta y Melilla);
- all the Italian regions, apart from one: IT8 (Campania);
- all the Greek regions, apart from three: GR11, GR41 and GR42;
- all the Austrian regions, apart from two: AT31 and AT34 (Oberösterreich and Vorarlberg);
- all the German regions, apart from nine (DE11, DE14, DE23, DE27, DE93, DE94, DEA3, DEA4
and DEA5);
- two large regions in the centre of Portugal : PT12 and PT14 (Centro and Alentejo).
35
Study of low fertility in the regions of the European Union: places, timetable and causes
Map 1 – The fertility of the European regions, 1991-1993
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
36
Study of low fertility in the regions of the European Union: places, timetable and causes
Map 2 – The fertility of the European regions, 1994-1996
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
37
Study of low fertility in the regions of the European Union: places, timetable and causes
Map 3 – The fertility of the European regions, 1997-1999
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
38
Study of low fertility in the regions of the European Union: places, timetable and causes
In conclusion, it would seem that 84 regions (over one third of the European regions examined)
showed low fertility at the end of the 1990s. These regions were all in six countries, including four
countries of southern Europe: Spain, Italy, Greece and Portugal, together with Germany and Austria.
They accounted for most of these six countries, except in the case of Portugal. In each of these
countries, the regions that were exceptions were either atypical (Ceuta y Melilla or Madeira and the
Açores), or close to 1.45 children/woman (the Greek islands in the Aegean sea, the German regions
and Campania in Italy).
63 of these 84 regions were already below the 1998 European fertility rate at the beginning of the
period. Two of the 65 regions (FR63, Limousin and NL11, Groningen) which had a fertility level
below 1.45 at the beginning of the period had risen above this level by the end of the period. The
geography of relatively low fertility therefore changed little in the 1990s. It had moved towards the
south in Spain (ES42, ES43, ES53, ES61 and ES62) and in Italy (IT91, IT93 and ITA), towards the
west in Austria (AT12, AT21, AT32 and AT33) and towards the centre of Portugal (PT12 and PT14);
it had continued in Germany and Greece, but had disappeared from a few regions of western Europe
where it had appeared in 1994-96 (Auvergne, Vlaams Brabant, Belgian and Dutch Limburg).
It is also interesting to isolate regions of very low fertility among these relatively low fertility levels.
The threshold of 1.30 children per woman has been proposed by Kohler, Billari and Ortega (2002, p.
641). Although arbitrary, this level is used by the authors because it would entail a mean annual rate of
increase of –1.5%13 in a stable population whose mean age at childbirth would be 30 and assuming a
very low female mortality prior to the age of 50. On the basis of this criterion, 49 European regions
would seem to show very low fertility in 1997-99. They are in five countries: Italy (16 regions), Spain
(14 regions), Germany (11 regions), Greece (5 regions) and Austria (3 regions). Among these 49
regions, 14 were not below the threshold of 1.3 children per woman at the beginning of the period: the
three Austrian regions, four of the five Greek regions, four Spanish regions (the centre and an island)
and three Italian regions (in the south and centre). In the same way as for relatively low fertility, most
very low fertility had already been recorded at the beginning of the period. It was not therefore, for the
most part, during the 1990s that these relatively compact sets of regions of low (1.45) and very low
(1.3) fertility appeared. In order to answer our second question (since when?), it is necessary to go
back in time: something which is not possible using the Regio domain of the New Cronos database
and its finely disaggregated data (births by year of age of the mother)!
Table 4 – List of regions showing fertility lower than the “European mean”,
1997-99
Region
1
2
3
4
5
6
7
8
9
10
11
12
13
14
13
ES12 Principado de Asturias
ES11 Galicia
ES41 Castilla y León
ES13 Cantabria
IT13 Liguria
ES21 Pais Vasco
GR24 Sterea Ellada
ITB Sardegna
IT51 Toscana
DEE1 Dessau
GR21 Ipeiros
DED3 Leipzig
IT4 Emilia-Romagna
IT33 Friuli-Venezia Giulia
TFR91-93
TFR94-96
TFR97-99
0.93
1.11
1.09
1.07
1.01
0.96
1.23
1.24
1.05
0.87
1.31
0.86
1.02
1.06
0.83
0.95
0.96
0.94
0.92
0.93
1.05
1.07
0.99
0.85
1.13
0.82
0.99
0.99
0.81
0.91
0.93
0.96
0.97
0.98
1.00
1.02
1.04
1.05
1.06
1.06
1.07
1.07
Leading to a halving of population numbers – and the cohort of births – every 45 years.
39
Study of low fertility in the regions of the European Union: places, timetable and causes
Table 4 – List of regions showing fertility lower than the “European mean”,
1997-99 (continued)
Region
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
ES24 Aragón
DEE2 Halle
IT11 Piemonte
IT71 Abruzzo
GR25 Peloponnisos
DEG Thüringen
DE4 Brandenburg
IT12 Valle d'Aosta
IT52 Umbria
IT92 Basilicata
DEE3 Magdeburg
ES23 La Rioja
DE8 Mecklenburg-Vorpommern
IT53 Marche
DED1 Chemnitz or DED Sachsen
DED2 Dresden
IT2 Lombardia
IT32 Veneto
IT6 Lazio
ES52 Comunidad Valenciana
DE3 Berlin
IT72 Molise
ES22 Comunidad Foral de Navarra
GR23 Dytiki Ellada
AT11 Burgenland
ES3 Comunidad de Madrid
ES51 Cataluña
ES43 Extremadura
AT13 Wien
DE6 Hamburg
IT93 Calabria
ES7 Canarias (ES)
ES42 Castilla-la Mancha
AT22 Steiermark
GR12 Kentriki Makedonia
DEC Saarland
ES61 Andalucia
GR22 Ionia Nisia
AT21 Kärnten
GR3 Attiki
GR14 Thessalia
IT91 Puglia
DE72 Gießen
PT14 Alentejo
DE91 Braunschweig
GR13 Dytiki Makedonia
DE5 Bremen
DE71 Darmstadt
DE12 Karlsruhe
AT12 Niederösterreich
DE24 Oberfranken
DE92 Hannover
AT32 Salzburg
AT33 Tirol
PT12 Centro (PT)
DE21 Oberbayern
DE25 Mittelfranken
DEA1 Düsseldorf
ES53 Illes Balears
IT31 Trentino-Alto Adige
DE26 Unterfranken
DE13 Freiburg
ITA Sicilia
ES62 Murcia
DEF Schleswig-Holstein
DEA2 Köln
DE73 Kassel
DEB Rheinland-Pfalz
GR43 Kriti
DE22 Niederbayern
40
TFR91-93
TFR4-96
TFR7-99
1.15
0.89
1.06
1.29
1.33
0.87
0.83
1.06
1.19
1.31
0.92
1.12
0.87
1.19
0.91
0.88
1.12
1.10
1.25
1.32
1.11
1.35
1.17
1.45
1.36
1.24
1.23
1.54
1.38
1.24
1.49
1.39
1.52
1.44
1.31
1.32
1.57
1.48
1.48
1.32
1.48
1.54
1.33
1.47
1.34
1.44
1.31
1.30
1.36
1.55
1.43
1.35
1.55
1.58
1.47
1.38
1.41
1.42
1.50
1.39
1.47
1.42
1.68
1.63
1.41
1.41
1.42
1.45
1.59
1.47
1.09
0.85
1.02
1.14
1.16
0.86
0.85
1.14
1.09
1.18
0.88
1.08
0.85
1.09
0.89
0.87
1.09
1.07
1.14
1.19
1.10
1.21
1.14
1.31
1.29
1.16
1.17
1.33
1.29
1.20
1.29
1.25
1.35
1.35
1.31
1.27
1.37
1.44
1.43
1.31
1.43
1.35
1.32
1.28
1.29
1.40
1.30
1.30
1.31
1.49
1.33
1.32
1.47
1.49
1.37
1.34
1.35
1.36
1.35
1.35
1.38
1.37
1.46
1.43
1.36
1.37
1.38
1.38
1.51
1.39
1.08
1.09
1.09
1.09
1.09
1.10
1.11
1.11
1.11
1.12
1.12
1.12
1.13
1.13
1.13
1.13
1.14
1.15
1.16
1.18
1.19
1.19
1.19
1.19
1.20
1.20
1.22
1.22
1.22
1.23
1.24
1.25
1.27
1.27
1.28
1.30
1.31
1.31
1.32
1.32
1.33
1.33
1.36
1.37
1.37
1.37
1.37
1.37
1.37
1.38
1.39
1.39
1.40
1.40
1.40
1.41
1.41
1.41
1.41
1.41
1.41
1.42
1.42
1.42
1.42
1.42
1.43
1.44
1.44
1.44
Study of low fertility in the regions of the European Union: places, timetable and causes
As a supplement to this answer to the first question, maps 4 to 6, which show the mean age at
childbirth for the three sub-periods, show both the clear increase in this mean age during the 1990s and
its lack of association with the TFR which continues to decline.
Map 4 – Mean age at childbirth in the European regions, 1991-1993
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
41
Study of low fertility in the regions of the European Union: places, timetable and causes
Map 5 – Mean age at childbirth in the European regions, 1994-1996
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
42
Study of low fertility in the regions of the European Union: places, timetable and causes
Map 6 – Mean age at childbirth in the European regions, 1997-1999
Additional statistical data: for the Federal Republic of Germany, statistical institutes of the Länder and
Statistiches Bundesamt; for Spain, 1999, Instituto Nacional de Estadística; for Italy, 1999, Istituto Nazionale di
Statistica.
43
Study of low fertility in the regions of the European Union: places, timetable and causes
3.2. Since when has fertility in these regions been lower than the European
mean?
Since when have the regions currently showing “relative under-fertility” (fertility below 1998
European fertility) been in this situation? It is not really possible to answer this question using the
statistics in Eurostat’s regional database. This database goes back only to 1990 and it was clear that,
with the exception of some regions of southern Europe (Thessalia, Ionia Nisia, Dytiki Ellada, Kriti,
Extremadura, Castilla-La Mancha, Andalucia, Murcia, Illes Balears, Puglia, Calabria, Sicilia, Centro
(PT), Alentejo), Germany (Unterfranken, Rheinland-Pfalz and Niederbayern) and Austria (Kärnten,
Salzburg, Tirol, Niederösterreich), the geography of low fertility as it currently stands was already in
place at the beginning of the period. For five of the six countries identified (Germany, Greece, Spain,
Italy, Austria 14), a specific analysis of regional data had to be carried out in order to go back further in
time. At present, a partial answer can be provided on the basis of four particular sets of available
statistics. These are, on the one hand, national data for the 15 current Member States of the EU, which
make it possible, in the case of the TFR, to go back to the beginning of the 1960s and, on the other
hand, data for three countries for which detailed regional studies have been conducted in the past:
Spain, Italy and Austria.
3.2.1 National fertility
The 84 European regions which are now below the mean fertility of the Union (as it stands at present)
are all in six countries and account for most of the territory of these countries (except in Portugal). The
trend in the TFR since 1960 (Graph 6) can be retraced for these countries. These are Austria, Spain,
Greece, Italy, Portugal and Germany which is shown here as the two separate political entities that
made it up prior to 1991: the Federal and Democratic Republics.
Graph 6 – Trend in the TFR in the six countries showing fertility lower than the “European
mean”, 1960-2000
3,50
3,00
2,50
Germany (Fed. Rep.)
Germany (Dem. Rep.)
2,00
Greece
TFR
Spain
Italy
Austria
1,50
Portugal
LF
VLF
1,00
0,50
0,00
1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
Years
14
Portugal is an exception as relatively low fertility levels appeared there during the 1990s.
44
Study of low fertility in the regions of the European Union: places, timetable and causes
Graph 6 shows that only Portugal remains, as a country, above the threshold of 1.45 children per
woman at the end of the period. Portuguese fertility fell below 1998 European fertility only from 1994
to 1996. In contrast, the Federal Republic of Germany (in its territory before 1991) fell below this
“mean” from 1977 onwards. The former German Democratic Republic shows a trend very similar to
that of the former Federal Republic, with two major exceptions, however: a jump in fertility from 1977
in comparison with the downward trend which started there, as in many other European countries, in
1964 and 1965 and then a sharp decline from 1991. From 1977 to 1990, East German fertility was
higher than West German fertility, before collapsing after reunification, falling largely below 1.45
children per woman (and even 1 child per woman) from 1991. In recent years, it seems to be rejoining
the other countries with low relative fertility.
Italy and Austria fell below the “European mean” in the mid-1980s: in 1985 in Italy (Section 3.2.3)
and in 1987 in Austria (Section 3.2.4). Spain and Greece fell below this mean at the end of the 1980s
(1989).
For the three countries for which no regional data are available prior to 1990 (Germany, Greece and
Portugal), the calculation of annual fertility levels nevertheless makes it possible to pinpoint those
regions which were already showing relative under-fertility (below 1.45 children per woman) in 1990.
In the Federal Republic of Germany, 25 out of 40 regions were already showing relative under-fertility
in 1991 (1990 is not available for all the Länder). Five of the 15 remaining regions fell below 1.45
children per woman over the period: Oberfranken in 1992, the three regions of the Land of RheinlandPfalz in 1993 and Unterfranken in 1994. Two regions never fell below this threshold (Weser-Ems and
Detmold), while the eight remaining regions fell below it only for a limited period (between one and
four years). In the case of Greece, five regions were already below the limit of 1.45 children per
woman in 1990 (Kentriki Makedonia, Ipeiros, Sterea Ellada, Peloponnisos and Attiki), while five
others fell below this threshold during the period: Dytiki Makedonia in 1992, Thessalia and Dytiki
Ellada in 1993, Ionia Nisia in 1994 and Kriti in 1998. Although Notio Aigaio never showed relative
under-fertility, Voreio Aigaio did in 1999 only and Anatoliki Makedonia and Thraki fell below in
1991, 1996 and 1999. In Portugal, no region was below 1.45 children per woman in 1991 (1990 not
available), with the result that the entire history of Portugal’s relative under-fertility can be retraced in
the 1990s. Although two regions (Centro and Alentejo) fell below 1.45 children per woman in 1993,
two further regions (Algarve and Açores) never fell below this threshold, while the three remaining
regions (Norte, Lisboa and Madeira) showed relative under-fertility from 1994 or 1995 to 1996.
Regional data are available over a longer period for three countries: Spain, Italy and Austria.
3.2.2. Spain
Data from the Instituto nacional de estadística supplement the data from New Cronos, making it
possible to reconstruct the fertility of Spanish regions from 1975 (Graph 7).
There are three different situations: in the first situation, a single region (Ceuta y Melilla) always
shows relatively high fertility; in the second situation, six regions fell into relative under-fertility
during the 1990s (Canarias in 1991, Castilla-La Mancha and Murcia in 1994; not shown on the graph,
Illes Balears in 1993, Extremadura and Andalucía in 1994); the third situation covers most of the
regions which fell below the threshold of 1.45 children per woman during the 1980s (Principado de
Asturias in 1984, La Rioja in 1986, Comunidad de Madrid in 1987 and, not shown on the graph, País
Vasco in 1984, Comunidad Foral de Navarra and Aragón in 1985, Galicia, Cantabria, Castilla y León
and Cataluña in 1986, Comunidad Valenciana in 1989). Murcia and the Illes Balears nevertheless rose
back above 1.45 children per woman in 2000.
45
Study of low fertility in the regions of the European Union: places, timetable and causes
Graph 7 – Trend in the TFR of some Spanish regions in comparison with the EU, 1975-2000
3,50
3,00
ES12
2,50
ES23
ES3
ES42
TFR
ES62
2,00
ES63
ES7
ES
LF
1,50
VLF
1,00
0,50
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Years
Sources: Instituto nacional de estadística; our calculations.
3.2.3 Italy
The respective works of J.L. Rallu, V. Terra-Abrami and M. Sorvillo, A. Golini and M. Livi-Bacci and
S. Salvini, and data from the Italian national statistical office make it possible to reconstruct the trend
in Italian regional fertility from 1959 (Graph 8).
Graph 8 – Trend in the TFR of some Italian regions in comparison with the EU, 1959-2000
3,75
3,00
IT11
IT13
IT32
IT8
TFR
2,25
IT93
ITB
IT
lF
VLF
1,50
0,75
1955
1960
1965
1970
1975
1980
1985
1990
Years
Sources: Rallu J.L., 1983; Terra-Abrami V. & Sorvillo M., 1993; our calculations.
46
1995
2000
Study of low fertility in the regions of the European Union: places, timetable and causes
Three situations can be seen: in the first situation, only one region remains above the “European
mean” throughout the observation period (Campania); the second situation includes regions which fell
below this mean before 1985 and which therefore precede Italy as a whole (Piemonte, Liguria and
Veneto, together Valle D’Aosta, Lombardia, Friuli-Venezia-Giulia, Emilia-Romagna, Toscana,
Umbria, Marche and Lazio, not shown in the graph); and lastly, the third situation covers regions
which fell below 1998 European fertility at a later date (Calabria and Sardegna, as well as Abruzzo,
Molise, Trentino-Alto-Adige, Puglia, Basilicata and Sicilia).
3.2.4. Austria
The works of F. Prioux on regional aspects of the family and illegitimacy in Austria make it possible
to go back to 1970 (Graph 9).
Graph 9 – Trend in the TFR of some Austrian regions in comparison with the EU,
1970-1998
2,75
2,50
2,25
AT11
2,00
AT13
AT21
TFR
1,75
AT33
AT34
AT
1,50
LF
VLF
1,25
1,00
0,75
1970
1975
1980
1985
1990
1995
2000
Years
Sources: Prioux F., 1993a; our calculations.
Three situations can be seen: in the first situation, two regions (AT34 Vorarlberg and, not shown in the
graph, Oberösterreich) remain above the “European mean” for the whole of the observation period; in
the second situation, a single region (AT13 Wien) falls below this mean at a very early date (in 1975)
and lastly, in the third situation, the six other regions fall below 1.45 children per woman at a later
date: before 1990 in Burgenland (AT11) and Steiermark (not included in the graph); in 1997, for Tirol
(AT33) and Salzburg (not included in the graph); and cyclically for AT21 Kärnten and
Niederösterreich.
47
Study of low fertility in the regions of the European Union: places, timetable and causes
3.3. How can these low and very low fertility levels be explained?
The answers to the two previous questions made it possible to locate low (TFR < 1.45 children per
woman) and very low (TFR < 1.30 children per woman) fertility exclusively in six of the 15 EU
Member States: Germany, Greece, Spain, Italy, Austria and Portugal. They also made it possible to
date the transition below the threshold of 1.45 children per woman, from the regional point of view in
most cases, and from a national point of view for Germany, Greece and Portugal. Five of Portugal’s
seven regions reached low fertility during the 1990s (between 1993 and 1995). As a country, Greece
fell below 1.45 children per woman from 1989, but five of its 13 regions had already reached low
fertility in 1990, five more fell below the threshold over the period and the last three in practice never
fell below the threshold. In Germany, a line needs to be drawn between the former Federal Republic
which passed into low fertility in 1997 and the former Democratic Republic which passed this
threshold in 1991; in the latter, all the East German regions declined sharply into very low fertility in
1991! Moreover, five West German regions reached low fertility during the 1990s (between 1992 and
1994).
For the remaining countries – Spain, Italy and Austria – which fell below 1.45 children per woman in
1989, 1985 and 1986 respectively, the data available make it possible to reconstruct the trend in
regional fertility from at least 1975. In Spain, 11 of the 18 regions recorded low fertility in the 1980s
(between 1983 and 1989) and six other regions did so in the first half of the 1990s (between 1991 and
1994). In Italy, 11 regions had recorded low fertility before 1985, while the remaining eight
(Campania being the only exception) did so after that date: four before 1990 and four during the
1990s. In Austria, Wien had the particular feature of experiencing low fertility from 1975. Four
regions joined it between 1985 and 1987, while three others did so at the end of the 1990s (between
1997 and 1999).
This history of relative under-fertility from a regional point of view goes with intra-national
differences. It should nevertheless be borne in mind that the descriptive analysis of regional fertility
differences in the period 1991-1999 had already led to a major finding which bore out one of the main
conclusions of the review of the literature: differences between countries explain most of the
variability of fertility between the 201 European regions examined here. In 1997-99, 71% of this
variability was due to differences between countries (66% in 1991-93). This finding justifies the use of
national characteristics in any explanatory approach, bearing in mind that the data available at regional
level are few and far between, that would make it possible to measure the variables potentially
offering an explanation.
Before looking for these explanatory variables, it is in any case necessary, in accordance with the most
robust theories and models of fertility (de Bruijn, 2002), to break down total fertility as measured by
the TFR or its longitudinal equivalent, completed fertility. Two breakdowns are necessary here: by
birth order and by births within and outside marriage. The importance of these breakdowns prior to
any attempt to explain regional fertility differences has been highlighted in the review of the literature
(Chapter I, p. 9). A few studies (Terra Abrami and Sorvillo, 1993; Prioux, 1993a) have been carried
out on specific national contexts (Italy and Austria), but regional data are not available for such
breakdowns for the 1990s. Moreover, even nationally, a breakdown by birth order is not possible
across the board, as biological order is not used everywhere (as in Belgium up to 1998).
In addition to these breakdowns, account also needs to be taken of the regionally differentiated effects
of some key causes which further reduce post-transitional fertility such as contraception, abortion, and
post-partum and secondary sterility. Again, the data needed for such a study for all the regions of the
European Union still needs to be collected.
It is only then the remoter causes of regional fertility differences can be brought into play. What are
these explanatory factors? The review of the literature showed that many factors had been put forward
by authors tackling the issue of regional fertility differences, but few studies had actually provided
48
Study of low fertility in the regions of the European Union: places, timetable and causes
conclusive proof of their effects. These potentially explanatory factors can be grouped under two
headings depending on the extent to which they are involved in the explanatory approach. Strictly
individual factors are few in number: level of education, labour market participation, religious belief
and nationality. These are difficult to measure, which explains why studies at an individual level are so
rare and why studies at an aggregate level are more common. At this level, individual variables are
taken into account in the form of structural factors: the proportion of young university students, the
divorce rate, the women’s participation rate, the proportion of votes for the Christian Democrats, the
proportion of foreigners. The second group contains contextual regional factors such as childcare
facilities for infants and school-age children, opportunities for women in the labour market, the
availability and the cost of resources such as housing, the quality of the environment and the
predominant perception of the family. Some of these latter factors may combine to provide a cultural
context which is more or less favourable to the reconciliation of working and family life.
Both individually and at a contextual level, these explanatory variables are not well documented at
regional level. However, in view of the importance of the state effect in the regional variability of
fertility, national factors, particularly social and family policies (measures on maternity leave, parental
leave, family allowances, etc.) take on a degree of importance in explaining fertility differences.
However, all the gaps observed in the availability of regional measurements, in terms of both
breakdowns or key and remoter explanatory factors, make any attempt to provide a detailed
explanation difficult and require major prior work to collect data.
49
Study of low fertility in the regions of the European Union: places, timetable and causes
Conclusions
This initial descriptive analysis of fertility shows that during the 1990s, fertility declined further in the
European regions. Not by much: 0.07 children per woman on average from 1991-93 to 1997-99
(Table 1); and not constantly: in most cases a decrease in the TFR during the first sub-period was
followed by a slight increase in the second sub-period (Graph 2). However, this decrease led to the
disappearance, with one exception (Pohjois-Suomi, in Finland), of all the TFRs of 2 or more children
per woman. In 1997-99, seven regions had a fertility of less than 1 child per woman: Sterea Ellada in
Greece, Galicia, Asturias, Cantabria, País Vasco and Castilla y León in Spain and Liguria in Italy.
The case of Germany is interesting as a result of the fertility trend in the East German regions: in these
nine regions (Brandenburg, Mecklenburg-Vorpommern, Chemnitz, Dresden, Leipzig, Dessau, Halle,
Magdeburg and Thüringen), fertility was already below 1 child per woman in 1991-93 and fell further
to below 0.9 children per woman in 1994-96. It then rose to above 1 child per woman in 1997-99.
There was also a remarkable trend in all the Swedish regions which were close to or above two
children per woman in 1991-93 and were all around 1.5 children per woman in 1997-99. Despite this
decline, Sweden continues to mirror the European “mean”. All or almost all of the regions of three
countries are below European fertility: Spain, Italy and Greece. In the case of Spain, Ceuta y Melilla is
a notable exception as a result of the extent to which the fertility (2.05 children per woman) of this
small region (136 000 people in an area of 31 km2) differs from that of the other Spanish regions. The
region of the Açores is also an exception, although to a lesser extent, among the Portuguese regions.
The advance of low and very low fertility in Europe can be seen from Maps to 1 to 3: low and very
low fertility are gaining ground in the south of Spain and the south of Italy, and throughout Greece,
with the exception of the islands and Anatoliki Makedonia and Thraki. In Germany and Austria, low
fertility is making inroads towards the west.
At the same time, the mean age at childbirth increased by an average of 0.8 years. The only country in
which this indicator did not increase was Ireland. Everywhere else, this “postponement” was
substantial with a particularly high figure (at least two years) in the regions of East Germany and, to a
lesser extent (between one and two years), in Greece, at one end of the distribution of mean ages at
childbirth and in Spain at the other end (Graph 3). Maps 1 to 3 highlight the spread of mean ages at
childbirth of over 29 in the European regions: in Spain, Italy and the Netherlands, mean ages are
mostly over 30; in France, Denmark, Finland and Sweden, mean ages of between 29 and 30 are
becoming widespread.
In 1997-99, 84 of the 211 European regions at the NUTS2 level showed a figure below 1.45 children
per woman, the fertility level observed for the 15 EU Member States during this sub-period. These
regions were in six countries; Spain (all the regions except one), Italy (all the regions except one),
Greece (all the regions except three), Portugal (two of the seven regions), Austria (all the regions
except two) and Germany (31 of the 40 regions). Among these low fertility regions, 49 recorded very
low fertility (below 1.30 children per woman). These regions were in five countries: Spain (14
regions), Italy (16 regions), Greece (5 regions), Austria (3 regions) and Germany (11 regions) (Map
6).
It is important to note that the geography of this low and very low fertility did not change a great deal
in the 1990s: 63 of the 84 low fertility regions and 35 of the very low fertility regions were already in
this situation in 1991-93. This low and very low fertility merely advanced towards the south of Spain,
Italy, Greece and Portugal and towards the west of Germany and Austria. Portugal was the only one of
these six countries where it was not necessary to go back further than 1990 to date the transition of
fertility below 1.45 children per woman. Longer chronological series had to be reconstructed for the
others to find out when the regions in question had made the transition into relative under-fertility.
This was possible for Spain, Italy and Austria, but not for Greece and Germany.
51
Study of low fertility in the regions of the European Union: places, timetable and causes
In the case of Spain, the chronological series of total fertility rates from 1975 to 2000 made it possible
to differentiate the 11 regions which had fallen below 1.45 children per woman in the 1980s (the north
of Spain down as far as Madrid) from the six regions which fell below this threshold during the 1990s
(the south and islands). In the case of Italy, the chronological series dated back to 1959. It also made it
possible to pinpoint the 15 regions which had made the transition into relative under-fertility between
1979 and 1988: the whole of the north and centre. Four regions (the south, except for Campania) fell
below this threshold during the 1990s. In Austria, the annual regional data were available from 1970 to
2000 and showed that five regions (the east and south-east) had made the transition into low fertility
before 1990: Wien in 1975 and the other four between 1985 and 1987. The whole of the west (with the
exception of Vorarlberg) reached low fertility only in the late 1990s (1997 and 1999).
It has been reiterated, as regards any examination of the factors able to explain this low fertility, that a
prior breakdown by birth order and legitimacy is needed, and that key factors and individual,
aggregated and contextual variables need to be taken into account at regional level, which is not
possible as the EU’s regional databases stand at present. Major data collection and harmonisation
work therefore remains to be carried out at regional level.
It is difficult to draw any findings from these observations and initial explanations in order to shed
light on fertility prospects. There is a “mechanical” method of projection of the curve of fertility rates
by age on the basis of a gamma function of the three parameters represented by the total fertility rate,
mean age at childbirth and standard deviation (Duchêne and Gillet-de Stefano, 1974).
During the 1990s, the fertility of European regions fell by an average of 0.07 children per woman
between 1991-93 and 1997-99. On the basis solely of this trend, it would be tempting to make all the
total fertility rates decline (annual decrease of 0.01 children per women). Looked at more closely,
however, this trend is not constant. At the same time, the mean age at childbirth increased by an
average of 0.8 years. This “postponement” was very marked everywhere except Ireland with a
particularly high figure (at least two years) in the regions of East Germany and, to a lesser extent
(between one and two years), in Greece and Spain.
In 1997-99, the standard deviation of age at childbirth varied from 4.30 to 6.38. On average, it
increased slightly during the period. Most of the European regions experienced an increase in the
dispersion of ages at childbirth but, with the exception of the regions of the United Kingdom where the
increase was substantial, there was little change in the other regions.
It may be that the mean age at childbirth can be extrapolated upwards, but to what level: for most of
the regions, the standard deviation can be kept unchanged or increased very slightly. How can the total
fertility rate be extrapolated from these observations? That is the question.
52
Study of low fertility in the regions of the European Union: places, timetable and causes
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Study of low fertility in the regions of the European Union: places, timetable and causes
ANNEXES
Annex 1 – List of NUTS2 regions of the European Union
R No C No
Country
1
1
Belgique-Belgïe
2
3
4
5
6
7
8
9
10
11
12
2
Danmark
13
3
BR Deutschland
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
R Code
BE1
BE21
BE22
BE23
BE24
BE25
BE31
BE32
BE33
BE34
BE35
DK
DE11
DE12
DE13
DE14
DE21
DE22
DE23
DE24
DE25
DE26
DE27
DE3
DE4
DE5
DE6
DE71
DE72
DE73
DE8
DE91
DE92
DE93
DE94
DEA1
DEA2
DEA3
DEA4
DEA5
DEB1
DEB2
DEB3
DEC
DED1
DED2
DED3
DEE1
DEE2
DEE3
DEF
DEG
Name of region
Région de Bruxelles capitale-Brussels hoofdstad Gewest
Antwerpen
Limburg (B)
Oost-Vlanderen
Vlaams-Brabant
West-Vlanderen
Brabant wallon
Hainaut
Liège
Luxembourg(B)
Namur
Danmark
Stuttgart
Karlsruhe
Freiburg
Tübingen
Oberbayern
Niederbayern
Oberpfalz
Oberfranken
Mittelfranken
Unterfranken
Schwaben
Berlin
Brandenburg
Bremen
Hamburg
Darmstadt
Gießen
Kassel
Mecklenburg-Vorpommern
Braunschweig
Hannover
Lüneburg
Weser-Ems
Düsseldorf
Köln
Münster
Detmold
Arnsberg
Koblenz
Trier
Rheinhessen-Pfalz
Saarland
Chemnitz
Dresden
Leipzig
Dessau
Halle
Magdebourg
Schleswig-Holstein
Thüringen
57
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
C No
Country
4
Ellada
5
Espana
6
France
R Code
GR11
GR12
GR13
GR14
GR21
GR22
GR23
GR24
GR25
GR3
GR41
GR42
GR43
ES11
ES12
ES13
ES21
ES22
ES23
ES24
ES3
ES41
ES42
ES43
ES51
ES52
ES53
ES61
ES62
ES63
ES7
FR1
FR21
FR22
FR23
FR24
FR25
FR26
FR3
FR41
FR42
FR43
FR51
FR52
FR53
FR61
FR62
FR63
FR71
FR72
FR81
FR82
FR83
FR91
FR92
FR93
FR94
Name of region
Anatoliki Makedonia, Thraki
Kentriki Makedonia
Dytiki Makedonia
Thessalia
Ipeiros
Ionia Nisia
Dytiki Ellada
Sterea Ellada
Peloponnisos
Attiki
Voreio Aigaio
Notio Aigaio
Kriti
Galicia
Principado de Asturias
Cantabria
Pais Vasco
Comunidad Foral de Navarra
La Rioja
Aragon
Comunidad de Madrid
Castilla y Leon
Castilla-La Mancha
Extremadura
Cataluña
Comunidad Valenciana
Baleares
Andalucia
Murcia
Ceuta y Melila
Canarias
Ile de France
Champagne-Ardenne
Picardie
Haute Normandie
Centre
Basse Normandie
Bourgogne
Nord-Pas de Calais
Lorraine
Alsace
Franche-Comté
Pays de la Loire
Bretagne
Poitou-Charente
Aquitaine
Midi-Pyrenées
Limousin
Rhône-Alpes
Auvergne
Languedoc-Roussillon
Provence-Alpes-Côte d’Azur
Corse
Guadeloupe
Martinique
Guyane
La Réunion
58
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
C No
Country
7
Ireland
8
Italia
9
10
Luxembourg (GD)
Nederland
11
Österreich
12
Portugal
13
Suomi/Finland
R Code
IE01
IE02
IT11
IT12
IT13
IT2
IT31
IT32
IT33
IT4
IT51
IT52
IT53
IT6
IT71
IT72
IT8
IT91
IT92
IT93
ITA
ITB
LU
NL11
NL12
NL13
NL21
NL22
NL23
NL31
NL32
NL33
NL34
NL41
NL42
AT11
AT12
AT13
AT21
AT22
AT31
AT32
AT33
AT34
PT11
PT12
PT13
PT14
PT15
PT2
PT3
FI13
FI14
FI15
FI16
FI17
FI2
Name of region
Border, Midlands and Western
Southern and Eastern
Piemonte
Valle d'Aosta
Liguria
Lombardia
Trentino-Alto Adige
Veneto
Friuli-Venezia-Giulia
Emilia-Romagna
Toscana
Umbria
Marche
Lazio
Abruzzo
Molise
Campania
Puglia
Basilicata
Calabria
Sicilia
Sardegna
Luxembourg (GD)
Groningen
Friesland
Drenthe
Overijssel
Gelderland
Flevoland
Utrecht
Noord-Holland
Zuid-Holland
Zeeland
Noord-Brabant
Limburg (NL)
Burgerland
Niederösterreich
Wien
Kärnten
Steiermark
Oberösterreich
Salzburg
Tirol
Vorarlberg
Norte
Centro (P)
Lisboa e Vale do Tejo
Alentejo
Algarve
Açores
Madeira
Itä-Suomi
Väli-Suomi
Pohjois-Suomi
Uusimaa
Etelä-Suomi
Àland
59
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
C No
Country
14
Sverige
15
United Kingdom
R Code
SE01
SE02
SE04
SE06
SE07
SE08
SE09
SE0A
UKC1
UKC2
UKD1
UKD2
UKD3
UKD4
UKD5
UKE1
UKE2
UKE3
UKE4
UKF1
UKF2
UKF3
UKG1
UKG2
UKG3
UKH1
UKH2
UKH3
UKI1
UKI2
UKJ1
UKJ2
UKJ3
UKJ4
UKK1
UKK2
UKK3
UKK4
UKL1
UKL2
UKM1
UKM2
UKM3
UKM4
UKN
Name of region
Stockholm
Östra Mellansverige
Sydsverige
Norra Mellandsverige
Mellersta Norrland
Övre Norrland
Smaland met Öarna
Västsverige
Tees Valley and Durham
Northumberland, Tyne and Wear
Cumbria
Cheshire
Greater Manchester
Lancashire
Merseyside
East Riding and North Lincolnshire
North Yorkshire
South Yorkshire
West Yorkshire
Derbyshire and Nottinghamshire
Leicestershire, Rutland and Northants
Lincolnshire
Herefordshire, Worcestershire and Warks
Shropshire and Staffordshire
West Midlands
East Anglia
Bedfordshire and Hertfordshire
Essex
Inner London
Outer London
Berkshire, Bucks and Oxfordshire
Surrey, East and West Sussex
Hampshire ans Isle of Wight
Kent
Gloucestershire, Wiltshire and North Somerset
Dorset and Somerset
Cornwall and Isles of Scilly
Devon
West Wales and the Valleys
East Wales
North Eastern Scotland
Eastern Scotland
South Western Scotland
Highlands and Islands
Northern Ireland
60
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 2a – Availability of fertility rates calculated on an annual basis,
1990 - 2000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
d
2000
Od
BE Belgium
O
O
O
O
O
O
O
O
N
O
DK Denmark
O
O
O
O
O
O
O
O
O
O
O
DE Germany (incl. GDR)
4
O*
O*
O*
O*
O*
O*
O*
O*
O*
3
GR Greece
O
O
O
O
O
O
O
O
O
On
N
ES Spain
O
O
O
O
O
O
O
O
O
O
O
FR France
O
O
O
O
O
O
O
O
O
Os
O
IE Ireland
O*
O*
O*
O*
O*
O*
O*
O*
O*
O*
O
IT Italy
O
O
O
O
O
O
O
O
Oi
O
O
LU Luxembourg
O
O
O
O
O
O
O
O
O
O
O
NL Netherlands
O
O
O
O
O
O
O
O
O
O
O
AT Austria
O
O
O
O
O
O
O
O
O
O
O
PT Portugal
N
O
O
O
O
O
O
O
O
O
O
FI Finland
O
O
O
O
O
O
O
O
O
O
O
SE Sweden
O*
O*
O*
O*
O*
O*
O*
O
O
O
O
UK United Kingdom
N
N
O*
O*
O*
O*
O*
O*
O*
O*
I
O Complete; N Not available; I Regions missing. ‘15’ number of Länder available.
* Merged regions; d Relative regional distribution transposed; n Relative national distribution; i Distribution estimated by
interpolation.
Annex 2b – Availability of fertility rates calculated for three-year periods
1991-93
1994-96
1997-99
BE Belgium
O
O
od
DK Denmark
O
O
O
DE Germany (incl. GDR)
O*
O*
O*
GR Greece
O
O
On
ES Spain
O
O
O
FR France
O
O
O
IE Ireland
O*
O*
O*
IT Italy
O
O
Oi
LU Luxembourg
O
O
O
NL Netherlands
O
O
O
AT Austria
O
O
O
PT Portugal
O
O
O
FI Finland
O
O
O
SE Sweden
O*
O*
O
UK United Kingdom
O*
O*
O
O Complete; o Missing year.
* Merged regions; d Relative regional distribution transposed; n Relative national distribution; i Distribution
estimated by interpolation.
61
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 3 – Main fertility indicators of the European regions (NUTS2 level),
1991 – 93, 1994 – 96 and 1997 – 99
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
Region
BE1 Région Bruxelles-capitale
BE21 Antwerpen
BE22 Limburg (B)
BE23 Oost-Vlaanderen
BE24 Vlaams Brabant
BE25 West-Vlaanderen
BE31 Brabant Wallon
BE32 Hainaut
BE33 Liège
BE34 Luxembourg (B)
BE35 Namur
DK Danemark
DE11 Stuttgart
DE12 Karlsruhe
DE13 Freiburg
DE14 Tübingen
DE21 Oberbayern
DE22 Niederbayern
DE23 Oberpfalz
DE24 Oberfranken
DE25 Mittelfranken
DE26 Unterfranken
DE27 Schwaben
DE3 Berlin
DE4 Brandenburg
DE5 Bremen
DE6 Hamburg
DE71 Darmstadt
DE72 Gießen
DE73 Kassel
DE8 Mecklenburg-Vorpommern
DE91 Braunschweig
DE92 Hannover
DE93 Lüneburg
DE94 Weser-Ems
DEA1 Düsseldorf
DEA2 Köln
DEA3 Münster
DEA4 Detmold
DEA5 Arnsberg
DEB Rheinland-Pfalz
DEC Saarland
DED1 Chemnitz or DED Sachsen
DED2 Dresden
DED3 Leipzig
DEE1 Dessau
DEE2 Halle
DEE3 Magdeburg
DEF Schleswig-Holstein
DEG Thüringen
GR11 Anatoliki Makedonia, Thraki
GR12 Kentriki Makedonia
GR13 Dytiki Makedonia
GR14 Thessalia
GR21 Ipeiros
GR22 Ionia Nisia
GR23 Dytiki Ellada
GR24 Sterea Ellada
GR25 Peloponnisos
GR3 Attiki
GR41 Voreio Aigaio
GR42 Notio Aigaio
GR43 Kriti
TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98
2
5
8
1.80
1.78
1.78
29.07 29.02 29.25 5.64 5.71 5.68
1.60
1.51
1.53
1.50
1.67
1.71
1.66
1.70
1.86
1.77
1.74
1.49
1.36
1.42
1.53
1.38
1.47
1.47
1.43
1.41
1.47
1.58
1.11
0.83
1.31
1.24
1.30
1.33
1.42
0.87
1.34
1.35
1.48
1.56
1.42
1.41
1.49
1.53
1.47
1.45
1.32
0.91
0.88
0.86
0.87
0.89
0.92
1.41
0.87
1.47
1.31
1.45
1.48
1.31
1.48
1.45
1.23
1.33
1.32
1.57
1.60
1.59
1.55
1.40
1.49
1.48
1.57
1.65
1.58
1.61
1.77
1.69
1.79
1.45
1.31
1.37
1.44
1.34
1.39
1.40
1.33
1.35
1.38
1.47
1.10
0.85
1.30
1.20
1.30
1.32
1.38
0.85
1.30
1.32
1.45
1.49
1.36
1.37
1.42
1.51
1.42
1.38
1.27
0.89
0.87
0.82
0.85
0.85
0.88
1.36
0.86
1.48
1.31
1.40
1.43
1.13
1.44
1.31
1.05
1.16
1.31
1.57
1.60
1.51
62
1.59
1.47
1.51
1.52
1.56
1.72
1.66
1.65
1.83
1.74
1.74
1.48
1.37
1.42
1.52
1.41
1.44
1.45
1.39
1.41
1.41
1.52
1.19
1.11
1.37
1.23
1.37
1.36
1.43
1.13
1.37
1.39
1.54
1.59
1.41
1.42
1.49
1.58
1.47
1.44
1.30
1.13
1.13
1.06
1.05
1.09
1.12
1.42
1.10
1.47
1.28
1.37
1.33
1.06
1.31
1.19
1.00
1.09
1.32
1.47
1.55
1.44
28.41
28.30
28.29
29.17
28.09
29.36
27.78
28.35
28.23
28.24
28.77
28.19
28.18
28.53
28.54
28.88
28.25
28.14
27.78
28.02
28.12
28.32
26.50
25.05
27.36
28.53
28.33
28.11
27.92
25.09
28.17
28.39
28.56
28.49
27.49
27.90
27.82
27.84
27.24
28.08
27.89
25.44
25.47
25.27
24.97
24.99
25.04
28.74
25.21
26.22
27.49
26.58
26.56
27.02
26.91
27.48
26.88
27.48
28.60
26.76
26.56
26.86
28.44
28.30
28.34
29.30
28.09
29.64
27.78
28.26
28.18
28.25
29.19
28.45
28.48
28.76
28.86
29.23
28.54
28.46
28.18
28.33
28.48
28.60
27.02
26.35
27.60
28.64
28.60
28.40
28.24
26.36
28.38
28.64
28.78
28.63
27.67
28.09
28.05
28.04
27.47
28.34
28.13
26.70
26.94
26.58
26.12
26.21
26.27
28.93
26.36
26.83
28.09
27.25
27.26
27.88
27.51
27.98
27.47
28.22
29.16
27.39
27.19
27.60
28.49
28.50
28.59
29.51
28.27
29.80
27.92
28.47
28.41
28.49
29.52
28.78
28.84
29.12
29.09
29.46
28.76
28.72
28.30
28.58
28.68
28.88
27.56
27.23
27.79
29.02
28.93
28.65
28.54
27.08
28.61
28.80
28.95
28.79
27.95
28.39
28.31
28.25
27.77
28.63
28.37
27.43
27.71
27.36
26.80
26.84
27.01
29.04
27.15
27.25
28.59
28.02
28.14
28.54
28.36
28.53
28.10
28.86
29.64
28.11
27.78
28.11
4.56
4.31
4.39
4.33
4.22
4.64
4.97
4.93
4.74
4.86
4.80
5.04
5.10
5.02
5.00
5.24
5.01
4.94
5.03
5.15
4.97
5.12
5.68
4.72
5.60
5.74
5.32
5.25
5.03
4.79
5.14
5.18
4.97
5.01
5.36
5.39
5.17
5.21
5.26
5.15
5.30
4.79
4.71
4.80
4.86
4.91
4.84
4.96
4.80
5.23
5.20
5.01
5.20
5.14
5.24
5.36
5.20
5.29
5.22
5.27
5.32
5.38
4.59
4.31
4.44
4.35
4.31
4.69
5.01
4.92
4.67
4.88
4.84
5.02
5.12
5.01
5.01
5.25
4.99
4.97
5.00
5.17
4.99
5.11
5.64
4.79
5.66
5.85
5.36
5.27
5.11
4.78
5.21
5.31
5.10
5.13
5.38
5.40
5.15
5.26
5.26
5.23
5.36
4.70
4.69
4.80
4.85
4.85
4.81
5.12
4.73
5.37
5.22
4.97
5.11
5.16
5.11
5.31
5.06
5.36
5.21
5.16
5.23
5.34
4.55
4.33
4.43
4.36
4.30
4.55
5.07
4.91
4.59
4.87
4.88
5.02
5.05
4.97
4.93
5.34
5.05
5.03
5.08
5.20
5.06
5.15
5.65
4.80
5.76
5.92
5.38
5.24
5.15
4.78
5.30
5.40
5.23
5.23
5.41
5.44
5.21
5.31
5.27
5.31
5.37
4.73
4.73
4.86
4.82
4.85
4.86
5.23
4.79
5.48
5.33
4.89
5.19
5.13
5.21
5.38
5.08
5.44
5.27
5.18
5.12
5.31
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 3 – Main fertility indicators of the European regions (NUTS2 level),
1991 – 93, 1994 – 96 and 1997 – 99 (continued)
No
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
110
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
Region
ES11 Galicia
ES12 Principado de Asturias
ES13 Cantabria
ES21 Pais Vasco
ES22 Comunidad Foral de Navarra
ES23 La Rioja
ES24 Aragón
ES3 Comunidad de Madrid
ES41 Castilla y León
ES42 Castilla-la Mancha
ES43 Extremadura
ES51 Cataluña
ES52 Comunidad Valenciana
ES53 Illes Balears
ES61 Andalucia
ES62 Murcia
ES63 Ceuta y Melilla (ES)
ES7 Canarias (ES)
FR1 Île de France
FR21 Champagne-Ardenne
FR22 Picardie
FR23 Haute-Normandie
FR24 Centre
FR25 Basse-Normandie
FR26 Bourgogne
FR3 Nord - Pas-de-Calais
FR41 Lorraine
FR42 Alsace
FR43 Franche-Comté
FR51 Pays de la Loire
FR52 Bretagne
FR53 Poitou-Charentes
FR61 Aquitaine
FR62 Midi-Pyrénées
FR63 Limousin
FR71 Rhône-Alpes
FR72 Auvergne
FR81 Languedoc-Roussillon
FR82 Provence-Alpes-Côte d'Azur
FR83 Corse
IE Irlande
IT11 Piemonte
IT12 Valle d'Aosta
IT13 Liguria
IT2 Lombardia
IT31 Trentino-Alto Adige
IT32 Veneto
IT33 Friuli-Venezia Giulia
IT4 Emilia-Romagna
IT51 Toscana
IT52 Umbria
IT53 Marche
IT6 Lazio
IT71 Abruzzo
IT72 Molise
IT8 Campania
IT91 Puglia
IT92 Basilicata
IT93 Calabria
ITA Sicilia
ITB Sardegna
TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98
2
5
8
1.11
0.95
0.91
28.29 29.21 30.02 5.42 5.30 5.30
0.93
1.07
0.96
1.17
1.12
1.15
1.24
1.09
1.52
1.54
1.23
1.32
1.50
1.57
1.63
2.05
1.39
1.83
1.79
1.92
1.89
1.75
1.82
1.71
1.99
1.76
1.78
1.84
1.85
1.79
1.64
1.58
1.58
1.45
1.83
1.54
1.72
1.79
1.66
2.01
1.06
1.06
1.01
1.12
1.39
1.10
1.06
1.02
1.05
1.19
1.19
1.25
1.29
1.35
1.74
1.54
1.31
1.49
1.68
1.24
0.83
0.94
0.93
1.14
1.08
1.09
1.16
0.96
1.35
1.33
1.17
1.19
1.35
1.37
1.43
1.90
1.25
1.74
1.69
1.77
1.77
1.65
1.76
1.63
1.86
1.65
1.65
1.73
1.75
1.69
1.57
1.50
1.51
1.40
1.71
1.45
1.64
1.70
1.54
1.89
1.02
1.14
0.92
1.09
1.35
1.07
0.99
0.99
0.99
1.09
1.09
1.14
1.14
1.21
1.52
1.35
1.18
1.30
1.46
1.07
63
0.81
0.96
0.98
1.19
1.12
1.08
1.20
0.93
1.27
1.22
1.22
1.18
1.41
1.31
1.42
1.90
1.25
1.80
1.76
1.87
1.83
1.72
1.80
1.69
1.91
1.71
1.72
1.80
1.82
1.77
1.67
1.56
1.58
1.48
1.74
1.54
1.67
1.74
1.51
1.90
1.09
1.11
0.97
1.14
1.41
1.15
1.07
1.07
1.04
1.11
1.13
1.16
1.09
1.19
1.48
1.33
1.12
1.24
1.42
1.02
28.65
29.13
30.32
30.29
29.80
29.82
29.94
29.59
29.22
28.85
29.53
29.31
29.02
28.94
29.01
28.65
28.50
29.31
27.83
27.88
27.99
28.28
28.03
28.07
27.81
27.91
28.21
28.05
28.32
28.60
28.03
28.56
28.86
28.27
28.77
28.25
28.52
28.66
28.37
30.47
29.51
29.07
30.30
29.94
29.79
30.09
30.05
29.65
29.75
29.43
29.58
29.72
29.06
28.71
28.63
28.71
28.87
28.40
28.20
29.96
29.56
30.02
31.17
31.04
30.48
30.63
30.74
30.37
29.86
29.40
30.24
29.98
29.72
29.53
29.66
29.01
29.05
29.83
28.26
28.24
28.40
28.75
28.48
28.56
28.05
28.34
28.59
28.55
28.83
29.09
28.52
29.02
29.33
28.71
29.25
28.70
28.86
29.04
28.78
30.22
30.15
29.84
30.83
30.61
30.28
30.63
30.56
30.23
30.40
30.01
30.26
30.34
29.90
29.29
28.93
29.17
29.46
28.89
28.65
30.57
30.34
30.86
31.88
31.62
31.06
31.23
31.35
31.05
30.39
29.93
30.80
30.57
30.10
30.00
30.10
29.40
29.37
30.18
28.53
28.49
28.70
29.08
28.80
28.90
28.35
28.64
28.93
28.86
29.18
29.41
28.90
29.34
29.66
29.01
29.53
28.99
29.15
29.34
29.15
30.46
30.56
30.24
31.27
30.89
30.52
30.97
30.89
30.59
30.82
30.43
30.58
30.95
30.29
29.90
29.30
29.60
29.89
29.43
29.13
31.15
5.21
5.12
4.46
4.74
4.75
4.73
4.85
5.14
5.11
5.43
4.78
4.92
5.16
5.39
5.40
6.00
5.66
5.32
5.06
5.21
5.10
4.95
4.91
4.96
5.25
5.02
5.14
5.06
4.67
4.62
4.76
4.90
4.86
4.84
4.98
4.84
5.12
5.20
5.30
5.63
4.88
4.95
4.86
4.87
5.10
4.86
4.89
5.04
4.96
4.84
4.81
5.05
4.91
5.04
5.25
5.25
5.01
5.35
5.51
5.58
5.15
5.00
4.33
4.42
4.60
4.56
4.70
4.99
4.97
5.31
4.66
4.81
5.02
5.30
5.30
5.86
5.61
5.17
5.01
5.16
5.08
4.88
4.90
4.89
5.23
5.00
5.09
4.98
4.63
4.63
4.80
4.92
4.86
4.86
4.90
4.90
5.16
5.14
5.22
5.54
4.87
5.01
4.82
4.89
5.08
4.85
4.85
5.03
4.94
4.88
4.83
4.98
4.97
4.98
5.27
5.27
5.03
5.37
5.53
5.56
5.29
4.98
4.36
4.50
4.66
4.65
4.76
5.00
4.99
5.32
4.79
4.91
5.13
5.36
5.44
5.88
5.83
5.24
5.07
5.24
5.13
4.95
4.98
4.99
5.26
4.99
5.13
4.98
4.71
4.71
4.88
4.96
4.94
4.91
4.92
4.89
5.20
5.18
5.32
5.68
4.98
4.95
4.93
4.94
5.14
4.86
4.95
5.19
5.06
4.98
4.88
5.03
4.91
4.97
5.26
5.25
5.03
5.26
5.47
5.48
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 3 – Main fertility indicators of the European regions (NUTS2 level),
1991 – 93, 1994 – 96 and 1997 – 99 (continued)
No
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
Region
LU Luxembourg
NL11 Groningen
NL12 Friesland
NL13 Drenthe
NL21 Overijssel
NL22 Gelderland
NL23 Flevoland
NL31 Utrecht
NL32 Noord-Holland
NL33 Zuid-Holland
NL34 Zeeland
NL41 Noord-Brabant
NL42 Limburg (NL)
AT11 Burgenland
AT12 Niederösterreich
AT13 Wien
AT21 Kärnten
AT22 Steiermark
AT31 Oberösterreich
AT32 Salzburg
AT33 Tirol
AT34 Vorarlberg
PT11 Norte
PT12 Centro (PT)
PT13 Lisboa e Vale do Tejo
PT14 Alentejo
PT15 Algarve
PT2 Açores (PT)
PT3 Madeira (PT)
FI13 Itä-Suomi
FI14 Väli-Suomi
FI15 Pohjois-Suomi
FI16 Uusimaa (suuralue)
FI17 Etelä-Suomi
FI2 Åland
SE01 Stockholm
SE02 Östra Mellansverige
SE04 Sydsverige
SE06 Norra Mellansverige
SE07 Mellersta Norrland
SE08 Övre Norrland
SE09 Smaland met O. or SE09 and SE0A
SE0A Vastsverige
UKC1 Tees Valley and Durham
UKC2 Northumberland, Tyne and Wear
UKD1 Cumbria
UKD2 Cheshire
UKD3 Greater Manchester
UKD4 Lancashire
UKD5 Merseyside
UKE1 East Riding and North Lincolnshire
UKE2 North Yorkshire
UKE3 South Yorkshire
UKE4 West Yorkshire
UKF1 Derbyshire and Nottinghamshire
UKF2 Leicestershire, Rutland and Northants
UKF3 Lincolnshire
UKG1 Herefordshire, Worcestershire & W.
UKG2 Shropshire and Staffordshire
UKG3 West Midlands
TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98
2
5
8
1.67
1.74
1.71
28.47 28.92 29.07 5.00 5.06 5.14
1.40
1.72
1.62
1.72
1.63
2.01
1.58
1.52
1.63
1.73
1.59
1.47
1.36
1.55
1.38
1.48
1.44
1.59
1.55
1.58
1.69
1.55
1.47
1.47
1.47
1.69
2.01
1.61
1.78
1.96
2.10
1.74
1.77
1.79
1.94
2.10
2.00
2.18
2.07
2.15
2.13
2.13
1.85
1.72
1.82
1.83
1.90
1.91
1.79
1.84
1.70
1.79
1.85
1.78
1.79
1.79
1.79
1.79
1.95
1.41
1.67
1.62
1.66
1.60
1.88
1.52
1.47
1.55
1.71
1.56
1.43
1.30
1.49
1.29
1.43
1.35
1.52
1.47
1.49
1.65
1.43
1.37
1.40
1.28
1.50
1.86
1.46
1.80
1.94
2.07
1.70
1.77
1.75
1.70
1.77
1.70
1.74
1.72
1.73
1.77
1.77
1.73
1.63
1.68
1.72
1.77
1.79
1.69
1.75
1.67
1.72
1.80
1.69
1.72
1.72
1.73
1.71
1.88
64
1.49
1.75
1.74
1.74
1.67
1.92
1.60
1.54
1.61
1.75
1.61
1.49
1.20
1.38
1.22
1.32
1.27
1.46
1.40
1.40
1.53
1.48
1.40
1.50
1.37
1.52
1.83
1.54
1.77
1.86
2.04
1.59
1.70
1.71
1.49
1.50
1.50
1.53
1.49
1.50
1.50
1.54
1.73
1.62
1.66
1.73
1.77
1.79
1.66
1.75
1.66
1.72
1.82
1.67
1.73
1.71
1.72
1.71
1.91
29.82
29.47
29.56
29.68
29.79
28.66
30.15
29.85
29.34
28.90
29.63
29.57
26.83
27.05
27.12
27.34
27.02
27.29
27.45
27.93
27.79
27.65
27.32
27.88
26.66
27.06
27.55
28.39
28.78
28.95
28.77
29.30
28.75
29.23
29.55
28.56
28.74
28.45
28.69
28.63
28.75
28.75
26.65
26.98
27.31
27.72
27.09
27.11
27.45
26.83
28.10
26.81
27.09
27.22
27.70
27.23
27.77
27.36
27.17
30.27
29.91
29.79
30.02
30.17
29.19
30.59
30.31
29.70
29.26
30.03
29.81
27.22
27.54
27.59
27.62
27.42
27.68
27.77
28.24
27.96
28.07
27.83
28.38
27.29
27.64
27.42
28.56
28.93
29.23
29.10
29.64
29.10
29.76
29.93
28.94
29.10
28.82
29.03
28.99
29.12
29.12
26.91
27.32
27.67
28.25
27.37
27.38
27.73
27.05
28.49
27.14
27.37
27.62
28.12
27.54
28.31
27.69
27.44
30.37
30.03
29.85
30.23
30.42
29.27
30.89
30.51
29.95
29.41
30.27
30.04
27.92
27.98
27.96
28.04
27.70
27.90
28.20
28.54
28.31
28.53
28.64
29.02
28.04
28.35
27.68
28.66
29.13
29.51
29.23
29.99
29.35
30.28
30.47
29.29
29.53
29.16
29.50
29.41
29.16
29.64
26.93
27.44
27.71
28.43
27.47
27.41
27.88
27.17
28.91
27.19
27.51
27.70
28.25
27.50
28.38
27.74
27.49
4.68
4.40
4.33
4.53
4.60
4.96
4.90
5.10
5.03
4.66
4.34
4.44
5.01
5.00
5.71
5.19
5.16
5.12
5.28
5.41
5.39
5.44
5.35
5.39
5.56
5.61
6.04
6.20
5.20
5.14
5.49
5.26
5.00
4.97
5.37
5.04
5.04
5.10
5.05
5.02
5.02
5.02
5.41
5.58
5.30
5.45
5.72
5.51
5.64
5.47
5.21
5.53
5.66
5.50
5.38
5.23
5.39
5.41
5.74
4.74
4.46
4.36
4.51
4.60
4.89
4.83
5.09
5.03
4.65
4.35
4.47
4.90
5.01
5.75
5.17
5.18
5.11
5.20
5.34
5.28
5.44
5.32
5.46
5.60
5.55
5.99
6.16
5.20
5.12
5.44
5.24
5.04
4.92
5.38
5.06
5.05
5.06
4.99
4.98
5.05
5.05
5.61
5.72
5.54
5.60
5.82
5.62
5.78
5.53
5.43
5.66
5.70
5.65
5.46
5.48
5.57
5.60
5.76
4.79
4.50
4.44
4.54
4.62
4.95
4.81
5.14
5.09
4.70
4.41
4.49
4.95
5.07
5.78
5.23
5.23
5.13
5.25
5.34
5.32
5.47
5.38
5.60
5.66
5.66
5.84
6.07
5.28
5.20
5.52
5.37
5.15
5.14
5.40
5.06
5.11
5.06
5.00
4.95
4.88
5.12
5.76
5.77
5.83
5.75
5.86
5.73
5.83
5.61
5.67
5.75
5.69
5.80
5.61
5.78
5.83
5.72
5.76
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 3 – Main fertility indicators of the European regions (NUTS2 level),
1991 – 93, 1994 – 96 and 1997 – 99 (continued)
No
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
211
Region
UKH1 East Anglia
UKH2 Bedfordshire, Hertfordshire
UKH3 Essex
UKI1 Inner London or UKI London
UKI2 Outer London
UKJ1 Berkshire, Bucks and Oxfordshire
UKJ2 Surrey, East and West Sussex
UKJ3 Hampshire and Isle of Wight
UKJ4 Kent
UKK1 Gloucestershire, Wiltshire & N.S.
UKK2 Dorset and Somerset
UKK3 or UKK3-4 Cornwall and Devon
UKK4 Devon
UKL1 or UKL Wales
UKL2 East Wales
UKM Scotland
UKN Northern Ireland
TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98
2
5
8
1.76
1.69
1.64
27.77 28.21 28.23 5.32 5.55 5.81
1.85
1.83
1.73
1.73
1.77
1.72
1.77
1.88
1.74
1.77
1.77
1.77
1.87
1.87
1.67
2.10
1.79
1.73
1.73
1.73
1.70
1.67
1.70
1.80
1.72
1.67
1.69
1.69
1.80
1.80
1.56
1.94
65
1.77
1.70
1.75
1.76
1.68
1.64
1.66
1.81
1.70
1.65
1.80
1.67
1.83
1.76
1.55
1.90
28.45
28.09
28.75
28.75
28.76
28.97
27.94
27.78
28.01
28.09
27.75
27.75
27.16
27.16
27.55
28.37
28.85
28.49
29.09
29.09
29.21
29.40
28.38
28.13
28.53
28.48
28.09
28.09
27.43
27.43
28.01
28.79
28.99
28.57
29.24
29.39
29.39
29.58
28.55
28.07
28.70
28.28
27.81
28.35
27.24
27.95
28.21
28.79
5.38
5.22
6.06
6.06
5.45
5.38
5.39
5.34
5.43
5.27
5.35
5.35
5.50
5.50
5.49
5.59
5.55
5.46
6.06
6.06
5.56
5.56
5.51
5.60
5.56
5.50
5.47
5.47
5.63
5.63
5.64
5.60
5.78
5.79
6.38
5.80
5.81
5.79
5.69
5.89
5.71
5.76
5.82
5.58
5.72
5.75
5.83
5.73
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 4 – List of tables, maps and graphs in the text
Tables
Table 1 – Regional fertility disparities in the European Union, 1991-1999 ...................................... 23
Table 2 – Composition and characteristics of the seven clusters obtained by classification in respect of
three principal components summarising TFR, AC and SD for the three sub-periods ........ 28
Tableau 3 – Composition and characteristics of the eight clusters obtained by classification in respect
of the standardised fertility rates for the 603 regions-periods ............................................ 30
Tableau 4 – List of regions showing fertility lower than the “European mean” ................................ 37
Maps
Map 1 – The fertility of the European regions, 1991-1993............................................................... 34
Map 2 – The fertility of the European regions, 1994-1996............................................................... 35
Map 3 – The fertility of the European regions, 1997-1999............................................................... 36
Map 4 – Mean age at childbirth in the European regions, 1991-1993............................................... 39
Map 5 – Mean age at childbirth in the European regions, 1994-1996............................................... 40
Map 6 – Mean age at childbirth in the European regions, 1997-1999............................................... 41
Graphs
Graph 1 – Total fertility rate in the European Union, 1991-1993 and 1997-1999 ............................. 24
Graph 2 – Trend (as %) in the total fertility rate in the European Union,
between 1991-1993 and 1994-1996, and between 1994-1996 and 1997-1999 ................... 24
Graph 3 – Trend (as %) in the mean age at childbirth in the European Union,
1991-1999 ....................................................................................................................... 25
Graph 4 – Trend (as %) in the standard deviation of age at childbirth in the European Union, 19911999 ................................................................................................................................ 26
Graph 5 – Projection of the three fertility indicators in the first factorial design............................... 27
Graph 6 – Trend in the TFR in the six countries showing fertility lower than the European mean,
1960-2000 ....................................................................................................................... 42
Graph 7 – Trend in the TFR of some Spanish regions in comparison with the EU,
1975-2000 ....................................................................................................................... 44
Graph 8 – Trend in the TFR of some Italian regions in comparison with the EU,
1959-2000 ....................................................................................................................... 44
Graph 9 – Trend in the TFR of some Austrian regions in comparison with the EU,
1970-1998 ....................................................................................................................... 45
--------------------------
66
Study of low fertility in the regions of the European Union: places, timetable and causes
STUDY OF LOW FERTILITY IN THE REGIONS OF THE
EUROPEAN UNION:
PLACES, PERIODS AND CAUSES
ACP des ‘calendriers’ de fécondité des 603 r égions-périodes
1,0
,5
tftr16
tftr18tftr17
tftr15
tftr19
tftr14
tftr20
tftr13
tftr21
Composante 2
tftr22
tftr46
tftr47
tftr48
tftr49
tftr12
tftr23
0,0
tftr24
tftr44tftr43tftr42
tftr41
tftr45
tftr40
tftr39
tftr38
tftr37
tftr36
tftr35
tftr34
tftr33
tftr32
tftr31
tftr25
-,5
tftr30
tftr26
tftr29
tftr27
tftr28
-1,0
-1,0
-,5
0,0
,5
1,0
Composante 1
PCA of fertility “timetables” in the 603 regions-periods
Composante = Component
ANNEX REPORT :
DATA COLLECTION AND EVALUATION
Josianne Duchêne
Michel Willems
Institut de démographie
Université catholique de Louvain
Louvain-la-Neuve
November 2003
67
Study of low fertility in the regions of the European Union: places, timetable and causes
Introduction
This annex report examines the data collection carried out for the study of low fertility in the regions
of the European Union and the evaluation procedures used before starting on a descriptive analysis of
regional fertility levels. This section, which we felt to be very important, was taken out of the main
report in order to simplify its presentation. It has four sections. Section 1 looks at the constraints that
the geographical division and sources of data imposed on the analysis in terms of the study’s
objectives. Section 2 looks at the collection of data per se, and at the problems raised by the
availability of fertility data in the regional database of New Cronos. Section 3 reports on the few
internal and external coherence checks used to evaluate the data taken from New Cronos. Section 4
looks briefly at definition problems encountered during data collection.
1. Geographical division and sources of data
As the objective of the study was to provide an international analysis of the reproductive behaviour of
women in the regions of level 2 of the Nomenclature of Territorial Units for Statistics (NUTS) of the
European Union, paying particular attention to regions whose fertility level was below the Community
mean, two constraints were immediately imposed on the collection of data:
a) a specific geographical division: the 211 regional units of level 2 of the Nomenclature of
Territorial Units for Statistics (NUTS2) of the European Union;
b) a main source of data: Eurostat’s existing regional databases, particularly the Regio domain of
New Cronos.
The first point that needs to be made as regards the geographical division is the major lack of
comparability of the units used for the study. Among the European regions examined here, there was a
substantial difference between the largest (SE08 – Övre Norrland, in Sweden) covering close on
155 000 km2 and the smallest (ES63 – Ceuta y Melilla, in Spain) covering only 31. Similarly, there
was a huge difference between the Île de France region (FR1) which had a population of close on
11 million on 1 January 2000 and Åland (FI2, in Finland) which had a population less than 26 000!
From the point of view of density, a variable which combines area and population and which may be
related to demographic phenomena, there were also substantial differences: on 1 January 2000, the
extremes were Inner London (UKI1) with 8 792 inhabitants per km2 and Övre-Norrland (SE08) with
only three inhabitants per km2. Gross domestic product (GDP) showed a similar dispersion to
population. These substantial disparities, resulting from the level of geographical division used
(NUTS2) and the way in which the various Member States of the Union have implemented it, were not
without effect on the analysis; we were unable, however, to measure or control these effects. The fine
division used in Germany, Belgium, the Netherlands and United Kingdom, likely to show more
differences, contrasts with the wider division used in Spain and France (see, in this respect, the case of
the Île de France discussed in Chapter I of the Final Report, p.8).
Denmark is an interesting case in terms of division and is worth looking at briefly. Considered as a
single region at NUTS2 level, this country is three times larger, three times more populated and four
times wealthier (in terms of GDP) than an average European region which covers 15 000 km2 and has
1.8 million inhabitants with a GDP of € 36 billion (Jacquier and Kirthichandra, 2001). In this case,
data were also available for the three lower-level regions (NUTS3). During this exercise, we found
that “Danish” fertility for the period 1997-99 (1.74 children per woman) was the result of the
aggregation of lower fertility in the capital region (1.64 in Copenhagen-Frederiksberg) with higher
fertility levels in the two other less urbanised regions (1.80 and 1.81 children per woman). Although
limited in absolute terms, there is no doubt that these differences increase the variation of the
phenomenon studied. This is obviously the main effect of a finer geographical division. In Italy, for
instance, the minimum and maximum values of the TFR were 0.93 and 1.61 respectively in 1994 at
regional level, but 0.79 and 1.69 at provincial level (Golini, 1999). In the case of Denmark, the change
of geographical division does not, however, change the position of the country and its component
regions with respect to mean European fertility. At both levels of division (NUTS2 and NUTS3), the
69
Study of low fertility in the regions of the European Union: places, timetable and causes
whole of Denmark remains above the Community mean, which explains our choice not to use the
NUTS3 level, despite the additional precision that it gave to the initial description.
Problems of data availability and internal coherence, which will be examined in more detail below,
also led us to exclude some regions and to merge others in order to use the NUTS1 level in these
cases:
- exclusion of the four French overseas Départements (FR91 to FR94);
- merger of the regions forming the Land of Rheinland-Pfalz (DEB1 to DEB3);
- merger of the regions forming the Republic of Ireland (IE01 and IE02);
- merger of the regions of Scotland (UKM1 to UKM4).
Ultimately, the analysis covered 201 regions of the European Union, three of which were not at
NUTS2 level: Rheinland-Pfalz, Republic of Ireland and Scotland. It should also be borne in mind that
other regions also had to be merged for some years. Details of and reasons for these mergers are given
in the following chapter. The serial numbers, codes and names of these 201 regions, as set out in New
Cronos, are given in Annex 1. They enable an unequivocal designation of the statistical units
analysed15.
As regards sources, most of the data came in practice from Eurostat: the Regio domain of New Cronos
and the 2002 Regional Statistical Yearbook (Eurostat, 2002a). A third source, made available to us by
Eurostat, i.e. the results of the work of Unit E3 of Eurostat (Health, education, culture), included only
data on morbidity and mortality and was not therefore used in this analysis. These two main sources
were not, however, enough on their own and it proved necessary to draw on additional sources.
Various population and fertility data were therefore obtained from publications or directly from the
national statistical institutes of Belgium, France and Austria. Requests were also sent to Germany,
Spain and Italy. For Austria, it was necessary to obtain the distribution of births for a particular year
for which the series was manifestly wrong in New Cronos. For France, it was necessary to obtain a
population structure missing from the database. For Belgium, Spain and Italy, it was necessary to
obtain births for one or more particular years which were missing from New Cronos. For Germany, it
was necessary to obtain all the data on fertility, as only the population structures were available in
Eurostat’s regional databases. These requests almost all met with a satisfactory response. In the
particular case of Germany, we received a whole range of fertility and mortality data largely through
the good offices of the Information Division of the Statistisches Bundesamt, but they took a long time
to come16. On the few occasions when it was impossible to obtain adequate data, simple techniques
were used to provide reliable estimates for them.
2. Data availability
Given the structure of the Regio domain of New Cronos, we decided to extract data which were as
detailed and broken down as possible, i.e.:
regional population numbers by gender and by year of age on 1 January;
regional births by year of age of the mother.
For each of these two themes and at this level of breakdown, the years available were from 1990 to
2000; no data were available prior to 1990 and 1990 itself was incomplete; after 2000, the data were
15
It is important to note that, for mapping purposes, we reconstructed the initial universe of the 211 units,
allocating missing values to the regions excluded from the analysis and, in the case of each of the component
regions of a merged unit, the value corresponding to that unit.
16
Following a request sent on 25 March 2003 to each of the statistical offices of the Länder, we obtained
assistance from the Statistisches Bundesamt (Mrs S. Kunze) for the coordination of data collection. After
invoicing for their services, the statistical offices agreed to provide us with the required data. Statistics from the
Land of Bayern (seven NUTS2 regions) reached us only on 6 September 2003.
70
Study of low fertility in the regions of the European Union: places, timetable and causes
available only for some countries17. A comparison of the data series on the two themes showed that the
set of nine years from 1991 to 1999 was relatively well covered (Annexes 2a and 2b), and had the
advantage of starting from the year of German reunification. It also made it possible to break down the
period of analysis into three sub-periods of the same length (1991-93, 1994-96 and 1997-99), making
it possible to retain a relatively detailed level of analysis (the year of age of the mother) while reducing
the effects of fluctuations linked to small numbers. These three sub-periods also echoed the three-year
periods used by Eurostat for the analysis of mortality by causes of death. Although the stress was
placed on these nine years, in particular in our request to the Länder, it did not rule out analysis of
regional fertility levels by year from 1990 to 2000.
Two concerns underpinned the extraction and formatting of the data on population structures and birth
numbers: to find data that were as complete as possible and to evaluate the quality of the data
extracted. The problems of availability encountered have been discussed above and evaluation
procedures will be examined in the following chapter.
Various solutions were used to cope with the various problems of availability encountered (Table 1).
1° Requests to the relevant statistical offices or to resource persons and the consultation of Internet
sites. These methods were used to obtain the population numbers of the French regions on
01/01/1999, births from 1996 to 2000 for the Belgian regions, all the fertility data for the German
Länder, births in 1999 for the Greek and Spanish regions and lastly births from 1998 to 2000 for
the Italian regions. When these requests and consultations did not provide a satisfactory result, we
used various techniques to fill any ongoing gaps.
2° Exclusion of the regions concerned: in the case of the four French overseas Départements (FR91
to FR94), the data available related only to the years 1998 to 2000 and showed clear-cut
differences of demographic behaviour in comparison with metropolitan France.
3° The merger of regions of NUTS2 level for the whole period of analysis: the three regions of the
Land of Rheinland-Pfalz in Germany (Koblenz, Trier and Rheinhessen-Pfalz), the two regions of
the Republic of Ireland (Border, Midlands and Western and Southern and Eastern) and the four
regions of Scotland (North Eastern Scotland, Eastern Scotland, South Western Scotland and
Highlands and Islands). In these three cases, either one or other or both of the data series were not
available at a regional level.
4° The merger of the NUTS2 regions for only part of the period of analysis 18 : Smaland met Öarna
and Västsverige, in Sweden; Inner London and Outer London, Cornwall and Isles of Scilly and
Devon, West Wales and the Valleys and East Wales in the United Kingdom. Changes of borders
had taken place in these regions during the period and statistical series for the new units had not
been retroactively constructed. In the case of Greater London (Inner and Outer London), it should
be added that the structure was obtained by subtracting the other regional structures available from
the national structure19.
17
Data collection took place from January 2003, when the initial data were extracted from the Regio domain of
New Cronos, to September 2003, when the last German data were received!
18
In contrast to the previous case, this “temporary” merger did not change the number of statistical units to be
used in the analysis. Simply, identical fertility rates, equivalent to the fertility rates of the higher level unit to
which they belonged, were allocated to the merged regions.
19
In this case, the entire “error” contained in the regional data for the United Kingdom could thus be attributed
to the age structure of Greater London (UKI).
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Study of low fertility in the regions of the European Union: places, timetable and causes
Table 1 – Problems of fertility data availability
-
-
Numbers by gender and age on 01/01
Missing
Solutions
DE4 Sachsen, 1991 to 2000
- Request sent to the Land and application of Sprague
formulae to the five-year structures
FR9 DOM, 1991 to 1997
- Exclusion of the overseas departments (FR91 to FR94)
FR, 1999
- Request to INSEE, then calculation of a mean structure
IE01 and 02, 1991 to 1996
- Merger of the NUTS2 regions for the whole period
IE, 1997 to 1999
- Application of Sprague formulae to the five-year structures
SE09 and SE0A, 1991 to 1996
- Merger of the NUTS2 regions to 1996
UK, 1990 and 1991
- Not available!
UK, women 15-49, 1992
- Application of the 1993 structure
UKI1 and UKI2, 1992 to 1996
- Merger of the two regions and subtraction
UKK3 and UKK4, 1992 to 1996 - Merger of the two regions to 1996
UKL1 and UKL2, 1992 to 1996
- Merger of the two regions to 1996
UKM1 to UKM4, 1992 to 1996
- Merger of the four regions for the whole period
UKM, 1997 and 1999.
- Application of Sprague formulae to the five-year structures.
Births by year of age of the mother
Missing
Solutions
BE, 1996 to 2000
- Request to the INS, 1998 not available, estimates for 1999
and 2000 by applying the regional distributions for 1997
DE, 1991 to 2000
- Requests to the Länder and merger of three regions (DEB)
GR, 1999
- Estimated by applying the relative distribution for 1998 to
the total number of births in 1999
ES, 1999
- Data obtained from the Centre d’Estudis Demografics of
the Universitat Autonoma of Barcelona
FR9 DOM, 1991 to 1997
- Exclusion of the overseas Departments
IE01 and 02, 1991 to 1993
- Merger of the two regions for the whole period
IT, 1998 to 2000
- 1999 data obtained from ISTAT and interpolation for the
1998 distribution
UKI1 and 2, 1992 to 1996
- Merger of the two regions and calculation by subtraction
UKK3 and 4, 1992 to 1996
- Merger of the two regions to 1996
UKL1 and 2, 1992 to 1996
- Merger of the two regions to 1996
UKM, 1992 to 1996.
- Merger of the four regions for the whole period.
5° The use of a five-year structure and a breakdown (Sprague formulae – Shryock and Siegel, 1976 –
or mean of the surrounding years): the three regions of the Land of Sachsen from 1991 to 1997; all
the French regions in 199920; the Republic of Ireland from 1997 to 1999, and Scotland in 1997 and
1999.
6° The estimation of a distribution of births by year of age of the mother by applying the regional
distributions by age in 1997 to the total regional numbers (Belgium, 1999 et 2000), by applying
the relative distribution of the preceding year to the total numbers of births (Greece, 1999) or by
interpolation from the surrounding regional distributions (Italy, 1998)21.
Even with these strategies and technical procedures, there were still some gaps which made it
impossible to calculate the fertility rates: for Belgium in 1998; for 12 of the 16 German Länder in
1990 and 2000; for Portugal in 1990 and for the whole of the United Kingdom in 1990 and 1991. The
20
INSEE was able to provide us only with five-year regional structures, given that “the ‘detailed age’ cross
reference is not reliable enough to be sent out”.
21
In the case of Greece in 1999, the decision to apply the structure of the previous year rather than interpolation
from the surrounding years was justified by the availability of the distribution of births by year of age of the
mother at national level, which was not the case for Italy in 1998, as a result of the “Bassanini bis” law.
72
Study of low fertility in the regions of the European Union: places, timetable and causes
division of the period studied into three sub-periods of three years each (1991-93, 1994-96 and 199799) and calculation of the rates on the basis of annual means over three years improved availability.
Only Belgium was then a particular case as the rates for the last sub-period (1997-99) were drawn up
on the basis of the years 1997 and 1999.
Following on from this review of data availability, it should be borne in mind:
- that an analysis of regional fertility in the European Union can be carried out only for the 1990s
using the Eurostat database;
- that various data, particularly birth numbers, are still missing with the result that implementing
procedures to calculate fertility rates that are fully comparable is problematic!
3. Data evaluation
During the data extraction, we used control procedures which made it possible to evaluate their
quality. These procedures covered internal coherence and external coherence.
As regards internal coherence, we found many problems from the point of view of both structures and
births (Table 2). Most of these problems concerned the aggregates: the total numbers reported were
often different from the sum of numbers by age and total births reported often differed from the sum of
births by year of age of the mother contained in the tables. Although these differences were common,
the distributions for some countries were always correct, as was the case for Spain, Portugal and
Finland. There was also a disturbing aggregate (“49 and over” before “50 and over”) containing births
in the conventional age group “50 and over” and those in the last year of age contained in the database
(49), with the result that births at 49 were counted twice unless care was taken. As regards the
beginning of reproductive life, births before the age of 15 were not reported in the same way by the
various countries (in the United Kingdom in 1998 and 1999, figures were not given for the years of
age from 10 to 14: there was an aggregate for the age group “0-15” and then figures for the years of
age from 15 onwards). The divisions used included: years of age from 10 to 15, “0-14”, “10-14” and
“0-15”: these were not used systematically, however, by the different countries. Another major
problem was the non-systematic reporting of births where the age of the mother was unknown: in the
Republic of Ireland and in Scotland, there were always unknown ages, although these were relatively
insignificant (between 0.1 and 0.5%); in Belgium, they were to be found in some cases and in Italy
they appeared only in 1997; they were not be found in all the other countries. In these latter cases, it
was necessary to find out whether births for which the age of the mother was unknown had been
reported and, where necessary, the way in which they had been “distributed”.
Lastly, there were two significant errors in the regional database of New Cronos :
- in Greece, in 1995, an inputting error had the result that all the births in Nisia Aigaiou, Kriti (GR4)
had been attributed to Voreio Aigaio (GR41), births in Voreio Aigaio to Notio Aigaio (GR42) and
births in Notio Aigaio to Kriti (GR43). It was possible to reconstruct the distributions of births in
these three regions by comparison and subtraction;
- in Italy, in 1997, a surprising calendar error appeared in Lazio (IT6). The distribution of births had
been rectified by applying the structure of births in 1996 by year of age of the mother to the total
number of births reported in 1997. Moreover, the distribution of births attributed to the region of
Centro (IT5) was not the sum of the births attributed to its component provinces (IT51 to IT53)!
For our purposes, we used only the distributions of birth by province (which corresponded to the
NUTS2 regions in this case).
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Study of low fertility in the regions of the European Union: places, timetable and causes
Table 2 – Internal coherence as regards regional fertility
Numbers by gender and age
Births by age of mother
- Total numbers reported often different from
the sum of numbers by age (these differences
being more common for numbers of women
than numbers of men!) : BE, DE, GR, FR, IT,
NL, AT, SE, UK.
- A disturbing aggregate: “49 and over”!
- Differences between total births reported and
sum of births by age: BE, GR, ES, UK.
- Italy: significant differences up to 1995,
complete concordance in 1996, small
differences in 1997 with the appearance of
many unknowns!
- Non-systematic reporting of births where age
of the mother was unknown: always and in
large numbers in IE and UKM; sometimes in
BE; in 1997, and not before, in IT.
- Births prior to the age of 15 were not input
regularly. UK in 1998 and 1999: no births
reported for each age, but a total for “0-15”!
Error for UKN in 1998.
- Greece, 1995: all births in GR4 attributed to
GR41!
- Italy, 1997: surprising calendar error for IT6!
IT5 was not the sum of its component regions!
- Some countries always accurate: ES, PT, FI!
As regards external coherence, it was possible to carry out various checks on the basis of national or
regional data available elsewhere for Belgium, Germany, France, Italy and Austria.
In some Belgian regions, it was possible to check 1996 births by comparing the data in New Cronos
with the corresponding data from the national statistical office. There was full concordance for these
regions, which enabled us to supplement the data recorded for the other regions from the INS data
which also provided us with the births for 1997.
In Germany, requests to the Länder meant that we had two sources on population numbers by gender
and year of age: the Regio domain of New Cronos and the statistics which the Länder supplied. It was
possible to compare these two sources for 13 Länder and nine dates from 31/12/1991 to 31/12/1999.
This detailed comparison provided an uneven picture. There was “complete” concordance between the
two sources from 1995 to 1999, albeit with small differences (of one unit or so) distributed at random
and due in all likelihood to reading or inputting errors 22; however, from 1991 to 1994, the two sources
were not at all concordant! With the surprising exception of the age 0, almost always concordant from
the point of view of men and women, all the numbers from the ages of 1 to 84 were different. These
differences were in most cases limited as relative values: less than or close to 1%. However, for some
regions (DE8 Mecklenburg-Vorpommern, DEB Rheinland-Pfalz, DEE1 Dessau, DEE2 Halle, DEE3
Magdeburg, DEG Thüringen), they were higher: up to 12% for the numbers in a year of age. In the
Land of Sachsen-Anhalt, the differences were such (largely negative for one region and positive for
the other two) that they pointed to a problem of division of the NUTS2 regions. The break in the series
of numbers in New Cronos between 31/12/1994 and 31/12/1995 would tend to bear out this
hypothesis. In any case, the lack of concordance observed between the German sources (the different
Länder) and Eurostat’s regional database raises the question of the regular updating of this database,
particularly for prior years for which the relevant statistical offices subsequently rectify estimates.
22
With the exception of Saarland on 31/12/1999 where unitary differences appeared throughout the age
distribution for both genders.
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Study of low fertility in the regions of the European Union: places, timetable and causes
For France, data from INSEE enabled us to compare population numbers by five-year age groups from
1990 to 2000 with the population data taken from New Cronos (except for 1999 which was not
available). In this comparison, only two years (1990 and 2000) were fully concordant. In all the other
years, there were major differences for all the regions and all age groups. As these were data between
censuses, it was not at all surprising to find these differences as the age structures of the years
following the census (1990) are obtained by projection up to the following census (1999) on which
date a correction is made for the previous years (1991 -1998) by interpolation between the two
censuses. It would undoubtedly be useful, in the case of France and other countries without a
population register, for these corrections to be included in the regional database of New Cronos, so
that this source of data does not conflict with national sources!
In the case of Italy, the data that we received from ISTAT made it possible to compare the regional
numbers of births for 2000 from two different sources. These numbers had also recently become
available in the Regio domain of New Cronos. They were unfortunately not distributed by year of age
of the mother so that only total regional numbers could be compared. In this case, however, the two
sources were completely at odds. In 2000, the total numbers of births of the Italian regions available in
New Cronos differed completely from the figures in the national source. The difference may be
explained by the fact that the total numbers in New Cronos are estimates. It is to be hoped that they
will be corrected soon on the basis of detailed national data.
In the case of Austria, we had statistics on live births by five-year age group of the mother and by
NUTS2 region from 1991 to 1998 (Statistik Österreich, 1997, 2000). Comparison of the two sources,
although showing that the series were almost fully concordant, also showed that births at less than 15
years of age (of the mother) reported by the Austrian statistical office were aggregated with those of
15 years of age or over in the New Cronos data. Wherever possible, we reconstructed a reasonable
hypothetical distribution of births prior to the age of 15 by removing the necessary number of births
recorded at 15 and distributing them over one or two previous years of age of the mother.
As a result of these problems of coherence, we decided to make use of the most disaggregated data
from New Cronos as often as possible so that we could ourselves recalculate the intermediate
aggregates and necessary totals. We used an external source (site or publications by statistical offices,
tables from the Eurostat national database, estimation techniques) only in cases where the distributions
showed obvious errors or gaps. The Eurostat regional database was therefore the main source of the
data used in this analysis. The lack of concordance noted here between New Cronos and the external
sources (chiefly for Germany and France) did not give rise to any data substitution, as we did not have
either the authority or the statistical arguments to opt for these external sources.
4. Definition problems
Before reaching a conclusion on data collection and evaluation, two definition problems need to be
tackled: the notions of birth and age as used in Eurostat’s regional database.
In the case of births, the main table from which the related statistics by year of age of the mother are
taken does not specify whether these are “live births”23. This is mentioned, however, in a further table
containing the total numbers of “live births”, expressed, however, in thousands24. A comparison of the
two tables highlights major rounding discrepancies for some countries (BE, ES, FR, IT), whereas this
rounding is accurate for all the years for other countries. This latter concordance shows that the same
data are certainly involved: live births, excluding still births. It nevertheless raises the question of
explaining the differences, which are positive in some cases (France), negative in others (Italy) and
sometimes positive and negative (Spain, 1998), observed elsewhere. Have the EU Member States all
responded in the same way to the questions drawn up by Eurostat? Despite a request to the relevant
23
24
Table p2natal of the Regio domain of New Cronos.
Table d3natmor of the Regio domain of New Cronos.
75
Study of low fertility in the regions of the European Union: places, timetable and causes
division, doubt still remains and it is only possible in principle to consider that live births are involved
for all the regions25.
In the case of the age of the mother at birth, the perspective in which this is defined needs to be
pinpointed (completed years or age reached during the year?) so that appropriate denominators and
mean durations can be used in the calculation of fertility rates. The original table of births by year of
age of the mother does not specify the definition used26. In the absence of this important detail, three
checking strategies were used:
1. a comparison at national level of mean ages at childbirth as published (Eurostat, 2002b, p. 89) and
mean ages at childbirth calculated assuming the mother’s age in completed years;
2. a comparison of the national statistics by completed years of age of the mother27 and the
aggregation by country of the regional statistics;
3. lastly, a request sent at our initiative but by Eurostat to the national statistical offices28.
None of these three strategies was completely conclusive. Pooling their partial results shows, however,
that both definitions of the age of the mother are to be found in the original regional table: five
countries use the age reached during the year (France, Luxembourg29, Netherlands, Finland and
Sweden), while nine other countries use age in completed years. In the case of the 15th country, i.e.
Germany, Eurostat has no regional distribution of births by age of the mother. Following the request
for data forwarded to the Länder, seven supplied an explicit definition of the age of the mother (always
in year differences!), while nine others sent no details.
On the basis of this information, and after extraction of the appropriate data series for Luxembourg,
we concluded that the regional distributions of births of 10 countries (Belgium, Denmark, Greece,
Spain, Ireland, Italy, Luxembourg, Portugal, Austria and the United Kingdom) were in completed
years and those of the other five countries (Germany, France, Netherlands, Sweden and Finland) were
in ages reached during the year. For all the regions of these five countries, a transformation was
carried out to obtain a distribution of births by completed age of the mother, from a breakdown by age
reached. This took place before the fertility rates were calculated and analysed. This transformation
took place assuming a uniform distribution of births at each age, except at the extreme ages of fertility
(Annex 3). It is important to note that this transformation was preceded, for all the distributions of
births, by a proportional distribution of the set formed by births for which the age of the mother was
unknown and by the “error”, the term “error” being used here to designate the difference often
recorded between total births reported and the sum of the births reported at each age. For the regions
concerned, the transformation of the distribution into completed ages was carried out after
proportional distribution of the unknowns and the error.
The checks discussed above highlighted many other inconsistencies that further complicate the use of
Eurostat’s regional database. These inconsistencies include the case of Belgium (in 1992, the national
distribution of births by age of the mother differed substantially from the sum of the regional
25
The issue of the definition of “live birth” and its comparability in the European area is important as some rates
(in particular mortality) may be very sensitive to it (Gourbin, 1995). The technical notes and definitions
accompanying statistical requests from Eurostat are clear in this respect. Nevertheless, the differences between
tables, whose specifications are, moreover, inadequate, which are supposed to deal with the same set raise
doubts.
26
Eurostat’s technical notes and definitions are again clear and, in this case, ask for a double ranking of births
(by age in completed years and by year of birth), but this double ranking is not published. The reason being that,
for a number of countries, it is undoubtedly not available for all the years.
27
These national statistics are taken from the fagec table “Live births by marital status and age of the mother in
completed years” in the Demo domain of New Cronos.
28
The partial results (eight out of 15 countries) of this strategy reached us only on 2 July 2003.
29
In the case of the Grand Duchy of Luxembourg, in the p2natal table of the Regio domain of New Cronos, all
the annual distributions of births are in ages reached during the year, except for 1998 which is in completed
years! The national distributions in completed years from the fagec table were therefore substituted for the
regional distributions from the p2natal table.
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Study of low fertility in the regions of the European Union: places, timetable and causes
distributions, for the same total of births), the case of France (total births differed substantially in
1992, 1995 and 1998 in particular, depending on whether they were taken from the national or the
regional statistics, irrespective of whether or not the overseas departments were taken into account)
and the case of the Republic of Ireland (from 1997, the national distribution was not equal to the sum
of the regional distributions)! Painstaking work is needed every time to identify the precise
specifications of the data used.
Conclusion
Following the collection of the data and the evaluation procedures used, two vast sets of data were
available. They covered 207 of the 211 regions of NUTS2 level making up the 15 Member States.
Four regions were excluded (the French overseas departments, FR91 to FR94) as data prior to 1998
were not available. Among these 207 regions, nine were merged, for the calculation of the fertility
rates, into higher level units (the three regions of the Land of Rheinland-Pfalz, the two regions forming
the Republic of Ireland and the four regions of Scotland). These NUTS2 regions were kept as units in
our database but were allocated the fertility rates calculated for the higher level unit (NUTS1 regions).
These two sets of data contained:
1. the numbers of women aged from 12 to 49, on 1 January from 1990 to 2001;
2. live births by year of age of the mother from 12 to 49 and over, from 1990 to 2000.
There were still a few gaps, but these did not, however, prevent the construction of a detailed picture
of the fertility of the regions of the European Union throughout the 1990s.
Faced with the many errors and inconsistencies found in Eurostat’s regional database, we can only
hope that periodical screening is carried out so that the data can be regularly updated to take account
of the information systematically forwarded by the relevant national and regional offices and that
checking procedures are implemented before the data are input into the database, and lastly that the
aggregates are calculated automatically as systematically as possible. This should make it possible to
reduce both errors and conflicts with national statistics.
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Study of low fertility in the regions of the European Union: places, timetable and causes
Additional sources
COUNCIL OF EUROPE, 2001, Recent demographic developments in Europe, Strasbourg, Council of
Europe Publishing.
EUROPEAN COMMISSION, 2002, European Social Statistics. Demography. 2002, Luxembourg,
Office for Official Publications of the European Communities, 171 p.
EUROSTAT, 2002a, Regions: Statistical yearbook 2002, Luxembourg, Office for Official
Publications of the European Communities, 151 p.
EUROSTAT, 2002b, European Social Statistics. Demography. 2002, Luxembourg, Office for Official
Publications of the European Communities, 171 p..
GOLINI A., 1999, Levels and trends of fertility in Italy: Are they desirable or sustainable?, Population
Bulletin of United Nations, 40/41, pp. 247-265
GOURBIN C., 1995, Critères d’enregistrement des événements ‘naissance vivante’ et ‘mort-né’ en
Europe, in DUCHÊNE J. & WUNSCH G., Collecte et comparabilité des données
démographiques et sociales en Europe, Chaire Quetelet 1991, Louvain-la-Neuve, Academia –
L’Harmattan, pp. 219-242
JACQUIER J. & KIRTHICHANDRA A., 2001, Les régions françaises dans l’Union européenne en
1998, INSEE Première, 810, pp. 1-4
SHRYOCK H.S., SIEGEL J.S. et al., 1976, The Methods and Materials of Demography, Condensed
Edition, New York, London, San Francisco, Academic Press
STATISTIK ÖSTERREICH, 1997, Demographisches Jahrbuch. Österreichs 1996, Wien, 330 p.
STATISTIK ÖSTERREICH, 2000, Demographisches Jahrbuch. Österreichs 1998, Wien, 381 p.
Copyright
All the regional data on births and deaths in the Federal Republic of Germany were obtained from the
statistical offices of the Länder. The latter sent us, either directly, or via the Information Division of
the Statistiches Bundesamt, the data requested together in some cases with the copyrights reproduced
below.
For Brandenburg :
 Landesbetrieb für Datenverarbeitung und Statistik, Postdam, June 2003. All rights reserved.
For Saarland :
 Statistisches Landesamt des Freistaates Sachsen, Kamenz, 2002
The data supplied are protected by copyright. Reproduction of all or some of the data and distribution
thereof without charge for non-commercial purposes is permitted, provided that acknowledgement of
the source is given. Distribution of all or some of the data via electronic systems/data media is subject
to prior consent. All other rights are reserved.
For Sachsen-Anhalt :
© Statistisches Landesamt Sachsen-Anhalt, Halle (Saale), 2003
Reproduction of all or some of the data and distribution thereof without charge for non-commercial
purposes is permitted, provided that acknowledgement of the source is given. Distribution of all or
some of the data via electronic systems/data media is subject to prior consent. All other rights are
reserved.
In the case of Spain, the data on births in 1999 were taken from a publication of the Instituto Nacional
de Estadística: copyright INE 2003.
79
Study of low fertility in the regions of the European Union: places, timetable and causes
Annexes
Annex 1 – List of reporting units included in the analysis
R No
C
Country
No
1 1 Belgique-Belgïe
2
3
4
5
6
7
8
9
10
11
12 2 Danmark
13 3 BR Deutschland
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
44
45
46
47
48
49
50
51
52
R Code
BE1
BE21
BE22
BE23
BE24
BE25
BE31
BE32
BE33
BE34
BE35
DK
DE11
DE12
DE13
DE14
DE21
DE22
DE23
DE24
DE25
DE26
DE27
DE3
DE4
DE5
DE6
DE71
DE72
DE73
DE8
DE91
DE92
DE93
DE94
DEA1
DEA2
DEA3
DEA4
DEA5
DEB
DEC
DED1
DED2
DED3
DEE1
DEE2
DEE3
DEF
DEG
Name of region
Région de Bruxelles capitale-Brussels hoofdstad Gewest
Antwerpen
Limburg (B)
Oost-Vlanderen
Vlaams-Brabant
West-Vlanderen
Brabant wallon
Hainaut
Liège
Luxembourg(B)
Namur
Danmark
Stuttgart
Karlsruhe
Freiburg
Tübingen
Oberbayern
Niederbayern
Oberpfalz
Oberfranken
Mittelfranken
Unterfranken
Schwaben
Berlin
Brandenburg
Bremen
Hamburg
Darmstadt
Gießen
Kassel
Mecklenburg-Vorpommern
Braunschweig
Hannover
Lüneburg
Weser-Ems
Düsseldorf
Köln
Münster
Detmold
Arnsberg
Rheinland-Pfalz (NUTS1)
Saarland
Chemnitz
Dresden
Leipzig
Dessau
Halle
Magdebourg
Schleswig-Holstein
Thüringen
80
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
C
No
Country
4
Ellada
5
Espana
6
France
R Code
GR11
GR12
GR13
GR14
GR21
GR22
GR23
GR24
GR25
GR3
GR41
GR42
GR43
ES11
ES12
ES13
ES21
ES22
ES23
ES24
ES3
ES41
ES42
ES43
ES51
ES52
ES53
ES61
ES62
ES63
ES7
FR1
FR21
FR22
FR23
FR24
FR25
FR26
FR3
FR41
FR42
FR43
FR51
FR52
FR53
FR61
FR62
FR63
FR71
FR72
FR81
FR82
FR83
FR91
FR92
FR93
FR94
Name of region
Anatoliki Makedonia, Thraki
Kentriki Makedonia
Dytiki Makedonia
Thessalia
Ipeiros
Ionia Nisia
Dytiki Ellada
Sterea Ellada
Peloponnisos
Attiki
Voreio Aigaio
Notio Aigaio
Kriti
Galicia
Principado de Asturias
Cantabria
Pais Vasco
Comunidad Foral de Navarra
La Rioja
Aragon
Comunidad de Madrid
Castilla y Leon
Castilla-La Mancha
Extremadura
Cataluña
Comunidad Valenciana
Baleares
Andalucia
Murcia
Ceuta y Melila
Canarias
Ile de France
Champagne-Ardenne
Picardie
Haute Normandie
Centre
Basse Normandie
Bourgogne
Nord-Pas de Calais
Lorraine
Alsace
Franche-Comté
Pays de la Loire
Bretagne
Poitou-Charente
Aquitaine
Midi-Pyrenées
Limousin
Rhône-Alpes
Auvergne
Languedoc-Roussillon
Provence-Alpes-Côte d’Azur
Corse
Guadeloupe
Martinique
Guyane
La Réunion
81
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
110
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
C
No
7
8
Country
Ireland
Italia
9 Luxembourg (GD)
10 Nederland
11 Österreich
12 Portugal
13 Suomi/Finland
R Code
IE
IT11
IT12
IT13
IT2
IT31
IT32
IT33
IT4
IT51
IT52
IT53
IT6
IT71
IT72
IT8
IT91
IT92
IT93
ITA
ITB
LU
NL11
NL12
NL13
NL21
NL22
NL23
NL31
NL32
NL33
NL34
NL41
NL42
AT11
AT12
AT13
AT21
AT22
AT31
AT32
AT33
AT34
PT11
PT12
PT13
PT14
PT15
PT2
PT3
FI13
FI14
FI15
FI16
FI17
FI2
Name of region
Ireland (NUTS1)
Piemonte
Valle d'Aosta
Liguria
Lombardia
Trentino-Alto Adige
Veneto
Friuli-Venezia-Giulia
Emilia-Romagna
Toscana
Umbria
Marche
Lazio
Abruzzo
Molise
Campania
Puglia
Basilicata
Calabria
Sicilia
Sardegna
Luxembourg (GD)
Groningen
Friesland
Drenthe
Overijssel
Gelderland
Flevoland
Utrecht
Noord-Holland
Zuid-Holland
Zeeland
Noord-Brabant
Limburg (NL)
Burgerland
Niederösterreich
Wien
Kärnten
Steiermark
Oberösterreich
Salzburg
Tirol
Vorarlberg
Norte
Centro (P)
Lisboa e Vale do Tejo
Alentejo
Algarve
Açores
Madeira
Itä-Suomi
Väli-Suomi
Pohjois-Suomi
Uusimaa
Etelä-Suomi
Àland
82
Study of low fertility in the regions of the European Union: places, timetable and causes
R No
C
No
Country
167 14 Sverige
168
169
170
171
172
173
174
175 15 United Kingdom
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
211
R Code
SE01
SE02
SE04
SE06
SE07
SE08
SE09
SE0A
UKC1
UKC2
UKD1
UKD2
UKD3
UKD4
UKD5
UKE1
UKE2
UKE3
UKE4
UKF1
UKF2
UKF3
UKG1
UKG2
UKG3
UKH1
UKH2
UKH3
UKI1
UKI2
UKJ1
UKJ2
UKJ3
UKJ4
UKK1
UKK2
UKK3
UKK4
UKL1
UKL2
UKM
UKN
Name of region
Stockholm
Östra Mellansverige
Sydsverige
Norra Mellandsverige
Mellersta Norrland
Övre Norrland
Smaland met Öarna
Västsverige
Tees Valley and Durham
Northumberland, Tyne and Wear
Cumbria
Cheshire
Greater Manchester
Lancashire
Merseyside
East Riding and North Lincolnshire
North Yorkshire
South Yorkshire
West Yorkshire
Derbyshire and Nottinghamshire
Leicestershire, Rutland and Northants
Lincolnshire
Herefordshire, Worcestershire and Warks
Shropshire and Staffordshire
West Midlands
East Anglia
Bedfordshire and Hertfordshire
Essex
Inner London
Outer London
Berkshire, Bucks and Oxfordshire
Surrey, East and West Sussex
Hampshire ans Isle of Wight
Kent
Gloucestershire, Wiltshire and North Somerset
Dorset and Somerset
Cornwall and Isles of Scilly
Devon
West Wales and the Valleys
East Wales
Scotland (NUTS1)
Northern Ireland
83
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 2a – Summary of the availability of data on population numbers by gender and year of
age in the Regio domain of New Cronos (on 1 January)
Prior 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
BE Belgium
N
O
O
O
O
O
O
O
DK Denmark
N
O
O
O
O
O
O
O
DE FRG (and GDR from N
N
I*
I*
I*
I*
I*
I*
91)
GR Greece
N
O
O
O
O
O
O
O
ES Spain
N
O
O
O
O
O
O
O
FR France
N
I
I
I
I
I
I
I
IE Ireland
N
I
I
I
I
I
I
I
IT Italy
N
O
O
O
O
O
O
O
LU Luxembourg
N
O
O
O
O
O
O
O
NL Netherlands
N
O
O
O
O
O
O
O
AT Austria
N
O
O
O
O
O
O
O
PT Portugal
N
N
O
O
O
O
O
O
FI Finland
N
O
O
O
O
O
O
O
SE Sweden
N
I
I
I
I
I
I
I
UK United Kingdom
N
N
N
I*
I
I
I
I
O: available; N: not available; I: available but incomplete at NUTS2 level.
* Five-year structures available, but not annual.
° Numbers estimated in thousands.
O
O
I*
O
O
O
O
O
O
O
O
O
O
O
N
O
O
I
I*
O
O
O
O
O
O
O
I*
O
O
O
I*
O
O
O
O
O
O
O
I
O
O
N
I*
O
O
O
O
O
O
O
I*
O
O
I
O
O
O
O
O
O
O
O
I
N
O
O
O
O
O
O
O
O
O
O
I°
Annex 2b – Summary of the availability of data on births by year of age of the mother in the
Regio domain of New Cronos
Prior 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 After
BE Belgium
N
O
O
O
O
O
O
I
DK Denmark
N
O
O
O
O
O
O
O
DE FRG (and GDR from N
N
N
N
N
N
N
N
91)
GR Greece
N
O
O
O
O
O
O
P
ES Spain
N
O
O
O
O
O
O
O
FR France
N
I
I
I
I
I
I
I
IE Ireland
N
I
I
I
I
O
O
O
IT Italy
N
O
O
O
O
O
O
O
LU Luxembourg
N
O
O
O
O
O
O
O
NL Netherlands
N
O
O
O
O
O
O
O
AT Austria
N
O
O
O
O
O
O
O
PT Portugal
N
N
O
O
O
O
O
O
FI Finland
N
O
O
O
O
O
O
O
SE Sweden
N
I
I
I
I
I
I
I
UK United Kingdom
N
I
I
I
I
I
I
I
O: available; N: not available; I: available but incomplete at NUTS2 level.
P: problem of concordance for a region or set of regions.
84
N
O
N
N
O
N
N
O
N
N
O
N
N
N
N
O
O
I
O
P
O
O
O
O
O
O
I
O
O
O
O
N
O
O
O
O
O
O
O
N
N
O
O
N
O
O
O
O
O
O
O
O
O
O
O
N
O
O
O
O
O
O
O
N
N
N
N
N
N
N
N
N
N
N
N
Study of low fertility in the regions of the European Union: places, timetable and causes
Annex 3 – Formulae for the transformation of a distribution of births by age reached in the year
into a distribution in completed ages 30
N 12T = N 12G + 0, 4 N 13G assuming no births prior to the mother’s twelfth birthday
N 13T = 0,6 N 13G + 0,4 N 14G assuming that there are more births, in the generation reaching the age of 13
in the year in question, at 13 years complete (60%) than at 12 years complete (40%)
N 14T = 0,6 N 14G + 0,5 N 15G assuming that there are more births, in the generation reaching the age of 14
in the year in question, at 14 years complete (60%) than at 13 years complete (40%)
(
)
N iT = 0,5 N iG + N iG+1 for the ages (i) between 15 and 47; it is assumed that there is a uniform
distribution between the two triangles of the generation
T
G
G
N 48
= 0,5 N 48
+ 0, 4 N 49
T
G
N 49
= 0,6 N 49
given that births occurring at ages above 49 are aggregated with those occurring at 49.
-------------------
30
For a theoretical explanation of this transformation, see Council of Europe, Recent demographic developments
in Europe, Strasbourg, Council of Europe Publishing, pp. 25-27.
85
Study of low fertility in the regions of the European Union: places, timetable and causes
WORKING PAPERS∗
E4/1997-1
Comparing data sources for measuring international migration in Central
and Eastern Europe
Michel Poulain - Université Catholique de Louvain
E4/1997-2
La mesure des courants de migration internationale entre la Belgique,
d’une part, le Danemark et la Suède, d’autre part
Ingvar Johannesson, Statistics Sweden, Örebro
Anita Lange, Danmarks Statistics, Copenhagen
Michel Poulain, Institut National de Statistique, Bruxelles
E4/1997-4
Birth expectations and their use in fertility forecasting
W. Van Hoorn, Statistics Netherlands
N. Keilman, Statistics Norway
E4/1997-5
Long-term internal migration scenarios for the countries of the European
Union
Nicole Van Der Gaag, Evert Van Imhoff, Leo VanWissen, NIDI
E4/1997-6
Long-term international migration scenarios for the European Economic
Area
Andries De Jong, Harry Visser, Statistics Netherlands
E4/1997-7
Now-casts of live births and deaths for 15 countries of the European
Economic Area
J. De Beer, K. Koldijk
E4/1997-8
Improved migration statistics - An evaluation
Ingrid Melin – Statistics Sweden
3/1998/E/n°1
Indicators of migration between the Republic of Ireland and the United
Kingdom
Central Statistics Office, Ireland
Office for National Statistics, United Kingdom
3/1998/E/n°2
Swiss-Swedish joint study on cohort-based asylum statistics
Torsten Torstensson, Krister Isaksson, Swedish Immigration Board
Stéphane Cotter, Marcel Heiniger, Swiss Federal Statistical Office Bern
3/1998/E/n°3
Analysis and projection of mortality by gender, age/generation, and main
causes of death for France, Italy, the Netherlands, and Norway
E. Tabeau, P. Ekamper, C. Huisman, A. Bosch, NIDI
3/1998/E/n°4
Stock de migrants et population d’origine étrangère – Comparaison des
concepts dans les pays de l’UE
B. Krekels, M. Poulain
3/1998/E/n°7
La mesure de la migration clandestine en Europe
D. Delaunay, G. Tapinos
∗
Most of the Working Papers are available in the internet site: http://europa.eu.int/comm/eurostat/
For requests: [email protected]
87
Study of low fertility in the regions of the European Union: places, timetable and causes
3/1998/E/n°8
Long-term mortality scenarios for the countries of the European Economic
Area
W. van Hoorn, J. de Beer
3/1998/E/n°12
International Migration Statistics in the Mediterranean Countries: current
data sources and statistics available from international organisations
D. Pearce
3/1998/E/n°15
Documentation of Eurostat’s database on international Migration: Central
European Countries, Cyprus and Malta
J. Bowman, J. Clarke, E. van Dam, V. Eidukiene, A. Herm, H. Prophet, I.
Salt, A. Singleton, U. Usackis
3/1998/E/n°16
Documentation of Eurostat’s database on International Migration: Labour
Data.
J. Clarke, M. Clarke, E. Van Dam, I. Salt, G. Cantisani, H. Eding,
A. Singleton
3/1998/E/n°17
Long-term fertility scenarios for the countries of the European Economic
Area.
A. de Jong – Statistics Netherlands
3/1998/E/n°18
Draft manual on statistics of Asylum-seekers and refugees
R. van der Erf
3/1998/E/n°19
Asylum-Seekers and Refugees a statistical report
Volume 3: Central European Countries
R. van der Erf, E. van Dam, NIDI
3/1998/E/n°20
International Migration Statistics in the Mediterranean countries: current
data sources and statistics available in the countries
Revised version, D. Pearce, D. Rotolone
3/1998/E/n°21
International Migration Statistics in the Mediterranean Countries: Report
on the legal situation
Revised version, C. Hein
3/1999/E/n°3
Investigation of the methods of estimating migrant totals
Sharon Bruce, Dave Elliot
3/1999/E/n°4
La fiabilité de la mesure des courants de migration internationale entre la
Belgique et l’Italie
E. Bisogno, M. Poulain
3/1999/E/n°5
Confrontation des statistiques de migration intra-européennes : Vers une
matrice complète ?
Michel Poulain
3/1999/E/n°6
Links between Stocks and Flows of the Foreign Population in Germany
Manfred Bretz
3/1999/E/n°7
Now-casts on international migration
Part 1: creation of an information database
Aarno Sprangers, Hans Sanders. Statistics Netherlands
3/1999/E/n°8
National and Regional Population Trends in the European Union
N. van der Gaag, L. van Wissen, E. van Imhoff, C. Huisman, NIDI
3/1999/E/n°9
Analysis and Forecasting of International Migration by Major Groups (Part
II)
N. van der Gaag , L. van Wissen, NIDI
88
Study of low fertility in the regions of the European Union: places, timetable and causes
3/1999/E/n°10
Guidelines and Table programme for the Community Programme of
Population and Housing Censuses in 2001
Volume II: Table Programme
Leitlinien und Tabellenprogramm für das gemeinschaftliche Programm der
Volks- und Wohnungszählungen im Jahre 2001
Vol. 2: Tabellenprogramm
Orientations relatives et Programme de Tableaux au Programme de
Recensements de la Population et des Habitations en 2001
Volume II : Programme de Tableaux
3/1999/E/n°11
Statistiques sur la migration internationale dans les pays méditerranéens.
Rapport de mission : Algérie, Maroc, Tunisie
Jamel Bourchachen
3/1999/E/n°12
International Migration Statistics in the Mediterranean Countries Mission
Report: Cyprus, Malta, Egypt
David Pearce, Barry Little
3/1999/E/n°13
International Migration Statistics in the Mediterranean Countries Mission
Report: Palestine, Jordan, Israel
Mauri Nieminen
3/1999/E/n°14
International Migration Statistics in the Mediterranean Countries Mission
Report: Turkey, Syria, Lebanon.
Jeannette Schoorl
3/1999/E/n°15
Report on demographic situation in 12 Central European countries, Cyprus
and Malta in 1997
3/1999/E/n°17
Population, migration and census in Eurostat – A guide to existing data
and publications
T. Chrissanthaki
3/1999/E/n°18
International Migration Statistics in the Mediterranean Countries.
Summary report of missions to the 12 project countries
David Pearce
3/2000/E/n°3
Documentation of Eurostat’s database on international migration :
Acquisition of Citizenship
J. Clarke, E. van Dam, H. Prophet, V. Robinson, I. Salt, A. Singleton, UCL
3/2000/E/n°4
Documentation of Eurostat’s database on international migration :
Population by country of birth
M. van de Klundert, NIDI
3/2000/E/n°5
Push and pull factors of international migration
Country report – Italy
3/2000/E/n°6
Facteurs d’attraction et de répulsion à l’origine des flux migratoires
internationaux
Rapport national – Le Maroc
3/2000/E/n°7
Push and pull factors of international migration
Country report – Egypt
3/2000/E/n°8
Push and pull factors of international migration
Country report – Turkey
3/2000/E/n°9
Push and pull factors of international migration
Country report – Spain
89
Study of low fertility in the regions of the European Union: places, timetable and causes
3/2000/E/n°10
Push and pull factors of international migration
Country report – Ghana
3/2000/E/n°11
Push and pull factors of international migration
Country report – The Netherlands
3/2000/E/n°12
Facteurs d’attraction et de répulsion à l’origine des flux migratoires
internationaux
Rapport national – Sénégal
3/2000/E/n°13
National and Regional Trends in the Labour Force in the European Union,
1985 – 2050
A. de Jong, R. Broekman. Statistics Netherlands
3/2000/E/n°14
Facteurs d’attraction et de répulsion à l’origine des flux migratoires
internationaux
Rapport comparatif
3/2000/E/n°16
National reports on the demographic situation in 12 central European
Countries, Cyprus and Malta in 1998
3/2001/E/n°5
Regional International Migration and Foreign Population within the EU A feasibility study
Final Report
N. van der Gaag, L. van Wissen – NIDI
J. Salt, Z. Lynas, J. Clarke – University College London
3/2001/E/n°6
Regional Differences in Labour Force Activity Rates of Persons Aged 55+
within the European Union
J.D. Vlasblom, G. Nekkers – Research Center for Education and the
Labour market, Maastricht University
3/2001/E/n°7
Regional Labour Force Differences among Young People in the European
Union
A.E. Green, D.W. Owen, R.A. Wilson – University of Warwick, UK
3/2001/E/n°8
Now-casts on International Migration
Part II : Searching for the most reliable method
H. Schapendonk-Maas, J. de Beer – Statistics Netherlands
3/2001/E/n°9
The Evaluation of Regional Population Projections for the European Union
P. Rees, M. Kupiszewski, H. Eyre, T. Wilson, H. Durham
3/2001/E/no 10
National reports on the demographic situation in 12 central European
Countries, Cyprus and Malta in 1999
3/2001/E/no 11
Sub-national cause-of-death profiles of chronic disease mortality in the
countries of the European Union
C. Huisman, E. Tabeau - NIDI
3/2002/E/no 17
Analysis and Forecasting of International Migration by Major Groups (Part
III)
H. Hilderink, N. van der Gaag, L. van Wissen, R. Jennissen, A. Roman –
NIDI
J. Salt, J. Clarke, C. Pinkerton – UCL
3/2002/E/no 19
National reports on the demographic situation in 12 central European
Countries, Cyprus and Malta in 2000
90
Study of low fertility in the regions of the European Union: places, timetable and causes
3/2003/E/no 25
Demographic statistics: Definitions and methods of collection in 31
european countries
Statistiques démographiques: Définitions et méthodes de collecte dans 31
pays européens
Bevölkerungsstatistik: Definitionen und Methoden zur Erhebung in
31 europäischen Ländern
3/2003/E/no 26
Methodology for the calculation of Eurostat’s demographic indicators
G. Calot, J.-P. Sardon – ODE
Méthodologie relative au calcul des indicateurs démographiques
d’Eurostat
G. Calot, J.-P. Sardon - ODE
3/2003/E/no 27
3/2004/F/no 01
3/2004/F/no 02
3/2004/F/no 03
3/2004/F/no 04
Basic methodology for the recalculation of intercensal population
estimates
M. Poulain – GéDAP, A. Herm – Statistical Office of Estonia
Documentation of the 2000 Round of Population and Housing Censuses in
the EU, EFTA and Candidate Countries
Part I+II
University of Thessaly
Documentation of the 2000 Round of Population and Housing Censuses in
the EU, EFTA and Candidate Countries
Part III
University of Thessaly
Regions with high life expectancy in the European Union: Places, periods
and causes
A. Gabadinho, J. Duchêne, P. Wanner, M. Willems
Institut de démographie, Université Catholique de Louvain
Study of low fertility in the regions of the European Union:
places, periods and causes
J. Duchêne, A. Gabadinho, M. Willems, P. Wanner
Institut de démographie, Université Catholique de Louvain
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