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
THEME 3 Population EUROPEAN and social COMMISSION 3conditions Europe Direct is a service to help you find answers to your questions about the European Union New freephone number: 00 800 6 7 8 9 10 11
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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
tftr16 tftr18tftr17 tftr44tftr43 tftr19 tftr15 tftr42 tftr41 tftr45 tftr40 tftr20 tftr14 tftr46 tftr39 ,5 tftr47 tftr38 tftr21 tftr13 tftr48 tftr49 tftr37 tftr36 tftr22 tftr12 tftr35 tftr23 tftr34 Component 2 tftr33 0,0 tftr24 tftr32 tftr31
tftr25 tftr30
-,5 tftr26 tftr29
tftr27 tftr28
-1,0 -1,0 -,5 0,0 ,5 1,0 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
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”, 1960- 2000 ...... 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 19th 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
13 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
14 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
15 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).
16 Study of low fertility in the regions of the European Union: places, timetable and causes
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
17 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 out- migration, 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.
18 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.
19 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”.
20 Study of low fertility in the regions of the European Union: places, timetable and causes
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 socio- economic 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 intra- national.
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.
21 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 493, 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 sub- periods 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 sub- periods (1991-1993, 1994-1996 and 1997-1999) in order to calculate the mean annual distributions of births focusing on 1992, 1995 and 19986. 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. 6 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.
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 Characteristic 1991-1993 1994-1996 1997-1999 TFR Mean* 1.55 1.47 1.48 Standard deviation 0.302 0.280 0.258 Minimum 0.83 0.82 0.81 Maximum 2.18 2.07 2.04 Mean age Mean* 28.2 28.6 29.0 Standard deviation 1.122 1.073 1.087 Minimum 25.0 26.1 26.8 Maximum 30.5 31.2 31.9 Standard Mean* 5.14 5.15 5.21 deviation of Standard deviation 0.345 0.371 0.400 age at Minimum 4.22 4.31 4.30 childbirth Maximum 6.20 6.16 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 sub- periods. 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 sub- periods, 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 Ceuta y Melila Belgium Denmark Açores Germany 1,75 Greece Spain TFR 1997-99 France Ireland 1,50 Italy 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 Greece 15 Spain France 10 Ireland Italy 5 Luxembourg Netherlands 0 Austria Valle D'Aosta Portugal -5 Finland Sueden -10 United Kingdom
-15 n = 73 n = 7 -20 -25 -20 -15 -10 -5 0 5 10 Relative increase 1992-95
26 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 regions10 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 Halle Finland Sueden 26 United Kingdom 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 6,0 Belgium Ceuta-y-Melila Denmark Açores Outer London Germany Greece Standard deviation 1997-99 Spain 5,5 France Ireland Italy Luxembourg 5,0 Netherlands 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
.5
tfrt92 0.0 tfrt98 tfrt95 Component 2
sdt92 -.5 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 (age- specific fertility rates divided by the TFR) for the three sub-periods taken together (603 regions- periods 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 sub- periods Cluster Parameter 1991-93 1994-96 1997-99 1 N 9 9 9 TFR mean 0.88 0.86 1.10 AC mean 25.2 26.4 27.2 SD mean 4.80 4.78 4.80 2 N 23 23 23 TFR mean 1.15 1.07 1.10 AC mean 29.6 30.3 30.8 SD mean 4.89 4.83 4.88 3 n 24 24 24 TFR mean 1.45 1.34 1.34 AC mean 29.0 29.5 29.9 SD mean 5.25 5.22 5.28 4 n 55 55 55 TFR mean 1.46 1.39 1.40 AC mean 27.6 28.0 28.4 SD mean 5.19 5.20 5.24 5 n 16 16 16 TFR mean 1.66 1.60 1.66 AC mean 29.0 29.2 29.4 SD mean 4.49 4.50 4.51 6 n 30 30 30 TFR mean 1.81 1.72 1.72 AC mean 27.4 27.8 27.9 SD mean 5.47 5.60 5.75 7 n 30 30 30 TFR mean 1.87 1.73 1.70 AC mean 28.4 28.7 29.0 SD mean 5.03 5.01 5.05 8 n 14 14 14 TFR mean 1.86 1.77 1.75 AC mean 29.0 29.3 29.5 SD mean 5.63 5.64 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 Parameter 1991-93 1994-96 1997-99 1991-99 1 N 9 9 9 27 TFR mean 0.88 0.86 1.10 0.95 AC mean 25.2 26.4 27.2 26.3 SD mean 4.80 4.78 4.80 4.79 2 n 21 25 26 72 TFR mean 1.13 1.06 1.08 1.09 AC mean 29.7 30.3 30.8 30.3 SD mean 4.88 4.87 4.94 4.90 3 n 66 62 53 181 TFR mean 1.44 1.38 1.38 1.40 AC mean 27.8 28.1 28.4 28.09 SD mean 5.23 5.24 5.30 5.25 4 n 9 15 31 55 TFR mean 1.47 1.41 1.46 1.45 AC mean 29.5 29.7 29.7 29.7 SD mean 5.07 5.08 5.11 5.09 5 n 10 11 11 32 TFR mean 1.60 1.56 1.62 1.59 AC mean 29.2 29.4 29.6 29.4 SD mean 4.39 4.44 4.45 4.43 6 n 10 24 23 57 TFR mean 1.86 1.74 1.72 1.75 AC mean 28.8 28.7 28.9 28.8 SD mean 5.76 5.62 5.78 5.71 7 n 49 39 30 118 TFR mean 1.82 1.72 1.75 1.77 AC mean 28.5 28.8 29.1 28.8 SD mean 5.03 4.99 5.01 5.01 8 n 27 16 18 61 TFR mean 1.81 1.75 1.75 1.78 AC mean 27.4 27.4 27.5 27.4 SD mean 5.49 5.67 5.77 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 TFR91-93 TFR94-96 TFR97-99 1 ES12 Principado de Asturias 0.93 0.83 0.81 2 ES11 Galicia 1.11 0.95 0.91 3 ES41 Castilla y León 1.09 0.96 0.93 4 ES13 Cantabria 1.07 0.94 0.96 5 IT13 Liguria 1.01 0.92 0.97 6 ES21 Pais Vasco 0.96 0.93 0.98 7 GR24 Sterea Ellada 1.23 1.05 1.00 8 ITB Sardegna 1.24 1.07 1.02 9 IT51 Toscana 1.05 0.99 1.04 10 DEE1 Dessau 0.87 0.85 1.05 11 GR21 Ipeiros 1.31 1.13 1.06 12 DED3 Leipzig 0.86 0.82 1.06 13 IT4 Emilia-Romagna 1.02 0.99 1.07 14 IT33 Friuli-Venezia Giulia 1.06 0.99 1.07
13 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 TFR91-93 TFR4-96 TFR7-99 15 ES24 Aragón 1.15 1.09 1.08 16 DEE2 Halle 0.89 0.85 1.09 17 IT11 Piemonte 1.06 1.02 1.09 18 IT71 Abruzzo 1.29 1.14 1.09 19 GR25 Peloponnisos 1.33 1.16 1.09 20 DEG Thüringen 0.87 0.86 1.10 21 DE4 Brandenburg 0.83 0.85 1.11 22 IT12 Valle d'Aosta 1.06 1.14 1.11 23 IT52 Umbria 1.19 1.09 1.11 24 IT92 Basilicata 1.31 1.18 1.12 25 DEE3 Magdeburg 0.92 0.88 1.12 26 ES23 La Rioja 1.12 1.08 1.12 27 DE8 Mecklenburg-Vorpommern 0.87 0.85 1.13 28 IT53 Marche 1.19 1.09 1.13 29 DED1 Chemnitz or DED Sachsen 0.91 0.89 1.13 30 DED2 Dresden 0.88 0.87 1.13 31 IT2 Lombardia 1.12 1.09 1.14 32 IT32 Veneto 1.10 1.07 1.15 33 IT6 Lazio 1.25 1.14 1.16 34 ES52 Comunidad Valenciana 1.32 1.19 1.18 35 DE3 Berlin 1.11 1.10 1.19 36 IT72 Molise 1.35 1.21 1.19 37 ES22 Comunidad Foral de Navarra 1.17 1.14 1.19 38 GR23 Dytiki Ellada 1.45 1.31 1.19 39 AT11 Burgenland 1.36 1.29 1.20 40 ES3 Comunidad de Madrid 1.24 1.16 1.20 41 ES51 Cataluña 1.23 1.17 1.22 42 ES43 Extremadura 1.54 1.33 1.22 43 AT13 Wien 1.38 1.29 1.22 44 DE6 Hamburg 1.24 1.20 1.23 45 IT93 Calabria 1.49 1.29 1.24 46 ES7 Canarias (ES) 1.39 1.25 1.25 47 ES42 Castilla-la Mancha 1.52 1.35 1.27 48 AT22 Steiermark 1.44 1.35 1.27 49 GR12 Kentriki Makedonia 1.31 1.31 1.28 50 DEC Saarland 1.32 1.27 1.30 51 ES61 Andalucia 1.57 1.37 1.31 52 GR22 Ionia Nisia 1.48 1.44 1.31 53 AT21 Kärnten 1.48 1.43 1.32 54 GR3 Attiki 1.32 1.31 1.32 55 GR14 Thessalia 1.48 1.43 1.33 56 IT91 Puglia 1.54 1.35 1.33 57 DE72 Gießen 1.33 1.32 1.36 58 PT14 Alentejo 1.47 1.28 1.37 59 DE91 Braunschweig 1.34 1.29 1.37 60 GR13 Dytiki Makedonia 1.44 1.40 1.37 61 DE5 Bremen 1.31 1.30 1.37 62 DE71 Darmstadt 1.30 1.30 1.37 63 DE12 Karlsruhe 1.36 1.31 1.37 64 AT12 Niederösterreich 1.55 1.49 1.38 65 DE24 Oberfranken 1.43 1.33 1.39 66 DE92 Hannover 1.35 1.32 1.39 67 AT32 Salzburg 1.55 1.47 1.40 68 AT33 Tirol 1.58 1.49 1.40 69 PT12 Centro (PT) 1.47 1.37 1.40 70 DE21 Oberbayern 1.38 1.34 1.41 71 DE25 Mittelfranken 1.41 1.35 1.41 72 DEA1 Düsseldorf 1.42 1.36 1.41 73 ES53 Illes Balears 1.50 1.35 1.41 74 IT31 Trentino-Alto Adige 1.39 1.35 1.41 75 DE26 Unterfranken 1.47 1.38 1.41 76 DE13 Freiburg 1.42 1.37 1.42 77 ITA Sicilia 1.68 1.46 1.42 78 ES62 Murcia 1.63 1.43 1.42 79 DEF Schleswig-Holstein 1.41 1.36 1.42 80 DEA2 Köln 1.41 1.37 1.42 81 DE73 Kassel 1.42 1.38 1.43 82 DEB Rheinland-Pfalz 1.45 1.38 1.44 83 GR43 Kriti 1.59 1.51 1.44 84 DE22 Niederbayern 1.47 1.39 1.44
40 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, Austria14), 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 1,50 Austria 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 Rheinland- Pfalz 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
2,50 ES12 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 TFR IT8 2,25 IT93 ITB IT lF VLF
1,50
0,75 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Years
Sources: Rallu J.L., 1983; Terra-Abrami V. & Sorvillo M., 1993; our calculations.
46 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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ETCHELECOU A., 2000, Approche des territoires de fécondité en France d’après les générations 1889 à 1949, in Régimes démographiques et territoires: les frontières en question, AIDELF Colloque international de la Rochelle, Paris, PUF, pp. 315-327 EUROSTAT, 2002a, Regions: Statistical yearbook 2002, Luxembourg, Office for Official Publications of the European Communities, 151 p. EUROSTAT, 2002b, European social statistics. Demography, Luxembourg, Office for Official Publications of the European Communities, 171 p. FESTY P., 1981, Diversité des comportements démographiques dans les pays occidentaux depuis un siècle: l’exemple de la fécondité, Revue suisse d’économie et de statistique, 3, pp. 453-478 FINNÄS F., 1991, The Effect of Religion of Fertility Differentials, Yearbook of Population Research in Finland, 29, pp. 28-35 GARCIA BALLESTEROS A., POZO RIVERA E. & MAYORAL M., 1998, Pratique religieuse et diminution de la fécondité en Espagne, Revue Belge de Géographie, 4, pp 407-418 GOLINI A., 1998, How low can fertility be? An empirical exploration, Population and Development Review, 24(1), pp. 59-73 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 GOZALVEZ PEREZ V., 1989, Crise et contrastes spatiaux de la fécondité espagnole, Espace, Populations, Sociétés, 2, pp. 201-214 HANK K., 2001, Regional Fertility Differences in Western Germany: An overview of the Literature and Recent Descriptive Findings, International Journal of Population Geography, 7, pp. 243-257 HANK K., 2002, Regional Social Contexts and Individual Fertility Decisions: A Multilevel Analysis of First and Second Births in Western Germany, European Journal of Population, 18, pp. 281- 299 HANK K. & KREYENFELD M., 2001, Childcare and Fertility in (Western) Germany, Rostock, MPIDR Working Paper, WP 2001-019, http://www.demogr.mpg.de/Papers/Working/wp-2001- 019.pdf IUSSP, 2001, International Perspectives on Low Fertility: Trends, Theories and Policies, Information available at: http://demography.anu.edu.au/VirtualLibrary/ConferencePapers/IUSSP2001/Program.html JACQUIER J. & KIRTHICHANDRA A., 2001, Les régions françaises dans l’Union européenne en 1998, INSEE Première, 810, pp. 1-4 KOHLER H.-P., BILLARI F. & ORTEGA J.A., 2002, The Emergence of Lowest-Low Fertility in Europe During the 1990s, Population and Development Review, 28 (4), pp. 641-680 KONIETZKA D. & KREYENFELD M., 2002, Travail féminin et fécondité hors-mariage en Allemagne au cours des années 90: comparaison entre l’Est et l’Ouest, Population-F, 57 (2), pp. 359-387 LAIHONEN A. & EVERAERS P., 1998, Changes in fertility and family sizes in Europe, Presentation to the Siena Group Meeting, Sydney (Australia), 7-9 December 1998, 14 p. LEGRAND J., 1992, La fécondité des départements de la France métropolitaine en 1989-90 comparée à celle du début de la décennie 80 (1981-1982), Population, 47 (3), pp.762-771
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LESTHAEGHE R. & NEELS K., 2002 , From the First to the Second Demographic Transition: An Interpretation of the Spatial Continuity of Demographic Innovation in France, Belgium and Switzerland, European Journal of Population, 18, pp. 325-360 LESTHAEGHE R. & WILLEMS P., 1999, Is Low Fertility a Temporary Phenomenon in the European Union?, Population and Development Review, 25 (2), pp. 211-228 LESTHAEGHE R. & WILSON C., 1982, Les modes de production, la laïcisation et le rythme de baisse de la fécondité en Europe de l’Ouest de 1870 à 1930, Population, 37 (3), pp. 623-645 LINCOT L. & LUTINIER B., 1998, Les évolutions démographiques départementales et régionales entre 1975 et 1994, INSEE-Résultats. Démographie-Société, 67-68, 242 p. LIVI-BACCI M. & SALVINI S., 2000, Trop de familles et trop peu d’enfants: la fécondité en Italie depuis 1960, Cahiers québecois de démographie, 29 (2), 231-254 MICHIELIN F., 2002, Lowest low fertility in an urban context. When migration plays a key role, Rostock, MPIDR Working Paper, WP 2002-050, http://www.demogr.mpg.de/Papers/Working/wp-2002-050.pdf MUNOZ-PEREZ F.,1991, Les naissances hors mariage et les conceptions prénuptiales en Espagne depuis 1975: II. Diversité et évolution régionales, Population, 46 (5), pp. 1207-1248 NAUCK B., 1993, Frauen und ihre Kinder : Regionale und soziale Differenzierungen in Einstellungen zu Kindern, im generativen Verhalten und in den Kindschaftsverhältnis-sen, in Nauck B (ed.), Lebensgestaltung von Frauen. Eine Regionalanalyse zur Integration von Familien- und Erwerbstätigkeit im Lebensverlauf, Weinheim and München, Juventa, pp. 45-86 NEYER G., 2003, Family Policies and Low Fertility in Western Europe, Rostock, MPIDR Working Paper, WP 2003-021, http://www.demogr.mpg.de/Papers/Working/wp-2003-021.pdf POPULATION DIVISION, 2000, Below Replacement Fertility, Population Bulletin of the United Nations, Special Issue 40/41, 348 p. PRIOUX F., 1993a, Aspects régionaux de la formation de la famille et de l'illégitimité en Autriche, Population, 48(3), pp. 735-752 PRIOUX F., 1993b, La fécondité hors-mariage en France depuis 1968: évolution des contrastes interdépartementaux, Espace, Populations, Sociétés, 2, pp. 281-292 RALLU J.L., 1983, Permanence des disparités régionales de la fécondité en Italie ?, Population, 38 (1), pp. 29-60 RYCHTARIKOVA J., 2000, Analyse nationale et spatiale du comportement procréateur en République Tchèque (fécondité et avortement), 1987-1996, in Régimes démographiques et territoires : les frontières en question, AIDELF Colloque international de la Rochelle, Paris, PUF, pp. 183-202 SANTINI A., 1986, Aree problematiche di fecondità, incluso aborto, Serie documenti e ristampe, 3, Rome, IRP, 88 p. SHRYOCK H.S., SIEGEL J.S. et al., 1976, The Methods and Materials of Demography, Condensed Edition, New York, London, San Francisco, Academic Press SCHWARZ K., 1983, Untersuchung zu den regionalen Unterschieden der Geburtenhäufigkeit, in Akademie für Raumforschung und Landesplanung (ed.), Regionale Aspekte der Bevölkerungsentwicklung unter den Bedingungen des Geburtenrückgangs, Hannover, Vincentz, pp. 7-30. STATISTIK ÖSTERREICH, 1997, Demographisches Jahrbuch. Österreichs 1996, Wien, 330 p. STATISTIK ÖSTERREICH, 2000, Demographisches Jahrbuch. Österreichs 1998, Wien, 381 p.
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STROHMEIER K-P., 1989, „Movers“ und „Stayers“. Räumliche Mobilität und Familienentwicklung, in HERLTH A., STROHMEIER K-P. (eds.), Lenbenslauf und Familienentwicklung: Mikroanalysen des Wandels familialer Lebensformen, Opladen, Leske & Budrich, pp. 165-188. TERRA ABRAMI V. & SORVILLO M.P., 1993, La fécondité en Italie et dans ses régions: analyse par période et par génération, Population, 48 (3), pp. 735-752 TUKEY J.W., 1977, Exploration Data Analysis, s.l., Addison-Wesley, 688 p. WATKINS S.C., 1990, From local to National Communities: The Transformation of Demographic Regimes in Western Europe, 1870-1960, Population and Development Review, 16, pp. 241-272
56 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 R Code Name of region 1 1 Belgique-Belgïe BE1 Région de Bruxelles capitale-Brussels hoofdstad Gewest 2 BE21 Antwerpen 3 BE22 Limburg (B) 4 BE23 Oost-Vlanderen 5 BE24 Vlaams-Brabant 6 BE25 West-Vlanderen 7 BE31 Brabant wallon 8 BE32 Hainaut 9 BE33 Liège 10 BE34 Luxembourg(B) 11 BE35 Namur 12 2 Danmark DK Danmark 13 3 BR Deutschland DE11 Stuttgart 14 DE12 Karlsruhe 15 DE13 Freiburg 16 DE14 Tübingen 17 DE21 Oberbayern 18 DE22 Niederbayern 19 DE23 Oberpfalz 20 DE24 Oberfranken 21 DE25 Mittelfranken 22 DE26 Unterfranken 23 DE27 Schwaben 24 DE3 Berlin 25 DE4 Brandenburg 26 DE5 Bremen 27 DE6 Hamburg 28 DE71 Darmstadt 29 DE72 Gießen 30 DE73 Kassel 31 DE8 Mecklenburg-Vorpommern 32 DE91 Braunschweig 33 DE92 Hannover 34 DE93 Lüneburg 35 DE94 Weser-Ems 36 DEA1 Düsseldorf 37 DEA2 Köln 38 DEA3 Münster 39 DEA4 Detmold 40 DEA5 Arnsberg 41 DEB1 Koblenz 42 DEB2 Trier 43 DEB3 Rheinhessen-Pfalz 44 DEC Saarland 45 DED1 Chemnitz 46 DED2 Dresden 47 DED3 Leipzig 48 DEE1 Dessau 49 DEE2 Halle 50 DEE3 Magdebourg 51 DEF Schleswig-Holstein 52 DEG Thüringen
57 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C No Country R Code Name of region 53 4 Ellada GR11 Anatoliki Makedonia, Thraki 54 GR12 Kentriki Makedonia 55 GR13 Dytiki Makedonia 56 GR14 Thessalia 57 GR21 Ipeiros 58 GR22 Ionia Nisia 59 GR23 Dytiki Ellada 60 GR24 Sterea Ellada 61 GR25 Peloponnisos 62 GR3 Attiki 63 GR41 Voreio Aigaio 64 GR42 Notio Aigaio 65 GR43 Kriti 66 5 Espana ES11 Galicia 67 ES12 Principado de Asturias 68 ES13 Cantabria 69 ES21 Pais Vasco 70 ES22 Comunidad Foral de Navarra 71 ES23 La Rioja 72 ES24 Aragon 73 ES3 Comunidad de Madrid 74 ES41 Castilla y Leon 75 ES42 Castilla-La Mancha 76 ES43 Extremadura 77 ES51 Cataluña 78 ES52 Comunidad Valenciana 79 ES53 Baleares 80 ES61 Andalucia 81 ES62 Murcia 82 ES63 Ceuta y Melila 83 ES7 Canarias 84 6 France FR1 Ile de France 85 FR21 Champagne-Ardenne 86 FR22 Picardie 87 FR23 Haute Normandie 88 FR24 Centre 89 FR25 Basse Normandie 90 FR26 Bourgogne 91 FR3 Nord-Pas de Calais 92 FR41 Lorraine 93 FR42 Alsace 94 FR43 Franche-Comté 95 FR51 Pays de la Loire 96 FR52 Bretagne 97 FR53 Poitou-Charente 98 FR61 Aquitaine 99 FR62 Midi-Pyrenées 100 FR63 Limousin 101 FR71 Rhône-Alpes 102 FR72 Auvergne 103 FR81 Languedoc-Roussillon 104 FR82 Provence-Alpes-Côte d’Azur 105 FR83 Corse 106 FR91 Guadeloupe 107 FR92 Martinique 108 FR93 Guyane 109 FR94 La Réunion
58 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C No Country R Code Name of region 110 7 Ireland IE01 Border, Midlands and Western 111 IE02 Southern and Eastern 112 8 Italia IT11 Piemonte 113 IT12 Valle d'Aosta 114 IT13 Liguria 115 IT2 Lombardia 116 IT31 Trentino-Alto Adige 117 IT32 Veneto 118 IT33 Friuli-Venezia-Giulia 119 IT4 Emilia-Romagna 120 IT51 Toscana 121 IT52 Umbria 122 IT53 Marche 123 IT6 Lazio 124 IT71 Abruzzo 125 IT72 Molise 126 IT8 Campania 127 IT91 Puglia 128 IT92 Basilicata 129 IT93 Calabria 130 ITA Sicilia 131 ITB Sardegna 132 9 Luxembourg (GD) LU Luxembourg (GD) 133 10 Nederland NL11 Groningen 134 NL12 Friesland 135 NL13 Drenthe 136 NL21 Overijssel 137 NL22 Gelderland 138 NL23 Flevoland 139 NL31 Utrecht 140 NL32 Noord-Holland 141 NL33 Zuid-Holland 142 NL34 Zeeland 143 NL41 Noord-Brabant 144 NL42 Limburg (NL) 145 11 Österreich AT11 Burgerland 146 AT12 Niederösterreich 147 AT13 Wien 148 AT21 Kärnten 149 AT22 Steiermark 150 AT31 Oberösterreich 151 AT32 Salzburg 152 AT33 Tirol 153 AT34 Vorarlberg 154 12 Portugal PT11 Norte 155 PT12 Centro (P) 156 PT13 Lisboa e Vale do Tejo 157 PT14 Alentejo 158 PT15 Algarve 159 PT2 Açores 160 PT3 Madeira 161 13 Suomi/Finland FI13 Itä-Suomi 162 FI14 Väli-Suomi 163 FI15 Pohjois-Suomi 164 FI16 Uusimaa 165 FI17 Etelä-Suomi 166 FI2 Àland
59 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C No Country R Code Name of region 167 14 Sverige SE01 Stockholm 168 SE02 Östra Mellansverige 169 SE04 Sydsverige 170 SE06 Norra Mellandsverige 171 SE07 Mellersta Norrland 172 SE08 Övre Norrland 173 SE09 Smaland met Öarna 174 SE0A Västsverige 175 15 United Kingdom UKC1 Tees Valley and Durham 176 UKC2 Northumberland, Tyne and Wear 177 UKD1 Cumbria 178 UKD2 Cheshire 179 UKD3 Greater Manchester 180 UKD4 Lancashire 181 UKD5 Merseyside 182 UKE1 East Riding and North Lincolnshire 183 UKE2 North Yorkshire 184 UKE3 South Yorkshire 185 UKE4 West Yorkshire 186 UKF1 Derbyshire and Nottinghamshire 187 UKF2 Leicestershire, Rutland and Northants 188 UKF3 Lincolnshire 189 UKG1 Herefordshire, Worcestershire and Warks 190 UKG2 Shropshire and Staffordshire 191 UKG3 West Midlands 192 UKH1 East Anglia 193 UKH2 Bedfordshire and Hertfordshire 194 UKH3 Essex 195 UKI1 Inner London 196 UKI2 Outer London 197 UKJ1 Berkshire, Bucks and Oxfordshire 198 UKJ2 Surrey, East and West Sussex 199 UKJ3 Hampshire ans Isle of Wight 200 UKJ4 Kent 201 UKK1 Gloucestershire, Wiltshire and North Somerset 202 UKK2 Dorset and Somerset 203 UKK3 Cornwall and Isles of Scilly 204 UKK4 Devon 205 UKL1 West Wales and the Valleys 206 UKL2 East Wales 207 UKM1 North Eastern Scotland 208 UKM2 Eastern Scotland 209 UKM3 South Western Scotland 210 UKM4 Highlands and Islands 211 UKN 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 2000 BE Belgium O O O O O O O O N Od Od 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 Region TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98 1 BE1 Région Bruxelles-capitale 1.802 1.785 1.788 29.07 29.02 29.25 5.64 5.71 5.68 2 BE21 Antwerpen 1.60 1.55 1.59 28.41 28.44 28.49 4.56 4.59 4.55 3 BE22 Limburg (B) 1.51 1.40 1.47 28.30 28.30 28.50 4.31 4.31 4.33 4 BE23 Oost-Vlaanderen 1.53 1.49 1.51 28.29 28.34 28.59 4.39 4.44 4.43 5 BE24 Vlaams Brabant 1.50 1.48 1.52 29.17 29.30 29.51 4.33 4.35 4.36 6 BE25 West-Vlaanderen 1.67 1.57 1.56 28.09 28.09 28.27 4.22 4.31 4.30 7 BE31 Brabant Wallon 1.71 1.65 1.72 29.36 29.64 29.80 4.64 4.69 4.55 8 BE32 Hainaut 1.66 1.58 1.66 27.78 27.78 27.92 4.97 5.01 5.07 9 BE33 Liège 1.70 1.61 1.65 28.35 28.26 28.47 4.93 4.92 4.91 10 BE34 Luxembourg (B) 1.86 1.77 1.83 28.23 28.18 28.41 4.74 4.67 4.59 11 BE35 Namur 1.77 1.69 1.74 28.24 28.25 28.49 4.86 4.88 4.87 12 DK Danemark 1.74 1.79 1.74 28.77 29.19 29.52 4.80 4.84 4.88 13 DE11 Stuttgart 1.49 1.45 1.48 28.19 28.45 28.78 5.04 5.02 5.02 14 DE12 Karlsruhe 1.36 1.31 1.37 28.18 28.48 28.84 5.10 5.12 5.05 15 DE13 Freiburg 1.42 1.37 1.42 28.53 28.76 29.12 5.02 5.01 4.97 16 DE14 Tübingen 1.53 1.44 1.52 28.54 28.86 29.09 5.00 5.01 4.93 17 DE21 Oberbayern 1.38 1.34 1.41 28.88 29.23 29.46 5.24 5.25 5.34 18 DE22 Niederbayern 1.47 1.39 1.44 28.25 28.54 28.76 5.01 4.99 5.05 19 DE23 Oberpfalz 1.47 1.40 1.45 28.14 28.46 28.72 4.94 4.97 5.03 20 DE24 Oberfranken 1.43 1.33 1.39 27.78 28.18 28.30 5.03 5.00 5.08 21 DE25 Mittelfranken 1.41 1.35 1.41 28.02 28.33 28.58 5.15 5.17 5.20 22 DE26 Unterfranken 1.47 1.38 1.41 28.12 28.48 28.68 4.97 4.99 5.06 23 DE27 Schwaben 1.58 1.47 1.52 28.32 28.60 28.88 5.12 5.11 5.15 24 DE3 Berlin 1.11 1.10 1.19 26.50 27.02 27.56 5.68 5.64 5.65 25 DE4 Brandenburg 0.83 0.85 1.11 25.05 26.35 27.23 4.72 4.79 4.80 26 DE5 Bremen 1.31 1.30 1.37 27.36 27.60 27.79 5.60 5.66 5.76 27 DE6 Hamburg 1.24 1.20 1.23 28.53 28.64 29.02 5.74 5.85 5.92 28 DE71 Darmstadt 1.30 1.30 1.37 28.33 28.60 28.93 5.32 5.36 5.38 29 DE72 Gießen 1.33 1.32 1.36 28.11 28.40 28.65 5.25 5.27 5.24 30 DE73 Kassel 1.42 1.38 1.43 27.92 28.24 28.54 5.03 5.11 5.15 31 DE8 Mecklenburg-Vorpommern 0.87 0.85 1.13 25.09 26.36 27.08 4.79 4.78 4.78 32 DE91 Braunschweig 1.34 1.30 1.37 28.17 28.38 28.61 5.14 5.21 5.30 33 DE92 Hannover 1.35 1.32 1.39 28.39 28.64 28.80 5.18 5.31 5.40 34 DE93 Lüneburg 1.48 1.45 1.54 28.56 28.78 28.95 4.97 5.10 5.23 35 DE94 Weser-Ems 1.56 1.49 1.59 28.49 28.63 28.79 5.01 5.13 5.23 36 DEA1 Düsseldorf 1.42 1.36 1.41 27.49 27.67 27.95 5.36 5.38 5.41 37 DEA2 Köln 1.41 1.37 1.42 27.90 28.09 28.39 5.39 5.40 5.44 38 DEA3 Münster 1.49 1.42 1.49 27.82 28.05 28.31 5.17 5.15 5.21 39 DEA4 Detmold 1.53 1.51 1.58 27.84 28.04 28.25 5.21 5.26 5.31 40 DEA5 Arnsberg 1.47 1.42 1.47 27.24 27.47 27.77 5.26 5.26 5.27 41 DEB Rheinland-Pfalz 1.45 1.38 1.44 28.08 28.34 28.63 5.15 5.23 5.31 44 DEC Saarland 1.32 1.27 1.30 27.89 28.13 28.37 5.30 5.36 5.37 45 DED1 Chemnitz or DED Sachsen 0.91 0.89 1.13 25.44 26.70 27.43 4.79 4.70 4.73 46 DED2 Dresden 0.88 0.87 1.13 25.47 26.94 27.71 4.71 4.69 4.73 47 DED3 Leipzig 0.86 0.82 1.06 25.27 26.58 27.36 4.80 4.80 4.86 48 DEE1 Dessau 0.87 0.85 1.05 24.97 26.12 26.80 4.86 4.85 4.82 49 DEE2 Halle 0.89 0.85 1.09 24.99 26.21 26.84 4.91 4.85 4.85 50 DEE3 Magdeburg 0.92 0.88 1.12 25.04 26.27 27.01 4.84 4.81 4.86 51 DEF Schleswig-Holstein 1.41 1.36 1.42 28.74 28.93 29.04 4.96 5.12 5.23 52 DEG Thüringen 0.87 0.86 1.10 25.21 26.36 27.15 4.80 4.73 4.79 53 GR11 Anatoliki Makedonia, Thraki 1.47 1.48 1.47 26.22 26.83 27.25 5.23 5.37 5.48 54 GR12 Kentriki Makedonia 1.31 1.31 1.28 27.49 28.09 28.59 5.20 5.22 5.33 55 GR13 Dytiki Makedonia 1.45 1.40 1.37 26.58 27.25 28.02 5.01 4.97 4.89 56 GR14 Thessalia 1.48 1.43 1.33 26.56 27.26 28.14 5.20 5.11 5.19 57 GR21 Ipeiros 1.31 1.13 1.06 27.02 27.88 28.54 5.14 5.16 5.13 58 GR22 Ionia Nisia 1.48 1.44 1.31 26.91 27.51 28.36 5.24 5.11 5.21 59 GR23 Dytiki Ellada 1.45 1.31 1.19 27.48 27.98 28.53 5.36 5.31 5.38 60 GR24 Sterea Ellada 1.23 1.05 1.00 26.88 27.47 28.10 5.20 5.06 5.08 61 GR25 Peloponnisos 1.33 1.16 1.09 27.48 28.22 28.86 5.29 5.36 5.44 62 GR3 Attiki 1.32 1.31 1.32 28.60 29.16 29.64 5.22 5.21 5.27 63 GR41 Voreio Aigaio 1.57 1.57 1.47 26.76 27.39 28.11 5.27 5.16 5.18 64 GR42 Notio Aigaio 1.60 1.60 1.55 26.56 27.19 27.78 5.32 5.23 5.12 65 GR43 Kriti 1.59 1.51 1.44 26.86 27.60 28.11 5.38 5.34 5.31
62 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 Region TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98 66 ES11 Galicia 1.112 0.955 0.918 28.29 29.21 30.02 5.42 5.30 5.30 67 ES12 Principado de Asturias 0.93 0.83 0.81 28.65 29.56 30.34 5.21 5.15 5.29 68 ES13 Cantabria 1.07 0.94 0.96 29.13 30.02 30.86 5.12 5.00 4.98 69 ES21 Pais Vasco 0.96 0.93 0.98 30.32 31.17 31.88 4.46 4.33 4.36 70 ES22 Comunidad Foral de Navarra 1.17 1.14 1.19 30.29 31.04 31.62 4.74 4.42 4.50 71 ES23 La Rioja 1.12 1.08 1.12 29.80 30.48 31.06 4.75 4.60 4.66 72 ES24 Aragón 1.15 1.09 1.08 29.82 30.63 31.23 4.73 4.56 4.65 73 ES3 Comunidad de Madrid 1.24 1.16 1.20 29.94 30.74 31.35 4.85 4.70 4.76 74 ES41 Castilla y León 1.09 0.96 0.93 29.59 30.37 31.05 5.14 4.99 5.00 75 ES42 Castilla-la Mancha 1.52 1.35 1.27 29.22 29.86 30.39 5.11 4.97 4.99 76 ES43 Extremadura 1.54 1.33 1.22 28.85 29.40 29.93 5.43 5.31 5.32 77 ES51 Cataluña 1.23 1.17 1.22 29.53 30.24 30.80 4.78 4.66 4.79 78 ES52 Comunidad Valenciana 1.32 1.19 1.18 29.31 29.98 30.57 4.92 4.81 4.91 79 ES53 Illes Balears 1.50 1.35 1.41 29.02 29.72 30.10 5.16 5.02 5.13 80 ES61 Andalucia 1.57 1.37 1.31 28.94 29.53 30.00 5.39 5.30 5.36 81 ES62 Murcia 1.63 1.43 1.42 29.01 29.66 30.10 5.40 5.30 5.44 82 ES63 Ceuta y Melilla (ES) 2.05 1.90 1.90 28.65 29.01 29.40 6.00 5.86 5.88 83 ES7 Canarias (ES) 1.39 1.25 1.25 28.50 29.05 29.37 5.66 5.61 5.83 84 FR1 Île de France 1.83 1.74 1.80 29.31 29.83 30.18 5.32 5.17 5.24 85 FR21 Champagne-Ardenne 1.79 1.69 1.76 27.83 28.26 28.53 5.06 5.01 5.07 86 FR22 Picardie 1.92 1.77 1.87 27.88 28.24 28.49 5.21 5.16 5.24 87 FR23 Haute-Normandie 1.89 1.77 1.83 27.99 28.40 28.70 5.10 5.08 5.13 88 FR24 Centre 1.75 1.65 1.72 28.28 28.75 29.08 4.95 4.88 4.95 89 FR25 Basse-Normandie 1.82 1.76 1.80 28.03 28.48 28.80 4.91 4.90 4.98 90 FR26 Bourgogne 1.71 1.63 1.69 28.07 28.56 28.90 4.96 4.89 4.99 91 FR3 Nord - Pas-de-Calais 1.99 1.86 1.91 27.81 28.05 28.35 5.25 5.23 5.26 92 FR41 Lorraine 1.76 1.65 1.71 27.91 28.34 28.64 5.02 5.00 4.99 93 FR42 Alsace 1.78 1.65 1.72 28.21 28.59 28.93 5.14 5.09 5.13 94 FR43 Franche-Comté 1.84 1.73 1.80 28.05 28.55 28.86 5.06 4.98 4.98 95 FR51 Pays de la Loire 1.85 1.75 1.82 28.32 28.83 29.18 4.67 4.63 4.71 96 FR52 Bretagne 1.79 1.69 1.77 28.60 29.09 29.41 4.62 4.63 4.71 97 FR53 Poitou-Charentes 1.64 1.57 1.67 28.03 28.52 28.90 4.76 4.80 4.88 98 FR61 Aquitaine 1.58 1.50 1.56 28.56 29.02 29.34 4.90 4.92 4.96 99 FR62 Midi-Pyrénées 1.58 1.51 1.58 28.86 29.33 29.66 4.86 4.86 4.94 100 FR63 Limousin 1.45 1.40 1.48 28.27 28.71 29.01 4.84 4.86 4.91 101 FR71 Rhône-Alpes 1.83 1.71 1.74 28.77 29.25 29.53 4.98 4.90 4.92 102 FR72 Auvergne 1.54 1.45 1.54 28.25 28.70 28.99 4.84 4.90 4.89 103 FR81 Languedoc-Roussillon 1.72 1.64 1.67 28.52 28.86 29.15 5.12 5.16 5.20 104 FR82 Provence-Alpes-Côte d'Azur 1.79 1.70 1.74 28.66 29.04 29.34 5.20 5.14 5.18 105 FR83 Corse 1.66 1.54 1.51 28.37 28.78 29.15 5.30 5.22 5.32 110 IE Irlande 2.01 1.89 1.90 30.47 30.22 30.46 5.63 5.54 5.68 112 IT11 Piemonte 1.06 1.02 1.09 29.51 30.15 30.56 4.88 4.87 4.98 113 IT12 Valle d'Aosta 1.06 1.14 1.11 29.07 29.84 30.24 4.95 5.01 4.95 114 IT13 Liguria 1.01 0.92 0.97 30.30 30.83 31.27 4.86 4.82 4.93 115 IT2 Lombardia 1.12 1.09 1.14 29.94 30.61 30.89 4.87 4.89 4.94 116 IT31 Trentino-Alto Adige 1.39 1.35 1.41 29.79 30.28 30.52 5.10 5.08 5.14 117 IT32 Veneto 1.10 1.07 1.15 30.09 30.63 30.97 4.86 4.85 4.86 118 IT33 Friuli-Venezia Giulia 1.06 0.99 1.07 30.05 30.56 30.89 4.89 4.85 4.95 119 IT4 Emilia-Romagna 1.02 0.99 1.07 29.65 30.23 30.59 5.04 5.03 5.19 120 IT51 Toscana 1.05 0.99 1.04 29.75 30.40 30.82 4.96 4.94 5.06 121 IT52 Umbria 1.19 1.09 1.11 29.43 30.01 30.43 4.84 4.88 4.98 122 IT53 Marche 1.19 1.09 1.13 29.58 30.26 30.58 4.81 4.83 4.88 123 IT6 Lazio 1.25 1.14 1.16 29.72 30.34 30.95 5.05 4.98 5.03 124 IT71 Abruzzo 1.29 1.14 1.09 29.06 29.90 30.29 4.91 4.97 4.91 125 IT72 Molise 1.35 1.21 1.19 28.71 29.29 29.90 5.04 4.98 4.97 126 IT8 Campania 1.74 1.52 1.48 28.63 28.93 29.30 5.25 5.27 5.26 127 IT91 Puglia 1.54 1.35 1.33 28.71 29.17 29.60 5.25 5.27 5.25 128 IT92 Basilicata 1.31 1.18 1.12 28.87 29.46 29.89 5.01 5.03 5.03 129 IT93 Calabria 1.49 1.30 1.24 28.40 28.89 29.43 5.35 5.37 5.26 130 ITA Sicilia 1.68 1.46 1.42 28.20 28.65 29.13 5.51 5.53 5.47 131 ITB Sardegna 1.24 1.07 1.02 29.96 30.57 31.15 5.58 5.56 5.48
63 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 Region TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98 132 LU Luxembourg 1.672 1.745 1.718 28.47 28.92 29.07 5.00 5.06 5.14 133 NL11 Groningen 1.40 1.41 1.49 29.82 30.27 30.37 4.68 4.74 4.79 134 NL12 Friesland 1.72 1.67 1.75 29.47 29.91 30.03 4.40 4.46 4.50 135 NL13 Drenthe 1.62 1.62 1.74 29.56 29.79 29.85 4.33 4.36 4.44 136 NL21 Overijssel 1.72 1.66 1.74 29.68 30.02 30.23 4.53 4.51 4.54 137 NL22 Gelderland 1.63 1.60 1.67 29.79 30.17 30.42 4.60 4.60 4.62 138 NL23 Flevoland 2.01 1.88 1.92 28.66 29.19 29.27 4.96 4.89 4.95 139 NL31 Utrecht 1.58 1.52 1.60 30.15 30.59 30.89 4.90 4.83 4.81 140 NL32 Noord-Holland 1.52 1.47 1.54 29.85 30.31 30.51 5.10 5.09 5.14 141 NL33 Zuid-Holland 1.63 1.55 1.61 29.34 29.70 29.95 5.03 5.03 5.09 142 NL34 Zeeland 1.73 1.71 1.75 28.90 29.26 29.41 4.66 4.65 4.70 143 NL41 Noord-Brabant 1.59 1.56 1.61 29.63 30.03 30.27 4.34 4.35 4.41 144 NL42 Limburg (NL) 1.47 1.43 1.49 29.57 29.81 30.04 4.44 4.47 4.49 145 AT11 Burgenland 1.36 1.30 1.20 26.83 27.22 27.92 5.01 4.90 4.95 146 AT12 Niederösterreich 1.55 1.49 1.38 27.05 27.54 27.98 5.00 5.01 5.07 147 AT13 Wien 1.38 1.29 1.22 27.12 27.59 27.96 5.71 5.75 5.78 148 AT21 Kärnten 1.48 1.43 1.32 27.34 27.62 28.04 5.19 5.17 5.23 149 AT22 Steiermark 1.44 1.35 1.27 27.02 27.42 27.70 5.16 5.18 5.23 150 AT31 Oberösterreich 1.59 1.52 1.46 27.29 27.68 27.90 5.12 5.11 5.13 151 AT32 Salzburg 1.55 1.47 1.40 27.45 27.77 28.20 5.28 5.20 5.25 152 AT33 Tirol 1.58 1.49 1.40 27.93 28.24 28.54 5.41 5.34 5.34 153 AT34 Vorarlberg 1.69 1.65 1.53 27.79 27.96 28.31 5.39 5.28 5.32 154 PT11 Norte 1.55 1.43 1.48 27.65 28.07 28.53 5.44 5.44 5.47 155 PT12 Centro (PT) 1.47 1.37 1.40 27.32 27.83 28.64 5.35 5.32 5.38 156 PT13 Lisboa e Vale do Tejo 1.47 1.40 1.50 27.88 28.38 29.02 5.39 5.46 5.60 157 PT14 Alentejo 1.47 1.28 1.37 26.66 27.29 28.04 5.56 5.60 5.66 158 PT15 Algarve 1.69 1.50 1.52 27.06 27.64 28.35 5.61 5.55 5.66 159 PT2 Açores (PT) 2.01 1.86 1.83 27.55 27.42 27.68 6.04 5.99 5.84 160 PT3 Madeira (PT) 1.61 1.46 1.54 28.39 28.56 28.66 6.20 6.16 6.07 161 FI13 Itä-Suomi 1.78 1.80 1.77 28.78 28.93 29.13 5.20 5.20 5.28 162 FI14 Väli-Suomi 1.96 1.94 1.86 28.95 29.23 29.51 5.14 5.12 5.20 163 FI15 Pohjois-Suomi 2.10 2.07 2.04 28.77 29.10 29.23 5.49 5.44 5.52 164 FI16 Uusimaa (suuralue) 1.74 1.70 1.59 29.30 29.64 29.99 5.26 5.24 5.37 165 FI17 Etelä-Suomi 1.77 1.77 1.70 28.75 29.10 29.35 5.00 5.04 5.15 166 FI2 Åland 1.79 1.75 1.71 29.23 29.76 30.28 4.97 4.92 5.14 167 SE01 Stockholm 1.94 1.70 1.49 29.55 29.93 30.47 5.37 5.38 5.40 168 SE02 Östra Mellansverige 2.10 1.77 1.50 28.56 28.94 29.29 5.04 5.06 5.06 169 SE04 Sydsverige 2.00 1.70 1.50 28.74 29.10 29.53 5.04 5.05 5.11 170 SE06 Norra Mellansverige 2.18 1.74 1.53 28.45 28.82 29.16 5.10 5.06 5.06 171 SE07 Mellersta Norrland 2.07 1.72 1.49 28.69 29.03 29.50 5.05 4.99 5.00 172 SE08 Övre Norrland 2.15 1.73 1.50 28.63 28.99 29.41 5.02 4.98 4.95 173 SE09 Smaland met O. or SE09 and SE0A 2.13 1.77 1.50 28.75 29.12 29.16 5.02 5.05 4.88 174 SE0A Vastsverige 2.13 1.77 1.54 28.75 29.12 29.64 5.02 5.05 5.12 175 UKC1 Tees Valley and Durham 1.85 1.73 1.73 26.65 26.91 26.93 5.41 5.61 5.76 176 UKC2 Northumberland, Tyne and Wear 1.72 1.63 1.62 26.98 27.32 27.44 5.58 5.72 5.77 177 UKD1 Cumbria 1.82 1.68 1.66 27.31 27.67 27.71 5.30 5.54 5.83 178 UKD2 Cheshire 1.83 1.72 1.73 27.72 28.25 28.43 5.45 5.60 5.75 179 UKD3 Greater Manchester 1.90 1.77 1.77 27.09 27.37 27.47 5.72 5.82 5.86 180 UKD4 Lancashire 1.91 1.79 1.79 27.11 27.38 27.41 5.51 5.62 5.73 181 UKD5 Merseyside 1.79 1.69 1.66 27.45 27.73 27.88 5.64 5.78 5.83 182 UKE1 East Riding and North Lincolnshire 1.84 1.75 1.75 26.83 27.05 27.17 5.47 5.53 5.61 183 UKE2 North Yorkshire 1.70 1.67 1.66 28.10 28.49 28.91 5.21 5.43 5.67 184 UKE3 South Yorkshire 1.79 1.72 1.72 26.81 27.14 27.19 5.53 5.66 5.75 185 UKE4 West Yorkshire 1.85 1.80 1.82 27.09 27.37 27.51 5.66 5.70 5.69 186 UKF1 Derbyshire and Nottinghamshire 1.78 1.69 1.67 27.22 27.62 27.70 5.50 5.65 5.80 187 UKF2 Leicestershire, Rutland and Northants 1.79 1.72 1.73 27.70 28.12 28.25 5.38 5.46 5.61 188 UKF3 Lincolnshire 1.79 1.72 1.71 27.23 27.54 27.50 5.23 5.48 5.78 189 UKG1 Herefordshire, Worcestershire & W. 1.79 1.73 1.72 27.77 28.31 28.38 5.39 5.57 5.83 190 UKG2 Shropshire and Staffordshire 1.79 1.71 1.71 27.36 27.69 27.74 5.41 5.60 5.72 191 UKG3 West Midlands 1.95 1.88 1.91 27.17 27.44 27.49 5.74 5.76 5.76
64 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 Region TFRT9 TFRT9 TFRT9 ACT92 ACT95 ACT98 SDT92 SDT95 SDT98 192 UKH1 East Anglia 1.762 1.695 1.648 27.77 28.21 28.23 5.32 5.55 5.81 193 UKH2 Bedfordshire, Hertfordshire 1.85 1.79 1.77 28.45 28.85 28.99 5.38 5.55 5.78 194 UKH3 Essex 1.83 1.73 1.70 28.09 28.49 28.57 5.22 5.46 5.79 195 UKI1 Inner London or UKI London 1.73 1.73 1.75 28.75 29.09 29.24 6.06 6.06 6.38 196 UKI2 Outer London 1.73 1.73 1.76 28.75 29.09 29.39 6.06 6.06 5.80 197 UKJ1 Berkshire, Bucks and Oxfordshire 1.77 1.70 1.68 28.76 29.21 29.39 5.45 5.56 5.81 198 UKJ2 Surrey, East and West Sussex 1.72 1.67 1.64 28.97 29.40 29.58 5.38 5.56 5.79 199 UKJ3 Hampshire and Isle of Wight 1.77 1.70 1.66 27.94 28.38 28.55 5.39 5.51 5.69 200 UKJ4 Kent 1.88 1.80 1.81 27.78 28.13 28.07 5.34 5.60 5.89 201 UKK1 Gloucestershire, Wiltshire & N.S. 1.74 1.72 1.70 28.01 28.53 28.70 5.43 5.56 5.71 202 UKK2 Dorset and Somerset 1.77 1.67 1.65 28.09 28.48 28.28 5.27 5.50 5.76 203 UKK3 or UKK3-4 Cornwall and Devon 1.77 1.69 1.80 27.75 28.09 27.81 5.35 5.47 5.82 204 UKK4 Devon 1.77 1.69 1.67 27.75 28.09 28.35 5.35 5.47 5.58 205 UKL1 or UKL Wales 1.87 1.80 1.83 27.16 27.43 27.24 5.50 5.63 5.72 206 UKL2 East Wales 1.87 1.80 1.76 27.16 27.43 27.95 5.50 5.63 5.75 207 UKM Scotland 1.67 1.56 1.55 27.55 28.01 28.21 5.49 5.64 5.83 211 UKN Northern Ireland 2.10 1.94 1.90 28.37 28.79 28.79 5.59 5.60 5.73
65 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, 1991- 1999 ...... 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
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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
tftr16 tftr18tftr17 tftr44tftr43 tftr19 tftr15 tftr45 tftr42tftr41 tftr46 tftr40 tftr20 tftr14 tftr39 ,5 tftr47 tftr38 tftr21 tftr13 tftr48 tftr49 tftr37 tftr36 tftr22 tftr12 tftr35 tftr23 tftr34 tftr33 0,0 tftr24 tftr32 tftr31 tftr25 Composante 2 tftr30
-,5 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 analysis18 : 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).
71 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 1997- 99) 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).
73 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 - A disturbing aggregate: “49 and over”! the sum of numbers by age (these differences - Differences between total births reported and being more common for numbers of women sum of births by age: BE, GR, ES, UK. than numbers of men!) : BE, DE, GR, FR, IT, - Italy: significant differences up to 1995, NL, AT, SE, UK. complete concordance in 1996, small differences in 1997 with the appearance of - Some countries always accurate: ES, PT, FI! 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!
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 errors22; 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.
74 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 Table p2natal of the Regio domain of New Cronos. 24 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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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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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 : ã Landesbetrieb für Datenverarbeitung und Statistik, Postdam, June 2003. All rights reserved.
For Saarland : ã 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 R Code Name of region No 1 1 Belgique-Belgïe BE1 Région de Bruxelles capitale-Brussels hoofdstad Gewest 2 BE21 Antwerpen 3 BE22 Limburg (B) 4 BE23 Oost-Vlanderen 5 BE24 Vlaams-Brabant 6 BE25 West-Vlanderen 7 BE31 Brabant wallon 8 BE32 Hainaut 9 BE33 Liège 10 BE34 Luxembourg(B) 11 BE35 Namur 12 2 Danmark DK Danmark 13 3 BR Deutschland DE11 Stuttgart 14 DE12 Karlsruhe 15 DE13 Freiburg 16 DE14 Tübingen 17 DE21 Oberbayern 18 DE22 Niederbayern 19 DE23 Oberpfalz 20 DE24 Oberfranken 21 DE25 Mittelfranken 22 DE26 Unterfranken 23 DE27 Schwaben 24 DE3 Berlin 25 DE4 Brandenburg 26 DE5 Bremen 27 DE6 Hamburg 28 DE71 Darmstadt 29 DE72 Gießen 30 DE73 Kassel 31 DE8 Mecklenburg-Vorpommern 32 DE91 Braunschweig 33 DE92 Hannover 34 DE93 Lüneburg 35 DE94 Weser-Ems 36 DEA1 Düsseldorf 37 DEA2 Köln 38 DEA3 Münster 39 DEA4 Detmold 40 DEA5 Arnsberg 41 DEB Rheinland-Pfalz (NUTS1) 44 DEC Saarland 45 DED1 Chemnitz 46 DED2 Dresden 47 DED3 Leipzig 48 DEE1 Dessau 49 DEE2 Halle 50 DEE3 Magdebourg 51 DEF Schleswig-Holstein 52 DEG Thüringen
80 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C Country R Code Name of region No 53 4 Ellada GR11 Anatoliki Makedonia, Thraki 54 GR12 Kentriki Makedonia 55 GR13 Dytiki Makedonia 56 GR14 Thessalia 57 GR21 Ipeiros 58 GR22 Ionia Nisia 59 GR23 Dytiki Ellada 60 GR24 Sterea Ellada 61 GR25 Peloponnisos 62 GR3 Attiki 63 GR41 Voreio Aigaio 64 GR42 Notio Aigaio 65 GR43 Kriti 66 5 Espana ES11 Galicia 67 ES12 Principado de Asturias 68 ES13 Cantabria 69 ES21 Pais Vasco 70 ES22 Comunidad Foral de Navarra 71 ES23 La Rioja 72 ES24 Aragon 73 ES3 Comunidad de Madrid 74 ES41 Castilla y Leon 75 ES42 Castilla-La Mancha 76 ES43 Extremadura 77 ES51 Cataluña 78 ES52 Comunidad Valenciana 79 ES53 Baleares 80 ES61 Andalucia 81 ES62 Murcia 82 ES63 Ceuta y Melila 83 ES7 Canarias 84 6 France FR1 Ile de France 85 FR21 Champagne-Ardenne 86 FR22 Picardie 87 FR23 Haute Normandie 88 FR24 Centre 89 FR25 Basse Normandie 90 FR26 Bourgogne 91 FR3 Nord-Pas de Calais 92 FR41 Lorraine 93 FR42 Alsace 94 FR43 Franche-Comté 95 FR51 Pays de la Loire 96 FR52 Bretagne 97 FR53 Poitou-Charente 98 FR61 Aquitaine 99 FR62 Midi-Pyrenées 100 FR63 Limousin 101 FR71 Rhône-Alpes 102 FR72 Auvergne 103 FR81 Languedoc-Roussillon 104 FR82 Provence-Alpes-Côte d’Azur 105 FR83 Corse 106 FR91 Guadeloupe 107 FR92 Martinique 108 FR93 Guyane 109 FR94 La Réunion
81 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C Country R Code Name of region No 110 7 Ireland IE Ireland (NUTS1) 112 8 Italia IT11 Piemonte 113 IT12 Valle d'Aosta 114 IT13 Liguria 115 IT2 Lombardia 116 IT31 Trentino-Alto Adige 117 IT32 Veneto 118 IT33 Friuli-Venezia-Giulia 119 IT4 Emilia-Romagna 120 IT51 Toscana 121 IT52 Umbria 122 IT53 Marche 123 IT6 Lazio 124 IT71 Abruzzo 125 IT72 Molise 126 IT8 Campania 127 IT91 Puglia 128 IT92 Basilicata 129 IT93 Calabria 130 ITA Sicilia 131 ITB Sardegna 132 9 Luxembourg (GD) LU Luxembourg (GD) 133 10 Nederland NL11 Groningen 134 NL12 Friesland 135 NL13 Drenthe 136 NL21 Overijssel 137 NL22 Gelderland 138 NL23 Flevoland 139 NL31 Utrecht 140 NL32 Noord-Holland 141 NL33 Zuid-Holland 142 NL34 Zeeland 143 NL41 Noord-Brabant 144 NL42 Limburg (NL) 145 11 Österreich AT11 Burgerland 146 AT12 Niederösterreich 147 AT13 Wien 148 AT21 Kärnten 149 AT22 Steiermark 150 AT31 Oberösterreich 151 AT32 Salzburg 152 AT33 Tirol 153 AT34 Vorarlberg 154 12 Portugal PT11 Norte 155 PT12 Centro (P) 156 PT13 Lisboa e Vale do Tejo 157 PT14 Alentejo 158 PT15 Algarve 159 PT2 Açores 160 PT3 Madeira 161 13 Suomi/Finland FI13 Itä-Suomi 162 FI14 Väli-Suomi 163 FI15 Pohjois-Suomi 164 FI16 Uusimaa 165 FI17 Etelä-Suomi 166 FI2 Àland
82 Study of low fertility in the regions of the European Union: places, timetable and causes
R No C Country R Code Name of region No 167 14 Sverige SE01 Stockholm 168 SE02 Östra Mellansverige 169 SE04 Sydsverige 170 SE06 Norra Mellandsverige 171 SE07 Mellersta Norrland 172 SE08 Övre Norrland 173 SE09 Smaland met Öarna 174 SE0A Västsverige 175 15 United Kingdom UKC1 Tees Valley and Durham 176 UKC2 Northumberland, Tyne and Wear 177 UKD1 Cumbria 178 UKD2 Cheshire 179 UKD3 Greater Manchester 180 UKD4 Lancashire 181 UKD5 Merseyside 182 UKE1 East Riding and North Lincolnshire 183 UKE2 North Yorkshire 184 UKE3 South Yorkshire 185 UKE4 West Yorkshire 186 UKF1 Derbyshire and Nottinghamshire 187 UKF2 Leicestershire, Rutland and Northants 188 UKF3 Lincolnshire 189 UKG1 Herefordshire, Worcestershire and Warks 190 UKG2 Shropshire and Staffordshire 191 UKG3 West Midlands 192 UKH1 East Anglia 193 UKH2 Bedfordshire and Hertfordshire 194 UKH3 Essex 195 UKI1 Inner London 196 UKI2 Outer London 197 UKJ1 Berkshire, Bucks and Oxfordshire 198 UKJ2 Surrey, East and West Sussex 199 UKJ3 Hampshire ans Isle of Wight 200 UKJ4 Kent 201 UKK1 Gloucestershire, Wiltshire and North Somerset 202 UKK2 Dorset and Somerset 203 UKK3 Cornwall and Isles of Scilly 204 UKK4 Devon 205 UKL1 West Wales and the Valleys 206 UKL2 East Wales 207 UKM Scotland (NUTS1) 211 UKN 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 O O O O O DK Denmark N O O O O O O O O O O O O DE FRG (and GDR from N N I* I* I* I* I* I* I* O O O N 91) GR Greece N O O O O O O O O O O O N ES Spain N O O O O O O O O O O O O FR France N I I I I I I I I O N I O IE Ireland N I I I I I I I I* I* I* O O IT Italy N O O O O O O O O O O O O LU Luxembourg N O O O O O O O O O O O O NL Netherlands N O O O O O O O O O O O O AT Austria N O O O O O O O O O O O O PT Portugal N N O O O O O O O O O O O FI Finland N O O O O O O O O O O O O SE Sweden N I I I I I I I O O O O O UK United Kingdom N N N I* I I I I 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.
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 N N N N N DK Denmark N O O O O O O O O O O O N DE FRG (and GDR from N N N N N N N N N N N N N 91) GR Greece N O O O O O P O O O N O N ES Spain N O O O O O O O O O N O N FR France N I I I I I I I I O O O N IE Ireland N I I I I O O O O O O O N IT Italy N O O O O O O O P N N N N LU Luxembourg N O O O O O O O O O O O N NL Netherlands N O O O O O O O O O O O N AT Austria N O O O O O O O O O O O N PT Portugal N N O O O O O O O O O O N FI Finland N O O O O O O O O O O O N SE Sweden N I I I I I I I O O O O N UK United Kingdom N I I I I I I I I O O O N O: available; N: not available; I: available but incomplete at NUTS2 level. P: problem of concordance for a region or set of regions.
84 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 ages30
T G G N12 = N12 + 0,4N13 assuming no births prior to the mother’s twelfth birthday
T G G N13 = 0,6N13 + 0,4N14 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%)
T G G N14 = 0,6N14 + 0,5N15 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%)
T G G N i = 0,5(N i + N i+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 = 0,5N 48 + 0,4N 49
T G N 49 = 0,6N 49 given that births occurring at ages above 49 are aggregated with those occurring at 49.
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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
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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 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 3/2004/F/no 01 the EU, EFTA and Candidate Countries Part I+II University of Thessaly Documentation of the 2000 Round of Population and Housing Censuses in 3/2004/F/no 02 the EU, EFTA and Candidate Countries Part III University of Thessaly Regions with high life expectancy in the European Union: Places, periods 3/2004/F/no 03 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: 3/2004/F/no 04 places, periods and causes J. Duchêne, A. Gabadinho, M. Willems, P. Wanner Institut de démographie, Université Catholique de Louvain
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