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Subnational spatial variations of fertility timing in Europe since 1990

Variations spatiales du calendrier de la fécondité Européenne à l’échelle infranationale depuis 1990
Variaciones espaciales y temporales de la fecundidad europea a escala subnacional desde 1990
Mathieu Buelens

Résumés

Si les tendances récentes de l'intensité de la fécondité en Europe sont bien connues, ce n'est pas le cas des variations spatiales du calendrier de la fécondité. C’est particulièrement vrai à l’échelle infranationale. Alors que des théories telles que la celle de la deuxième transition démographique ont permis une bonne compréhension à la fois de la nature et des origines des récents changements dans les pratiques de la fécondité, y compris le report de la fécondité à des âges plus avancés, la prédilection des recherches empiriques pour les analyses transnationales laisse relativement méconnue l'organisation spatiale des calendriers de la fécondité. Cependant, l’idée d’une fécondité différenciée dans l'espace est soutenue théoriquement. Cette analyse géographique présente les variations spatiales du calendrier de la fécondité en Europe. Grace à l’utilisation de données infranationales sur un large espace transnational, elle contribue à mettre en évidence l'importance des contextes locaux sur les schémas de fécondité.
Après évaluation du choix des indicateurs et du bienfondé des méthodes, l'âge moyen à la naissance dans les régions NUTS-2 et les taux de fécondité par âge pour les unités spatiales NUTS-3 sont utilisés afin de cartographier les calendriers de la fécondité types dans plus de 30 pays sur la période 1990-2017. Il en découle plusieurs résultats importants. Malgré un report général de la fécondité vers des âges plus avancés en Europe, qui se traduit par une légère tendance à l'homogénéisation, les résultats révèlent l'existence de différences infranationales. Mais ils révèlent surtout que ces différences sont spatialement organisées. Au cours de la période le clivage Est-Ouest reste un contraste structurant mais d'autres disparités importantes se renforcent, mettant en évidence la spécificité des modèles de fécondité en Europe méridionale et celle en place dans les grandes aires urbaines.
Pour terminer, cette analyse inductive aborde les théories permettant d'interpréter les principales variations spatiales exposées. Cette recherche prouve l'importance de prendre en compte les contextes infranationaux, notamment métropolitains, dans l'étude des comportements de fécondité.

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1 Introduction

1Theoretically and empirically, the delayed transition to parenthood is considered one of the most important features of the dramatic changes European fertility underwent since the last quarter of the 20th century (Lesthaeghe & Neels, 2002; Sobotka, 2008; Van de Kaa, 2002). Postponement of fertility tremendously influenced recent European fertility level, transition to adulthood and family formation processes. A substantial body of literature has brought good understanding of both the causes and consequences of this ‘postponement transition’ (Billari et al., 2006; Kohler et al., 2002; Mills et al., 2011).

2Changes in values and attitudes towards partnership, parenthood and family (Billari et al., 2006; Bongaarts, 2001; Van de Kaa, 2001) and shift from incidental to planned parenthood (Bajos, Ferrand, 2006; Goldin, 2006; Knibiehler, 1997) foster motivations for delayed fertility. Individually, later fertilities are encouraged when childbearing is expected to climax a difficult to achieve situation such as a stable union of adults (Billari, Philipov, Testa, 2009; Hobcraft, Kiernan, 1997; Myers, 1997; Testa, 2007), each with a career solidly initiated (Bajos, Ferrand, 2006; Girard, Roussel, 1981), with sufficient economic and residential assets to support family expansion (Moguérou, Bajos, Ferrand, Leridon, 2011), and where future parents have been able to enjoy childfree couple-life (Mazuy, Rozée, 2008; Régnier-Loilier, Leridon, 2007).

3These motivations and their realisation are context dependent. Individuals act differently under different environmental conditions, which result in spatially differentiated fertility behaviours (Lesthaeghe, Neels, 2002). As exposed in the following section (2.1), fertility (timing) determinants have a geographically defined scope of action. Unfortunately studies that incorporate geographical perspectives in research on fertility are lacking for contemporary trends in the West (Boyle, 2003; Compton, 1991). Little is known on spatial dimension of fertility postponement across time, especially within countries. This research aims to fill this knowledge gap. It will reveal if and how the postponement transition unfolds spatially within European countries, whether or not spatial variations remains or tend to converge, and will hopefully contribute to a better understanding of the recent changes in fertility timing.

4Existing attempts to address spatial variations of fertility behaviours suffer from a dichotomy between cross-country comparisons (Burkimsher, 2015; Frejka, Sardon, 2006a; Kohler et al., 2002; Lesthaeghe, 2010; Nathan, Pardo, 2019) and single country subnational analysis. For contextualisation purposes, we sum up the spatial description obtained with the first approach in section 2.2. The second approach is motivated by at least two reasons. First, cross-country comparisons risk overlooking potential differences within countries, leading to serious bias (Snyder, 2001). Second, some fertility determinants highlighted in the literature present a local scope of action. Researches using subnational data therefore present spatial variations that we summarise in section 2.3. So far, this literature is fragmented and their results are difficult to generalise as most of these studies focus on specific contexts in a single country at the time. The multilevel approach of this study aims at bridging the gap between these two bodies of literature.

5After discussing the data and methodology used, we will present the general trends and spatiality of fertility timing in Europe over the last decades (section four). The result section identifies the overriding geographical structures organising the spatial variations of fertility timing. Then, in section six, we will discuss which determinants are most likely to have shaped this spatial organisation. The last section resumes the main advantages of such geographical approach and the important findings of this study.

2 Literature review

2.1 Spatial variations of fertility behaviours

6Determinants of fertility behaviours have a geographically defined scope of action. Reviewing the determinants of fertility quantum and tempo in advanced societies, Balbo et al. (2013) grouped them based on the spatial level in which they operate: from the individual to the macro level.

7Individual’s characteristics influence fertility actions. Education level (Berrington, Stone, Beaujouan, 2015; Gustafsson, Kalwij, 2006; Neels, De Wachter, 2010; Ní Bhrolcháin, Beaujouan, 2012), socio-professional occupation (Kerckhoff, 2001; Sigle-Rushton, 2008; Steele, Kallis, Goldstein, Joshi, 2005) and (expected) earnings (Blackburn, Bloom, Neumark, 1993; Ekert-Jaffé et al., 2002; Smith, Ratcliffe, 2009) have been consistently reported to influence fertility timing in particular. In short, women with low-paid employment expectations have less incentives to delay their transition to motherhood and thus become mothers at a younger age than women spending long periods of time in education who expect big returns to education. But these factors interact with factors located at different analytical levels (Balbo et al., 2013).

8At meso-level, social interactions (Bernardi, Keim, Von der Lippe, 2007; Billari et al., 2009; Liefbroer, Billari, 2010; Lutz, Skirbekk, Testa, 2006; Rossier, Bernardi, 2009) are believed to shape both fertility quantum as well as timing decisions. These hypotheses are mostly theoretical because of the lack of suitable data and the difficulty to disentangle both factors. Living environments and their perceived “family friendliness” (Boyle, Graham, Feng, 2007; Kulu, Vikat, Andersson, 2007) as well as housing stock (Kulu, Boyle, 2009; Lutz, Qiang, 2002) have also been presented to influence fertility behaviours. Similarly lower housing affordability can be held accountable for later fertility in urban centres. Indeed, cost of housing and homeownership in particular forces postponement of marriage and eventually competes with childbearing cost for couples willing to become married / homeowners (Mulder, 2006; Murphy, Sullivan, 1985). Hence, unavailability of appropriate housing to family-style living leads inhabitants of the centre of Turin to postpone family formation (Michielin, 2004).

9Within countries distinctive regional contexts exist. They result from economic, social, cultural, or ethnic contrasts, some spatially organised (Lesthaeghe, Lopez-Gay, 2013; Lesthaeghe, Neels, 2002; Van de Kaa, 1997). Hence, most SDT values are spatially differentiated, including secularisation, rise in individualistic values, and promotion of (women’s) emancipation and self-fulfillment through work or economic independence rather than through marriage and family. Consequently, motivation for fertility postponement is spatially differentiated as well, particularly along the urban-rural gradient (Lesthaeghe & Neels, 2002; Sobotka, 2008; Sobotka & Adiguzel, 2002; Van de Kaa, 2002). Major urban centres support alternative lifestyles to family formation for women in their twenties and early thirties if not discourage parenthood in general (Fiori, Graham, Feng, 2014). On the contrary, small towns and rural areas hold more traditional ‘family-oriented’ attitudes and lifestyles (Heaton, Lichter, Amoateng, 1989; Trovato, Grindstaff, 1980).

10Distinctive context also exists at macro-level. (Trans-)national historic and cultural continuities influence fertility behaviours (Dalla Zuanna, 2004; Micheli, 2000; Reher, 1998). Cross-country comparative studies often highlight the same factors shaping individuals’ characteristics and thus enabling or inhibiting childbearing and the ability to bear a child at a specific age or period of a livetime. Among them are the employment and education systems, the welfare regimes, and the social policies, family, and gender systems. These factors are nation-specific institutions (Mayer, 2004). They are thus considered accountable for the variations observed between nations (Billari, Kohler, 2010; Holdsworth, Elliott, 2001; Mills, Blossfeld, Klijzing, 2005). Social policies in particular have been found to affect the timing of fertility (Andersson, Hoem, Duvander, 2006; Ermisch, 1999; Hoem, 2005).

11From this structuration of fertility determinants derive a new analytical strategy that incorporates geographical perspectives in research on fertility. Multiscale analyses seem particularly appropriate to describe how the different factors interact. Disentangling their impact based on the spatial extension of their scope of action may also contribute to a better understanding.

2.2 Contextualisation: national variations in fertility timing

12Despite cross-country comparison not being intended as geographic, it contains spatial information. From the factors presented above, such perspective actually focuses merely on the macro-level ones. Studies considering recent national trends in fertility timing highlight national differences within Europe (Kohler et al., 2002; Lesthaeghe, 2010; Philipov, Kohler, 2001; Sardon, 2009). For contextualisation purpose, we quickly ran through main observations based on the mean age at birth (MAB) (Fig. 1 a-c) and other fertility timing indicators such as mean age at first birth (MAB1).

Figure 1 a-c: MAB in selected countries

Figure 1 a-c: MAB in selected countries

Note: UK: England and Wales only

Source: Human Fertility Database, Eurostat

13Between the early 1960s and today, each European national MAB average presented a two phases change: first a period of slow decrease until a minimum, then a quick rise. Today’s European average (just under 31 years) is higher than the early 1960s one, but it also results from distinctive fertility patterns. Fertility was still relatively high back whereas recent values are mostly due to delayed family formation. The two phases change occurred with different chronology and magnitude across Europe. Lesthaeghe (2010) sees in the uneven national trends three major groups.

  • 1 Indeed period fertility as measured by TFR depends on variations in the timing of fertility. Hence (...)

14a) Northwestern countries lead the postponement transition. In the 1960s already, high order birth rates decreased, driving down the MAB. MAB1 decreased as well reflecting earlier unions and rejuvenation of family formation (Sardon, 2009). MAB reached their minimum in the mid-1970s, around 26.5 years (except in the Netherlands). Since then, MAB is increasing (Fig. 1a). This rise is happening while total fertility rates (TFR) are rather steady and relatively high for European standards; between 1.6 and 2. This suggest a fair proportion of the postponed births are recovered at older ages.1

  • 2 Germany, Austria and Switzerland usually constitute this sub-group (Frejka, Sardon, 2006b; Neyer, 2 (...)

15b) Lesthaeghe (2010) gathers Mediterranean and German-speaking countries2 in a second group, where cohort TFR are expected to remain below replacement level. Compared with the first group, the rise of their MAB started later (Fig. 1b). A closer analysis differentiates two sub-groups.

  • 3 For instance in the 2000s, Spanish MAB plateaued around never seen before level of 30.9 years, MAB1 (...)

16In Italy and Spain, MAB1 started to rise in the late 1970s (early 1980s in Greece) (Kohler et al., 2002). In these countries, fertility ageing is faster than in Northwestern countries, but more importantly it is associated with decreasing TFR. Since the 1990s TFR remained low (under 1.5). Fertility in younger age groups thus declined and recovery by later age groups is absent or too low to compensate. In the 2000s postponement seemed to slowdown only to resume vigorously after 2008.3 Today, MAB1 in Spain and Italy exceed 31 years (EU average 29.2).

17In German-speaking countries MAB reach their minimum values slightly before Mediterranean countries. The main difference however is the stronger, however still scarce, recovery of delayed births after 30 years (Lesthaeghe, 2010). Here low fertility also results from higher level of childlessness and low family size ideal (Goldstein et al., 2003; Miettinen et al., 2015). Today MAB1 in Luxembourg and Switzerland reach almost 31 years, 30 in West Germany and 29 in East Germany.

  • 4 Caucasian and other easternmost former soviet states are not considered here. Their relatively late (...)

18c) The last group (Central and Eastern European (CEE) countries) presents younger fertilities but mostly later minimums and sharper rises of their MAB (Fig. 1c). Additionally to these timing features, these countries share an abrupt fall of their fertility level in the 1990s. Closer analysis distinguishes sub-groups here as well. Going Eastward, MAB1 progressively starts rising later. Similarly, going eastward, postponement play a lesser role in the 1990s fertility decline. At least three sub-groups exist (Kohler et al., 2002).4

19In Central European countries (Poland, Czechia, Hungary, Slovakia and Slovenia), MAB slowly decreased until the 1980s. Strong postponement of births explains quickly rising MAB and dramatic acceleration of period fertility loss in the (early) 1990s. However TFR are likely to rise once postponement transition slows down or comes to an end.

20In Bulgaria and the Baltic states, the MAB plunged around 1990 consecutively to a reduction of high order births. Postponement transition started later and therefore account for a smaller proportion of 1990s period fertility loss.

21In Easternmost countries (Russia, Ukraine, Romania, Belarus) MAB decreased for a substantial part of the 1990s, both because of high order births becoming rare and rejuvenation of mean age at first and second births. Childbirth delaying trends came later and more progressively than in Central Europe (Philipov, Kohler, 2001).

2.3 Spatial variations within countries

22However fragmented, studies at lower levels also reveal spatially uneven fertility behaviours. It has been recognized based on a large number of countries both in terms of fertility trends (Buelens, 2021a; Fox et al., 2019) and specifically non-marital fertility trends (Decroly, 1992; Klüsener, Perelli-Harris, Sánchez Gassen, 2013). Subnational level studies addressing the spatiality of fertility timing in particular too often consider a limited spatial extent (generally a single country). This strategy compromises the ability to generalise their results. However some general observations appear.

23Considering the timing of the transition to adulthood and parenthood, some researches notice regional contrasts within countries. They refer to local specificities for instance in Germany (Goldstein, Kreyenfeld, 2011); Italy (Bacci, 1977) ; Portugal (Bacci, 1971) and Slovakia (Šprocha, 2018). As an example Busetta and Giambalvo (2012) noticed postponement transition in Italy started in the North, spread into the Centre, then hit the South, Sicily and Sardinia. However once it started in the South it became more vigorous. Eventually every Italian region present MAB above 31 years, but for uneven reasons : low fertility at young ages plays a major role in the South while stronger recovery of fertility by older women is more important in the North and Centre (Giorgi, Mamolo, 2007).

24Other studies acknowledge important contrasts between urban and rural environments, with fertility being both lower and later in large urban settlements (Busetta, Giambalvo, 2012; Ďurček, Šprocha, 2017; Kulu et al., 2007; Noin, Chauviré, 1989; Šprocha, Šídlo, 2016). Later fertility in urban areas is generally resulting from earlier and more pronounced postponement transitions. Elsewhere later start and lower speed of the transition may have led to the impression that rural areas are following behind (Kotowska, Jóźwiak, Matysiak, Baranowska, 2008). As much as reduced number of children has characterised fertility in urban settlements for a while (Decroly, Vanlaer, Grimmeau, Roelandts, Vandermotten, 1991; Kulu et al., 2007), it is not clear if later fertility timing in urban centres is as firmly established. Urban MAB may be younger than those in rural areas because of an earlier decline in fertility (for instance in nineteenth century Paris : Brée, 2017); bigger desire to limit or halt childbearing specifically at older ages (for instance in 1960s Russia : Zakharov & Ivanova, 1996); earlier transition to parenthood successive to earlier age at marriage (see for instance in 1960s Ireland : Wilson, 2007); or a combination of these three reasons.

25Finally, spatial differences within urban areas are also noticed. Considering fertility timing, suburban areas show a more pronounced postponement of parenthood (Kulu, Boyle, Andersson, 2009; Wanner, 2000), a characteristic that persist when controlling for socio-economic compositions (Kulu, Boyle, 2009). Equally, fertility before 20 years old is more common in deprived neighbourhoods than suburban areas no matter the income group (Buelens, 2021b).

3 Data

3.1 Analysis strategy

26To gain control over historical, ecological, and cultural conditions and to improve the ability to generalise results, Snyder, in his paper “Scaling Down: The subnational comparative method” (2001), proposes to compare subnational units from different countries. Balbo et al., (2013) pushes a similar approach in conclusion of their review on fertility determinants. This subnational and transnational approach raises several methodological challenges. First it requires to find a method able to measure fertility timing using only the limited data available. Data is particularly limited across time and space at (subnational) regional level. The indicator we chose is based on such data and will be discussed along with the data gathering process in the next two sections. Secondly it requires mixed methods. We first proceeded in exploratory analyses in section 4. The next sections measure the relevance of the geographic structures highlighted in the exploratory analysis and looks at how the relevance of these structures changes with time (5.1) with specific focus on urban areas which are increasingly different from the rest (5.2). Lastly we zoomed in for further description of fertility timing differences within urban areas using more detailed information (5.3).

3.2 NUTS-2 and NUTS-3 levels

27Only national level data exists to study transnational spatiality of the ‘postponement transition’ across European countries before 1990. For deeper spatial analysis, I collected regional data at the NUTS-2 level. Therefore, the following analysis does not consider the early phases of the transition especially in western European countries (cf. contextualisation in section 2.1). In general, ASFR and related MAB are available at the NUTS-2 level since 1990 in Eurostat databases. However approximately a fifth of the needed MAB values were missing, most of them for the year 1990. Thus, we first had to complete the databases either using data from a different year (~15% of the missing regional values for 1990 have been replaced by 1991 values weighted by the changing ratio between those two dates at the appropriate national level), or computing MAB based on fertility rates in five-year age groups (instead of ASFR) available in other databases such as national statistics offices. Because NUTS regions are not consistent with time, we also had to reconstruct spatially consistent regions. Eventually we obtained a database of 286 regions covering the period 1990 - 2017.

  • 5 For instance, Paris figures are diluted in the broad region Ile de France while the city of Prague (...)
  • 6 The average population size of NUTS-3 regions in European countries lies between 150.000 and 800.00 (...)

28For even further investigation in urban areas we used NUTS-3 level data, more spatially detailed than the NUTS 2 ones (results presented in section 5.1). Indeed, the spatial extension of NUTS-2 regions poorly segregates urban environments from their surroundings.5 At such fine level, Eurostat provides the numbers of live births by age groups of the mothers and the number of women by age groups since 2013. We used the ratio between these two variables to compute age groups fertility rates. The small historical depth at the time of downloading this data limited the benefits of a temporal evolution analysis. Instead, we offer a snapshot of fertility patterns in the middle of the last decade studied. Some of these data were very likely to present statistical hazards values due to rare events in relatively small population groups.6 To reduce statistical hazard, we averaged three years of observation using 2014, 2015 and 2016 figures.

3.3 Mean age at birth to measure the timing of fertility

29We used the women’s mean age at birth of child (MAB) as synthetic indicator of fertility timing. This choice raises two main concerns. First, MAB masks dispersion around the central value which is problematic as recent re-increase in both Eastern and Western European fertility came with an increasing width of the fertility curve (Burkimsher, 2015), but mostly because MAB does not only consider the first child, it is interdependent with the total number of children. In theory, the more children a woman has, the later her average age at giving birth, hence the fall in the number of children per women during the (first) demographic transition brought MAB down (see Fig. 1). However considering recent European trends, such relationship is no longer clearly observed empirically at the regional level. The coefficient of determination (or R²) between MAB and period fertility is low (<0.01) during the 1990-2017 period. Across the continent, regions with the highest TFR are not those with the highest MAB and reversely.

  • 7 Based on 2015 data, tempo-related information cast 47% of the regional differences in ASFR, while q (...)
  • 8 The only major difference between PCAs is that with time the association of 25-29 years old fertili (...)

30Principal component analyses (PCAs) allow further investigation on the (in)dependence between fertility level and fertility timing. Summarising information contained in the age specific fertility rates (ASFR) of European (subnational) regions, PCAs oppose tempo-related information from quantum-related information on two orthogonal (so uncorrelated) axes.7 Fig. 2 exemplifies with 2015 and NUTS-3 level data the results from one of the PCAs conducted. Results from the other PCAs based on ASFR from the 1990-2017 period are closely related.8 The first components (horizontal axes) are defined thanks to their positive relations with fertilities at later older ages and negative ones with fertilities at younger ages. They gather tempo-related information and are closely associated with MAB. The second components (vertical axes) depend on fertilities at the medium and most fertile ages and is therefore closely related to fertility level as measured with TFR.

Figure 2: First and second component factor loading based on 2015 NUTS-3 region information

Figure 2: First and second component factor loading based on 2015 NUTS-3 region information

Note: TFR and MAB as supplementary variables

Source: own calculation

31Using MAB as central indicator of fertility timing for this research has many pros. The indicator is widely available including at subnational levels and across time. It is measured in years which is relatively intuitive. It is independent from the population age structure which makes it more appropriate for comparison across different time and space. Finally, its close association with the first axis of the above mentioned PCAs makes it a good summary indicator of tempo-related fertility information.

4 Exploratory analyses

4.1 Postponement and increasing homogeneity in fertility timing across Europe

  • 9 Averages are based on regional figures, weighed by the female population in childbearing ages.

32The dataset of 286 regional MAB shows a clear trend towards further postponement of fertility timing between 1990 and 2017. When in 1990 lowest and highest regional MAB spread from 23.7 to roughly 30 years, this maximum was topped by the European average9 since at least 2010. The 2017 average reached 30.8 years, with only 30% of the regions presenting a MAB under 29 years (the 2000 average), and only nine of them under the 1990 average of 27.8 years. MAB increased by almost three years in the 27 year period while TFR is pretty similar in 1990 and 2017 (around 1.6 children per woman).

Table 1: Subnational MAB statistics in Europe

Statistic on MAB / Dates

1990

2000

2010

2017

Average

27,8

29,0

30,0

30,8

First quartile

26,7

28,0

29,2

30,0

Third quartile

28,6

29,6

30,7

31,4

Inter-quartile range

1,97

1,64

1,46

1,40

Standard deviation

1,42

1,42

1,17

1,18

Coefficient of variation

5,1%

4,9%

3,9%

3,8%

Note: Based on 286 NUTS-2 regions

Source: Own calculation

33The aging trend took place within a converging movement. As shown with Table 1, statistical dispersion decreased by approximately a quarter since 1990. Regions that started with the youngest MAB generally experienced the biggest increases. However, homogenous behaviour is still far out of the reach as strong differences persist, converging trend slows down in the last period and homogeneity is not recorded either in terms of other family and fertility behaviours (Billari, Wilson, 2001; Kuijsten, 1996).

4.2 From one clear spatial distinction to a softer gradient

Figure 3: Timing of fertility in European NUTS-2 regions in 1990, 2000, 2010 and 2017

Figure 3: Timing of fertility in European NUTS-2 regions in 1990, 2000, 2010 and 2017

Source: Eurostat and national statistic offices

34Mapping the dataset like in Fig. 3 exposes the spatial discontinuities in fertility timing. This way, it is possible to identify the spatial structures organising fertility behaviours in Europe without preconceptions on the hypothetical priority of international differences over intra-national ones (unlike graphs similar to Fig. 1). The general increase of MAB across Europe is well visible, and so are the different spatial distributions trough time.

  • 10 Especially since high order births are still relatively common in the East back then. However, late (...)

35In 1990, a clear contrast is visible. MAB is lower in the East, resulting from much younger fertility patterns.10 Fertility there depends a lot on relatively young age groups. In Bulgaria up to two thirds of the childbirths occur to women younger than 25. Consequently, Bulgarian regions present the youngest MAB (< 25). With 1990 rates, women there would have had 1.2 children before turning 25, twice more than the European average. East German and Czech regions (beside Berlin and Prague) also present young MAB, both because of high fertility among younger age groups and low fertility among the other groups. In the West, youngest MAB (< 27) are found in Greece, South Portugal, Western Austria (with low TFR), as well as some Welsh and North English regions (with above average fertility). Latest MAB (> 29) are organised in four clusters: the Netherlands; Ireland and Sweden (where fertility is relatively high at 2.1 children per woman); Northern Spain and Northern Italy despite lowest fertility on the continent (<1.2); and the major urban centres (Ile-de-France, capital city regions of Nordic countries).

36Ten years later, in 2000, the MAB average almost reaches 29 years. The East-West division is softened but a gradient is still visible. MAB increased quickly in East Germany, Bohemia (western regions of Czechia), Western Hungary and Slovenia (between 2 and 2.5 years in a decade) to reach 27 years or more. Eastward figures are getting increasingly lower, unless regional fertility is relatively high (but rapidly decreasing as in Albania, Poland and Romanian Moldavia). Differences also exist in the West, some corresponding clearly with national borders. Hence Spanish, Italian, Dutch and Irish regions present MAB over 30 years while such levels are the prerogative of capital city-regions outside those four countries. In the UK, fertility timing and intensity is still comparable with that in Eastern Poland.

37In 2010, 30 years became the average. The East-West divide is getting more blurred as witnessed on the third map in Fig. 3. Bulgaria and Romania still display the youngest MAB, only in those two countries did regions not yet top the 1990 European average. Slower postponement of fertility timing in Bulgaria described above (Section 2.2 Contextualisation) also applies to East Slovakia. Both share exceptionally high fertility for Eastern European standards (TFR < 1.6). There, fertility before 20 years may have not even decreased in the 2000s. In Czechia and capital-city regions of CEE, TFR is low but increases in pair with the aging of MAB, giving credits to the postponement of early births to older age groups.

38Dissimilarities between capital city regions and their surrounding appears more strikingly than 10 years earlier. Differences reach two years in France, Belgium, Norway and Demark while it topped at 2.6 years of difference in Romania. In Spain and Italy such difference is absent: every region in these countries present late MAB.

39The last map represents regional MAB in 2017. On average MAB continued to grow as quickly as it did in the previous periods, increasing by 1 year every decade. However, where biggest increases used to take place in Central Europe (and capital city regions in particular), they now are happening in Greece and Portugal (up to 1.5 years more in 7 years). Beside one Northern-English region, every region with a MAB under 29 years are located in Eastern Europe, with the youngest ones specifically in South-Eastern Europe. MAB above 31 years (and up to 33) are combined with very low fertility in all of Spain and Italy but now also in most of Greece and Portugal. In Switzerland and South Germany it is combined with TFR around the average but increasing. Finally, later MAB is also displayed in relatively high fertility areas such as Ireland, the Netherlands and most Europeans metropolitan regions.

40Overall, between 1990 and 2017 European regional MAB have increase everywhere. The increase has been limited to 0.5 – 1.5 years in most Dutch provinces and formerly high fertility regions (Albania, Romanian Carpathians), but is substantial in the Central and Southern parts of the continent (+ 3 to 5.5 years). These differences over the whole period reflect less the intensity of the postponement transition than the later start of the transition in Eastern Europe. The transition indeed started before 1990 in most of Western Europe. However new spatial organisation of fertility timing emerged, which will be the focus of the following sections.

5 Results

5.1 Towards a loose spatial organisation?

41Exploratory analysis reveals contigious spaces and spatial discontinuities in the distribution of MAB that echos with known geographical structures (countries, historical regions, urban-rural dichotomy, etc.). The relevance of these structures in the spatial organisation of fertility timing can be measured thanks to the consistency of (reconstructed) NUTS 2 regions.

42Fig. 4 presents the proportion of regional variance associated with different geographical structures (from broad divisions of the continent to smaller subnational regions). For comparison purposes similar analysis is done for fertility timing and intensity (respectively using MAB and TFR). In terms of fertility timing, the East-West divide remains highly relevant throughout the 1990-2017 period (but this is not the case is not the case for fertility intensity).

Figure 4-a: NUTS-2 spatial MAB variance associated with specific geographic structures / 4-b: NUTS-2 spatial TFR variance associated with specific geographic structures

Figure 4-a: NUTS-2 spatial MAB variance associated with specific geographic structures / 4-b: NUTS-2 spatial TFR variance associated with specific geographic structures

Note: The number in brackets is the number of units considered.
Only 21 countries are considered, those with more than three NUTS-2 regions : Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Greece, Germany, Hungary, Italy, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Spain Sweden, Switzerland and the United Kingdom

Source: own calculation

  • 11 These five groups are inspired by researches considering fertility intensity (Pinnelli, Hoffmann-No (...)

43Around 75% of the regional variance in MAB is associated with the division in 21 countries. The magnitude of this association is rather stable over the period despite diminishing since 2000. From a fertility intensity perspective European countries have become increasingly homogenous since at least 1960, much earlier according to Watkins (1990). Such a trend does not exist considering fertility timing. Similarly, division of the continent in five groups of countries (Nordic, Western, Southern, German-speaking and Central/Eastern)11 is clearly more relevant when it comes to TFR than MAB. Former political blocs, opposing until the late 1980s communist to capitalist ruled countries and dividing the continent between East and West along the Iron Curtain are fairly relevant for the MAB through the whole period. In 1990, 56% of regional variance is associated with this division in just two groups. Almost three decades after the end of communist rule in CEE the division is still associated with 44% of the European regional MAB variance.

Figure 5: NUTS-2 spatial MAB variance associated with nested geographic structures (spatial organisation of European subnational MAB)

Figure 5: NUTS-2 spatial MAB variance associated with nested geographic structures (spatial organisation of European subnational MAB)

Source: own calculation

44Spatial structures tested here are nested within each other like Russian dolls. Relevance of the supranational division thus depends on the relevance of every lower scale division inside. Fig. 5 presents the part of regional variance strictly associated with each geographical space between 1990 and 2017. Although the clear East-West distinction is softened, this first level division remains highly accurate. As said here above, this sole division accounts for 44% of the geographical variation of fertility timing between European regions. From the variance left to explain within former political blocs it appears regions are getting increasingly organised according to the group of countries they belong to as the proportion of variance associated more than tripled since 1990 (from 5% to 16%). It means increasingly similar patterns within groups and increasingly dissimilar patterns between groups, especially between the southern group and the others within the former Western political bloc. Within these groups, the national level is not getting more significant. Despite Italy and Germany (previously most diverse countries) becoming much more coherent since 1990, homogeneity declined in many other countries. Countries still participate at a fair level to a better description of spatial organisation (between 13 and 20% of additional variance).

45These analyses on the regional variance expose how accurately spatial organisation of European fertility timing may be described with the sole national and supra-national levels. These levels are associated with almost three quarters of the spatial variance between NUTS-2 units. Such result supports analyses based on national data such as those exposed in section 2.1 (Contextualisation). However Fig. 5 also exposes growing proportion of spatial differences within countries. This is discussed in the following sections.

5.2 Metropolitan regions race ahead

  • 12 For the following analysis Zurich region has been considered as the “capital-city region” of Switze (...)

46Fig. 5 presented a growing importance of subnational spatial variations, including an increasing differences between regions that contain the capital-city12 and other regions in the country. In 1990 this distinction was associated with less than 7% of the total variance in Europe but a third of the variance within countries (Fig. 5). In 2017 it represented half the variance within countries (and 13% of the total variance). Fig. 6a-d show the trends of MAB in metropolitan regions compared with their respective national average. Fertility in metropolitan areas is usually later than in the rest of the country, and this specificity usually has strengthened with time.

Figure 6a-d: MAB trends in selected metropolitan regions

Figure 6a-d: MAB trends in selected metropolitan regions

Note: Based on NUTS-2 regions
Curves are smoothed using three-years averages

Source: own calculation

47From the almost 30 metropolitan regions selected, three major trends appear.

48The highest increase of the difference between metropolitan and national MAB between 1990 and 2016 took place in CEE countries (Fig. 6a). These countries also experienced large MAB increase in general, despite important declines of their spatial homogeneity. The great differences in Bulgaria, Czechia, Hungary, Romania and Slovakia (> 1.25 year) reflect the tighter delineation of their capital-cities at the NUTS-2 level if not the high levels of regional inequality in these countries (Petrakos, 2001). In the 1990s, the differences between metropolitan and national MAB were mostly due to lower fertility at young ages in metropolitan regions. Lately (2017) these regions also have a greater fertility at older age (see Fig. 7a). Around Bratislava and Warsaw, the catch up of postponed births by older age groups is even big enough for the capital-city regions TFR to surpass the national average.

Figure 7a-c: National and metropolitan age specific fertility rates at different date

Figure 7a-c: National and metropolitan age specific fertility rates at different date

Source: Eurostats

49In Manchester and Birmingham regions (second order cities in the UK), the differences with national average are also growing rapidly. There, fertility was characterised by its young pattern, much higher around 20 years than in the national average as shown in Fig. 7b with Manchester example. With the 2000 rates, a young woman in Manchester would have had 0.60 children before she turned 25. A number closer to the Bulgarian (0.68) than the British average (0.48). Since then, fertility rates at young ages decrease dramatically (with 2016 rates, the British average is only 0.07), and the decrease has been the largest in Manchester-like regions. Additionally the decrease has been largely overbalanced by increasing fertility above 27 years in these regions. Greater Manchester keeps a younger MAB than the UK average because the national average is heavily influenced by London Region where fertility below 30 years is comparatively very low.

50In France and in the Nordic countries the specificity of the capital-cities grows more progressively (Fig. 6b). The spatial structure of fertility timing strengthens no matter if the national TFR increases (in France) or declines (in Nordic countries). In the other North-western countries, the differences between metropolitan and national MAB are relatively stable between 1990 and 2016 (see Fig. 6c).

51Finally, Fig. 6d exposes diminishing differences in the South of Europe. Here metropolitan MAB remains generally above national average but the gap is closing in. It does not mean fertility postponement stops in metropolitan regions but rather that fertility is ageing more rapidly in the other regions of these countries. Northern Italian regions such as Lombardy (containing Milan) presented the highest MAB in 1990. Then, postponement has been so important in the rest of the country that Central regions such as Lazio (including Rome) and lately even Southern regions outstripped the Northern ones. Fig. 7c shows how fertility distribution across age groups shifts to older ages in Portugal and Lisbon Area in the last 25 years. The national curve in particular shows the great decrease in young (pre-modal) fertilities and the rise in older (post-modal) fertilities. Unlike in CEE, the shift has been more important for the national average than for the capital-city regions. Such rapid ageing throughout country made Portuguese and Greek fertility distribution more similar to those of Italy and Spain.

5.3 Not a single but multiple urban patterns of fertility timing

  • 13 To keep the focus on fertility timing only, the cluster analysis is based on the proportion of the (...)

52Metropolitan regions across Europe did not experience similar trends and it would be an oversimplification to consider a single, trans-European, metropolitan fertility pattern. For further investigation we used a cluster analysis on NUTS-3 level data.13 The results show late fertility is characteristic from urban areas, however different fertility timing patterns exist in urban environments.

Figure 8: Spatiality of the six major fertility timing patterns in European NUTS-3 regions (2015)

Figure 8: Spatiality of the six major fertility timing patterns in European NUTS-3 regions (2015)

Note: NUTS-3 regions distorted according to the female population in childbearing age using the Gastner/Newman (2004) diffusion-based algorithm.
Average based on observations from 2014, 2015 and 2016.

Source: own calculation

  • 14 The size of each spatial units does not represent its surface but its population. It emphasizes vis (...)

53Six clusters result from a Ward’s hierarchical gathering. They are mapped in Fig. 8 where regions are distorted according to the female population in childbearing age.14 Neither the number of clusters nor any boundaries were assumed a priori. The spatial distribution of these cluster show fertility timing patterns are spatially organised and dependent from several political and morphological borders. The following descriptions focus on the three clusters gathering NUTS 3 regions with major European cities. Together they contain two third of the European population. We refer to them as the urban clusters. They all present later than average fertility patterns (as shown in Fig. 9a). Fig. 9b reports age specific fertility in the three other (non-urban) clusters for comparison purposes.

Figure 9a-b: Specificity of age distribution of fertility in six clusters compared to the European average

Figure 9a-b: Specificity of age distribution of fertility in six clusters compared to the European average

Note: colours refer to clusters in Figure 8

Source: own calculation

54Most metropolitan regions in the North and West of Europe display a fertility timing pattern similar to Italy, Spain and Ireland (in orange). Examples include (in decreasing order of population) regions of London, Paris, Athens, Stockholm, Budapest, Hamburg, Helsinki, Munich, Zurich, Amsterdam, Thessaloniki or Cologne. The cluster has the latest MAB: 32 years, 1.4 years later than the continental average. Fig. 9a compare proportions of fertility due to age groups in the cluster with those of the average. Here women have the lowest fertility at every age group below 30 years but the highest after 35. TFR is very dependent on older age groups: 46% of childbirth are due to women above 30 years (compared with 34% on average).

55A second cluster (in yellow) also consists in metropolitan environments. It gathers most of (West-) Germany, the Netherlands and Switzerland with Central European biggest cities (Warsaw, Prague, Zagreb, Krakow, Bratislava, …) and regions containing a second order city in Western or Northern Europe (Lyon, Toulouse or Bordeaux in France; Gothenburg, Malmö, Aarhus, Bergen or Tampere in the Nordic countries, etc.). Where NUTS-3 regions provide information within metropolitan areas, it seems this cluster is also typical from suburban environments (see regions around Hamburg, Brussels, Vienna or Copenhagen). This suburban patterns may overwhelm that of city-centres in case of regions much larger than the morphological extend of the cities. At 31 years the cluster has the second oldest MAB but most of all fertility is very concentrated around 30 years, which could result from a rectangularisation of fertility patterns (Kohler et al., 2002). Here 41% of childbirths are accountable by the sole 25-34 years groups. As shown on Fig. 9a, middle aged women are here more fertile than average Europeans, unlike youngest and oldest age groups.

56A third cluster also consists of mostly metropolitan regions. That cluster (in light blue) brings together most of the UK with urban regions from across the continent such as Berlin, Lisbon, Bucharest, Vienna, Bouche-du-Rhone (containing Marseilles), Sofia, Brussels, and large sections of the German Ruhr region. The cluster has both a fertility distribution and a MAB close to the average (30,4 years), thus much younger than in the two clusters above. Looking at the specificity of age groups distribution of fertility (Fig. 9a), this third group is in some ways the opposite of the above “suburban cluster” (in yellow). There are at least two explanations to this heterogeneity in the timing of fertility. Either it is due to high (total) fertility, like in poorest urban districts in France and the UK (with high TFR such as Seine-Saint-Denis in North-East Paris and Eastern London). Either it is because very distinctive groups of population are responsible for the early and late childbirths which suggests high levels of social inequality and fertility patterns stratified by socioeconomic groups (see next section).

57The other three clusters (in light blue, dark blue and green) gather NUTS 3 regions that include very few major European cities. We refer to them as non-urban clusters in opposition to the first three clusters. MAB in these non-urban clusters are lower than 30 years and proportions of birth due to later age groups are low too (see Fig. 9b). The diversity of fertility patterns in urban and metropolitan areas is further studied in Buelens (2021).

6 Discussion: interpretation of the spatial variations

58The above spatial analysis suggests the geographical structures organising fertility timing in Europe. Thanks to the structuration of fertility determinants based on their spatial scope of action (section 2.1), it allows interpretations of the exposed spatiality in terms of fertility determinants. It mostly confirms known relationships but also suggest factors likely to influence fertility timing.

6.1 Fading of the East-West divide and diversity within CEE

59Earlier fertilities in CEE have long been recognised. They are attributed to later start of the postponement transition. The slowly fading East-West divide witnessed above took place while regimes transitioned away from communist rules and the European Union deepened / widened, which lead to great changes in fertility behaviours (Kohler et al., 2002). This happened together with changes in values and attitudes regarding family formation in CCE (Zakharov & Ivanova, 1996), particularly in East-Germany, Czechia and Estonia where stronger post-modern values lead to stronger postponement of fertility in these countries (Sobotka, 2003). Material constraints also played a role as rapid deterioration of material conditions accelerated postponement of (first) births (Kohler et al., 2002; Mills, Blossfeld, 2003; Sardon, 1998). The following economic recovering did not happen evenly which causes fertility postponements trends in CEE to be spatially differentiated both between countries (Billingsley, 2010; Philipov, Kohler, 2001) and within countries as new employment structures shift more clearly in favour of metropolitan (capital-city) regions.

6.2 Late fertility across the South

60The increasing significance of the supranational group level in figure 5 is mostly due to later fertilities in Mediterranean countries. The literature explains it by later transitions to adulthood and a common family-centred culture in inadequacy with recent female status with education system, labour market and housing market (Feyrer, Sacerdote, Stern, 2008; Mills, Mencarini, Tanturri, Begall, 2008). In Mediterranean societies norms, values and attitudes attached to family, gender relationship and transition to adulthood impose a fairly rigid family formation process (Billari, Kohler, 2010; Fux, 2008; Reher, 1998). The greater role of religion, and Catholicism in particular, as key social institution is also held responsible for later fertilities, both nationally and within countries (Adsera, 2006; Bacci, 1971; Dalla Zuanna, 2004; Pearce, 2010). The requirements to family formation are also harder to fulfill because of deteriorating economic conditions : young adults employment is high (above 50% in Greece and Southern Italy, Eurostat 2018) and once on the labour market young adults are likely to get low-paid and temporary jobs, which induce later transition to parenthood (Aassve, Arpino, Billari, 2013; Kohler et al., 2002; Tello, 1995). Difficult homeownership regimes in Southern European countries (Mulder, Billari, 2010) also lead to later residential independence and ultimately further postponement of parenthood (Castiglioni, Dalla Zuanna, 1994; Mulder, 2006). Later ages at leaving parental home has been shown to be closely related to later transition to parenthood both between countries (Aassve et al., 2013; Breen, Buchmann, 2002; Cordón, 1997; Kiernan, 1986; Van de Velde, 2008), and within countries such as Italy and Spain [Holdsworth et al., 2002 and Santarelli & Cottone, 2009].

6.3 Subsistence of regional specificities

61Some regional specificities subsist throughout the 1990-2017 period. Most have already been explained in the literature by either contextual factors or by population composition.

62Youngest fertility patterns in the west are mostly circumscribed in Northern England and Wales. Locally, it is characteristic to industrial areas and deprived neighbourhoods (Johns, 2011; McCulloch, 2001) as exemplified with the difference between East and West London fertility distribution (see Fig. 7b). Individual level determinants cannot satisfactorily explain the British specificity compared with other western countries. In continental European countries (France and the Nordic countries especially), policies aim to reduce the fertility differences induced by socio-economic stratification unlike the traditional relative “laissez faire” of British national family (Rendall et al., 2010; Sigle-Rushton, 2008; Wellings et al., 2016).

63Even if the homogeneity of European nations in terms of ethnic, linguistic or religious groups strengthen spatial organisation of fertility at the national level (Decroly, Grasland, 1992), counter examples emerge where minorities account for a large proportion of the local population composition. Examples include younger fertility patterns among the Roma communities (Janky, 2005; Koytcheva, Philipov, 2008), which people represent a higher share of population in east Slovakia and north-east Hungary, north-west and central Bulgaria. However their younger fertility pattern is not immutable but rather associated with lower educational attainment and lower earning perspectives. In favourable market conditions Roma women tend to adjust their fertility patterns towards those of the majority (Janky, 2005).

6.4 Increasingly differentiated fertility in urban areas

64The specificities of fertility timing in urban areas exposed in this analysis are less known from the literature. Crossing over spatial descriptions with the knowledge of fertility determinants structured based on their scope produce hypotheses for the interpretation of fertility behaviours. For this reason, geographic approaches such as the one adopted by this study contribute, at least heuristically, to a better understanding of demographic phenomenon.

65Results in section 5.2 have shown an increasing differentiation of fertility timing in most metropolitan areas. Postponement trends have been more radical in metropolitan areas. It happened in parallel with the increasing regional inequality and fragmentation between the ‘core’ and ‘peripheries’ at both European and national scales (Petrakos, 2001). Metropolises become increasingly different from the rest of their country because of the economic transformations and globalisation processes of the last decades (Sassen, 1991; Veltz, 1996). The ‘metropolisation’ of the economy resulted among other things in deep changes in the local structure of labour demand. It could be hypothesised further postponement of fertility has been promoted as an adaptation to these changes. Indeed it gives the urban citizens the opportunity to improve their human capital and employment ability. Adaptation to the changing labour market demand is accountable for fertility postponement in CEE countries in general (Kotowska et al., 2008). Local conditions that could enable fertility postponement in urban areas vary within cities along with population composition. It includes the perceived quality of living environment, the specificity of housing stock and prices and the spread of SDT-values in social environment.

7 Conclusion

66In this research we presented the spatial variations of fertility timing in European regions and how they changed in the last three decades. Once we overcame the inherent challenges of data gathering, this research benefits from a unique subnational level perspective across many countries. Thanks to this extended data set, the results give a more global understanding of spatial differences in European which foster generalisation. Three major outcomes emerge from this unique subnational yet transnational level approach of European fertility timing. First in recent European context, there is no clear relationship between fertility intensity and mean age at motherhood. Unlike theoretical expectations regions with latest mean ages at motherhood are not those with the highest fertility. Secondly, during the last three decades, postponement continued across Europe with European mean age at birth growing by one year every decade. Finally, despite general postponement spatial variations subsist, including within countries. The analysis portrays various fertility timing trends and patterns. Postponement transition has not been equal in onset and speed nor has it been sustained by the same environmental conditions across the continent or even within each individual nation. The prime spatial contrast across the continent switched away from an East-West divide after two decades of strong postponement in Central Europe. On the contrary two other major spatial distinctions strengthened. One sets apart Southern Europe with its very late fertility timings from the other European regions, the other isolates major urban areas from their respective countries because of a fertility highly dependent from the women over 30 years.

67We acknowledge several methodological limitations, most originating from data quality. Subnational data are not available before 1990 even though the postponement transition started before this date, especially in northwestern Europe. The spatial units used (NUTS-2 and NUTS-3 regions) are based on administrative delineations, not on differences in terms of morphology or population composition. The study used the age specific fertility rates which doesn’t provide birth order information. However, this last limitation is not as problematic as one could have expected as we showed mean age at birth turns to be a valid indicator to sum up tempo-related information of period fertility for the time and space considered. Some may consider the inductive approach rather than the more traditional deductive, model-testing approach as another limitation. The spatial approach executed here cannot disentangle the factors of an early start of the postponement transition or the current late fertility timing. But it pursues another goal: exposing the spatial impact of the different factors combine. The inductive approach allows to start without preconceptions such as a hypothetical primacy of international differences over intra-national ones. Only after exposing the spatial organisation do we open the discussion on the determinants of fertility timing.

68This research fulfills the aim to present how regional variation in terms of fertility timing change in Europe in the last decades. It highlights a switch from an East-West divide to a North-South divide as prime spatial contrast across the continent, but also the increasing specificity of fertility patterns in metropolitan areas. It proves subnational differences persist and calls for more consideration of subnational contexts, especially metropolitan contexts, beyond the traditional cross-country comparisons when studying fertility behaviours.

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Notes

1 Indeed period fertility as measured by TFR depends on variations in the timing of fertility. Hence when women postpone births to older ages, period fertility likely underestimates the ultimate number of children women will have.

2 Germany, Austria and Switzerland usually constitute this sub-group (Frejka, Sardon, 2006b; Neyer, 2003). Based on major fertility characteristics, Luxembourg could be added to the sub-group.

3 For instance in the 2000s, Spanish MAB plateaued around never seen before level of 30.9 years, MAB1 around 29.4 years but after 2008 it grew by nearly two years in a decade. In Greece MAB1 increases three times quicker after 2008 than it did in the 2005-2008 period.

4 Caucasian and other easternmost former soviet states are not considered here. Their relatively late MAB reflect higher occurrence of high order births than in the rest of Europe.

5 For instance, Paris figures are diluted in the broad region Ile de France while the city of Prague is considered a NUTS-2 region on its own

6 The average population size of NUTS-3 regions in European countries lies between 150.000 and 800.000. However roughly a quarter of the NUTS-3 regions considered gather less than 30.000 women in childbearing age.

7 Based on 2015 data, tempo-related information cast 47% of the regional differences in ASFR, while quantum-related information only 23%. This is another argument for more studies on European fertility differences considering timing rather than the sole fertility intensity.

8 The only major difference between PCAs is that with time the association of 25-29 years old fertility with fertility at earlier age is growing and the association of 30-34 years old fertility with later fertility is diminishing.

9 Averages are based on regional figures, weighed by the female population in childbearing ages.

10 Especially since high order births are still relatively common in the East back then. However, latest MAB in Eastern regions match particularly high TFR (3 in Albania; 2,40 in South-East Poland,…)

11 These five groups are inspired by researches considering fertility intensity (Pinnelli, Hoffmann-Nowotny, Fux, 2001). They are similar to the (sub-)groups presented in the contextualisation.

12 For the following analysis Zurich region has been considered as the “capital-city region” of Switzerland, instead of Espace Mittelland which contains Bern.

13 To keep the focus on fertility timing only, the cluster analysis is based on the proportion of the local total fertility (TFR) imputable to each age groups. These proportions are computed based on ASFR by five years age groups in 2014, 2015 and 2016.

14 The size of each spatial units does not represent its surface but its population. It emphasizes visibility of densely populated and urban regions.

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Table des illustrations

Titre Figure 1 a-c: MAB in selected countries
Légende Note: UK: England and Wales only
Crédits Source: Human Fertility Database, Eurostat
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-1.png
Fichier image/png, 29k
Titre Figure 2: First and second component factor loading based on 2015 NUTS-3 region information
Légende Note: TFR and MAB as supplementary variables
Crédits Source: own calculation
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-2.png
Fichier image/png, 9,7k
Titre Figure 3: Timing of fertility in European NUTS-2 regions in 1990, 2000, 2010 and 2017
Crédits Source: Eurostat and national statistic offices
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-3.png
Fichier image/png, 289k
Titre Figure 4-a: NUTS-2 spatial MAB variance associated with specific geographic structures / 4-b: NUTS-2 spatial TFR variance associated with specific geographic structures
Légende Note: The number in brackets is the number of units considered. Only 21 countries are considered, those with more than three NUTS-2 regions : Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Greece, Germany, Hungary, Italy, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Spain Sweden, Switzerland and the United Kingdom
Crédits Source: own calculation
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-4.png
Fichier image/png, 19k
Titre Figure 5: NUTS-2 spatial MAB variance associated with nested geographic structures (spatial organisation of European subnational MAB)
Crédits Source: own calculation
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-5.png
Fichier image/png, 10k
Titre Figure 6a-d: MAB trends in selected metropolitan regions
Légende Note: Based on NUTS-2 regions Curves are smoothed using three-years averages
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-6.png
Fichier image/png, 43k
Titre Figure 7a-c: National and metropolitan age specific fertility rates at different date
Crédits Source: Eurostats
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-7.png
Fichier image/png, 56k
Titre Figure 8: Spatiality of the six major fertility timing patterns in European NUTS-3 regions (2015)
Légende Note: NUTS-3 regions distorted according to the female population in childbearing age using the Gastner/Newman (2004) diffusion-based algorithm. Average based on observations from 2014, 2015 and 2016.
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-8.png
Fichier image/png, 121k
Titre Figure 9a-b: Specificity of age distribution of fertility in six clusters compared to the European average
Légende Note: colours refer to clusters in Figure 8
Crédits Source: own calculation
URL http://journals.openedition.org/cybergeo/docannexe/image/37887/img-9.png
Fichier image/png, 18k
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Référence électronique

Mathieu Buelens, « Subnational spatial variations of fertility timing in Europe since 1990 », Cybergeo: European Journal of Geography [En ligne], Espace, Société, Territoire, document 1000, mis en ligne le 10 décembre 2021, consulté le 22 janvier 2022. URL : http://journals.openedition.org/cybergeo/37887 ; DOI : https://doi.org/10.4000/cybergeo.37887

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Auteur

Mathieu Buelens

Université libre de Bruxelles, 1050, Brussels, Belgium
mathieu.buelens@ulb.be

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