Dykstra, Pearl

Puur, A

Põldma, A

Sakkeus, L

Schenk, N (Niels)

English

EUR Research Repository

          Project title: How demographic changes shape intergenerational solidarity, well-being, and social integration: A multilinks framework  Policy brief #4: Puur, A., Sakkeus, L, Schenk, N., Põldma, A., & Dykstra, P. A. (2010).  New insights on kin availability, using the Generations & Gender Surveys.  SUMMARY Objectives of  the research Providing new insights on kin availability using data from the Generations & Gender Surveys (GGS). Scientific approach /  methodology We illustrate the advantages of the GGS by focusing on diversity in partnerships, childlessness among women ánd men, and the generational structure of kin networks.  New knowledge and/or European added value (1) The multitude of partnership types and partnership histories visible in the findings underscore that the traditional civil registry categories of “married”, “divorced”, “widowed” and “never-married” do a poor job of representing reality.   (2) Childlessness rates tend to be higher among men than women. Men thus stand a higher chance of being without a primary source of support. (3) Contrary to popular belief, vertically extended kin networks with four or five generations alive at the same time are not the norm. The majority of adults are members of three-generation families. Key messages for policy-makers,  businesses, trade unions and  civil society actors The size and composition of kin networks are relevant in view of support responsibilities and support provision. An inventory of family members can be used to identify persons at risk of being without the help they need, or at risk of being burdened by family obligations. Such an inventory is not possible with standard demographic measures. Survey data such as those of the GGS are required.            EUROPEAN POLICY BRIEF   2Objectives of  the research The newly available data from the Generations & Gender Surveys (GGS) have several advantages compared to conventional sources of demographic information. In this policy brief we illustrate the advantages of the GGS by focusing on  (a) diversity in partnerships, providing information about ‘new’ forms of partnership such as unmarried cohabitation and Living Apart Together relationships (LAT), and providing information on partnership history — which standard demographic measures with their focus on official marital status cannot do — (b) childlessness among women and men — which standard demographic measures cannot do because fertility data are only collected for women —, and  (c) the generational structure of kin networks — which standard demographic measures cannot do because they cannot provide insight into the joint effect of demographic trends. Scientific approach /  methodology The analyses reported in this policy brief are from seven countries for which harmonized wave 1 Generations & Gender survey data (~2004) were available: Bulgaria, Estonia, France, Georgia, Germany, Hungary, and Russia. The data are based on national probability samples of men and women aged 18-79 years, living in non-institutional households. The analyses can be expanded as data from a larger number of countries become available. So far, the GGS has been carried out in 15 countries. The Generations and Gender Programme (GGP) aims at improving the knowledge base for policy-making in UNECE countries. The main substantive goal of the Generations and Gender Programme is to improve understanding of demographic and social development and of the factors that influence these developments, with a particular attention towards relationships between children and parents (generations) and relationships between partners (gender).              EUROPEAN POLICY BRIEF   3New knowledge and European added value  The size and composition of kin networks are relevant in view of support responsibilities and support provision. An inventory of family members can be used to identify persons at risk of being without the help they need, or at risk of being burdened by family obligations. Alternatively, it is an indicator of available resources (e.g. the back-up support of an older generation). Thus, knowledge about kin networks is important for forecasting and the creation of social policies and programs.   To describe kin networks, one cannot rely on demographic statistics. One reason is that standard demographic measures disregard links between family members, making analyses of patterns across successive generations, and of clustering in families, difficult. Another reason is that the joint effect of demographic trends is not always obvious. The opposing effects of increasing longevity and postponed childbearing on the generational structure of families are a good example. Whilst the extended life span means that older family members are living longer than they did in the past, which increases the likelihood that three, four or even five generations may be alive at the same time, delayed childbearing means that the age gap between generations is relatively large, which reduces the likelihood that multiple generations will be alive at the same time. A third reason is that demographic statistics are often based on registry data, which provide no information about ‘new’ forms of partnership and parenthood. A fourth reason is that registry data on fertility are collected for women only; information on men is lacking. A fifth reason pertains to the unit of data collection, which is often the household. Measures limited to the household unit disregard non-co-residing kin and connections beyond the household that could noticeably affect the well-being of individuals. For instance, the implications of solitary living in the old age in case children can be called on for help, may be very different from the situation where such a possibility does not exist.  The newly available data from the Generation and Gender Surveys (GGS) have several advantages compared to conventional sources of demographic information. They make it possible to examine vertical and horizontal kin ties of men and women which link, irrespective of co-residence, successive family generations and various types of exchange that are channeled along these ties. Such analyses can only partially be carried out with other existing large-scale cross-nationally comparative datasets. For instance, SHARE (Survey of Health and Retirement in Europe) collects information from the older (50+) age groups only; ECHP (EC Household Panel) and its current successor EU-SILC (EU Survey on Income and Living Conditions) has only limited information on family members living outside the household.   In this policy brief we illustrate the advantages of the GGS by focusing on diversity in partnerships, childlessness among women and men, and the generational structure of kin networks. The analyses reported here are from seven countries for which harmonized wave 1 data (~2004)          EUROPEAN POLICY BRIEF   4were available: Bulgaria, Estonia, France, Georgia, Germany, Hungary, and Russia.   From the historical point of view, the GGS-countries cover the entire spectrum of demographic modernization stretching from the vanguard (France) to the latecomers (e.g. Georgia and the Russian Federation). With respect to current patterns, they feature a considerable variation in mortality, fertility and nuptiality regimes, which sets a favorable ground for exploring the interplay between kin network structures and the underlying demographic processes.  Diversity in partnerships  Figure 1 shows the proportion ever-partnered for three age groups (20-39, 40-59 and 60-79) in the seven countries. It also has information on the type of partnership (marriage, unmarried cohabitation or Living Apart Together (LAT, i.e. not sharing the same household)), and on ever having been widowed or divorced/separated.  Figure 1. Partner status and partner history, by age group and country BG EE FR GE DE HU RU BG EE FR GE DE HU RU BG EE FR GE DE HU RU020406080100% age group0,00,30,60,91,21,5mean number of partnershipsMarriedCohabitingDivorced/separatedWidowedMean Source: Generations & Gender Surveys, 2004.  Not surprisingly, the proportion ever-partnered is lowest in the youngest age group. The age at partnership formation is lowest in Russia, and highest in Georgia. Germany shows the highest rates of ever-singles in the middle and oldest age groups. The proportion ever-partnered in these age groups is highest in Estonia, France, Hungary, and Russia.  Marriage is the dominant form of partnership in each of the age groups. Cohabitation is most prevalent in the youngest age group, but certainly not restricted to that age group. Estonia and France have relatively high proportions of cohabiting in the          EUROPEAN POLICY BRIEF   5middle age group. LAT-relationships are also most prevalent in the youngest age group. In France and Germany LAT-relationships are also observed in the oldest age group. Note, however, that in the youngest age group a LAT-relationship is often a precursor to either cohabitation or marriage. In the oldest age group it tends to be engaged in after divorce or widowhood, and at higher ages it is often a permanent form of partnership.  Country differences in the prevalence of divorce are greater in the oldest age group than in the younger and middle age groups. Estonia, Hungary and Russia have the highest proportion of divorcees in the oldest age group. The rates of widowhood are greatest in Russia and Georgia. The likelihood of having had multiple partnerships is highest in the intermediate age group. Multiple partnerships are most often observed in France.   Overall, the country differences are not large. Russia and Georgia stand out with relatively high rates of widowhood in the oldest age group. Estonia stands out with relatively high rates of “non-traditional” partnership behaviour (unmarried cohabitation, divorce, but not LAT) at all ages.   The multitude of partnership types and partnership histories visible in Figure 1 underscore that the traditional civil registry categories of “married”, “divorced”, “widowed” and “never-married” do a poor job of representing reality.    Childlessness among women and men  Being childless is defined as having no surviving biological children. The GGS allow drawing the distinction between never having had children, and outliving them, a distinction that is not possible if registry data are used. As noted previously, another distinctive feature of the GGS is that information on childlessness among men is available.   As Table 1 shows, the proportion of childless is higher among men than among women. The difference is attributable to lower proportions of men entering partnerships. Note that the childlessness rates among men might be overestimated; men might yet become fathers for the first time over the age of 40. Childlessness rates (no children ever born) range from a high of 24% among German men to a low of 6% among Russian women. Bulgaria, Estonia, and Russia show the greatest likelihood of becoming childless due to outliving all one’s offspring. The circumstances of those who never had children are starkly different from those who have outlived their children. For the first, childlessness has always been a part of their lives. For the second, childlessness involves a turn of events they did not anticipate.  As Table 1 shows, the proportion of childless is higher among men than          EUROPEAN POLICY BRIEF   6among women. The difference is attributable to lower proportions of men entering partnerships. Note that the childlessness rates among men might be overestimated; men might yet become fathers for the first time over the age of 40. Childlessness rates (no children ever born) range from a high of 24% among German men to a low of 6% among Russian women. Bulgaria, Estonia, and Russia show the greatest likelihood of becoming childless due to outliving all one’s offspring. The circumstances of those who never had children are starkly different from those who have outlived their children. For the first, childlessness has always been a part of their lives. For the second, childlessness involves a turn of events they did not anticipate.  Table 1. Rates of childlessness among women and men aged 40 - 79, by country    No Biological Children (%)  Women Men  Ever Born Alive Ever Born Alive Bulgaria 7 8 10 11 Estonia 8 10 11 12 France 12 12 15 15 Georgia 12 12 8 8 Germany 19 19 24 24 Hungary 10 11 14 14 Russia 6 8 8 9      Source: Generations & Gender Surveys, 2004.  Generational structure kin network  The generational structure of kin networks was established on the basis of information on surviving parents, grandparents, children, grandchildren, and great-grandchildren. The information pertains to biological kin.  The conventional portrayal of family change under the influence of demographic trends is that the extension of life and the drop in birth rates result in “beanpole” families with relatively many vertical ties and relatively few horizontal ties. Contrary to popular belief, vertically extended kin networks with four or five generations alive at the same time are not the norm (see Figure 2). The majority of adults are members of three-generation families. The proportions in that type of generational structure vary from 55% (Germany) to 63% (Estonia). In a sample with a wider age range (i.e. a sample including the very young and the very old), the proportion four- and five-generation families would probably be higher.          EUROPEAN POLICY BRIEF   7 Figure 2. Number of generations in the kin network of adults aged 18-79, by country1  Bulgaria Estonia France Georgia Germany Hungary Russia020406080100% population 20-790,00,51,01,52,02,53,03,5mean number of generations4+321mean Source: Generations & Gender Surveys, 2004.  In Bulgaria, France and the Russian Federation, four-generation families outnumber two-generation families. In Estonia, Georgia and Germany, the opposite is the case. The least common kin network structure is that of a single generation. On average, four per cent of the respondents are so-called solo individuals, i.e. they have neither ascending nor descending kin. Solos are deprived of intergenerational kin ties but at the same time they are not necessarily family-less: an overwhelming majority of them has one or more siblings, and/or a partner. The prevalence of one-generation kin network structures is highest in Germany where it amounts to eight per cent of the population aged 20-79. In Estonia, France and Georgia, the likelihood of being a solo individual is about twice as low as in Germany (around 4%). Being deprived of any vertical family ties is even less common in the Russian Federation (3%) or in Bulgaria (2%).  GGS-data make it possible to examine the opposing effects of increased longevity and postponed childbearing on the generational structure of kin networks. For example, Figure 2 shows that the proportions in one-, two-, three- and four-generation families are virtually identical in France and in Russia. The underlying demographic processes are quite different, however, as is illustrated in Figure 3. In France, where people tend to live long lives, adults have relatively many ascending family generations. In Russia, where people tend to have children at a young age, adults have relatively many descending family                                                  1 Data for Hungary are lacking.          EUROPEAN POLICY BRIEF   8generations.   Figure 3. Mean number of ascending and descending generations in the kin networks of adults aged 18-79, by country  20 25 30 35 40 45 50 55 60 65 70 750,00,51,01,52,0mean number of descending generationsBulgariaEstoniaFranceGeorgiaGermanyHungaryRussia20 25 30 35 40 45 50 55 60 65 70 750,00,51,01,52,0mean number of ascending generationsBulgariaEstoniaFranceGeorgiaGermanyRussia              EUROPEAN POLICY BRIEF   9Key messages for policy-makers,  businesses, trade unions and  civil society actors There are many myths about the impact of demographic changes on the size, composition and structure of kin networks. Researchers have not always been able to redress misconceptions due to a lack of appropriate data. Broad developments in childbearing, marriage, divorce and remarriage have been sketched, but when it comes to telling the story of how these changes come together in people’s lives and families researchers have often ended up with more questions than answers. With data from surveys like the GGS, researchers can describe what contemporary kin networks look like and what is going on in those networks.          PROJECT IDENTITY   10Coordinator Pearl A. Dykstra, Erasmus University Rotterdam Consortium Erasmus University Rotterdam, Netherlands Interdisciplinary Demographic Institute, Vrije Universiteit Brussel, University of Antwerp, Wissenschaftszentrum Berlin für Sozialforschung, Utrecht University, Estonian Interuniversity Population Research Centre, Dondena Centre for Research on Social Dynamics, Bocconi University  Duration March 1, 2008 – March 1, 2011 (36 months)  Funding Scheme SEVENTH FRAMEWORK PROGRAMME  THEME 8 Socio-Economic Sciences and Humanities (SSH),  Collaborative project; Small or medium-scale focused research project  SSH-2007-3.1.1 The impact of demographic changes in Europe  Budget € 1,499,694  Website www.multilinks-project.eu Further reading Puur, A., Sakkeus, L., Schenk, N., & Põldma, A. (2009). Family constellations in Europe. Deliverable 3.1. MULTILINKS-project. Related websites http://www.ggp-i.org/ For more information allan@ekdk.estnet.ee  

New insights on kin availability, using the Generations &amp; Gender Surveys.

https://pure.eur.nl/files/47213169/Fourth%20Policy%20brief%20Multilinks.pdf

New insights on kin availability, using the Generations & Gender Surveys.

Abstract

Similar works

Full text

Available Versions

EUR Research Repository

New insights on kin availability, using the Generations &amp; Gender Surveys.

Abstract

Similar works

Full text

Available Versions

EUR Research Repository

New insights on kin availability, using the Generations & Gender Surveys.