Introduction
The 2030 Agenda for Sustainable Development, which includes 17 global goals, charts an ambitious course for the coming decade and beyond. Attached to the goals are 169 targets, which lay out the specific aims towards which the global community is working. In total, 95 of the targets are either directly (48) or indirectly (47) connected to children. The SDGs can only deliver on the promise of equity if the world knows which children and families are thriving and which are being left behind (UNICEF, 2017). Understanding the situation of children in relation to the SDGs is, therefore, crucial both for the well-being of children and for reaching the targets of the global goals.
Childhood well-being has an impact on a range of outcomes such as adult health, educational attainment, and employment and socioeconomic status in adulthood (Statham and Chase, 2010). Even though there is general agreement that policies to ensure child well-being are necessary there are disparities in the practical implementation of policy between countries and on regional levels. In some countries, a lack of financial resources may be the main reason for not having an appropriate monitoring system that has the potential to inform politicians on a regular and reliable basis; on the other hand, it could be a question of competing priorities. There is, however, an acknowledged
A longitudinal survey design has the unique capacity to capture the dynamics of childhood and young person development and helps to capture the impact of major life events, such as starting school, reaching adolescence and leaving home as well as giving a rare insight into the effects of socio-political changes and historical events that take place during their lifetime. This crucial phase of the life course is included in the focus of SDG 4 – Quality Education and SDG 8 – Quality Work (UN, 2020). Birth cohort surveys across the world have been central to our understanding of the factors which contribute to enhancing child well-being. These longitudinal data sets provide the highest quality data, which reveal demographic patterns of difference, changes over time and factors associated with high levels of well-being. They are an important source of evidence for policy makers seeking to protect and enhance the lives of children as they grow up. However, until now such surveys have been developed independently and, while there are some common features, in order to compare data in different countries there are many challenges in post hoc data harmonization. The merits of collecting national longitudinal data are widely recognized, and yet the current studies are not easily comparable as they contain different questions and are conducted at different times and on different age groups.
The European Commission (2012) recognizes that there is a significant gap in European data on children. These European data needs present a clear case-input harmonized comparative birth cohort survey. The European Cohort Development Project has been funded by the EU and has developed the design and business case for such a survey since 2018. The proposed survey – EuroCohort – comprises a common questionnaire, common sampling and fieldwork procedures and will thus allow a direct comparison of the well-being of children as they grow up across Europe in different national contexts. In the future, researchers the world over will be able to learn from the lived experiences of children and young people as they grow up in a diverse range of European countries.
This chapter begins with an overview of the development of child well-being research perspectives over the past few decades, including debates on models of participation for children and measurement debates on child well-being research. Secondly, it discusses birth cohort surveys as increasingly popular tools of measuring individual change over time and the growing desire to undertake comparisons and associated analytic challenges. Thirdly, it demonstrates the value of
Perspectives on and the measurement of child well-being
This section focuses on the development of child well-being research perspectives over the past few decades. It starts with debates on models of participation for children in research. It then focuses on measurement debates on child well-being research. Finally it discusses the development of research on child well-being demonstrating the gradual shift from tokenistic participation of children to co-creative model which appears to be aligned with the move in using subjective over objective measures of well-being. In academic literature, well-being is used as an overarching concept to indicate the quality of life of people in society (Rees et al, 2010a). From a hedonic point of view, it refers to a ‘person’s cognitive and affective evaluations of his or her life’ (Diener et al, 2002, p 63).
Models of participation in research with children
There are a number of models of how children and young people can/should be involved in a study. Depending on the degree of control and participation children may have in a study, Shaw and colleagues (2011) identified four models (ranging from the lowest to the highest level of participation): children are sources of research data, children are consulted about the research, children are collaborators in the study, and children are owners of the research. Hart (1992) also developed a ‘Ladder of Participation’ to describe the level of children and young people’s participation, going from rung 1, where young people are manipulated, to rung 8, where young people and adults share decision making. Current best practice for involving children in research focuses on a stronger and more active involvement, one that empowers them (Goswami et al, 2016).
Since childhood is not static and children’s abilities to participate vary by their age, the participation model used for research with children in a longitudinal design must be flexible. Lansdown (2001), a long-standing advocate of children’s participation in democratic decision making, argued for different levels of support and to use a variety of methods of working and expression to enable all children to participate
Objective versus subjective measures of well-being
In child well-being research, a distinction is made between objective and subjective measures. Objective measures of social reality are those which are not filtered by perceptions and are independent from personal evaluations. On the other hand, subjective measures are supposed to explicitly express subjective states, such as perceptions, assessments and preferences (Noll, 2013).
The use of objective measures such as GDP, household income, household wealth and income distribution, the proportion of children in education, educational attainment, life expectancy and crime rates are well established in research with children and young people’s well-being. Although objective measures provide useful information on well-being at the macro level, there are many criticisms and caveats to be taken into account when confronting such measures (McGillivray, 2007). For example, Hicks (2011) terms the approach to using objective well-being measures as ‘paternalistic’. This approach, he argues, assumes that certain things are good or bad for well-being and these are included in any indicator set; although there may be a model underpinning the choice, there is the danger that what is measured becomes what matters rather than what matters being measured. Some researchers (for example, Pollard and Lee, 2003) argue that the growth of the ‘developmental perspective’ in analysing childhood well-being has influenced the research on child well-being by objective indicator-based measures. The major UK birth cohort surveys (1958 National Child Development Study, 1970 British Cohort Study, 2000 Millennium Cohort Survey) have been guided by this perspective. A developmental perspective, they suggest, tends to adopt measures associated with deficits, such as poverty, ignorance and physical illness. While such indicators are important to begin to redress issues of inequalities and social exclusion which negatively impact on children’s health and well-being – for example, lower family socioeconomic position is linked to poor educational for children in schools (see the review by Blanden and Gregg, 2004) – they tend to ignore the potential, attributes and strengths of children.
In order to explain the usefulness of subjective measures in well-being research, Kroll and Delhey (2013) used the famous Thomas
This holistic approach (Goswami et al, 2016) to measuring children’s well-being is adopted in EuroCohort (2019). Since this approach gives equal emphasis on both measures of well-being, it allows researchers to estimate more comprehensively how changes in children’s and young people’s lives affect their well-being (measured using both subjective and objective indicators) as they grow up.
Paradigm shifts in child well-being research
Research on child well-being, especially in the context of measurement and participatory models, has made significant progress over the last decade. Therefore, the relevance of well-being research for children across the life course needs to be evaluated in the context of scientific advancement in this area of research. This section briefly reviews this development by using the Rees and colleagues (2010b) typology.
Social indicators movement
Influenced by the wider social indicators movement, this approach initially focused on measurement and trends in child well-being primarily using ‘survival indicators’ (Ben-Arieh, 2008) such as rates of mortality, disease and social problems affecting children (e.gfor example, illiteracy and school failure). Major work informed by this approach includes the Child and Youth Well-being Index (Land et al, 2001) in the USA, the National Set of Child Well-being Indicators (Hanafin and Brooks, 2005) in the Republic of Ireland, the Children and Young People’s Well-being Monitor (Welsh Assembly Government, 2008) for Wales, the Local Index of Child Well-being (Bradshaw et al, 2009) published by the Department for Communities and Local Government in England, Kids Count, a national and state-by-state effort to track the
These indicator-based measures are useful to understand children and young people’s well-being at the macro level. However, as Moore and colleagues (2014) argue, these macro indices predominantly focus on describing children’s well-being at the expense of analysing the contexts that may contribute to or undermine their well-being. Using data from the 2007 US National Survey of Children’s Health, Moore and colleagues (2014) developed micro-level indices (positive and negative) of child well-being by focusing on the three contextual domains of family, neighbourhood and socio-demographic factors. Their indices significantly contributed to child well-being research as they clearly revealed how the independent variables (environment or context of children) play crucial roles in determining children’s development and well-being. While such indicators are important to begin to redress issues of inequalities and social exclusion that negatively affect children’s health and well-being, they tend to ignore the potential, attributes and strengths of children. More specifically, this approach can be argued to treat children as passive agents not capable of evaluating their own lives (Goswami et al, 2016).
Self-report surveys
The second approach emphasizes measuring child well-being through self-report surveys. A number of instruments have been developed over the last decade to measure young people’s own assessment of their lives. One of the most widely used is Huebner’s Multi-Dimensional Student Life Satisfaction Scale (Huebner, 1994), which measures well-being in five domains: family, friends, school, living environment and self. Similarly, Cummins and Lau (2005), in their work with children and young people in Australia, have developed a Personal Well-being Index covering the domains of standard of living, personal health, achievement in life, personal relationships, personal safety, feeling part of the community and future security.
The international Health Behaviour in School-aged Children (HBSC) survey covers a number of key areas of young people’s health and well-being. In the UK, several waves of the Tellus survey (Ofsted/DCSF) have surveyed young people about their well-being and views under the five themes of the Every Child Matters framework – Be
More broadly, the Danish Longitudinal Survey of Children, the Youth component of the German Socio-Economic Panel (SOEP), French Longitudinal Survey of Children, Swiss Survey of Children and Youth, the European Social Survey and the European Quality of Life Survey and some cross-sectional surveys (such as Progress in International Reading Literacy Study, Progress for International Student Assessment, Trends in International Mathematics Science Study, the European School Project on Alcohol and Other Drugs) have included questions on well-being and its various domains for young people in various age groups. For a full review of these surveys, see Richardson (2012), Gabos and Kopasz (2013) and Gabos and Toth (2011).
The main advantage of this approach is that it focuses on self-reported well-being. More specifically, the international surveys among children and young people provide precious comparable data on child well-being covering countries in the EU and beyond. For example, the OECD conducted a comparative analysis that provides useful insights on the state of child well-being among 30 OECD countries by focusing on six well-being domains: material well-being; housing and environment; education; health; risk behaviours; and quality of school life (OECD, 2009). Moreover, household panel surveys (for example, Understanding Society) provide new opportunities to explore the effect of changes in young people’s lives on their overall well-being. However, the concepts and domains of well-being used in this work were developed primarily from concepts which originated from the study of adult well-being. Fattore and colleagues (2007) argue that these concepts are not directly transferable to the measurement of the well-being of children and young people. Moreover, as Bradshaw (2009) argues, most of these studies include only a limited number of well-being domains and therefore do not provide the full picture on the state of well-being for children and young people. These limitations influence the development of the third approach: child and young people centred studies.
The third approach focuses on developing concepts and frameworks which incorporate children’s perspectives. This strand is still at a relatively early stage, but there are a few examples of attempts to develop well-being frameworks from children’s perspectives. Consultation exercises with children and young people in the Republic of Ireland (Gabhainn and Sixsmith, 2005, Hanafin et al, 2007) and Australia (Fattore et al, 2007) have identified important differences in children and young people’s ideas about well-being which often vary significantly by age (Rees et al, 2010a).
In this regard, the first large-scale project took place in the UK in 2005, undertaken by the Children’s Society when it included open-ended questions asking young people about their views on well-being and the factors which promoted and hindered it in its national survey of 11,000 young people aged 14 to 16. The thematic and content-based analyses of these responses identified ten key areas (The Children’s Society, 2006). These were, roughly in order of their frequency of occurrence in the responses: (1) family, (2) friends, (3) leisure, (4) school, education and learning, (5) behaviour, (6) the local environment, (7) community, (8) money, (9) attitudes and (10) health. Following this child-centric approach, Rees and colleagues (2010a) developed an index of children’s subjective well-being in England. This ten-domain index includes young people’s satisfaction on family or carer, friends, health, appearance, time use, future, home, money and possessions, school and amount of choice.
A number of similar initiatives are also observed across Europe. For example, the Danish Youth Survey 2002 (Helweg-Larsen et al, 2004) examined young people’s experiences and views on six themes including family, school, leisure and social networks, health and health behaviour, sexual experiences with peers and adults and violence in immediate surroundings. The DJI Youth Survey in Germany explores adolescents’ trust in social institutions, their political attitudes, interest in politics, value orientation as well as their willingness regarding political activity (DJI, 2000). Similarly, Mihálik and colleagues (2018) identified four common domains (family, friends, school, material conditions) of well-being in their research with children and young people from four contrasting countries in Europe: Estonia, Slovakia, Greece and Portugal.
This third approach has been taken further by an international group of researchers linked to Children’s Worlds (2019). The study aims to collect solid and representative data on children’s lives and
Having the unique position of ‘research with and by children’, this third approach reflects a major paradigm shift in child well-being research (Mason and Danby, 2011). Thus, the importance of including children as active agents whose perspectives are heard in matters concerning them, especially in child well-being policies, is gaining momentum. However, child well-being researchers (Richardson, 2012; Bradshaw et al, 2009; Casas, 2011) are increasingly concerned about the shortage of internationally comparable subjective data on children’s and adolescents’ perceptions, evaluations and aspirations which they consider useful for decision-making and evaluating social change. In this regard, the data from the International Survey on Children’s Well-being (ISCWeB) by Children’s Worlds (2019) supplies invaluable comparative data on subjective well-being among a number of EU member states and countries beyond Europe. Several waves of data from these countries will also help researchers to examine aggregate change over time for specific cohorts. However, as Howieson and colleagues (2008) argued, such data lack being able to detect change at the individual level. Therefore, they do not enable an understanding of an individual’s transition through different activities and statuses that might be linked to their subjective well-being (for example, moving from primary school to high school or changes in family structure for a child living with a divorced mother to a family structure where a step-father joins and then back to living with the single mother after a subsequent divorce and the influence of those changes on children’s educational and family well-being). Since childhood is not static but dynamic, a holistic view taking into account changes at different stages of children and young people’s development and transitions is required. This explains why there is a growing belief that in order to better understand how these changes and other socioeconomic factors related to these changes affect children’s and young people’s well-being, a longitudinal survey using a ‘children and young people centric approach’ is necessary (Pollock et al, 2018; Goswami et al, 2016). The European Cohort Development Project (2018) draws on this child-centric approach in developing a longitudinal framework on
Birth cohort surveys
This section describes the way in which birth cohort surveys have become increasingly used as a tool of measuring individual change over time.
The breadth of current birth and child cohort surveys
While the UK was a leader in the development of cohort surveys there now exists a plethora of such survey across the world. Moreover, the advantages of cross-cohort comparisons was also identified and addressed through collecting data from parallel cohorts of different ages in a single study in order to bring forward the analytic potential. This has become known as the ‘accelerated’ cohort model (Farrington, 1991) and is commonly used to facilitate quicker cross-cohort comparisons that would be possible were single cohorts used. An important example of an accelerated design includes the Young Lives project which covers Ethiopia India, Peru and Vietnam (Briones, 2018). This project was developed to address MDGs and can plausibly demonstrate that policies in each country have been directly influenced as a result of cohort data analysis (see for example Crivello and Morrow, 2019). In Germany, the National Education Panel Survey (NEPS) includes a series of six parallel cohorts, following more than 60,000 participants, making it perhaps the richest educational data source (Blossfeld et al, 2016). An analysis of the existence of surveys across the world, including cohort surveys, shows that there remains a high degree of variability in terms of the country coverage, survey content and research design (Richardson, 2012).
Identification of problems of comparative analysis using post hoc harmonization
The growing range of data sources on babies and children being followed as they grow up give us important representations of similarities and differences across the world. It has been only to be expected that scientists and policy makers became interested in similarities and differences across cultural and national boundaries, especially where welfare arrangements differ and policy interventions
Longitudinal data has the unique capacity to capture the dynamics of one’s development through life and allows the impact of a variety of factors, major life events, and even socio-political changes on future outcomes to be evaluated, making it a particularly valuable source of evidence for policy makers. While insights from surveys have much to offer to policy, it is worth noting that different designs will be associated with answering different research questions and responding to different policy challenges associated with a variety of policy drivers at regional, national and international levels. In Germany, the National Educational Panel Study (NEPS) is one of the largest studies of its kind in Europe. Due to its longitudinal nature it acts as a fundamental source of data for studying the cause–effect relationships between a person’s educational paths and multiple dimensions of well-being. By the end of 2018 more than 600 academic publications have used NEPS data (NEPS Bibliography, www.neps-data.de/en-us/datacenter/publications.aspx). The NEPS data is used for educational reporting and monitoring (Federal Ministry of Education and Research, 2017) and is used to inform the decisions of policy makers in Germany. In addition, as a result of cooperation with the German Ministry of Education and Research, the NEPS team has conducted evaluations of federal educational reforms in Thuringia (Rieger et al, 2018) and Baden-Wuerttemberg (Hübner et al, 2017), therefore providing additional information for policy makers on the regional level.
Three important UK cohort studies (Millennium Cohort Study, 1958 National Child Development Study and 1970 British Cohort Study) have provided data for over 4,000 publications that have generated profound and significant insights into how health, education and family backgrounds of children have lasting impacts on outcomes and achievements throughout their lives. One of the key findings of the Millennium Cohort Study (MCS) was that breastfeeding protects against infant hospitalization for diarrhoea and respiratory tract infections and was associated with lower prevalence of overweight at age 3 and higher cognitive scores at ages 3 and 5. These data are used by NICE guidance, the UK Department of Health and Social Care, and documents supporting the Baby Friendly Initiative led by UNICEF and WHO that has been implemented in 134 countries (UCL, 2014), presenting a case of a national survey being used to inform policy on both national and international level.
The ESRC Longitudinal Studies Strategic Review notes that the ‘the proliferation of cohort studies around the world lacks a methodology to make sense of comparative analyses’ (Davis-Kean et al, 2017, p 26). It asserts the ongoing need for high-quality longitudinal data, while stressing the importance of the data to be internationally comparable, in order to enable cross-national analysis of phenomena in an increasingly globalized world. Internationally comparable longitudinal data allows for addressing international topics of interests, such as ways in which policy and circumstances affect well-being, health and other outcomes by comparing life-course trajectories and identifying factors that foster successful integration of migrants by comparing experiences in different countries, among many others. Moreover, a vast number of policy questions would profit from larger sample sizes by pooling samples (Davis-Kean et al, 2017).
For instance, findings from the longitudinal comparative Survey of Health, Ageing and Retirement in Europe (SHARE) allow for evidence-based policy making by EU member states, the European Commission and international organizations. In Germany, SHARE data was used to compare people’s future pension expectations with the actual projected amount and directly evaluate policy measures incentivizing citizens to prepare for retirement through increased private retirement savings. The Estonian Government Office has used SHARE data to inform the parliament in the ongoing discussion about working opportunities beyond retirement age. The European Commission and the OECD has used SHARE research to analyse the recipients and providers of informal care in Europe and to develop ways of providing support to help the caregivers remain in employment and in good health (the Survey of Health, Aging and Retirement in Europe, now the European Research Infrastructure Consortium [SHARE-ERIC]), 2018).
The case for EuroCohort, an input-harmonized comparative birth cohort study
Drive from the European Union
Although it is important that healthy emotional, physical and psychological life-styles should start from an early age, very little European comparative social and educational research is being done in order to ascertain what are the best policies and approaches to effectively promote the well-being of children and young people. Research into the perspective of children and young people with regard to the various aspects of care, education, leisure and well-being seems to be even more overdue – although it involves very significant methodological challenges. Moreover, in order to understand the development of demographic trends in Europe, an investigation of the lower end of the demographic pyramid is required. To do this, we need a robust, representative and comparable dataset on the well-being of children/young people, child/youth related policies, childhood care and access to education, as well as on the environment in which a child grows up, which is primarily the family. Relational, organisational, participation, civic and leisure activities could also be included. To ensure comprehensive coverage of this topic, it might be necessary to conduct a longitudinal survey, which would capture the full picture of the growing-up process from birth to the end of a child’s education – possibly including aspects related to the transition to work and parenthood. A multidisciplinary approach is needed in order to grasp the dynamic character of this process. … The conduct of the survey should ensure an ex-ante harmonisation across countries. Ideally, such a survey should be implemented at least in a large, representative sample of the EU countries, in cooperation with Member States. (EC, 2012)
The EU already recognizes the importance of high-quality survey data to inform policy development through its investment in surveys such as SHARE and the European Social Survey. In addition to the requirement that EU member states undertake a Survey of Income and Living Conditions (EU-SILC), the existence of complementary socioeconomic surveys strengthens the evidence base for future policy making. These surveys provide the benchmark, worldwide, for comparative survey methodology and will continue to provide a growing body of data which details the lived, contextual experience of the survey respondents. There is, however, a gap as no prospective
Planning for EuroCohort
The EU call for a feasibility study to scope the development of a Europe-wide longitudinal survey with a focus on child well-being was undertaken by the MYWeB project which began in 2013. Working with policy makers and academics, as well as children and young people, the MYWeB project systematically showed that not only was such a survey technically doable (Ozan et al, 2018a), but that there was also a strong desire among policy makers, practitioners and academics for the data that this survey would generate (Ozan et al, 2018b). From the outset MYWeB ensured that children and young people were involved in the research to inform the study about their priorities as well as to give an insight into fieldwork strategies such as response modes, answer structures and question wording (Franc et al, 2018). The challenges of including children and young people in such a large and complex project were explored in order to ensure that they could be used maximally at the highest possible rung on Hart’s (1992) ladder of participation (Nico et al, 2018). Having established the feasibility and desirability of a Europe-wide longitudinal child well-being survey, the MYWeB team progressed to undertaking a ‘Design Study’, again funded by the EU, this time with a view to initiating the preparation of a Europe-wide ‘Research Infrastructure’. This new study was known as the European Cohort Development Project (ECDP), or ‘EuroCohort’. ECDP completed its work at the end of 2019, by which time the research design, thematic content and draft questionnaires were finalized at the same time as a systematic cost–benefit exercise to show what each country would have to invest to make it happen, as well as what they stand to gain in terms of robust evidence for policy making. At the time of writing (2020) the ECDP team are preparing
EuroCohort design
EuroCohort is an accelerated birth cohort with data collection for each cohort taking place (mostly) alternately. Figure 7.1 shows how this would work with a birth cohort (C2) starting in 2024 and an age 8 cohort (C1) starting in 2022. Of importance is the collection of age-equivalent data for each cohort; hence from age 8 each cohort is surveyed every three years. There is a common sampling strategy where national criteria are set to ensure that the highest quality available data are used to draw both the birth cohort and child participants. There are minimum target sample sizes for each participating country which are set in conjunction with population and birth rates as well as anticipated attrition rates over the life of the project to ensure that by the age of 24 the samples remain statistically representative and will, therefore, be large enough throughout the whole study to facilitate multivariate analysis using demographic variables. The aim is to facilitate as many EU countries as possible joining EuroCohort; in practice the study has wide coverage with a presence in almost all EU countries (and some beyond the EU). During the preparation phases there will remain the possibility for countries not yet included to join.
The content of the questionnaires is broad and captures familial context for demographic analysis, and a range of themes which contribute to an understanding of how well-being, both objectively and subjectively, changes over the life course. The major themes for the eight-year-old children are: subjective well-being, family and home, friends, school, local area, material conditions, time use, safety, health child rights. The parental questionnaires cover a broad range of demographic issues as well as themes that cross over with those answered by the children.
EuroCohort’s accelerated design and an example of a potential timeline
Source: Author’s ownEuroCohort’s accelerated design and an example of a potential timeline
Source: Author’s ownEuroCohort’s accelerated design and an example of a potential timeline
Source: Author’s ownThe survey is child-centric, including children and young people throughout the development of the research design and the content of the questionnaires. It is also child-centric in giving primacy to the voice of the child when it comes to reporting on well-being-related issues. In this regard parental responses are not used as proxy measures for the child. Nonetheless, parents are an important source of data, especially in the earliest year of the study, providing information about the context within which the babies and children are growing up. This is also the case during those years when young children do
The strategy for the EuroCohort questionnaire content is to prioritize collecting data that will be of use in developing policies to promote child well-being. The survey is not, therefore, a medically driven survey, although there is a health theme which will exist throughout its existence. Socioeconomic conditions, educational experiences, lifestyle, technology and the ways in which these are increasingly subject to volatility and change are included. As well as including the views of children, this strategy looks to the future through an innovative foresight scenario planning exercise involving policy makers and scientists across Europe (EuroCohort, 2019).
Scenario planning is a medium- to long-term analysis and planning technique used to ‘develop policies and strategies that are robust, resilient, flexible and innovative’ (Rhydderch, 2009, p 5). A number of scenario planning methods exists, each considering different timescales and providing a varying degree of depth of analysis. Scenarios developed for EuroCohort used the ‘Two Axes’ method. This method is particularly appropriate for investigating medium- to long-term policy directions, by ensuring that it will remain robust in a range of future environments (Rhydderch, 2009). The intention of thinking about the future of Europe was to futureproof the EuroCohort survey to make sure it covers the most pertinent and crucial topics and will allow us to make sense of the lives of the future children and young people in Europe. The purpose of this exercise was to confirm priority policy areas in child and youth well-being in Europe and identify future policy trends by beginning to think about how global and local changes might affect the policy priorities in Europe in ten years’ time. It engaged policy makers, practitioners, researchers, academics and young people across Europe and asked them to think about the possible future trends that will impact child and youth well-being in Europe. As with all longitudinal studies, there is a need for a long-term approach to data collection, ensuring comparability over time as well as being responsive to unanticipated contingencies. The findings from the foresight exercise were one of the key content drivers for the EuroCohort questionnaires, allowing the possible needs of evaluating the well-being of the children of the future Europe to be considered and ensuring that the instruments will remain relevant over the 25-year course of the survey.
Central to any initiative seeking to address grand challenges through objectives such as the SDGs are mechanisms whereby progress can be measured. By their very nature, the SDGs are both international and longitudinal: different countries and regions will be at varying stages along the road of achieving them. Cross-sectional measures of poverty, education, gender inequality and so forth are able to show how progress is made at an aggregated level and these measures are important headline figures in being able to understand relative differences and aggregate progress from one country to another. Such data, however, tends to mask individual experiences and is not good at exposing dynamic time-based patterns which are easily hidden in averaged figures. In so doing, cross-sectional data unfairly misrepresents individual life-course experiences and undermines the ability of policy makers to find robust solutions to social problems. As has been argued in this chapter, only through the collection of longitudinal survey data are we able to measure individual experiential changes, such as the varying financial position of a family and then connect this to other spheres of life, such as education, health and lifestyle. Longitudinal analysis has shown us that life circumstances and events are associated with differential outcomes. We are also aware that there are some individuals who appear to be particularly resilient in the face of adverse experiences and mange to overcome multiple disadvantages.
EuroCohort is an important initiative in a number of respects: firstly it prioritizes child well-being above all else and seeks to develop a high-quality evidence base by which it will be possible to monitor comparatively and longitudinally the dynamics of child well-being and find ways of improving it. This imperative is helped by the project being both child-centric and policy focused. Child-centricity is important in order to ensure relevance to the children themselves of the indicators (alongside those survey measures that scientists believe ought to be of importance). Moreover, including children and young people as active participants in as much of the scientific project as is suitable will enhance technical elements such as fieldwork processes. Policy relevance is important as there is a need for public spending to be seen to be cost effective as well as efficient in having the desired impact. Prospective longitudinal data is able to show how policy interventions manifest over time.
Governments across the world spend a vast amount of money on child and family policies, and much of this is devoted to providing
Acknowledgments
This chapter is based on research funded under EU FP7 (MYWeB grant agreement no. 613368) and EU H2020 (ECDP grant agreement no. 777449) programmes.
References
Annie E. Casey Foundation (2012) Kids Count. Available at www.aecf.org/MajorInitiatives/KIDSCOUNT.aspx
Ben-Arieh, A. (2008) The child indicators movement: past, present and future. Child Indicators Research 1: 3–16.
Blanden, J. and Gregg, P. (2004). Family income and educational attainment: a review of approaches and evidence for Britain. Oxford Review of Economic Policy 20(2): 245–63.
Blossfeld, H.P., von Maurice, J., Bayer, M. and Skopek, J. (Eds) (2016) Methodological Issues of Longitudinal Surveys: The Example of the National Educational Panel Survey. Wiesbaden: Springer.
Bradbury, B., Corak, M., Waldfogel, J. and Washbrook, E. (2011) Inequality during the Early Years: Child Outcomes and Readiness to Learn in Australia, Canada, United Kingdom, and United States. IZA Discussion paper.
Bradshaw, J. (2009) Social inclusion and child poverty. In P. Perrig-Chiello (Ed) Interdisciplinary Workshop Report on Changing Childhood in a Changing Europe (pp 29–36). Strasbourg: ESF Social Sciences Unit.
Bradshaw, J., Bloor, K., Huby, M., Rhodes, D., Sinclair, I., Gibbs, I. et al (2009) Local Index of Child Well-Being: Summary Report. London: Department for Communities and Local Government.
Briones, K. (2018) A Guide to Young Lives Rounds 1 to 5 constructed files. Young Lives Technical Note, 48.
Casas, F. (2011) Subjective social indicators and child and adolescent well-being. Child Indicators Research 4: 555–75.
Chapple, S. and Richardson, D. (2009) Doing Better for Children. Paris: OECD Publishing.
CHICOS (nd) Strategy for European Birth Cohort Research. Available at http://chicosproject.eu/assets/181/CHICOS_strategic_report_20130712.pdf
Children’s Worlds (2019) International Survey of Children’s Well-being. Available at www.isciweb.org/
Crivello, G. and Morrow, V. (2019) Against the odds: why some children fare well in the face of adversity. Journal of Development Studies.https://doi.org/10.1080/00220388.2019.1626837
Cummins, R. and Lau, A. (2005) Personal Wellbeing Index – School Children. Melbourne: School of Psychology, Deakin University.
Davis-Kean, P., Chambers, R., Davidson, L., Kleinert, C., Ren, Q. and Tang, S. (2017) Longitudinal Studies Strategic Review. London: Economic and Social Research Council.
Diener, E., Lucas, R.E. and Oishi, S. (2002) Subjective well-being: the science of happiness and life satisfaction. In C.R. Snyder and S.J. Lopez (Eds) Handbook of Positive Psychology (pp 63–73). Oxford: Oxford University Press.
DJI (2000) DJI-Jugendsurvey 2000, GESIS Datenarchiv, Köln. ZA3609 Datenfile Version 1.0.0. doi: 10.4232/1.3609.
European Commission (2012) Work Programme 2013. Cooperation: Theme 8: Socio-economic Sciences and Humanities. Brussels: European Commission.
Emery, T., Pollock, G. and Scherpenzeel, A. (2019) Securing high-quality data on populations: why we need EuroCohort, GGP and SHARE in Europe. Population Europe Policy Brief. Available at https://population-europe.eu/policy-brief/securing-high-quality-data-populations
ESFRI (2019) Roadmap 2021 Public Guide. Available at www.esfri.eu/sites/default/files/ESFRI_Roadmap2021_Public_Guide_Public.pdf
EUCCONET (2008) European Child Cohort Network. Available at www.slls.org.uk/eucconet-reports
EuroCohort (2019) Future Trends and Scenarios Shaping the Future of Child and Youth Well-being in Europe. Project briefing. Available at www.eurocohort.eu/files/filemanager/files/ECDP_Future_trends.pdf
European Cohort Development Project (2018) European Cohort Development Project. Available at www.eurocohort.eu/
Farrington, D.P. (1991) Longitudinal research strategies: advantages, problems and prospects. Journal of American Academic Child and Adolescent Psychiatry 30(3): 369–74.
Fattore, T., Mason, J. and Watson, E. (2007) Children’s conceptualisation(s) of their well-being. Social Indicators Research 80(1): 5–29.
Federal Ministry of Education and Research (2017) Research Report on Vocational Education and Training. Available at www.bmbf.de/pub/Berufsbildungsbericht_2017_eng.pdf
Franc, R., Sucic, I., Babarović, T., Brajša-Žganec, A., Kaliterna-Lipovčan, L. and Dević, I. (2018) How to develop well-being survey questions for young children: lessons learned from cross-cultural cognitive interviews. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 91–109). Springer International.
Freed-Taylor, M. and Schaber, G. (1995) An integrated longitudinal database for comparative analysis: the panel comparability project. Innovation: The European Journal of Social Science Research 8(1): 53–59.
Gabhainn, S. and Sixsmith, J. (2005) Children’s Understandings of Well-Being. Dublin: National Children’s Office.
Gabos, A. and Kopasz, M. (2013) Conception Paper for an Integrated Poverty and Living Condition Indicator System (IPOLIS) Database. Unpublished Paper.
Gabos, A. and Toth, I.G. (2011) Child Well-Being in the European Union: Better Monitoring Instruments for Better Policies. Budapest: TARKI Social Research Institute.
Goswami, H., Fox, G. and Pollock, G. (2016) The current evidence base and future needs in improving children’s well-being across Europe: is there a case for a comparative longitudinal survey? Child Indicators Research 9(2): 371–88.
Guillén-Royo, M. and Velazco, J. (2006) Exploring the relationship between happiness, objective and subjective well-being: evidence from rural Thailand. ESRC Research Group on Well-being in Developing Countries, WeD Working Paper 16. Bath: WeD.
Hanafin, S. and Brooks, A.M. (2005) Report on the Development of a National Set of Child Well-Being Indicators. Dublin: National Children’s Office/The Stationery Office.
Hanafin, S., Brooks, A.M., Carroll, E., Fitzgerald, E., Gabhainn, S.N. and Sixsmith, J. (2007) Achieving consensus in developing a national set of child well-being indicators. Social Indicators Research 80: 79–104.
Hart, R. (1992) Children’s Participation from Tokenism to Citizenship. Florence: UNICEF Innocenti Research Centre.
Helweg-Larsen, K., Sundaram, V., Curtis, T. and Larsen, H.V. (2004) The Danish Youth Survey 2002: asking young people about sensitive issues. International Journal of Circumpolar Health 63(2): 147–52.
Hicks, S. (2011) The Measurement of Subjective Well-Being. Paper for Measuring National Well-Being Technical Advisory Group. Newport: ONS.
Howieson, C., Croxford, L. and Howat, N. (2008) Meeting the Needs for Longitudinal Data on Youth Transitions in Scotland – An Options Appraisal. Edinburgh: Scottish Government Social Research.
Hübner, N., Wagner, W., Kramer, J., Nagengast, B. and Trautwein, U. (2017) The G8 reform in Baden-Württemberg: competencies, wellbeing and leisure time before and after the reform. Zeitschrift für Erziehungswissenschaft 20(4): 748–71.
Huebner, E.S. (1994) Preliminary development and validation of a multidimensional life satisfaction scale for children. Psychological Assessment 6(2): 149–58.
Kroll, C. and Delhey, J. (2013) A happy nation? Opportunities and challenges of using subjective indicators in policymaking. Social Indicators Research 114(1): 13–28.
Land, K.C., Lamb, V.L. and Mustillo, S.K. (2001) Child and youth well-being in the United States, 1975–1998: some findings from a new index. Social Indicators Research 56(3): 241–320.
Lansdown, G. (2001) Promoting Children’s Participation in Democratic Decision-Making. Innocenti Insights. Florence: UNICEF Innocenti Research Centre.
Mason, J. and Danby, S. (2011) Children as experts in their lives. Child Indicators Research 4(2): 185–90.
McGillivray, M. (2007) Human well-being: issues, concepts and measures. In M. McGillivray (Ed) Human Well-Being: Concept and Measurement (pp 3–16). Basingstoke: Palgrave MacMillan.
Mihálik, J., Garaj, M., Sakellariou, A., Koronaiou, A., Alexias, G., Nico, M. et al (2018) Similarity and difference in conceptions of well-being among children and young people in four contrasting European countries. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 55–69). Springer International.
Moore, K.A., Murphey, D., Bandy, T. and Lawner, E. (2014) Indices of child well-being and developmental contexts. In A. Ben-Arieh, F. Casas, I. Frones and J.E. Korbin (Eds) Handbook of Child Well-Being: Theories, Methods and Policies in Global Perspective (pp 2807–22). New York: Springer.
Nico, M., de Almeida Alves, N., Ferrer-Fons, M., Serracant, P. and Soler, R. (2018) Methodological challenges when involving children and young people in survey research on well-being. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 131–36). Springer International.
Noll, H.-H. (2013) Subjective social indicators: benefits and limitations for policy making – an introduction to this special issue. Social Indicators Research 114(1): 1–11.
OECD (2009) Doing Better for Children. Available at www.oecd.org/social/family/doingbetterforchildren.htm
O’Leary, C. and Fox, C. (2018) Understanding the potential policy impact of a European longitudinal survey for children and young people. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 147–161). Springer International.
Ozan, J., Pollock, G., Goswami, H. and Lynn, P. (2018a) Challenges in conducting a new longitudinal study on children and young people well-being in the European Union. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 111–30). Springer International.
Ozan, J., Mierina, I. and Koroleva, I. (2018b) A comparative expert survey on measuring and enhancing children and young people’s well-being in Europe. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy, pp 35–53. Springer International.
Pollard, E.L. and Lee, P.D. (2003) Child well-being: a systematic review of the literature. Social Indicators Research 61(1): 59–78.
Pollock, G., Ozan, J., Goswami, H. and Fox, C. (2018) With a view towards the future: working towards an accelerated European cohort survey. In G. Pollock, J. Ozan, H. Goswami, G. Rees and A. Stasulane (Eds) Measuring Youth Well-being: How a Pan-European Longitudinal Survey Can Improve Policy (pp 163–78). Springer International.
Rees, G. and Dinisman, T. (2015) Comparing children’s experiences and evaluations of their lives in 11 different countries. Child Indicators Research 8(1): 5–31.
Rees, G., Goswami, H. and Bradshaw, J. (2010a) Developing an Index of Children’s Subjective Well-being in England. London: The Children’s Society.
Rees, G., Bradshaw, J., Goswami, H. and Keung, H. (2010b) Understanding Children’s Well-Being: A National Survey of Young People’s Well-Being. London: The Children’s Society.
Richardson, D. (2012) An Evaluation of International Surveys of Children. Social Policy Division. Paris: OECD.
Rieger, S., Hübner, N. and Wagner, W. (2018) NEPS Technical Report for English Reading: Scaling Results for the Additional Study Thuringia (NEPS Survey Paper No. 39). Bamberg, Germany: Leibniz Institute for Educational Trajectories.
Rhydderch, A. (2009). Scenario Planning. Foresight Horizon Scanning Centre. Available at https://webarchive.nationalarchives.gov.uk/20140108141323/http://www.bis.gov.uk/assets/foresight/docs/horizon-scanning-centre/foresight_scenario_planning.pdf
Sarriera, J.C., Casas, F., Bedin, L., Abs, D., Strelhow M.R., Gross-Manos, D. and Giger, J. (2015) Material resources and children’s subjective well-being in eight countries. Child Indicators Research 8(1): 111–31.
Share-Eric (2018) Annual Activity Report 2017/18. Available at www.share-project.org/fileadmin/pdf_documentation/SHARE_AnnualActivityReport_2017-18.pdf
Shaw, C., Brady, L. and Davey, C. (2011) Guidelines for Research with Children and Young People. London: National Children’s Bureau.
Statham, J. and Chase, E. (2010) Childhood Wellbeing: A Brief Overview. Loughborough: Childhood Wellbeing Research Centre.
Stiglitz, J.E., Sen, A. and Fitoussi, J.-P. (2009) Report by the Commission on the Measurement of Economic Performance and Social Progress. Paris. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.58&rep=rep1&type=pdf
Technopolis Group (2016) Comparative Impact Study of the European Social Survey (ESS) ERIC. Available at www.europeansocialsurvey.org/docs/findings/ESS-Impact-study-Final-report.pdf
The Children’s Society (2006) Good Childhood? A Question for Our Times. London: The Children’s Society.
Thomas, W.I. and Thomas, D.S. (1928) The Child in America: Behaviour Problems and Programmes. New York: Knopf.
UCL (2014) Millennium Cohort Study: building a picture of a new generation. University College London. Available at www.ucl.ac.uk/impact/case-studies/2014/dec/millennium-cohort-study-building-picture-new-generation
UN (2020) Sustainable Development Goals. Available at www.un.org/sustainabledevelopment/sustainable-development-goals/
UNICEF (2007) Child Poverty in Perspective: An Overview of Child Well-Being in Rich Countries. Innocenti Report Card 7. Florence: UNICEF Innocenti Research Centre.
UNICEF (2010) The Children Left Behind: A League Table of Inequality in Child Well-Being in the World’s Richest Countries. Innocenti Report Card 9. Florence: UNICEF Innocenti Research Centre.
UNICEF (2017) Is Every Child Counted? Status of Data for Children in the SDGs. Division of Data, Research and Policy, New York.
Veenhoven, R. (2002) Why social policy needs subjective indicators. Social Indicators Research 58(1–3): 33–46.
Welsh Assembly Government (2008) Children and Young People’s Well-being Monitor for Wales. Available at http://orca.cf.ac.uk/69801/1/110328evidencereviewmonitoren.pdf
