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This paper identifies subjective well-being trajectories through happiness measures as influenced by time, socio-economic, demographic and behavioural determinants. Hierarchical age-period-cohort models are applied to European Social Survey (2002–2016) data on the population aged 30 and older in 10 countries. A U-shaped relationship between age and happiness is found for some countries, but a rather flat pattern and considerable diversity beyond age 80 are detected for other countries. Lower happiness levels are found for baby boomers (1945–1964) than for preboomers and post-boomers, and also for late boomers (1955–1964) than for early boomers (1945–1954). Women, highly educated and native people are shown to have higher happiness levels than men, less educated and non-native people, respectively. Moreover, a positive assessment of income, having a partner, and being a parent, in good health, employed and socially active are all found to have a positive impact on happiness levels. We find evidence of gaps in happiness levels due to differences in socio-economic characteristics over the life course in some, but not in all of the countries analysed.

The Antwerp COR*-database is a longitudinal micro-level database, which covers all entries from individuals whose last names started with the letters COR (and individuals who shared at some moment in time a household with a COR*-person) from the population registers and the vital registration of births, marriages and deaths for the 19th- and early-20th-century Antwerp district in Flanders, the northern Dutch-speaking part of Belgium. As such the database allows the reconstruction of historical life courses and families, and the analysis of key demographic characteristics and developments regarding marriage, fertility, migration, social mobility, health, mortality and longevity, as well as their interplay within and across households, families and generations. After a short description of the source material and the construction of the database, a review of the literature based on the database is presented in order to provide the reader with an encompassing overview of the research that has been carried out with this database and the knowledge and insights it has generated since its first release in 2010. The article ends with a discussion of potential pathways for future research, including new topics, and future extension of the database through citizen science projects.

The Leuven research team working on historical demography is grateful for their opportunity to have collaboratively and intensively worked with Professor Dr. Kees Mandemakers over an extended period of time. We wish him a wonderful emeritus status, not only in academia, but also in the warm nest of his family, relatives, children, and grandchildren. The three of us have known Kees for some time, but most closely since 2014, when we became formally engaged as project partners under the so-called LONGPOP-project, an EU funded Marie Curie grant, named Methodologies and Data mining techniques for the analysis of Big Data based on Longitudinal Population and Epidemiological Registers. The importance of our close professional relationship is best demonstrated by our work in producing the COR*-IDS 2020 database. The historical demographic dataset for the Antwerp arrondissement, a letter sample COR*-2010, recorded total sample size of +/- 33,000 residents of Antwerp for nearly seven decades and was already available. The LONGPOP project began in Autumn 2016, setting in motion a collaboration between ourselves at KU Leuven and Kees and his colleagues at the IISH. From the outset, we had purposefully worked closely with Kees' team, utilising their premier expertise in database management and the IDS towards the new release of our database, the Antwerp COR*-IDS dataset. Here we set out our recollections of that intellectual process, encompassing the personal and professional reflections of our close working relationship.

The Antwerp COR*-IDS database 2020 is a transformed and harmonized historical demographic database in a cross-nationally comparable format designed to be open and easy to use for international researchers. The database is constructed from the 2010 release of the Antwerp COR*-historical demographic database, which was created using a letter sample of the whole district of Antwerp (Flanders, Belgium). It has a total sample size of +/- 33,000 residents of Antwerp. The sample spans nearly seven decades. The data is collected from historical records: including population registers and vital registration records covering births, marriages, in/external migrations and deaths. The database covers up to three linked generations (in some cases more), and contains micro-data on individual level life courses, and relationships deriving from addressbased household composition methods. An important characteristic is the sample's large migrant population, including the timings of their demographic events and living arrangements, whilst resident in the district of Antwerp. In addition, the sample also contains a large array of occupational level information. This paper presents the processes, methodologies and documentation regarding the evaluation and development of a pre-existing historical database. This includes the systematic evaluation of the original samples, methodologies for address based reconstructing of households, and the geocoding of a historical database which took place during the current development of this new version of the database.

The postponement of motherhood is one of the most important trends in fertility behaviour in the last few decades. The consequences of late motherhood for child health are not well understood, however. One reason is that in the study of child health, the focus is either on birth (e.g., risk factors for low birth weight), or on child health after birth (e.g., child health consequences of low birth weight). The comprehensive view to child health underlying this paper is that both sides are closely linked. Those perinatal, behavioural and socio-demographic factors which affect birth weight also affect child health after birth. This paper addresses both issues together on the basis of two sets of Belgian regional data. The focus is on the relation between maternal age on the one hand, and birth weight and weight gain after birth (the latter conceptualised in an innovative way) as proxies of child health on the other hand. Our results confirm the importance of high maternal age as a risk factor for low birth weight. They also point to a long-term, though not necessarily permanent, effect of high maternal age on child health after birth for low birth weight children. These results differ from those of studies using other proxies for child health after birth such as physical and cognitive development, which point to permanent negative health effects of low birth weight, and even raise the question of negative intergenerational fertility effects

In earlier rounds of the European Social Survey, non-response bias was studied by using population statistics and call record information (para data). In the third round, a new feature was introduced: two kinds of non-respondent surveys were set up using a short list of questions which were designed to study non-response bias. In Belgium, a very short questionnaire was offered to all refusals at the doorstep (doorstep questions survey, DQS). In Norway and two other countries, somewhat longer versions of the basic questionnaire were offered to all main survey non-respondents and to samples of respondents (non-response survey, NRS). Logistic regression models were applied in order to estimate response propensities. This paper shows that propensity score weighting adjustment of non-response bias, on the basis of key socio-demographic and attitudinal variables, is effective for most demographic and non-demographic variables in both Belgium and Norway. Application of the weighting procedure balances the samples of cooperative respondents and non-respondents according to the key variables studied since systematic differences between cooperative respondents and non-respondents have disappeared.

This article examines the relation between well-being and fertility intentions in Europe and addresses three main research questions: Does overall well-being influence fertility intentions? What kind of well-being factors are more important in the determination of fertility intentions (individual-level subjective ones vs. individual-level objective ones vs. country-level ones)? Does the role of specific well-being variables change over the course of the life course, i.e. as age and parity increase? In accordance with the theory of planned behaviour (Ajzen, 1991), fertility intentions are studied as important predictors of actual fertility behaviour. And in line with established studies, a broad approach is taken towards the concept of well-being. The analysis is theoretically grounded in the framework of methodological individualism (i.e. micro-macro linkages). Use is made of data on women aged 20-39 in 27 countries, which were taken from the 'Family, work and well-being' module in the 5th round (2010) of the European Social Survey. The analysis of a comparable European population sample is made possible by taking account of both unit and item non-responses, and correcting for them. Our analysis shows overall positive but small correlations between well-being and fertility intentions in all countries: the higher the level of well-being, the higher the intended fertility, although the strength of the correlation differs between countries. Also, overall, individual-level objective well-being factors, such as level of education and employment status, have a larger impact on fertility intentions than individual-level subjective well-being factors and country-level well-being factors regarding human development, gender inequality and region. Changes in the effects of these well-being factors are found depending on the stage of the life course: as parity and age increase, the importance of country-level well-being effects increases. This shows that family-friendly country policies targeted to these groups can have positive effects on fertility. [ABSTRACT FROM AUTHOR]