Demography and Social Inequality
The research cluster “Demography and Social Inequality” is aimed at conducting rigorous and relevant research on demographic change and its interrelationship with social inequalities. The latter aspect is of particular importance, because demographic change has been fueled by social change (and vice versa) and the underlying social processes are characterized by a clear social gradient. For example, research has shown that (a) wealth is positively associated with health and longevity, (b) socio-economic status affects both the timing and quantum of fertility, and (c) education is an important factor in determining individuals’ propensity to migrate as well as their chances for a successful integration into the host society.
The causal relationship between demographic behavior and socioeconomic status (social inequalities, respectively), however, is complex and often bi-directional: Wealth is likely to impact individuals’ health, but health is also likely to impact individuals’ capacity to accumulate wealth. High socioeconomic status offers the resources to bear the costs of children, but – at the same time – opportunity costs of childrearing are high for the highest qualified.
Recent years brought about significant improvements in our – theoretical and empirical – understanding of the causal mechanisms underlying demographic outcomes/processes and social inequalities as described above. Another important aspect we still know relatively little about, though, relates to the coping strategies of actors facing the demographic challenges lying just ahead of us. More generally phrased – and following a general model of sociological explanation – we might thus ask:
- How do actors at different levels of aggregation (macro-, meso-, and micro-level) adapt their behaviors to fundamentally changing (social, economic, demographic) environments, coping with new challenges and opportunities?
- How might behavioral changes at the micro-level result in structural changes at higher levels of aggregation? And how are such behavioral changes related to social inequalities (both as a determinant and as a consequence)?
These questions relate all three demographic processes – fertility, migration, and mortality – and various dimensions of social inequality – education, income, social participation, etc. – alike. Answering them requires competence in several substantive fields as well as broad methodological expertise and knowledge of a variety of theoretical approaches to human behavior. The research initiative clusters the individual competences of its members, thereby offering the broad expertise necessary to advance our knowledge in the field of “Demography and Social Inequality”.
The research initiative is in the unique position that its members are involved in a number of long-term data collection enterprises (pairfam, CILS4EU, NRW Survey of Oldest-Old) providing the research community with high-quality research infrastructures. This involvement not only allows us to collect ‘tailor-made’ data needed for our own projects, but also poses us in a position as an attractive partner for external collaborations.
Researchers
While the professors at the ISS form the core of the research initiative, the research cluster includes other associated researchers from the Faculty of Management, Economics and Social Sciences and other Faculties of the University of Cologne.
Selected publications
Relationship‐Based Selective Participation of Secondary Respondents in a German Multi‐Actor Panel Study
Hünteler, B. and Wetzel, M. (2020). Journal of Marriage and Family.
Geographic Proximity to Parents, Intergenerational Support Exchange, and Migration Within Germany.
Hünteler, B., and Mulder, C.H. (2020). European Journal of Population.
On Increasing Divorce Risks
Wagner, M. (2020). In: Divorce in Europe. Springer, Cham.
Perceptions of Society’s Necessary Standard of Living: Are Perceptions Determined by What People Have, or Do They Reflect a Social Consensus?
Gutfleisch, T. and H.-J. Andreß (2020). Social Indicators Research.
Choosing priors in Bayesian measurement invariance modeling: A Monte Carlo simulation study
Pokropek, A., P. Schmidt and E. Davidov (2020). Structural Equation Modeling.
Educational inequalities in health after work exit: the role of work characteristics
De Breij, Sascha; Yogachandiran Qvist, Jeevitha; Holman, Daniel; Mäcken, Jana; Seitsamo, Jorma; Huisman, Martijn; Deeg,…
Grandparents’ Relationship to Grandchildren in the Transition to Adulthood
Martin Wetzel and Karsten Hank (2020). Journal of Family Issues.
Housing Affordability, Housing Tenure Status and Household Density: Are Housing Characteristics Associated with Union Dissolution?
Sandra Krapf and Michael Wagner (2020). European Journal of Population.
In Search of the Healthy Immigrant Effect in Four West European Countries
Dina Maskileyson, Moshe Semyonov, Eldad Davidov (2019). Social Inclusion.
Measurement invariance analysis using multiple group confirmatory factor analysis and Alignment optimisation
Davidov, E. and B. Meuleman. In Van de Vijver, F.J.R., F. Avvisati, E. Davidov, M. Eid, J.-P. Fox, N. Ledonne, K. Lek,…