Social networks, fertility and wellbeing in ageing populations: Building demographic resilience in Finland (NetResilience) consortium investigates how social networks vary between regions and demographic groups and over time, and how they affect fertility, active ageing, individual wellbeing, and demographic resilience.
Three broad research questions are considered:
- How does fertility decline and population ageing reshape individual social networks?
- How do social networks feed back to population change?
- How can the interplay between social networks and institutions support the wellbeing and resilience of ageing populations?
These questions are approached through five themes and working packages (WP): fertility (WP1), older age (WP2), regional variation (WP3), temporal change (WP4), and network methodology (WP5).
WP1: Fertility and childbearing motivations
Lead by Anna Rotkirch, Population Research Institute.
WP1 focuses on social network effects on fertility and childbearing motivations (personal fertility ideals and intentions, perceived obstacles to and benefits of childbearing) using the register data, Finnish Family Barometers and cross-national Generations and Gender Survey (GGS), and Survey of Health, Ageing and Retirement in Europe (SHARE). The following themes are considered WP1: (1) Type of social networks favouring childbearing; (2) Perceptions of uncertainty, social support and fertility decline; (3) Regional characteristics and fertility resilience.
WP2: Ageing Finns
Lead by Mirkka Danielsbacka, University of Turku.
WP2 explores the role of social and kin relations (social networks) for healthy, happy and active ageing using longitudinal Generational Transmissions in Finland (Gentrans) surveys combined with population registers and SHARE allowing wider country comparisons. The following questions are investigated: (1) Structure of informal social networks of older adults; (2) How social networks relate to the wellbeing of older adults and what kind of network and support structure enhance resilience of older adults; (3) Older adults as promoters of the wellbeing of others.
WP3: Regional perspective
Lead by Markus Jokela, University of Helsinki.
WP3 integrates the topics of the other WPs with a geographic perspective with register data with detailed geospatial locations for monthly to yearly migration flows. It determines how people’s social networks, and the correlates of these networks, vary by geographical location in Finland. The following themes are addressed: (1) Variation of structures of social networks by region; (2) Density of social networks and regional fertility rates; (3) Association of regional rates of in-migration and out-migration with social network structures and wellbeing of older people; (4) Association of kin network density to demographic resilience.
WP4: Temporal perspective
Lead by Virpi Lummaa, University of Turku.
WP4 focuses on quantifying the long-term changes in the structure of family networks, and provides knowledge on how these changes are associated with fertility and mortality outcomes over time and during quantifiable hardships such as famines, disease outbreaks and political conflicts (resilience). These key questions are studied with an unique life history data gathered from church records and population registers in Finland 1731–2019. The main themes are: (1) Structures of kin networks across the demographic transition in Finland; (2) Effects of changing network structures on fertility and mortality; (3) The type of network structures providing fertility and mortality resilience during hardship.
WP5: Network methods
Lead by Jari Saramäki, Aalto University.
WP5 supports the other WPs with statistical and computational tools. The aim is to distill information from complex, multidimensional data in the form of relationships between entities and quantitative descriptors of these relationships (individuals, families, regions, etc.) using the tools of network science and data science. WP5 addresses the following themes: (1) Network indicators; (2) Mobility patterns; (3) Predictive modelling and regional forecasts