A data-driven approach shows that individuals' characteristics are more important than their networks in predicting fertility preferences

• Published in: Royal Society open science 2023-12, Vol.10 (12), p.230988
• Main Authors: Stulp, Gert, Top, Lars, Xu, Xiao, Sivak, Elizaveta
• Format: Article
• Language: English
• Subjects: Computer Science and Artificial Intelligence; fertility; LASSO regression; personal network; predictive ability; social influence
• ISSN: 2054-5703; 2054-5703
• DOI: 10.1098/rsos.230988

People's networks are considered key in explaining fertility outcomes-whether people want and have children. Existing research on social influences on fertility is limited because data often come from small networks or highly selective samples, only few network variables are considered, and the strength of network effects is not properly assessed. We use data from a representative sample of Dutch women reporting on over 18 000 relationships. A data-driven approach including many network characteristics accounted for 0 to 40% of the out-of-sample variation in different outcomes related to fertility preferences. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, particularly those people in the network desiring children or those choosing to be childfree. Structural network characteristics, which feature prominently in social influence theories and are based on the relations between people in the networks, hardly mattered. We discuss to what extent our results provide support for different mechanisms of social influence, and the advantages and disadvantages of our data-driven approach in comparison to traditional approaches.