Volume 34 - Article 23 | Pages 657-688 Author has provided data and code for replicating results

Types of social networks and the transition to parenthood

By Daniel Lois

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Date received:21 Nov 2014
Date published:05 Apr 2016
Word count:9097
Keywords:cluster analysis, social networks, transition to parenthood
DOI:10.4054/DemRes.2016.34.23
Additional files:readme.34-23 (text file, 1009 Byte)
 demographic-research.34-23 (zip file, 7 kB)
 

Abstract

Background: A growing body of literature acknowledges the importance of social interaction and ideational factors for generative behavior. Building on this research, the present study identifies specific types of social network and gauges their value for predicting fertility behavior.

Methods: Based on data from the German Family Panel (N = 3,104 respondents aged 20 to 42), four types of ego-centric social networks were identified using cluster analyses. Clusters were used to prospectively predict the transition to parenthood using a discrete-time event history analysis.

Results: In the event history analyses, the highest propensity to start a family was found for ‘family-centered’ social networks, which were characterized primarily by a high share of persons with young children, a high amount of network support in case of parenthood, and a high proportion of strong ties to members of the nuclear family. By contrast, respondents who were embedded in ‘family-remote’ networks had the lowest transition rate to parenthood. Family-remote networks were characterized by a high share of friends and acquaintances, a high proportion of weak ties, and a low amount of social support and social pressure. Regarding selection effects, a comparison of cluster affiliation over time does not consistently confirm that persons who start a family select themselves into ‘fertility-promoting’ network types. In sum, the results enhance our understanding of how mechanisms of social influence and structural features of ego-centric social networks are interlinked.

Author's Affiliation

Daniel Lois - Universität der Bundeswehr München, Germany [Email]

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