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

Social influences on fertility behaviour are well-established. Individuals learn from, receive support from, and perceive pressure from people in their social network regarding having children. Previous research has focused on identifying specific network characteristics in small networks in relation to fertility. In this study, we take a comprehensive, data-driven approach to assess the impact of various network characteristics on people’s fertility outcomes. We use unique personal network data from Dutch women to predict different fertility outcomes and employ LASSO regression, which can handle the inclusion of multiple variables, prevent overfitting, and leads to sparse models including only the most important variables. Our models were able to explain between 0% and 40% of the out-of-sample variation in the different outcomes we used. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, in particular, people in the network that wanted children and people that wanted to be childfree. Structural network characteristics, 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.

Summary

     ODISSEI conference

     Utrecht, the Netherlands

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Description

Social influences on fertility behaviour are well-established. Individuals learn from, receive support from, and perceive pressure from people in their social network regarding having children. Previous research has focused on identifying specific network characteristics in small networks in relation to fertility. In this study, we take a comprehensive, data-driven approach to assess the impact of various network characteristics on people’s fertility outcomes. We use unique personal network data from Dutch women to predict different fertility outcomes and employ LASSO regression, which can handle the inclusion of multiple variables, prevent overfitting, and leads to sparse models including only the most important variables. Our models were able to explain between 0% and 40% of the out-of-sample variation in the different outcomes we used. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, in particular, people in the network that wanted children and people that wanted to be childfree. Structural network characteristics, 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.