Identifying interaction effects using random fertility shocks

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

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.

Explaining Residential Clustering of Large Families

Numerous studies have shown that fertility behavior is spatially clustered. In addition to pure contextual effects, two causal mechanisms could drive this pattern. First, neighbors may influence each other's fertility and second, family size may influence decisions about where to live. In this study we examine these two potential causal mechanisms empirically, using the sex composition of the two eldest children and twin births as instrumental variables (IVs) for having a third child. We estimate how having a third child affects three separate outcomes: the fertility of neighbors; the propensity to move houses; and the likelihood of living in a family-friendly neighborhood with many children. We draw residential and childbearing histories (2000-2018) from Norwegian administrative registers (N ~ 167,000 women). Individuals' neighborhoods are defined using time-varying geocoordinates for place of residence. We identify selective moves as one plausible causal driver of residential clustering of large families. This study contributes to the understanding of fertility and relocation, and to the literature on the social interaction effects of fertility, by testing the relevance of yet another network: that of neighbors.

Preferences for a mixed-sex composition of offspring: A multigenerational approach

Parents with two boys or two girls are more likely to have a third child than those with a 'sex mix'. However, little is known on whether these 'mixed-sex preferences' extend beyond the nuclear family. This study leverages the random variation in sex at birth to assess whether the sex of nieces and nephews, in combination with own children, matters for fertility choices. Using three-generational data from the US Panel Study of Income Dynamics (PSID), I show that extended families (including grandparents, their children, and their grandchildren) are collectively more likely to have three or more grandchildren when lacking sex mix, whether the first two grandchildren are siblings or cousins. I explore the pathways for these offspring sex preferences, finding support for a preference for an uninterrupted line of male descendants. This multigenerational approach also contributes a new estimation strategy that causally estimates the effects of family sizes on outcomes beyond fertility.

Sibling influence on family formation: A study of social interaction effects on fertility, marriage, and divorce

This study examined social interaction effects on family formation and dissolution, asking whether fertility, marital, and divorce behavior spread in the sibling network. Using panel data from the German SOEP (1984 - 2016; N = 4,521 individuals), we estimated discrete-time event history models with random effects at the individual to examine whether siblings' transitions to parenthood, marriage, and divorce influence an individual's chance to have children, marry, and divorce. Results show that the hazard of becoming a parent increased in the short term after a sibling had a child. Similarly, the hazard of getting married increased following a sibling's marriage. Tentative evidence also suggested that transition rates to divorce increased in the longer term following a sibling's divorce. Furthermore, we found evidence for social interaction effects across different transitions in the process of family formation, as the transition rates to marriage decreased after a sibling divorced. Conversely, the risk of divorce decreased following a sibling's entry to marriage. Overall, these findings illustrate that the impact of network partners on demographic behavior is not limited to the same behavioral domain and might be negative as well as positive.