Causal Modelling in Fertility Research: A Review of the Literature and an Application to a Parental Leave Policy Reform

Abstract

This paper reviews empirical studies that have examined the causal determinants of fertility behaviour. In particular, we compare the approaches adopted in the different disciplines to improve our understanding of how birth dynamics are influenced by changes in female employment and changes in family policies. The wide array of panel data that have become available in recent years provide great potential for advanced causal modelling in this field. Event history modelling has been a dominant approach in sociology and demography. However, researchers are increasingly turning to other methods to unravel causal effects, such as fixed-effects modelling, the regression discontinuity approach, and statistical matching. We summarise selected studies, and discuss the advantages and the shortcomings of the different approaches. In an empirical section, we analyse the impact of the German 2007 policy reform on birth behaviour to illustrate the difficulties involved in isolating policy effects. The final chapter concludes by underscoring that even simple modelling strategies may be beneficial for improving our understanding of how policy effects shape demographic behaviour, and for laying the groundwork for more fine-grained causal investigations.
 * This article belongs to a special issue on “Identification of causal mechanisms in demographic research: The contribution of panel data”.

Measuring the effect of employment uncertainty on fertility in low-fertility contexts: an overview of existing measures

“…However, the evidence for the influence of social policy on fertility remains inconclusive and insufficient (Gauthier, 2007 ). Causally linking social policies with fertility has proven to be difficult (Kreyenfeld, 2021 ). Social policies like child tax benefits, parental leave, and expanded childcare likely influence individuals’ decisions about if their current employment provides enough income and stability to pursue their fertility desires.…”

“…An alternative to event history analysis is causal models. However, births are fairly uncommon events making the data demands quite large (for a discussion on the use of causal modelling in fertility research please see Kreyenfeld, 2021 ). With births being relatively rare events, many of the most common causal models struggle to show significant results.…”

Editorial on the Special Issue “The identification of causal mechanisms in demographic research”

“…political interventions, on the outcomes of individuals who experienced the event or realised the interventions (e.g. Gangl/ Ziefl e 2015;Hofmann et al 2017;Kreyenfeld 2021). However, non-random selection of cases into the treatment group must be examined in close detail and with indepth substantive knowledge of the treatment assignment mechanism.…”

“…While retrospective information is already of great use, prospective panel designs enable a more appropriate and manifold collection of relevant information, as well as more refi ned statistical modelling of the interdependence between individual behaviour, its dispositional and motivational drivers, its situational conditions, and its outcomes over time. Panel data are also useful for another prominent class of methods, i. e. techniques of event history analysis (Blossfeld/Rohwer 2002;Kreyenfeld 2021).…”

“…Next, Michaela Kreyenfeld (2021) evaluates the study of causal factors for reproductive behaviour. She focusses on three important substantive topics under investigation in this fi eld of research: the important "female employment and fertility nexus", the impact of family policy on reproductive behaviour, and the explanation of fertility intentions and their behavioural link.…”