Fertility and Well-Being
At a Glance
Contemporary and Future Trends in Fertility
The 20th century witnessed substantial declines in human fertility and population growth rates. At the same time, global populations experienced unprecedented improvements in socioeconomic development. The negative relationship between fertility and socioeconomic development has been one of the most solidly established and generally accepted empirical patterns described in the social sciences. In the late 20th and early 21st centuries, however, a number of highly developed countries saw increases in their fertility levels. Projects in this research area aim to gain a deeper understanding of contemporary trends in fertility and to develop methods for improving fertility forecasting.
Socioeconomic and demographic factors both appear to have influenced recent trends in period fertility. An important demographic factor is the postponement of births, which temporarily suppresses fertility levels. If postponement slows down, this temporary suppression comes to an end, and fertility increases follow. Socioeconomic factors, including overall socioeconomic development, have also contributed positively to fertility trends, as have further improvements in socioeconomic conditions at high levels of socioeconomic development. But it remains unclear whether socioeconomic development influences fertility postponement. Moreover, the potentially critical role of gender equality as a link between socioeconomic development and fertility is poorly understood. Our goal is to provide a full description of the connections between socioeconomic development, gender equality, fertility postponement, and fertility trends.
The impact of fertility timing on period-fertility measures is well-known, and can be seen as either a feature or a bug. Regardless of how they are viewed, period-fertility measures do not describe the fertility experiences of any real cohort of women. A cohort perspective is needed to determine how many children real female cohorts are having. However, the cohorts of interest are often too young to have completed their childbearing. We are thus developing new methods for forecasting cohort fertility from incomplete data. We are also considering a wide range of approaches that vary in their levels of complexity and in their data demands. Examples are the straightforward linear extrapolation of past trends, the application of stochastic diffusion models, and Bayesian modeling.
Projects of this Research Area
Shifts in the Fertility-Development Nexus at the Macro and Micro Level Project details
Causal Inference Approaches to Fertility over the Life Course Project details
Comparing Age- and Parity-Specific Approaches to Project Cohort Fertility in Developing Countries with Defective Data Project details