Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation

TL;DR

This paper introduces a Bayesian framework using neural posterior estimation to infer individual reproductive behaviors from aggregate fertility data, enabling detailed demographic modeling from limited data.

Contribution

It presents a novel likelihood-free Bayesian approach that combines a demographically interpretable simulation model with SNPE to recover individual reproductive parameters from aggregate rates.

Findings

Successfully recovers behavioral parameters from ASFRs

Predicts individual outcomes like age at first sex and family size

Reduces data needs for demographic microsimulations

Abstract

Age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, but their aggregate nature obscures the individual-level behavioral mechanisms that drive fertility trends. To bridge this micro-macro divide, we introduce a likelihood-free Bayesian framework that couples a demographically interpretable, individual-level simulation model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). We show that this framework successfully recovers core behavioral parameters governing contemporary fertility, including preferences for family size, reproductive timing, and contraceptive failure, using only ASFRs. The framework's effectiveness is validated on cohorts from four countries with diverse fertility regimes. Most compellingly, the model, estimated solely on aggregate data, successfully predicts out-of-sample distributions of individual-level outcomes, including age at first sex, desired family size, and birth intervals. Because our framework yields complete synthetic life histories, it significantly reduces the data requirements for building microsimulation models and enables behaviorally explicit demographic forecasts.