2023/12/13 by Daniel Ciganda, Ciganda, Daniel, Nicolas Todd +1
Decision Sciences · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Family Dynamics and Relationships #Insurance, Mortality, Demography, Risk Management #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2312.08185
openalex publication_date 2023/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Macro-level modeling is still the dominant approach in many demographic applications because of its simplicity. Individual-level models, on the other hand, provide a more comprehensive understanding of observed patterns; however, their estimation using real data has remained a challenge. The approach we introduce in this article attempts to overcome this limitation. Using likelihood-free inference techniques, we show that it is possible to estimate the parameters of a simple but demographically interpretable individual-level model of the reproductive process from a set of aggregate fertility rates. By estimating individual-level quantities from widely available aggregate data, this approach can contribute to a better understanding of reproductive behavior and its driving mechanisms. It also allows for a more direct link between individual-level and population-level processes. We illustrate our approach using data from three natural fertility populations.