  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.
