Demographic models of the reproductive process

Past, interlude, and future

Bibliographic Data

ID4268639
AuthorsDaniel Ciganda (0000-0002-6181-9947, Max Planck Institute for Demographic Research), Nicolas Todd (0000-0002-7223-0759, Max Planck Institute for Demographic Research)
Year2022
Volume76
Issue3
Pages495-513
Publication date2022-09-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePopulation Studies (JOURNAL)
Journal identifiersISSN: 0032-4728 • E-ISSN: 1477-4747
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00324728.2021.1959943
PMID34486942
OpenAlexW3196594289
LanguageEN
Citations received3
References cited44

After 30 years of active development, mechanistic models of the reproductive process nearly stopped attracting scholarly interest in the early 1980s. In the following decades, fertility research continued to thrive, relying on solid descriptive work and detailed analysis of micro-level data. The absence of systematic modelling efforts, however, has also made the field more fragmented, with empirical research, theory building, and forecasting advancing along largely disconnected channels. In this paper we outline some of the drivers of this process, from the popularization of user-friendly statistical software to the limitations of early family building models. We then describe a series of developments in computational modelling and statistical computing that can contribute to the emergence of a new generation of mechanistic models. Finally, we introduce a concrete example of this new kind of model, and show how they can be used to formulate and test theories coherently and make informed projections

Data science · Management science · Model building · Operations research · Process (computing) · Statistical model · Work (physics) · Artificial Intelligence · Birth, Development, and Health · Computer Science · demographic modeling and climate adaptation · Engineering · Insurance, Mortality, Demography, Risk Management · Software

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