a7ef21d714eecd619b19edd880daf4c9  Learning_Individual_Reproductive_Behaviour.pdf
9864fb1261db242ae8574d4df907f91c0b58b0b5  Learning_Individual_Reproductive_Behaviour.pdf
ec203f1fc3961adf0c747b850f0f9bf213da768fe03d5fb94f5ef330f090962e  Learning_Individual_Reproductive_Behaviour.pdf

Title:           Learning individual reproductive behaviour from aggregate fertility rates via neural posterior estimation
Subject:         DOI: 10.1093/jrsssa/qnag045; Journal of the Royal Statistical Society Series A: Statistics in Society, 2026-03-20.; Abstract: Age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, but their aggregate nature obscures the individual-level behavioural mechanisms that drive fertility trends. To bridge this micro–macro divide, we introduce a simulation-based 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 behavioural 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 behaviourally explicit demographic forecasts.
Keywords:        age-specific fertility rates; individual-level modelling; microsimulation; reproductive behaviour; simulation-based inference
Author:          Daniel Ciganda; Ignacio Campón; Iñaki Permanyer; Jakob H. Macke
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     Item: 
      Title: Learning individual reproductive behaviour from aggregate fertility rates via neural posterior estimation
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       Item: 
        Title: 1 &!--skipFloatSeq--;Introduction
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         Item: 
          Title: 1.1 Previous work
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         Item: 
          Title: 1.2 Organization of the article
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       Item: 
        Title: 2 Model
        Destination: secqnag045-s2
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         Item: 
          Title: 2.1 Implementation
          Destination: secqnag045-s2.1
       Item: 
        Title: 3 Data
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       Item: 
        Title: 4 Simulation-based inference
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         Item: 
          Title: 4.1 Inference scenarios
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         Item: 
          Title: 4.2 Prior specification
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       Item: 
        Title: 5 Results
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         Item: 
          Title: 5.1 Cross-validation
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          Title: 5.2 Model validation
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           Item: 
            Title: 5.2.1 Posterior predictive checks
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         Item: 
          Title: 5.3 Estimated marginal posteriors
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           Item: 
            Title: 5.3.1 Out-of-sample validation
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             Item: 
              Title: 5.3.1.1 Overall fit
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             Item: 
              Title: 5.3.1.2 Age at first sex
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             Item: 
              Title: 5.3.1.3 Desired family size
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             Item: 
              Title: 5.3.1.4 Birth intervals
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       Item: 
        Title: 6 Discussion
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       Item: 
        Title: Conflicts of interest
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       Item: 
        Title: Funding
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       Item: 
        Title: Data availability
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       Item: 
        Title: Supplementary material
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       Item: 
        Title: References
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    Title: Learning individual reproductive behaviour from aggregate fertility rates via neural posterior estimation
    Author: Daniel Ciganda; Ignacio Campón; Iñaki Permanyer; Jakob H. Macke
    Subject: DOI: 10.1093/jrsssa/qnag045; Journal of the Royal Statistical Society Series A: Statistics in Society, 2026-03-20.; Abstract: Age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, but their aggregate nature obscures the individual-level behavioural mechanisms that drive fertility trends. To bridge this micro macro divide, we introduce a simulation-based 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 behavioural 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 behaviourally explicit demographic forecasts.
    Keywords: age-specific fertility rates; individual-level modelling; microsimulation; reproductive behaviour; simulation-based inference
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   Type: CRC32
  Checksum: a7ef21d714eecd619b19edd880daf4c9
   Type: MD5
  Checksum: 9864fb1261db242ae8574d4df907f91c0b58b0b5
   Type: SHA-1
  Checksum: ec203f1fc3961adf0c747b850f0f9bf213da768fe03d5fb94f5ef330f090962e
   Type: SHA-256