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Maternal circumstances and intergenerational transmission of health: A model-based recursive partitioning approach from Machine Learning

Author

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  • Brahma, Dweepobotee
  • Ghosh, Souvik

Abstract

This paper investigates how different maternal circumstances affect the intergenerational transmission of health from mother to infants differentially. Using a novel model-based recursive partitioning algorithm from the Machine Learning literature that uses econometric tests for parameter instability, this study identifies different circumstance profiles characterized by varying coefficients of intergenerational health transmission. Results exhibit considerable heterogeneity by both short-run and long run markers of maternal health and reveal that a global model for investigating intergenerational transmission is inadequate. Worse-off circumstances have stronger intergenerational transmission rates of maternal health to newborns. The results have implications for building more targeted social safety net policies and improve our understanding of how to break intergenerational cycles of ill-health.

Suggested Citation

  • Brahma, Dweepobotee & Ghosh, Souvik, 2025. "Maternal circumstances and intergenerational transmission of health: A model-based recursive partitioning approach from Machine Learning," Economics & Human Biology, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:ehbiol:v:59:y:2025:i:c:s1570677x2500084x
    DOI: 10.1016/j.ehb.2025.101551
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    Keywords

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    JEL classification:

    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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