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Machine Learning and Multiple Abortions

Author

Listed:
  • Kumar, Pradeep

    (University of Exeter)

  • Nicodemo, Catia

    (University of Oxford)

  • Oreffice, Sonia

    (University of Exeter)

  • Quintana-Domeque, Climent

    (University of Exeter)

Abstract

This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We find that Random Forest and an Ensemble method are most effective in the highest risk decile, capturing about 55% of cases, whereas linear models and Extreme Gradient Boosting excel in mid to lower deciles. We also show that targeting the top 20% most at-risk could yield cost savings of 5.44 to 8.2 million EUR, which could be reallocated to prevent unintended pregnancies arising from contraceptive failure, abusive relationships, and sexual assault, among other factors.

Suggested Citation

  • Kumar, Pradeep & Nicodemo, Catia & Oreffice, Sonia & Quintana-Domeque, Climent, 2024. "Machine Learning and Multiple Abortions," IZA Discussion Papers 17046, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17046
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    More about this item

    Keywords

    Extreme Gradient Boosting; Ridge; random forest; multiple abortions; Logit; Lasso; Ensemble; reproductive healthcare;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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