Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/300942 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 17046
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
Extreme Gradient Boosting
Ridge
random forest
multiple abortions
Logit
Lasso
Ensemble
reproductive healthcare
JEL: 
I12
I18
C53
J13
C55
Dokumentart: 
Working Paper

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