Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning

Md Mohsan Khudri and Andrew Hussey ()
Additional contact information
Md Mohsan Khudri: Austin Community College
Andrew Hussey: University of Memphis

No 17080, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Using data from the Panel Study of Income Dynamics, we estimate the impact of breastfeeding initiation and duration on multiple cognitive, health, and behavioral outcomes spanning early childhood through adolescence. To mitigate the potential bias from misspecification, we employ a doubly robust (DR) estimation method, addressing misspecification in either the treatment or outcome models while adjusting for selection effects. Our novel approach is to use and evaluate a battery of supervised machine learning (ML) algorithms to improve propensity score (PS) estimates. We demonstrate that the gradient boosting machine (GBM) algorithm removes bias more effectively and minimizes other prediction errors compared to logit and probit models as well as alternative ML algorithms. Across all outcomes, our DR-GBM estimation generally yields lower estimates than OLS, DR, and PS matching using standard and alternative ML algorithms and even sibling fixed effects estimates. We find that having been breastfed is significantly linked to multiple improved early cognitive outcomes, though the impact reduces somewhat with age. In contrast, we find mixed evidence regarding the impact of breastfeeding on non-cognitive (health and behavioral) outcomes, with effects being most pronounced in adolescence. Our results also suggest relatively higher cognitive benefits for children of minority mothers and children of mothers with at least some post-high school education, and minimal marginal benefits of breastfeeding duration beyond 12 months for cognitive outcomes and 6 months for non-cognitive outcomes.

Keywords: breastfeeding; human capital; cognitive and non-cognitive outcomes; doubly robust estimation; machine learning (search for similar items in EconPapers)
JEL-codes: C21 C63 I12 I18 J13 J24 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2024-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hea and nep-neu
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://docs.iza.org/dp17080.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp17080

Ordering information: This working paper can be ordered from
IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany

Access Statistics for this paper

More papers in IZA Discussion Papers from Institute of Labor Economics (IZA) IZA, P.O. Box 7240, D-53072 Bonn, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Holger Hinte ().