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Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods

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  • Evan D. Peet
  • Dana Schultz
  • Susan Lovejoy
  • Fuchiang (Rich) Tsui

Abstract

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has an extensive literature documenting positive effects on infant health outcomes, specifically preterm birth, low birthweight, small size for gestational age, and infant mortality. However, existing studies focus on average effects for these relatively infrequent outcomes, thus providing no evidence for how WIC affects those at greatest risk of negative infant health outcomes. Our study focuses on documenting how WIC's infant health effects vary by level of risk. In doing so, we leverage a uniquely rich database describing maternal and infant outcomes and risk factors. Additionally, we use high dimensional data to generate predictions of risk and combine these predictions with the novel double machine learning method to stratify the effects of WIC by predicted risk. Our estimates of WIC's average treatment effects align with those in the existing literature. More importantly, we document significant variation in the effects of WIC on infant health by predicted risk level. Our results show that WIC is most beneficial among those at greatest risk of poor outcomes.

Suggested Citation

  • Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    • Handle: RePEc:wly:hlthec:v:32:y:2023:i:1:p:194-217
    DOI: 10.1002/hec.4617
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    1. Khanani, I. & Elam, J. & Hearn, R. & Jones, C. & Maseru, N., 2010. "The impact of prenatal WIC participation on infant mortality and racial disparities," American Journal of Public Health, American Public Health Association, vol. 100(S1), pages 204-209.
    2. Hilary W. Hoynes & Diane Whitmore Schanzenbach, 2009. "Consumption Responses to In-Kind Transfers: Evidence from the Introduction of the Food Stamp Program," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 109-139, October.
    3. Janet Currie & Ishita Rajani, 2015. "Within-Mother Estimates Of The Effects Of Wic On Birth Outcomes In New York City," Economic Inquiry, Western Economic Association International, vol. 53(4), pages 1691-1701, October.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    5. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    6. Oliveira, Victor & Racine, Elizabeth & Olmsted, Jennifer & Ghelfi, Linda M., 2002. "The Wic Program: Background, Trends, And Issues," Food Assistance and Nutrition Research Reports 33847, United States Department of Agriculture, Economic Research Service.
    7. Ted Joyce & Andrew Racine & Cristina Yunzal-Butler, 2008. "Reassessing the WIC effect: Evidence from the Pregnancy Nutrition Surveillance System," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(2), pages 277-303.
    8. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    9. Figlio, David & Hamersma, Sarah & Roth, Jeffrey, 2009. "Does prenatal WIC participation improve birth outcomes? New evidence from Florida," Journal of Public Economics, Elsevier, vol. 93(1-2), pages 235-245, February.
    10. Michael C. Lovell, 1963. "Seasonal Adjustment of Economic Time Series and Multiple Regression," Cowles Foundation Discussion Papers 151, Cowles Foundation for Research in Economics, Yale University.
    11. Christopher R. Knittel & Samuel Stolper, 2019. "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers 26531, National Bureau of Economic Research, Inc.
    12. Alberto Abadie & Matthew M. Chingos & Martin R. West, 2018. "Endogenous Stratification in Randomized Experiments," The Review of Economics and Statistics, MIT Press, vol. 100(4), pages 567-580, October.
    13. Brent Kreider & John V. Pepper & Manan Roy, 2020. "Does The Women, Infants, And Children Program Improve Infant Health Outcomes?," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1731-1756, October.
    14. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    15. Hope Corman & Dhaval M. Dave & Nancy Reichman, 2018. "Effects of Prenatal Care on Birth Outcomes: Reconciling a Messy Literature," NBER Working Papers 24885, National Bureau of Economic Research, Inc.
    16. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    17. Anna Chorniy & Janet Currie & Lyudmyla Sonchak, 2020. "Does Prenatal WIC Participation Improve Child Outcomes?," American Journal of Health Economics, University of Chicago Press, vol. 6(2), pages 169-198.
    18. Swann Christopher A, 2010. "WIC Eligibility and Participation: The Roles of Changing Policies, Economic Conditions, and Demographics," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-37, March.
    19. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    20. Marianne P. Bitler & Janet Currie, 2005. "Does WIC work? The effects of WIC on pregnancy and birth outcomes," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(1), pages 73-91.
    21. Ted Joyce & Diane Gibson & Silvie Colman, 2005. "The changing association between prenatal participation in WIC and birth outcomes in New York City," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(4), pages 661-685.
    22. Kowaleski-Jones, L. & Duncan, G.J., 2002. "Effects of participation in the WIC program on birthweight: Evidence from the National Longitudinal Survey of Youth," American Journal of Public Health, American Public Health Association, vol. 92(5), pages 799-804.
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