Childhood Circumstances and Health of American and Chinese Older Adults: A Machine Learning Evaluation of Inequality of Opportunity in Health

Shutong Huo, Derek Feng, Thomas M. Gill and Xi Chen

No 1384, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: Childhood circumstances may impact senior health, prompting this study to introduce novel machine learning methods to assess their individual and collective contributions to health inequality in old age. Using the US Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS), we analyzed health outcomes of American and Chinese participants aged 60 and above. Conditional inference trees and forest were employed to estimate the influence of childhood circumstances on self-rated health (SRH), comparing with the conventional parametric Roemer method. The conventional parametric Roemer method estimated higher IOP in health (China: 0.039, 22.67% of the total Gini coefficient 0.172; US: 0.067, 35.08% of the total Gini coefficient 0.191) than conditional inference tree (China: 0.022, 12.79% of 0.172; US: 0.044, 23.04% of 0.191) and forest (China: 0.035, 20.35% of 0.172; US: 0.054, 28.27% of 0.191). Key determinants of health in old age were identified, including childhood health, family financial status, and regional differences. The conditional inference forest consistently outperformed other methods in predictive accuracy as measured by out-of-sample mean squared error (MSE). The findings demonstrate the importance of early-life circumstances in shaping later health outcomes and stress the earlylife interventions for health equity in aging societies. Our methods highlight the utility of machine learning in public health to identify determinants of health inequality.

Keywords: Life Course; Inequality of Opportunity; Childhood Circumstances; Machine Learning; Conditional Inference Tree; Random Forest (search for similar items in EconPapers)
JEL-codes: C53 I14 J13 J14 O57 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-age and nep-big
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/281669/1/GLO-DP-1384.pdf (application/pdf)

Related works:
Working Paper: Childhood Circumstances and Health of American and Chinese Older Adults: A Machine Learning Evaluation of Inequality of Opportunity in Health (2024)
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:zbw:glodps:1384

Access Statistics for this paper

More papers in GLO Discussion Paper Series from Global Labor Organization (GLO) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().