Dario Sansone; Anna Zhu (2023) Academic journal article

Using Machine Learning to Create an Early Warning System for Welfare Recipients

Oxford Bulletin of Economics and Statistics (Wiley)

Ref: SRC-002-AUS-001

https://onlinelibrary.wiley.com/doi/10.1111/obes.12550

Accessed: 3/23/2026

Summary

Peer-reviewed journal version of the IZA working paper. Confirms ML models improve prediction of long-term welfare dependency by at least 22% relative to standard profiling tools, using Australian longitudinal administrative data from 2014-2018.

View Harvard reference

Sansone, D. and Zhu, A. (2023) 'Using Machine Learning to Create an Early Warning System for Welfare Recipients', Oxford Bulletin of Economics and Statistics, 85(5), pp. 959-992. doi:10.1111/obes.12550.

Linked Cases (1)

AUS-001 AUS Design & Development Phase

Machine-Learning Early Warning System for Income Support Recipients (Australia — Research Prototype)

Social assistance Classical ML