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

Dario Sansone and Anna Zhu ()
Additional contact information
Anna Zhu: RMIT University

No 14377, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially provide governments with large savings in accrued welfare costs.

Keywords: income support; machine learning; Australia (search for similar items in EconPapers)
JEL-codes: C53 H53 I38 J68 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2021-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://docs.iza.org/dp14377.pdf (application/pdf)

Related works:
Journal Article: Using Machine Learning to Create an Early Warning System for Welfare Recipients (2023)
Working Paper: Using Machine Learning to Create an Early Warning System for Welfare Recipients (2021)
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:dp14377

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 ().