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

Dario Sansone and Anna Zhu

Papers from arXiv.org

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.

Date: 2020-11, Revised 2021-05
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (8)

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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)
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