Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236408 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14377
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
income support
machine learning
Australia
JEL: 
C53
H53
I38
J68
Document Type: 
Working Paper

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