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

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  • Dario Sansone
  • Anna Zhu

Abstract

Using high‐quality nationwide 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 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 4 years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R‐squared), 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 enable governments and institutions to offer timely support to these at‐risk individuals.

Suggested Citation

  • Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    • Handle: RePEc:bla:obuest:v:85:y:2023:i:5:p:959-992
    DOI: 10.1111/obes.12550
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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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