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α: calibrated so average coauthorship-adjusted count equals average raw count
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.