  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.
