Modelling Social Assistance Take-Up with Machine Learning in Slovakia

The Material Need Benefit (MNB) constitutes the core component of the Slovak social assistance system. It is a means-tested transfer whose eligibility depends on household composition, income thresholds, and asset tests. In the TATRASK model, which is a static microsimulation model of the Slovak tax and transfer system based on linked administrative data collected by government authorities, the MNB is simulated through the application of legislative rules subject to data availability constraints. As is common in microsimulation models of this type, both the number of simulated beneficiaries and the aggregate fiscal cost are substantially overestimated. This limitation stems primarily from the inability to model benefit take-up accurately due to missing or unobserved information in the data. To address this issue, first an expert-based adjustment approach is implemented, where the number of potential beneficiaries is reduced through a set of rules reflecting observed behavioural patterns in the data. While this method improves model performance and reduces overestimation, it remains limited in its ability to fully capture take-up behaviour. As an alternative, this contribution applies machine learning techniques to model MNB take-up. Specifically, XGBoost models are trained to predict the probability of benefit receipt and to identify likely beneficiaries within the underlying dataset. The results demonstrate that the machine learning approach clearly and substantially outperforms both the baseline TATRASK simulation and the expert rule-based adjustment in terms of accuracy. Moreover, cross-temporal validation confirms the robustness and stability of the ML model, even in the presence of policy changes affecting MNB eligibility.