Algorithmic Profiling: Effective Tool for Targeting Active Labour Market Policies?

ABSTRACT Digitisation has sparked interest in automated decision making in Public Employment Services (PES). We evaluate an algorithmic profiling model in Austria that predicts the reemployment prospects of unemployed individuals to classify and assign them to active labour market policies. Our analysis shows that reallocating resources from jobseekers with low to medium prospects, as proposed by the PES, does not yield the expected efficiency gains. We find no systematic evidence that programmes are less effective for individuals with low predicted employment prospects than for those with medium prospects. These findings caution against crude algorithmic profiling and highlight the need for nuanced targeting strategies that prioritise the most disadvantaged jobseekers. JEL Classification: J24, J64, J68