Publications - Working Papers IER SAS

Using rich administrative data on job seekers (JSs) registered by the public employment service (PES), we describe the implementation of the Youth Guarantee (YG) initiative through the Slovak Active Labour Market Policy (ALMP). By adopting a dynamic estimation technique based on double machine learning (DML), we generate evidence on the impact of various types of ALMP programs provided in different periods of unemployment. The spectrum of ALMP programs ranges from classroom training through hiring incentives and subsidized employment in the private sector to public works organized at the municipality level. We identify the impact of participation in a particular ALMP program or sequences of ALMP programs on the absence of individuals from registered unemployment after three years. We demonstrate that due to the functionality of the dynamic DML estimator, one case study can generate comparative evidence affirming the conclusions of ALMP impact evaluation meta-analyses. Additionally, aiming to address the operational-level PES case-worker dilemma, we quantify the impact of the evaluated ALMP programs compared with those of two alternative counterfactual situations, assuming a more and less employable client.