Statistical Profiling as a Targeting Tool: Can It Enhance the Efficiency of Active Labor Market Policies?

Autor/inn/en	Eppel, Rainer; Huemer, Ulrike; Mahringer, Helmut; Schmoigl, Lukas
Institution	Österreichisches Institut für Wirtschaftsforschung
Titel	Statistical Profiling as a Targeting Tool: Can It Enhance the Efficiency of Active Labor Market Policies? Gefälligkeitsübersetzung: Statistisches Profiling als Targeting-Instrument: Kann es die Effizienz aktiver arbeitsmarktpolitischer Maßnahmen erhöhen?
Quelle	Wien (2025), 33 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	WIFO working papers. 694
Sprache	englisch
Dokumenttyp	online; Monografie
Schlagwörter	Aktivierung; Algorithmus; Automatisierung; Arbeitsverwaltung; Arbeitsmarktpolitik; Arbeitsvermittlung; Berufliche Integration; Qualifizierungsmaßnahme; Schwer vermittelbarer Arbeitsloser; Arbeitsförderungsmaßnahme; Teilnehmer; Arbeitspapier; Auswahl; Auswahlverfahren; Auswirkung; Effizienz; Profilanalyse; Zielgruppe; Österreich
Abstract	"Digitization has spurred interest in the potential of statistical profiling to improve the targeting of active labor market policies. Despite growing adoption, empirical evidence on the effectiveness of such profiling in program allocation is scarce. We evaluate a semi-automated statistical profiling model in Austria that aims to target policies based on predicted reemployment prospects (low, medium, high). Our analysis shows that a reallocation of resources from low-chance to medium-chance segments, as envisaged by the Public Employment Service, would not yield the desired efficiency gains. Employment programs have a stronger impact on jobseekers with low job prospects than on those with medium prospects, and training programs are not consistently less effective in the low-chance segment either. Our findings suggest that the focus should remain on the most disadvantaged, both from an efficiency and an equity perspective. They caution against relying on overly coarse profiling and stress the need for nuanced targeting strategies." The study refers to the period 2014-2022 (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2025/3