Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/260717 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] IZA World of Labor [ISSN:] 2054-9571 [Article No.:] 483 [Publisher:] Institute of Labor Economics (IZA) [Place:] Bonn [Year:] 2021
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
Statistical models can help public employment services to identify factors associated with long-term unemployment and to identify at-risk groups. Such profiling models will likely become more prominent as increasing availability of big data combined with new machine learning techniques improve their predictive power. However, to achieve the best results, a continuous dialogue between data analysts, policymakers, and case workers is key. Indeed, when developing and implementing such tools, normative decisions are required. Profiling practices can misclassify many individuals, and they can reinforce but also prevent existing patterns of discrimination.
Schlagwörter: 
statistical profiling
long-term unemployment
benefit exhaustion
labor market discrimination
JEL: 
C1
J64
J7
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
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