Statistical profiling of unemployed jobseekers

Bert Van Landeghem, Sam Desiere and Ludo Struyven
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Ludo Struyven: KU Leuven, Belgium

IZA World of Labor, 2021, No 483, 483

Abstract: 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.

Keywords: statistical profiling; long-term unemployment; benefit exhaustion; labor market discrimination (search for similar items in EconPapers)
JEL-codes: C1 J64 J7 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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