Optimal Welfare-to-Work Programs with Worker Profiling

Sergio Cappellini ()
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Sergio Cappellini: Department of Economics, Ca' Foscari University of Venice; University of Padua

No 2023:08, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: Profiling has a pivotal role in welfare-to-work programs in classifying jobless workers according to their abilities and assigning them to suitable labor-market policies. This analysis designs an optimal profiling policy, by embedding dynamic learning about a recipient's ability within principal-agent framework. In optimal profiling, a certain proportion of low-skilled workers may be persuaded that they are in fact high-skilled and referred to delegated search, together with actual high-skilled workers (positive type II error). This occurs whenever the government prefers that overly optimistic low-skilled workers search for jobs (with low incentive costs) rather than referring them to passive labor-market policies. On the other hand, a high-skilled worker is never classified as being low-skilled, nor she is referred to a passive policy (no type I error). In the US, an optimal profiling strategy would generate annual savings that range from around $0:6 million (South Dakota) to $201:1 million (California).

Keywords: Bayesian Persuasion; Job-Search Assistance; Non-Contractible Effort; Social Assistance; Unemployment Insurance; Worker Profiling (search for similar items in EconPapers)
JEL-codes: D82 I38 J65 J68 (search for similar items in EconPapers)
Pages: 83 pages
Date: 2023
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