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All co-authors learning machine performance unemployed data unemployment assessments caseworkers probability inflow covid long term pandemic individuals whether methods arbeitsuche predicting versus workers administrative pandemie predict information compare measures random forest prediction expectations arbeitslosigkeit arbeitslosenversicherung prognoseverfahren analyze unique sources months sampled asked perceived second revealed expected trained individual predictive consider combinations improve caseworker contain captured beschäftigungseffekt re‑employment evidence techniques labor lesser extent self reported logistic regression terms beginning declines
Composed unemployment insurance künstliche intelligenz artificial intelligence long term job search learning versus versus assessments assessments unemployed unemployed workers workers caseworkers term unemployed random forest prediction performance forecasting model predicting employment employment machine covid pandemic analyze unique unique data data sources sources information information probability months individuals individuals sampled sampled inflow inflow unemployment unemployment asked asked perceived perceived probability probability second second caseworkers caseworkers revealed revealed whether whether expected learning methods methods trained trained administrative data inflow inflow predict predict individual individual compare compare predictive predictive performance performance measures measures consider combinations improve improve performance caseworker assessments assessments contain contain information information captured captured machine learning algorithm employment effect machine learning learning techniques covid pandemie caseworkers predicting consider combinations lesser extent extent caseworker individuals long probability employment employment months expected random forest machine self reported performance methods long term unemployment re‑employment machine effects minimum enriching administrative data using using survey survey data data machine predict long unemployment times times crisis crisis evidence policy mix
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The information on the author is retrieved from: Entity Facts (by DNB = German National Library data service), DBPedia and Wikidata
Max Kunaschk Dr. rer. pol. Biblio: Diss. Rechts- und Wirtschaftswissenschaftlichen Fakultät / Fachbereich Wirtschafts- und Sozialwissenschaften der Univ. Erlangen-Nürnberg
Profession Economist
Affiliations Institut für Arbeitsmarkt- und Berufsforschung
Publishing years Series IAB discussion paper : Beiträge zum wissenschaftlichen Dialog aus dem Institut für Arbeitsmarkt- und Berufsforschung (2) IAB-Kurzbericht (1) Discussion paper series / IZA (1) Working paper series (1) Working papers / Institute for Evaluation of Labour Market and Education Policy (1) 
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