Van Landeghem, B. orcid.org/0000-0001-6759-8893, Desiere, S. and Struyven, L. (2021) Statistical profiling of unemployed jobseekers : the increasing availability of big data allows for the profiling of unemployed jobseekers via statistical models. IZA World of Labor. 483. ISSN 2054-9571
Abstract
Statistical models can help public employment services to identify factors associated with long-term unemployment and to identify groups at risk. Statistical profiling models will probably even become more prominent as new machine learning techniques in combination with the increasing availability of big data will improve their predictive power. However, a policy maker cannot just define an outcome variable at the start of the project and walk away: a continuous dialogue between data analysts, policy makers and caseworkers is very important. Indeed, throughout the process, normative decisions are to be made: profiling practices misclassify many individuals. They can reinforce but also prevent existing patterns of discrimination.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an author-produced version of a working paper published in IZA World of Labor. For re-use permissions, please contact the authors. |
Keywords: | Statistical Profiling; Long-term Unemployment; Benefit Exhaustion; Labour Market Discrimination |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Oct 2020 10:41 |
Last Modified: | 09 Feb 2021 08:07 |
Status: | Published |
Publisher: | IZA-Institute of Labor Economics |
Refereed: | Yes |
Identification Number: | 10.15185/izawol.483 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166746 |