Selecting Suitable Job Quality Items in Profiling and Job Matching Algorithms for Public Employment Services

Clément Brébion, Janine Leschke, Pavle Boskoski, Biljana Mileva Boshkoska

Publikation: KonferencebidragPaperForskningpeer review

Abstrakt

This paper takes its starting point in the lessons learned from first generation statistical profiling systems in Public Employment Services (PES) and the sociological vision of labour market integration and good-quality work. Commonly job quality is not considered in statistical profiling, instead exit to any type of employment is modelled. We discuss and operationalize the multi-dimensional concept of job quality with a view on whether and how job quality items can be integrated into a tool computing the probability of exit into good jobs.
OriginalsprogEngelsk
Publikationsdato2022
Antal sider36
StatusUdgivet - 2022
Begivenhed20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022: Social Policy Change between Path Dependency and Innovation - Wien, University of Vienna, Østrig
Varighed: 14 sep. 202216 sep. 2022
Konferencens nummer: 20
https://www.espanet-vienna2022.org/

Konference

Konference20th Annual European Network for Social Policy Analysis Conference. ESPAnet 2022
Nummer20
LokationWien
Land/OmrådeØstrig
ByUniversity of Vienna
Periode14/09/202216/09/2022
Internetadresse

Emneord

  • Algorithms
  • Job quality
  • Matching
  • Profiling
  • Public employment services

Citationsformater