Arbeitspapier

Trainspotting: `Good Jobs', Training and Skilled Immigration

While skilled immigration ceteris paribus provides an immediate boost to GDP per capita by adding to the human capital stock of the receiving economy, might it also reduce the number of 'good jobs', i.e. those with training, available to indigenous workers? This paper analyzes this issue theoretically and empirically. The theoretical model shows how skilled immigration may affect the sectoral allocation of labor and how it may have a positive or negative effect on the training and social mobility of native born workers. The empirical analysis uses UK data from 2001 to 2018 to show that training rates of UK born workers have declined in a period where immigration has been rising strongly, and have declined significantly more in high wage non-traded sectors. At the sectoral level however this link is much less strong but there is evidence of different effects of skilled immigration across traded and non-traded sectors and evidence that the hiring of UK born workers in high wage non-traded sectors has been negatively affected by skilled immigration, although this effect is not large. Taken together the theoretical and empirical analyses suggest that skilled immigration may have some role in allocating native born workers away from 'good jobs' sectors but it is unlikely to be a major driver of social mobility.

Sprache
Englisch

Erschienen in
Series: CReAM Discussion Paper Series ; No. 07/19

Klassifikation
Wirtschaft
Thema
Immigration
Training
Income Distribution

Ereignis
Geistige Schöpfung
(wer)
Mountford, Andrew
Wadsworth, Jonathan
Ereignis
Veröffentlichung
(wer)
Centre for Research & Analysis of Migration (CReAM), Department of Economics, University College London
(wo)
London
(wann)
2019

Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Mountford, Andrew
  • Wadsworth, Jonathan
  • Centre for Research & Analysis of Migration (CReAM), Department of Economics, University College London

Entstanden

  • 2019

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