The paradoxical role of social class background in the educational and labour market outcomes of the children of immigrants in the UK.

  • Published In: British Journal of Sociology, 2023, v. 74, n. 4. P. 733 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zuccotti, Carolina V.; Platt, Lucinda 3 of 3

Abstract

Despite predominantly lower social class origins, the second generation of established immigrant groups in the UK are now attaining high levels of education. However, they continue to experience poorer labour market outcomes than the majority population. These worse outcomes are often attributed in part to their disadvantaged origins, which do not, by contrast, appear to constrain their educational success. This paper engages with this paradox. We discuss potential mechanisms for second‐generation educational success and how far we might expect these to be replicated in labour market outcomes. We substantiate our discussion with new empirical analysis. Drawing on a unique longitudinal study of England and Wales spanning 40 years and encompassing one per cent of the population, we present evidence on the educational and labour market outcomes of the second generation of four groups of immigrants and the white British majority, controlling for multiple measures of social origins. We demonstrate that second‐generation men and women's educational advantage is only partially reflected in the labour market. We reflect on the implications of our findings for future research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:British Journal of Sociology. 2023/09, Vol. 74, Issue 4, p733
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0007-1315
  • DOI:10.1111/1468-4446.13047
  • Accession Number:171349192
  • Copyright Statement:Copyright of British Journal of Sociology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.