@dutaSocialClassInequalities2018

(2018) - Adriana Duta, Cristina Iannelli

Journal: Social Sciences
Link:: http://www.mdpi.com/2076-0760/7/10/201
DOI:: 10.3390/socsci7100201
Links::
Tags:: #paper #Transition #school-to-work #LabourMarket #SocialClass
Cite Key:: [@dutaSocialClassInequalities2018]

Abstract

This paper provides new important evidence on the spatial dimension of social class inequalities in graduates’ labour market outcomes, an aspect largely overlooked within the existing literature. Using data from the HESA Destinations of Leavers from Higher Education Early and Longitudinal Survey (DLHE) for the 2008/09 graduate cohort and applying multilevel logistic regression models, we investigate whether and the extent to which social class inequalities in graduates’ occupational outcomes vary depending on the job opportunities in the geographical area where they find employment. By examining different macro-level indicators, we find wider social inequalities by parental social class in areas with fewer opportunities in high professional and managerial occupations and smaller inequalities in areas with more opportunities. Interestingly, this pattern applies only to graduates who moved away from their place of origin. We interpret this finding as the result of selective migration, that is, areas with more opportunities attract the better-qualified graduates irrespective of their social origin. Finally, graduates’ HE experiences—in particular, their field of study—and sector of employment explain most of the social class gap in areas with fewer job opportunities.

Notes

“By examining different macro-level indicators, we find wider social inequalities by parental social class in areas with fewer opportunities in high professional and managerial occupations and smaller inequalities in areas with more opportunities. Interestingly, this pattern applies only to graduates who moved away from their place of origin.” (Duta and Iannelli, 2018, p. 1)

“Finally, graduates’ HE experiences—in particular, their field of study—and sector of employment explain most of the social class gap in areas with fewer job opportunities.” (Duta and Iannelli, 2018, p. 1)

“Graduates from different social origins tend to differ in their HE experiences–such as the prestige of the HE institution attended (Iannelli et al. 2016; Sullivan et al. 2014; Boliver 2011), chosen field of study (Iannelli et al. 2018; Laurison and Friedman 2016; Van de Werfhorst et al. 2003) and class of degree achieved (Crawford 2014; Crawford et al. 2016)—but also in earlier cognitive skills (Sullivan et al. 2017).” (Duta and Iannelli, 2018, p. 2)

“Given the larger supply of university graduates and the weaker links between HE qualifications and labour market outcomes in the UK, employers may need to rely on factors other than credentials (e.g., soft skills; candidates’ social and cultural capital) to select their future employees. This ultimately favours people from higher social origins (Jackson et al. 2005).” (Duta and Iannelli, 2018, p. 2)

“Recent policy reports have stressed the fact that regional characteristics can enable or hamper social mobility (Friedman et al. 2017; Social Mobility Commission 2017) and hence, those coming from less privileged backgrounds and who live in areas with fewer opportunities can suffer a double disadvantage, not only related to their family of origin but also to the geographic area.” (Duta and Iannelli, 2018, p. 3)

“Based on the social mobility research (e.g., Goldthorpe 1987; Payne 1987; Gordon et al. 1988; Erikson and Goldthorpe 1992), we could argue that social class inequalities tend to reproduce themselves irrespective of the economic conditions of a place. Thus, better regional job opportunities may not translate into a more equal distribution of professional jobs (as was the case in the 1970s and 1980s when professional and managerial jobs expanded).” (Duta and Iannelli, 2018, p. 4)

“On the other hand, following the ‘escalator’ regions literature (e.g., Fielding 1992; Van Ham et al. 2012; Champion et al. 2013), we could envisage an alternative scenario.” (Duta and Iannelli, 2018, p. 4)

“In the statistical modelling, we adopt a multilevel framework with graduates nested into NUTS3 regions of the area of employment7 to analyse the variance in graduates’ destinations at both individual (level 1) and area level (level 2) and to disentangle the effect of macro- and micro- level factors on the outcome.” (Duta and Iannelli, 2018, p. 7)

“n line with other studies (e.g., Gibbons et al. 2010), we found that the overall area-level variation in the chances of getting a top-level job and avoiding a low-level job is low compared to the individual-level variation. However, area characteristics such as the percentage of professional jobs available and” (Duta and Iannelli, 2018, p. 14)