The influence of socioeconomic status on changes in young people’s expectations of applying to university

(2017) - Jake Anders

Journal: Oxford Review of Education
Link:: https://www.tandfonline.com/doi/full/10.1080/03054985.2017.1329722
DOI:: 10.1080/03054985.2017.1329722
Links::
Tags:: #paper #Methods #DurationModels
Cite Key:: [@andersInfluenceSocioeconomicStatus2017]

Abstract

A much larger proportion of English 14-year-olds expect to apply to university than ultimately make an application by age 21, but the proportion expecting to apply falls from age 14 onwards. In order to assess the role of socioeconomic status in explaining changes in expectations, this paper applies duration modelling techniques to the Longitudinal Study of Young People in England, analysing transitions in young people’s expectations both from being ‘likely to apply’ to being ‘unlikely to apply’ and vice versa. Young people’s socioeconomic background has a significant association with changes in expectations, even after controlling for prior academic attainment and other potentially confounding factors; in addition, young people’s backgrounds affect their responsiveness to new evidence on academic attainment at age 16. This suggests more could usefully be done to maintain the educational expectations of academically able young people from less advantaged families, especially providing guidance on how to view new academic results.

Notes

“A much larger proportion of English 14-year-olds expect to apply to university than ultimately make an application by age 21, but the proportion expecting to apply falls from age 14 onwards” (Anders, 2017, p. 381)

“There is a large socioeconomic gradient in university attendance in England. Much of this gap can be explained by differences in academic achievement that emerge long before the point at which young people apply to university (Chowdry et al., 2013). However, there remains a socioeconomic gradient in university application (Anders, 2012a), despite the fact that a larger proportion of English 14-year-olds from disadvantaged backgrounds expect to apply to university than the overall proportion who have ultimately done so by age 21 (Anders & Micklewright, 2015, pp. 42–43)” (Anders, 2017, p. 381)

“his paper applies duration modelling to analyse changes in young people’s expectations.” (Anders, 2017, p. 384)

“This is dichotomised into a distinction between ‘likely’ (‘very likely’ or ‘fairly likely’) or ‘unlikely’ (‘not very likely’ or ‘not at all likely’) to apply to universit” (Anders, 2017, p. 384) But why?

“Duration modelling is typically employed to analyse transitions between defined states such as employment and unemployment.” (Anders, 2017, p. 384)

“Household income is measured at each wave between 1 and 4. As previous studies have highlighted the particular role of permanent, rather than transitory, income on educational outcomes (Jenkins & Schluter, 2002, p. 2), an approximation of the household’s equivalised ‘permanent’ income is made by averaging across these four measures and dividing by the square root of household size” (Anders, 2017, p. 387)

“Parental education is likely to play a role in the formation of young people’s educational expectations (Ganzach, 2000)” (Anders, 2017, p. 387)

“Social class is also a key element of an individual’s SES (Goldthorpe & McKnight, 2004). In particular, as ‘young people (and their families) have, as their major educational goal, the acquisition of a level of education that will allow them to attain a class position at least as good as that of their family of origin’ (Breen & Yaish, 2006, p. 232)” (Anders, 2017, p. 387) Breen paper is pretty good

“(NS-SEC), designed to capture social class differences between occupational types (Rose & Pevalin, 2001).” (Anders, 2017, p. 387)

“The above measures are combined using principal component analysis with a polychoric correlation matrix (Kolenikov & Angeles, 2009; Olsson, 1979) to construct a single SES index. This explains roughly three quarters of the variation in the three individual measures, but provides a broader measure of family circumstances than any one measure would provid” (Anders, 2017, p. 387)

“a duration modelling approach is able to take into account‘censoring’, where the start and/or end points of a spell are not observed, meaning that the true length of the spell is unknown.” (Anders, 2017, p. 388)

“ot observing the end of a spell is referred to as‘right censoring’, which occurs in the final report for all individuals, whether this is due to the end of the period under analysis (at age 17) or earlier as a result of attrition” (Anders, 2017, p. 388)

“SES is dichotomised into ‘high’ (comprising the top 40% of the distribution of the SES index) and ‘low’ (comprising the bottom 60% of the distribution), although the results are robust to splitting at different points.” (Anders, 2017, p. 388)

“Figure 3.  Probability that an individual who reports being ‘likely to apply’ at age 14 has not moved to” (Anders, 2017, p. 389)

“omparing Figure 4 with Figure 3, it is clear that the differences in rates of transition from being ‘unlikely’ to being ‘likely’ by SES are markedly smaller than for the transition in the opposite direction:” (Anders, 2017, p. 390)

“A regression-based duration model may be estimated as a binary choice model applied to a dataset organised such that there is one observation for each time point that each individual is‘at risk’of making the relevant transition (Jenkins, 1995). This exposition concentrates on the transition from‘likely to apply’to‘unlikely to apply’to avoid unnecessary duplication; it is easy to see how the model is modified for the transition from ‘unlikely to apply’to ‘likely to apply’.” (Anders, 2017, p. 391)

“In summary, there continues to be a strong relationship between young people’s socioeconomic background and their probability of continuing to report being ‘likely to apply’ to university” (Anders, 2017, p. 394)

“iven the likely endogeneity of performance at age 16, estimates from M3 are a better guide to the ‘conditional’ association between SES and the probability of transition than those from M4, although there are only slight changes in practice.” (Anders, 2017, p. 394)

“The least advantaged fifth of young people have more than twice the probability of switching from reporting being‘likely to apply’to reporting being‘unlikely to apply’as the most advantaged fifth, conditional on prior attainment. Conversely, the most advantaged fifth of young people have more than twice the probability of changing from reporting being‘unlikely to apply’to reporting being‘likely to apply’as the most advantaged fifth, again conditional on prior attainment.” (Anders, 2017, p. 398)

“There is also evidence that young people from differing SES backgrounds react differently to new information on their academic attainment at age 16. This differential is also asymmetric, helping to explain the growth in inequality of expectations: more advantaged young people are significantly more responsive to improved results in raising their expectations” (Anders, 2017, p. 399)