What is the relationship, if any, between key indicators of wealth and the propensity to take up a student loan?

What is the relationship, if any, between key indicators of wealth and the propensity to take up a student loan?

What are the important debt avoidance mechanisms that students use, and do these apply equally to maintenance and to tuition loans?

Data and indicators

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To answer these questions, we use the first seven waves of Next Steps (formerly known as the Longitudinal Study of Young People in England (LSYPE)), which follows the lives of English people born in 19891990. The survey started in 2004, when the respondents were aged 1314, was undertaken annually until 2010. Footnote 5 It collected information on parental economic and social background, academic attainment, health and wellbeing, family life, education and employment. It also assesses respondents’ attitudes, including towards debt.

Information from early waves of the survey are exploited, especially the seventh wave undertaken between when respondents were aged 19 or 20 and therefore likely to have started higher education. The initial sample selected was nationally representative of young people in England, with an over-sample of schools in deprived areas. By wave 7, nearly half of the original sample of 15,770 had dropped out of the study, reducing the sample at wave 7 to 8682. The response rate at wave 7 was 90%. Survey weights, which account for attrition (Department of Education 2011), and sampling strata are used in this paper to keep the representativeness of the original sample. With less than 10% missing data on any individual variable included in the models, listwise deletion is used throughout.

The sample used in this paper consists of all respondents who had enrolled in higher education by wave 7 (N = 4368) and therefore had , 74% of first-year undergraduates in England were aged 20 and under (Higher Education Statistics Agency, 2018). Our analysis, therefore, captures the vast majority of respondents who entered higher education. Part-time students are excluded too because they were ineligible for loans. Nevertheless, unlike any alternative dataset, Next Steps provides very rich information on students’ socio-economic backgrounds, attitudes towards debt, and opportunities to compare borrowers and non-borrowers-advantages that make it stand out and enable us to answer our research questions.

At waves 6 and 7 of Next Steps, cohort members enrolled in higher education institutions were asked how they financed their studies. The variables tied to student loan debt in both waves are combined to obtain an indicator of whether the respondents took a student loan. In wave 7, the distinction is made between tuition fee and maintenance loans.

Nevertheless, mature students are excluded from this analysis but their relationship to student loans is usually different and, generally, they are less likely to rely on loans

Following practice elsewhere (e.g. Henderson et al. 2018), we use variables adopting the family as the unit of analysis for the socio-economic background. Therefore, parental social class, family’s highest educational level and housing tenure are used as indicators of parental wealth. They are taken from wave 4 (when respondents were aged 16), the latest wave at which parental information is available. In England, social class is based on occupational types and we adopt this system to categorise parental social class (Office for National Statistics n.d.). To these variables, we have added two indicators from wave 1: the income deprivation affecting children index’ (IDACI) and whether the respondent was attending private or state school. The latter is, specifically in Britain, a useful indirect proxy for wealth since private school fees http://getbadcreditloan.com/payday-loans-ma/bridgewater are especially high and, for the most part, can only be afforded by families with considerable wealth (Henseke et al. 2018). We also include an indicator, created by Anders (2012), of permanent equivalised income based on family income measures in the first four waves of the survey.