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Dropping out of post-compulsory education in the UK: an analysis of determinants and outcomes


We analyse the decision to drop out of post-compulsory education over the period 1985–1994 using data from the Youth Cohort Surveys. We show that the dropout rate declined between 1985 and 1994, in spite of the rising participation rate in education, but is still substantial. Dropping out is more or less constant over the period of study, though the risk of dropout does vary with young people’s prior attainment, ethnicity, family background and the state of the labour market. The course of study has a substantial effect on the risk of dropout.

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  1. Our data refer to England and Wales where young people complete their compulsory schooling at the age of 16, and may then proceed to a period of continued education, typically up to the age of 18, before entrance to the labour market or to university. The period of education between 16 and 18 is voluntary and is referred to throughout this paper as post-compulsory education.

  2. The Local Authority District is regarded in this study as a self-contained labour market because young people tend to be less geographically mobile than adults and they are therefore likely to respond to ‘local’ labour market conditions.

  3. In 1984, the staying on rate was comparatively low with only 41% of all 16 year olds entering post-compulsory education, whereas by 1994 this figure had risen to 71%.

  4. A potential limitation of the method we adopt is that the effects of unobservables are assumed to be uncorrelated with the observed covariates.

  5. As an aside, it is worth noting that the previous literature has typically estimated cross-sectional binary choice models of the decision to drop out, conditional on having stayed on. The few longitudinal models of dropout in higher education do not take into consideration the initial decision made by the individual to enter university.

  6. An alternative approach that was recently applied to an investigation of dropouts by Jakobsen and Rosholm (2003) is the screening/signaling model (e.g. Spence 1974).

  7. In 1997, 11 states required the youth to attend school until age 18 (National Center for Education Statistics 1998).

  8. YCS2–YCS6 are the only versions where sample members complete an annual survey. In YCS7–YCS9, respondents complete a retrospective diary covering a 2 year period (i.e. for the period of 16–18), which may exacerbate the problem of recall bias. Since this is the period in which young people pursue post-compulsory education, we decided not to use the more recent data. In addition, YCS10–YCS11 had only one sweep at the time of going to press, which means that it is not useful for our purposes.

  9. Specifically, young people are sent a postal questionnaire, which they are asked to complete and return.

  10. There is some concern about the quality of the retrospective diary information contained in the YCS, which may lead to measurement error in the dependent variable. To reduce the likelihood of measurement error we carefully examined the diary information and made the following assumptions. First, if a young person in post-compulsory education indicated that they had a spell of employment, for instance, between two spells of education, then the spell of employment was recoded to education. This occurred most frequently in the Christmas and Easter holiday period, which implies that the employment spell referred to a casual job. Since the young person returned to education, it is safe to assume that their main activity is still as a student. Second, the imposition of two conditions for a young person to be a dropout (see the text above) also reduces measurement error because young people whose diaries are inaccurate but nevertheless sit for the examination are counted as graduates. Of course, we cannot completely rule out the presence of measurement error in our data, however, this is unlikely to be any worse than the measurement error associated with many other longitudinal datasets, such as the BHPS and the NCDS, which are routinely used by researchers to estimate models similar to those estimated in this paper.

  11. ‘High’ academic education refers to young people taking A levels, the traditional route to higher education, whereas ‘low’ refers to young people repeating their GCSE exams (see footnote 8). Similarly, a ‘high’ vocational education, for instance, refers to BTEC National Diplomas in Business Studies, Science, Engineering, which are ‘equivalent’ to A levels, and ‘low’ vocational education includes basic business, typing and similar courses.

  12. For the single risk model, r i  = 1 and r i  = 2 are combined but the econometric methods are identical to the competing risks model discussed in the text.

  13. In the econometric analysis, since a young person cannot by definition be observed to start and quit post-compulsory education in the same month, we combine the months October and November thereby giving a total of 20 time periods.

  14. The baseline hazard is estimated non-parametrically, which means that the hazard can vary freely over time but is assumed to be constant within each time interval. This is equivalent to assuming an exponential survival in each time interval.

  15. The marginal effects are actually computed at \(\gamma = 12\) for females and \(\gamma = 10\) for males.

  16. Students sit for the General Certificate of Secondary Examination (GCSE) at the end of their compulsory schooling, typically at age 16, in up to ten subjects dictated by the National Curriculum. The grades that could be achieved at the time of this study were A (high) through to G (low).

  17. We also estimated a model with interaction effects between academic attainment and the local unemployment rate to see if their response to labour market conditions differed. For males, there was no statistically significant effect and for females, the model would not converge because of the small number of observations in some categories.

  18. This version of the paper is available at the following web address:


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We would like to thank two anonymous referees and the editor of the journal for the helpful comments, which have greatly improved our paper. The authors are also grateful to the Social and Community Planning Research for providing the Youth Cohort Surveys and for the information that enabled us to combine several data sets. We are grateful to Anna Vignoles and the participants at the Human Resource Economics Study Group (Lancaster) for the helpful comments, particularly to Rob Crouchley, Geraint Johnes, Anh Nguyen, Dave Stott and Jim Taylor. The authors accept responsibility for the remaining errors and omissions.

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Correspondence to Steve Bradley.

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Bradley, S., Lenton, P. Dropping out of post-compulsory education in the UK: an analysis of determinants and outcomes. J Popul Econ 20, 299–328 (2007).

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  • Dropouts
  • Competing risks
  • Employment

JEL Classification

  • I21
  • J24