A first look at the latest LEO data

The Department for Education has released a surprise update to the Longitudinal Educational Outcomes data (LEO), making it the fifth experimental release to date. With lots of new breakdowns and additional international stats, there is lots of data to crunch.

Here’s upper quartile, median quartile, and lower quartile salary data for all students by year of graduation, filterable by gender. As you can see, there are no unexpected jumps, and the data continues rather than alters trends we already knew about.

But there are some new aspects to the way data is broken down that are of interest.

• We’ve got an additional tax year from 2015/16 included, which means the data now goes back 11 years.
• There’s more data on POLAR3 quintiles, prior attainment tariff points, and free school meal (FSM) students.
• For the first time, we can separate out graduates who lived away from home, from those who lived at home while studying.
• Modes of study now include sandwich courses.
• There’s much more data on international students. We plan to look at this data separately as it’s huge.
• But crucially, for this release, we don’t have data broken down by institution.

However, with more data and better breakdowns comes more caveats.

• The only intersectional group data available is with gender, so drawing evidenced conclusions from the data remains difficult. We don’t know, for instance, how closely FSM graduates correlate with graduates in POLAR3 quintile 1 (with the lowest HE participation) – so we can’t make a judgement as to which of these aspects has a greater effect on earnings.
• When DfE released the institutional-level LEO data they removed data from 10 years ago due to a limited ability to measure current activity. We have a 10th (and 11th!) year of data here, so clearly the data is good enough for sector-level aggregation – but data may not be at as high a quality there than in more recent years.
• Although the addition of international student data, broken down by country and subject, will be welcomed by many, it is incredibly limited. A large proportion of the data is missing, so its very difficult to meaningfully draw any conclusions.
• We can’t follow cohorts – the 2015/16 data looks at different graduating cohorts to the 2014/15 data. This preserves the idea of looking 1, 3, 5, and 10 years on, but we can’t see how individual salaries change year-on-year – so we can’t test hypotheses that see graduates progressing more quickly due to a given attribute.

We’ve got all the UK data for you to look at here:

For now, this is just a simple table showing median income – you can filter to view male/female splits. Each separate factor is shown as a table, and you can use the tabs at the top to scroll through.

Six lessons to learn from this LEO release

As above there are no huge surprises – as we’ve reported before, LEO data shows that a plethora of different factors influences earning potential, particularly student background and environmental attributes, which are all outside the control of HE institutions.

1. Prior attainment is more important than ever

Previously, we’ve reported that the LEO data showed us that your earnings potential was more likely to be indicated by prior attainment. The greater breakdown provided in this data further emphasises that point, with earning outcomes varying by over £10,000 between the prior attainment categories.

2. South/East of England is the place to be!

Outside of London, students whose home region is either the East of England or the South East, have the highest earning outcomes. The data further highlights north-south regional differences in outcomes.

3. POLAR is trending

As you’d expect, students from areas with higher historic HE participation earn more than their peers. This is a consistent trend across all cohorts – and suggests that:

• Socio-economic background remains a great predictor of earnings;
• POLAR3 is a good proxy for this.

4. Free school meals unknowns

As you’d expect, those who were on free school meals have lower earning outcomes across the cohorts. Interestingly, those whose access to free meals is not known, have the highest earning outcomes across the cohort – could this be linked to public school attendance, where such data is often not available?

5. Part-time and (particularly) sandwich courses are faring better than full-time

Unsurprisingly, sandwich course students significantly outearn other modes of study. With a year in industry, these students enter the graduate market with more work experience (and more contacts) than their counterparts. However, it appears that the more recent cohort of part-time students is also, on average, earning more than full-time students. Due to the recent funding changes to part-time education, this is likely to reflect a more mature, financially well-off (and smaller) cohort.

6. Home students have lower earning outcomes

Students who live elsewhere (i.e. not at home) during their studies have greater earning outcomes. Many students who live at home do so for financial reasons, so this could further reflect the impact of existing wealth on earning outcomes.