What or Who is LEO you might ask?
“Longitudinal Education Outcomes (LEO) data enables us to know how much UK graduates of different subjects at different universities are earning now, either one, three or five years since graduating. It does this by linking up tax, benefits, and student loans data”. (WONKHE beginners guide to LEO).
Yep you read it right, LEO is based on HMRC tax data for ALL UK graduates working or
claiming benefits in the UK, NOT a survey like the long established Destination of leavers survey (DLHE). LEO data brings together information from the Department for Education with employment info, AND benefits & earnings info from the Department for Work and Pensions and Her Majesty’s Revenue and Customs.
What does this experimental data do for us?
The figures allow us to compare variation in employment and earnings according to the subject a graduate studied and by various demographic characteristics (including gender, ethnicity, age, home region and prior attainment). LEO also reports employment outcomes according to the specific institution that a graduate attended. Previously, such granular information was reported only as a snapshot six months after graduation, so we (the sector) welcome this much more long-term and detailed data.
This richer information could prove immensely helpful to prospective students. It should also be very useful for universities (Me as a business intelligence analyst) and our efforts for strategic interventions to support the employability of our students. This data may help tailor the investment in the years ahead and this data will help us target our efforts to where it is needed most.
(paraphrased from various readings and publications – HEFCE/WONKEHE/UUK)
There is no doubt that this development is innovative, but for it to be helpful we do need to recognise the key limitations (and there are a few!). Due to the collection method of tax records, most of the earnings and employment figures published exclude graduates who are self-employed. As per the publication release notes identify, the exclusion of self-assessment data has a particularly large impact on ‘arts’ graduates and, therefore providers focused on delivering art subjects have a larger than average proportion of their graduates are self-employed, therefore provider level and subject level Median salaries may skew the real sector picture.
In addition, the data excludes graduates who are working abroad. Neither can they account for whether a graduate is in full- or part-time work, or for the region in which a graduate currently works. All these limitations mean those in well-paid part-time work could appear to be earning very little, providers operating in challenging local economic conditions could appear to produce graduates with below average employment outcomes even if their graduates’ employment rate is substantially higher than the regional average. – detail detail detail.
Frankly LEO does to some extent, raise more questions than it answers at this point in time, but as development goes, experimental and innovative it certainly is!
#VisualisingHE hope to provide an intro to the dataset and hopefully glean some insights into what the data may tell us given the numerous caveats and limitations the data comes laden with.
By way of an introduction to the dataset, please find below a very simple (and slightly tongue in cheek) #Coffeetableviz that presents the Median Annual Earnings 1, 3 and 5 years after graduating per subject area of study.
If you wish to see the absolute figures (in tooltips) or explore a comparison of Female | Male or Female+Male pay differences between subject areas, take a look at the interactive viz: Are your earnings focused?
Are you earnings focused – Takeaway
The TOP5 earning subjects area are:
- Medicine and Dentistry
- Veterinary Sciences
- Engineering & Technology
- Mathematical Sciences
Question: If I were to do it all again would I follow my love for the Arts, or my wallet and choose a subject more financially rewarding like the TOP5?
If I’m honest, I think today you go to university for a different reason I did some ‘X’ years ago, and rightly or wrongly I think i chose to read a degree for LOVE not entirely focused on post degree EARNINGS. However with fees being what they are I don’t think students have that luxury any more, graduate destinations and earnings are at the forefront of applicants minds before even setting through the door of HE.
What would you do and would you do it differently second time over?
Below is an exploratory viz again focusing on the Median annual earnings 1, 3 and 5 years after graduating created by Stephen encouraging you to filter the visual by gender and by provider. The viz provides you with a clear overview of the subject level earnings of graduates compared to the sector median earnings, provider by provider 1, 3 and 5 years out. The viz then encourages you to take a delve into ‘a’ subject area plotting the distribution of earnings provider by provider.
I’ve had a lot of fun investigating this viz, curious to know how graduates earnings of various providers perform against sector median salaries and other providers (i.e does it matter what Uni you went to?)
Link to viz: Exploring Annual Earnings in LEO data
Exploring Annual Earnings – Takeaway
- £20,800 – it’s the magic number……Oh yes it is….
“The big number that a top-level analysis of this plethora of data will be compared to is probably £20,800. Why? Because according to the Office for National Statistics, this was the median salary for all 25-29 year olds in work in 2014-15.” – WONKHE winners and losers
Next I specifically wanted to take a peek into the gender pay gap debate and understand how this dataset would help display the problem, here is what the data shows:
For a tinker with the interactive viz (and much clearer look at the image): The Gender Pay GAP
Pay Gap – Takeaway
- Female earnings fall short of Male earnings in 83% (19/23) of subject areas (focusing on 5 years after graduating).
- 3 years after graduating Females Median salary falls short of males earnings in 74% (17/23) of subject areas
- 1 year after graduation this figure is less noticeable but still lower than Male earnings in 70% (16/23) of subject areas.
- It’s not all bad news, in the subjects of Economics, Mathematics and Mass Communications & Documentations Females consistently outperform Males in graduate earnings in all three assessed years after graduating (1, 3 & 5).
Hope you have found this intro to LEO useful. keep #VisualisingHE in your sights for our next foray into HE public datasets.
Sector discussions and links to general reading on LEO
- A beginner’s guide to Longitudinal Education Outcomes (LEO) data
- Digesting the Longitudinal Educational Outcome data
- Why is there such a large gender pay gap for graduates?
- Prior attainment, gender gaps and other lessons from LEO
- Graduate outcomes for all subjects by university (June 17)
- Graduate outcomes, by degree subject and university (December 16)