September 2019 update: The Office for Students has introduced an experimental but more sophisticated successor to POLAR called TUNDRA. Wonkhe have published a helpful summary with some of the limitations and context. The links below may no longer work following the closure of HEFCE, but POLAR can be found on the same page as TUNDRA.
I’ve been working with some of the Higher Education Funding Council for England’s (HEFCE) datasets on young people’s participation in higher education, and was thinking how useful and interesting this information would be on a global scale.
There are two key maps. The first is Participation of Local Areas (POLAR); see an example for Brighton, UK, above. The POLAR classification looks at how likely young people are to participate in higher education across the UK and shows how this varies by area. Areas in red are those that have the lowest participation rates while areas in dark blue are those that have the highest participation rates. Yellow is in the middle.
The second is more nuanced and perhaps more useful. It shows gaps in participation – the proportion of young people participating in higher education compared to that expected given GCSE-level attainment and ethnic profile. In the example above, also for Brighton, in those areas shaded red young participation is much lower than expected. In those areas shaded blue, participation is much higher than expected. In those areas shaded white, young participation rates are as expected.
Whilst higher education participation data is probably available at country or broader sub-country level for most countries, the real value is in the hyper-local detail – in this case at the level of electoral wards, of which there are 7,669 in England alone. The potential insights are obvious from the discrepancies in colour between neighbouring wards. I’d be fascinated to see other countries divided into 7,500 sections and each section scored for higher education participation – would countries with similar inequality levels or income levels look similar? How do urban and rural areas compare across countries? How about those areas in close proximity to a cluster of universities or a major transport network? Or those close to a border of a wealthier neighbour?
As an aside, there’s a very good podcast on data and development on the Development Drums podcast.