The big moment in this morning’s annual report from Scotland’s Fair Access Commissioner – the first in John McKendrick’s tenure – concerns area-based measures of deprivation.

The Scottish Index of Multiple Deprivation (SIMD – usually SIMD2020 these days though there have been iterations in 2012 and 2016) identifies relative deprivation in small areas (these are “Data Zones”, broadly analogous to the Lower layer Super Output Areas – LSOAs – used in England and Wales).

In previous years universities in Scotland have been given access targets based on the proportion of undergraduate intake from SIMD quintile 1 – the last Peter Scott report recommended replacing these targets with a basket of indicators defined by the institution. McKendrick takes the middle ground here, ditching the targets but still requiring an increase in admissions from SIMD quintile 1 from all providers.

The full recommendation (recommendation 4) is to:

Withdraw the SIMD Institutional target but introduce a commitment from each HEI to take action to increase the proportion of SIMD20 among its entrants or, if this is demonstrably not possible without adverse consequences, to match the highest proportion and number of SIMD20 entrants that it achieved since 2013-14.

This addresses a number of common complaints about the way these targets have been set (the most common one being that there’s not many SIMD quintile 1 areas in rural Scotland, as we shall see), from a number of providers, but also makes solid data-definition sense.

To be clear, the focus on socio-economic disadvantage and SIMD in particular remains. It will still be used to set a national target (though with deciles rather than quintiles), and providers may well choose to use SIMD as part of a basket of metrics and indicators used to report on their own progress. The target in FE has traditionally been SIMD10, and the Commissioner recommends harmonisation, in that he should gain oversight over access to all of tertiary education.

What is SIMD and why does it matter

SIMD is a single index drawing on more than 30 indicators of deprivation (including stuff like income, housing conditions, skill levels, public transport, and educational or employment opportunities). These are combined to create an overall ranking of data zones – the most deprived at the top, and the least deprived at number 6,976). Classically, these are presented as quintiles – with the attention of universities in Scotland being drawn, as above, to the twenty percent most deprived areas (SIMD quintile 1).

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Area-based measures of disadvantage are very useful in planning terms, or to understand the character of a small area, but it is entirely possible for a comfortably off family to reside in a SIMD quintile 1 area, or for tiny pockets in SIMD quintile 5 areas to experience extreme deprivation. It’s generally reckoned to work better in urban areas – each Data Zone has around the same number of people (somewhere between 500 and 1000), and although attempts are made to make these areas homogenous this is difficult for a larger rural area.

Clearly the ideal might be to use indicators relating to individuals (for example has a person benefited from free school meals, or is family income below a set level). Indeed, the report recommends that the government prepares to move to a greater emphasis on measures like this from 2026. This will come alongside a gradual shift to (dare we say?) an England-style examination of the student experience and outcomes of those who enter university from a disadvantaged background, and a move beyond the default full time undergraduate routes. This is a big data ask, so there are many recommendations focused on preparing the disaggregated datasets that would be used to support providers in doing this.

But my favourite recommendation along these lines has to be recommendation 19:

Stakeholders should explore the prospects for introducing a single student identifier to improve tracking and to facilitate more robust evaluation of the impact of fair access activity

It’s a Wonkhe favourite, but the ability to track the impact of disadvantage and the efficacy of measures used to address these impacts from primary school through to postgraduate study would be a game-changer.