Using Census data to generate a UK-wide measure of disadvantage - measures of widening participation
2. Widening participation: What measures are or could be available?
Chapter 7 of the 1997 Dearing Report highlighted that ‘increasing participation in higher education is a necessary and desirable objective of national policy over the next 20 years. This must be accompanied by the objective of reducing the disparities in participation in higher education between groups’. While the likely need for performance indicators in the higher education sector dates back to the 1985 Jarratt Report, it was shortly after the publication of the Dearing Report that the various funding councils of the UK began the development of suitable UK Performance Indicators (UKPIs) for the sector, with one of these revolving around access to higher education among under-represented groups. The majority of prospective undergraduate students will enter higher education following the submission of an application form to UCAS. As part of this process, individuals aged 18 to 20 are asked an optional question on the job title of their highest earning parent (with those who are 21 or above required to supply their own employment details). This information is then used to group workers into various categories based on the skills and qualifications demanded by the role, which are utilised to indicate one’s socioeconomic position according to the National Statistics Socio-economic Classification (NSSEC). Specifically, UCAS use the job title provided by each responding individual to assign them a four-digit unit group code of the Standard Occupational Classification (SOC), which is subsequently mapped to one of the fourteen functional or three residual operational categories of NSSEC. This is then collapsed into the eight-class version of NSSEC, before being supplied to HESA and stored within the Student record. From the outset, disparities by socioeconomic status were monitored through the production of UKPIs that examined the entry rates of those aged 18 to 20 whose (highest earning) parent was based in groups 4 to 7 of the eight-class categorisation of NSSEC.
Quality concerns with this field may arise through a number of channels. Firstly, there are different ways in which employment information can be collected and used to derive the NSSEC field. As noted by the Office for National Statistics (ONS), the procedure currently adopted by UCAS (known as the simplified method) will not lead to the same level of accuracy in coding as the full approach. Furthermore, as the data is self-reported, there is the possibility that either respondents are unable to correctly recall this information regarding another individual or are unwilling to supply an answer. While the vast majority of full-time undergraduates aged between 18 and 20 on entry will have gone through the UCAS system, there will be a small proportion who will have directly applied to their provider (e.g. through submission of a record of prior acceptance or due to their chosen place of study not utilising the UCAS system route for their admission process). For those who enrol through such alternative pathways, we cannot be certain that they are asked the same question (if at all) on parental occupation. Such concerns led to the NSSEC field being withdrawn from use in the production of the UKPIs from 2017 onwards.
The UCAS application form also provides prospective students with the option to give details on whether or not their (step-) parents or guardians hold higher education qualifications. Data on this variable is then transferred to HESA on an annual basis. During the review of the UKPIs in 2007, this field was considered as a possible indicator of a student’s background, but was ultimately not deemed suitable for use in the UKPI publications. Reasons for this included the fact that this variable has the same potential drawbacks as the information collected on parental occupation that we discuss above (aside from the NSSEC coding matter, which is specific to that variable).
Our own recent investigations into the quality of the parental education and occupation fields using linked HESA-Census 2011 data did, however, alleviate some of the worries around the potential accuracy of the information. That being said, we did note that for both variables, around 15% of students are still categorised as having ‘missing information’ in the most recent academic years we considered, with there also being some evidence to suggest that ‘missing’ data could be more of a concern among those from disadvantaged backgrounds.
As with NSSEC, state school marker was a variable utilised in the very first UKPI publication, given those from independent schools are disproportionately represented in higher education. The marker continued to be used in the latest dissemination of the UKPIs, though it too suffers from limitations. For example, the variable is generated based on the last school attended by the student, which may be less helpful than information on schooling up to the age of 16. In Northern Ireland, as fee paying schools will receive state funding, very few schools are classified as private, rendering the measure ineffective in this part of the UK. Previous research by Gorard et al. (2017) has also noted that such a binary distinction fails to consider the level of heterogeneity within state and independent schools. For example, within the state sector, there are a number of selective schools where attainment is high and levels of disadvantage among its pupils are quite low.
Following a review of the UKPIs in 2007, the original postcode-based measure (used along with NSSEC and state school marker) was replaced with the introduction of POLAR. For the most recent iteration of this variable (POLAR4), this was derived by dividing the proportion of young people who enter higher education by age 18 or 19 in a particular area [Middle layer Super Output Areas (MSOAs) for England and Wales, Intermediate Zones (IZs) in Scotland and Super Output Areas (SOAs) in Northern Ireland] by the proportion of young people in that locality. Data from the academic years 2009/10 to 2013/14 was utilised to calculate this. Areas are then ranked before being placed into quintiles, with quintile 1 defining the lowest participation neighbourhoods. As the OfS highlight, this is not a measure of socioeconomic disadvantage of an individual or an area (though the initial research carried out on POLAR did indicate a positive correlation between low participation areas and the extent of socioeconomic disadvantage in such localities within England). Consequently, they encourage those trying to evaluate the background of a (prospective) student to do so using additional information alongside POLAR. Rather, this measure provides an insight into those parts of the country where participation remains low over time and can thus offer providers useful data to support the development of their outreach programmes when trying to increase participation among under-represented groups. Indeed, many providers in England do use POLAR in the implementation of their access and participation plans. Alongside state school marker, this has remained a variable that is drawn upon in the production of the widening participation UKPIs, though due to the high levels of participation in certain regions of the UK (particularly Scotland and London), few students from these parts of the country will be identified as being in the lowest quintile of the marker. It should be noted that POLAR data has been suppressed for Scotland in the final widening participation UKPI publications since 2007/08. One further point to make about the POLAR measure is that due to it being based on participation levels among those aged 18 or 19 in an area, it is less applicable when one wishes to undertake an analysis of mature students in higher education and hence an alternative approach is needed when investigating this group.
With some of the above measures having their limitations in particular parts of the UK, we have seen increasing use of country-specific measures in supporting the widening participation agenda, such as the Indices of Multiple Deprivation. For example, back in 2016, the Commission on Widening Access in Scotland set a target for 16 percent of first degree entrants to Scottish universities to be from the poorest 20% of areas of the country, based on the Scottish Index of Multiple Deprivation (SIMD). HEFCW also utilise the equivalent Welsh measure (WIMD) in evaluating access to higher education participation. In each of the four nations, the final index is derived by bringing together several domains (while these vary slightly by country, common dimensions include income, education, health, employment and crime). Each of these elements is then assigned its own weight in the generation of the final composite variable. Areas (of various size) are then ranked according to the extent of deprivation they exhibit. In England and Wales, lower layer super output areas (LSOAs) are utilised in the construction of the index, which average approximately 1,500 inhabitants. Data zones are the geography level employed in Scotland, which have populations of 500 to 1,000, while in Northern Ireland, SOAs will generally consist of around 2,000 people. Areas are partitioned into deciles or quintiles, through which those living in the most deprived parts of the country are identified. Aside from not being UK-wide, there are other limitations associated with IMD in each country. For example, in England and Scotland, concerns have been raised on the extent to which this measure can adequately capture deprivation in more rural parts of the country.
The limitations of existing individual and area-level variables have led to continued research in this field to understand whether better measures could be made available for the sector to utilise. Both Jerrim (2020) and Gorard et al. (2017) highlight the various advantages of utilising free school meal eligibility as a measure of socioeconomic disadvantage and endorse the use of the number of years an individual has been eligible for free school meals as a suitable individual-level variable to use in the contextualised admissions process. Indeed, UCAS have recently announced that, for English 18 and 19-year old applicants, access will be provided to free school meal status from the 2021 cycle.
However, the widening participation agenda extends beyond the contextual admissions process, as we have alluded to earlier in this paper. Indeed, there are instances where drawing upon measures based on individual-level data is not always possible or practical. For example, providers will often conduct local outreach work within disadvantaged communities (e.g. to try and raise aspirations around education). Furthermore, given current UK policy objectives around equality of opportunity and equitable growth, as well as the role that higher education is expected to play in meeting these aims, it is important for providers to be able to locate those areas that would be most beneficial to support. In these circumstances, area-based measures can be helpful in enabling practitioners to identify the localities where they should prioritise their resources and are thus likely to continue assisting the widening participation agenda.
We depart from previous work in this area as follows. Firstly, while we also construct an area-level measure, we rely on a smaller geographic domain than both POLAR and IMD (regardless of which nation we are analysing), where average population sizes are generally in the hundreds. Moreover, we illustrate how our measure has potential UK-wide applicability, as well as the value it can add alongside existing area-level variables that are used in widening participation policy.
 The 2022 UKPIs will be the last in their current form – see https://www.hesa.ac.uk/blog/19-05-2021/measure-measures for further information.
 See https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2020/soc2020volume3thenationalstatisticssocioeconomicclassificationnssecrebasedonthesoc2020#toc for further information.
 NSSEC class 8 (long-term unemployed or never worked) were not included in the calculation.
 See, for example, section 13 at this webpage https://webarchive.nationalarchives.gov.uk/20160106042025/http://www.ons.gov.uk/ons/guide-method/classifications/current-standard-classifications/soc2010/soc2010-volume-3-ns-sec--rebased-on-soc2010--user-manual/index.html#13
 Please note that the state school marker does not form part of the Welsh widening access agenda.
 See the notes at the following link https://www.hesa.ac.uk/news/01-02-2018/widening-participation-summary.
 More information on this can be found under the ‘Low-participation neighbourhoods (Super Profiles)’ section of this webpage https://www.hesa.ac.uk/data-and-analysis/performance-indicators/definitions
 https://www.hefcw.ac.uk/en/statistics-and-data/postcode-data/. Note that HEFCW are also currently consulting on the methodology for allocating the access and retention premium, with it being proposed that it should be based on WIMD (2019), as opposed to the Communities First programme (discussed later in this report).
 See, for example, https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019, https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/, https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019 and https://www.nisra.gov.uk/publications/nimdm17-results for further information.
 See, for example, https://ocsi.uk/2011/03/24/why-the-imd-is-still-important-in-the-open-data-age/ and https://www.gov.scot/binaries/content/documents/govscot/publications/progress-report/2016/03/blueprint-fairness-final-report-commission-widening-access/documents/00496620-pdf/00496620-pdf/govscot%3Adocument/00496620.pdf
 While variables such as free school meal eligibility and IMD are also incorporated into the Multiple Equality Measure and Association Between Characteristics of Students (ABCS) measures developed by UCAS and OfS respectively, these are not discussed in detail here as they do not solely relate to socioeconomic disadvantage.
 See, for example, this blog by Mark Cover for further discussion around such matters - https://wonkhe.com/blogs/polar-mem-and-equality-you-dont-have-to-choose/.
- Executive summary
- 1. Introduction and policy context
- 2. Widening participation: What measures are available?
- 3. Data
- 4. The derivation of a new measure of disadvantage
- 5. Results
- 6. Further remarks and next steps
- Appendix 1: English domiciled entrants
- Appendix 2: Welsh domiciled entrants
- Appendix 3: Scottish domiciled entrants
- Appendix 4: Northern Irish domiciled entrants