by Tej Nathwani
The 2020s will be a pivotal period in determining the UK’s economic future. That’s the primary message of a recent report published by the Resolution Foundation and Centre for Economic Performance at LSE. While major events such as the pandemic and Brexit have undoubtedly played a part in this, there are also longer-term factors that have contributed to the country reaching this position. Examples noted by the researchers include stagnant productivity levels, large disparities in economic performance between areas and inequalities in our education system.
Naturally, one of the questions being increasingly asked of the UK higher education sector is how it is helping to resolve some of the latter issues. Yet being able to tackle these matters successfully, as well as understand the outcomes from various interventions, requires the provision of suitable data. As the body responsible for the collection and dissemination of information about UK higher education, HESA has a role to play in supplying appropriate variables and statistics to our users that support them in their decision-making. Hence, the past few years have seen us develop new fields designed to be relevant and valuable in meeting the current needs of our customers. Across two separate blogs we will be outlining what these are and the potential value they can deliver. In this first piece we begin with a focus on our work relating to socioeconomic disadvantage.
The uses of data on deprivation in higher education
One of the ways in which providers seek to improve equality of opportunity in education is through outreach activity. These are initiatives that aim to raise aspiration and attainment among those from disadvantaged backgrounds, as well as helping to inform them of the potential benefits that studying for a degree can offer. Area-based data on deprivation will typically be used in two ways. Firstly, as part of the eligibility criteria that an individual must meet to participate (for example, at Surrey). Secondly, it can help providers determine the areas of the country which they believe would be most useful to target given their strategic ambitions (for example, at King’s College London).
The current problem
The most commonly used area-based measure of disadvantage across the sector in each of the four nations is the index formed from the Indices of Deprivation. However, while this is especially effective in capturing deprivation in major urban areas, it is known to be less useful in identifying this in rural locations. For example, Na h-Eileanan Siar in Scotland has no localities that emerge in the bottom quintile of the Scottish Index of Multiple Deprivation (SIMD), despite income levels being below the national average. (Indeed, local government looking at poverty in the area highlight that ‘There are difficulties in using the SIMD in rural areas. Areas such as the Outer Hebrides are sparsely populated, socially heterogeneous and less sensitive to area-based measures such as SIMD. This can lead to a situation where households in rural areas are omitted from policy and targeting by national interventions designed to address poverty and inequalities’.) Furthermore, the size of the areas used to derive the index can also make it difficult to fully understand the levels of deprivation within localities. For example, there may be pockets within a zone that are experiencing higher levels of disadvantage compared with other vicinities, but the use of a more aggregated geographic domain can lead to this being masked. The consequence of this for the higher education sector is that there may be some prospective students who live in deprived neighbourhoods, but due to the limitations of existing data, find themselves unable to participate in outreach activity (eg as a result of not meeting the eligibility criteria or providers not targeting their place of residence).
Comparability is also an important aspect of high-quality statistics. Each nation of the UK, however, adopts a different approach in generating its index from the Indices of Deprivation. This means it is not a UK-wide variable and does not enable statistics to be evaluated across nations. Both the Office for Statistics Regulation and the latest State of the Nation report by the Social Mobility Commission (see p20) have highlighted this as an existing data gap that inhibits our understanding of wider societal trends in social mobility.
A potential solution
The question we therefore asked ourselves was ‘Can we create a UK-wide area-based measure of deprivation that can also address some of the drawbacks of existing indicators?’. To do so, we relied upon the 2011 Census, given the questions asked across the nations are harmonised as far as possible, meaning a UK-wide metric can be created. Data are also released at ‘output area’ level (output areas are often referred to as ‘small areas’ in Northern Ireland), which is a smaller level of geography than is used for the Indices of Deprivation. Output areas will typically contain less than 500 individuals.
With earnings data not available in the Census, our measure of deprivation was derived using the qualifications and occupations of residents in output areas, given these two factors are key determinants of low income. Having generated this, and to understand the potential value it could bring, we compared the bottom quintile of our measure to the equivalent group in the index produced from the Indices of Deprivation (ie the most deprived neighbourhoods). In each of the four nations, we found that our measure picked up a greater proportion of rural areas, albeit to varying degrees. Furthermore, when looking at those output areas that emerged in the lowest fifth of our measure, but a higher quintile of the index developed using the Indices of Deprivation, we observe that the most prevalent localities are based in local authorities/council areas/local government districts where there appear to be lower levels of economic activity (eg County Durham in England, North Lanarkshire in Scotland, Rhondda in Wales, as well as Armagh City, Banbridge and Craigavon in Northern Ireland).
In summary, our measure does seem to overcome some of the existing shortcomings of area-based indicators of deprivation. Over the next few years, we shall therefore be looking to supply the measure to users in an accessible format, alongside updating it using information from the most recent Census. As well as supporting equality of opportunity, if the measure can help to raise participation and skill levels in some of our most deprived neighbourhoods, there is also the possibility that this will assist with reducing spatial disparities in output. For example, the study by the Resolution Foundation and the Centre for Economic Performance notes that the ability of the Shared Prosperity Fund to successfully increase growth may well depend on the levels of human capital in the area. Through upskilling local residents living in disadvantaged localities, providers may therefore be able to facilitate the creation of the conditions needed for growth-enhancing initiatives to succeed. Of course, this rests on the assumption that these areas do not subsequently see residents move to other parts of the country. Understanding the geographical mobility of graduates will thus be the topic of our next blog.
Read more about our measure, its correlation with income and how it compares to the Indices of Deprivation https://www.hesa.ac.uk/insight/08-11-2022/new-area-based-measure-deprivation-summary.
Feedback on our measure of deprivation is most welcome. Please send this to email@example.com.
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Tej Nathwani is a Principal Researcher (Economist) at HESA, which is now part of Jisc.