Using Free School Meals (FSM) data in education research: potential and pitfalls

By Karina Berzins, Continuum – University of East London

Free School Meals (FSM) data is commonly used in education research here in the UK, and is collected annually at the time of the school census for both primary and secondary schools. There are a number of relevant FSM data columns in the publicly available census data – both numbers and percentages of those who take up the offer of free school meals, as well as the numbers and percentages of those learners who are eligible. In addition there are other metrics – those who are eligible (for performance tables) which is the most robust as this is an average of three and six years’ worth of data.

To be eligible for FSM the family must be in receipt of one of a number of state benefits, including jobseekers, disability, housing etc. As such, the FSM eligibility data points have been independently verified and as such are a very robust and useful to use as a proxy to determine the numbers of learners in low income households at a particular school.

Interestingly there is a differential between those learners who are eligible for FSMs and those who take advantage of the offer. This discrepancy varies considerably between Local Authority (LA) areas, and many LAs run programmes and drives to ensure those who are eligible take up the offer, as this also has an impact on the pupil premium funding that the school receives from central government.

When the latest school census was released, it was noted by various commentators that the numbers of those who were in receipt of school meals were fewer than in the previous years. In fact, across all school types there were 14% of learners eligible for FSM provision, the lowest proportion since 2001 when records began.

While this is true – it does not reflect fewer learners from low income households – rather this reflects the continuation of austerity economics whereby many benefits have been cut, and where the earning threshold for state assistance has also been lowered. As FSM eligibility depends on the receipt of income related benefits – when there are fewer benefits claimants the proportion of learners who are eligible for FSMs also decreases. The following graph shows the average FSM data for those learners who are eligible for and who claimed FSMs at maintained nursery and state-funded primary schools, state funded secondary schools, special schools and pupil referral units.

Graph 1: Percentage of learners’ eligible for, and claiming FSM

The following graph shows the average FSM data for those learners who are eligible for and who claimed FSMs at both primary and secondary levels. We can easily see the dips in numbers at the 2013 and the 2016 marks where the effects of the benefits caps were felt.

Graph 2: Percentage of pupils eligible for and claiming FSM, secondary and primary level

It is clear then that FSM data should no longer be used alone as a proxy for low income households, but instead should be used as one of a bundle of measures to establish the numbers of learners from low income households.

This does present a number of problems for the schools’ data analyst. The other data sets available to establish household income are not based on school level data and these data are not collected annually. The Indices of Deprivation – in particular the Index of Multiple Deprivation (IMD) and the income deprivation affecting children index (IDACI) are geographically based series of data which although robust, are difficult to analyse alongside school based data as the areas of geography do not necessarily correspond to where learners live in relation to the school postcode. Alongside this the latest IMD data is from 2015, so there will be a mismatch of years. While there will be more technical skill involved, and time taken to marry these data sets, it is clear that relying solely on the FSM data as an indicator of poverty at school level is problematic. Certainly any FSM data since 2013, and particularly after 2016 needs to be understood within the wider context of the change in state benefits and therefore no longer adequately reflects the numbers of young people from low income households.

Karina Berzins is a Research Fellow with Continuum: the Centre for Widening Participation Policy Studies, based at the University of East London where she previously taught on the Masters of Research. As an academic and data analyst her specialisms include: widening participation in education, the use of contextual data, outreach evaluation, school level and school census data, learner data and data wrangling. She is also a certified Safe Researcher with the Office for National Statistics, and is on the Data panel for the QAA.

Karina is lead for the FACE Special Interest Group (SIG) for Data. If you are a FACE member and would like to get involved in the SIG, please contact Karina:


Graph 2 taken from Department for Education, Schools, pupils and their characteristics. Statistical Publication, 28 June 2018, page 1, available from here.

Photo by Nick Hillier

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