Updated with corrections to sensitivity analysis in Table 4.2 in the methodology and Table 7a of the underlying tables
Background
Income Contingent Repayment (ICR) student loans are provided by Government to higher education students and some further education students to cover course fees and living costs while they are studying. They were first introduced in the UK for new undergraduate students in 1998, at the same time as tuition fees. Prior to 1998, university students were provided funding by Government through a mixture of grants and, from 1990, mortgage style loans that were available to help with living costs. Mortgage style loans are not covered in this publication.
Each of the four constituent countries of the UK now have their own student loan policies, but only students who are eligible through Student Finance England are considered in this publication. These are loans issued to English domiciled students at UK providers and a small number of students of other residencies that attend learning providers in England. A summary timeline of income contingent repayment loans is available in Table 1.1 below.
Table 1.1: Income Contingent Repayment loan timeline: England
1998
Plan 1 loans introduced for new UK domiciled undergraduate students, to cover living costs.
Annual tuition fee of up to £1,000 also introduced in 1998 Teaching and Higher Education Act.
2006
Maximum annual tuition fee limit increased to £3,000 for new full-time undergraduate entrants.
Tuition fee loans introduced to meet the costs of tuition for new full-time undergraduates.
Maintenance grants introduced for new full-time undergraduate entrants on lower incomes.
EU domiciled students became eligible to take out tuition fee loans.
Repayment term changed to 25 years for new entrants, rather than ending at age 65.
2012
Plan 2 loans introduced for new entrants.
Maximum annual tuition fee limit increased to £9,000 for new full-time undergraduate entrants.
New eligible part-time undergraduates subject to maximum tuition fee limit of £6,750 and entitled to fee loans for the first time to meet the full costs of their tuition.
2013
Advanced Learner Loans introduced for students aged 24+ on designated Level 3-4 further education courses in England, on the Plan 2 system. Advanced Learner Loans Bursary Fund introduced.
2016
Plan 3 loans introduced for new students taking postgraduate Master’s courses, who could borrow up to £10,000 over the length of their course.
Maintenance grants replaced by additional loans for living costs for new full-time undergraduate entrants on lower incomes.
Advanced Learner Loans and Bursary Fund extended to students aged 19-23 and to Level 5-6 designated courses.
2017
Nursing, midwifery and most allied health students become eligible for student loans, in place of receiving NHS bursaries.
Maximum annual tuition fee limits increased for new entrants and continuing students who started their courses on or after 1 September 2012 (£9,250 for a full-time course, £6,935 for a part-time course).
2018
Plan 2 repayment threshold increased from £21,000 to £25,000. It had previously been announced that it would remain at £21,000 until April 2021.
Doctoral degree loans of up to £25,000 across the length of a borrower’s course introduced for new starters, on the Plan 3 system.
Loans for living costs introduced for new part-time undergraduates attending degree-level courses and level 5 pre-registration healthcare courses only.
2019
Plan 2 repayment threshold increased to £25,725 for tax year 2019-20, and was thereafter increased annually in-line with average earnings growth figures published by the Office for National Statistics (ONS) until subsequent freezes were announced.
2020
Plan 3 repayment threshold remains at £21,000 until April 2022.
2021
Student Finance support for EU Domiciles withdrawn.
2022
Plan 3 repayment threshold remains at £21,000 until April 2023
Plan 2 repayment threshold remains at the financial year 2021-22 level of £27,295 until April 2025 increasing annually with RPI (Retail Price Index) rather than earnings thereafter
Interest rate for plans 2 and 3 capped at 7.3% for academic year 2022/23 in line with forecast prevailing market rate
2023
Plan 3 repayment threshold remains at £21,000 until April 2025.
Plan 5 loans introduced for new entrants starting from 1st August 2023, with repayment threshold at £25,000 until April 2027 (increasing annually with RPI thereafter), a repayment term of 40 years and a rate of interest in and after study of RPI+0%
Maximum annual tuition fee limits frozen at 2022/23 levels for the 2023/24 and 2024/25 academic years for new and continuing undergraduate students (£9,250 for full-time courses, £6,935 for part-time courses)
Student loans are issued by and administered by the Student Loans Company (SLC) on behalf of the Government and the devolved administrations in the UK. The Department for Education produces forecasts for its outlay on, and the repayments it expects to receive from, the English student loans that it is responsible for. These forecasts are audited by the National Audit Office (NAO) annually and are subject to the Department for Education’s quality assurance framework for business critical models. The forecasts are scrutinised and cleared by quarterly internal Models and Funding Boards before they are used in financial planning, policy development and to value the loans that have been issued in its annual accounts. The forecasts presented in this publication are produced across multiple models, as follows:
Student entrants model – this model forecasts the number of full-time English domiciled undergraduate entrants eligible for tuition fee loans in England. The growth rates from this forecast are used in the student loan outlay and repayment models to estimate the future growth in English domiciled loan borrower numbers.
Student loan outlay model – this model produces forecasts of loan outlay on higher education ICR loans issued to undergraduate and postgraduate students.
Student loan earnings model – this model produces forecasts for the future earnings of higher education ICR loan borrowers.
Student loan repayments model – this model produces forecasts for the future repayments that will be made by higher education ICR loan borrowers.
Advanced Learner Loans model – this model produces forecasts for loan outlay and repayments that will be made on Advanced Learner Loans, which are available for some further education courses. This document provides information on these models, including the methodology, data sources and assumptions used in producing the forecasts.
With the exception of the Lifelong Learning Entitlement (LLE) (opens in a new tab), these forecasts incorporate existing government policy announced by April 2024, which is when this forecast was approved by DFE. From September 2025, learners will be able to apply for LLE funding for courses and modules commencing from January 2026 onwards. The introduction of the LLE and any other changes to student loan eligibility, loan amounts, or terms and conditions, if implemented by Government after 24 April 2024, are not accounted for in the forecasts in this publication and may have an impact on their validity.
DfE’s higher education (HE) student entrants model forecasts the number of England-domiciled, full-time undergraduate student entrants to UK providers. These are all student entrants, whether eligible for a student loan or not. The model then forecasts a subset of these student entrants as the population eligible for tuition fee loans from Student Finance England (SFE). The model assumes a constant proportion of loan-eligible entrants, based on the latest estimated proportion of loan-eligible entrants in HESA’s Core Student Record (2021/22). Growth rates for loan-eligible entrants are then applied to the latest year of outturn SLC data in the student loans outlay model (2022/23), which inform the department’s financial accounts regarding student loan outlay via SFE. The forecasts are also used by the Office for Budget Responsibility (OBR) in the Office for Budget Responsibility – Economic and Fiscal Outlook, March 2024 (opens in a new tab) which forecasts public spending, including student finance over a five-year period.
Scope
The forecasts are based on full-time undergraduate entrants to the following provider types:
Higher education institutions (HEIs)
Designated Alternative Providers (APs) registered with the Office for Students (OfS) as Approved (fee cap)
HEIs in the devolved administrations.
These providers capture the vast majority of HE full-time undergraduate student entrants to Approved (fee cap) providers eligible to charge the maximum annual tuition fee loans of £9,250. Of the remaining Approved (fee cap) providers, Further Education Colleges (FECs) and non-designated APs are excluded. Fee-loan eligible students at these providers are captured within DfE’s higher education student loans outlay model. Since the maximum fees that providers can charge under each OfS registration category is also impacted by Access and Participation Plans (APP) and Teaching Excellence Framework (TEF) awards, the forecast does not include all providers that can charge maximum fees and may also include some providers that cannot.
The following students are excluded from the student entrants forecast:
England domiciled, full-time undergraduate entrants at English providers that have not registered with the OfS, that are registered as Approved and former non-designated APs and FECs registered as Approved (fee cap).
England-domiciled, full-time undergraduate student entrants to APs and FECs in the devolved administrations.
EU-domiciled students
UK domiciled full-time undergraduate student entrants that are not domiciled in England.
Continuing full-time undergraduate students – of any domicile to any providers.
Overseas students (including EU) – of any levels and modes of study.
Entrants from groups 1-3, 5-7 who are eligible for tuition fee loans from SFE are captured within DfE’s higher education student loans outlay model.
Methodology
The student entrants model forecasts England-domiciled student entrants and loan-eligible entrants over a six-year period, in two distinct modelling stages.
The HESA student statistics products for 2024 were not published on the normal schedule in January and February, due to delays in HESA collecting the 2022/23 HESA Student Record. HESA are now expect to publish these products in August 2024. (opens in a new tab) The version of the student entrants model in this publication was finalised and approved by DfE on 24th April 2024, before the release of the 2022/23 data. As a result, the model input data does not include 2022/23 HESA student entrants data.
England-domiciled student entrants
England-domiciled student entrants are forecast by age group and gender. Linear regression underlies most of the entrant forecast (exceptions are detailed in the relevant sections). At a high level, England-domiciled entrants are calculated as:
There are some minor exceptions to this method for some age groups, which are discussed below. Overall, the model calculates four volumes: population, applicants, acceptances, and entrants. Entrants are calculated over two stages:
Core entrants are modelled based on the interaction between population projections, applicant rates and acceptance rates (i.e., provider behaviour).
Entrants to former designated APs registered as Approved (fee cap) are forecast separately by applying the growth rates obtained from the core entrants forecast to the AP count measured in the most recent academic year of data (i.e., 2021/22).
Table 2.1:Forecast population, applicants, acceptances and entrants, England-domiciled
Core population of model
Core plus additional groups
Academic year
18-34-year-old population, England
Total main scheme applicants
Total main scheme accepted applicants
Total accepted applicants
Entrants
Total Entrants
2022/23*
12,463,000
459,000
364,000
418,000
427,000
444,000
2023/24
12,718,000
447,000
355,000
414,000
429,000
446,000
2024/25
12,882,000
446,000
354,000
415,000
434,000
451,000
2025/26
13,009,000
455,000
360,000
420,000
439,000
456,000
2026/27
13,116,000
468,000
370,000
430,000
448,000
466,000
2027/28
13,181,000
467,000
369,000
428,000
448,000
466,000
2028/29
13,227,000
469,000
369,000
428,000
448,000
465,000
*The HESA student statistics products for 2024 were not published on the normal schedule in January and February, due to delays in HESA collecting the 2022/23 HESA Student Record. The version of the student entrants model in this publication was finalised and approved by DfE in April 2024, before the release of the 2022/23 data. As a result, the model input data does not include 2022/23 HESA student entrants data and the number of entrants for 2022/23 is a forecast.
Population
ONS population estimates and projections are aggregated by sex (male, female) and age group (18, 19, 20, 21-24 and 25-34). These estimates and projections are used as the denominator population count for each age-sex group of applicants (see the “main scheme applicants” section below) to obtain the applicant rate, under the assumption that population data for England are a suitable proxy for the England-domiciled student population. Population data is not used in the models for age 17 and under, and 35 and over.
Main scheme applicants
The main UCAS application scheme has two deadlines, January and June. Main scheme applicants refer to those who submitted at least one application prior to the UCAS June deadline and do not include those who applied through non-main scheme routes, such as clearing. UCAS main scheme applicant counts are published shortly after each deadline.
The models are based on June applicant data. If at the time the model is run the applicant counts for the most recent application cycle are only available up to the January deadline (for instance, we are currently in the 2024/25 cycle and June applicant data has not yet been released), the model estimates the expected June applicant counts for that application cycle year from the proportion of June applicants that January applicants have made up historically and its average growth in the latest three years. For those aged 18-34, the model then calculates the proportion of the population (by age group and gender) that have applied through the main scheme (i.e., applicant rate). For those aged 35 and over, it sums the number of applicants that have applied. The resulting applicant rates (or counts for those aged 35 and over) are then log transformed and entered into bounded linear regression models, from which the forecast applicant rates (or counts) are extracted. Forecast applicant rates are then multiplied with population projections to obtain forecast applicant counts. Due to the small number of applicants, those aged 17 and under are forecast by calculating a three-year average of applicants and carrying it over to all forecast years.
The historic time-series entered into the regression begins at 2014/15 for groups aged 18-20, and at 2013/14 for groups aged 21 and over, to ensure that the input data reflects recent applicant trends. Data from the academic year 2021/22 has now been removed from the estimation of this year’s June applicant counts and from the historic time-series for all age groups since application growth was exceptionally high in this year due to the uncertainty caused by the Covid-19 pandemic. Academic year 2022/23 has been excluded from the applicant rate forecasts for 18-year-olds since the applicant rates were as unusually high as they were in 2021/22. Excluding these data points ensures the forecast does not overestimate the proportion of applicants that have already applied in January this year and the expected number of applicants in future years.
Main scheme accepted applicants
UCAS end of cycle acceptances are published by acceptance type (main scheme and non-main scheme). For applicants aged 18 and over, main scheme acceptance rates (i.e. the proportion of main scheme applicants that were accepted through the main scheme) are calculated for each age and gender subgroup. These are then log transformed and entered in bounded linear regression models, from which forecast rates are extracted and multiplied with the forecast applicant numbers obtained in the previous section. The historic time-series entered in the regression models start at 2016/17, since earlier data points do not reflect recent trends. Academic years 2020/21 and 2021/22 show unusual acceptance rates due to the Covid-19 pandemic and were therefore excluded from all input data. Accepted applicants aged 17 and under are forecast by applying a three-year average (excluding 2021/22) of the number of main scheme accepted applicants to all forecast years.
Non main scheme accepted applicants
The model forecasts the number of non-main scheme accepted applicants using bounded linear regression to model the ratio of non main scheme to main scheme applicants over time, separately for applicants accepted directly through clearing and for records of prior acceptance. Records of prior acceptance are acceptances where an application is submitted to UCAS via an institution when an unconditional firm has been offered and accepted by the applicant. Variability in these forecasts are limited to a third of historical variability, to prevent the forecast from reaching unrealistic levels of accepted applicants. To obtain the number of non-main scheme applicants, the forecast ratios are applied to the forecast for main scheme applicants.
Tables 2.2 and 2.3 set out the 18-year-old population against applicants and acceptances (for 18-year-olds and all ages). Eighteen-year-olds make up around half of total entrants, so trends in this population are key to understanding trends in applicants (and therefore accepted applicants).
Table 2.2: Estimated population and historical applicant and accepted applicant counts, England-domiciled. Excludes applications and acceptances made directly with the providers.
Academic year
18-year-old population
Total main scheme applicants (18-year-olds)
Total main scheme accepted applicants (18-year-olds)
Total main scheme applicants (all ages)
Total main scheme accepted applicants (all ages)
2012/13
674,000
215,000
177,000
429,000
323,000
2013/14
655,000
218,000
184,000
442,000
344,000
2014/15
656,000
224,000
190,000
457,000
357,000
2015/16
661,000
232,000
199,000
461,000
366,000
2016/17
648,000
234,000
202,000
459,000
367,000
2017/18
644,000
236,000
205,000
438,000
360,000
2018/19
627,000
232,000
202,000
422,000
348,000
2019/20
619,000
236,000
205,000
419,000
347,000
2020/21
621,000
243,000
218,000
427,000
363,000
2021/22
639,000
267,000
233,000
456,000
373,000
2022/23
657,000
281,000
236,000
459,000
364,000
2023/24
276,000
232,000
447,000
355,000
Table 2.3: Forecast population, UCAS applicants and UCAS acceptances, England-domiciled. Excludes applications and acceptances made directly with the provider.
Academic year
18-year-old population
Total main scheme applicants (18-year-olds)
Total main scheme accepted applicants (18-year-olds)
Total main scheme applicants (all ages)
Total main scheme accepted applicants (all ages)
2023/24
669,000
2024/25
676,000
279,000
233,000
446,000
354,000
2025/26
693,000
294,000
244,000
455,000
360,000
2026/27
722,000
310,000
257,000
468,000
370,000
2027/28
718,000
313,000
258,000
467,000
369,000
2028/29
724,000
319,000
263,000
469,000
369,000
Entrants
HESA entrants are aggregated by the same groups as applicants and accepted applicants and include entrants to both first degree and other undergraduate courses (levels 4, 5 and 6 (opens in a new tab)).
For ages 18 and over, entry rates (i.e. the proportion of accepted applicants that appear on the HESA data) are calculated as a function of age and gender. These rates are log-transformed and entered in bounded regression models, from which forecast rates are extracted. These forecast rates are then multiplied by the forecast accepted applicant counts obtained in the previous step, resulting in entrant counts. The input data starts at academic year 2016/17 for entrants aged 18 and 2013/14 for entrants aged 19 and over, to ensure that the input data reflects recent entrant trends Entrants aged 17 and under are forecast by applying a three-year average of the number of entrants to all forecast years.
Office for Students (OfS) registration
Since 2019/20, providers in England (offering higher education courses) that wish to obtain designated status are required to register with the OfS under the Approved (fee cap), or Approved (opens in a new tab) category. tThe student entrants model includes the impact of former designated APs that registered as Approved (fee cap) by the 20th of March 2024. This is not the total potential impact of entrants to Approved (fee cap) providers. The forecast does not include students at any former designated APs that have since registered, or are yet to register as Approved (fee cap) or students at non-designated APs registered as Approved (fee cap), since very little is known about the number of students at these providers.
To estimate these entrants, the model extracts the number of England-domiciled full-time undergraduate entrants at Approved (fee cap) providers listed on the OfS register (downloaded on 20th of March 2024) from the HESA AP Student Record (2021/22). It then applies the entrant growth rates obtained from the main model to this baseline, under the assumption that entrant numbers at these providers will increase at the same rate as those at HEIs.
Although the number of designated APs that register under the Approved (fee cap) category may grow, there is currently no data to suggest which providers are likely to register in future. To avoid relying on a purely judgement-based estimate, this method assumes no future changes in registration behaviour. As the number of entrants at these providers (up to 18,000 in total, annually) remains much smaller than those at HEIs, these assumptions are believed to have minimal impact on the total growth in student entrants and therefore pose little risk to the accuracy of the forecast.
The student entrants model does not include the impact of entrants to Approved providers; loan borrowers to these providers are captured within DfE’s higher education student loans outlay model.
Loan-eligible entrants
The numbers of England-domiciled loan-eligible entrants are forecast by calculating the proportion that loan-eligible entrants make up out of all England-domiciled entrants, from the most recent year of HESA’s Core Student Record (this publication uses analysis based on academic year 2021/22). This proportion is then applied to every year of the forecast. As a result, the growth forecast for loan-eligible entrants is equal to that of student entrants.
Nursing, midwifery and allied health profession (AHP)
Loan-eligible entrants to nursing, midwifery and AHP courses are forecast separately from the wider population as they are expected to grow at a different rate, with specific Government policies aimed at increasing the uptake of these courses.
Together with the Department of Health and Social Care (DHSC) a separate set of forecast growth rates is established for nursing, midwifery and AHP entrants, which reflect the expected growth of these courses given applicant demand and capacity for funded clinical places. For academic year 2022/23, these growth rates are currently based on the growth observed in UCAS applicants accepted to these courses in each respective year. From 2023/24 onwards, the growth is determined by places made available as part of the NHS Long-term Workforce plan (opens in a new tab), combined with assumptions of displacement from other subjects allied to medicine. The forecast growth rates are applied to a baseline number of loan-eligible nursing, midwifery and AHP entrants, derived from the HESA Core Student Record (AY 2021/22).
Long-term forecasts
Beyond the six-year forecasting period, long-term growth in loan-eligible entrants is forecast within a separate model that runs to academic year 2082/83. This forecast is used in the student loans outlay and the student loans repayment models. This model projects population projection-based growth rates for loan-eligible full-time and part-time entrants, at undergraduate, master’s and doctoral level. To obtain these growth rates, ONS principal population projections for each age are first converted into cumulative growth rates, with the final year of the short-term forecast as the baseline (i.e. 2028/29). For undergraduates, these are then weighted according to the distribution of age observed in the last forecast-year of the Student Loans Outlay Model (2028/29) with the effective date of November 2023. Student Loans Outlay Model sampling (which the forecast is dependent on) identifies age at course start date from the difference in years between the date of birth and the course start date of the loan-borrowing entrant. For master’s and doctoral entrants, the growth rates are weighted by the age distribution observed in SLC data for these levels of study. At the time of this analysis, the effective date of the SLC data was April 2023. For each study level, weighted growth rates are then summed across age to obtain total cumulative growth (i.e. for each of the student population), from which the year-on-year growth is then calculated.
Data quality
The nature of any forecast is inherently uncertain and dependent on the quality of the source data, modelling methodology and assumptions made throughout. The student entrants model uses published data from ONS, UCAS and HESA to forecast full-time undergraduate entrants.
ONS Population Estimates and Projections
ONS population estimates and projections used in the short term and long term student entrants model are the main population statistics used by Government. They are based on calendar years, whilst the student entrants forecasts are based on academic years (August to July). Although this misalignment may impact the accuracy of applicant rates to a small degree, it is assumed that ONS population projections are accurate enough for forecasting purposes, since the majority of full-time undergraduate entrants will commence study in the first calendar year of the academic year (matching the population data).
Data quality guidance for population projections (opens in a new tab) and estimates (opens in a new tab) is published by ONS. Short-term principal projections are largely considered reliable; given that the student entrants model only forecasts over a six-year period, this increases confidence in the base population which forecast entrant are modelled from. ONS do not make any predictions of future political or economic changes that could affect population numbers.
Post-2012/13 higher education data
Due to the 2012/13 tuition fee reform that increased fees to £9,000 per year, the earliest historical data point in the short term student entrants model is academic year 2013/14. This reduces the number of data points which increases the model’s sensitivity to substantial deviations in the data trends and the forecasts’ uncertainty. Deviation from the overall trend have historically occurred due to various factors including changes to HE policy and student funding as well as the Covid-19 pandemic.
Student entrants model assumptions
Instances where the sex is classified as ‘Other’ in HESA data are combined with female entrants because females account for the largest number of entrants. While this assumption has made minimal impact to the forecast historically, it is continually being reviewed.
HESA data records sex whereas UCAS data reports gender. While these two classifications do not completely align, carrying over gender from the applicant and accepted applicant rate forecasts to sex in the entry rate forecast will have minimal impact on the overall accuracy of the forecast and is therefore deemed valid enough for the current purpose. We are continually reviewing this assumption.
The model rests on the assumption that due to the Covid-19 pandemic academic years 2021/22 and 2022/23 were unusual in terms of applicant behaviour and that 2020/21 and 2021/22 were unusual in terms of provider behaviour, therefore these data points are excluded from the respective forecasts. These assumptions were assessed with the release of 2024/25 January applicant and 2023/24 accepted applicant data.
It’s yet unclear whether entry rates observed over the Covid-19 pandemic reflect a real trend change or whether they are unusual. The impact of these data points on the forecast cannot be fully reviewed until HESA 2022/23 data is released, and these data points can be compared to points either side of them in the timeseries. Since the most recent entry rates (i.e., 2020/21 and 2021/22) generally fall in line with the rest of the historical trend and do not result in an unreasonable forecast, these data points were retained in the model until they can be fully assessed with new data (i.e., HESA 2022/23).
No assumptions are explicitly made regarding the growth of non-EU students and how this could impact growth for the loan-borrowing entrant population. Given the many factors that influence non-EU applicant demand, there are limited sources of forecasts. However, we continue to monitor UCAS data and provider forecasts. Whilst the non-EU student population is expected to grow, its growth isn't assumed to largely constrict the growth of domestic student entrants because non-EU students account for a relatively small proportion of the full-time undergraduate student entrant population.
Student entrants model uncertainties
There is uncertainty around both future behaviour in both students and providers. In terms of student behaviour, the 2024/25 application cycle saw a decline in the number of January applicants, that followed a prolonged period of sustained growth. The current model forecasts we will return to pre-pandemic trends after 2024/25. This is however subject to some uncertainty, because while potential entrants might well be less likely to apply in the end this academic year, it is possible that they are exercising caution and waiting to apply at a later point in the application cycle. There is also uncertainty around the current and upcoming economic climate and its potential effects on current and future applicant behaviour.
The uncertainty around provider behaviour stem from questions about provider capacity, including whether providers will be able to continue growing at the current rates and how long they will be able to continue growing for. Accepted applicant data from 2022/23 suggest some restrained acceptance behaviours from providers, with a decline in the number of main scheme accepted applicants and lower growth than previous years in accepted applicants, overall. This could be a response to the unusually high intakes across the Covid-19 pandemic, uncertainty over grade inflation in the previous two academic years, or the start of providers reaching capacity.
The model under-estimated the number of entrants in the first forecast-year from publication year 2019 to 2022. The first year of the entrant forecast relies on the product of that year’s outturn of accepted applicants and the first forecast entry rate, since the accepted applicant data is published at this point, but HESA data is not yet released. The difference observed in the forecast value and the eventual outturn of entrants therefore originates from underestimating the entry rate for the first forecast year. The growth in entry rates of entrants aged 25 and over saw a sharp increase around 2018/19 that was likely not fully reflected by the linear model fit. The even larger deviations from the previous trends observed in data recorded during the Covid-19 pandemic likely led to the difference increasing in the subsequent years. The most recent forecast published in July 2022 for academic year 2021/22 came very close to the outturn seen now in the HESA data, for the model’s target population.
Due to the delay in the 2024 release of the HESA Student bulletin and open data to August 2024, we were not able to make a comparison between the entrant forecast published on 29 June 2023 for academic year 2022/23 and the eventual outturn for this year, at the time of this publication.
Table 2.4: Previous published England-domiciled student entrant forecasts against outturn
The student loan outlay model forecasts loan amounts that the Department for Education (DfE) expects to pay higher education students (and their providers) via the Student Loans Company (SLC).
A range of sub-models are used to capture the various loan types available to students. The loan products that outlay forecasts are produced for are:
Full-time undergraduate loans (Plan 2) – the loan system for students on full-time courses that started between September 2012 to 31st July 2023 and that are eligible for undergraduate student support funding, consisting of fee loans and maintenance loans.
Part-time undergraduate loans (Plan 2) – the loan system for students on part-time courses that are eligible for undergraduate student support funding. These first became available in September 2012, consisting of a tuition fee loan. From August 2018, maintenance loans were also available to some part-time students.
Full-time undergraduate loans (Plan 5) – the loan system for students on full-time courses that start on or after 1st August 2023 and are eligible for undergraduate student support funding, consisting of fee loans and maintenance loans.
Part-time undergraduate loans (Plan 5) – the loan system for students on part-time courses that start on or after 1st August 2023 and are eligible for undergraduate student support funding, consisting of fee loans and maintenance loans for some part-time students.
Postgraduate Master’s loans (Plan 3) – loans available to Master’s students to help cover fees and living costs. They were introduced in August 2016 and are on the Plan 3 repayment system.
Postgraduate Doctoral loans (Plan 3) – loans available to Doctoral students from August 2018 to help cover fees and living costs. They are on the Plan 3 repayment system.
Outlay forecasts for Plan 1 loans are no longer produced due to the very small number of recipients expected in 2023/24, because most students who started a course before September 2012 have now completed those studies. The Student Loans Company recorded (opens in a new tab) £55,000 in Plan 1 loan outlay in the tax year 2023-24.
Eligible English domiciled students are entitled to tuition fee and maintenance loans for courses that are eligible for undergraduate funding, and Master and Doctoral loans for courses that are eligible for postgraduate funding. Previous publications included a separate “EU borrower” forecast, for EU domiciled students who were eligible to take out tuition fees loans only. In 2021/22 this student finance support was withdrawn following the UK’s exit from the EU and the residency status that defines this small group of borrowers with partial eligibility has changed. Partial eligibility covers those who do not have access to undergraduate maintenance funding but have access to postgraduate funding and undergraduate tuition fee-only funding. This group now comprises a mixture of:
EU nationals with pre-settled status,
EU, other EEA and Swiss nationals still eligible for tuition fee loans as a result of the Withdrawal Agreement, including Irish nationals who have not been domiciled in the UK for 3 years before starting studies,
Other residencies granted partial higher education support under the regulations.
This group is now labelled “Borrowers granted partial higher education support under the regulations”. Both this group and English domiciled students are entitled to the same amount for postgraduate loans, but the two groups are forecast separately to capture the difference in their expected growth. For further details on loans and eligibility, please see the Student Finance England practitioner website (opens in a new tab).
Maintenance loans for eligible students depend on their location and household income (where a borrower applies for a means tested loan). The maximum maintenance loan for full-time Plan 5 borrowers living away from home and studying outside of London, in 2023/24 is £9,978, as outlined in the financial memorandum (opens in a new tab). Table 1A of the Student Loans Company statistical publication Student support for Higher Education in England (opens in a new tab)presents the maximum rates of maintenance loans and tuition fee loans for full-time students domiciled in England.
Maintenance loans became available in 2018/19 to part-time, on-campus, degree students. These loans mirror the full-time maintenance loan, with the intensity of study taken into account alongside means testing and location. Students studying courses at less than 25% intensity are not eligible for part-time maintenance loans.
As part of a wider reform of the higher education system, the Government introduced the Lifelong Learning Entitlement (opens in a new tab) Bill. Students will be able to apply for Lifelong Learning Entitlement funding from September 2025 and commence study from January 2026. This policy change is not included in the forecasts presented but will be integrated for future publications.
Postgraduate Master’s loans
The postgraduate Master’s loan was introduced in 2016/17. Eligibility for a Master’s loan depends on the duration and intensity of the student’s course, their age on the first day of the first academic year of their course, and their nationality or residency status. The course must also be provided by a university or college in the UK, which is either publicly funded or a designated private provider. From 2019/20, English providers are required to register with the OfS as Approved (fee cap) or Approved to be eligible for student support funding.
Unlike undergraduate loans, Master’s loan entitlement for eligible students depends on the start date of their course, rather than location or household income. The maximum Master’s loan amount for a course starting in 2023/24 is £12,167 across the length of the course.
Postgraduate Doctoral loans
The postgraduate Doctoral loan was introduced in 2018/19. Eligibility for a Doctoral loan is based on the duration of the student’s course, their age on the first day of the first academic year of their course, and their nationality or residency status. The course must also be provided by a university or college in the UK, which is either publicly funded or a designated private provider. From 2019/20, English providers are required to register with the OfS as Approved (fee cap) or Approved to be eligible for student support funding.
Doctoral loan entitlement for eligible students depends on the start date of their course. The maximum Doctoral loan amount in 2023/24 is £28,673 across the length of the course.
Methodology
Student loan outlay, for undergraduate higher education loan products (and including postgraduate Initial Teacher Training, which is funded through undergraduate student finance), is forecast based on historical data from the Student Loans Company. For postgraduate loan products, where historical information is limited, an alternative forecasting method is required.
Undergraduate higher education loan products
The student loan outlay forecasts for the higher education Plan 2 or Plan 5 loan products use current and historical anonymised data on individual loan borrowers from the Student Loans Company. Individual-level data on loan borrowers were provided by SLC in April 2023 providing nearly complete information on student loans up to and including 2022/23. Using this and other information from SLC and growth rates from the student entrants model, forecast students were generated and allocated loans according to announced loan caps or OBR RPIX (Retail Price Index All Items Excl. Mortgage Interest) forecasts. Note that fee and maintenance loan levels available to students are typically already known for the first two or three academic years for which the model produces forecasts, currently 2023/24 and 2024/25. Maximum fee amounts in 2023/24 and 2024/25 have been set at the same levels as in 2017/18, while maintenance loan entitlements were increased in line with the latest Office for Budget Responsibility (OBR) RPIX central forecast (opens in a new tab)for the Jan-Mar quarter in each year.
This modelling method assumes that the distribution of characteristics of future loan borrowers, such as means-testing, gender and degree subject, is the same as for loan borrowers from the recent past. Internal analysis of historic trends in recent SLC data indicates that this assumption is generally accurate for most characteristics. Changes to student finance brought in from 2023/24 may impact on the distribution of the characteristics of loan borrowers and on their loan amounts in the short-term. This is being monitored by DfE, and if needed, or where future policies are announced, an assessment will be made on the impact of the policy on the borrower numbers and loan amounts and the forecasts may be adjusted accordingly.
When the individual-level data was received from SLC in April 2023, it contained nearly complete data for 2022/23. To use this most recent year of data for modelling, estimates of the missing information until the end of the academic year, from April to August 2023, were made and the dataset amended accordingly. There are three main types of missing information between April and August, and these were quantified to produce a mini forecast of a full year of data for 2022/23. These three types of information are:
Missing borrowers. By April, most loan borrowers in the academic year have been paid a loan amount by SLC. However, some students may not yet appear in the dataset because their first loan payment is late in the year, for example if their course starts between April and August, or if a student’s circumstances change and they require student finance when they did not earlier in the year. Borrowers can apply for funding up to 9 months after starting and later applications can also be honoured by SLC, so borrower numbers can change after the end of the academic year. The number of missing borrowers was calculated using SLC data on the total number of students at the end of August 2023, split by institution type, study mode, student domicile (England or “Borrowers granted partial higher education support under the regulations”), and loan product. To account for late reporting after the end of the academic year, the number of August borrowers was uplifted by the growth in the number of enrolled borrowers in 2021/22 from the SLC August 2022 to the SLC April 2023 individual-level data, split by study mode, domicile, and for England-domiciled full-time students, by institution type too. For each combination of characteristics, this uplifted figure was compared to the number of students in the individual-level SLC April 2023 data, and a number of students equal to the difference in the April and uplifted August totals was added to the individual-level dataset by randomly sampling and duplicating pre-existing entrants and amending the unique identifiers of the duplicated records. It was assumed that all missing borrowers after April 2023 were entrants (first year students).
Missing withdrawals. Some students will withdraw from university between April and August of an academic year, and this information will not have been contained in the April 2023 extract for 2022/23. In addition, there is a (non-negligible) time lag between a student’s withdrawal and the HE provider notifying SLC of that event, so some information on withdrawals up to end April 2023 is missing too. To calculate the number of extra withdrawals in 2022/23, the 2023 extract was compared with the 2022 extract, with the 2023 extract providing an updated view on withdrawals in 2021/22. Specifically, the proportion of missing withdrawals by study mode, institution type and loan product received was estimated. Within each of these combinations of characteristics, the expected number of extra withdrawals in 2022/23 was then randomly sampled and their records amended to add withdrawal information.
Missing loan outlay. Most loan outlay is paid prior to April in each academic year and is therefore recorded in the April cut of the SLC data. However, students who start later in the academic year, or whose circumstances change, may receive loan payments later than April and not all borrowers are paid their requested loan amount. The payments and requested loan amount for the 2021/22 academic year, i.e., the most recent full academic year of data in the 2023 extract, were compared across two years of April SLC data extracts (2022 and 2023). The proportion of borrowers with a requested loan amount in the April 2022 extract that had been paid less than that amount in the April 2023 extract was calculated, split by study mode, provider type and (requested) loan product. For each of these combinations of characteristics, the April 2023 SLC data was searched for students who had received less than their requested loan amounts and had not withdrawn in 2022/23. A random sample of these students had their loan amounts increased to their requested amounts, based on the aforementioned proportions.
Once this missing 2022/23 data had been imputed, the now-full year of data was used as the undergraduate outlay forecast baseline.
For borrowers who have not finished their course yet, their original course length is not always representative of the number of years of loans a borrower will go on to receive. For example, a borrower may repeat a year, switch courses to include or remove a placement year, transfer onto a new course entirely (starting in year 1 again) or withdraw. To reflect this in the outlay forecasts, once the full year of 2022/23 data had been imputed, continuation rates reflecting average course lengths were applied to all students who were studying in 2022/23 and started their course after 2017/18. These continuation rates were derived from 2014/15 and 2015/16 HESA student record data for full-time students, whereas for part-time students the continuation rates are based on 2016/17 and 2017/18 aggregate SLC data on part-time students taking up tuition fee loans. This adjustment was applied randomly within each study mode and for each original course length.
This data could now be used to generate students starting in future academic years. Entrant borrowers in 2022/23 were sampled, duplicated and renamed with new date information to generate entrants in future years. Generated entrants were all assigned Plan 5 repayments terms. The number of students to be generated for each future year was calculated by applying the student entrants forecast growth rates to the number of 2022/23 entrants from aggregate SLC data. Different growth rates are calculated for part and full-time students and England and tuition fee loan-only eligible students, so the new students were generated for each combination of these characteristics. Some subsections of the student population were separated out and generated using a set entrants forecast, for example where a student numbers cap was in place. These sets of students included Nursing, Midwifery and Allied Health (NMAH) students, students studying Medicine and Dentistry, postgraduate Initial Teacher Training students, students studying at alternative providers, and Higher Technical Qualification (HTQ) students. Students in these groups were identified by a combination of CAH (Common Aggregation Hierarchy) 01 code, qualification level, course duration and course level.
Some students may be eligible to receive funding for more than one HE Plan 2 or Plan 5 course; examples include postgraduate teacher training students, students completing a foundation degree before continuing to study for a bachelors-level degree, or students studying an Equivalent or Lower Qualification (ELQ)-exempt course. Students studying more than one funded course therefore usually already have a loan balance when they start their second course. The entrants generated by the method above did not have pre-existing loan balances. To generate a realistic loan borrower population where some entrants each year already have a loan from a previous course, a random sample (by study mode and domicile) of previous loan borrowers was chosen. These borrowers were allocated to start new courses, with the proportion starting at different institution types, course levels and subjects (NMAH, Medicine and Dentistry, Initial Teacher Training, and all other courses) determined by analysis of historic SLC data. These borrowers with previous loans made up a small portion of the forecast entrant numbers.
At this point in the modelling process, the SLC data consisted of a row per student per course with their loan outlay recorded up to the end of 2022/23 (some of which is estimated). It also included future students generated from current and previous students and renamed with their date information shifted forwards. With this data, future outlay can be generated.
To generate future outlay, the outlay of each student was calculated as the lower of their most recent year of outlay (2022/23, or their course start year if later) uprated by the OBR forecast of RPIX, and the relevant loan cap. For full-time students that started prior to 2022/23 and were identified to be on a year overseas or a placement year in 2022/23, their future outlay was based on their outlay in 2021/22 instead. Separate tuition fee caps were applied to students at Approved and Approved (fee cap) providers, set to the maximum tuition fees for each type of institution. Maintenance loan caps were applied separately to medical and dentistry students in their fifth and sixth years (who are eligible for smaller loans because they receive an NHS bursary in these years), and to all other students. Additionally, a random sample of students on courses of length 4 years were chosen to be sandwich placement students and were given lower fee and maintenance loans in their third years, where their third year was 2023/24 or later. A random sample of entrants starting in 2022/23 who had fee loans below the cap in that year were chosen to be studying courses as fee waiver students, and allocated fee loans at the cap in their second year, even where this was an above-inflation increase. For all future years, a random sample of nurses were chosen to take out a reduced maintenance loan as a result of the re-introduction of NHS bursaries for NMAH students. A random sample of part-time HTQ borrowers were allocated an HTQ-specific maintenance loan calculated based on SLC data on existing borrowers taking level 4 & 5 qualifications. The proportions of students who have sandwich placement, fee waiver, nursing bursary loan or HTQ maintenance loan adjustments were determined by an analysis of SLC data or by agreement with the OBR.
The academic year loan outlay forecasts were converted into financial years. This was done based on an analysis of academic year payments and which financial year they fall in, using SLC data. This analysis took into account at which point within an academic year a student started. Payments relating to one academic year can span multiple financial years. Those students starting later in an academic year will have larger portions of their loan paid in the later relevant financial years. At this point, forecasts of total loan outlay as well as a breakdown by Plan type were produced.
Master’s loans
Master’s loans were introduced in academic year 2016/17 and since then, historic aggregate level borrower data has been monitored and used as the baseline for the Master’s outlay forecast. Given the differences in the loan products, with the Masters’ loans entitlement for the course rather than each year, the method for forecasting future loan outlay uses a different approach to undergraduate loans. Instead, estimates of the total number of entrants who are likely to take up a loan each academic year are derived by assuming that the number of loan recipient entrants in 2022/23 grows annually by the rates displayed in Table 3.1. The baseline number of loan recipient entrants in 2022/23 is based on SLC provisional data from October 2023.
From 2023/24, England domiciled loan-recipient entrants are assumed to grow at a constant rate of 2.5%, which is based on average historical growth of HESA taught and research Master’s entrants. Based on provisional loan payment and application data for 2023/24 and the disappearance of the “pre-settled" residency status for those who are EU domiciled in 2026/27, substantial declines in the group “Borrowers granted partial higher education support under the regulations” are anticipated in academic years 2023/24 and 2026/27. After this point, this group will consist mostly of Irish nationals who have not been domiciled in the UK for 3 years. For further information on this category of borrowers, please see the Student Loan Outlay introduction.
Growth rate for ‘Borrowers granted partial higher education support under the regulations’
2023/24
2.5%
-43.4%
2024/25
2.5%
0.6%
2025/26
2.5%
0.0%
2026/27
2.5%
-91.0%
2027/28
2.5%
0.0%
2028/29
2.5%
0.0%
Table 3.2: Core Master’s loans model parameters by course duration
Course duration
Proportion of loan recipient entrants
2016/17 average loan (per year)
1 year
0.80
£9,300
2 years
0.17
£4,400
3 years
0.03
£2,700
Annual academic year loan outlay is calculated using a cohort approach, based on start year and the proportion of students within each course duration. The parameters used are shown in Table 3.2, where average loan amounts are rounded to the nearest £100. The average loan amounts per year are derived from 2016/17 SLC payments data . The proportions of loan recipient entrants are based on a combination of 2017/18 SLC data, and comparisons between the 2018-19 model forecast to outturn. The expected number of loan borrowers in each cohort is multiplied by an estimated average loan amount. The estimated average loan amounts per year for a cohort are calculated by uprating the 2016/17 average loan amounts by OBR outturn and forecast RPIX, from 2017/18 to the corresponding start year. The loan amount is spread across one, two or three years depending on course duration. The sum of the outlay from each cohort is aggregated to produce a final academic year outlay figure. The financial year forecast is calculated by assuming the outlay in a given financial year is equal to the sum of one third of the outlay in the first academic year it overlaps with and two thirds of the outlay in the second academic year it overlaps with.
Doctoral loans
Doctoral loans were introduced in academic year 2018/19, and since then, historic aggregate level borrower data has been monitored and used as the baseline for the doctoral outlay forecast. To qualify for a Doctoral loan, the borrower's course must last between 3 and 8 academic years. For each course duration, estimates of the total number of student entrants who are likely to take up a loan each academic year are calculated by assuming that the number of loan recipient entrants in 2022/23, grows annually by the rates displayed in Table 3.3. The number of loan recipient entrants in 2022/23 is based on SLC provisional data from October 2023.
From 2023/24 onwards, the estimated annual growth in England domiciled recipient entrants is assumed to remain constant at the average of the growth rates from 2011/12 to 2018/19 of HESA doctoral entrants. ‘Borrowers granted partial higher education support under the regulations’ growth is forecast based on trends in monthly SLC payment and application data. Based on provisional loan payment and application data for 2023/24 and with the disappearance of the “pre-settled" residency status for those who are EU domiciled in 2026/27, substantial declines in the group “Borrowers granted partial higher education support under the regulations” are anticipated in academic years 2023/24 and 2026/27. After this point, this group will consist mostly of Irish nationals who have not been domiciled in the UK for 3 years. It is assumed the doctoral “Borrowers granted partial higher education support under the regulations” growth rates are the same regardless of course length. For further information on this category of borrowers, please see the Student Loan Outlay Introduction.
Table 3.3: Forecast Doctoral loan recipient entrant growth rate by domicile and course duration
Academic year
Growth rate for England domiciles (course duration of 3 to 4 years)
Growth rate for England domiciles (course duration of 5 to 8 years)
Growth rate for “Borrowers granted partial higher education support under the regulations”
2023/24
3.3%
1.0%
-77.7%
2024/25
3.3%
1.0%
0.0%
2025/26
3.3%
1.0%
0.0%
2026/27
3.3%
1.0%
-39.1%
2027/28
3.3%
1.0%
0.0%
2028/29
3.3%
1.0%
0.0%
Assuming that the course duration split of entrant students taking up Doctoral loans does not change year-on-year, provisional 2018/19 SLC applicants’ data, as at mid November 2018, was used to estimate the split of loan recipient entrants by course duration displayed in Table 3.4.
Table 3.4:Estimated proportion of Doctoral loan recipient entrants by course duration
Course duration
Proportion of loan recipient entrants
3 years
0.43
4 years
0.33
5 years
0.06
6 years
0.10
7/8 years
0.07
Figures may not appear to sum due to rounding.
The average loan for the whole course is estimated using a similar uprating approach to the Master’s forecast. For example, the average loan for new entrants in 2023/24 is estimated to be £27,412 for their whole course. This is calculated by uprating the average requested amount in 2018/19 (opens in a new tab) as at end December 2018, by outturn and forecast RPIX, from 2019/20 to the corresponding start year . Note, the maximum doctoral loan amount for a course starting in 2023/24 is £28,673.
Annual academic year outlay is then calculated using a cohort approach based on course duration. The number of borrowers per course duration as outlined above, is multiplied by continuation rates from HESA data, to estimate the expected number of borrowers in each year for each course length. The expected number of loan borrowers in each cohort is multiplied by the corresponding average loan amount spread evenly across the course duration. The sum of the outlay from each cohort is aggregated to produce a final academic year outlay figure. Like in the Master’s loans forecast, the financial year forecast is calculated by assuming the outlay in a given financial year is equal to the sum of one third of the outlay in the first academic year it overlaps with and two thirds of the outlay in the second academic year it overlaps with.
Long-term outlay forecasts
The same methodology as documented above was used to forecast the long-term full-time and part-time undergraduate outlay, however an alternative method was used to forecast the long-term postgraduate outlay.
Undergraduate higher education loan products
The methodology described above, used to forecast outlay for undergraduate loans was also used to produce the long-term undergraduate outlay forecast. Using individual-level data on loan borrowers provided by SLC and long-term growth rates from the student entrants model, forecast students were generated for each academic year from 2023/24 to 2082/83. These students were allocated loans using forecasted loan caps based on announced loan caps and the long-term OBR RPIX forecast. The loan outlay forecast was converted to a financial year forecast by apportioning loan payments made in each academic year between the financial years they straddle, according to analysis of SLC data.
Postgraduate loans
The postgraduate long term forecast is calculated using a cohort (based on start year) approach. The growth rate for each cohort is found by taking the entrant growth from the long-term student numbers model for the academic year that the cohort started. This is then multiplied by the proportion of total borrowers from each cohort, by product, shown in Table 3.5, to give a total student loan borrower growth rate for the academic year. This student loan borrower growth rate is then multiplied by the previous academic year outlay forecast and multiplied by forecast RPIX. The financial year forecast is calculated by assuming the outlay in a given financial year is equal to the sum of one third of the outlay in the first academic year it overlaps with and two thirds of the outlay in the second academic year it overlaps with.
Table 3.5: Proportion of total borrowers by postgraduate loan product and year of study
Year of study
Proportion of total borrowers (Master’s)
Proportion of total borrowers (Doctoral)
1st year
0.80
0.35
2nd year
0.15
0.30
3rd year
0.05
0.25
4th year
−
0.10
Data quality
Student loan outlay forecasts are uncertain as they are primarily driven by borrower behaviour as well as economic factors such as inflation and household residual income. This model assumes that the characteristics and behaviour of future borrowers will be similar to historic ones derived from SLC administrative data, which may not necessarily be the case. The model is also dependent on the Spring 2024 OBR macroeconomic forecasts that are used to uprate loans. Any significant changes to the economy from these forecasts could affect the outlays that will be made on student loans.
The model uses SLC administrative data to determine borrower numbers and loan amounts. The DfE receives data extracts from the Student Loans Company on an academic year basis that are used in the student loan outlay model. This data is consistent with the data published in the SLC Student Support for higher education in England publication (opens in a new tab).
The model uses the growth in forecasted entrants from the DfE student entrants model; see Section “Student entrants model” – “Data quality”. The population covered largely aligns with those eligible for tuition fee loans from Student Finance England; see Section “Student entrants model” for more detail. However, the entrants forecast does not include students studying higher education courses at FECs registered as Approved (fee cap). The outlay model therefore assumes that the growth in these students is the same as that of the population covered by the entrants model. It also assumes that the growth in entrants eligible for tuition fee loans from Student Finance England is the same as for maintenance loans. If the growths in these populations are not consistent then this will have an impact on the number of new entrants who are taking out loans in the model.
The model assumes that loans will be uprated by forecast RPIX in future years for which the maximum loan amounts have not yet been announced. With the exception of the introduction of the LLE in 2025, the model incorporates existing government policy announced by 24 April 2024 and assumes such policy will remain unchanged. Therefore, the introduction of the LLE and, if implemented by Government, any other changes to student loan eligibility, quantum or terms and conditions could affect the forecasts presented in this publication.
Table 3.6 compares the performance of the loan outlay model to SLC outturn data in 2023-24 and shows that the overall outlay forecast was approximately 0.5%, or £93 million, higher than the outturn figure. Both the Undergraduate and Master’s forecast were higher than the actuals (0.5% and 0.5% respectively), with the Doctoral forecast having an under forecast (-10.6%). The -10.6% difference between the Doctoral 2023-24 forecast and actuals is likely due a larger than expected number of Doctoral entrant borrowers in 2023/24 or a larger than expected increase in average loan amounts.
Table 3.6: Difference between Higher Education Outlay Forecast and SLC outturn for financial year 2023-24
The DfE student loan earnings and repayments model is the financial model used to estimate the cost of income contingent student loans to Government. It forecasts the repayments that the Department expects to receive on its expenditure on student loans.
The model is a micro-simulation model. It forecasts student loan repayments by estimating future earnings for a sample of individual student loan borrowers, and applies the loan repayment policy to each borrower, before aggregating the results to estimate totals for the population as a whole. For each loan borrower, it predicts their next year’s earnings, and when this is repeated it generates an earnings path. Where historical information on earnings is available the model makes use of this. Earnings predictions are based on the borrower’s level of study, gender, years since SRDD (Statutory Repayment Due Date), age and other information. This allows the model to capture individual changes in earnings over the borrower’s working lifetime.
Once a borrower’s earnings have been forecast, their repayments, interest and loan balances are calculated year by year for the length of their repayment term, or until they finish repaying their loan. Further adjustments are made to some borrowers’ repayments to allow for voluntary repayments, overseas repayments, and differing obligatory repayments resulting from non-standard earnings distributions across months of the year or across multiple jobs.
The model forecasts repayments for income-contingent student loans eligible through Student Finance England. Earnings forecasts are made for undergraduates (first degrees and sub-degrees) and PGCE loan borrowers based on historical administrative data for comparable loan borrowers and historical administrative earnings data for the UK residents. For Master’s and Doctoral loan borrowers, earnings are modelled by applying a percentage uplift to an earnings forecast for a comparable first degree student.
The main data sources used in the model are:
SLC administrative data – provides details of borrowers and the loans they take out, used to forecast earnings and employment status in early repayment years. Used for modelling migration, repayment frictions and repayments made directly to the SLC.
Longitudinal Education Outcomes (LEO) - used in earnings and employment models in early repayment years.
HMRC administrative earnings data – used in earnings model from year 11 of repayments onwards.
Office for National Statistics (ONS) life tables – data on deaths.
ONS Average Weekly Earnings (AWE) data – used to adjust earnings between 2014-15 earnings values and nominal terms.
Higher Education Statistics Agency (HESA) data – course completion rates, characteristics information for borrowers taking out new loan products for which there is no historical SLC data.
Office for Budget Responsibility (OBR) macroeconomic forecasts – forecasts of earnings growth, the Bank of England base rate, RPI and RPIX.
DfE Student numbers model – forecasts of entrant numbers.
DfE Outlay model – forecasts of student loan outlay.
Figure 4.1: Processes and sources underlying the student loan repayment model
Figure 4.1 explains, at a high level, the processes that the model goes through to produce the forecasts, along with how each data source feeds into the full model.
Student loan repayment policies
ICR loans require borrowers to make repayments based on their annual income, starting from the April after they have left their course. Under each policy, borrowers are required to make repayments each tax year equal to a percentage of their income above a set repayment threshold until either they have fully repaid their loan balance, or their loan is cancelled. Loans are cancelled if the borrower dies, if they still have an outstanding loan balance at the end of their repayment term, or if they are in receipt of a disability related benefit and are permanently unfit for work. Loans accrue interest during and after the borrower’s course, which is added to their loan balance.
A borrower becomes liable to repay their loan on the 6 April (start of the UK tax year) after they complete or withdraw from their course, at which point their repayment term starts on what is known as their Statutory Repayment Due Date (SRDD). There are two exceptions to this:
Part-time loan borrowers will enter repayment at the start of the tax year after four years have elapsed since the first day of the first academic year of the course, even if they are still studying.
When a loan product is first introduced the earliest SRDD for some borrowers may be later than it would usually be. For example, all Plan 2 borrowers that completed or left their courses before April 2016 had an SRDD of April 2016, even though under the usual rule some would have had an SRDD up to three years earlier. For Plan 5 borrowers, the first possible SRDD is April 2026.
A summary of the key repayment policy details for each loan product is shown in Table 4.1 below.
Table 4.1: Key policy details for each loan product
Plan 1
Plan 2
Plan 3 (Postgraduate)
Plan 5
Earliest year of entrants
1998/99
2012/13
2016/17 (Master’s)
2018/19 (Doctorate)
2023/24
Earliest SRDD cohort
April 2000
April 2016
April 2019 (Master’s)
April 2020 (Doctorate)
April 2026
Length of repayment term
Until age 65 (entrants up to 2005/06);
25 years after SRDD (2006/07 entrants onwards)
30 years after SRDD
30 years after SRDD
40 years after SRDD
Repayment rate
9% of earnings above repayment threshold
9% of earnings above repayment threshold
6% of earnings above repayment threshold (in addition to any Plan 1, 2 or 5 repayments)
9% of earnings above repayment threshold
Interest rate
The lower of either RPI, or the Bank of England base rate +1%
RPI+3% during course, variable between RPI and RPI+3% after SRDD depending on earnings
Each loan product has a separate income repayment threshold, above which repayments are made. Figure 4.2 shows the forecast repayment thresholds for each policy. All historic Plan 1 and Plan 2 threshold levels are published (opens in a new tab). The Plan 1 threshold is set at £22,015 for tax year 2023-24, and at £24,990 for tax year 2024-25. It will subsequently increase each year based on RPI. The Plan 2 threshold was initially set at £21,000 from 2016-17 to 2017-18 before rising to £25,000 in 2018-19,to £26,575 in 2020-21, and to £27,295 in 2021-22. The threshold will remain at £27,295 until the end of tax year 2024-25, (opens in a new tab) after which it will increase in line with RPI. The initial Plan 5 repayment threshold is set at £25,000 until 2027-28, after which it will increase in line with RPI.
If a borrower has loans under multiple undergraduate plan types, they repay in line with the rules outlined at Repaying your student loan: How much you repay - GOV.UK (www.gov.uk) (opens in a new tab). For example, if a borrower holds both a Plan 1 and a Plan 2 loan, they pay back 9% of income over the Plan 1 threshold. If their income is under the Plan 2 threshold, repayments only go towards the Plan 1 loan. If income is over the Plan 2 threshold, repayments will be split between both loans. We assume that the rules for borrowers with Plan 1/2 and Plan 5 repayments will be analogous for those with both Plan 1 and 2 loans. Thestudent loan undergraduate repayment model forecasts future repayment thresholds for all undergraduate plan types using OBR forecasts for RPI.
The Plan 3 repayment threshold is £21,000 from when the first borrowers became liable to repay in 2019-20 and 2020-21. More details on how much the borrower repays are available at gov.uk (opens in a new tab). To enable future repayments to be forecast, for modelling purposes it is assumed that from 2025-26 the Plan 3 loan will rise in line with Office for National Statistics (ONS) average earnings growth statistics; there is no set policy for increases to the Plan 3 repayment threshold.
Figure 4.2: Forecast repayment thresholds for each loan product
England, financial years 2023-24 to 2053-54
In addition to the repayment threshold, Plan 2 also has two interest thresholds. Once Plan 2 borrowers are past their SRDD their interest rate varies depending on income. If their income is below or equal to the lower interest threshold their interest rate is equal to RPI, at or above the upper interest threshold it is RPI+3%, and for anyone with an income in between it varies linearly between the two. The lower interest threshold is the same as the repayment threshold, while the upper interest threshold was set to £41,000 in 2016-17 before increasing to £45,000 in 2018-19, and £49,130 in 2021-22. It will remain at £49,130 until the end of tax year 2024-25. (opens in a new tab) In subsequent years both thresholds are forecast to rise in line with RPI.
Student loan borrowers resident in the UK generally make their loan repayments through the tax system to Her Majesty's Revenue and Customs (HMRC), either in-year through their employer via Pay As You Earn (PAYE) or the following year via a Self-Assessment tax return. Borrowers residing overseas are required to contact the Student Loans Company (SLC) and arrange to make repayments directly to them. Borrowers can also choose to make early repayments on their loan directly to SLC, and when a borrower is close to fully repaying their loan SLC will alert them and, to avoid over-repaying via the tax system, they can arrange to make their repayments via direct debit directly to SLC rather than through HMRC.
Methodology
Loan borrower population
A population of past and future loan borrowers is created containing information about borrowers’ loan amounts, their courses, and various other information about them. To forecast a borrower’s earnings the model needs data on their characteristics such as:
Higher Education provider group (see list of higher education provider groups below)
1994 Group: Loughborough, East Anglia, Leicester, Lancaster, Sussex, Essex, Goldsmiths, Royal Holloway, IoE, SOAS, Birkbeck
University Alliance: Manchester Metropolitan, Sheffield Hallam, Nottingham Trent, UWE, Liverpool John Moores, Northumbria, Plymouth, De Montfort, Portsmouth, Kingston, Hertfordshire
MillionPlus: Leeds Metropolitan, Central Lancashire, Wolverhampton, Middlesex, Birmingham City, London Metropolitan, East London, Staffordshire, Derby, Sunderland
GuildHE: Southampton Solent, Worcester, York St John, Winchester, Chichester and many arts university colleges
Large non-affiliated: Brighton, Hull, Westminster, Kent, Edge Hill, Brunel, Strathclyde, Reading, Swansea, Roehampton, Gloucestershire, Bath, Heriot-Watt
Small non-affiliated: Numerous small colleges
Course subject classifications, with typical subjects of study, are as follows:
Medicine and Dentistry: Medicine, Dentistry (both pre-clinical and clinical)
Subjects allied to Medicine: Anatomy, Pharmacy, therapies, nutrition, optometry, audiology, nursing, medical technology, environmental health
Biological Sciences: Anatomy, Pharmacy, therapies, nutrition, optometry, audiology, nursing, medical technology, environmental health
Veterinary Sciences, Agriculture: Veterinary Medicine and Dentistry (both pre-clinical and clinical), animal science, agriculture, forestry, food studies
Architecture, Building & Planning: Architecture, Surveying, Building, Landscape design, Planning
Social Studies: Economics, Politics, Sociology, Social Policy, Social Work, Anthropology, Human geography, Development studies
Law: Law by area, law by topic
Business & Administrative Studies: Business Studies, Management, Finance, Accounting, Marketing, HR management, office skills, hospitality/tourism
Mass Communication and Documentation: Information Services, public relations, Media studies, Publishing, Journalism
Linguistics and Classics: Linguistics, Literature, English studies, Ancient language studies, Celtic studies, Latin studies, Classical Greek studies, Classics
European Languages and Literature: French studies, German studies, Italian studies, Spanish studies, Portuguese studies, Scandinavian studies, Russian and East European Studies, European Studies
Other Languages and Literature: Chinese studies, Japanese studies, South Asian studies, Asian studies, African studies, Modern Middle Eastern studies, American studies, Australasian studies
Historical and Philosophical Studies: History by period, History by area, History by topic, Archaeology, Philosophy, Theology and religious studies, Heritage studies
Creative Arts and Design: Fine art, Design studies, Music, Drama, Dance, Cinematics and photography, Crafts, Imaginative writing
Education: Teacher training, research and study skills in education, academic studies in education
Combined courses and others not coded: Combined or unknown subject area
Earnings forecasts
The part of the student loan earning and repayment model that forecasts earnings is known as the earnings model. The earnings model predicts earnings and earnings status for each individual for up to 43 years past their SRDD, by which point any outstanding loan balances will have been written off.
An estimate of the earnings path of loan borrowers is necessary to estimate a borrower’s repayments across their repayment term. As loans generate interest throughout their repayment period, the path of an individual’s earnings, rather than their total earnings over the repayment period, has a significant effect on the amount of the loan the borrower will repay.
Figure 4.3 depicts two hypothetical earnings scenarios, A and B, for a Plan 2 loan borrower with the same SRDD and same nominal loan balance at their SRDD of c. £35,000. In both cases the individual has the same total nominal earnings over their supposed 30 year repayment period (for Plan 2 loans) however in scenario B the loan is completely repaid, whereas in scenario A part of the loan (c. £12,000 nominal loan balance) is written off.
Figure 4.3: Earning and loan balance paths for a hypothetical loan borrower with earnings scenarios A and B
The earnings model is divided into two parts, based on the number of years between the forecast year and a borrower’s SRDD. The first 10 years after SRDD are predicted using the ‘early-career earnings model’ and years 11 to 43 are predicted using the ‘long-term earnings model’. The models are split in this way due to different data availability for these periods.
Forecasts are produced based on the assumption that future earnings will follow the same trajectories as those seen in recent historic earnings. In the early-career model these historic earnings are derived from two sources:
Student Loans Company (SLC) administrative data from 2001 to 2020
Longitudinal Education Outcomes (LEO) data from 2014 to 2020
The SLC data includes earnings and employment status in each tax year following a borrower’s SRDD as well as characteristics of the borrower’s most recent period of study, such as subject of study, provider and course level. SLC data does not, however, include the earnings of those who have fully repaid their loan and therefore, once high earners start to repay their loans in full, the dataset provides an unbalanced picture of the earnings of the graduate population. To overcome this, where borrowers have repaid their loans, earnings are taken from the LEO dataset where available, based on fuzzy matching between the SLC and LEO datasets. The model is trained on a subset of the data, from 2014 to 2020, containing earnings and characteristics for borrowers up to 10 years after SRDD.
In the long-term earnings model, historic earnings are derived from HMRC administrative earnings data for 10% of the UK population from 2012 to 2020. This data includes a person’s age and gender, as well as their PAYE and self-assessed earnings. It is not possible to distinguish whether or not people in this dataset are graduates or not. Therefore, it would not be suitable for predicting earnings earlier in a graduate’s career, where we expect a degree qualification to have a signalling effect resulting in graduates earning more on average than non-graduates, even once gender, age and earnings history are taken into account. By 11 or more years after SRDD we assume the label of graduate or non-graduate is immaterial, as any signalling effect is already encompassed within individual’s earnings history (given age and gender). Therefore, the HMRC data offers a richer source of data on earnings for people later in their careers, compared to the SLC/LEO datasets. The Average Weekly Earnings (AWE) index, published by the ONS, is used to re-baseline historic earnings into 2014-15 financial year terms, allowing us to make predictions based on the full time-series of earnings, having already accounted for wage inflation between years.
To create an earnings path for each borrower, both models search their respective training datasets to find the 10 most similar individuals based on various characteristics. In the early-career model, similar individuals are selected from the population with the same number of years since SRDD. The variables that define the proximity between individuals are their Higher Education Institute type, domicile (England or “Borrowers granted partial higher education support under the regulations”), subject of study, gender, age, whether or not the borrower withdrew from studies before graduation and up to three years of previous PAYE and self-assessed earnings. In the long-term model, individuals are selected from the population with the same age and gender, and the previous three years of PAYE and self-assessed earnings are used to define proximity. This proximity is measured by Euclidean distance. To produce an earnings forecast, the model then selects one of those individual’s known earnings in the following year, choosing the individual based on a weighted probability, where weights are the square inverse of the Euclidean distance. Where the weights of the 10 selected individuals are identical, the model selects one at random. Annual PAYE and self-assessed earnings are forecast for each borrower in the sample, and this step is repeated over subsequent years, to generate an earnings path for each individual. Forecasts are produced in 2014-15 financial year terms, and later re-scaled using OBR earnings growth forecasts. Whilst undertaking a course we do not forecast earnings for borrowers. This includes part-time loan borrowers studying for longer than 4 years who have an SRDD in the fifth April after the start of their course, even if they are still studying. Similarly, we do not account for the impact of earnings prior to taking up a university course on earnings on completion. As such, estimated earnings for part-time loan borrowers may be lower than actuals on entry into the labour market. However, we do not expect this impact to be long lasting and modelled earnings for part-time loan borrowers will tend towards those of full-time loan borrowers as they progress through their career.
Mortality
ICR loans can be cancelled prior to the end of the repayment term if the borrower dies. The probability of death in a given year is derived, based on the borrower’s age and gender, from ONS life tables and SLC data. SLC data shows lower write-off levels than would be expected from ONS mortality rates, most likely reflecting that graduates have lower mortality rates than non-graduates of the same age. But the historic SLC data has little coverage of student loan borrowers aged above their mid-30s, so a weighted average of the two sets of mortality rates is taken that gives a high weighting to the SLC data at younger ages and to ONS data at older ages.
There are reasons other than death why a loan may be written off before the end of a borrower’s repayment term, however, the level of these write-offs is comparatively small and as such they are not included in the model.
Repayments made directly to SLC
In addition to obligatory repayments collected through the UK tax system, repayments can also be paid directly to the SLC. These fall into three main categories:
Voluntary repayments (prepayments) – These are (early) repayments made by the individual in addition to their obligatory repayments.
Payments from overseas – Repayments from borrowers situated overseas cannot be collected through the tax system. Overseas borrowers make obligatory repayments direct to the SLC based on their income and the earnings threshold for their country of residence.
Direct debits – In the last couple of years of payment the SLC offer the borrower the opportunity to repay the rest of their loan through a direct debit to prevent overpayment.
The probability of a borrower making a voluntary repayment each year is generated from a multinomial logistic regression model based on SLC administrative data. Voluntary repayments are particularly dependent on the magnitude of the debt outstanding and the number of years into the repayment period, as well as whether a borrower has previously made a voluntary repayment. The regression model also takes into account obligatory repayments, whether a borrower is close to repaying their loans in full, earnings and the proportion of the balance previously paid via voluntary repayments. Most voluntary repayments come from borrowers with low amounts of debt in the first few years of repayment. If a borrower is due to make a voluntary repayment in the model, they are either categorised as repaying their outstanding balance in full or repaying a percentage of the debt outstanding as a direct repayment. For those borrowers not repaying their outstanding balance in full, the percentage to be repaid is derived using a second logistic regression model. This model takes into account voluntary repayments in prior years, the numbers of years into the repayment period, outstanding balance, earnings, and obligatory repayments.
Borrowers can also make overseas repayments. The probability of a borrower making an overseas repayment each year is generated from a decision tree model based on SLC administrative data. The decision tree model considers the borrower’s plan type, earnings over threshold, and previous overseas repayments. The size of the repayment is selected at random from a distribution of repayment amounts based on the borrower’s overseas repayments last year and their plan types.
Postgraduate loan borrowers
Limited historic data on postgraduate loan borrowers is available as they are new loan products (introduced in 2016). In addition, information on postgraduate earnings and behaviours from survey data is limited, as in population surveys the proportion of the survey respondents that have postgraduate degrees is very small. Therefore, the student loan repayment model generates employment and earnings forecasts for postgraduate loan borrowers using the same earnings model as for first degree students with the same characteristics, to which it then applies a fixed uplift to earnings in all years to account for the higher earnings postgraduates are expected to have.
For Master’s borrowers an earnings uplift of 8.9% is applied for male borrowers and 10.3% for female borrowers. This is based on research that estimated this to be the average marginal earnings gain for Master’s students on top of their undergraduate degree (Conlon & Patrignani, 2011). For Doctoral students, an earnings uplift of 8.0% is applied for male borrowers and 6.0% for female borrowers. These uplifts are based on results for Doctoral students from the same research, though they have been adjusted down to account for trends associated with subject of study and HEI group. This adjustment aims to account for factors such as this, which were not considered directly in the research due to the available sample size but are important in student finance forecasting. For example, the population of students expected to take up Doctoral loans is not representative of all Doctoral students since the availability of other funding sources (such as industry or research council funding) may differ by subject of study. These uplifts for Master’s and Doctoral students are not directly comparable, as the other factors will also affect the average earnings for each course level.
As there is only very limited administrative information available, postgraduate loan borrowers are assumed to have similar behaviours as an equivalent first degree student for factors such as such as voluntary repayments, overseas repayments and mortality.
Obligatory repayment amounts
Once annual earnings are calculated and non-employment, migration, and mortality are taken into account, the obligatory repayments are calculated according to the deterministic repayment rules for that year. Once obligatory repayments have been calculated, a repayment frictions model is applied to them to reflect the historic differences observed between repayments due based on the deterministic calculation and observed obligatory repayments. These frictions can be due to e.g. seasonal earnings patterns (which could result in higher repayments than otherwise) or the borrower’s earnings coming from multiple jobs (which could result in lower repayments than otherwise).
Repayment frictions
The probability of a borrower having a repayment friction each year is generated from a decision tree model based on SLC administrative data. Incidence of repayment frictions are dependent on the magnitude of the debt outstanding for loans of various plan types, earnings in both the current and prior year, the change in earnings, the number of years into the repayment period, as well as a borrower’s repayment frictions in the prior year.
If a borrower is due to have a repayment friction in the model, they are either categorised as having a positive friction (borrower repaying more than they are obliged to based on earnings), a large negative friction (borrower repaying less than they are obliged to based on earnings), or a small negative friction (less than £15).
For those borrowers due to have a small negative friction, the repayment adjustment is taken from a random distribution of historic small negative repayment frictions. For borrowers due to have a positive friction, the repayment adjustment is selected at random from a distribution of repayment amounts (expressed as percentages of earnings over the repayment threshold) based on the borrower’s plan types, earnings over threshold and earnings change from the prior year. Finally, for borrowers due to have a large negative friction, a multiple regression model is used to determine the size of the repayment friction. This model uses a borrower’s frictions in the prior year, earnings change from the prior year, obligatory repayments in the prior year, and obligatory repayments in the current year.
Repayment amounts and debt outstanding
All obligatory, voluntary, and overseas repayments that the borrower makes each year are summed together, up to a maximum of the borrower’s remaining loan balance. Borrowers are assumed to stop repaying their loan once their loan balance reaches zero. The model does not account for borrowers making overpayments or receiving refunds after overpaying.
To calculate the size of a borrower’s loan balance, borrowers are given annual outlay amounts while on their course based on the distribution of outlay amounts of historical borrowers in the SLC data, uprated in line with forecast RPIx depending on the appropriate loan policy. Capitalised interest is accumulated and added to the borrower’s balance each year, with any repayments subtracted from it. The size of the borrower’s debt is calculated on this basis each year until they either fully repay their loan or until their loan is cancelled, either due to mortality or because they reached the end of their repayment term.
In reality HMRC share repayment data with SLC on a weekly basis, but as an approximation all PAYE and voluntary repayments in the model are assumed to be made in the middle of the financial year. Repayments from self-assessment (SA) are correctly assumed to be made at the end of each January, for the previous financial year. Interest for the first half of the financial year is added to the debt outstanding at the start of the year before PAYE and voluntary repayments are made, then the interest for the next four months is added after they have been deducted, then finally the interest for the final 2 months is added after any SA repayments have been deducted. In academic years where a borrower is forecast to receive loan outlay, these are assumed to occur in three instalments at the end of September, January and April. Interest is accrued on these payments and applied to the loan balance accordingly.
If a borrower’s loan is cancelled this is assumed to happen at the end of the financial year, as this is the point when cancellations will occur at the end of a borrower’s repayment term, which are expected to account for the large majority of cancellations.
RPI and Bank of England base rate figures are based on OBR forecasts. The interest rates for each part of the year are calculated and then combined into an annual average that is used across the financial year. For all loan plans, the RPI figure used in calculating interest rates changes each September to the March RPI figure published by ONS in the same year, but as OBR only publishes quarterly forecasts (and in the long run only annual forecasts) the model uses the forecast for the equivalent January to March quarter in the short run, and the annual figure for the same financial year in the long run. For Plan 1 loans, quarterly base rate forecasts are averaged over each financial year, with this average +1% compared to the financial year RPI to determine which to use for interest rates that financial year.
Population totals
Forecasts for individual loans are aggregated together to estimate totals for the whole student loan population. Rather than making estimates for the whole population of loans, to make the model more efficient (i.e. quicker to run), forecasts are made for a sample of loans, with weightings applied to these loan results to “scale up” to the correct totals for the whole population.
A random sample of 500,000 borrowers is used, covering entrants from 1998/99 to 2084/85 (excluding those that have already finished repaying their loans or had them cancelled).
As the Doctoral loan is a new product from 2018/19, limited outturn data is available and so borrowers are based upon historical HESA data. Until sufficient administrative data becomes available, the same sample size of Doctoral borrowers is used, and the results are scaled down to match new forecasted entrant numbers.
The scaling used to increase the sample results to population totals is weighted based on several variables to reduce the sampling bias in the model. The variables used in the weighting are course start year, SRDD, gender, course level, whether the course is a STEM subject, plan type, whether the loan is for full time or part time study, and how many loans the borrower has.
The Resource Accounting and Budgeting (RAB) charge and the stock charge
The RAB and stock charges are the estimated cost to Government of providing a subsidy for the student finance system. They are the proportion of loan outlay (the RAB charge) and of the total outstanding loan balances (the stock charge) that are expected to not be repaid when future repayments are valued in present terms.
To calculate the RAB charge, the total outlay in a given year is added up and compared to the total net present value (NPV) of the repayments that are anticipated in connection with this same outlay. The RAB charge is calculated as
Similarly, the stock charge is calculated by summing all outstanding loan balances at the start of the year and comparing this to the total net present value (NPV) of the repayments that are anticipated in connection with these loans. The stock charge is calculated as
The NPV of future repayments is calculated by discounting all future repayments at a rate of RPI-1.05% per year until the end of financial year 2029-30, and RPI-0.05% per year from financial year 2030-31, to the same point in time as the loan outlay or loan balance. This is the discount rate for financial instruments set by HM Treasury (HMT) (opens in a new tab) and is intended to reflect of the cost of Government borrowing.
Student loans are valued in DfE’s annual accounts in line with the International Financial Reporting Standard (IFRS) 9, under which where future cash flows are discounted to measure the fair value of a financial asset, this should be done using the higher of the rate intrinsic to the financial instrument or the Her Majesty's Treasury (HMT) discount rate. For student loans the intrinsic rate would be the discount rate that gave a RAB or stock charge of 0%, so the HMT discount rate is used provided the RAB charge is greater than 0%. Should the HMT discount rate result in a RAB charge calculation giving a negative value then the intrinsic rate is used instead, meaning that that RAB charge will take a value of 0%.
In the model, RAB charges are calculated for the loan book as a whole by first calculating the NPV of individual borrowers’ repayments, then for each year aggregating these together across all borrowers and comparing them to their total loan outlay in that year. Stock charges are calculated in the same way, aggregating the NPV of individual’s repayments before aggregating them to a population total and comparing this to the face value of the loans at that point in time. Where a borrower has more than one year of outlay or has both future loan outlay and an existing loan balance that will be included in the stock charge, future repayments are allocated between each year of their loan outlay and their existing loan balance in proportion to the relative balances of each loan when valued at the same point in time (i.e. taking into account interest accrued on the earlier loan balances). A RAB charge is no longer produced for Plan 1 loans as very few Plan 1 students are still receiving loans.
Data quality
RAB and stock charge estimates require earnings and repayments forecasts covering the subsequent 40 years. These forecasts depend heavily on input data sources, modelling techniques and assumptions used in the model. Consequently, forecasts are inherently uncertain owing to the inevitable uncertainties associated with these sources, assumptions, and methods. For example, the model assumes that the distribution of future earnings paths will be similar to historic distributions derived from SLC and HMRC administrative data, which will not necessarily be the case, particularly in the long term.
The model is dependent on the OBR macroeconomic forecasts that it uses to uprate earnings, calculate interest rates and repayment thresholds, and to discount future repayments to present values. Any significant changes to the economy from these forecasts could affect the repayments that will be made on student loans.
“Where future cash flows are discounted to measure fair value, entities should use the higher of the rate intrinsic to the financial instrument and the real financial instrument discount rate set by HM Treasury (promulgated in Public Expenditure System (PES) papers) as applied to the flows expressed in current prices.”
For the purpose of valuing student loan repayments when the discount rate falls the value of future repayments goes up, and vice versa.
Table 4.2 shows that when the discount rate and the earnings growth rate are flexed in the same direction the RAB charges undergraduate borrowing don’t move by more than a few percentage points. When these numbers are flexed up plan 2 RABs fall, as the majority of Plan 2 borrowers do not fully repay, and the increased earnings growth pushes more borrowers into repayment. Plan 5 RABs are much less affected as more borrowers are expected to fully repay, and as none are expected to repay significantly more than they borrowed.
When the discount rate and the earnings growth rate are flexed in the opposite direction, larger changes in the RAB charges. When the discount rate is dropped 0.2pp (valuing future repayments more) and the earnings growth rate is dropped 1pp, future repayments are forecast to drop (as earnings decrease), while the value of those “lost” repayments goes up. The result is a rise in RAB charge as the model forecasts less of the loans outlaid will be repaid.
Varying RPI has a larger effect on RABs and stock charges. For all plans increasing RPI increases interest rates, and therefore future loan balance face values. With no change to the earnings growth rate less borrowers in the model with fully repay their loans, and both the stock charge of Plan 1 loans and the RAB charges of Plan 2 & 5 loans are driven up. Increasing the RPI uplift from 1pp to 2pp, shows this relationship is not linear, as the changes to stock/RAB charges do not double.
When the RPI forecasted is flexed downwards the opposite happens, lowering both the stock and RAB charges. However, RAB charges for Plan 2 and Plan 5 loans don’t fall by the same amount as the raised when RPI was increased, reinforcing that the relationship between RPI and RAB is not linear.
Table 4.2: Sensitivity of Stock and RAB charge forecasts to variations of key economic inputs
Macroeconomicchange
Plan 1 stock charge
Plan 2 full-time RAB charge
Plan 2 part-time RAB charge
Plan 5 full-time RAB charge
Plan 5 part-time RAB charge
HMT discount rate +0.2pp, earnings growth +1.0pp, until 2030
-2pp
-4pp
-3pp
-2pp
-1pp
HMT discount rate +0.2pp, earnings growth -1.0pp, until 2030
+3pp
+5pp
+5pp
+3pp
+2pp
HMT discount rate -0.2pp, earnings growth +1.0pp, until 2030
-3pp
-5pp
-5pp
-3pp
-3pp
HMT discount rate -0.2pp, earnings growth -1.0pp, until 2030
+2pp
+4pp
+3pp
+2pp
+1pp
RPI & RPIX +1.0pp throughout the forecast period
+5pp
+14pp
+13pp
+11pp
+7pp
RPI & RPIX +2.0pp throughout the forecast period
+9pp
+26pp
+25pp
+22pp
+16pp
RPI & RPIX -1.0pp throughout the forecast period
-5pp
-14pp
-12pp
-9pp
-5pp
RPI & RPIX -2.0pp throughout the forecast period
-10pp
-27pp
-21pp
-15pp
-9pp
Table 4.2 shows the percentage point (pp) change to the forecast 2023-24 stock (Plan 1 loans) and RAB (Plan 2 and Plan 5 loans) charges as a consequence of varying each listed macroeconomic input up or down by 1pp from years where there aren’t published outturn values. For discount rate and earnings growth rate changes were varied up to 2029-30, RPI was varied for the length of the forecast from 2024-25 onwards.
Table 4.3: Sensitivity of RAB charge forecasts to variations of key policy inputs
Policy change
Plan 1 Stock charge
Plan 2 full-time RAB charge
Plan 2 part-time RAB charge
Plan 5 full-time RAB charge
Plan 5 part-time RAB charge
Repayment threshold
+£1,000
+2pp
+2pp
+2pp
+1pp
+1pp
-£1,000
-2pp
-2pp
-2pp
-1pp
+0pp
Table 4.3 shows the percentage point (pp) change to the forecast 2023-24 RAB charges as a consequence of varying the repayment threshold by £1,000 in 2024-25 (with the threshold growing at the usual rate after that).
The model uses SLC administrative data to determine borrower characteristics, loan amounts, earnings in the first three years of their repayment term and repayments made directly to SLC. As an administrative source the historical SLC data should be broadly accurate, although the earnings and direct repayment forecasts rely on future borrowers having similar behaviours to historic borrowers.
Where new loan products are being introduced, the forecasts are more uncertain as there is less historical information available on which to base forecasts and more uncertainty about what student behaviours will be in response to policy. This is particularly the case for the two postgraduate loan products, for which the earnings forecasts are less well developed than for undergraduates and for which there is very limited historical information about loan borrowers’ characteristics and behaviours.
Since the 2023 publication there have been a number of updates to the forecast. There have been revisions to the data, economic assumptions, policies and modelling methodology. These have all combined to change the forecast RAB charges for 2023-24 in comparison to the previous publication. The effect of these factors on the RAB forecast is shown in Table 4.4. The figures are rounded to the nearest percent or percentage point.
Methodology updates: improvements and fixes to the repayments model.
Table 4.4a: Changes in the 2023-24 RAB charge for Plan 2 full-time higher education loans in comparison to the previous publication (June 2023).
23-24
24-25
25-26
26-27
27-28
June-2023 annual publication
28%
29%
31%
34%
37%
June-2024 annual publication
30%
30%
32%
36%
42%
Change since June 2023 publication
+1pp
+1pp
+1pp
+2pp
+5pp
Note: all figures are rounded to the nearest percentage / percentage point. The change is calculated from the raw numbers and then rounded – therefore the change may differ from subtracting the rounded components.
Table 4.4b: Changes in the 2023-24 RAB charge for Plan 5 full-time higher education loans in comparison to the previous publication (June 2023).
23-24
24-25
25-26
26-27
27-28
June-2023 annual publication
27%
26%
25%
24%
23%
June-2024 annual publication
29%
29%
29%
29%
29%
Change since June 2023 publication
+2pp
+3pp
+4pp
+5pp
+6pp
Note: all figures are rounded to the nearest percentage /percentage point. The change is calculated from the raw numbers and then rounded – therefore the change may differ from subtracting the rounded components.
Comparison of forecast earnings with actuals
Earnings forecasts from prior years can be compared to actual earnings that subsequently become available in the SLC administrative data. One way to assess the accuracy of the forecasts is to calculate the differences (positive or negative) between forecasts and actuals for each individual borrower. Figure 4.4 shows the distribution of these individual-level absolute errors as the number of years since a borrower’s SRDD increases. Accuracy is lower in the first few years after SRDD as there is relatively little data on the previous earnings of these borrowers, and improves as data on prior earnings becomes available.
Figure 4.4: The distribution of absolute differences in predicted and actual earnings for borrowers in repayment between 2020-21 and 2021-22
The individual level errors shown in Figure 4.4 can be fairly large, for example if the employment status of a borrower is incorrectly predicted. While minimising individual-level errors can help to predict accurate earnings trajectories, accuracy at the individual level is not necessary when calculating population-level financial metrics such as the RAB charge. Here, it is more important that the distribution of earnings is accurate, particularly around repayment thresholds. This can be assessed by comparing predicted and actual earnings distributions, as shown in Figure 4.5. Note that assessment of the long-term model only includes years up to a maximum of 22 years after SRDD, as actual earnings are not available beyond that yet. The distribution of long-term earnings is lower than that of the early-career earnings as these are primarily plan 1 borrowers who are yet to repay their loans, and are therefore skewed towards lower earners.
Figure 4.5: Forecasted and actual distributions of earnings between 2020-21 and 2021-22 for borrowers still repaying, with different numbers of years since their SRDD
The difference between predicted and actual earnings distributions can be summarised as the Wasserstein distance. When updating the model we carry out testing to check that any changes lead to reductions in accuracy metrics such as the individual-level absolute error and the Wasserstein distance.
Comparison of forecast repayments with actuals
The student loan repayment model is designed to forecast repayments across loan borrowers’ repayment terms. Comparisons between forecast repayment totals (one or two years ahead) for individual years and the actual outturn data can give an indication of how well the model has been performing. Table 4.5 shows how recent outturn figures compare with the forecasts made at the time. Improvements are made to the student loan earnings and repayment model each year and the data used in it are updated, so forecasts are shown as made at both the start and end of each tax year. The 22-23 forecast might be less accurate due to covid effects, and we are still investigating this.
Table 4.5: Forecast and outturn repayments across all higher education loan products
Outturn
Forecast
Tax Year
Date
£ million
Date
£ million
Difference
2013-14
30/04/2014
1,590
31/03/2014
1,630
2.5%
2014-15
30/04/2015
1,750
31/03/2014
31/03/2015
1,870
1,920
6.9%
9.7%
2015-16
30/04/2016
1,930
31/03/2015
31/03/2016
2,140
1,930
10.9%
0.0%
2016-17
30/04/2017
2,220
31/03/2016
31/03/2017
2,320
2,250
4.5%
1.4%
2017-18
30/04/2018
2,340
31/03/2017
31/03/2018
2,570
2,470
9.8%
5.6%
2018-19
30/04/2019
2,530
31/03/2018
31/03/2019
2,700
2,600
6.7%
2.8%
2019-20
30/04/2020
2,402
31/03/2019
2,990
24.5%
31/08/2020
2,690
12.0%
2020-21
30/04/2021
2,794
31/08/2020
2,885
3.3%
31/03/2021
2,840
1.6%
2021-22
30/04/2022
3,394
31/03/2021
3,190
-6.0%
14/07/2022
3,318
-2.2%
2022-23
30/04/2023
4,230
14/07/2022
4,004
-5.3%
29/06/2023
3,475
-17.9%
2023-24
30/04/2024
4,646
29/06/2023
4,245
-8.6%
24/04/2024
4,489
-3.4%
These figures include repayments made directly to SLC and PAYE and Self Assessment repayments made via HMRC. Direct repayments are recorded against the year they are received by SLC, HMRC repayments are recorded against the year of the earnings they relate to.
Repayments across all loan products are included in the data. Up to 2015-16 only Plan 1 borrowers were eligible to make obligatory repayments, though Plan 2 borrowers could make voluntary repayments. From 2016-17 the first Plan 2 borrowers became liable to make obligatory repayments, and the first Plan 3 borrowers will in 2019-20. Variances between forecasts and actuals will be due to a range of factors, including Macroeconomic shifts and new data
Modelling variances (i.e. simplifications and assumptions made in the modelling) and random variation (e.g. through sampling)
Operational factors that result in lower than expected collections.
By the time of the second forecast shown for each year the macroeconomic data for the year will largely be known, so the forecast is less dependent on OBR macroeconomic forecasts. However, modelling changes and other data updates will also have occurred so changes between the two forecasts also include other factors.
Advanced Learner Loans (ALLs) are tuition fee loans to help those aged 19+ at the start of their course meet the up-front costs of regulated Further Education (FE) qualifications at Levels 3 (equivalent to 2 A levels) to Level 6 (equivalent to an undergraduate degree) in England. ALLs were introduced in 2013/14 to those aged 24+ and at levels 3-4 following a refocusing of the Adult Education Budget on adults requiring skills and learning to equip them for work, an apprenticeship or further learning.
Following a public consultation in 2014, the extension of ALLs in academic year 2016/17 to those aged 19-23 and to Levels 5-6 has been the programme’s most significant change.
The RAB charge for ALLs is the estimated cost to Government of borrowing to support the ALLs system. The purpose of the DfE ALLs model is to assist in valuing the existing ALLs loan book and to provide forecasts for budgeting purposes.
The RAB charge is an estimate, and it is heavily dependent on assumptions around the future income of ALLs borrowers. The methodology, data sources and assumptions are presented in the section below.
Methodology
The ALLs model is a micro-simulation model. The model creates thousands of simulated borrowers with a variety of characteristics. Each borrower is assigned a debt and their earnings are projected for the length of their repayment term. Then the repayment rules are applied to each borrower to estimate their repayments, individual loan balance and interest for the length of their repayment term. The assumptions used in the simulation model fall into five main sections below:
Borrowers’ characteristics and their loan details
Macroeconomic assumptions: Average Earnings Index (AEI) and Retail Price Index (RPI)
Loans policy assumptions
Annual income post learning: employment status, income and income distributions
A. Labour market status
B. Position on income distribution
C. Annual income in nominal terms
Life events
A. Mortality
B. Migration
C. Permanently unfit for work
D. Extending working lives
E. Voluntary Repayments
1. Borrowers’ characteristics and their loan details
Analysis of administrative Student Loans Company (SLC) data informs the input assumptions on the characteristics of borrowers in each academic year, the courses they study and the average loan size they take. The complete list of input parameters are:
Total number of new borrowers
Proportion of borrowers on multiple courses
Course type (A levels, Access to HE, Level 3 Diploma, Level 4 Diploma, Level 3 Certificate, Level 4 Certificate and Level 5/Level 6 courses)
Gender split of borrowers
Age distribution: 19-71 by single year of age
Course duration in months: 1-24 months
Course start month across academic year
Average loan size by type of course (A levels, Access to HE, Level 3 Diploma, Level 4 Diploma, Level 3 Certificate, Level 4 Certificate and Level 5/Level 6 courses)
Non-completion rates by type of course
2. Macroeconomic assumptions
The model uses the Office for Budget Responsibility (OBR) assumptions on future average earnings growth (AEI) and RPI projections to calculate future repayment thresholds, interest rates and discount rates for the ALLs. The model also uses the AEI projections to uprate borrowers’ future earnings. Course costs are uprated by Consumer Price Inflation (CPI).
3. Loans policy assumptions
Loans are currently available to learners aged 19+ who are studying Level 3 and above qualifications. The loans are repaid at a rate of 9% of pre-tax earnings above the lower repayment threshold. ALL borrowers are either liable for a plan 2 or plan 5 loan product and their main repayment conditions are described in Table 4.1: Key policy details for each loan product.
Additionally, there are a number of scenarios where a borrower may have their outstanding loan written off. These are:
borrowers who study Access to HE courses and complete a higher education course. The model assumes that half of Access to HE borrowers are eligible for this.
if a borrower’s course is no longer offered by the provider. The model assumes that there is a 1 in 600 chance that the borrower will be eligible for this.
From April 2021, learners aged 19 and older who take their first full level 3 qualification will be grant funded for a number of level 3 qualifications, and therefore do not require an advanced learner loan. From April 2022, any adult in England earning under the National Living Wage will also be able to access these qualifications for free, regardless of their prior qualification level.
4. Annual income post learning – employment status, income and income distributions
The key assumption in the model is the future annual income of borrowers after finishing their course.
ALLs are income contingent, i.e. borrowers repay the loan only if their annual income is above the lower repayment threshold, and their repayment amount is based on their income. Annual incomes are the basis for calculating loan repayments and interest; and getting good income estimates over the working life of borrowers is critical to estimating the RAB charge.
Income modelling starts for a borrower in the first financial year after completing their course. In most cases this will be the first year they are due to make repayments, except during the first years of the policy where it was legislated that the first repayments would not be due until 2016-17.
In every simulation each modelled borrower will go through processes to:
Set labour market status – transition between ‘employed’, ‘self-employed’ and ‘no income’
Assign position on income distribution – one of the 25 quantiles
Assign annual income based on their position in the income distributions.
The income distribution is split into 25 equal quantiles, so each quantile represents 4% of the income distribution. Quantile 1 = the lowest earning. The number of quantiles was determined by considering the greatest detail that the LFS could provide whilst still providing robust estimates.
The process is summarised below in figure 5.1:
Figure 5.1: Income modelling for ALL borrowers
A. Labour market status
Labour market status in a given year is modelled using a discrete probability matrix which supports three possible outcomes: ‘no income’, ‘employed’ and ‘self-employed’. The matrix has different probabilities depending on the characteristics of the borrower:
Course level: Level 4+, Level 3
Gender: male or female
Age: single year of age
Years since completed learning: one year, or more than one year
Current labour market status: no income, employed or self-employed
Current year income: one of 25 quantiles, or zero if not in employment.
The data source for the analysis is 68 quarters of Labour Force Survey (LFS). The model uses two sets of labour market assumptions:
Initial labour market status for those in the first full financial year after their SRDD.
Probabilities of changing labour market status in all subsequent years.
In the first year after SRDD the model has no knowledge about the working history of each borrower so borrowers are assigned a labour market status only in accordance with the overall employment rates for that age, gender and course level. (Ideally, the data would be further restricted to learners that are one year post learning but there is insufficient data in the LFS to support this. The initial employment status is quickly eroded by the subsequent employment transition probabilities so has a fairly limited impact on the forecast.) In all subsequent years, each borrower has a probability of changing labour market status, this time taking into account their current labour market status and income quantile (if in employment).
The income based labour market changes from the employee population are used as a proxy for the self employed, i.e. if an employed borrower at the bottom of the income distribution has a greater chance of becoming unemployed compared to someone at the top, then a self-employed borrower at the bottom of the income distribution also has a greater chance of becoming unemployed compared to someone at the top.
B. Position on income distribution
After labour market status is established in the previous stage, the model assigns the borrower to a quantile on the income distribution. As with labour market status, the model assigns future income quantiles, using different probabilities depending on the characteristics of the borrower:
Course Level: Level 4+, Level 3
Gender: male or female
Age: single year of age
If in work: current income: one of 25 quantiles
If out of work: previous income when last in work: one of 25 quantiles or zero if never previously modelled in work.
Income information for the self-employed is not used, and so it is assumed that transitions across the income distribution for those who are self-employed occur with the same likelihood as for those who are employed.
The LFS data only have sufficient information to compare year on year income, they do not hold robust income information from previous employment spells further back in time. As a simplification it is assumed that a borrower returns to work at the same position on the income distribution as they were in their most recent modelled income spell, e.g. if they left work in quantile 10 then when they return to work they will return in quantile 10.
The LFS data suggest that typically those entering work are on lower incomes than the equivalent group leaving work. This would suggest a natural decay in position on the income distribution between jobs, i.e. that learners should return at a lower position on the income distribution. In practice this does not produce a good modelling solution as it creates a heavy penalty for being out of work in a single year and produces unexpected lifetime income paths inconsistent with the original simplified assumption.
C. Annual income in nominal terms
The last step in modelling income is to convert an income quantile into actual income for a given financial year. This is achieved by matching the simulated income quantile to historic income quantile distributions. There are different distributions by:
Course Level: Level 4+, Level 3
Gender: male or female
Age: single year of age
Years since left learning bands: 1-3 years; 4-10 years; 11+ years.
Income distributions in the first 3 years post training are constructed using actual ALL learner earnings from Longitudinal Education Outcomes (LEO) data. For the years 4-10 post training, we also use LEO data, but use a proxy group of learners who started before the introduction of ALLs that have similar characteristics to those that now on ALL courses. Beyond 10 years post training, we use 14 years of the Annual Population Survey (APS). Income is inflated for future growth using OBR projections of average earnings growth for the entire working population. It is also assumed that average earnings growth is even across all quantiles.
The use of LEO data is a change to the methodology relative to the 2018-19 publication. Previously only APS data was used to inform the model income forecasts, as this was the best source available at the time. It was not possible to isolate ALL-funded students in the APS data. That led to overstating the incomes of the students. Changing to LEO data has lowered the income forecasts for the ALL-funded students, meaning fewer students will reach the repayment threshold. This has led to an increased RAB and an increased transfer proportion.
5. Life events
From the time a borrower takes out a loan there are additional events that need to be factored into the modelling, such as mortality, migration, and repayments made directly to SLC. All are modelled at the individual level, but to varying levels of detail.
A. Mortality
The Office for National Statistics (ONS) England & Wales population projections include assumptions on mortality rates by gender and single year of age up to 2084. These are used directly to estimate the chance of death for a borrower in any year post taking out a loan, with the key assumption that ALLs borrowers have the same mortality rates as the rest of the population.
It is likely there is some difference in mortality rates for this group compared to the whole of England & Wales, particularly as there is a known link between increased wealth and improved mortality. However, any difference will be minimal and the mortality rates for the working age population are generally very low, further reducing any possible impact.
B. Migration
The migration assumptions are split into two components
Probability of migrating out of the country (out-migrant rate)
Length of spell out of the country: 3 years, 7 years or 20 years.
Out-migrant rates by gender and age are derived by combining Office for National Statistics (ONS) out-migrant estimates with population projections. These are used directly to provide each borrower with a probability of becoming an out-migrant each year.
Once an out-migrant is identified the model assigns a length of stay for the duration abroad. The length and probability of duration are estimated from International Passenger Survey (IPS) data between 1978 and 2012, over which time actual length of stay figures have been relatively stable.
C. Permanently unfit for work
The model uses expected flows into the Support Group of Employment Support Allowance (ESA) as a proxy for the number of people expected to become unfit for work each year. Once identified as unfit for work, a simulated borrower has their remaining loan amount written off.
The ESA Support Group is not a perfect measure of the unfit for work rate, as it is a State Benefit that needs to be actively claimed so is not received by everyone that is eligible. It does, however, provide the closest match in definition to unfit for work which is sufficient for the relatively low number of people that will be affected.
D. Extending working lives
State Pension Age (SPA) is due to rise to 67 for men and women by 2024. For women this represents an additional 7 years of work before they will receive their State retirement benefits compared to the observed population in the model data sources.
The model includes a timetable to assign each borrower a State Pension age, from which there is an assumption around what employment rates and income to use given to reflect that people are likely to work for longer. For example, a man with SPA of 65 has the employment rate of a 66-year-old when aged 66, but a man with SPA of 66 has the employment rate of a 65-year-old when aged 66. This assumes that for men, the employment rate and income of a 65 year old is held constant between the ages of 65 and the new State Pension age; for women the employment rate and income of a 60 year old is held constant between the ages of 60 and the new State Pension age.
The effects of retirement can be seen in LFS data from as early as age 50, however, given the high uncertainty about how people will respond to the rises in State Pension age, and future employment in general, the model uses the simplified assumption.
E. Voluntary Repayments
A borrower may make voluntary repayments in addition to the repayments collected via the UK tax system, The model assumes that each borrower will make an additional yearly voluntary payment. The size of the repayments is based on historic data and depends on the outstanding debt and the number of years in the repayment period.
Data quality
Forecasting repayments are inherently uncertain and relies on the data sources, modelling techniques and assumptions. Specifically, the model assumes the characteristics of future borrowers will be the similar as historic ones derived from SLC administrative data.
Both administrative and survey data are used to inform the assumptions underpinning the ALLs model. SLC administrative data are used to determine borrowers' characteristics and loan amounts. Historic SLC data should be broadly accurate.
The Labour Force Survey (LFS) is used to determine employment and income state movements of ALLs borrowers and the Annual Population Survey (APS) and Longitudinal Education Outcomes (LEO) are used for the income distributions. The LFS is a survey of the employment circumstances of the UK population. It is the largest household survey in the UK and provides the official measures of employment and unemployment. The APS is a supplement to the LFS data. The APS is published quarterly, and each dataset contains 12 months of data. The sample size for each dataset is approximately 170,000 households and 360,000 individuals. LEO (opens in a new tab) brings together information from the Department for Education with employment, benefits and earnings information from the Department for Work and Pensions and Her Majesty’s Revenue and Customs.
The employment and income status assumptions are two of the most important assumptions within the model. We derive both of those from the LFS assuming that Further Education achievers in the LFS data are representative of ALLs borrowers.
The LFS data has various limitations and a key drawback is that only one year of earnings history is available. The key assumptions arising from the data limitations are listed below:
The model 'forgets' a borrower’s past history of employment statuses prior to the current year, since a one-step transition approach is used. This means that a learner who has been unemployed for 10 years has equal chance of employment as someone who has been unemployed for just one year.
Transitions between employment states for the employed are used as a proxy for self-employed people. Self-employed people are assumed to have the same income distribution as employed learners.
We assume that a borrower who has left the labour market will return to work at the same position on the income distribution as they were in their most recent employment spell.
Despite these limitations, the LFS is the only data source currently available that has a sufficiently large sample to allow analysis of income by age and type of qualifications and transitions in and out of employment. We now have some actual repayment data from the Longitudinal Education Outcomes, but only for a limited number of years. We will seek to use this data alongside the LFS data to improve these assumptions.
The model is also dependent on the OBR macroeconomic forecasts that it uses to uprate earnings, calculate interest rates and repayment thresholds, and to discount future repayments to present values. Any significant changes to the economy from these forecasts could affect the repayments that will be made on the Advanced Learner Loans.
These forecasts show how much Government outlay on student loans is expected to be in future, how much is expected to be repaid and how the student loan book may grow in the future. The Department for Education uses these models for financial planning and in the development of student funding policies.
These forecasts are also used in the Department for Education’s annual accounts in the valuation of the student loan book and in the public sector finance statistics. The stock charge, RAB charge and transfer proportion are used to impair the face value of the loan books and the value of new loans being issued respectively, to reflect that the value of the future repayments that will be received in relation to these loans is less than the long term cost of Government borrowing that would be necessary to cover its outlay on student loans.
These models are used by the Office for Budget Responsibility as part of its estimates of public sector borrowing, including in its Economic and Fiscal Outlook that presents economic forecasts five years into the future and its Fiscal Sustainability Report that presents long term projections of UK public finances.
The year from 1 August to 31 July. Throughout the publication this is denoted in the format ‘2012/13’ to describe the year from 1 August 2012 to 31 July 2013.
Advanced Learner Loan (ALL)
A fee loan payable to Further Education (FE) providers on behalf of FE learners who meet the eligibility criteria and started a FE course on or after 1 August 2013.
Cancelled loans
The borrower no longer has any liability to repay, as provided for in the loan’s regulations. A borrower’s liability is cancelled:
On the death of the borrower;
On reaching the age or length of time cancellation criteria for their loan (which varies by loan product); or,
If borrower is in receipt of a disability related benefit and permanently unfit for work.
Capitalised interest
The interest accrued on student loans is added to a borrower’s loan balance, rather than requiring repayment at the time it is accrued.
Doctoral loan
Loans issued to students on Doctoral courses, on the Plan 3 repayment system. They are paid directly to students and can be used to cover fees or living costs.
Domicile
The usual residence of a student in the period prior to commencement of study. The financial support available to students from Government can vary for students from different domiciles. This publication includes forecasts of entrant numbers for English and EU domiciled students. Wherever ‘EU domiciled’ students are referred to this includes students domiciled in countries other than the UK that count as EU domiciled for funding purposes.
Entrants
Students in their first year of study. Defined as those starting a course in the academic year who have not been active at the same broad level of study at the same provider in either of the two previous academic years.
Face value of loan book
The total outstanding balance of the loan book. This will include all previous loan outlay and accrued interest, less any repayments or loan cancellations.
Financial year
The year from 1 April to 31 March. Throughout the publication this is denoted in the format ‘2012-13’ to describe the year from 1 April 2012 to 31 March 2013.
Some aspects of the student loan system are based on tax years (the 12-month period starting on 6 April), but as a simplification the student loan models assume that this is the same as the equivalent financial year.
Fully repaid loan
The borrower has repaid the loan in full during their repayment term without it being cancelled.
Higher education full-time loan
Loans available to students on full-time higher education courses, including first degrees, sub-degrees and certain postgraduate courses (e.g. Postgraduate Certificate in Education or PGCEs) that are eligible for the undergraduate loan system
Higher education part-time loan
Loans available to students on part-time higher education courses with an intensity of 25% or higher.
Household Residual Income
The household gross income minus payments to private pension schemes, additional voluntary contributions and employment related costs as well as allowances for dependants and students.
Income Contingent Repayment (ICR) loan
Loans for which the required repayments are based on the borrower’s income. The type of student loan that has been available to students since 1998.
Liable to make repayments
The borrower has a remaining loan balance and has reached their Statutory Repayment Due Date (SRDD).
Maintenance loan
Maintenance loans are loans to cover living costs, paid directly to the student.
Master’s loan
Loans issued to students on Master’s courses, on the Plan 3 repayment system. They are paid directly to students and can be used to cover fees or living costs.
Plans 1, 2, 3 and 5
The ICR loan scheme has been separated into different repayment arrangements called Plans 1, 2, 3 and 5. While they operate in a similar manner, they differ in some ways such as the repayment thresholds, interest rates and the length of borrowers’ repayment terms.
Plan 1 is the loan system for undergraduate students that started courses before September 2012. Plan 2 the system for undergraduates and for Advanced Learner Loans between September 2012 and September 2023. Plan 3 the system for postgraduate loans introduced in 2016. Plan 5 the system for undergraduates and for Advanced Learner Loans since September 2023.
Resource Account Budgeting (RAB) charge
Used in the DfE annual accounts, this is the proportion of loan outlay that is expected to not be repaid when future repayments are valued in present terms.
Repayment term
The period for which a loan borrower is liable to make repayments based on their income. At the end of a borrowers’ repayment term any remaining loan balance is cancelled.
Repayment threshold
The annual income threshold above which borrowers are required to make repayments on any eligible income. Plan 1, Plan 2 and Plan 5 loan borrowers are required to pay 9% of any earnings above the threshold and Plan 3 borrowers will be required to repay 6%.
Statutory Repayment Due Date (SRDD)
The point a borrower becomes liable to begin repaying a loan, normally the start of the tax year (6 April) after graduating or otherwise leaving their course. After their SRDD, borrowers are required to make repayments if their income is above the repayment threshold.
Stock charge
Used in the DfE annual accounts, this is the proportion of the total outstanding face value of the loan book that is expected to not be repaid when future repayments are valued in present terms.
Tax year
The 12-month period starting on 6 April. As a simplification, the student loan models assume that this is the same as the equivalent financial year running for 12 months from 1 April. Repayment thresholds are fixed for the duration of each tax year and borrowers’ SRDDs are at the start of the tax year after they graduate or otherwise leave their course.
Transfer proportion
Under the partitioned loan transfer approach, student loan outlay is partitioned into loaned and transferred funds. Conceptually the transfer proportion is the fraction of student loan outlay identified at loan inception as government expenditure, in recognition that this portion of the loan is unlikely to be repaid.
Tuition fee loan
Tuition fee loans are loans to cover all or part of the cost of tuition. They are paid directly to the learning provider.
Voluntary repayment
A borrower can at any time choose to repay some or all of their loan balance early, in addition to any repayments they are liable to make based on their income.
