Estimating teacher demand
To estimate future teacher demand, the model considers the Department’s National Pupil Projections[1] by school phase and uses assumptions on how national pupil:teacher ratios (PTRs) will rise and fall with projected pupil numbers based upon historic trends.
The model assumes that as pupil numbers grow, PTRs will grow in line with the historical relationship between pupil numbers and PTRs. By contrast, the model assumes that PTRs will fall if pupil numbers fall.
The demand for individual secondary subjects is based upon the current split of teaching time across subjects as recorded within the School Workforce Census.
Pupil projections are a key driver of the future demand for new teachers. All else being constant, if pupil numbers start to grow, or begin to grow at a faster rate year-on-year, then teacher numbers would also need to grow at a faster rate. Similarly, if pupil numbers were to fall, or to grow at a slower rate year on year, this would lead to a fall in the demand for new teachers.
The impact of pupil projections upon 2022/23 PGITT targets compared to 2021/22 is downward for both primary and secondary, despite pupil populations moving in opposite directions:
- The primary pupil population is forecast to fall by 1.7% in 2023/24, falling faster than in preceding years.
- The secondary pupil population is forecast to grow by 1.5% in 2023/24, a slower rate of growth than in preceding years, i.e. pupil numbers are still growing, but more slowly. Whilst the model still assumes that the secondary workforce needs to grow with growing pupil numbers, it assumes it can grow less rapidly because pupil numbers aren’t growing as quickly, reducing the demand for PGITT trainees in 2022/23.
Estimating future teacher leaver numbers
The TWM estimates the number of qualified teachers that will leave the workforce between 2020/21 and 2023/24 and assumes that leavers will need to be replaced by teachers entering into the workforce.
The TWM uses forecasts of leaver rates, as the number of leavers would vary depending on the size of the workforce. Separate forecasts are produced for those leaving via retirement or death in service, and those that leave for other reasons, such as working in other sectors (including within the wider education sector).
In 2020/21, the latest year for which data was available, there was an unprecedented decrease in the number of teachers leaving service, with leaver rates falling to the lowest level observed since the School Workforce Census started being collected in 2010.
This fall in leaver rates was likely because of the circumstances and economic impacts of COVID-19 at that time. We have assumed that future leaver numbers in the short-term will exceed the levels from immediately before the pandemic. This is because we assume that some of those teachers that chose to remain in service during the pandemic, but might otherwise have left, will actually leave service, leading to short-term increases in post-pandemic leaver rates.
Within our forecasts we also consider the latest (as of December 2021) economic forecasts from the OBR, and any other known factors that will impact on future teacher numbers not yet reflected in the baseline data. The model also accounts for existing teachers that stay in service but reduce their working hours between years; the reduction in capacity is expressed in terms of FTE teachers that need to be replaced[2].
The TWM leaver number estimates for 2023/24 exceed those for 2022/23 that influenced the 2021/22 PGITT targets in last year’s model. This anticipated increase in leaver numbers in 2023/24 is the key driver behind increased 2022/23 PGITT targets for both primary and secondary overall.
Estimating future entrant numbers, excluding PGITT
The TWM estimates the number of qualified entrants that will enter the workforce between 2020/21 and 2023/24, that do not fall within the category of newly qualified teachers that have trained via PGITT. As with leaver numbers, future entrant numbers are forecasted in the form of FTE (full time equivalent) rather than headcount, reflecting differences in working pattern between different types of inflow/outflow.
Both the number of returners (those teachers returning to the state-funded schools sector in England) and those that are new to the sector (including newly trained teachers that do not join the workforce immediately after ITT) are estimated using forecasts that consider the latest workforce and economic data as of December 2021.
The model then estimates the number of teachers that will enter service having just gained qualified teacher status (QTS) via either the Assessment Only route or undergraduate ITT using the latest ITT recruitment data. As some undergraduate trainees will not both complete ITT and immediately enter service after training, ITT completion and post-ITT employment rates are applied to the numbers of undergraduate trainees previously recruited.
Adjustment to counter under-recruitment from previous ITT rounds
One of the key drivers behind changes in 2022/23 PGITT targets for specific subjects is an adjustment included within the TWM to build in any impacts of recruitment being below targets for the two ITT rounds prior to 2022/23 (the 2020/21 and 2021/22 rounds).
The TWM uses ITT recruitment data, and ITT completion & post-ITT employment rates to estimate the number of NQTs entering the workforce having trained via PGITT (both mainstream PGITT and High Potential ITT routes) from the two ITT cycles immediately before 2022/23, for primary, and each secondary subject.
The model uses these PGITT NQT figures for 2021/22 and 2022/23 along with estimates for both the corresponding numbers of entrants into the stock via other routes (e.g. returners) and leavers to estimate the size of the workforce in 2022/23. This 2022/23 stock size is then compared to the previously estimated teacher demand for that year to identify whether we have recruited the teachers we need from the two PGITT rounds prior to 2022/23.
If this comparison shows that we have under recruited in particular subjects, an adjustment is applied to correct for it by sourcing additional teachers via 2022/23 PGITT for those subjects. No adjustment is required for subjects where there is no under-recruitment impact. This action is taken as the recruitment impacts of the 2020/21 and 2021/22 PGITT cycles had not yet fed into the School Workforce Census as of December 2021.
This adjustment is a key driver of some of the subject level changes in PGITT targets this year:
- In the previous two ITT rounds, recruitment for modern languages was below target, so we have increased the 2022/23 target for modern languages to account for this previous under-recruitment. This is the first time we have made such an adjustment for the subject, leading to modern languages having the largest percentage increase in targets this year.
- For some subjects, the impact of previous under-recruitment against targets can be offset by other factors. A good example of this is for mathematics where we have seen a decrease in the 2022/23 target compared to last year’s target. Whilst the 2020/21 and 2021/22 PGITT targets for mathematics were not met, the impact of this under-recruitment was more than offset by increases in the numbers of PGITT trainees, returners, and teachers that are new to the state-funded sector being recruited. Furthermore, there was an increase in the proportion of mathematics trainees entering the workforce immediately after ITT.
- The falls in targets for chemistry, business studies, and music were in part driven by a reduction in the scale of the adjustment this year.
- PGITT targets increased for computing, geography, design & technology, and the group of subjects called ‘others’ because the scale of the adjustment used was greater this year than last.
[1]National pupil projections, Reporting Year 2021 – Explore education statistics – GOV.UK (explore-education-statistics.service.gov.uk).
[2]Some existing teachers that stay in service increase their working hours between years. The model estimates and accounts for the net impact of teachers changing their working hours between years, which is a reduction.