Fixing public services: Methodology

General 

Public services spending, including estimates of the total spend on public services 

To estimate the real cost of public spending, we deflate government spending figures using the GDP deflators published by the Office for Budget Responsibility (OBR) in the Economic and Fiscal Outlook from March 2024. ³⁵ Office for Budget Responsibility, ‘Economic and fiscal outlook – March 2024’, 6 March 2024, retrieved 17 July 2024, https://obr.uk/efo/economic-and-fiscal-outlook-march-2024  To better reflect the underlying inflation conditions present in 2020/21, we estimate our own figures by generating a mid-point that averages across values from 2019/20 and 2021/22. We deflate spending figures in our financial analysis across the entire report to 2024/25 prices. There are some instances throughout where we put spending into prices from a year other than 2024/25. Where this is the case, we explain our reasoning below. 

In cases where we calculate real-terms changes in figures that relate to individuals – for example, wages or the adult social care means test – we use the consumer price index (CPI) rather than the GDP deflator. The CPI that we use also comes from the OBR’s Economic and Fiscal Outlook from March 2024. 

Change in demand for public services (used most prominently in Figure 6.3) 

General practice 

To project likely growth in demand for general practice, we use analysis from The Health Foundation. Its main published analysis for ongoing health demand, published in May 2024, includes an estimate for how much demand for general practice will increase due to increasing size and morbidity of the population. ³⁶ Rocks S, Issa Z, Thorlby R and others, How much funding does the NHS need over the next decade? Technical annex, The Health Foundation, May 2024, p.5, https://www.health.org.uk/sites/default/files/2024-06/funding_ projections_technical_annex.pdf

To ensure comparability with the demand projections shown for other services (for which we do not include service-specific cost pressures or possible productivity gains), we only factor in increases in demand, rather than additional assumptions around changes to pay, productivity or to the health care model. 

Hospitals 

To project likely growth in demand in hospitals, we again draw on analysis from The Health Foundation. Its analysis provides an estimate of the rate of growth in activity, adjusted for morbidity, needed to meet growing demand for acute care, while maintaining its scope and quality. We assume that demand for acute and specialist trusts (our focus in this chapter) changes in the same way as The Health Foundation’s projection of demand for acute care. 

To ensure comparability with the demand projections we show for other services – where we do not include service-specific cost pressures or possible productivity gains – we only factor in increases in demand. This means that we use their “minimal change” scenario ³⁷ Rocks S, Issa Z, Thorlby R and others, How much funding does the NHS need over the next decade? Technical annex, The Health Foundation, May 2024, p.5, https://www.health.org.uk/sites/default/files/2024-06/funding_ projections_technical_annex.pdf  which assumes that activity in hospitals will grow more quickly than demographic and morbidity pressures, reflecting the trend between 2010/11 and 2018/19.

The Health Foundation kindly provided us with a breakdown of its model to allow us to derive an overall estimate of acute care, based on a weighted average of elective, emergency, A&E and outpatient activities. 

Adult social care 

For adult social care, we take the projected increase in demand from The Health Foundation’s REAL Centre, published in October 2021. This model incorporates several factors, including increases in pay and projected changes in productivity. We take only the increase in demand projected in the model, as the outlook for pay has changed since it was published.

Children’s social care 

To estimate demand for this service, we split it into two component parts, weighted by the amount that local authorities spent on those services in 2022/23. The two halves of that spending are on “looked after children” and “other” spending, which includes “safeguarding children and young people’s services”, “family support services” and “other” spending. 

We assume that demand for looked after children will increase in line with the number of section 47 referrals that local authorities receive. We project this by calculating the average annual growth rate of section 47 referrals between 2015/16 and 2022/23. We start in 2015/16 because there has been a significant and sustained increase in the rate of section 47 referrals since that year that would not be captured by starting earlier. 

We assume that demand for other parts of children’s social care spending increases in line with assessments under section 17 of the Children Act 1989. Similarly, we use the period of 2015/16 to 2022/23 as the years to calculate the average annual growth rate.

Table 8.1 Projected growth rates for children’s social care

We then create a demand index starting in 2022/23, weighting the two parts of the index by spending on those areas in 2022/23. We increase the index by the average annual increase of section 47 referrals and section 17 assessments every year until 2028/29.

Neighbourhood services

We assume that demand for neighbourhood services increases in line with population growth in England.

Schools

• To project how much schools would have to spend to meet increased demand, we separate primary and secondary schools because:
• on average, the government spends slightly more on each secondary school pupil than on each primary school pupil

the DfE projects that the number of primary school pupils will fall over the period 2019/20–2024/25 while the number of secondary school pupils will increase.

As 2020/21 was an unusual year, we base our projections on spending in 2019/20 (see Table 8.2). We multiply the 2019/20 level of spending per pupil in primary and secondary schools by expected growth in pupil numbers between 2019/20 and 2028/29 and add together the implied figures for spending on primary and secondary schools. We assume that the costs of the inputs used in providing school services rise in line with economy-wide inflation.

Table 8.2 Projected growth rates for schools

Police

To project police demand, we divide demand on the police into the following categories:

• reactive demand, subdivided into: reactive crime demand and reactive non-crime demand
• protective demand, such as intelligence-gathering and safeguarding
• internal demand, including HR, training and professional standards. 

We then calculate likely trends in each of these separately to project overall demand. 

We calculate the proportion of current demand made up by each of category based on the number of FTE officers, staff and police community support officers (PCSOs) within each function, drawn from Home Office police workforce data. We weight the number of FTE roles according to the mean salary of these groups, to account for police officers being more expensive on average than staff or PCSOs. Mean salary data comes from the Annual Survey of Hours and Earnings, Table 13.7, for officers and PCSOs, and the Police Staff Council Earnings Survey 2023 for police staff. 

Some functions within reactive demand deal exclusively with crime (e.g. investigations) but it is not possible to separate local policing and dealing with the public into crime and non-crime. We assume that reactive demand is two-thirds due to crime and one third due to non-crime, based on existing estimates of police demand and the greater time involved in responding to crime than non-crime incidents. Other estimates of police demand suggest all non-crime demand, including protective and internal demand, accounts for 21– 35% of demand. ³⁸ Crest Advisory, ‘Modelling demand on Nottinghamshire Police’, 2019, https://www.nottinghamshire.pcc.police. uk/Document-Library/Public-Information/Newsletters-and-Publications/Publications/Modelling-Demand-on- Nottinghamshire-Police.pdf ³⁹ Hadjipavlou S, Redgrave H and Desroches C, ‘Rethinking Police Demand’, 2018, https://static.wixstatic.com/ ugd/b9cf6c_2e5f620f265a42b2a7c385c68308b68b.pdf

Table 8.3 Police demand categorisation

Internal demand we assume will remain flat, based on maintaining the current workforce. Non-crime reactive demand and protective demand we project will grow in line with the population in England and Wales. We model reactive crime demand based on the trend in police recorded crime since 2018/19, adjusted for the growing complexity of the crime mix and investigations. We do this by generating a ‘complexity score’ for each offence group, based on the median length of time between an offence being reported and an outcome recorded for that offence type, relative to that for all offences. We based these complexity scores on data across 2022 and 2023.

We then multiplied the number of offences for each offence group each year by the relevant complexity score to calculate the complexity-adjusted trend in police recorded crime. Finally, we applied a 0.5% annual increase in reactive crime demand to account for the increasing volume of digital evidence. ⁴⁰ Home Office, ‘Crime outcomes in England and Wales 2022 to 2023’, 2023, https://www.gov.uk/government/ statistics/crime-outcomes-in-england-and-wales-2022-to-2023/crime-outcomes-in-england-and-wales- 2022-to-2023  This is likely to be an underestimate of the impact of increasing complexity and growing volumes of evidence, given the large increase in recent years in the time it takes for the police to record an outcome once an offence has been reported. However, it is not possible to determine how much of this is due to increasing complexity as opposed to reduced police productivity.

Table 8.4 Projected growth in police demand

Criminal courts 

We project demand for the crown and magistrates’ courts separately. 

For the crown court, we calculate demand as the number of cases received each year, weighted by the average hearing time for cases completed in each year. We do this separately for cases that are ‘for trial’ and other cases (such as appeals and sentencing). We assume that: (i) longer hearing times are a result of cases being more complex, rather than inefficient use of hearing time; and (ii) the average hearing time for cases received would have been the same as the average hearing time of the ones disposed of, within case type (cases for trial and others), in the year in question. 

For magistrates’ courts, where the data we have is less detailed, we measure demand simply as the number of cases received each year. 

We weight magistrates’ and crown court demand to come to an overall measure of court demand. We do this using two components. First, we use the number of sitting days in the crown court and magistrates’ courts in 2018. Second, we use the average costs per sitting day in the crown court and magistrates’ courts, which the National Audit Office reported in 2016, as these are the latest available figures. This implies that 61% of court demand comes from the crown court and around 39% comes from the magistrates’ courts. We then project demand forward separately for the crown and magistrates’ courts. 

The main driver of our projection of court demand is the increase in police officers; the government met its commitment to increase officer numbers by 20,000 on top of 2018/19 figures by April 2023. We assume that an increase in the number of officers means the police can charge more cases, because as it stands the number of charges is only a small fraction of total crimes reported. The number of charges per police officer has fallen steadily for several years. 

We assume that once officers are embedded the number of charges per officer will return to and stay at 2019/20 levels. However, we assume that there is a lag of three years between recruitment and a return to 2019/20 levels of charges, as this is the time it has taken between the start of the uplift programme and an increase in that indicator. We therefore project that charges per officer will return to 2019/20 levels in 2026/27, increasing uniformly between 2022/23 and 2026/27. 

In the magistrates’ courts, we assume that the least serious ‘summary’ cases are unaffected by the number of police officer charges as some of these are brought by non-police organisations and they are simple, routine offences. With all other cases, in both the crown and magistrates’ courts, increases occur in line with the lag described above.

Prisons 

To project demand for prisons, we use the Ministry of Justice’s (MoJ) central estimate for prisoner numbers over the five years from 2023 to 2028, which was published in November 2023. 

This projection incorporates the recruitment of the additional 20,000 police officers and the estimated impact of other policies, including new offences and changes in agreed sentencing, such as the Police, Crime, Sentencing and Courts Act and the Release of Prisoners Order 2020. It does not account for emergency early release measures or changes to the point of automatic release being considered to ease the emergency capacity crisis. These measures reflect insufficient supply rather than lower demand.

1. The NHS 

Hospital productivity (Figure 1.1) 

Hospital doctors includes all NHS Hospitals and Community Health Service (HCHS) doctors from the NHS England ‘organisation’ dataset. Hospital nurses includes adults and children’s nurses. 

Elective activity is a combination of admitted and non-admitted completed pathways from the ‘referral to treatment’ dataset. Outpatient appointments are total appointments from the ‘hospital episodes statistics’ dataset. Diagnostic tests come from the activity table in the ‘DM01’ dataset. Cancer appointments are urgent suspected cancer referrals seen at a first outpatient attendance from ‘cancer waiting times’ datasets.

The time series for both staffing and activity levels start in January 2010. The time series for activity levels (elective activity, outpatient appointments, cancer appointments and diagnostic tests) end in April 2024. The time series for staff ends in March 2024. Both of these are the most recent months for which there is data. 

For staff, the annualised growth rate is taken by calculating a compounded monthly growth rate between January 2010 and December 2019 and January 2020 and March 2024, then converting those numbers into compounded annual growth rates. 

For activity levels, we first create a time series of the average level of activity in the previous 12 months. We do this to avoid variation that comes from using any individual month, where activity may be temporarily lower or higher than is typical for unobservable reasons. Thus the first data point in this time series is the average activity in the previous 12 months in December 2010. We then calculate a compounded monthly growth rate between that point and December 2019 and between January 2020 and April 2024. We then convert those into a compounded annual growth rate. 

Gross capital formation in health care as a percentage of GDP, by OECD country (Figure 1.3) 

The weighted OECD average is calculated by first calculating the total spent on gross capital formation in every year by multiplying the percentage given in the dataset by the GDP number for the relevant year. This is then summed and divided by the sum of GDP for every country that has a data point in that year. Any country that does not have a data point in a given year is excluded from the analysis for that year. 

Change in the number of managers per FTE NHS staff member (Figure 1.5) 

We calculated the number of senior managers and managers per NHS staff on an FTE basis for every month of this time series. We then calculated how this changed over the course of the time series. 

Patient to GP ratio by decile of deprivation (Figure 1.10) 

For this we use a snapshot of the number of patients and the number of fully qualified, permanent GPs in March 2016 and March 2024, to account for any seasonality in either number. To find how many GPs and patients there are in each decile of deprivation, we use the practice postcode to place a practice into a Lower layer Super Output Area (LSOA). We then use the English indices of multiple deprivation at a LSOA level to assign a decile of deprivation to each practice. From there it is possible to sum both the patients and the number of fully qualified permanent GPs and then divide the former by the latter to come to a patient–GP ratio for each decile of deprivation. 

Proportion of GP trainees entering the fully qualified workforce (Figure 1.12) 

To calculate the annual proportions, we sum the number of trainees that entered the workforce in each of the preceding four quarters. We then sum the total number of trainees that finished training in those four quarters and use that as the denominator in the calculation. For example, for the June 2022 cohort, we include those who finished training in September 2021, December 2021, March 2022 and June 2022. This removes any seasonal variation there might be in the rate at which trainees join the workforce.

Staff working in primary care, by staff group (Figure 1.14) 

All staff are shown in terms of FTEs. 

Data for the full-qualified GPs, nurses, and GP trainees comes directly from the NHS Digital GP workforce bulletin tables. This therefore only captures those GPs that work in general practice rather than the wider primary care workforce. We discussed this approach with experts on primary care data and they agreed that given how few additional GPs worked outside general practice, this was an acceptable approach.

For the direct patient care (DPC) staff, three approaches were used for different time periods: 

• Between September 2015 and December 2018, the DPC staff shown on the chart are taken directly from the GP workforce bulletin tables. Given that primary care networks did not exist at this time, this measure should capture all the DPC staff working in primary care.
• Between March 2019 and June 2021, the DPC staff are a combination of the GP workforce bulletin tables DPC staff and those from the primary care network workforce bulletin tables. 
• From September 2021 onwards, the DPC staff comes from the primary care workforce – nurses, DPC and admin/non-clinical staff dataset.

2. Local government 

Local authority core spending power, actual and forecast, by fiscal event (Figure 2.2) 

For the years 2015/16 onwards, this chart shows local authorities’ core spending power (CSP). This measure did not exist before 2015/16, when the government instead measured local authorities revenue spending power (RSP). To create a comparable CSP for the years before 2015/16, we apply the annual percentage growth in RSP to the first year of the CSP metric until we have an artificial CSP total for 2010/11. 

For the outturn line between the years 2015/16 and 2024/25, the totals come from the final local government finance settlement 2024/25, put into 2021/22 prices using the spring budget 2024 deflator. 

The “Spending review 2021” and “Autumn statement 2022” lines take the CSP settlements outlined in those fiscal events and put them in 2021/22 prices using the GDP deflators that were used in each respective fiscal event. 

We keep this chart in 2021/22 prices so that the anticipated path of CSP growth for the spending review period can be compared to the actual growth of CSP in that time. 

Annual real terms change in local authority spending power, by deprivation and time period (Figure 2.3) 

The earliest time period (2010/11–2014/15) shows the change in RSP ordered by deprivation. 

The later two time periods (2015/16–2019/20 and 2020/21–2024/25) show the change in CSP, ordered by level of deprivation. 

Change in local authority spending power, by decile of deprivation (Figure 2.4) 

As with the CSP timeline in Figure 2.2, we constructed a consistent CSP time series for each upper- and single-tier local authority between 2010/11 and 2024/25. We then used the 2019 English index of multiple deprivation (IMD) to assign a deprivation score to each local authority. 

We excluded those local authorities that were either abolished or created during this period and then grouped local authorities into deciles of deprivation, summed their CSP in 2010/11 and 2024/25 and then calculated the percentage change. 

Spending by local authorities in England, by type (Figure 2.6)

We take total expenditure from the revenue summary for each type of local authority (shire districts, shire counties, unitary authorities, metropolitan districts, London boroughs, and others). 

We then deduct spending on schools, public health (because local authorities only had responsibility for this from 2013/14 onwards), fire and rescue services, and police services. We then split out adult and children’s social care and add in local authorities’ spending for the better care fund to create a total amount that local authorities spend on social care. 

For the years in which there was Covid spending (2020/21 and 2021/22), we calculate the total spent on social care and neighbourhood services from the Covid-19 financial impact monitoring returns and subtract that from the spending for each category in the revenue outturns to avoid double counting spending.

3. Schools 

Attainment at the end of primary and secondary school, 2010–23 (Figure 3.1) 

All figures include pupils not in mainstream education. 

Primary (KS2) assessments were reformed between 2015 and 2016 and did not take place in 2020 and 2021. 

Between 2013 and 2014 a number of changes occurred to secondary qualifications. In 2017 reformed English and maths GCSEs were awarded for the first time. In 2020 and 2021 GCSE results were awarded by centre- or teacher-based assessments rather than external exams. In 2022 GCSE results were set between pre-pandemic (2019) levels and 2021 levels. In 2023 results were allowed to return to pre-pandemic levels with some grade protections in place. SEND top-up funding provided to schools by local authorities, by provision type, 2015/16–2022/23 (Figure 3.5) Figures have been deflated using the smoothed deflator described at the start of the Methodology. Median teacher pay by role, 2010–23 (Figure 3.7) We take teacher pay from estimates of median full-time equivalent pay using teacher pension scheme data, which retroactively includes backdated pay awards. Figures have been deflated using CPI figures from the spring budget, as described at the start of the Methodology.

4. The criminal justice system 

On 26 June 2024, the Ministry of Justice announced a potential data quality issue affecting criminal court statistics. ⁴⁹ Ministry of Justice, ‘Accredited official statistics announcement: criminal court statistics quarterly: January to March 2024’, 4 July 2024, retrieved 4 July 2024, www.gov.uk/government/statistics/announcements/criminalcourt- statistics-quarterly-january-to-march-2024#full-publication-update-history  The scale of this issue is currently unknown. Findings drawing on criminal court statistics should therefore be interpreted with caution and numbers may be subject to change. 

Charges/summonses recorded by police forces (Figure 4.1) 

All figures in this chart are for a 12-month period. Values for 2009/10–2022/23 are for 12 months ending in March and the value for 2023 is 12 months ending December. This means there is a three-month (one quarter) overlap between the 2022/23 figure and the 2023 figure. Greater Manchester only supplied data for Q1 2019/20 and Devon and Cornwall Police only supplied data for Q1 and Q2 2022/23 and not at all in 2023. To represent the national trend as accurately as possible, we have imputed missing figures for these quarters based on their average share of national charges in other years. For Devon and Cornwall, the average share was calculated based on data from 2014/15 to 2021/22, as values were similar throughout the period. For Greater Manchester, the average share was calculated based on figures from 2016/17 to 2020/21, excluding 2019/20 when data is missing. This is because of changing trends in the proportion of charges GM is responsible for, driven partly by an increased emphasis on charging over other outcomes in recent years. 

Data for this chart is drawn from two different sources. Values for 2003/04 to 2013/14 are headline national figures from Home Office, ‘Crime outcomes in England and Wales’ (Table B2), 2022/23. Values from 2014/15 to 2022/23 and for calendar year 2023 are from Home Office, ‘Police recorded crime and outcomes open data tables’, 2018/19–2022/23. Totals may vary slightly across different data sources. 

Outstanding cases in the crown court (Figure 4.4) 

The latest official statistics for the backlog in the criminal courts are taken from the Criminal Court Statistics Quarterly up to December 2023. Jury trials were disproportionately likely to be delayed during the pandemic, so outstanding cases (cases in the ‘backlog’) are more likely to require jury trials than the average case. Jury trials are more complex and require much more hearing time in courts, so adjusting for this allows us to better compare the current backlog with pre-pandemic levels.

The number of outstanding cases (the ‘backlog’) is the cumulative difference between cases entering the court system (receipts) and cases completed (‘disposed’) each quarter. We adjust for the greater complexity of the backlog in several stages: 

• We calculate a pre-pandemic baseline for the proportion of case receipts that result in a jury trial by dividing the number of trials by the total number of disposals. This is based on data from Q2 2019 to Q4 2019 and we include both effective trials (those that go ahead as planned and result in a verdict) and cracked trials (those that are cancelled on the day of the listing and not rescheduled; for example, because of a late guilty plea or discontinued prosecution). 
• We assume that the proportion of cases coming into the crown court that end up as jury trials is the same as before the pandemic and use this to estimate the number of jury trial receipts. We then subtract the number of jury trial disposals to estimate the number of outstanding jury trials. We use the same process to calculate outstanding non-jury trial cases, by subtracting other disposals from estimated non-jury trial receipts.
• We calculate a complexity value for jury trial cases by dividing [trial cases share of total hearing time] by [trial cases share of total cases]. We multiply the number of outstanding cases by this complexity value. We repeat this process for nonjury trial cases, calculating a separate complexity value by dividing [non-jury trial cases share of total hearing time] by [non-jury trial cases share of total cases] and multiplying it by the number of outstanding non-jury trial cases. We then add them together for a total complexity-adjusted increase in the backlog. 
• Even before the pandemic, outstanding cases were more complex than the average of all cases processed. We adjust for this to make our backlog consistent with the pre-Covid backlog. We calculate a complexity value for pre-pandemic backlog cases by dividing [average hearing time of pre-pandemic backlog case mix] by [average hearing time of all pre-pandemic cases]. We multiply this by the complexity-adjusted increase in the backlog for a comparable complexity adjusted backlog figure. 

Crown court hearing hours per sitting day (Figure 4.6) 

We calculated total annual hearing hours by multiplying [average hearing time in hours per case] by [total number of cases disposed]. We then divided this by the total number of sitting days to calculate hearing hours per sitting day. 

Prison population and useable operational capacity, actual and projected (Figure 4.7) 

We use month-end figures for both actual and projected population and actual useable operational capacity. Operational capacity is the total number of prisoners that an establishment can hold taking into account control, security and the proper operation of the planned regime. Useable operational capacity is the operational capacity minus an ‘operating margin’ that reflects the constraints imposed by the need to appropriately accommodate different classes of prisoner (for example, by sex, age, conviction status, single cell risk assessment, geographic distribution and security category). In any given monthly data release, either the total operational capacity or useable capacity is given, along with the operating margin in the notes. In cases where the total operational capacity is published, the useable figure is calculated by subtracting the operating margin from the total. 

The population projection is the Ministry of Justice’s (MoJ) central estimate for prisoner numbers over the five years from 2022 to 2027, which was published in February 2023. This projection incorporates the recruitment of the additional 20,000 police officers and the estimated impact of other policies, including new offences and changes in agreed sentencing, such as the Police, Crime, Sentencing and Courts Act and the Release of Prisoners Order 2020. It does not account for emergency early release measures or changes to the point of automatic release being considered to ease the emergency capacity crisis. These measures reflect insufficient supply rather than lower demand. 

We divide the capacity projection into two phases, first from June 2024 to December 2025 and second from January 2026 to December 2030. The Labour Party confirmed its commitment to delivering the planned 20,000 new prison spaces during the 2024 election, ⁵⁰ Labour Party, ‘Labour Party prisons policy: How we will fix the prisons crisis and keep criminals behind bars’, 9 Jun 2024, retrieved 9 July 2024, https://labour.org.uk/updates/stories/labour-party-prisons-policy-how-wewill- fix-the-prisons-crisis-and-keep-criminals-behind-bars  so we assume these will be delivered on the same schedule planned by the Sunak government. The Sunak government stated it had delivered 5,600 new spaces of the planned 20,000 by October 2023, and would have delivered 10,000 (that is, 4,400 further spaces) by the end of 2025. ⁵¹ Ministry of Justice, ‘Written evidence submitted by the Ministry of Justice’, Parliament, October 2023, https:// committees.parliament.uk/writtenevidence/126249/pdf  

Useable operational capacity has remained flat from October 2023 to May 2024, so we assume the remaining 4,400 spaces will be delivered between June 2024 and December 2025. Without more specific information on when additional spaces will become operational, we assume that they will be delivered at a consistent rate from June 2024 until December 2025. We recognise that this is unlikely to reflect actual delivery patterns, but it provides a rough indication of how capacity is likely to grow. The date at which the remaining 10,000 places will become operational is unknown, but is likely to take at least until December 2030, so again we assume they will be delivered at a consistent rate from January 2026 to December 2030. We do not have data on the number or rate of cells becoming unusable, so have assumed that all cells currently in use remain in use throughout the forecast period. As such, this should be viewed as a maximum capacity projection rather than a central scenario.

5. Cross-cutting problems 

Change in median gross earnings of selected public sector professionals since 2009/10 (Figure 5.2) 

The ONS has changed how it classified professions twice since 2009/10. To ensure consistency we cross-referenced codes against volumes of employees to check similar numbers of staff were being assessed. The most affected data series was nursing professionals, for which we used data for health associate professionals for 2009/10, nursing and midwifery professionals for 2010/11–2019/20 and nursing professionals for 2020/21–2021/22. Additionally, figures for primary education teaching professionals include nursery staff up to 2020/21. 

Figures were deflated using the consumer price index as published by the OBR. 

Capital spending index, public service departments (Figure 5.3) 

The primary problem in the creation of this chart is change in departmental structures and responsibilities. To circumvent this issue, we create a retrospective time series, using the most recent outturn as the baseline for the years 2018/19 to 2024/25 (the years for which there is data in the PESA tables). We then input the PESA results for previous years. We transform the last outturn year (2018/19) by the change in spending for the previous PESA outturn. We then replicate this method for all departments and for all years going back to 2004/05. 

For 2023/24 and 2024/25 numbers, we use the forecast from the March 2024 spring budget. We call the 2023/24 numbers outturn rather than forecast because the budget was close enough to the end of the financial year that we believe that there will be very little change between that number and the outturn. 

CDEL to RDEL transfers, % of department CDEL (Figure 5.4) 

We find both the CDEL to RDEL and the RDEL to CDEL transfers in either HMT PESA tables, or in House of Commons Library revised government spending plans. ⁵² Brien P, Brook P, Broughton A and others, ‘Revised Government spending plans for 2021/22’, House of Commons Library Research Briefing, 7 March 2022, retrieved 17 July 2024, https://commonslibrary.parliament. uk/research-briefings/cbp-9420  From there, we create a percentage by dividing that number by the total CDEL budget in a given year.

6. Implications of sticking with the status quo 

Government capital spending, outturn and forecast (Figure 6.1) 

The outturn and forecast lines for this chart show numbers that come from the Office for Budget Responsibility’s March 2024 Economic and Fiscal outlook. It shows total government CDEL to 2028/29. 

The dot “Including Labour’s spending plans” shows the total after accounting for the increase that comes from the Green prosperity plan in their manifesto. ⁵³ Labour Party, ‘Labour’s fiscal plan’, 13 June 2024, retrieved 17 July 2024, https://labour.org.uk/change/ labours-fiscal-plan  

Average annual real-terms change in spending planned at successive multiyear fiscal events (Figure 6.2) 

We first increased total RDEL across government by 1% in real terms for each year between 2024/25 and 2028/29, as laid out in the March 2024 spring budget. From that we subtracted protected spending. 

For this chart, protected spending is spending on health, defence, education and foreign aid. To calculate health spending, we increased spending for the NHS (in this case, general practice and hospitals) by 3.6% in real terms for each year of this future spending review as this is the amount that the Institute for Fiscal Studies (IFS) estimates that it would require to meet the commitments laid out in the NHS Long Term Workforce Plan. ⁵⁴ Warner M and Zaranko B, ‘Implications of the NHS workforce plan Green Budget 2023 – Chapter 8’, Institute for Fiscal Studies, 2023, retrieved 17 July 2024, https://ifs.org.uk/publications/implications-nhs-workforce-plan  For foreign aid, we increased the budgets of the Foreign, Commonwealth and Development Office (FCDO) by the real-terms GDP forecast from annex A of the OBR’s March 2024 Economic and Fiscal Outlook. For defence, we assume that the Labour government will remain committed to the previous government’s ambition to increase defence spending to 2.5% of GDP by 2030. ⁵⁵ Sunak government press release, ‘PM announces ‘turning point’ in European security as UK set to increase defence spending to 2.5% by 2030’, Gov.uk, 23 April 2024, www.gov.uk/government/news/pm-announcesturning- point-in-european-security-as-uk-set-to-increase-defence-spending-to-25-by-2030  Finally, for education we keep spending flat in real terms per pupil. 

Please note, that the definition of unprotected spending in this chart differs from the measure used in the one below, where we also exclude spending on childcare and assume that per-pupil spending on schools is held flat in real terms. We do this to make the unprotected spending in this Figure 5.2 comparable across different spending reviews. 

Average annual real-terms change in spending between 2024/25 and 2028/29 under current government plans, relative to demand (Figure 6.3) 

As noted, we use a slightly different unprotected spending definition for this chart than for Figure 6.2. We include childcare spending and schools spending (held flat in real terms on a per-pupil basis) in protected spending. 

We then include analysis of Labour’s commitments in its manifesto. ⁵⁶ Labour Party, ‘Labour’s fiscal plan’, 13 June 2024, retrieved 17 July 2024, https://labour.org.uk/change/ labours-fiscal-plan  We do not think that all of the spending that it has committed to will be in addition to the baseline RDEL commitments. Table 8.3 gives details of the Labour spending commitments as outlined in its manifesto and whether or not we believe that they are additional to the baseline or included in it.

Table 8.5 Labour manifesto spending commitments

After adding the spending from the Labour manifesto that we assumed was truly additional, we assumed that all unprotected departments would change in line with the change in unprotected RDEL. From there, we offset the spending increases by demand for each service as has already been described at the beginning of this Methodology section.