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Key Takeaways
The share of public-sector employment in total employment has been relatively stable once reclassification effects are taken into account
The share of public-sector employment in total UK employment dropped from 22% in 2010 to around 16.5% in 2018 and has since only gradually increased again. In absolute numbers public-sector employment was – at 5.9m – around 560,000 lower in 2023 than in 2010.
However, these headline changes reflect more sectoral reclassifications than actual trends. Among other reclassification changes, Further Education and Sixth Form College corporations in England were included in the public sector before mid-20212 but have been included in the private sector since then. Going the other way, Lloyds Banking Group plc and Royal Mail plc were reclassified from the public to the private sector at the end 2013. Once these reclassification changes are taken into account, the drop in the share of public-sector employment in total employment was much more modest and public-sector employment was in fact marginally higher in absolute terms in 2023 than in 2012.
The share of public-sector employment in total employment varies considerably across the regions of the UK. In 2023, in the East and South East only 15% of the total workforce was employed in the public sector. This contrasts with 26% in Northern Ireland.
Within the public sector, central government is taking an ever larger employment share, rising from 44% in 2010 to 62% in 2023. Both local government and public corporations saw declines in their shares over that period.
Much of this can be explained by the rapid rise in NHS employment, which went from 1.6m in 2010 to 2m in 2023. By contrast, employment in the civil service and in education remained reasonably stable over that period.
Since mid-2021 earnings have been higher in the private than in the public sector, reversing a long-term trend
Average weekly earnings in the private sector dropped sharply at the onset of the Covid pandemic in early-2020 when many workers were moved to the government’s furlough employment support scheme. Average weekly earnings recovered quickly though and with the exception of March 2021 (when bonus pay was below trend) has seen steady growth since.
By contrast, average weekly earnings in the public sector continued to grow during the first year of the Covid pandemic but then trended sideways until early-2023. Since then it has grown more or less in line with private-sector earnings (the spike in June 2023 reflects the one-off bonus payment to NHS workers).
Notably, as a result of these diverging trends, average weekly earnings in the private sector have been higher than in the public sector since mid-2021.
This contrasts with the long-term trend. In the 290 months between January 2000 and February 2024, average weekly earnings in the private sector were higher than in the public sector in only 49 of them (16%) – 30 of which since mid-2021. The last time average weekly earnings in the private sector were higher than in the public sector over several consecutive months was in 2000.
According to the Institute of Fiscal Studies, several professions in the public sector including nurses but in particular teachers and hospital doctors have experienced much worse pay growth than the average public-sector worker. This reflects a (deliberate) compression of public-sector pay within and across professions. This contrasts with developments in the private sector over the same period. Historically average pay in the public sector has been higher than in the private sector as the average level of educational attainment and skills is higher in the former than in the latter.
Between mid-2021 and mid-2023 public-sector real wage growth was negative
With consumer price inflation accelerating from early-2021 onwards (taking annual CPIH from 0.8% in December 2020 to a peak of 9.6% in October 2022), moderate nominal average weekly earnings growth initially turned into modest real growth and then negative real growth. This was particularly notable in the public sector: in the 24 months between July 2021 and June 2023, real average weekly earnings growth was negative in 22 of them. In the private sector this was the case in 14 out of those 24 months.
As a result (and ignoring the one-off spike in June 2023), real average weekly earnings in the public sector were well below their pre-Covid level between mid-2022 and late-2023 and are only now close to pre-pandemic levels. This contrasts with developments in the private sector, where real wages have been higher than pre-Covid since the end of the furlough period.
Why does it matter?
Public-sector pay accounts for a large part of the public-sector spending and as such plays an important role in the financial stability of local governments and other public-sector bodies. Recent earnings trends show a compression of public-sector pay within and across professions, with the result that public-sector average weekly earnings have fallen behind those in the private sector. This suggests that public-sector wages in general and for certain professions in particular face significant upward pressure, potentially straining public-sector budgets even further.
If you would like to discuss what insights and lessons you could take away from recent public-sector and private-sector employment and wage trends, please talk to us.
Suggested further reading
Camdor Global Advisors evaluates accuracy, depth and strategic insight
AI adoption is accelerating. Decision makers, researchers and policy professionals increasingly use AI-generated output to drive strategic assessments, policy advice and investment discussions. The question is no longer what AI can do, but how much we can trust it. AI adopters should be cautious and consider the risks regarding accuracy, credibility and reliability. While we acknowledge AI significantly accelerates research, it has not removed the need for human oversight.
At a high level, we note that AI excels at speed, structure and coverage, but falls short on policy alignment and judgement. Additionally, a few of the models can reach near-human reasoning through multi-step prompting, but continue to produce hallucinated references and generic outputs. The UK’s competition regulator and other authorities flag hallucination/unreliability as known risks – demonstrating that AI still fails to address regulatory concerns.
Camdor Global Advisor’s (CGA) Test :
We designed a test to evaluate AI-generated outputs against human analysts’ prompt responses using three criteria: Factual Accuracy, Analytical Depth and Policy & Strategic Insight.
Prompt topics:
We employed distinct prompts to make our experiment robust, while testing the AI models’ ability to extract data accurately and provide analytical insights.
Samples From Our Testing
Prompt 1: Historical local authority capex data: Testing Factual Accuracy
What AI did and responded with:
Several AI models extracted £25.9bn from the Department for Levelling Up, Housing and Communities (DLUHC) 2018-19 outturn report for the FY2018-19 figure and then applied a GDP inflator (a few hallucinated the inflation level to apply) to arrive at a standardised 2022-23 price. The models failed to notice that DLUHC in their 2022-23 report published the real term figure of £29.6bn. The models needlessly introduced complexity and arrived at an incorrect figure, demonstrating inconsistency.
Risk summary: Even with a precise and well-framed prompt, AI models selected an outdated price base and generated inaccurate comparisons. This highlights an issue with data retrieval and inflation-adjustment logic.
VS
What a human analyst would respond with:
An analyst would accurately identify the correct DLUHC dataset, verify the price base, and correctly cite £29.6bn (real, 2022-23 prices) for 2018-19 and £27.5bn for 2022-23. They also clarified that the £25.9bn remains valid only in 2018-19 prices, adding transparency and eliminating ambiguity.
Stakeholders should be aware that hallucinations and other errors are not isolated incidents. A recent example was highlighted by the Guardian where a company used AI to produce a report for a local Australian government containing errors and hallucinated references to non-existent sources.
Prompt 2: Local authorities investing responsibly: Testing Analytical Depth
What AI did and responded with:
The response is partially surface level, remaining very general. It is not fully tailored to local authorities or pension funds, presenting strategies in a mainly institutional context rather than addressing the specific audience. While it does offer strategies and practical steps, it requires significant prompting to produce a response approaching the depth and relevance of a human analysis.
Risk summary: AI struggles to tailor analysis to specific audiences and can ignore key principles.
VS
What a human analyst would respond with:
A human analyst would refer to key policies such as LGPS Frameworks, Task Force on Climate-related Financial Disclosures (TCFD) and the UN’s six PRI principles etc. They would analyse how each can be applied to real-world scenarios associated with local authorities and pension funds. Drawing on experience with clients, a human can tailor the analysis to specific contexts, adding valuable insights that enhance understanding. Human insight provides additional depth and practical relevance, resulting in analytical reasoning that is more comprehensive.
A recent example reiterates our findings. CNET published AI-generated articles on topics such as personal finance, but many of these pieces were later found to contain numerous factual and analytical errors.
Prompt 3: Responsible Investment policy frameworks: Testing Policy & Strategic Insight
What AI did and responded with:
When prompted to explain financing mechanisms for UK local authority climate projects, several AI models, particularly Copilot and Perplexity, confidently stated that “local councils can issue sovereign green bonds to fund energy-efficiency projects” or cited the EU Green Bond Standard as the relevant framework. Both claims are incorrect in the UK context.
Risk summary: The output showed limited awareness of UK’s financing architecture and local borrowing limits, underscoring the risk of over-relying on unverified AI in policy advice.
VS
What a human analyst would respond with:
An analyst would recognise that Councils can finance capital via PWLB and the UK Municipal Bonds Agency, as per the Local Government Act 2003 and CIPFA Prudential Code and not by issuing sovereign debt. Additionally, the analyst would propose i) place-based climate infrastructure debt; ii) green municipal or UK municipal-style bonds; iii) retrofit outcome partnerships based on the client’s requirements. The response would demonstrate nuanced understanding of fiscal limits, policy coherence and implementation pathways – ensuring the recommendations are both legally compliant and investment-ready.
CGA’s Experiment Conclusion
We clearly find that AI excels at speed, structure and breadth, producing solid first-pass insight and analysis. A positive example of this efficiency benefit is indicated in Visual capitalist, allowing professionals to focus more on analysis and decision-making rather than routine work. However, AI models continue to be plagued by hallucinated sources, contextual gaps, factual inaccuracy and other errors. If purely AI generated reports or highly AI dependent opinions were presented to councillors/local government officers, it could potentially lead to incorrect decision-making, non-compliant financial strategies or reputational risks.
Human analysts will produce accurate, nuanced and contextually grounded outputs by applying their experience/expertise in institutional and policy awareness that AI still lacks. These qualities remain indispensable when precision, accountability and policy alignment underpin credibility.
At Camdor Global Advisors (CGA), we recognise these challenges and address them through extensive research and expert analysis. We also acknowledge that AI is improving, so these limitations found in the experiment may gradually be mitigated over time.
Implications for Firms
AI tools are invaluable for drafting, summarising and early-stage research, but their insights must be reviewed, verified and contextualised by human experts. At CGA, we view AI as a strategic augmentation tool, not an autonomous analyst. The combination of human judgment and AI efficiency offers the most credible and compliant path forward for regulated and advisory environments. Many AI solution providers frame automation as a path to cost reduction. Yet what is gained in speed is often lost in accuracy, context and accountability. At CGA, we view such shortcuts as false economies. Precision, policy alignment and informed judgment cannot currently be automated, they require expertise. For investors and public institutions, the path forward lies not in replacing human expertise but in integrating AI responsibly, as a tool for research and insight generation, supported by human validation.
*Note: This experiment was conducted in late 2025. Given the rapid pace of AI development, some findings may not fully reflect the capabilities of newer models. A follow-up experiment will be conducted in the future to reassess results against updated models.
Key Takeaways
The population of England is growing and ageing
In mid-2021 the resident population in England was 56.5m, up from 53.1m in mid-2011 (+6.6%). There were marginally more women than men (51% vs 49%), while the median age was 40.5 years, up from 39.4 years a decade earlier.
Between mid-2011 and mid-2022 the number of people aged 0 to 19 years increased by 2.6%, that of people aged 20 to 64 years (commonly defined as ‘the working age’) by 4.3% and that of people aged 65 years and over by 19.9%. As a result, for every person aged 65 years and over there were around three people of working age – down from nearly four people in mid-2011. This is referred to as the old-age dependency ratio.
London and the South West stand out among English regions in terms of age structure
The age structure among English regions varies considerably. In terms of the old-age dependency ratio, the South West has by far the oldest population of all the English regions, with only 2½ people of working age for every person aged 65 years and over, followed by the North East at 2¾. At the other end of the spectrum is London with nearly 5½ people of working age for every person aged 65 years or over. The differences are much wider still on a more detailed geographic breakdown, with around 12 people of working age for every person aged 65 years and over in the London Borough of Tower Hamlets and less than 1½ in North Norfolk.
The picture is more similar when it comes to young people of schooling age (5 to 19 years): in every English region there are between 3 and 4 people of working age for every person of schooling age.
Total fertility rates vary widely across local authority districts and are too low to replace the population
There were 595,948 live births in England in 2021. The total fertility rate (TFR) for England was 1.62 children per women in 2021, the second lowest since the early 1940s. Westminster had the lowest TFR at 1, while Luton had the highest at 2.23. Luton was also the only local authority district in which TFR exceeded the replacement rate of 2.1 required to stabilise the population in the absence of immigration.
Similarly, age-standardised mortality rates and life expectancy vary widely
In 2021 there were 549,349 deaths from all causes in England, the second highest number since the mid-1970s because of the Covid pandemic. Age-standardised mortality rates (ASMRs), which take account of the population size and age structure of a particular location, vary widely across English regions (and males and females), with the lowest recorded in London at 906 per 100,000 people, followed by the South East (908.7) and South West (913.1). By far the highest was recorded in the North East at 1110.1.
These regional differences are also reflected in life expectancy at birth and later on in life. In the period 2012-14 (the latest available date), male life expectancy at age 65 years ranged from 17.9 years in the North East to 19.3 years in the South East and South West. Female life expectancy at age 65 years was lowest in the North East at 20 years and highest in London at 21.9 years. The differences are much more pronounced on a local authority level, with male life expectancy at age 65 years of 15.9 years in Manchester and 21.6 years in Kensington and Chelsea. Life expectancy was also lowest in Manchester for females (18.8 years) and highest in Camden (24.6 years).
London has the highest share of foreign-born residents but is losing population to other parts of England
International migration statistics are not available on a regional level. In the period July 2022 to June 2023 UK net migration (immigration minus emigration) has been provisionally estimated at 672,000, up from 607,000 in the year-earlier period, and more than twice as high as in any other respective period in previous years. Net migration of EU nationals is now negative, with the sharp jump reflecting net migration of non-EU nationals. The main reason for immigrating to the UK was work, followed by study. Current migration statistics are volatile because of global humanitarian crises.
In 2021, the usual resident population in English regions was overwhelmingly born in the UK. The share of those born outside the UK in the resident population was lowest in the North East at 6.8%, followed by the South West at 10.2%. Outside London, the highest share was in the South East at 15.8%. London is the exception with a share of 40.6%.
London’s unique population characteristics are also reflected in internal migration. For the year ending in June 2020 (the latest available data), London lost more than 100,000 residents to other English regions, with positive net migration only for those aged between 20 and 29 years. The pattern for London was similar in the previous year, suggesting that this significant outflow was not mainly due to the COVID-19 pandemic in 2020.
Why does it matter?
The size and composition of the UK population on the national, regional and local levels is one of the most important factors policymakers will need to consider when designing and implementing policies. It is of crucial importance for the local labour market and demand for public services at all levels of government. The UK population also differs significantly across the regions and smaller geographical units, giving rise to particular location-specific challenges and opportunities for policymakers.
If you would like to find out more about the topics discussed in this factsheet and what these might mean for your operations, please talk to us.
Suggested further reading
Population estimates for the UK: mid-2021 (Office for National Statistics)
Deaths registered in England and Wales 2021 (Office for National Statistics)
Vital Statistics in the UK: Births, Deaths and Marriages (Office for National Statistics)
Life expectancies (Office for National Statistics)
The Changing picture of long-term international migration (Office for National Statistics)
Long-term international migration Provisional Year Ending June 2023 (Office for National Statistics)
Internal migration by local authority and region (Office for National Statistics)