In recent weeks, claims of a “productivity miracle” in some of the UK’s northern cities have sparked lively debate (which I have been part of!). New subregional productivity data appears to show that places like Manchester, Liverpool, Leeds and others have dramatically outpaced national trends since 2019, but with a very specific high jump in Manchester. But how much of this change reflects real economic transformation and how much is an artefact of shifting datasets, new classifications, or anomalies in how we measure labour input and output?
This challenge led me down a rabbit hole of looking at the drivers behind the Manchester data blip and suggesting reasons in the debate as to why this has happened (bear in mind I did this in about 20 mins, so it’s not robust. I’m just applying my 25 years’ experience in the data and policy! A robust follow up analysis would need doing!). The first issue is anything that looks too good to be true, usually isn’t, unless there has been a significant transformational investment, i.e. Trafford Centre scale investment.
So, what would drive a blip in the south of Manchester? These blips are often misallocation of HQ data, usually caught by the ONS cleaning and redistribution approach, but occasionally slip through. It’s a well-established phenomenon. It happened a few years ago with company numbers in the West Midlands, which the Economic Intelligence Unit and I explored, and it was an accountant who let it slip through the review net. Where all its clients were registering at one address (and therefore all their accounts info).
For this area of Manchester, my own experience points to the footballer belt effect, high net worth individuals who set up ltd companies and register and run them through their accountant’s address. This can occasionally lead to misallocation, and also shows high productivity, high wage companies, with a low number of employees. A quick Google and you find specialist sports accountants in the area where the spike is (as there is a big market locally). This might be worth a detailed investigation by ONS.
One of the issues we have with productivity data is that wages are used both as an input into productivity measures and as a proxy for labour quality. However, wages omit unearned income (work we did at City-REDI illustrates how this can lead to underestimates of the values of these people and the impact on inequality). Using wages also assumes that higher earnings imply higher skills and greater productivity, but are we saying footballers are more skilled and more productive than doctors?
So, the cause of the data blip needs to be explored. Is it real or a data error?
Investigating the Manchester productivity blip: Why apparent surges demand caution
The Manchester data spike prompted me to dig, quickly, into possible explanations. This wasn’t a full analytical exercise, but even this light‑touch exploration highlights why unusual surges should be treated with scepticism until fully validated. Sudden jumps without obvious catalysts are typically a sign of data artefacts rather than genuine productivity change.
Misallocated headquarters data: A well‑known phenomenon
One of the most common explanations for localised productivity spikes is the misallocation of company headquarters or accounting addresses. Although the ONS usually redistributes such data to correct for these distortions, the system is not foolproof and occasionally anomalies do slip through. This is not speculative: similar issues occurred previously in the West Midlands, where an unusually high rise in company registrations was traced back to a single accounting firm whose clients all listed the same address, leading to inflated local business counts and misleading productivity signals before being rectified.
This process mirrors the concerns identified in recent debates about subregional productivity data, where ONS has warned of errors in chained volume GVA affecting productivity measures and advised caution in interpretation.
The “footballer belt” effect in South Manchester
For the specific area south of Manchester, my experience suggests another plausible driver: the footballer belt effect. South Manchester hosts a high concentration of elite athletes and other high‑net‑worth individuals who frequently set up limited companies for image rights, sponsorship income, and personal services. These companies are often registered via specialist accountants located in precisely the same area where the productivity spike appears.
A quick scan reveals these specialist sports accountancy firms operating locally, aligning with what LinkedIn commentary has already suggested in relation to misallocated legal and accounting jobs following IR35 rule changes. These companies typically have:
- very high turnover,
- very high declared income,
- very few employees.
This combination can artificially inflate local productivity (output per job) without reflecting the underlying economic structure of the area. If a cluster of such companies were caught in the labour/output data at the same time and not corrected, it could easily generate the sort of anomalous spike we’re seeing.
This is not to say the phenomenon is unique to Manchester; rather, it is a familiar pattern wherever large numbers of high‑earning professionals consolidate through shared accountants or management firms. Given the scale of the anomaly, this area may warrant additional ONS scrutiny to ensure the data accurately reflects real economic activity.
The use of wages in multi-factor productivity measures
One additional challenge is that regional productivity metrics often rely on wages both as an input into productivity and as a proxy for labour quality, which disguises the unearned income of high-net-worth individuals and hides real levels of inequality. Yet wages exclude unearned income and can therefore undervalue high‑asset individuals, something City‑REDI research (see our report here on exploring all personal income) has demonstrated in work examining the role of unearned income and its impact on inequality.
Using wages also assumes a straightforward correlation:
higher wage = higher-skilled worker = higher productivity.
But this raises conceptual questions. If productivity is defined as output relative to labour input, and labour quality is proxied by wages, then under current measurement frameworks, a Premier League footballer appears vastly more “productive” than a hospital consultant or experienced teacher. The metric begins to lose explanatory power when comparing sectors where income reflects market power, scarcity, or branding, not efficiency or societal value. The market value of an activity does not always reflect productivity or skill.
This is a known limitation of productivity measures, and one reason policymakers must be careful when using local productivity as a proxy for economic health or wellbeing.
What does that mean for researchers attempting to interpret this data?
As researchers, however, we welcome this scrutiny, as it helps us improve our analysis. The conversation reveals something bigger than any single dataset, the fragility of regional productivity measurement, and the urgent need for smarter, more holistic approaches to understanding local economic change.
Below, I explore several angles that City‑REDI believes are essential for interpreting the claims of a productivity surge in the North (or anywhere!). Things to bear in mind:
1. Data anomalies are real, and we need more transparency
Productivity statistics rely heavily on estimates of economic output (GVA) and labour input. When either of these changes, even subtly, the resulting productivity numbers can shift dramatically.
As mentioned above, the Office for National Statistics recently confirmed errors in chained‑volume GVA data for ITL2 and ITL3 geographies, warning policymakers not to use certain subregional productivity tables until corrected data are released. This acknowledgement is significant: it means some of the very data underpinning claims of a “miracle” are in flux.
Meanwhile, hyper‑local anomalies like an MSOA north of Altrincham showing a sudden rise of 21,000 legal and accounting jobs between 2019 and 2023 raise further questions. The timing coincides with IR35 rule changes and another explanation from the footballer effect (more likely in other places without high-performance athletes!), the presence of a major umbrella company in the area, suggesting administrative reclassification rather than genuine economic transformation. This isn’t to say productivity growth isn’t happening. But it is to say, we need a comprehensive cross‑institution review of how we measure productivity below the regional level.
2. Productivity ≠ prosperity – and sometimes rises when labour markets shrink
One of the most important messages for policymakers is that productivity growth does not automatically signal better living standards. Centre for Cities highlights a major data issue: a decline in survey‑based estimates of self‑employment (from the Labour Force Survey and Annual Population Survey) has artificially shrunk measured hours worked in many places. Fewer recorded hours inflate productivity per hour, even if nothing has changed on the ground. In other words, a city can look more productive when its labour market data becomes less accurate.
This raises important questions:
- Are northern cities genuinely becoming more productive?
- Or are changes in survey methodology, response rates, and labour classifications creating statistical illusions?
A productivity miracle may make headlines. But prosperity is about wages, wellbeing, job quality and availability, housing availability and resilience, all of which require deeper analysis as well as having a basket of indicators to look to for change.
3. Structural change matters more than short‑term spikes
Even if productivity has risen in some northern cities, the drivers differ significantly across places.
Oxford Economics shows that although some northern districts outperformed the UK for GVA growth pre‑pandemic, their success came from different sectors and structural conditions, “with no over‑arching pattern of success” across the North. Similarly, Centre for Cities notes that while big cities may appear to be converging with London, much of the convergence is driven by London’s stagnation, not northern acceleration.
This raises a crucial point for policymakers:
Short-term productivity spikes do not equal long-term economic strategy.
If anything, the data show that the North’s growth story remains uneven, sector‑specific, and fragile.
4. Why measurement quality matters for Levelling Up
Subregional productivity figures shape:
- central government funding allocations,
- levelling‑up assessment frameworks,
- Combined Authority priorities, and
- investment zones and industrial policies.
If those metrics are unstable, or worse, misleading, then billions in public investment risk being misdirected. ONS explicitly warns policymakers to treat recent subregional productivity figures with caution until corrected data are released. This points to a broader issue: policy decisions are being made on datasets that are increasingly complex, volatile, and in some cases incomplete. The productivity debate is not just about economics. It is about the governance and understanding of local growth.
5. Toward a “productivity plus” framework
The current productivity debate shows the need to move beyond a single, narrow indicator. We propose a Productivity Plus approach that considers:
- Productivity + skills
Are workers equipped for high‑value roles? - Productivity + resilience
How well can local economies withstand shocks? - Productivity + sectoral diversification
Are cities building balanced, future‑oriented economic bases? - Productivity + institutional capacity
Do local bodies have the powers and resources to drive change?
This framework recognises that productivity is necessary, but not sufficient, for sustainable and inclusive regional growth.
The researcher’s view: What we still don’t know
Many important questions remain unresolved:
- How much of the North’s productivity growth is real vs. data‑driven?
- Are we seeing genuine shifts in economic structure, or relocations driven by tax and regulatory changes?
- What does productivity look like at the neighbourhood level, where do inequalities persist even in high‑growth cities?
- How much of the apparent convergence with London is due to structural changes in London’s economy post‑COVID?
London’s own recent productivity weakness, particularly since the pandemic, has contributed substantially to apparent national rebalancing. Understanding this dynamic is essential before drawing conclusions about northern resurgence.
Next steps: How researchers can respond
City‑REDI proposes three clear actions:
- Improving the interpretation of productivity data
- There is a need for better guidance on how to interpret recent subregional productivity figures, covering known data issues.
- Working with ONS and city‑region partners
- To improve the communication of uncertainty and help strengthen future labour input and output measures.
- Publishing qualitative and case‑study evidence
- To unpack the real-world dynamics behind the numbers, looking at sectoral change, business behaviour, labour markets, and how places respond to shocks.
- This is the kind of analytical leadership the debate now needs.
The real story: Not a miracle, but a complex picture
Rather than a miracle, what the data shows is:
- Patchy structural change,
- Measurement volatility,
- Divergence in sectoral performance, and
- A London slowdown that exaggerates northern gains.
None of this means the North is stagnating. In many places, there are encouraging signs of resilience, diversification, and new economic activity. But the story is far more nuanced and far more interesting than headline claims suggest.
The West Midlands: persistent gaps, structural roots
Our own work on the West Midlands underscores why headline productivity shifts must be read carefully. In Shedding Light on Productivity in the West Midlands, we set out how subregional productivity estimates are sensitive to both how output is apportioned (for example, head‑office effects) and how labour input is captured (hours vs. jobs), with modelling choices potentially underestimating output outside London and overstating it in the capital. The associated WMREDI report by Melisa Wickham (with WMCA) draws these conclusions: the regional productivity gap is driven more by lower productivity within industries than by sector mix, and is especially pronounced in less knowledge‑intensive services, where firm‑level dispersion is widest, whereas manufacturing shows a relatively tighter dispersion. These findings point away from quick fixes, toward longer‑term capability, capital deepening and diffusion challenges that have dogged the region since the 2008 downturn.
Why measurement quality matters for West Midlands policy choices
The national data context reinforces this caution. ONS has flagged errors in chained‑volume GVA feeding into subregional productivity, advising users to treat certain 2023 tables with care until corrections are processed; that matters for a region like the West Midlands, often benchmarked on “GVA per hour” and “GVA per job” in policy documents and funding cases. Recent WMCA‑level summaries show the area still trails the UK average on GVA per hour, with uneven performance across local authorities, useful for diagnostics, but not a substitute for granular, quality‑assured evidence on firm dynamics, skills, capital and management practices that actually move the needle. In short: measurement volatility plus structural headwinds can easily be misread as either sudden deterioration or improvement; the West Midlands record argues for a careful reading of the stats, coupled with a Productivity‑Plus agenda focused on firms’ capabilities, sectoral upgrading and institutional capacity.
Summary: Key issues to watch out for
Overall, the emerging “productivity miracle” narrative, whether in Manchester, the West Midlands, or other parts of the UK, highlights the need for caution when interpreting subregional productivity data. Recent ONS corrections to GVA and productivity estimates show how easily headline figures can be distorted by data anomalies, survey shifts, and modelling assumptions, particularly around hours worked and head‑office allocation.
Localised spikes may stem from administrative, tax and regulation effects, such as IR35‑driven relocations or clusters of high‑earning individuals routed through specialist accountants, rather than genuine structural change. And in regions like the West Midlands, long‑standing patterns of within‑industry underperformance, combined with limitations in wage‑based productivity measures, underline that the real challenges lie in firm capabilities, skills, capital deepening and diffusion, not in chasing short‑term statistical shifts. Ultimately, policymakers and analysts should treat sharp movements in the data as prompts for deeper investigation, not proof of transformation, and focus on building a more reliable evidence base to understand what is truly changing on the ground.
If we want real levelling up, we must start with better data, better theory, and better policy.
This blog was written by Rebecca Riley, Professor for Enterprise, Engagement and Impact, City-REDI, University of Birmingham and the Director of the LPIP Hub.
Find out more about the Local Policy Innovation Partnership Hub.
Disclaimer:
The views expressed in this post are those of the author and not necessarily those of City-REDI or the University of Birmingham.