How do health inequalities shape regional economic performance in the UK?

The UK has some of the widest disparities in regional economic performance among advanced countries. Evidence suggests that health inequalities between different parts of the country are an important part of the explanation for the geographical variations in employment, inactivity and productivity.

Health inequalities are an important contributor to regional differences in economic performance across the UK, although the relationship is complex and runs in both directions. At a glance, areas with poorer health tend to have lower employment, higher economic inactivity and lower productivity.

The scale of these disparities is substantial: people living in the most deprived parts of England spend around 20 fewer years in good health than those in the least deprived areas; while productivity in the most productive local areas is almost three times higher than in the least productive.

Evidence suggests that reducing health inequalities could support labour market participation and economic performance, but it remains difficult to separate the effects of health on the economy from the effects of economic conditions on health.

How unequal are health and productivity outcomes across regions?

Geographical inequalities in health are substantial. In England, men living in the most deprived 10% of areas can expect just under 20 fewer years of good health than those living in the least deprived areas: 49.8 compared with 69.2 years. For women, the gap is slightly larger, at just over 20 years: 48.2 compared with 68.5 years (Office for National Statistics, ONS, 2026).

These inequalities also have a clear regional pattern, with people in the North of England having greater rates of both mortality and morbidity than those in the rest of the country (Simpson et al, 2025a).

Figure 1. UK healthy life expectancy at birth by area deprivation, 2022-24

Source: ONS, Healthy life expectancy by national area deprivation, April 2026.

Economic outcomes are similarly unevenly distributed. Productivity, which is commonly measured as economic output per worker or per hour worked, varies substantially across the UK. In 2021, output per hour worked in ‘Inner London-West’ was 54% above the UK average, while around half of UK sub-regions had productivity at least 10% below the national average (ONS, 2023). At a more local level, productivity in Tower Hamlets in central London was almost three times that of Powys in Wales (ONS, 2023).

Figure 2. Regional labour productivity by ITL3 region (UK=100), 2023

Source: ONS, Sub-regional productivity: labour productivity indices, June 2025

These productivity differences exist alongside pronounced geographical inequalities in employment, earnings and economic inactivity. For example, in the period from April to June 2026, the employment rate varied by more than seven percentage points between the lowest and highest employment regions of England – from 71.1% in the North East to 78.3% in the East of England. Similarly, economic inactivity ranged from 18.4% in the South East, the lowest rate, to 25.1% in the North East, the highest (ONS, 2026).

While employment and economic inactivity are not measures of productivity themselves, they are important indicators of regional economic performance because they determine how much of the potential workforce is in work. In this article, we take a broader view of regional economic performance, considering how health inequalities affect labour force participation alongside productivity and economic output.

How can health inequalities translate into productivity differences?

The relationship between individual health and labour market outcomes is both well-established and multifaceted within and across different conditions (Pintor et al, 2024). Regional inequalities in health can then translate into productivity differences through their effects on labour market participation.

Many of the health conditions contributing to regional inequalities in employment and productivity, including obesity-related conditions and musculoskeletal conditions, are more prevalent in deprived areas.

There is also evidence that health-related economic inactivity is around 50% higher in the North of England than in the rest of England, with regional differences in health accounting for more than two-fifths of this gap (Simpson et al, 2025a).

Figure 3. Share of working-age population economically inactive due to ill health, 2024

Source: (Simpson et al, 2025a), Health for Wealth.

This geographical pattern reflects the greater prevalence of poor health in the North as well as its unequal labour market consequences. For example, people living in northern regions are more likely to leave employment following the onset of poor health than comparable people elsewhere in England (Simpson et al, 2025a). It’s a similar story with mental health, with the employment consequences of poor mental health varying substantially across different areas (Bryan et al, 2022).

This is consistent with evidence on spatial differences in the disability-employment gap, which varies substantially across the UK. The gap between the employment rates of disabled and non-disabled people ranges from 17 to 43 percentage points across local areas.

Differences in the characteristics of local populations explain part of this geographical variation, but where people live also matters. Local labour demand and industrial structure contribute to geographical differences in employment outcomes among people with disabilities, consistent with earlier evidence on the importance of local labour market conditions (Bryan et al, 2026; Little, 2009).

These differences have potentially substantial consequences for regional and national economic performance. Research suggests that closing the health gap between the North and the rest of England could generate around £18.4 billion in additional economic output each year, with around £6.6 billion associated with closing the regional mental health gap (Simpson et al, 2025a; Mignon et al, 2026).

Although these are modelled counterfactual estimates rather than forecasts of the returns to a particular intervention, they illustrate the potential scale of the economic consequences of regional health inequalities. It is also worth emphasising that these estimates are best viewed as an indication of the scale of the economic costs associated with health inequalities rather than forecasts of what any specific policy intervention would achieve.

Can reducing health inequalities improve economic outcomes?

If health inequalities carry substantial economic costs, an important question is whether reducing them can improve economic outcomes.

Improving health and access to effective treatment is one potential route. Recent evidence from NHS Talking Therapies provides a useful example (Dodd et al, 2025). A reduction in waiting times of around 10-11 days was associated with a 1.5 percentage points reduction in the employment gap between people with good and poor mental health, alongside a reduction in inequalities in time away from work.

Further analysis estimates that hiring 14 additional therapists per commissioning region could generate up to £335 million in additional income tax revenue nationally after one year. While this is not a guaranteed fiscal return, it illustrates how investment in healthcare may generate economic benefits alongside improvements in health.

An alternative route to healthcare access is to invest in integrated health-skills-employment pathways at local level. Evidence from modelling of return-to-work support for Universal Credit recipients who are sick or disabled and aged between 25 and 64 suggests that helping even 5% of them into employment could generate substantial fiscal benefits, around £12 billion over the period 2026-29 (Bambra et al, 2025).

But healthcare and return-to-work programmes alone cannot eliminate regional health inequalities. A large body of evidence shows that health is shaped by wider social, economic and political conditions (Bambra, 2019). Recent research identifies household income and the uneven effects of welfare reforms as important contributors to health inequalities between the North of England and the rest of the country (Simpson et al, 2025b).

Reducing regional health inequalities therefore requires action not only within the healthcare system, but also on the wider economic and social conditions that shape health.

How reliable is the evidence?

Health can affect productivity both through participation effects (who is able to work) and through within-work productivity (how effectively people can work while employed). Much of the current evidence is stronger for labour market participation than for productivity per hour worked, although both channels are likely to be important.

The evidence linking health inequalities to regional economic performance is extensive, but it has important limitations. A major challenge is establishing causality. Poor health may reduce employment and productivity, but weaker economic conditions, lower incomes and poor-quality jobs can themselves lead to worse health.

Many studies attempt to address this problem using longitudinal data and fixed-effects models that control for stable differences between individuals or places. While these approaches strengthen causal inference, they cannot fully rule out reverse causality or the influence of unobserved factors that change over time.

A further limitation is that some evidence is based on comparisons between geographical areas rather than individuals. Such analyses are useful for identifying broad spatial patterns, but relationships observed at the area level do not necessarily reflect the experiences of people living in those places. As a result, caution is needed when interpreting regional associations between health and productivity as direct causal effects.

Even so, the consistency of findings across multiple studies, datasets and methodological approaches provides increasing evidence that health inequalities are an important contributor to regional economic inequalities, even if the precise magnitude of that contribution remains uncertain.

What else do we need to know?

Understanding how reducing health inequalities can boost regional productivity remains an active area of research. We still know relatively little about how links between health and employment at the individual level translate into differences in economic performance between regions.

Researchers also need to account for labour market connections and spillover effects that extend beyond local administrative boundaries. In addition, more evidence is needed to establish causal evidence on whether improvements in health lead to subsequent gains in productivity and economic performance.

We also know relatively little about which policies produce the greatest economic returns from health improvement. For example, investments in healthcare, prevention, housing, welfare support and employment services may all improve health, but their relative impacts on productivity remain uncertain.

What does this mean for policy?

Three lessons follow from the argument set out in this article.

First, reducing health inequalities should be seen as part of regional economic policy, not solely as a health policy objective. If inequalities in health contribute to differences in labour market participation and productivity, investment in prevention, healthcare and population health has the potential to generate economic benefits as well as health benefits. This provides an economic rationale for targeting health investment towards places with the greatest health needs.

Second, policy-makers trying to address health-related economic inactivity also need to recognise substantial geographical differences in employment opportunities. Recent evidence shows more than a six-fold difference in the availability of suitable jobs for young people across local labour markets in the UK (Institute for Public Policy Research, IPPR, 2026). Opportunities to enter employment vary considerably depending on where people live. Improving health or providing employment support may therefore be insufficient where suitable employment opportunities are limited.

Third, policies that reduce income and wider inequalities in material circumstances may be particularly important for narrowing health inequalities between places (Simpson et al, 2025b). This highlights the role of the welfare system as an important public health policy lever, as well as a means of providing income protection and economic security (Johnson et al, 2025).

Could devolution help?

Devolution could provide a route for bringing these three different policy levers together. Greater Manchester offers an important example, where devolved responsibility has enabled greater coordination across health, social care and wider public services. Evaluations suggest that life expectancy has improved relative to comparable areas following devolution, with larger improvements in more income-deprived areas (Britteon et al, 2022).

Over the first decade of devolution, the city-region experienced strong economic growth, with its economy growing by 28% between 2015 and 2025. This was accompanied by particularly strong productivity performance: between 2015 and 2023, Greater Manchester recorded the highest average annual productivity growth of any UK ITL2 area (Greater Manchester Combined Authority, GMCA, 2025).

While these trends do not establish that devolution caused the economic improvements, they illustrate the potential of place-based approaches that integrate health and economic policy. This is consistent with a ‘Health in All Policies’ approach, which recognises that improving population health requires action across policy sectors rather than within the healthcare system alone (World Health Organization, WHO, 2012).

Of course, many factors contributed to Greater Manchester's subsequent economic performance, and it would be inappropriate to attribute these trends solely to devolution. Even so, what should be clear is that health, place and economic performance are closely linked. Policy-makers seeking to improve regional productivity as well as health outcomes must keep this three-way connection in mind when designing solutions.