Labour Market Outlook· Labour market Labour Market Outlook Q1 2024 29 January 2024 Charlie McCurdy When comparing the pre-pandemic period to today, the national employment story has generally focused on the rise in inactivity due to ill health and the resultant fall in employment. However, in this note, we show that this is not the picture in all parts of the country. First, not everywhere has seen employment fall. Low-employment areas including West Central Scotland (which includes Glasgow) have seen the fastest employment growth compared to before the pandemic. In contrast, some high-employment areas’ employment rates (like Surrey, Sussex and Cheshire) are well down on pre-pandemic levels. Second, the rise in economic inactivity due to ill health has been geographically uneven. Areas of the country with high rates of pre-pandemic ill health and low shares of the population with degrees, like Lincolnshire, have seen particularly steep increases in the share of the working-age population who are economically inactive due to ill health. Finally, putting these two trends together isn’t as simple as you’d have thought. Some areas (like Tees Valley and Durham) have seen both rising inactivity due to ill health and rising employment – with other forms of economic inactivity pulling in the opposite direction. In the ‘Lifting the Lid’ section, we explore regional differences in median pay, what the transformation of the Labour Force Survey could reveal about unemployment, and how UK employment compares internationally. Spotlight | The geography of employment today compared to pre-pandemic Britain’s long-term labour market backdrop has been one of regional employment gaps opening up during the period of de-industrialisation in the 1980s, followed by a welcome fall in employment gaps between places over the 2000s and 2010s. More recently, UK-wide headlines have focused on the rise in economic inactivity (primarily due to sickness) and the resultant fall in employment compared to the pre-pandemic period. But this national story of inactivity up and employment down has played out differently across the country. In this spotlight we zoom in on the geography of employment and sickness-related inactivity today compared to where it was before the pandemic. Areas with lower employment rates before the pandemic have seen faster employment growth since then – with the reverse true for higher-employment areas Comparing the pre-pandemic period with today, there has been some return to normality in the UK labour market. As Figure 1 shows, the national working-age employment rate in the 12 months to September 2023 was only 0.1 percentage points shy of pre-pandemic levels. However, the local experience of employment changes is very different to the headline national picture. Compared to before the pandemic, low employment areas – like Tees Valley and Durham (+1.6 percentage points) and West Central Scotland, which includes Glasgow (+1.5 percentage points) – have generally seen the fastest employment growth. Low-employment parts of Inner and Outer London, too, have continued with the strong employment performance that was a feature of the 2010s. The reverse is true for high employment areas – Cheshire (-2.2 percentage points), Surrey and Sussex (-1.9 percentage points) have seen some of the largest falls. As a result, the positive pre-pandemic story of falling employment gaps between places has continued. The coefficient of variation of employment rates across areas also fell between 2019 and 2023. The difference this time is that employment has fallen in some higher employment areas, rather than the pre-pandemic trend of rising everywhere but fastest in low-employment areas. Figure 1: Areas with lower employment rates before the pandemic have seen faster employment growth since then – with the reverse true for higher-employment areas There are spatial differences in the rise in inactivity due to ill health Next, we turn to the post-pandemic rise in inactivity (those out of work and not looking for work) due to ill health, which has dominated recent discussions on the nexus between health and the UK labour market. It is well established that regional differences in economic inactivity due to ill health exist. The share of working-age people not working due to ill health in the 12 months to September 2023 (the latest data point) was around 50 per cent higher in West Wales (9.1 per cent), Tees Valley and Durham (8.8 per cent) and Merseyside (8.7 per cent) than the UK-wide average (5.8 per cent). Historical census data also confirms those areas with high rates of sickness-related inactivity today tend to have a long history of health-related inactivity (dating back to at least the 1980s). Despite these clear differences, there has been very little focus on the geographic nature of the recent rise in inactivity due to ill health. Figure 2 shows that UK-wide inactivity due to ill health as a share of the working-age population has risen by 0.7 percentage points since the 12 months ending March 2020 (up from 5.1 per cent to 5.8 per cent). But there are some clear spatial differences in this rise – places with already-high rates of sickness-related inactivity like Merseyside (+1.6 percentage points), Tees Valley and Durham (+1.5 percentage points) and West Wales (+1.5 percentage points) have experienced twice the national increase. In contrast, Inner London East (-0.4 percentage points) and West (-0.3 percentage points) have actually seen falls in the share of working-age people who are inactive due to ill health. Figure 2: There are spatial differences in the rise in inactivity due to ill health In fact, regional variation in economic inactivity due to ill health looks to have risen since the onset of the pandemic. Figure 3 plots a measure of the spread (the standard deviation) of sickness-related inactivity for the working-age population, with a higher value indicating that rates have become more geographically dispersed. At both the NUTS2 region and detailed region (a group of 22 sub-regions using Labour Force Survey data) level, regional variation in economic inactivity due to ill health for the working-age population was flat over the 2010s but has been rising since the start of 2020. Figure 3: Regional variation in economic inactivity due to ill health has risen compared to the pre-pandemic period Places with pre-existing health problems – and low graduate shares – have seen particularly steep increases in inactivity due to ill health If geographic differences in health-related inactivity have risen since the pandemic (as shown in Figure 3), what, then, are some common characteristics of places that have seen particularly large increases? We might expect the share of the working-age population aged 50-64 to help explain the rise in economic inactivity due to ill health (because inactivity rates are higher for older age groups as health tends to worsen at older ages). But there is not a clear relationship between the share of the working-age population aged 50-64 and the change in the number of people out of work due to ill health. Why might this be the case? Even at older ages there are different rates of sickness-related inactivity across regions. In Inner London, for example, just 11 per cent of those aged 50-64 are economically inactive due to ill-health, compared to 17 per cent in Tyne and Wear and 16 per cent in South Yorkshire. There is likely to be a contributing role played by local – particularly health-related – factors. For instance, other measures of health, like life expectancies, differ considerably across the country. Indeed, as the left-hand side of Figure 4 shows, places with high shares of working-age people who classify as disabled have seen the biggest deterioration in inactivity due to ill health. West Wales had nearly twice the proportion of working-age people who classify as disabled (using the Equality Act definition) compared to Inner East London in 2019 (25 per cent vs 15 per cent) and has seen inactivity due to ill health increase rather than fall between 2019 and 2023 (+1.5 percentage points vs -0.4 percentage points). Separate administrative data available at the local authority level on Personal Independence Payment claims (PIP) also displays a very clear relationship between the share of working-age residents receiving PIP pre-pandemic and the recent rise in PIP claims.  Put differently, places with pre-existing health problems have seen the steepest decline in the health of working-age people. Figure 4: Places with pre-existing health problems – and low graduate shares – have seen particularly steep increases in inactivity due to ill health The right-hand side of Figure 4 shows that areas with high shares of graduates tend to have seen the smallest increases in the share of working-age people who are economically inactive due to long-term sickness. Lancashire, for example, has seen a far greater increase in the share of working-age people who are inactive due to sickness since 2019 compared to Inner London West (+2.0 percentage points vs -0.3 per cent), and in 2019 had a far smaller share of working-age residents with degrees (25.2 per cent vs 62.3 per cent). Lincolnshire, too, has a relatively low concentration of graduates and has seen the share of 16-64-year-olds not working due to sickness increase by 2.5 percentage points since the pandemic – the biggest increase of any NUTS2 region. As previous Resolution Foundation work has highlighted, graduates are very unlikely to be workless due to ill health, which is consistent with the finding that areas with larger pools of graduates (generally parts of London) have been least affected by the recent rise in health-related inactivity. On the whole, the same places saw rising sickness-related inactivity and falls in employment – but this is not true for all areas Stepping back, is there an overlap between the areas which have seen the biggest rises in sickness-related economic inactivity and those with the largest falls in employment since the pandemic began? On average, the answer is yes. For places like The West and North West area of Outer London (which includes the likes of Barnet, Brent, Ealing and Hillingdon), Lincolnshire and East Wales a rise in sickness-related inactivity appears to have been associated with a fall in the employment rate – because fewer people are in each local labour market. The reverse is true for most other NUTS2 regions of London, in which the share of the working-age population who are inactive due to ill health has fallen slightly at the same time as employment rates have risen. In these parts of London, the number of people unable to work due to ill health hasn’t changed since before the pandemic, but population growth has far exceeded the national average. But there are exceptions: for example, traditionally low-employment urban areas like Merseyside, Tees Valley and Durham, West Midlands and West Wales (which includes Swansea) have maintained strong pre-pandemic employment growth despite above-average increases in sickness-related inactivity. For these areas, we can observe in the data that other reasons for inactivity (like retirement) have moved in the opposite direction. Figure 5: Some, but not all, parts of the country have seen both rises in inactivity due to ill health and falls in employment Overall then, there are parts of the country where inactivity due to ill health has risen sharply – primarily areas with pre-existing health problems. In some of these areas, rising ill health is associated with employment falls. But in other parts of the country the story is less straightforward: for example, where inactivity due to ill health and employment have both risen. Policy makers should be alive to these challenges that are experienced differently across the country. It’s unclear whether or not these geographic differences will endure. Nonetheless, boosting UK-wide labour market participation should remain a core priority for the Government. . In this spotlight we use the Annual Population Survey which combines 12 months of Labour Force Survey data to provide sufficient sample sizes for small geographical areas. . In this note our analysis is conducted using the 40 NUTS2 regions, the smallest geographical disaggregation which also ensures sufficient sample sizes.  This positive relationship also holds if we compare the change in the working-age population who are economically inactive due to ill health since pre-pandemic with the share of the pre-pandemic population who were economically inactive due to ill health in 2019. . PIP, which is the main working-age disability benefit, is used here as a proxy for ill health.