Policy paper

Get Britain Working White Paper: Analytical Annex

Published 26 November 2024

Analytical annex to the Get Britain Working White Paper

November 2024

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Purpose of the statistics

This annex accompanies the Get Britain Working White Paper. It provides details of analysis referenced in the paper and additional analysis that further contextualises the themes of the paper.

Section 1 provides information on transitions in labour market status for those on Universal Credit and demographic breakdowns of the “Searching for work” labour market regime.

Section 2 provides information on the grouping of local authorities by labour market type.

Section 3 provides information on the persistence of low pay.

Section 4 provides information on the main reason for inactivity broken down by age.

Section 5 provides information on the rates of young people classed as not in education, employment or training across England’s regions.

Section 6 provides information on economic inactivity rates within each qualification group.

Section 7 provides information on economic inactivity across England’s upper tier local authorities, excluding students.

Section 8 provides contextual analysis on reaching an 80% employment rate, including how many more people need to be in work.

1. Changes in labour market status for those on Universal Credit and demographic breakdowns of the “Searching for work” labour market regime

The analysis in section 1 focuses on changes in circumstances for customers of Universal Credit (UC). The analysis explores customers’ movements between UC conditionality regimes^{[footnote 1]}, as well as movements into work, with a focus on customers in the “Searching for work” conditionality regime. To fully understand trends in these movements, proportions of characteristics related to those customers in “Searching for work” are presented over the last 5 years, at the start of this section. Changes in these proportions over time add necessary context to the trends observed in the analysis of movements into work and between conditionality groups.

To contextualise the analysis, the compositional changes in demographic proportions in the “Searching for work” group are explored at the start of this section.

Definitions:

UC Conditionality regimes: customers are required to do certain work-related activities to receive UC. These activities depend on which of the 6 conditionality regimes the customer is placed in. Each person will be assigned one of 6 conditionality regimes based on their assessed capability and circumstances. These 6 conditionality groups are:

1. Searching for work

2. Working - with requirements

3. No work requirements

4. Working - no requirements

5. Planning for work

6. Preparing for work

For more information on the definition of the UC conditionality groups please see the ‘About these Statistics’ section of the analytical annex.

Assessment period: UC is calculated based on a customer’s circumstances each month. These are called ‘assessment periods’. A customer’s UC payment is based on their circumstances within the preceding assessment period and the first assessment period starts on the day a claim to UC is first made. Assessment periods are monthly, beginning on the same day each month.

Into-work rate: this is when a UC customer has earnings in one assessment period when they did not have earnings in the preceding assessment period. Due to this, the rate could miss some movements out of, and back into, work which happens within the time of 2 assessment periods if earnings are present in both.

1.1 Current and past demographic breakdowns of customers in the “Searching for work” labour market regime

The UC “Searching for work” group includes people newly claiming UC and those who have moved from other conditionality regimes who are required to take action to find or increase their levels of work and earnings. The term ‘caseload’ is used to refer to all the customers claiming UC in the “Searching for work” group at a specific time. The caseload includes both customers out of work, as well as customers in work and in receipt of earnings below the UC Administrative Earnings Threshold^{[footnote 2]}. This threshold is based on a household’s earnings within an assessment period. If these earnings are below the Administrative Earnings Threshold a customer will remain in “Searching for work”; those with higher earnings will move into a different conditionality regime. These figures will differ from Official Statistics published on the DWP Stat-Xplore website^{[footnote 3]} due to the use of different underlying datasets to allow for more accurate reporting of earnings information and movement off UC (see ‘About these Statistics’ section for more information).

In September 2024, 1.7 million customers made up the “Searching for work” conditionality group, of these: 1 million were out of work, and 0.6 million were in receipt of earnings. Figure 1 shows the volumes of this group from 2019 and highlights the large increase in the number of customers in the “Searching for work” group during the Covid-19 pandemic. The number of customers in the group decreased during 2021 and stabilised from around September 2022. More recently, in the past 6 months, there have been increases in the volumes of customers in receipt of earnings, this coincides with an increase to the Administrative Earnings Threshold in May 2024^{[footnote 4]} and the migration of customers from certain working age legacy benefits, such as Working Tax Credits, that began in April 2024.

The proportion of those out of work on the “Searching for work” caseload has reduced since 2020, with further reductions in the past year. In 2024 the caseload has begun to increase following a more stable period in 2022 to 2023.

Figure 1: “Searching for work” caseload, and “Searching for work” caseload restricted to those out of work, September 2019 to September 2024, Great Britain.

The following demographic compositional charts focus on the “Searching for work” group, restricted to those out of work as shown in Figure 1. This is to contextualise the analysis in Section 1.2 on into-work rates, as the into-work rate is based on the out of work group and not the total “Searching for work” group.

In September 2024, almost 40% of the “Searching for work” out of work group had resided in this conditionality group for 1 year or more, with just over 20% relatively new to the conditionality group having spent fewer than 3 months in “Searching for work”. Since 2019 there has been a considerable change in the duration customers spend in the “Searching for work” group. Figure 2 highlights the proportions of groupings of customers based on their duration in “Searching for work”, restricted to those out of work at each specific month. This illustrates the significant shift in the composition of the group as a result of the first Covid-19 lockdown: with a spike in customers with a duration of less than 3 months between April and June 2020 (the impacts of the first national lockdown of the pandemic). This increase led to staggered increases in the 3 up to 6 month, 6 up to 12 month, and one year and over groups’ proportions of the caseload in subsequent months. The share with a duration of one year and over has particularly grown from April 2021 onwards. While there have been some decreases to the 1 year or more group, the proportion of customers in this group remains significantly higher than it was before the Covid-19 pandemic.

Proportions of customers who have spent 1 year or more in the “Searching for work” conditionality group are significantly higher than prior to the pandemic.

Figure 2: “Searching for work” caseload restricted to those out of work, split by duration in the “Searching for work” conditionality group proportions, September 2019 to September 2024, Great Britain

Other customer characteristics are more stable across the 5-year period. Gender is particularly stable, with the proportion of female customers in the “Searching for work” out of work group consistently lower than male customers. 43% of customers in this group were women compared to 57% who were men in September 2024.

Figure 3 shows proportions of the “Searching for work” out of work group based on specific age groupings, highlighting the lower proportions of older age groups, with around one-fifth age 50 and over.

60% of the out of work, “Searching for work” group were aged under 40 in August 2024. All age group proportions have been relatively stable since September 2022.

Figure 3: “Searching for work” caseload restricted to those out of work, split by age group proportions, September 2019 to September 2024, Great Britain

1.2 Into-work rates of customers in the “Searching for work” labour market regime

This subsection focuses on into-work rates, the proportion of the customers in the UC “Searching for Work” group who move from being out of work in one month to being in work the following month.

Figure 4 shows the trends over time between September 2019 and September 2024, while Figure 5 shows rates for successive 12-month periods between 2019 and 2024. On average, in the 12-months between October 2023 and September 2024, 8% of those in the “Searching for work” group who were out of work moved into being in work the following month. There is significant seasonal variation in the into-work rate. The rate also reflects changes in wider economic conditions. Rates are consistently higher in the run-up to Christmas, typically around 9 to 10% between October and December, with lower rates in the summer months and at the start of the year, with the lowest rates in January at around 6 to 8%. The Covid-19 pandemic highlighted the reactiveness of the into-work rate to economic conditions with atypically low into-work rates during the periods of national lockdowns, such as between April and July 2020 as well as February to March 2021. There were also atypically high into-work rates from September to December 2020 and June to August 2021, both periods immediately after lockdowns ended.

Into-work rates are volatile month-to-month, however, have generally sat within a range of 6% to 10% in recent years.

Figure 4: Monthly into-work rates and 12-month average into-work rate, September 2019 to September 2024, Great Britain

The highest into-work rates are seen in October, with dips at the start of the year and summer.

Figure 5: Monthly into-work rates, October to September, 2019 to 2024, Great Britain

Figure 6 shows how the into-work rates are higher for customers who have newly joined the “Searching for work” conditionality group compared to those who have been in this group for longer. While these customers are new to the “Searching for work” group they may not be new customers of UC, as they may have moved from other UC conditionality regimes following a change in their circumstances. The impacts of the Covid-19 pandemic are also reflected in this graph, with a pronounced reduction in into-work rates in mid-2020 followed by considerable increases in the summer of 2021, across all durations in “Searching for Work”. These elevated into-work rates persisted through late 2021 and into 2022 but are now more in line with pre-pandemic trends. Into-work rates are lowest for those in the 1 year or over group. As shown in Figure 2, this group makes up around two-fifths of the caseload and therefore has a significant influence over the headline into-work rate.

Into-work rates are higher for customers that have been in the “Searching for work” group for up to 3 months.

Figure 6: Monthly into-work rates by duration in the “Searching for Work” group, September 2019 to September 2024, Great Britain

Figure 7 shows how into-work rates tend to be higher for women, particularly in October, suggesting most of the increases seen in the headline into-work rate in October is related to women.

Into-work rates are generally higher for women, particularly in recent years. Into-work rates between men and women tend to diverge around October.

Figure 7: Monthly into-work rates by a customer’s gender in the “Searching for work” group, September 2019 to September 2024, Great Britain

Figure 8 shows how into-work rates are highest for younger customers, particularly those aged under 25 and lowest for people aged 50 and over. Into-work rates were particularly high for young people in the aftermath of the Covid-19 pandemic, particularly between July 2021 and July 2022. However, rates have reduced significantly since then and are lower now than they were before the Covid-19 pandemic both for those aged under 25 and those aged 25 to 39. People aged over 50 saw less variation in into-work rates over the post-pandemic period. Into-work rates for this group remain low, however. As shown in Figure 4 this group makes up a smaller proportion of the “Searching for work” caseload.

Into-work rates are lowest for customers aged 50 and over, and higher for younger customers. 2021 saw particularly higher into-work rates for “Under 25” customers, however rates for this group have since fallen.

Figure 8: Monthly into-work rates by age group of customer in the “Searching for work” group, September 2019 to September 2024, Great Britain

1.3 Flows in and out of employment for customers in the “Searching for work” labour market regime

Focussing on movements into work alone does not fully capture how the UC system is working. Cohort analysis between April 2023 to April 2024 shows that over half of those entering the “Searching for work” group during this time were in receipt of earnings, with around 39% employees. Analysis which follows this cohort for a 6-month period, shows continual movements in and out of receipt of earnings over this period, with only a marginal increase in those receiving earnings from an employer at Month 6, to 45%, as shown in Figure 9. For Figure 9, customers are placed in ‘employment status’ groupings based on their earnings, and data collected through a UC claim on if they’re self-employed, further information on how these groupings are produced is contained in the About these Statistics section of the analytical annex. The movement between different employment states shown in Figure 9 applies to a minority of people in the cohort analysis. The majority (64%) of customers remain in the same ‘employment status’ for the full 6-month period, shown in Table 1.

Between months there are continual movements between those out of work into employment and vice versa, with marginal increases in the proportion of those Employed with an employer over the 6-month period.

Figure 9: ‘Employment status’ by months since flowing into the “Searching for work” group, April 2023 to April 2024 cohorts, Great Britain

64% of customers, consisting of 37% in sustained employment and 27% sustained out of work, remain in the same ‘employment status’ as they were when they flowed into the “Searching for work” group over the 6-month period.

Table 1: Proportions of the “Searching for work” inflow between April 2023 and April 2024 that remain in or change their ‘employment status’ over 6-month period, Great Britain

	Proportion of “Searching for work” inflow
Employed with employer or self-employed throughout	37%
Out of work throughout	27%
Changed employment status once or more since inflow	36%

1.4 Flows between conditionality groups

There is movement between the UC conditionality groups by customers in the months after they join the “Searching for work” group, shown in Figure 10. By Month 6, 15% have a closed UC claim and 45% remain in the “Searching for Work” group.

45% of those who flow into the “Searching for work” group are also in this group by Month 6, with considerable minorities flowing into other conditionality groups.

Figure 10: Conditionality groups by months since flow into the “Searching for work” group, April 2023 to April 2024 cohorts, Great Britain

*Due to small proportions the Planning for work and Preparing for work conditionality groups have been merged for this analysis.

Considering the whole UC caseload, most customers on UC stay in the same conditionality group from one month to the next. This remains the case when looking at customers’ status one month and then 6 months later. The left-hand chart in Figure 11 shows the transitions between UC conditionality groups for an average month between September 2023 and September 2024. The right-hand chart shows the flows between one month and 6 months later (averaged March 2023 and March 2024). This shows that 85% of the “Searching for work” group (as a whole) remained in that group from one month to the next, and 60% were there after 6 months. The percentage who remained in the “No work requirements” group was 98% after one month and 92% were there after 6 months. The thickness of the lines represents the size of the flows between the conditionality groups.

Most customers stay in the same conditionality group month to month.

Figure 11: Month-to-month and 6 month transitions between UC conditionality groups, Great Britain. The percentage of the group that remains in that group from one time point to the next is shown.

*the “Not on Universal Credit” group captures flows into and out of the Universal Credit conditionality groups.

**average month-to-month transitions between September 2023 and September 2024.

***average 6 month transitions between March 2023 and March 2024.

2. Grouping local authorities by labour market type

2.1 Background to local labour market type analysis

Analysis of sub-national labour markets typically focuses on the differences between different regions of the UK. However, regional analysis masks considerable differences in labour market outcomes at the local labour market level within regions. Focus on regions can also obscure the fact that that there are similarities in local labour markets that are not geographically close to each other.

To obtain a better understanding of sub-national labour markets, we have conducted ‘cluster analysis’ to categorise local authorities (LAs) into ‘labour market types’, based on key labour market variables, rather than geographic location. Cluster analysis has been used in the past by the Office for National Statistics to classify area types^{[footnote 5]}

This kind of categorisation differs from grouping LAs based on an index such as the English indices of deprivation^{[footnote 6]}. That approach aggregates multiple variables into a single scale. The cluster approach recognises that local areas may have weaker or stronger labour markets for reasons that vary between each LA. For example, deprived inner-city areas may have different labour market issues than coastal towns, but an index of deprivation would group these sorts of areas together; cluster analysis separates them out.

2.2 Findings from local labour market type analysis

To create the local labour market types, we used cluster analysis. For our analysis this was based on 6 local labour market variables. In Table 2 these are referenced as A-F. Please refer to the “About these Statistics” section for more information on the data sources for these variables.

A. Number of UC Searching for Work and Jobseekers’ Allowance claimants in the LA

B. LA employment rate

C. LA working-age work-limiting disability rate

D. Proportion of LA working-age population with at least level 4 qualifications^{[footnote 7]}

E. LA musculoskeletal condition rate

F. Proportion of LA population with a mental health condition

This cluster method creates groups of LAs such that the difference between each cluster group member within each cluster is minimised in terms of the 6 variables, yet the difference between cluster groups of local authorities is maximised. This is intended to create coherent groups of LAs that are distinct from each other. Applying this method to our data resulted in 14 groups of LAs. We reviewed the groups created and drew out common features within each group to generate pen portraits of the different local labour market types. These pen portraits and some information on each group is shown in the table below.

The 6 local labour market variables are rated as better than average, average and worse than average – represented by green (G), amber (A), and red (R) respectively. Please refer to Table 9 in the annex of this document to see table that shows the information below and a full list of the LAs by labour market type.

Table 2: Local labour market type descriptions.

LA labour market type	A	B	C	D	E	F	Pen portrait
1 - Coastal industry (15 LAs)	A	R	A	R	R	A	This cluster is characterised by low employment and high rates of disability and poor health, particularly musculoskeletal (MSK) conditions. These areas typically have seen the decline of major employers in heavy industry and are disproportionately ports.
2 - Small cities and large towns (22 LAs)	R	A	A	A	G	R	This cluster tends to be urban areas with young populations that do not have as high levels of high skilled jobs as compared to other major cities. The claimant count is high as is the rate of mental health conditions.
3 - Rural industrial legacy (29 LAs)	G	G	R	R	R	A	These areas tend to be more remote rural areas with healthy economies, but with a lack of connectivity to major employment centres and/or high skilled industry. They tend to have high employment rates, but lower skill levels and higher rates of MSK conditions. They are likely to have an older than average population.
4 - Representative agent (36 LAs)	A	A	A	A	A	G	Areas in this cluster score in the middle of the range on most measures. The cluster is a mix mainly of rural areas and smaller towns.
5 - Remote rural (18 LAs)	G	G	A	A	A	G	This cluster is characterised by high labour demand, with low claimant count and high employment rates, and less pronounced health problems. They tend to be sparsely populated rural areas.
6 - High growth centres (9 LAs)	A	G	G	G	G	A	These areas are those with strong labour demand and high skilled labour supply. They have a low rate of health and disability, though the incidence of mental health conditions is around the national average. These areas are mainly affluent areas of outer London and London commuting areas.
7 - Urban industrial legacy (20 LAs)	R	R	R	A	A	R	This cluster is characterised by weak labour markets with low labour demand and low skilled supply. They have higher rates of mental health conditions. They tend to be large urban areas with diverse populations. There are 2 distinct sub-groups within this cluster – ‘Older ex-industrial towns’ e.g. Burnley and ‘Younger ex-industrial cities; e.g. Birmingham
8 - Traditional affluent (29 LAs)	G	A	G	A	G	G	These areas have a highly skilled workforce, with low rates of health conditions, unemployment is low. These areas tend to be semi-rural or towns on the fringes of urban centres with strong economies.
9 - Semi-rural Britain (29 LAs)	G	A	A	G	A	G	This cluster is characterised by low rates of disability and poor health, with average employment rates and high skilled workers. These tend to be areas that historically would have had agriculture as a significant part of the economy with little infrastructure to connect the area to larger economic centres.
10 - Towns in transition (34 LAs)	A	R	R	R	R	A	This cluster is characterised by low rates of disability and poor health, with average employment rates and high skilled workers.
11 - Industrial retirement (12 LAs)	R	R	R	R	R	R	This cluster is made up of areas with higher levels of deprivation with the most adverse levels for each of the indicators of labour demand, skills and health. They tend to be towns with less well performing economies. In some cases these economies have seasonal industries.
12 - Affluent commuter belt (39 LAs)	G	G	G	G	G	G	This cluster is made up of areas that have strong indicators of labour demand, skills and health - these tend to be areas that are mainly rural but with good connectivity to strong economic centres, particularly London.
13 - London and diverse inner city (28 LAs)	R	R	G	G	G	R	This cluster is made up of almost all the local authorities of London, but also the central LAs of Greater Manchester (i.e. Manchester and Salford). These areas tend to have higher than average unemployment combined with high levels of skills in the workforce and a health profile that is consistent with a young population (i.e. low rates of disability and MSK, but high rates of mental health conditions).
14 - Trade towns (29 LAs)	A	A	R	G	A	A	Areas in this cluster have lower levels of skills in the workforce, with average levels of unemployment, MSK and mental health conditions with low rates of disability. These areas tend to be towns or areas on the periphery of big cities with medium skill economies.

We can show that using these groups to analyse sub national labour market dynamics is more informative than relying on regional analysis. One such application has been to show that the changes in inactivity post-pandemic varies for different demographic groups and for different reasons depending on labour market type – a finding that would be missed if this question was analysed using regional breakdowns.

Changes in economic inactivity and employment over the last 5 years have varied significantly according to local labour market type. Some have seen an increase in inactivity and a decrease in employment while for others the opposite is true.

Figure 12 shows that the national post-pandemic rise in economic inactivity is not uniform across the country. Since the pandemic, inactivity has increased for 7 labour market types and decreased for the other 7. ‘Coastal industry’ and ‘Industrial retirement’ have seen large rises in their inactivity rate, 3.3%pts and 3.0%pts respectively. This is almost entirely driven by increases in long-term sickness. These 2 labour market types also saw the largest fall in employment rate. ‘High growth centres’ has seen the largest fall in its inactivity rate of 3.5%pts. ‘High growth centres’ and ‘Trades towns’ are the only 2 local labour market types to have seen a post-pandemic fall in inactivity due to long-term sickness. ‘High growth centres’ was also the labour market type to have seen the largest increase in employment over the same period. ‘Rural industry legacy’ was the only labour market type to see an increase in both its inactivity and employment rates. Its unemployment rate fell by 0.9%pts meaning that some people that were unemployed moved into employment or became economically inactive. ‘London and diverse inner city’ was the only labour market type to see a fall in both its inactivity and employment rates, although these were both small changes at 0.2%pts and 0.1%pts respectively.

Figure 12: Percentage point change in 16 to 64 employment and inactivity rates by LA labour market type, 2019 to 2023

3. Persistence of low pay

This analysis tracks the earnings of people in the UK who were low paid in 2017 from 2017 to 2023. The definition of low pay used is less than two thirds of median hourly pay.

The analysis breaks the outcomes for low paid people down into 4 categories:

• Stuck: people who were low paid in every time period.

• Escapers: people who earn above the low pay threshold in both of the final 2 time periods the analysis covers.

• Cyclers: people who escape low pay at some point during the analysis but have not consistently escaped low pay, as the escapers have, by 2023.

• Missing: people who were missing from the analysis at some point by 2023.

Only 2 in 5 low paid people in 2017 were consistently earning enough by 2023 to no longer be considered low paid.

Table 3: earnings outcomes in 2023 of people who were low paid in 2017.

	Proportion of people in each category in 2023
Stuck	10%
Escapers	41%
Cyclers	44%
Missing	6%

The analysis finds of those low paid in 2017, only 2 in 5 (41%) were consistently earning enough by 2023 to no longer be low paid (escapers). 1 in 10 (10%) remained stuck throughout the period while 2 in 5 (44%) were cyclers, moving onto higher wages at some point throughout the period but not consistently earning enough by 2023 to no longer be low paid. The remaining 1 in 20 (6%) were missing from the analysis at some point by 2023.

4. Economic inactivity by reason and age

Definitions:

Economically inactive: the economically inactive are categorised by the main reason they give for inactivity.

Student: not available to start work or not looking for work due to education or being a student.

Sick/disabled: the sum of those inactive due to being short term-sick/injured, and long-term sick/disabled.

Retired: those who describe themselves as retired from paid work.

Carers: those inactive due to looking after family or home.

All other reasons: this includes those who are discouraged from looking for work; are waiting the results of a job application; say have not yet started looking for work; state that they do not need or want employment; have given an uncategorised reason for being economically inactive; or who have not given a reason for being economically inactive.

Over half of those inactive due to sickness/disability are in the 50 to 64 year-old age bracket.

Table 4: Economic inactivity levels (000s), 16 to 64-year-olds, April to June 2024 and change in year since April to June 2023.

	16 to 24 Level	16 to 24 Change	25 to 34 Level	25 to 34 Change	35 to 49 Level	35 to 49 Change	50 to 64 Level	50 to 64 Change	All ages Level	All ages Change
Student	2,393	+212	173	+24	72	-10	20	+0	2,659	+227
Sick / disabled	286	+56	403	-13	721	+42	1,625	+46	3,035	+130
Carers	124	+19	445	+53	668	-4	506	-1	1,741	+67
Retired	..	..	..	..	..	..	1,063	-15	1,069	-16
All other reasons	284	-41	153	+16	168	-23	407	-11	1,012	-59
Total	3,087	+246	1,174	+80	1,634	+4	3,621	+20	9,516	+350

”..” indicates too small a sample to present.

Those aged 16 to 24-years-old have seen the biggest rise in inactivity in the year to April to June 2024 - much of this has been driven predominantly by increased numbers of inactive students. Sickness/disability has risen as a reason for inactivity overall and has risen for all age groups on the year except for those aged 25 to 34-years-old.

5. Statistics on young people aged 16 to 24-years-old who are not in education, employment or training (NEET) across England’s regions

Definitions:

Young people: people aged 16 to 24-years-old.

Education and training: people are considered to be in education or training if they are enrolled on an education course and are still attending or waiting for term to start or restart; are doing an apprenticeship; are on a government-supported employment or training programme; are working or studying towards a qualification; have had job-related training or education in the last 4 weeks.

Young people not in education, employment or training (NEET): anybody who is not in any of the forms of education or training listed above and not in employment is considered to be NEET. As a result, a person identified as NEET will always be either unemployed or economically inactive^{[footnote 8]}.

NEET rate: the proportion of people aged 16 to 24 who meet the NEET definition above. This is calculated by dividing the number of 16 to 24-year-old people who are NEET by the 16 to 24 population (and multiplied by 100).

England has considerable variation in NEET rates. For the year ending April to June 2024, the North East had the highest, while London had the lowest.

For the year ending April to June 2024, the North West of England had the highest 16 to 24 NEET level in England while the North East had the lowest.

Table 5: 16 to 24 NEET rates and levels, by England’s regions.

	16 to 24 NEET rate	16 to 24 NEET level (000s)
England[footnote 9]	13.0%	786
North East	15.4%	45
North West	15.0%	124
Yorkshire and the Humber	11.4%	70
East Midlands	13.8%	74
West Midlands	14.6%	96
East of England	13.5%	82
London	10.6%	108
South East	11.7%	108
South West	13.6%	79

6. Economic inactivity by highest qualification

This analysis presents the economic inactivity rate for the 16 to 64-year-old population within each qualification level for the years 2015 to 2024. This is measured by the highest qualification an individual has.

People with no qualifications are more likely to be inactive than those with any other level of qualification.

Figure 13: Percentage of people aged 16 to 64 that are inactive by highest qualification, 2015 to 2024

Percentage of people aged 16 to 64 that are inactive by highest qualification, 2015 to 2024

Figure 13 shows that people with no qualifications are more likely to be inactive than active in the labour market. The rate of inactivity for people aged 16 to 64-years-old with no qualifications has been increasing since 2015 – from 50.1% to 54.1%. The only other qualification group that has seen an increase in their inactivity rate since 2015 is those whose highest qualification is “GCSE or equivalent A-C”, who have seen a 4.4 percentage point increase. This means that the difference in inactivity rates between the “No Qualification” and “GCSE or equivalent A-C” groups has decreased slightly over this period. The inactivity rates for the “A Level” and “Degree or equivalent” groups have both fallen between 2015, 0.5 percentage points and 1.3 percentage points respectively.

This analysis shows that there is an inverse relationship between inactivity and qualification – i.e., inactivity rate falls as level of qualification increases. However, it cannot be determined to what extent one factor contributes to the other.

7. Economic inactivity across England’s upper tier local authorities, excluding students

Excluding students, of the 20 upper tier local authorities in England with the highest rates of economic inactivity, 11 are in the North of England, while none are in the South East and just 2 are in London.

Table 6: The top 20 upper tier local authorities by inactivity rate, excluding students

Upper Tier Local Authority	Economic inactivity rate (excluding people whose main reason for inactivity is study)
Blackpool	28.9%
Bridgend	28.7%
Wolverhampton	28.4%
North Ayrshire	28.4%
Clackmannanshire	26.0%
Hartlepool	25.1%
Rhondda Cynon Taff	25.1%
South Tyneside	25.1%
Wigan	24.9%
Knowsley	24.8%
Rotherham	24.6%
Blackburn with Darwen	24.6%
Bury	24.5%
Bolton	24.4%
Enfield	24.1%
Kensington with Chelsea	23.9%
Halton	23.8%
Neath Port Talbot	23.3%
Middlesbrough	23.1%
Sandwell	23.0%

People who are economically inactive with a main reason for economic inactivity as study are taken out of the of the 16 to 64 years old economic inactivity rate calculation. The economic inactivity rate has a numerator of economically inactive people aged 16 to 64 years old in the local authority and denominator of population aged 16 to 64 years old in the local authority. 

8. 80% employment rate – contextual analysis

The Office for National Statistics estimated in their October United Kingdom Labour Market Overview that in June to August 2024 the UK’s headline working age (age 16 to 64-years-old) employment rate was 75.0%^{[footnote 10]}. This is 5.0% points away from an 80% employment rate.  31.8 million people aged 16 to 64-years-old were employed out of a population of 42.4 million aged 16 to 64-years-old. 

If 80% of the UK’s current estimated 42.4 million working age population were employed, 34.0 million people would be in work – 2.1 million more people than currently in work^{[footnote 11]}. 

An 80% employment rate would make the UK one of the best performing labour markets in the OECD in terms of getting people into work. Currently the OECD estimate that the top performing labour markets in the world, with employment rates of above 80% are the Netherlands (82.5%), Switzerland (80.4%) and Iceland (85.3%)^{[footnote 12]}.

9. About these Statistics

Details of the analysis in section 1

The analysis in section 1 uses administrative data to explore the transitions between labour market statuses and UC conditionality groups.

Proportions in this publication are not comparable with the monthly on the number of people on UC^{[footnote 13]}. This is because they are drawn from the underlying administrative databases rather than the extracts that comprise the monthly Official Statistics (which would not facilitate this analysis). The metadata on Stat-Xplore for the People on UC Official Statistics^{[footnote 14]} provides details of the methodology used for those statistics, which differs in various ways from the definitions in this publication.

Table 7: Universal Credit conditionality groups

Conditionality group	Definition
Searching for work	Not working, or with very low earnings. A customer is required to take action to secure work - or more or better paid work. The Work Coach supports them to plan their work search and preparation activity. Typical examples of people in this regime include jobseekers and self-employed in start-up period. Customers are only in this regime if they do not fit into one of the other regimes.
Working – with requirements	In work, but could earn more, or not working but has a partner with low earnings.
No work requirements	Not expected to work at present. Health or caring responsibility prevents the customer from working or preparing for work. Examples of people on this regime include those in full time education, over state pension age, has a child under 1 and those with no prospect for work.
Working – no requirements	Individual or household earnings over the level at which conditionality applies. Required to inform DWP of changes or circumstances, particularly at risk of earnings decreasing or job loss.
Planning for work	Expected to work in the future/ Lead parent or lead carer of child aged 1 (aged 1 to 2, prior to April 2017). The customer is required to attend periodic interviews to plan for their return to work.
Preparing for work	Expected to start work in the future even with limited capability to work at the present time or a child aged 2 (aged 3 to 4, prior to April 2017). The customer is expected to take reasonable steps to prepare for working including Work Focused Interview.

The analysis

The dataset analysis in 1.1, 1.2, 1.3, and 1.4 is produced from contains a combination of UC administrative data and HM Revenue and Customs’ (HMRC) Pay As You Earn (PAYE) Real Time Information (RTI) data. This additional data on a customer’s earnings, beyond that collected by DWP, enables more accurate identification of a movement into work, and the level of earnings received. It also provides functionality to observe earnings data, and by proxy ‘employment status’, of a previous customer following the closure of their UC claim.

For all the analysis in Section 1, a customer’s circumstances are taken as at the end of their assessment period, and a month relates to the month where an assessment period ends. Some customers may have changes to their circumstances within an assessment period, such as their ‘employment status’ changing and their conditionality group, however this analysis will only capture circumstance changes between assessment periods and not within.

The latest data available is for assessment periods up to those ending in August 2024. For cohort flows analysis in 1.3 and 1.4, this covers new inflows into the “Searching for work” conditionality group between March 2023 to March 2024, and follows these claimants for 6 months, the last being August 2024.

Analysis in 1.2 focusses on the into-work rate. Producing this analysis involves the identification of whether a customer has moved into work. To calculate the into-work rate for a specific month, firstly the cohort of applicable customers needs to be identified. This consists of all customers who are in the “Searching for work” group without earnings, where their assessment period end date falls in the preceding month. This is the base month, or the into-work denominator. The same customers are looked at in the following assessment period and any customers with earnings are included in the reporting month, or into-work numerator. The into-work rate is calculated by dividing the counts of these 2 groups. As the rate is based purely on the presence of earnings within assessment periods, the rate could miss some movements out of, and back into, work which happen within the time of 2 assessment periods if earnings are present in both.

The caseload characteristics analysis in Figures 2 and 3 is based on those customers in the base month, or Into-Work denominator.

The analysis in both section 1.2 and 1.3 focusses on whether customers are in work or out of work, and in section 1.3 what their ‘employment status’ is. To produce these distinctions, specific assumptions are made, the first being that a customer is in work or employed within an assessment period if they are in receipt of earnings. Therefore, customers who have started work within an assessment period but did not receive earnings from this employment till the next, would not be included, as well as customers who may have left work but are still receiving earnings from this work in the same assessment period. For the ‘employment status’ groupings used in section 1.3, ‘Employed with Employer’ relates to a customer receiving earnings from an employer, and therefore present in the HMRC PAYE RTI data. For ‘Self-employed’, when claiming UC, a flag in administrative data is produced to indicate that a customer is undertaking self-employed work with the expectation of profit, and it is their main form of work. Unfortunately, data on self-employment for those not claiming UC, but who have claimed UC previously, is not available, therefore all people in this grouping are also claiming UC. For ‘out of work’ this grouping relates to where no earnings are present in the assessment period. The definitions used may differ from definitions produced elsewhere.

The analysis in section 1.3 and the first part of 1.4 follows customers newly flowing into the “Searching for work” conditionality group of UC, having either been not on UC or in a different conditionality group at the end of the previous assessment period. In the analysis, a customer’s first month relates to where they have completed one assessment period, with this assessment period ending with the customer in “Searching for work”. These customers are then followed for 6 months with data on their circumstances in each of these included in the dataset. This enables the analysis of changes in circumstances from month-to-month and over the full 6 months.

Earnings data is aligned to assessment periods, and for months where a previous customer has stopped claiming UC a ‘notional’ assessment period is utilised. This follows the same dating system as if they were still claiming UC, which ensures consistency across the analysis.

Beyond the first month, if a customer stops claiming UC and their claim closes, the earnings data received from HMRC is utilised. If a previous customer has earnings in these months following a UC claim they are classed as Employed with an Employer, if no earnings are present in the HMRC data these previous customers are classed as out of work. Due to data limitations, the out of work group may include small proportions of those self-employed, as self-employment data is not available after a customer closes their UC claim. Flows into out of work from self-employed represent less than 1% of the Out of Work group. For this specific analysis, flows from self-employment into the Out of work group make up less than 1% of the group.

UC claims are closed either by a customer themselves, or if the customer has had 6 months of nil payments. The analysis in 1.3 and 1.4 includes these customers with nil payments, so for conditionality group proportions these proportions include both in-payment and out-of-payment UC claims.

Details of the analysis in section 2

In section 2 the 6 local labour market variables that were used to classify local authorities into labour market types were from the following data sources. ONS denotes Office for National Statistics.

Table 8: Variables used to classify local authorities

Labour market type	Data source
A. Searching for work Number of UC Searching for Work and Jobseeker’s Allowance claimants in the local authority.	ONS Claimant Count – includes UC Searching for Work and Jobseeker’s Allowance claims - 2023[footnote 15]
B. Local authority employment rate.	ONS Annual Population Survey - 2022-23 average[footnote 16]
C. Local authority working-age work-limiting disability rate.	ONS Annual Population Survey - 2022-23 average[footnote 17]
D. Proportion of local authority working-age population with at least level 4 qualifications[footnote 18].	ONS Annual Population Survey - 2022-23 average[footnote 19]
E. Local authority musculoskeletal condition rate.	OHID Local Authority Health Profiles - 2023[footnote 20]
F. Proportion of local authority population with a mental health condition.	OHID Local Authority Health Profiles - 2017[footnote 21]

There are some caveats with the Annual Population Survey (APS) data. While the ONS considers the quality of the APS to be robust for national and headline regional estimates, there are concerns with the quality of estimates for smaller segments of the population, such as local authority geographies. ONS outputs produced using the APS data, including those disseminated on NOMIS, are now labelled as “official statistics in development”. There is greater uncertainty associated with estimates at a local authority level compared to estimates at a regional or national level. Areas with smaller populations will, other things being equal, have smaller samples and thus wider margins of error.

There are some limitations to this analysis, summarised below.

The groupings are only based on the 6 variables listed above. The choice of variables to construct the groups is, to an extent, subjective. The variables are intended to provide a good description of labour market strengths and weaknesses. We did however experiment with a number of different labour market variables and used decision rules to narrow down our choice to the 6 listed above. Ideally in cluster analysis you want dimensionality - i.e. variables that relate to the central construct of interest (in this case the local labour market) but are not highly correlated with each other - in this way you get cluster groups forming out of combinations of high and low values of the variables. This explains why there are some variables (e.g. wages or vacancy rates) that might seem relevant labour market measures that are not included in the analysis.

The groupings will vary depending on the variables selected and the time period to which the data pertains, so they should not be taken as rigid groupings that define the characteristics of group members. Our analysis is a data driven exercise to generate groupings that will be practically useful rather than intended to identify fixed qualities of local areas.

The groupings are of local authorities – these are administrative groupings rather than areas that might be recognised as spatially distinct labour markets. The reason for this is that most sub-national data is readily available at the local authority level and hence it made sense to maximise the data available for this exercise. In addition, policy devolution is at the local authority or the combined authority level so it makes sense to produce analysis that matches this.

Details of the analysis in section 3

The persistence of low pay analysis in section 3 builds upon earlier analysis published by the Resolution Foundation in 2017, which tracked the proportion of people who were persistently low paid on an hourly basis over a 10-year period (2006 to 2016), using longitudinal Annual Survey of Hours and Earnings data^{[footnote 22]}. This analysis differs slightly from the Resolution Foundation’s analysis by covering 5 waves of the Understanding Society longitudinal survey^{[footnote 23]}. This analysis maintains the concepts used in the Resolution Foundation study of being stuck in low paid work, cycling in and out of low paid work and escaping low paid work.

This analysis tracks people who were initially low paid in the first wave of the analysis (wave 9, January 2017 to May 2019) across a 5 wave period to assess their earnings outcomes in the final period (wave 13, January 2021 to May 2023). In section 3 where the year 2017 is used this refers to wave 9, and 2023 refers to wave 13.

The analysis is based on a sample of 1,481 Understanding Society respondents who were in low paid employment in wave 9 of the survey and are in employment until wave 13 of the survey.

Details of the analysis in sections 4 and 5

The statistics in sections 4 and 5 are calculated using Labour Force Survey micro-data. The Labour Force Survey (LFS) - conducted by the Office for National Statistics (ONS) - is a large, representative household study and provides detailed information related to the UK population’s activity in the labour market. The LFS is the basis of the headline labour market statistics produced by ONS; and a valuable data source to conduct analysis into more detailed aspects of the labour market.

Sample surveys like the LFS provide estimates of population characteristics, rather than exact measures. In principle, many random samples could be drawn, and each would give different results, because each sample is made up of different people who give different answers to the questions asked. The spread of these results is the sampling variability, which generally reduces with increasing sample size, but is present in all iterations of the survey data.

The micro-data estimates we have calculated use recent LFS data which is subject to heightened volatility due to ongoing data quality problems with the ONS Labour Force Survey^{[footnote 24]}. The ongoing challenges with response rates and other aspects of the survey mean the LFS is currently considered ‘official statistics in development’ until further notice. Because of increased volatility of LFS estimates, estimates of change should be treated with additional caution.

The statistics in section 4 are calculated using LFS micro-data of the economically inactive population, split by main reason for economic inactivity and age (between 16 to 64-year-olds). Some reasons for inactivity have been grouped to provide a more coherent narrative.

The statistics in section 5 are calculated using LFS micro-data of young people aged 16 to 24-years-old who are not in education, employment or training (NEET) across the England’s regions. The 16 to 24 group is a smaller population group, has wider margins of error/volatility than for other, larger groups of the population. Therefore, micro-data on the youth labour market is both volatile and uncertain (due to the relatively high level of sample variability in the data for this smaller population group and because the data is not seasonally adjusted); meaning estimates produced at different points in the year will vary significantly.

To counteract these issues the data is calculated as a 4 quarter average (for the year ending April to June 2024). The NEET rates and levels are only given for England’s regions and not the other UK countries, i.e. Scotland, Wales and Northern Ireland. This is due to issues with comparability between the methodology used for each nation’s NEET statistics.

Details of the analysis in sections 6 and 7

The analysis in sections 6 and 7 uses data provided by the Office for National Statistics (ONS) from the Annual Population Survey (APS), which is based on data collected through the Labour Force Survey (LFS).

There are some caveats with the APS data – which we set out in “Details of analysis in section 2” that also apply to the analysis in section 6 and 7.

In section 6 the figure for each year is based on data from April to March e.g. 2024 denoted the period from April 2023 to March 2024. The analysis covers the total population aged 16 to 64-years-old and is weighted to ensure results are representative of the overall population. When recording individuals’ highest qualification those with higher degrees, unspecified qualifications, or qualifications categorised as “Other” were excluded from the analysis.

In section 7 the analysis is based on APS data covering July 2023 to June 2024. We have used upper tier (county or unitary) local authorities to minimise sample variability at local authority level. However, at this sub-national geography and level of sample variability present in the data and due to issues with achieving the desired APS sample exacerbating this issue, the APS data at this level should be interpreted cautiously.

Details of the analysis in section 8

This analysis is based on published ONS statistics that are based on the Labour Force Survey, the main source of regular and detailed market information in the UK and the basis of the ONS’ headline labour market statistics.

The OECD infra-annual labour market statistics are an internationally recognised source of comparable data on different countries.

Statement of Compliance with the Code of Practice for Statistics

The Code of Practice for Statistics (the Code) is built around 3 main concepts, or pillars: 

• Trustworthiness – is about having confidence in the people and organisations that publish statistics

• Quality – is about using data and methods that produce statistics

• Value – is about publishing statistics that support society’s needs

The following explains how we have applied the pillars of the Code in a proportionate way. 

Trustworthiness

HM Government analysts work to a professional competency framework and Civil Service core values of integrity, honesty, objectivity, and impartiality. The analysis in this release has been scrutinised and quality assured in line with the Aqua Book^{[footnote 25]} and received sign off by the subject expert lead Senior Civil Service analyst.

The figures have been seen in advance by Ministers and officials, in line with the Code, where pre-release access does not apply for an ad hoc analysis release.

Quality

The analysis presented in this publication is based on a range of administrative and survey data sources.

Quality assurance has taken place in line with the standards usually applied to HM Government ad hoc releases, with internal checks that the results shown are robust.

Value

Publishing this annex ensures that the public have access to the analysis that supports and complements the Get Britain Working White Paper.

Contact information

For media enquiries contact the DWP Press Office.

Annex

The 6 local labour market variables used for this analysis are referenced as A to F. Please refer to the “About these Statistics” section for more information on the data sources for these variables.

A. Number of UC Searching for Work and Jobseekers’ Allowance claimants in the LA

B. LA employment rate

C. LA working-age work-limiting disability rate

D. Proportion of LA working-age population with at least level 4 qualifications^{[footnote 7]}

E. LA musculoskeletal condition rate

F. Proportion of LA population with a mental health condition

The 6 local labour market variables are rated as better than average, average and worse than average – represented by green (G), amber (A), and red (R) respectively.

Table 9: Local labour market type descriptions, including full list of local authorities’ classifications.

LA labour market type	A	B	C	D	E	F	Pen portrait	Local authorities
1 - Coastal industry (15 LAs)	A	R	A	R	R	A	This cluster is characterised by low employment and high rates of disability and poor health, particularly musculoskeletal (MSK) conditions. These areas typically have seen the decline of major employers in heavy industry and are disproportionately ports.	Babergh, Bridgend, Chorley, Clackmannanshire, Dumfries and Galloway, Dundee City, East Ayrshire, Inverclyde, Isle of Wight, Neath Port Talbot, North Ayrshire, North Lanarkshire, Rhondda Cynon Taf, South Ayrshire, Wyre
2 - Small cities and large towns (22 LAs)	R	A	A	A	G	R	This cluster tends to be urban areas with young populations that do not have as high levels of high skilled jobs as compared to other major cities. The claimant count is high as is the rate of mental health conditions.	Brighton and Hove, Bristol, City of Cardiff, Crawley, Derby, Glasgow City, Hounslow, Ipswich, Kirklees, Lancaster, Leeds, Lincoln, Newcastle upon Tyne, Newport, Norwich, Peterborough, Portsmouth, Preston, Sheffield, Southampton, West Dunbartonshire, Worcester
3 - Rural industrial legacy (29 LAs)	G	G	R	R	R	A	These areas tend to be more remote rural areas with healthy economies, but with a lack of connectivity to major employment centres and/or high skilled industry. They tend to have high employment rates, but lower skill levels and higher rates of MSK conditions. They are likely to have an older than average population.	Adur, Amber Valley, Arun, Breckland, Castle Point, Cornwall, Cumberland, Dorset, East Riding of Yorkshire, East Suffolk, Flintshire, Forest of Dean, Gosport, Herefordshire, County of King’s Lynn and West Norfolk, Mid Devon, New Forest, North Devon, North Northamptonshire, North Warwickshire, North West Leicestershire, Nuneaton and Bedworth, Redditch, South Derbyshire, South Holland, South Lanarkshire, Staffordshire Moorlands, Tamworth, Wrexham
4 - Representative agent (36 LAs)	A	A	A	A	A	G	Areas in this cluster score in the middle of the range on most measures. The cluster is a mix mainly of rural areas and smaller towns.	Angus, Argyll and Bute, Ashford, Broxbourne, Broxtowe, Caerphilly, Canterbury, Carmarthenshire, Ceredigion, Cheshire West and Chester, Chichester, Conwy, Dacorum, Falkirk, Fife, Gedling, Gwynedd, High Peak, Lewes, Maidstone, Moray, North Tyneside, Northumberland, Pembrokeshire, Powys, Rother, Scottish Borders, South Kesteven, Stafford, Swansea, Teignbridge, Vale of Glamorgan, West Lancashire, West Suffolk, Worthing, Wychavon
5 - Remote rural (18 LAs)	G	G	A	A	A	G	This cluster is characterised by high labour demand, with low claimant count and high employment rates, and less pronounced health problems. They tend to be sparsely populated rural areas.	Aberdeenshire, Basingstoke and Deane, Blaby, Cheshire East, Fareham, Huntingdonshire, Mole Valley, North Somerset, Orkney Islands, Rushmoor, Shetland Islands, South Gloucestershire, South Ribble, Stroud, Test Valley, Tewkesbury, Torridge, Wiltshire
6 - High growth centres (9 LAs)	A	G	G	G	G	A	These areas are those with strong labour demand and high skilled labour supply. They have a low rate of health and disability, though the incidence of mental health conditions is around the national average. These areas are mainly affluent areas of outer London and London commuting areas.	City of Edinburgh, City of London, Kingston upon Thames, Merton, Oxford, Reading, Richmond upon Thames, Wandsworth, Watford
7 - Urban industrial legacy (20 LAs)	R	R	R	A	A	R	This cluster is characterised by weak labour markets with low labour demand and low skilled supply. They have higher rates of mental health conditions. They tend to be large urban areas with diverse populations. There are 2 distinct sub-groups within this cluster – ‘Older ex-industrial towns’ e.g. Burnley and ‘Younger ex-industrial cities; e.g. Birmingham	Birmingham, Blackburn with Darwen, Bolton, Bradford, Burnley, Harlow, Hartlepool, Kingston upon Hull, City of Leicester, Liverpool, Middlesbrough, Nottingham, Oldham, Pendle, Rochdale, Sandwell, South Tyneside, Stoke-on-Trent, Walsall, Wolverhampton
8 - Traditional affluent (29 LAs)	G	A	G	A	G	G	These areas have a highly skilled workforce, with low rates of health conditions, unemployment is low. These areas tend to be semi-rural or towns on the fringes of urban centres with strong economies.	Aberdeen City, Bath and North-East Somerset, Cambridge, Cheltenham, Cherwell, Derbyshire Dales, East Dunbartonshire, East Hampshire, East Lothian, East Renfrewshire, Epping Forest, Malvern Hills, Midlothian, Monmouthshire, Perth and Kinross, Renfrewshire, Ribble Valley, Runnymede, South Cambridgeshire, South Hams, South Oxfordshire, Stirling, Stockport, Tandridge, Trafford, Warwick, Welwyn Hatfield, West Lothian, York
9 - Semi-rural Britain (29 LAs)	G	A	A	G	A	G	This cluster is characterised by low rates of disability and poor health, with average employment rates and high skilled workers. These tend to be areas that historically would have had agriculture as a significant part of the economy with little infrastructure to connect the area to larger economic centres.	Braintree, Broadland, Central Bedfordshire, Charnwood, Colchester, Cotswold, East Devon, Exeter, Fylde, Harborough, Hertsmere, Highland, Hinckley and Bosworth, Maldon, Melton, Mid Suffolk, North Kesteven, North Yorkshire, Oadby and Wigston, Rochford, Rutland, Sevenoaks, Shropshire, Somerset, South Norfolk, Spelthorne, Wealden, West Devon, Westmorland and Furness
10 - Towns in transition (34 LAs)	A	R	R	R	R	A	This cluster is characterised by low rates of disability and poor health, with average employment rates and high skilled workers.	Barnsley, Bassetlaw, Bolsover, Calderdale, County Durham, Denbighshire, Doncaster, Dover, Eastbourne, Fenland, Folkestone and Hythe, Gateshead, Great Yarmouth, Halton, Havant, Merthyr Tydfil, North East Derbyshire, North East Lincolnshire, North Norfolk, Plymouth, Redcar and Cleveland, Rossendale, Rotherham, St. Helens, Stockton-on-Tees, Sunderland, Tameside, Telford and Wrekin, Thanet, Torbay, Wakefield, West Lindsey, Wigan, Wirral
11 - Industrial retirement (12 LAs)	R	R	R	R	R	R	This cluster is made up of areas with higher levels of deprivation with the most adverse levels for each of the indicators of labour demand, skills and health. They tend to be towns with less well performing economies. In some cases these economies have seasonal industries.	Ashfield, Blackpool, Blaenau Gwent, Boston, Chesterfield, East Lindsey, Hastings, Hyndburn, Knowsley, Mansfield, Tendring, Torfaen
12 - Affluent commuter belt (39 LAs)	G	G	G	G	G	G	This cluster is made up of areas that have strong indicators of labour demand, skills and health - these tend to be areas that are mainly rural but with good connectivity to strong economic centres, particularly London.	Bracknell Forest, Brentwood, Bromley, Bromsgrove, Buckinghamshire, Chelmsford, Dartford, East Cambridgeshire, East Hertfordshire, Eastleigh, Elmbridge, Epsom and Ewell, Guildford, Hart, Horsham, Lichfield, Mid Sussex, Na h-Eileanan Siar, North Hertfordshire, Reigate and Banstead, Rugby, Rushcliffe, South Staffordshire, St Albans, Stratford-on-Avon, Surrey Heath, Sutton, Three Rivers, Tonbridge and Malling, Tunbridge Wells, Uttlesford, Vale of White Horse, Waverley, West Berkshire, West Oxfordshire, Winchester, Windsor and Maidenhead, Woking, Wokingham
13 - London and diverse inner city (28 LAs)	R	R	G	G	G	R	This cluster is made up of almost all the local authorities of London, but also the central LAs of Greater Manchester (i.e. Manchester and Salford). These areas tend to have higher than average unemployment combined with high levels of skills in the workforce and a health profile that is consistent with a young population (i.e. low rates of disability and MSK, but high rates of mental health conditions).	Barking and Dagenham, Barnet, Brent, Camden, Coventry, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Hillingdon, Islington, Kensington and Chelsea, Lambeth, Lewisham, Luton, Manchester, Newham, Redbridge, Salford, Slough, Southwark, Tower Hamlets, Waltham Forest, Westminster
14 - Trade towns (29 LAs)	A	A	R	G	A	A	Areas in this cluster have lower levels of skills in the workforce, with average levels of unemployment, MSK and mental health conditions with low rates of disability. These areas tend to be towns or areas on the periphery of big cities with medium skill economies.	Basildon, Bedford, Bexley, Bournemouth, Christchurch and Poole, Bury, Cannock Chase, Darlington, Dudley, East Staffordshire, Erewash, Gloucester, Gravesham, Havering, Isle of Anglesey, Medway, Milton Keynes, Newark and Sherwood, Newcastle-under-Lyme, North Lincolnshire, Sefton, Solihull, Southend-on-Sea, Stevenage, Swale, Swindon, Thurrock, Warrington, West Northamptonshire, Wyre Forest

1. Stat Xplore People on Universal Credit, Conditionality Regime – Department of Work and Pensions (https://stat-xplore.dwp.gov.uk). Available at: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Monthly/Conditionality%20Regime.html#:~:text=Conditionality%20Regime%20is%20based%20on,closest%20to%20the%20count%20date. ↩

2. Guidance Universal Credit and earnings – GOV.UK (www.gov.uk), published 6th November 2023. Available at: https://www.gov.uk/guidance/universal-credit-and-earnings ↩

3. Stat-Xplore People on Universal Credit – Department for Work and Pensions (https://stat-xplore.dwp.gov.uk), published 12th November 2024. Available at: https://stat-xplore.dwp.gov.uk/webapi/jsf/tableView/tableView.xhtml ↩

4. 2 previous increases to the Administrative Earnings Threshold occurred in September 2022 and January 2023. ↩

5. For example: 2021 residential-based area classifications- Office for National Statistics (www.ons.gov.uk), last updated 17th November 2023. Available at: https://www.ons.gov.uk/methodology/geography/geographicalproducts/areaclassifications/2021residentialbasedareaclassifications ↩

6. English indices of deprivation – GOV.UK (www.gov.uk), published 13th December 2012. Available at: https://www.gov.uk/government/collections/english-indices-of-deprivation ↩

7. What qualification levels mean – gov.uk (www.gov.uk). Available at: https://www.gov.uk/what-different-qualification-levels-mean/list-of-qualification-levels ↩ ↩²

8. Young people not in education, employment or training (NEET), UK: August 2024 - Office for National Statistics (www.ons.gov.uk), published 22nd August 2024. Available at:  https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/bulletins/youngpeoplenotineducationemploymentortrainingneet/latest This refers to the young people not in education, employment or training (NEET) definitions. ↩

9. The England figures are calculated by summing the total number of young people and young people who are NEET in all of England’s regions. ↩

10. A01: Summary of labour market statistics, UK - Office for National Statistics (www.ons.gov.uk), published 15 October 2024. Available at: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/datasets/summaryoflabourmarketstatistics ↩

11. Figures presented in the annex have been rounded to 1 decimal place so may not sum exactly. ↩

12. OECD infra-annual labour market statistics employment rate quarter two 2024 – Organisation for Economic Co-operation and Development (www.oecd.org), published 2024. Available at: https://www.oecd.org/en/data/indicators/employment-rate.html?oecdcontrol-40985420ae-var3=2024-Q2&oecdcontrol-40985420ae-var5=Q ↩

13. Universal Credit statistics – GOV.UK (www.gov.uk), published on 15 October 2024. Available at: https://www.gov.uk/government/collections/universal-credit-statistics ↩

14. Dataset: People on Universal Credit – GOV.UK (https://stat-xplore.dwp.gov.uk). Available at: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Monthly/UC_Monthly.html ↩

15. ONS Claimant Count – includes UC Searching for Work and Jobseekers’ Allowance claims – 2023. Available at: https://www.nomisweb.co.uk/ ↩

16. ONS Annual Population Survey - 2022-23 average. Available at: https://www.nomisweb.co.uk/ ↩

17. ONS Annual Population Survey - 2022-23 average. Available at: https://www.nomisweb.co.uk/ ↩

18. What qualification levels mean – gov.uk (www.gov.uk). Available at: https://www.gov.uk/what-different-qualification-levels-mean/list-of-qualification-levels ↩

19. ONS Annual Population Survey - 2022-23 average. Available at: https://www.nomisweb.co.uk/ ↩

20. OHID Local Authority Health Profiles – 2023. Available at: https://www.gov.uk/government/organisations/office-for-health-improvement-and-disparities ↩

21. OHID Local Authority Health Profiles – 2017. Available at: https://www.gov.uk/government/organisations/office-for-health-improvement-and-disparities ↩

22. The Great Escape? Low pay and progression in the UK’s labour market – Resolution Foundation (www.resolutionfoundation.org), published 19th October 2017. Available at: https://www.resolutionfoundation.org/publications/the-great-escape-low-pay-and-progression-in-the-uks-labour-market/ ↩

23. Understanding Society: The UK Household Longitudinal Study, waves 9-13: https://www.understandingsociety.ac.uk/  ↩

24. Labour Force Survey: planned improvements and its reintroduction – Office for National Statistics (www.ons.gov.uk), published 2nd November 2023. Available at: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/methodologies/labourforcesurveyplannedimprovementsanditsreintroduction#further-update ↩

25. The Aqua Book: guidance on producing quality analysis – gov.uk (ww.gov.uk), published 26th March 2015. Available at: https://www.gov.uk/government/publications/the-aqua-book-guidance-on-producing-quality-analysis-for-government ↩