Chapter 3: Mobility across the UK

Question 1

Highlights

Accepted Answer

We have developed new composite indices of intermediate outcomes (mobility outcomes earlier in life), and drivers (the enablers of mobility), at the local authority (LA) level.^{[footnote 1]}

We now have a single composite index for intermediate outcomes at the upper-tier LA level.^{[footnote 2]} This gives us 203 geographical regions across the UK, instead of the 41 regions that we had last year. This index, called Promising Prospects, covers highest qualifications, hourly earnings, and also professional and working-class occupations of young people.

In common with other work on the topic, we have found that most LAs have similar levels of mobility, with a few at the top and bottom ends. The most favourable areas tend to be in London or in the adjoining Home Counties.

Similarly, we have developed 3 new composite indices of drivers at the upper-tier LA level, giving the same 203 geographical regions.

The first index based on drivers is called Conditions of Childhood. This covers childhood poverty, parental education, parental working-class occupation and parental professional occupation. The most favourable conditions of childhood tend to be found in affluent areas, mainly Greater London and the Home Counties but also parts of the North and Scotland.

The second index based on drivers is Labour Market Opportunities for Young People. This covers unemployment, professional employment and working-class employment of young people. Results are similar, although the LAs with the less favourable opportunities for young people tend to be in the North East and North West of England, as well as older industrial and port areas.

We have retained our composite index that looks at research and development (R&D), but improved it so that it also gives us 203 regions. This index is now called Innovation and Growth. The most favourable areas are clustered around London, mainly in the South of England, but a few other areas score well on this index.

Our measurement framework has shown that there are differences in mobility prospects for people from different backgrounds, with, for example, parental education, occupation and ethnicity playing important roles. But there is an increasing realisation that geography – where people grow up – can also affect mobility chances. This is why we introduced geographical breakdowns of mobility chances and drivers in last year’s report.

However, looking at geographical breakdowns of single indicators of social mobility, like unemployment or highest qualification, could be misleading, for 2 reasons. Firstly, results have to be estimated from sample surveys, and sample sizes at a regional or local level can be small. Secondly, we need to take a holistic view of conditions in an area, rather than using only one indicator, no matter how reliable.

To deal with these problems, we introduced 5 summary measures last year (composite indices). These provided a snapshot of how regions performed across a range of indicators. Two of these indices were based on intermediate outcomes and the remaining 3 on drivers, with each index composed of 3 underlying measures. These gave a much more reliable picture of what was going well, and what could be improved, across the UK.

Using these indices, we learned that Greater London and some adjoining areas stand out as places where young people do particularly well. Someone growing up in London was more likely to have promising prospects – attain higher qualifications, higher earnings and a professional job – than someone from the same socio-economic background (SEB) who grew up in a more rural or remote area. However, their risk of facing precarious situations such as unemployment, economic inactivity and lower working-class employment was also higher in London.

Question 2

Measuring social mobility in local authorities

Accepted Answer

We have built on this work by constructing a composite index of mobility prospects at LA level, instead of the regional level (International Territorial Level 2, ITL2) that we used last year.^{[footnote 3]}^{[footnote 4]} By moving from ITL2 to upper-tier LA level, we have increased the number of areas from 41 to 203. This increases both granularity and statistical power for investigating why areas differ in their mobility prospects. Additionally, this type of breakdown aligns more closely with policy responsibilities than any other breakdowns at ITL, since LAs have responsibility for local services, including maintained schools.

A new composite index for the intermediate outcomes

Like the previous Promising Prospects composite index, the new index is based on 3 intermediate outcomes (4 measures) – highest qualification, occupational level, and hourly earnings among young people in the UK. As with all intermediate outcomes, we control for SEB. This means that the new index identifies the LAs where the young people who grew up there do better (or worse) than people with the same SEB who grew up elsewhere. We should think of this index as providing a measure of absolute mobility chances, not of relative mobility.^{[footnote 5]}

Table 3: Summary of composite indices for the intermediate outcomes this year.

Index	Indicator	LFS data used
Promising Prospects	IN2.3 Highest qualification (university degree)	Net levels of a university degree among young people in each area after controlling for SEB
	IN3.3a Occupational level (professional occupation)	Net proportions of young people in professional-class jobs in each area after controlling for SEB
	IN3.3b Occupational level (working-class occupation)	Net proportions of young people not in working-class jobs in each area after controlling for SEB
	IN3.4 Hourly earnings	Mean hourly earnings among young people in each area after controlling for SEB

We pooled Labour Force Surveys (LFS) from several years, with areas based on where the respondent was living when aged 14 years. However, LA areas have much smaller sample sizes than the ITL2 areas (which approximate to groups of about 4 upper-tier LAs) that we used in our report last year.^{[footnote 6]} We have therefore made various changes to improve the precision of the estimates. We have also changed the methodology to provide a stronger statistical foundation.^{[footnote 7]}

Increasing the precision of the estimates

To compensate for the much smaller sample sizes at LA level than at ITL2, we have made the following changes:

We have added one extra year of data, so that we now use pooled LFS data for 2018 to 2022.
We have broadened the social class categories by merging the higher and lower working classes, and the higher and lower professional classes.
We have added one extra indicator to the index. We now include a working-class position in the index in addition to the professional class indicator that we used previously. As a result, we are no longer reporting on the ‘precarious situations’ composite index from last year.
We have broadened the age range of young people from 25 to 29 years to 25 to 44 years. This increases the sample sizes by a factor of 4.

We should still emphasise that, even with these changes, there remain some LAs where the sample size is very small – in a few cases, it is fewer than 50 respondents. In these cases, we cannot be sure whether the LA shows better prospects, average prospects or poorer prospects.

As previous work has shown, most LA areas have similar mobility chances for the people who grew up in them.^{[footnote 8]}^{[footnote 9]} Since we are reporting estimates based on survey data, the estimates are not precise enough to give a rank order of LAs. Instead, the main interest is which areas fall into the 2 distinctive ‘tails’ of the distribution: the LAs with the most and least favourable scores.

In our findings, there are a number of very small LAs, with correspondingly small sample sizes. In these cases, we cannot be confident about their position in the distribution. They might be disadvantaged or alternatively highly advantaged, rather than average or near the middle. But we cannot be sure either way. The LAs with the smallest sample sizes are Clackmannanshire (45), East Renfrewshire (40), Hammersmith and Fulham (43), Kensington and Chelsea (26), Midlothian (41), Na h-Eileanan Siar (27 – Outer Hebrides), Rutland (33), Orkney Islands (19), Shetland Islands (28) and Westminster (37). We should also note that the LFS does not allow us to distinguish LAs within Northern Ireland, which we therefore treat as a single entity.

We have made several changes to the composite indices for the drivers. In last year’s report we included 3 composites at ITL2: sociocultural advantage, childhood poverty and disadvantage, and R&D.^{[footnote 10]}

We explored whether we could build equivalents of the first 2 indices at LA level. For the first 2 indices, we have largely used the same indicators as in our State of the Nation 2023 report, but have made a few changes, consistent with those described in the previous section. First, we have expanded the age ranges for some indicators to increase sample sizes. For consistency with the new index of intermediate outcomes, we also include the percentage of young people with a working-class occupation as an additional indicator of the labour market situation facing young people. For the Conditions of Childhood index, we’ve added parental education and professional jobs, and omitted youth unemployment. The list of indicators used this year is shown in table 3.1.

Table 3.1: Summary of composite indices for the drivers (DR) this year.

Index	Indicator	Data used
Conditions of Childhood	Driver (DR) 1.2 Childhood poverty	% of children in relative poverty (Department for Work and Pensions (DWP) and Households Below Average Income (HBAI) statistics, pooled years 2018 to 2022)
	DR 1.3 Distribution of parental education	% of families (with a dependent child) containing a graduate parent or adult (pooled LFS 2014 to 2022)
	DR 1.4a Distribution of parental occupation (professional)	% of families with a professional parent or adult (pooled LFS 2014 to 2022)
	DR 1.4b Distribution of parental occupation (working class)	% of families with a working-class parent or adult (pooled LFS 2014 to 2022)
Labour Market Opportunities for Young People	DR 3.2 Youth unemployment	% of young people in employment (pooled LFS 2014 to 2022)
	DR 3.3a Type of employment opportunities for young people (professional)	% of young people with a professional occupation (pooled LFS 2014 to 2022)
	DR 3.3b Type of employment opportunities for young people (working class)	% of young people with a working-class occupation (pooled LFS 2014 to 2022)
Innovation and Growth	DR 5.1 Broadband speed	% of premises with gigabit-capable broadband (Ofcom)
	DR 5.2 Business expenditure on R&D	Business expenditure (logged) per 100,000 people on R&D
	DR 5.3 Postgraduate education	% of working-age (age 25 to 64 years) people with postgraduate education (pooled LFS 2014 to 2022)

Please see our technical annex for a detailed account of our methodology.

Question 3

Intermediate outcomes: Promising Prospects

Accepted Answer

This index brings together 4 measures capturing promising prospects for young people, as measured by their levels of education, occupational positions and earnings. The index adjusts for SEB, and measures how well young people from similar backgrounds do in education and the labour market.

In table 3.3 we list the LAs with the most favourable, favourable, near average, unfavourable and least favourable outcomes. Within each of the bands listed we order the names alphabetically, as it would be misleading to rank-order them, given the imprecision of the estimates. Since the distribution is skewed towards more favourable outcomes, we show 32 LAs with the more favourable outcomes and 22 LAs with the less favourable outcomes (corresponding to the 2 tails shown in the figure 3.2). We must also emphasise that there is more uncertainty about the membership of the ‘favourable’ and the ‘unfavourable’ bands than about the ‘most favourable’ and ‘least favourable’ bands.

Figure 3.2: Most LAs have scores in the middle or average on the index of Promising Prospects but there are 2 tails with distinctively favourable or unfavourable prospects.

LAs’ scores in the composite index of Promising Prospects, covering 4 intermediate outcomes. LAs with near-average outcomes are shown in grey.

Explore and download the data: Promising prospects (State of the Nation data explorer).

Source: LFS, from 2018 to 2022. Source data used from the following indicators: intermediate outcomes 2.3, 3.3a, 3.3b and 3.4.

Note: The histogram shows the distribution of scores for the unitary and upper-tier LAs where respondents lived when they were aged 14 years. For more information on how each LA was scored, please see the technical annex.

Case study: Conor Warren, 18, Devon, South West England

I grew up in Tiverton in mid- Devon. In Year 6 my parents divorced. Me and my sister moved from mid-Devon to north Devon with my mum who is a teacher. When I was younger, we were comfortable but after my parents split up, we had to be a bit more careful.

I really enjoyed school. I did as many different clubs as I could. I was head boy. I did OK in my GCSEs, not amazingly, but I scraped passes. I preferred the social aspects.

When I was 14, I founded my own mental health organisation. Me and a group of friends were ranting about the mental health system and what it would be like in an ideal world. And then we thought why are we saying “in an ideal world”? Why don’t we make it happen? We started creating resources such as worksheets and PowerPoints on subjects including anxiety and self-care. We’ve now got over 300 schools using our resources. We also created a digital advent calendar campaign around mental health with celebrities like Judi Dench, Emma Thompson and Stephen Fry giving tips.

After my GCSEs, I decided to stay on at sixth form to do business, media and drama because they were all coursework-focused and I didn’t do amazingly well in my GCSEs.

At the same time, I got involved in Young Enterprise. I built a greetings card company with friends as part of their company programme and we made it to the UK finals.

Running my own business has helped me grow my contacts and learn lots of skills. I am someone who learns by doing and everything I’ve learned in business has been by making mistakes and learning on the job.

You don’t have to quit education or your job straight away. It’s about starting small and building up over time. I’m now travelling across the world through work. Being 18, living in north Devon, there are not so many opportunities and it’s allowed me to see the wider world. I have decided not to go to university for the time being. In the future, I want to carry on with the business and grow it as much as I can. I love the freedom. I am a very ambitious person and it means if I have an idea, I can run with it and see where it leads.

Table 3.3: The LAs with the most favourable, favourable, unfavourable and least favourable scores on the index of Promising Prospects. LAs with near-average outcomes are omitted.

Most favourable outcomes for Promising Prospects:

Barnet
Brent
Camden and City of London
Ealing
Harrow
Hillingdon
Hounslow
Redbridge
Richmond upon Thames
Surrey county council

Favourable outcomes for Promising Prospects:

Bexley
Bedford
Brighton and Hove
Buckinghamshire
Central Bedfordshire
Cheshire East
Enfield
Hackney
Hammersmith and Fulham
Haringey
Hertfordshire county council
Islington
Kensington and Chelsea
Kingston upon Thames
Lambeth
Lewisham
Luton
Newham
Southwark
Tower Hamlets
Wandsworth
Warwickshire county council

(LAs with near-average outcomes are omitted)

Unfavourable outcomes for Promising Prospects:

East Ayrshire
Hartlepool
City of Kingston upon Hull
Kirklees
Northumberland
Newcastle upon Tyne
North Ayrshire
North East Lincolnshire
North Tyneside
Renfrewshire
Rochdale
Wakefield

Least favourable outcomes for Promising Prospects:

Barnsley
Cornwall plus Isles of Scilly
Dumfries and Galloway
County Durham
Gateshead
Northern Ireland
North Lanarkshire
Scottish Borders
South Tyneside
Sunderland

Source: LFS, from 2018 to 2022. Source data used from the following indicators: intermediate outcomes 2.3, 3.3a, 3.3b and 3.4.

Note: The index of Promising Prospects is a composite index covering 3 intermediate outcomes (4 measures) for unitary and upper-tier LAs, based on the LA where respondents lived when they were aged 14 years. The suffix ‘CC’ indicates that the authority is a two-tier county council. Data constraints mean that Northern Ireland has to be treated as a single unit and in a few other cases LAs have had to be combined. The distinctions between the 5 categories (most favourable, favourable, near-average, unfavourable and least favourable) are based on their positions within the overall distribution. LAs with near-average outcomes are omitted.

The most striking finding is that nearly all the most favourable and favourable areas are either in London or in the adjoining Home Counties of Surrey, Hertfordshire and Buckinghamshire surrounding London. As shown in figure 3.4, these Home Counties are often included in the London travel-to-work area and in the London Metropolitan area (alongside parts of other counties adjoining London such as Kent county council and Essex county council). Broadly speaking these areas all have good transport links to London and can be thought of as a commuter belt around London.

However, there are 2 notable exceptions to the dominance of London and the Home Counties: Cheshire East and Warwickshire. These 2 authorities outside the London area with favourable scores might also constitute commuter areas for their nearby metropolitan areas of Manchester and Birmingham, respectively.

Figure 3.4: Index of Promising Prospects: London boroughs and LAs in adjoining Home Counties of Surrey county council, Hertfordshire county council and Buckinghamshire have the most promising prospects for young people.

Explore and download the data: Promising prospects (State of the Nation data explorer).

Source: LFS, from 2018 to 2022. Source data used from the following indicators: intermediate outcomes 2.3, 3.3a, 3.3b and 3.4.

Note: Areas are where respondents lived when they were aged 14 years. For more information on how each area was scored, please see the technical annex.

We can get a sense of the nature and magnitude of the differences between the most favourable and the least favourable areas from figures 3.5a and b. In the figure, we show the relationship between SEB and the intermediate outcome of occupational class (a component of the composite index), in the most favourable and least favourable LAs. To create the top panel of figure 3.5a, we pool the results for the top 10 LAs with the most favourable outcomes (Barnet to Surrey county council) and in the bottom panel (figure 3.5b) we pool the results for the 10 LAs with the least favourable outcomes (that is, Barnsley to Sunderland). We pool the results from 10 LAs because the results for individual LAs are too imprecise for separate analysis.

Both panels show a strong relationship between SEB and occupational outcomes, with young people who come from working-class backgrounds having the lowest chances of reaching the professional class, and those from professional backgrounds having the highest chance. This pattern applies more or less equally to people who grew up in the most favourable areas and those brought up in the least favourable areas.^{[footnote 11]}

We also see from figures 3.5a and b that 31% of people from working-class backgrounds who grew up in the least favourable areas experienced long-range upward mobility to the professional class whereas 50% of people from working-class backgrounds who grew up in the most favourable areas experienced long-range upward mobility – a difference of 19 percentage points.

We must however remember that these are simply descriptions of the differences in mobility chances in the most and least favoured areas – they are not causal claims about the effects of place on mobility. Almost certainly some of the variation between areas will be due to unmeasured characteristics of the individuals who lived there, such as their educational background or ethnicity.

Figures 3.5a and 3.5b: The relationship between SEB and social class positions of people aged 25 to 44 years in the most favourable (upper panel) and least favourable (lower panel) LAs.

Explore and download the data: Promising prospects (State of the Nation data explorer).

Source: LFS, from 2018 to 2022. Source data used from the indicator of intermediate outcomes 3.3a.

Note: Areas are where respondents lived when they were aged 14 years. The areas in each panel correspond to those shown for the LAs with the most favourable and least favourable scores in figures 3.5a and b. For more information on how each area was scored, please see the technical annex.

Turning to the specific areas that are included as having more or less favourable intermediate outcomes, one notable finding is that young and mid-career people who grew up in some less affluent London boroughs, such as Hackney, Haringey, Southwark and Wandsworth have favourable outcomes. This pattern may be driven by the expanding opportunities that the London Metropolitan area as a whole has seen, rather than by the policies and conditions of specific boroughs. In effect, the London economic environment can be thought of as a ‘rising tide that raises all ships’.

Turning to the less favourable areas, there is no single pattern comparable to the London effect. The closest parallel to the London effect is a negative ‘North East effect’ comprising less favourable prospects for people who grew up in Newcastle upon Tyne, Sunderland, Gateshead, Hartlepool, South Tyneside, Northumberland and Durham.

The less favoured areas tend to be relatively remote from London, pointing to a possible centre or periphery distinction. There do however seem to be several different types of less-favoured areas. Some of them such as Cornwall and Isles of Scilly, Dumfries and Galloway, and the Scottish Borders are predominantly rural and are relatively distant from major metropolitan areas. Northumberland and Durham also share some features with this rural type of cluster. A second type of less-favoured area comprises former mining areas, such as Barnsley, Wakefield, and North Lanarkshire. A third type consists of manufacturing areas, such as Kirklees and Rochdale, and a fourth type consists of east coast ports such as Hartlepool, Hull and Grimsby (North East Lincolnshire).

These patterns probably reflect the shift of the UK’s economy away from the primary sectors of agriculture and mining, as well as away from the secondary sector of traditional manufacturing and shipbuilding, towards high-tech and service sectors. Even though some of these changes have been long-standing, the declining size of the primary and some components of the secondary sector appear to have left an enduring legacy.

These findings are broadly in line with those that we reported last year at a regional level (ITL2), but provide greater granularity.^{[footnote 12]} They are also broadly in line with those shown in the SMC’s report The Long Shadow of Deprivation, which covered income mobility among young people across both upper and lower-tier LAs in England (but which excluded Scotland, Wales and Northern Ireland).^{[footnote 13]}^{[footnote 14]} This report showed that the 10 most advantaged lower-tier LAs were nearly all in the Home Counties surrounding London. For example, these included districts in Berkshire (Wokingham), Essex, Hertfordshire, Oxfordshire, Suffolk and Surrey. The 10 most disadvantaged LAs included Hartlepool and Gateshead in the North East of England, Bradford, Nottingham and Sheffield, alongside rural districts in Devon, Worcestershire, Lancashire and Buckinghamshire.^{[footnote 15]} Researchers Breen and In’s recent study of regional variation in intergenerational social mobility in Britain also shows a very similar geographical pattern of absolute mobility.^{[footnote 16]}

However, we must be very careful about attributing causal relationships to these descriptive findings. Almost certainly, some of the variation between areas will be due to the unmeasured characteristics of the individuals who lived there, such as their educational background or ethnicity. Some variation between areas, especially in the case of small neighbouring districts within the same travel-to-work area, may reflect processes of families relocating to provide more favourable environments for their children. Families who earn more can move to more desirable areas with higher housing prices while families who earn less will be forced into areas with cheaper housing. Indeed, our report The Long Shadow of Deprivation shows that the level of house prices in an area is one of the best predictors of absolute mobility chances in the area.^{[footnote 17]} Other important predictors were found to be the level of deprivation in an area as measured by the Index of Multiple Deprivation, population density (an indication of the degree of urbanisation), the percentage of ‘outstanding’ schools as rated by Ofsted, and the percentage of the labour force in a professional occupation.^{[footnote 18]}^{[footnote 19]}

Question 4

Drivers: Conditions of Childhood index

Accepted Answer

We turn to the composite indices of the drivers, beginning with Conditions of Childhood index.^{[footnote 20]} These composite indices are built in the same way as the index of Promising Prospects described earlier in the report. There are however 2 important differences between the composite indices for drivers and that for intermediate outcomes. First, in the case of the intermediate outcome index of Promising Prospects we controlled for SEB. However, in the case of the drivers we do not control for SEB, but instead, use data on the overall conditions in each area. This is because the drivers try to capture the overall level of opportunity, or lack of opportunity, in each area. The composite indices for the drivers resemble our original index from 2016, and other indices such as those of the World Bank.^{[footnote 21]}^{[footnote 22]} This is because they look at conditions that might help mobility, rather than actual levels of mobility.

The second important difference is that the index of Promising Prospects was based on where young people had grown up, whereas the 3 composite indices of drivers are based on people’s current location. This is because the drivers are intended to provide a forward look at where future mobility chances might be more or less good for those currently living there, rather than a backward look at the areas from which people had already gone on to better or worse outcomes.

Table 3.6 shows the unitary and upper-tier LA that have favourable and unfavourable scores on the Conditions of Childhood index. As with the new composite index of intermediate outcomes, we distinguish between areas with the most favourable, favourable, middle (near-average), unfavourable and least favourable conditions. Areas in the middle group comprise roughly two-thirds of the total number of authorities. We do not attempt to make any finer distinctions within this middling group, as the scores are close together and will be subject to considerable measurement error. In effect, they would be distinctions without a difference.

The more favourable conditions of childhood tend to be found in affluent areas, predominantly in the Greater London area (such as Kingston upon Thames and Richmond upon Thames), the Home Counties around London (Surrey county council, and Windsor and Maidenhead), as well as affluent areas in or around Manchester (Trafford), and Glasgow (East Dunbartonshire).^{[footnote 23]} Conversely, less favourable conditions of childhood tend to be found in the North East of England, in coastal cities, industrial and former mining areas (for example, Oldham and Stoke-on-Trent), as well as some inner-city areas in London.

These patterns are well-known. They correspond fairly well to those found, for example, by the Legatum Institute in its 2021 Prosperity Index of LAs and districts across the UK.^{[footnote 24]} The government’s Levelling-Up White Paper also shows a similar pattern with coastal cities, parts of the North and Midlands with industrial legacies, and rural parts of Scotland, Wales and Northern Ireland being left behind.^{[footnote 25]}

The map for conditions of childhood shown in figure 3.7 demonstrates that, within all parts of Great Britain – that is within Scotland, Wales, the North of England, the Midlands and the South (including London) – there are both advantaged and disadvantaged areas. While the advantaged areas predominate in London and the South of England, there are also areas of high deprivation in London too. The same applies in Scotland and Wales. Childhood deprivation cannot be characterised solely along North and South lines.

We should however recognise that the geographical pattern may be partly driven by ‘geographical sorting’. In other words, areas with larger houses and gardens and within easy commuting distance to major centres of employment will tend to attract higher-wage families who can afford the high house prices. Concentrations of affluent families in such areas may well have broader spillover benefits, especially if the quality of local schools is greater. This is consistent with our The Long Shadow of Deprivation report, which showed that higher house prices and more schools rated as outstanding were associated with improved mobility chances for children who grew up there.^{[footnote 26]}

Table 3.6: The LAs with the most favourable, favourable, unfavourable and least favourable scores on the Conditions of Childhood index. LAs with near-average conditions are omitted.

Most favourable conditions of childhood:

Brighton and Hove
East Dunbartonshire
East Renfrewshire
Kingston upon Thames
Oxfordshire county council
Richmond upon Thames
Surrey county council
Trafford
Wandsworth
Windsor and Maidenhead
Wokingham

Favourable conditions of childhood:

Barnet
Bath and North East Somerset
Bracknell Forest
Bromley
Buckinghamshire
Camden and City of London
Cheshire West and Chester
City of Edinburgh
Hammersmith and Fulham
Hertfordshire county council
Hampshire county council
Kensington and Chelsea
Merton
Reading
Rutland
Stirling
Stockport
Sutton
Vale of Glamorgan
West Berkshire
Wiltshire

(middle-ranked LAs not listed)

Unfavourable conditions of childhood:

Barking and Dagenham
Barnsley
Birmingham
Blackpool
Blaenau Gwent
Bradford
Doncaster
Hartlepool
Luton
Manchester
Merthyr Tydfil
Newham
North Ayrshire
North Lincolnshire
Nottingham
Peterborough
Rochdale
Redcar and Cleveland
Rotherham
Sunderland
Torfaen
Tower Hamlets
Walsall
West Dunbartonshire
Wolverhampton

Least favourable conditions of childhood:

Blackburn with Darwen
Hull
Leicester
Middlesbrough
North East Lincolnshire
Oldham
Sandwell
Stoke-on-Trent

Source: DWP – HBAI statistics, and LFS, from 2014 to 2022. Source data used from the following indicators: DR 1.2, 1.3, 1.4a and 1.4b.

Note: Areas are those where respondents were currently living at the time of data collection. The Conditions of Childhood index is a composite index covering 3 drivers (4 measures) for unitary and upper-tier LAs (DR 1.2, 1.3, 1.4a, 1.4b). Data constraints mean that Northern Ireland has to be treated as a single unit and in a few other cases LAs have had to be combined. The distinctions between the 5 categories (most favourable, favourable, near-average, unfavourable and least favourable) are based on their positions within the overall distribution. LAs with near-average outcomes are omitted.

Figure 3.7: Conditions of Childhood index: more favourable conditions of childhood tend to be found in affluent areas in and around the Greater London area, the Home Counties, and Manchester, Glasgow and City of Edinburgh.

Explore and download the data: Conditions of childhood (State of the Nation data explorer).

Source: DWP – HBAI statistics, and LFS, from 2014 to 2022. Source data used from the following indicators: DR 1.2, 1.3, 1.4a and 1.4b.

Note: Areas are based on current residence. For more information on how each area was scored, please see the technical annex.

Question 5

Drivers: Labour Market Opportunities for Young People index

Accepted Answer

The composite index of Labour Market Opportunities for Young People is designed to show which areas offer more or less favourable employment opportunities for young people. While it would be expected that these employment opportunities would have some similarities with the Conditions of Childhood index, there is not a one-to-one correspondence between them.

Table 3.8 and figure 3.9 show that there is a similar over-representation of favourable areas in and around London in both lists of favourable areas, but that there are also some striking differences. For example, few of the areas in Scotland and Northern England with favourable conditions of childhood appear in the list of areas with favourable labour market opportunities for young people. So Vale of Glamorgan in Wales, East Dunbartonshire, East Renfrewshire, City of Edinburgh and Stirling in Scotland, Cheshire West and Chester, Stockport and Trafford in the north of England all obtain favourable scores on the Conditions of Childhood index, but only 3 of these appear in the list of areas with favourable scores on the index of labour market opportunities. This means that the latter index has a much more explicit North and South division than the former index.

Table 3.8: The LAs with the most favourable, favourable, unfavourable and least favourable scores on the index of Labour Market Opportunities for Young People, for unitary and upper-tier LAs. LAs with near-average conditions are omitted.

Most favourable labour market opportunities for young people:

Bristol
Hackney
Hammersmith and Fulham
Havering
Islington
Lambeth
Lewisham
Southwark
Tower Hamlets
Wandsworth

Favourable labour market opportunities:

Bath and North East Somerset
Bracknell Forest
Bromley
Buckinghamshire
Camden and City of London
City of Edinburgh
Essex county council
Hertfordshire county council
Kensington and Chelsea
Merton
Oxfordshire county council
Reading
Stockport
Surrey county council
Sutton
Trafford
West Berkshire
Westminster
Windsor and Maidenhead

(middle-ranked LAs not listed)

Unfavourable labour market opportunities:

Argyll and Bute Islands
Carmarthenshire county council
Darlington
Doncaster
Dumfries and Galloway
Durham
Gwynedd
Hartlepool
Moray
Neath Port Talbot
Northumberland
Oldham
Sandwell
Shetland Islands
West Lothian

Least favourable labour market opportunities:

Birmingham
Middlesbrough
North Lincolnshire
Redcar and Cleveland
Stockton-on-Tees
Sunderland

Source: Source data used from the following indicators: DR 3.2, 3.3a, and 3.3b.

Note: Areas are based on current residence. The index of Labour Market Opportunities for Young People is a composite index covering 3 drivers for unitary and upper-tier LAs (DR 3.2, 3.3a and 3.3b). Data constraints mean that Northern Ireland has to be treated as a single unit and in a few other cases LAs have had to be combined. The distinctions between the 5 categories (most favourable, favourable, near-average, unfavourable and least favourable) are based on their positions within the overall distribution. LAs with near-average outcomes are omitted.

Case study: Amy Stevens, 35, Mansfield, Nottinghamshire

My dad was in the navy. My mum did a bit of everything – part time working around us, a few odd jobs, then after that they went into pub management. I’m a bit of a wildcard. I left school and worked with my parents in the pub trade. Then I decided I wanted to do something of my own and find a profession where I could continually develop. So I thought… nursing!

I applied to do a course but I had to take a step back. I had two children and my life was taken up by looking after them. Once my daughter was old enough, I looked into it again but realised the entry requirements had changed and I needed a maths GCSE. I contacted a couple of the local secondary schools to see if they had places and ended up taking the exam in a room full of 15 and 16-year-olds thinking, “what am I doing?” The anxiety was indescribable!

Once I had that GCSE, I realised I wanted to try nursing again. It’s the variety. It ticked so many boxes for me. I took a university access course at West Notts College, then a general nursing degree at Nottingham Trent. I was in my 30s, juggling taking my youngest to nursery and my oldest to school. If I had any upcoming assignments or exam practice, I had to be quite disciplined and take myself to the university library then get back to pick up the children, make dinner and do the evening routine. There have been a lot of late nights, sometimes working until two in the morning and then getting up again at half past six or seven for the school run. There are times when I’ve been pulling my hair out because I can’t split myself into three different people.

There have been times where I was not sure whether I would be able to continue but I’ve managed to grit my teeth and weather the storm. I am now on the final placement of the programme and currently job searching, with a potential job lined
up. If you’d come to me five years ago and said “in five years’ time this is where you’re going to be and this is what you will have achieved” I would have laughed and said I don’t believe you.

Retraining will make things much more financially comfortable. There was a lot of anxiety involved in retraining all over again but I’m so glad I did it.

Figure 3.9: A North and South divide is more evident regarding the index of Labour Market Opportunities for Young People than with the Conditions of Childhood index.

Explore and download the data: Labour Market Opportunities for Young People (State of the Nation data explorer).

Source: LFS, from 2014 to 2022. Source data used from the following indicators: drivers 3.2, 3.3a and 3.3b.

Note: Areas are based on current residence. For more information on how each area was scored, please see the technical annex.

Comparing conditions of childhood with labour market opportunities for young people

The 2 lists of favourable areas for these 2 indices have some interesting differences. The list of areas with favourable conditions of childhood includes several in Scotland (East Dunbartonshire, East Renfrewshire, City of Edinburgh, Stirling), in the North West of England, in and around Manchester (Cheshire West and Chester, Trafford, Stockport), and one from Wales (Vale of Glamorgan). In contrast, there are fewer Scottish or North Western authorities in the list of areas with favourable opportunities for young people, and none from Wales (Labour Market Opportunities index). A North and South divide is therefore even more marked in the case of the Labour Market Opportunities index than the Conditions of Childhood index.

These differences between these indices may be due to the different underlying mechanisms involved. Geographical sorting is likely to occur everywhere in and around major urban areas across the UK and is likely to lead to areas with more and with less favourable conditions of childhood. In contrast, labour market opportunities for young people may depend on factors such as investment, economic growth and expansion of the professional and managerial classes, that have been concentrated in the south of England in recent years.

Turning to the 2 lists of less favoured areas, we also see both similarities and differences between the Conditions of Childhood index and index of Labour Market Opportunities for Young People. While older industrial and port areas in the north and midlands of England are present in both indices (Birmingham, Hartlepool, Sunderland), the Labour Market Opportunities for Young People index also contains some largely rural areas (Northumberland, Shetland Islands) whereas rural areas are not strongly represented in the corresponding list for the Conditions of Childhood index. We also see a greater concentration of LAs with unfavourable conditions in the North East of England for the Labour Market Opportunities for Young People index. Again, as with the favourable areas, mechanisms of geographical sorting (in the case of conditions of childhood) and of economic growth (or its lack in the case of labour market opportunities) can plausibly account for the patterns. In this context it is noteworthy that rates of economic growth over the last decades show a clear North and South gradient with the rate of growth (gross value added) being markedly higher in London than in the rest of the South of England or Scotland, and being markedly lower in the North of England, Midlands and Wales.^{[footnote 27]}^{[footnote 28]}

Question 6

Drivers: Innovation and Growth index

Accepted Answer

Last year we introduced an experimental set of indicators to measure environments that potentially were favourable to innovation and growth and so for future social mobility. We have changed the name of this composite index because it is more focused on the conditions that can help economic growth and innovation rather than entrepreneurship or R&D. Apart from changing the geographical level of the index – it now breaks the UK down into LA areas – we have also made 2 changes to the method used to calculate it. Firstly, the postgraduate research indicator now focuses on the proportion of people with postgraduate skills, rather than the number of research students. Secondly, the broadband speed metric now tracks the proportion of premises with gigabit internet availability rather than broadband speed itself.

Innovation and growth:

Innovation and its commercial development have long been part of national industrial strategy. A favourable educational, technical and economic infrastructure can promote local economic growth, stimulating investment and expanding professional and business opportunities in the area. This provides opportunities for upward mobility. Conversely, areas with lower levels of what economists term ‘human capital’ (people available to work), a less favourable infrastructure and less investment are more likely to miss out on economic growth. The impact on social mobility will tend to be indirect, operating via local growth rates, but is potentially important. It is of considerable interest to measure the innovation environment and to test whether a favourable environment promotes growth and upward mobility in the future.

To develop the composite index for this driver we use 3 indicators – broadband speed, business R&D expenditure and the number of people in the area with postgraduate degrees – to tap into different potential components of an environment that is helpful for innovation and growth. Because of the lack of data availability for business expenditure at the LA level, the details of the indicators differ from those used last year, although the general principles are the same.

Table 3.10 shows that, once again, LAs in London and the Home Counties predominate in the list of those with favourable environments for innovation and growth. However, there are interesting differences between the 3 indices with respect to the authorities that have favourable scores. In particular, there are several ‘new entries’ outside London in the list of authorities with favourable environments for innovation and growth, notably Cambridgeshire county council, Cardiff, Milton Keynes, Slough, Southampton and Warrington (interestingly, all with good transport links).

On the unfavourable side, we also see an even larger number of new entries, mainly from Wales and Scotland. These include the more rural and less densely-populated areas like the Isle of Anglesey, Caerphilly, Ceredigion county council, Cornwall and Isles of Scilly, Highland, Lincolnshire county council, Na h-Eileanan Siar (Outer Hebrides), Orkney Islands, Pembrokeshire county council, Powys and the Scottish Borders. Given the inclusion of fixed broadband as one of the indicators, this is hardly surprising although both the other indicators for this index also show strong rural-to-urban differences.

More surprising perhaps is that the North-East disadvantage that was evident with the Conditions of Childhood index and index of Labour Market Opportunities for Young People is not so strongly evident in the case of the environment for innovation and growth. Areas such as Hartlepool, Sunderland City, Middlesbrough and Redcar and Cleveland (which had unfavourable scores on both the other indices) do not have unfavourable scores on this third index.

Table 3.10: The LAs with the most favourable, favourable, unfavourable and least favourable scores on the index of Innovation and Growth for unitary and upper-tier LAs. LAs with near-average conditions are omitted.

Most favourable for innovation and growth:

Camden and City of London
Hammersmith and Fulham
Kensington and Chelsea
Richmond upon Thames
Wandsworth
Westminster

Favourable for innovation and growth:

Barnet
Bracknell Forest
Brighton and Hove
Bristol
Cambridgeshire county council
Cardiff
Cheshire East
Cheshire West and Chester
City of Edinburgh
Ealing
Hackney
Hertfordshire county council
Hounslow
Islington
Lambeth
Milton Keynes
Oxfordshire county council
Reading
Slough
Southampton
Warrington
West Berkshire
Windsor and Maidenhead
Wokingham

(middle-ranked LAs not listed)

Unfavourable for innovation and growth:

Barnsley
Caerphilly
Carmarthenshire county council
Ceredigion county council
Durham
East Ayrshire
Gwynedd
Isle of Anglesey
Lincolnshire county council
Merthyr Tydfil
Neath Port Talbot
North Ayrshire
Powys county council
Rhondda Cynon Taf
South Ayrshire

Least favourable for innovation and growth:

Argyll and Bute Islands
Blaenau Gwent
Cornwall and Isles of Scilly
Dumfries and Galloway
Highland
Moray
Na h-Eileanan Siar
Orkney Islands
Pembrokeshire county council
Scottish Borders
Shetland Islands
Torfaen

Source: Source data used from the following indicators: DR 5.1 (OfCom), 5.2 (NESTA) and 5.3 (pooled LFS 2014 to 2022).

Note: Areas are the current ones at the time of data collection. NESTA only provides data for ITL2 areas. We have therefore given each LA within a given ITL2 area the score of that ITL2 area. The distinctions between the 5 categories (most favourable, favourable, near-average, unfavourable and least favourable) are based on their positions within the overall distribution. LAs with near-average outcomes are omitted.

Figure 3.11: London and the Home Counties predominate in the list of LAs with favourable environments for innovation and growth, but several outside London have favourable environments too, notably Cambridgeshire county council, Cardiff, Milton Keynes, Slough, Southampton and Warrington.

Explore and download the data: Innovation and growth (State of the Nation data explorer).

Source: Ofcom, NESTA, LFS, from 2014 to 2022. Source data used from the following indicators: DR 5.1, 5.2 and 5.3.

Note: Areas are based on current residence. For more information on how each area was scored, please see the technical annex.

Question 7

Comparing the 3 composite indices of drivers

Accepted Answer

There are some consistent geographical axes on all 3 composite indices, with London and the Home Countries predominating among the more favourable areas and the midlands, northern areas of England, Wales and Scotland predominating among the less favourable areas. Particularly notable is the finding that 4 London boroughs and 4 authorities near London (Bracknell Forest, Hertfordshire county council, Oxfordshire county council and Reading) score favourably on all 3 indices. However, they are joined by the City of Edinburgh, while Trafford, Stockport and Cheshire West and Chester also score favourably on 2 of the 3 indices.

Table 3.12: provides a summary of the broad geographical distribution of favourable and unfavourable scores on the 3 indices. The table departs however from the usual regional analyses of England in one respect – given our previous findings about the favourable results for areas in the South and East of England which are within commuting distance of London, we distinguish between the Home Counties (defined as those adjoining London) and other areas of the South and East of England further from London.

Table 3.12: Numbers of favourable and unfavourable scores in each region across all 3 composite indices of drivers.

	Favourable	Unfavourable
North East England	0	13
Midlands (East and West)	1	10
Wales	2	18
Scotland	6	18
Other Northern England	8	15
Other Southern and Eastern England	14	3
London Boroughs	37	3
Home Counties^{[footnote 29]}	23	0

Source: Data derived from the 3 composite indices for drivers.

Note: Favourable includes all regions which are either ‘Favourable’ or ‘Most favourable’. Unfavourable includes all regions which are either ‘Unfavourable’ or ‘Least favourable’. ‘Other Northern England’ consists of all regions in the ITL1 region of Yorkshire and the Humber and North West England. Regions in the ITL1 region of South East England and the East of England are split between ‘Home Counties’ and ‘Other Southern and Eastern England’.

The table shows a clear pattern of differentiation, with the North East of England, the Midlands, and Wales having LAs with predominantly unfavourable or average scores. Then come Scotland and the rest of Northern England whose authorities’ scores are skewed towards the unfavourable more than the favourable side but not as severely as with the North East, Midlands and Wales. The scores in the other Southern and Eastern England regions are markedly skewed in the opposite direction, while the most favoured regions are London and the Home Counties.

In this context, it is noteworthy that this pattern of inter-regional disadvantage broadly parallels that concerning rates of economic growth (measured by per capita gross value added) over the last 2 decades and in the changes in house prices (which are likely proxies for the changes in spending power).^{[footnote 30]}

In other respects, there are some important differences between the results of the 3 composite indices. The list of areas with favourable conditions of childhood includes several in Scotland (East Dunbartonshire, East Renfrewshire, City of Edinburgh, Stirling), in and around Manchester, and one from Wales (Vale of Glamorgan). In contrast, there are fewer Scottish or northern areas in the list of areas with favourable labour market opportunities for young people and none from Wales. A North-South divide is therefore even more marked in the case of the Labour Market Opportunities for Young People index than the Conditions of Childhood index.

Turning to the less favoured areas, we also see both similarities and differences between the indices of Conditions of Childhood and Labour Market Opportunities for Young People. While older industrial and port areas in the north and midlands of England tend to have unfavourable scores on both indices (Birmingham, Hartlepool, Sunderland), the Labour Market Opportunities for Young People index also shows several rural areas with unfavourable scores whereas rural areas are not especially disadvantaged on the Conditions of Childhood index. We also see a somewhat greater concentration of areas with unfavourable conditions in the North East of England for the Labour Market Opportunities for Young People index. In contrast, there are many more rural Welsh and Scottish areas with unfavourable scores on the index of Innovation and Growth.

These differences between the scores on the indices may be due to the different underlying mechanisms involved. Geographical sorting is likely to occur everywhere in and around major urban areas across the UK and could well account for the geographical distribution of more and less favoured areas on the Conditions of Childhood index. In contrast, labour market opportunities for young people may depend on mechanisms, such as recent economic growth and expansion of the professional and managerial classes, that have been concentrated in the south of England in recent years in contrast to the deindustrialisation of the Midlands and North. In the case of the third index, factors such as current business expenditure or R&D, broadband, and concentrations of postgraduates may be indicative of areas with potential for growth in the new digital and high-tech economy.

Chapter 3: Mobility across the UK

Highlights

A new composite index for the intermediate outcomes

Increasing the precision of the estimates

Results

Intermediate outcomes: Promising Prospects

Case study: Conor Warren, 18, Devon, South West England

Drivers: Conditions of Childhood index

Drivers: Labour Market Opportunities for Young People index

Case study: Amy Stevens, 35, Mansfield, Nottinghamshire

Comparing conditions of childhood with labour market opportunities for young people

Drivers: Innovation and Growth index

Comparing the 3 composite indices of drivers

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Highlights

Measuring social mobility in local authorities

A new approach to monitoring social mobility by local authority

A new composite index for the intermediate outcomes

Increasing the precision of the estimates

Composite indices for the drivers of social mobility

Results

Intermediate outcomes: Promising Prospects

Case study: Conor Warren, 18, Devon, South West England

Drivers: Conditions of Childhood index

Drivers: Labour Market Opportunities for Young People index

Case study: Amy Stevens, 35, Mansfield, Nottinghamshire

Comparing conditions of childhood with labour market opportunities for young people

Drivers: Innovation and Growth index

Comparing the 3 composite indices of drivers

Is this page useful?

Help us improve GOV.UK

Help us improve GOV.UK