Technical annex

Question 1

1. Data sources

Accepted Answer

In the State of the Nation 2024 report, we use a range of data sources to construct the components of the Index. We draw on the Office for National Statistics (ONS) Labour Force Survey (LFS) for the majority of indicators, including both drivers and outcomes. The LFS is a representative sample survey with a large sample size, covering the whole of the UK, which enables us to chart trends over time and across the regions of the UK from 1992 onwards. We supplement the LFS with a range of other large-scale and authoritative data sources.These include other government surveys such as the regular Wealth and Assets Survey (WAS), the Community Life Survey (CLS), the Annual Survey of Hours and Earnings (ASHE), and the Vacancy Survey. We also draw on data sources based on governmental administrative data such as the Department for Work and Pensions’ (DWP) Housing Below Average Incomes (HBAI) statistics and, for England only, the Department for Education’s (DfE) Education Statistics. We also draw on authoritative non-governmental studies such as the UK Household Longitudinal Study (UKHLS) and the European Social Survey (ESS).

All these data sources are constructed according to high methodological standards. For example, all the administrative data that we use are Designated National Statistics. However, we should emphasise that all data sources are subject to various limitations and potential biases. Thus ‘hard to reach’ groups (such as undocumented residents) will tend to be under-represented both in administrative datasets and in sample surveys of the population. In the case of sample surveys, all our data sources use weighting techniques in order to mitigate known biases (although weighting does not necessarily correct for all sources of potential bias). Weighting is not however used in the case of administrative data.

Sample surveys will also be subject to sampling error, and we therefore show confidence intervals wherever possible.

Please note, the description of most of our data sources this year remain unchanged from our 2023 State of the Nation report.^{[footnote 1]}

1.1 Office for National Statistics (ONS) Labour Force Survey

The Labour Force Survey (LFS) is a survey of households living at private addresses in the UK. Its purpose is to provide information on the UK labour market which can then be used to develop, manage, evaluate and report on labour market policies. The survey is managed by the Office for National Statistics (ONS) in Great Britain, and the Northern Ireland Statistics and Research Agency (NISRA) in Northern Ireland. The LFS excludes people, such as those resident in communal establishments or who are homeless or are not living in private households. While response rates to the LFS have fallen considerably over the years (down to around 50% just before the COVID-19 pandemic), comparisons between the results from the LFS and those obtained by the Census suggest that (after weighting) the extent of response bias is quite small.^{[footnote 2]}

The LFS is a nationally-representative government survey that covers England, Wales, Scotland and Northern Ireland. The survey has a rolling panel design over 5 quarterly waves, with one-fifth entering the survey and one-fifth leaving at each wave. The July to September wave has been used in each year for our analyses, as this wave has questions on respondents’ socio-economic background. It asks about the household composition, the main wage earner (including if no parent was earning) and the occupation of the main wage earner when the respondent was age 14 years. This has been included in each July to September wave since 2014, meaning that there is now 9 years’ worth of data. Since 2018 the LFS has also included questions in this wave on where respondents were living when aged 14. These data permit a granular measure of socio-economic background based on the official National Statistics Socio-economic Classification – NS-SEC).

A quarterly main LFS dataset typically contains around 75,000 individuals. However during the COVID-19 pandemic, from July to December 2020, some important temporary changes were made to the methodology of the LFS as a result of the COVID-19 pandemic, with a move from face-to-face to telephone interviewing (in order to minimise social contact). This had implications both for response rates and for response bias. Weighting methods were also changed in order to address the potential biases.^{[footnote 3]}

Weights are used throughout the analysis. These weights ensure that estimates reflect the sample design so that cases with a lower probability of selection will receive a higher weight to compensate. They also compensate for differences in the non-response rate among different sub groups of the population. The LFS provides person weights, household weights and income weights. For most analyses we use person weights, but income weights are used when the outcome variable relates to income or earnings.^{[footnote 4]} We use these weights to help make our estimates derived from the LFS more representative of the actual population. We refer to these weights as person weights in our report and throughout this annex.

In some instances indicated in the report (notably geographical and intersectional analyses), data is pooled across multiple years in order to achieve sufficient sample sizes. As discussed in the Geographical Analysis section of this annex, the large sample size of the pooled LFS’s means that the data can be disaggregated into International Territorial Levels regions and into Local Authority areas.

The version of the LFS that we have been given access to for this report includes some measures, such as where the respondent was living when aged 14, which are not available in the version that is available through the UK Data Archive for wider dissemination.

1.2 ONS Annual Survey of Hours and Earnings (ASHE)

The Annual Survey of Hours and Earnings (ASHE) is managed by the Office for National Statistics. It is based on a 1% sample of employee jobs taken from HM Revenue and Customs’ (HMRC’s) Pay As You Earn (PAYE) records. It started in 2004, replacing the New Earnings Survey, and is carried out in April of each year with a sample of approximately 300,000. Earnings of the self-employed are excluded.

ASHE is the most comprehensive source of information on the structure and distribution of earnings in the UK. ASHE provides information about the levels, distribution and make-up of earnings and paid hours worked for employees in all industries and occupations. The ASHE tables contain estimates of earnings for employees by sex, full-time or part-time status and other characteristics.

More information: Annual Survey of Hours and Earnings (ASHE) methodology and guidance.

1.3 ONS Vacancy Survey

The ONS Vacancy Survey produces monthly estimates of job vacancies across the whole economy of Great Britain. Questionnaires are sent to a sample of approximately 6,100 businesses every month, approached mainly via head offices. Responses are collected via an electronic questionnaire. The survey covers all sectors of the economy and all industries in England, Scotland and Wales (Great Britain) with the exception of employment agencies (to avoid double-counting of vacancies) and agriculture, forestry and fishing. Northern Ireland businesses are not approached because of the risk of overlap with other surveys conducted by Northern Ireland departments. Overall, in 2019, the average response rate for the Vacancy Survey was 80.2%.

More information: ONS Vacancy Survey methodology and guidance.

1.4 Department for Business, Energy & Industrial Strategy (BEIS) / Nesta spatial data tool

BEIS and Nesta have co-developed a research and development spatial data tool that allows users to compare indicators that show the scale of R and D systems at a sub-regional (ITL2) level. The tool is an open data repository. As much as possible, the tool uses data from official sources such as ONS and Higher Education Statistics Agency (HESA) although a few proprietary sources are also used. Formally known as the National Endowment for Science, Technology and the Arts, Nesta is registered as a charity, which supports innovation. It was originally funded by a £250 million endowment from the UK National Lottery. The endowment is managed through a trust, and Nesta uses the interest from the trust to meet its charitable objectives and to fund and support its projects.

More information: BEIS/Nesta Research and Development spatial data tool: Methodology.

1.5 The Department for Digital, Culture, Media & Sport (DCMS) Community Life Survey

The Department for Digital, Culture, Media & Sport (DCMS) took on responsibility for publishing results from the Community Life survey (CLS) for 2016-17 onwards, after it was commissioned by the Cabinet Office in 2012. The survey is representative of adults in England aged 16+ and living in private residences. In 2020/21 the survey was conducted online and paper using ANOS methodology. There were 8787 online interviews and 2130 paper questionnaires returned. The household online response rate was 21.2%.

The Community Life Survey is a key evidence source for understanding more about community engagement, volunteering and social cohesion, sampling adults (aged 16+) throughout England. The CLS moved to a self-completion online and paper mixed method approach from 2016-17 onwards, with an end to the previous face-to-face method. Fieldwork for the Community Life Survey 2021/22 was conducted between October 2021 and September 2022, with samples issued on a quarterly basis. Overall, 10,126 interviews were achieved over the year, which represents a population-representative overall (online or paper) individual response rate of 23%.

More information: Community Life Survey: Technical report.

1.6 DCMS Taking Part Survey

The Taking Part survey is a continuous face to face household survey of adults aged 16 and over and children aged 5 to 15 years old in England. In 2018/9 the sample size was just over 8000 and the response rate was 50.5%. The survey is commissioned by the Department for Culture, Media and Sport (DCMS) and 3 partner organisations (Arts Council England, Historic England and Sport England). The survey has run since 2005 and is the main evidence source for DCMS and its sectors. Its main objective is to provide a central, reliable evidence source that can be used to analyse cultural, digital, and sporting engagement, providing a clear picture of why people do or do not engage.

More information: Taking Part Survey.

1.7 Department for Education (DfE) Early Years Foundation Stage Profile (EYFSP) results in England

The EYFSP is a teacher assessment of children’s development in England at the end of the early years foundation stage (the end of the academic year in which the child turns 5 years old – this is typically at the end of the reception year). All providers of state-funded early years education in England are within the scope of the EYFSP teacher assessments including academies, free schools and private, voluntary and independent (PVI) providers. However, the proportion of the age group that is covered by the EYFSP is not reported.

Data is collected by the DfE from local authorities covering state-funded schools and private, voluntary and independent (PVI) providers (including childminders) as part of the EYFS profile return. This data is then matched to other data sources, including the school and early years censuses, to obtain information on pupil characteristics such as ethnicity and eligibility for Free School Meals (the main measure of socio-economic background that is available in the dataset). It is likely that there is some missing data on characteristics and eligibility for Free School Meals but this is not reported. The results are published by the DfE.

More information: Early years foundation stage profile results.

1.8 Income Deprivation Affecting Children Index (IDACI)

IDACI is a supplementary index of the English indices of deprivation. This is calculated by DfE as part of their Early years foundation stage profile results. Each lower-layer super output area (LSOA), or neighbourhood, is given a score showing the percentage of pupils aged 5 that live in income deprived households. These neighbourhoods are grouped into deciles so that the 10% of neighbourhoods with the highest scores (that is, with the most deprived children) make up decile 1, and the 10% of neighbourhoods with the lowest scores (that is, with the fewest deprived children) make up decile 10.

More information: Income deprivation affecting children index.

1.9 DfE National Curriculum Assessments at KS2 in England

Statutory testing and assessment for pupils in primary schools in England is the responsibility of the Standards and Testing Agency (STA), an executive agency of the Department for Education. KS2 tests must be administered by state-funded schools, the STA organises test marking and the return of results to schools. KS2 teacher assessments are also collected by STA and the information is collated and passed on within the department. Independent schools, non-maintained special schools and pupil referral units may take part in the KS2 assessments if they wish to do so. The position of home-schooled children is not specified and the proportion of the age group taking part in the KS2 assessments is not reported.

The attainment data is combined with information on pupil characteristics such as ethnicity and eligibility for Free School Meals (the main source of socio-economic background available in the dataset) taken from the school census. Details of this data are provided in a separate quality and methodology document. It is likely that there is some missing data on ethnicity and eligibility for free school meals but this is not reported. In addition eligibility for Free School Meals is only known for students at state-funded schools. Those who are in private education, or who are home schooled are therefore excluded from analyses which take account of disadvantage status. However, the DfE does not report what proportion of the age group are excluded in this way. Given that pupils in private education may be less likely to be disadvantaged than those in state-funded education, it is likely that measures such as the disadvantage gap underestimate the true extent of inequality among children and young people in English schools.

More information: Attainment in primary schools in England: Quality and methodology information (PDF).

1.10 DfE National Curriculum Assessments at KS4 in England

The ‘total’ includes pupils for whom Free School Meal eligibility (FSM), Special Educational Needs status (SEN provision) or SEN primary need could not be determined. This figure also includes pupils at further education colleges: as FE colleges do not complete the school census, we do not have matched pupil characteristics data of pupils in FE colleges and therefore these pupils are not included in characteristics breakdowns. This means that there are some cases where the individual characteristics breakdowns will not add up to the ‘all pupils’ figure. From 2014/15, disadvantaged pupils include pupils known to be eligible for free school meals (FSM) in any spring, autumn, summer, alternative provision or pupil referral unit census from year 6 to year 11 or are looked after children for at least one day or are adopted from care.

More information: Key stage 4 performance: methodology.

1.11 DfE Participation in education, training and employment age 16-18 statistics

These statistics from the Department for Education cover young people who reside in England and are based on their academic age, that is their age at the start of the academic year, 31st August. The data are at national level only and cannot be disaggregated to sub-national levels, or by characteristics other than gender.

The population at each age is based on Office for National Statistics (ONS) mid-year estimates, adjusted so that they relate to academic age and the end of the calendar year. Participation data from administrative sources is then subtracted from this total. Participation estimates are made by combining administrative data from schools, further education, work-based learning (apprenticeships) and higher education. Procedures are included to identify young people in more than one form of provision, to give a view of the cohort as a whole.

The labour market status (whether a young person is employed, unemployed or economically inactive) is then estimated from the Labour Force Survey (LFS) for each of the major groups:

Full time education (FTE)
Work based learning (WBL), comprises solely of apprenticeships from 2013
Employer funded training (EFT)
Other education or training (OET)
Not in education or training (NET)

Those in the NET group whose labour market status is economically inactive or ILO^{[footnote 5]} unemployed are concluded to be NEET.

More information: Participation in education, training and employment age 16 to 18: Methodology.

1.12 Department for Work and Pensions (DWP) Households Below Average Income (HBAI) statistics

The Department for Work and Pensions’ (DWP) Households Below Average Income (HBAI) report presents information on living standards in the United Kingdom and is the foremost source for data and information about household income and inequality in the UK. It has provided annual estimates on the number and percentage of people living in low-income households since 1995. The HBAI statistics report on the percentage of children living in low income households both before and after housing costs. However, data after housing costs are not published by the DWP for local authorities in their release on Children in Low Income Families: local area statistics.

The HBAI statistics are based on the Family Resources Survey (FRS). The FRS is a continuous household survey which collects information on a representative sample of private households in the United Kingdom with a sample size of around 10,000 households and a response rate of around 25%.

More information: Households below average income series: quality and methodology information report FYE 2022.

1.13 Higher Education Statistics Agency (HESA) UK performance indicators

The Higher Education Statistics Agency (HESA) produced UK performance Indicators from 2002/3 but these were discontinued after the 2020/21 statistics were published in 2022.

UK Performance Indicators (UKPIs) were statistics which compared universities and colleges against benchmarks for Widening participation, Non-continuation, and the Employment or further study of graduates. All the tables were based on undergraduate students who were residents of England, Scotland, Wales or Northern Ireland before starting their course. The statistics appear to have been based on returns from HE providers^{[footnote 6]}.

More information: Higher Education Statistics Agency (HESA) UK performance indicators.

1.14 OECD online education database

This database includes raw data used for the computation of indicators published in Education at a Glance. The database is compiled on the basis of national administrative sources, reported by Ministries of Education or National Statistical offices according to international standards, definitions and classifications. The collected annual data cover the outputs of educational institutions, the policy levers that shape educational outputs, the human and financial resources invested in education, structural characteristics of education systems, and the economic and social outcomes of education.

More information: OECD online education database

1.15 OECD online education database and the Programme for International Student Assessment (PISA)

The OECD also manages the Programme for International Student Assessment (PISA). This is a worldwide study in OECD member and non-member countries designed to evaluate educational systems by measuring 15-year-old school pupils’ performance on mathematics, science, and reading. The tests and procedures are centrally designed and harmonised but are implemented by the participating countries. PISA was first conducted in 2000 and then repeated every 3 years (apart from during the COVID-19 pandemic). Results are available for the UK from 2003 onwards, although caution is required when interpreting the 2003 estimates for the UK because PISA sampling standards were not met.

More information: Programme for International Student Assessment

1.16 UK Household Longitudinal Study

Some indicators that form the basis for the Index draw on Understanding Society, also known as the UK Household Longitudinal Study (UKHLS). The UKHLS is a longitudinal survey of the members of approximately 40,000 households in the UK with a household response rate of 57% at round 1.

The study is based at the Institute for Social and Economic Research at the University of Essex and is funded by the Economic and Social Research Council (ESRC) and the British Academy. The study covers the whole of the UK and also has booster samples for ethnic minority and immigrant groups. Information is collected on all members of each household, and each year recruited households are visited to collect information on changes to their individual and household circumstances.

The purpose of the UKHLS is to provide high-quality longitudinal data on subjects such as health, work, education, income, family, and social life. This helps to understand the long-term effects of social and economic change, as well as policy interventions designed to impact the general wellbeing of the UK population. To do this the study collects both objective and subjective indicators and offers opportunities for research within and across multiple disciplines including sociology, economics, geography, psychology and health sciences.

The UKHLS started with a representative sample of households in 2009/10. It also incorporated respondents who had previously participated in the British Household Panel Study (see further below). Since 2009/10 there have been annual waves with repeat interviews of sample members. As with all panel studies of this kind, there is attrition over time, with some participants dropping out of the study. The sample is however replenished, through existing sample members who leave the original household and establish a new household of their own.

The attrition of the original sample members is not random but tends to be greater among some ethnic minority groups and among those from disadvantaged backgrounds. Weighting is therefore used in order to mitigate any resulting bias. However, weighting cannot guarantee that there will be no biases with respect to particular subgroups of the population.

The greatest strength of the UKHLS for social mobility research is that it follows up as many young people as possible from their original households where they lived as children into adulthood and the new household that they establish. This enables us to link data on parents with that on their adult children and thus permits analysis of intergenerational educational and income mobility.

More information: Panel attrition in the General Population Sample and the Immigrant and Ethnic Minority boost of Understanding Society (PDF).

The European Social Survey is an academically driven cross-national survey that has been conducted every 2 years across Europe since 2001. The ESS was awarded European Research Infrastructure Consortium (ERIC) status in November 2013. It is directed by a Core Scientific Team led from City, University of London (UK) alongside 6 other partner institutions. The UK has participated in every round since the inception of the ESS. The survey has been funded in the UK by the ESRC.

ESS samples are representative of all persons aged 15 and over (no upper age limit) resident within private households in the UK, regardless of their nationality, citizenship or language. Individuals are selected by strict random probability methods at every stage and a minimum effective achieved sample size of 1,500 is aimed for after discounting for design effects.

The ESS sample design in the UK is a clustered and stratified 2-stage random probability design and excludes the following areas: Highlands and Islands, the Isle of Man and the Channel Islands. The sampling frame used is the Post Office Address File and is a sample of addresses. 5885 issued sample units in round 10 yielding 1249 valid interviews, a response rate of 21%.

More information: European Social Survey.

1.18 Ofcom Connected Nations Report

Connected Nations is Ofcom’s annual report on progress in the availability of broadband and mobile services in the UK, including the roll-out of fixed full-fibre and mobile 5G networks. The report contains data from or about the companies that Ofcom regulates.

More information: Ofcom Connected Nations Report

Question 2

2. Methodology and analysis

Accepted Answer

Not all indicators are in our report this year. To avoid repetition with our 2023 report, we have only included indicators in which we found an interesting development compared to last year. For the indicators we do include in the report, we have at times not included all breakdowns by protected characteristics or region.

For our regional analysis, we have used the following geographical variables. For all intermediate outcomes, we use the region in which people grew up in. For the intermediate outcomes derived using the LFS, this is the area where they lived at age 14. In contrast, all of our drivers are based on the area where people were living at the time of data collection. We do this because for the intermediate outcomes we want to understand the outcomes of people who grew up in different areas, whereas for our drivers we want to take a forward look at which areas are likely to have different conditions for enabling social mobility for those growing up there now.

To see our most up to date data on all of the indicators in the Social Mobility Index, please look at our online data explorer, which contains updated intermediate outcomes and drivers. We have not updated the mobility outcomes this year because these are mainly based on cohort surveys which are conducted on an irregular basis spanning many decades, and moreover we do not expect them to change significantly from one year to the next.

Table 1. Code and name of the measures in the State of the Nation report

Indicator Type	Indicator Number	Name
Intermediate outcome	1.1	Level of development at age 5
Intermediate outcome	1.2	Attainment at age 11
Intermediate outcome	1.3	Attainment at age 16
Intermediate outcome	1.4	Skills at age 15
Intermediate outcome	2.1	Destinations following the end of compulsory full-time education
Intermediate outcome	2.2	Entry to higher education
Intermediate outcome	2.3	Highest qualification
Intermediate outcome	2.4	Skills in early adulthood
Intermediate outcome	3.1	Economic activity
Intermediate outcome	3.2	Unemployment
Intermediate outcome	3.3	Occupational level of young people aged 25 to 29 years
Intermediate outcome	3.4	Earnings of young people aged 25 to 29 years
Intermediate outcome	3.5	Income returns to education
Intermediate outcome	3.6	Direct effect of social origin on earnings
Intermediate outcome	4.1	Further training and qualifications
Intermediate outcome	4.2	Occupational progression
Intermediate outcome	4.3	Income progression
Driver	1.1	Distribution of earnings
Driver	1.2	Childhood poverty
Driver	1.3	Distribution of parental education
Driver	1.4	Distribution of parental occupation
Driver	2.1	Further education and training opportunities
Driver	2.2	Availability of high-quality school education
Driver	2.3	Access to higher education
Driver	3.1	Job vacancy rate
Driver	3.2	Youth unemployment
Driver	3.3	Type of employment opportunities for young people
Driver	3.4	Labour market earnings of young people
Driver	4.1	Civic engagement
Driver	4.2	Level of trust, fairness and helpfulness
Driver	5.1	Broadband speed
Driver	5.2	Business spending on research and development
Driver	5.3	Postgraduate education
Composite Index	Promising prospects	Promising prospects
Composite Index	Labour market opportunities for young people	Labour market opportunities for young people
Composite Index	Conditions of childhood	Conditions of childhood
Composite Index	Innovation and growth	Innovation and growth

2.1 Indicators: Intermediate outcomes

Intermediate outcome 1.1: Level of development at age 5 years

Definition: Percentage of students in England achieving a ‘good level of development’ at age 5 years by eligibility for FSM.
Unit of measurement: Percent
Time period covered: Figure 2: Academic year 2012/13 to 2022/23, Figures 2.1, 2.2, 2.3 : Academic year 2022/23
Methodology: This indicator captures the percentage of students achieving a good level of development at age 5 years in England. A child achieving at least the expected level in the early learning goals within the 3 prime areas of learning and within literacy and numeracy is classed as having ‘a good level of development’. The early years foundation stage profile (EYFSP) is a teacher assessment of children’s development at the end of the early years foundation stage (the end of the academic year in which the child turns 5 years old – this is typically at the end of the reception year). All providers of state-funded early years education in England are within the scope of the EYFSP teacher assessments including: academies, free schools and private, voluntary and independent (PVI) providers. To capture socio-economic background, results are split by claimed eligibility for free school meals (FSM). FSM eligibility is defined as collected in the school census which states whether a child’s family have claimed eligibility. Parents are able to claim FSM if they receive certain benefits. To look at attainment by level of neighbourhood deprivation in England we also used the income deprivation affecting children index (IDACI). Deciles are calculated based on the percentage of children living in income-deprived households within a certain neighbourhood. 1 = 10% of neighbourhoods with highest percentage of children living in income-deprived households nationally, 10 = 10% of neighbourhoods with lowest percentage of children living in income-deprived households nationally. See the methodology page for more information.
Data source: Department for Education. Early years foundation stage profile results from the 2012 to 2013 academic year to 2022 to 2023 academic year.
Notes: The EYFS was significantly revised in September 2021 which means we cannot directly compare the outcomes for 2020 to 2021 with earlier years. Due to the COVID-19 pandemic, EYFSP results in England publication was cancelled for 2019 to 2020.
Figure(s): 2, 2.1, 2.2, 2.3

Intermediate outcome 1.2: Attainment at age 11 years

Definition: Percentage of students in England reaching the expected standard in reading, writing and maths at key stage 2 (KS2) by disadvantage status.
Unit of measurement: Percent
Time period covered: Figure 2.4: Academic year 2015/16 to 2022/23, Figures 2.6, 2.7 and 2.8: Academic year 2022/23
Methodology: Proportion of pupils who meet the expected standard in all 3 subjects (reading, writing and maths) at key stage 2 (age 11 years). Disadvantaged pupils are defined as those who were registered as eligible for free school meals at any point in the last 6 years, and children looked after by a local authority (LA) or who left LA care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order. For more details see Methodology of KS2 attainment.
Data source: Department for Education. National curriculum assessments at KS2 in England, 2022.
Notes: Attainment in all of reading, writing and maths is not directly comparable to some earlier years (2016 and 2017) because of changes to teacher assessment frameworks in 2018. No data was collected for the 2 academic years starting in 2019 and 2020 due to the COVID-19 pandemic.
Figure(s): 2.4, 2.6, 2.7, 2.8

Intermediate outcome 1.2: Disadvantage attainment gap index at key stage 2 (KS2)

Definition: Disadvantage attainment gap index in England at key stage 2 (KS2)
Unit of measurement: Disadvantage gap index units
Time period covered: Academic year 2010/11 to 2022/23
Methodology: To create the index comparisons are made by ordering pupil scores in reading and maths assessments at the end of key stage 2 and assessing the difference in the average position of disadvantaged pupils and others. The mean rank of pupils in the disadvantaged and other pupil groups are subtracted from one another and multiplied by a factor of 20 to give a value between -10 and +10 (where 0 indicates that both groups have the same mean rank). Disadvantaged pupils are defined as: those who were registered as eligible for FSM at any point in the last 6 years, children looked after by a local authority or have left local authority care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order.
Data source: Department for Education. National curriculum assessments at KS2 in England, 2010/11 to 2022/2023.
Notes: Between the academic years 2019 to 2020 and 2021 to 2022, there was a break in assessments due to the pandemic.
Figure(s): 2.5

Intermediate outcome 1.3: Attainment at age 16 years

Definition: Percentage of students in England achieving a grade 5 or above in both GCSE English and maths by disadvantage status.
Unit of measurement: Percent
Time period covered: Figure 2.9: 2018/19 to 2022/23, Figures 2.11, 2.12, 2.13: Academic year 2022/23
Methodology: This covers the attainments in maths and English GCSEs of students at state-funded schools using a positional measure of attainment (students achieving a pass at grade 5 or above in both subjects). Pupils are defined as disadvantaged if they are known to have been eligible for FSM at any point in the past 6 years (from year 6 to year 11), if they are recorded as having been looked after for at least one day or if they are recorded as having been adopted from care. For more details see Methodology of KS4 performance.
Data source: Department for Education (DfE). National curriculum assessments at key stage 4 in England, 2018/19 to 2022/23.
Notes: The 2021 to 2022 year assessment returned to the summer exam series, after they had been cancelled in 2020 and 2021 due to the impact of the COVID-19 pandemic. During this time alternative processes were set up to award grades (centre assessment grades, and teacher assessed grades).
Figure(s): 2.9, 2.11, 2.12, 2.13

Intermediate outcome 1.3: Disadvantage attainment gap index at key stage 4 (KS4)

Definition: Disadvantage attainment gap index in England at key stage 4 (KS4)
Unit of measurement: Disadvantage gap index units
Time period covered: Academic years 2010/11 to 2022/23
Methodology: The disadvantage gap index is intended to provide a more resilient measure of changes over time in attainment that may have been affected by, for example, the GCSE reforms introduced in 2017 and associated changes to headline measures (for example, moving away from 5 or more GCSEs to average attainment 8 scores). The disadvantage gap index summarises the relative attainment gap (based on the average grades achieved in English and maths GCSEs) between disadvantaged pupils and all other pupils. The index ranks all pupils in state-funded schools in England and asks whether disadvantaged pupils typically rank lower than non-disadvantaged pupils. A disadvantage gap of zero would indicate that pupils from disadvantaged backgrounds perform as well as pupils from non-disadvantaged backgrounds. We measure whether the disadvantage gap is getting larger or smaller over time. While the absolute differences (in English and maths GCSE grades) may differ between years the gap index measures results in terms of how disadvantaged pupils are ranked in comparison to non-disadvantaged pupils therefore it offers greater comparability between years. Pupils are defined as disadvantaged if they are known to have been eligible for FSM at any point in the past 6 years (from year 6 to year 11), if they are recorded as having been looked after for at least one day or if they are recorded as having been adopted from care. For more details see ‘Statistical working paper measuring disadvantaged pupils’ attainment gaps over time (updated)’.
Data source: Department for Education (DfE). National curriculum assessments at key stage 4 in England, 2010/11 to 2022/23.
Notes: The 2021 to 2022 year assessment returned to the summer exam series, after they had been cancelled in 2020 and 2021 due to the impact of the COVID-19 pandemic. During this time alternative processes were set up to award grades (centre assessment grades, and teacher assessed grades).
Figure(s): 2.10

Intermediate outcome 1.4: Skills at age 15

Definition: Average pupil attainment scores in mathematics, science and reading by highest level of education of parents, UK and Organization for Economic Cooperation and Development (OECD) average, for 2022
Unit of measurement: Program for International Student Assessment (PISA) score
Time period covered: 2022
Methodology: PISA scores were used as they aim to assess the knowledge and skills of students in maths, science and reading. It uses an internationally agreed metric to collect data from students, teachers, schools and systems to understand performance differences between OECD countries at age 15 years. Parental education level is used as a measure of socio-economic status, as no direct measure of parental occupational class background is available. Parental educational attainment refers to the highest educational qualification ever reported by either of the parents.
Data source: OECD, PISA, 2022.
Notes: ISCED refers to the international classification for organising education programmes and related qualifications by levels. Missing data has not been explicitly accounted for. The results for those with parents educated to level 8 (doctoral degree) have been omitted, as this is a small group. Level 1 = primary education, level 2 = lower secondary education (lower than GCSE level but having gone to secondary school), level 3.3 = upper secondary education with no direct access to tertiary education, level 3.4 = upper secondary education with direct access to tertiary education, level 4 = post-secondary non-tertiary education (such as a HE Access course), level 5 = short-cycle tertiary education (below degree-level qualifications of a minimum of 2 years study such as a level-4 apprenticeship), level 6 = bachelor’s degree or equivalent, level 7 = master’s degree or equivalent. PISA scores do not have a maximum or minimum, instead they are scaled so that the mean for OECD countries is around 500 score points and one standard deviation is around 100 score points.
Figure(s): 2.14, 2.15, 2.16

Intermediate outcome 2.1: Further education and training opportunities

Definition: Proportion of young people aged 16 to 24 years in the UK who are in education and training, employment, or NEET by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: Our charts for this year are the same as what was featured in last year’s report and it covers the year 2022.
Methodology: Education and training captures young people who satisfy the following definition: people aged 16 to 24 years who are in full-time education or training of any type. Those in training were included with those in education due to small sample sizes. Employment is defined as those aged 16 to 24 and who did at least one hour of work in the reference week (as an employee, as self-employed, as unpaid workers in a family business, or as participants in government-supported training schemes) and those who had a job that they were temporarily away from (for example, if they are on holiday).
NEET is defined as ‘not in employment, education or training’ in the week before the survey.
SEB refers to the main wage earner’s occupation when the respondent was aged 14 years. Where there was no earner in the family, SEB is included in the lower working class.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 16 to 24 years in the UK, data collected from July to September 2022. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey 2014 to 2022, respondents aged 16 to 24 years in the UK, data collected from July to September each year.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer for the latest version.

Intermediate outcome 2.2: Entry of young people into higher education

Definition: Percentage of young people aged 18 to 20 years in the UK enrolled in higher education by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: Figure 2.17 covering 2022, Figure 2.18 comparing 2014 to 2022
Methodology: The proportion of young people aged 18 to 20 years studying in higher education by socio-economic background in 2022. Being in higher education is defined as currently studying degree-level qualifications, this includes foundation degrees. To look at the higher education enrolment gap, we used the ratio between young people of a higher professional background to those of lower working background, aged 18 to 20 years, studying for degree-level qualification. The gap in 2014 was compared to that in 2022 to look at differences over time.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 18 to 20 years in the UK.
Notes: This indicator only covered age 19 last year. The age range has been extended to 18-20 to increase the sample size and improve the precision of the estimates. The data used is weighted using the LFS person weights.
Figure(s): 2.17, 2.18

Intermediate outcome 2.3: Highest qualification of young people

Definition: Highest level of qualification achieved by young people aged 25 to 29 years in the UK by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: For this indicator we distinguish 6 levels of educational qualification: Higher degree, First degree, Further education below degree, A level and equivalent, O level, GCSE and equivalent, Lower level (below CSE grade 1). This indicator includes breakdowns by gender, ethnicity, disability status and area of the percentage of the highest qualifications achieved by socio-economic background. Parental social class is measured by the main wage earner’s occupation when the respondent was aged 14 years.
For the ethnicity breakdown the outcome measure is simplified to whether the respondent has a university degree or not due to the small sample sizes of some ethnic groups. The estimated percentages and confidence intervals are derived from a logistic regression model on the likelihood of attaining a degree by ethnic group and SEB, controlling for sex. The model assumes that class effects are the same within each ethnic group. The percentages shown are those for men, aged 27. Percentages are shown only for those with lower working-class and higher professional-class backgrounds for illustrative purposes. For regional analysis, people are classified according to the area where they lived at age 14.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 25 to 29 years in the UK. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer for the latest version.

Intermediate outcome 3.1: Economic activity of young people

Definition: Percentage of young people aged 25 to 29 years in the UK who were economically active by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: Proportions of people aged 25 to 29 years who were economically active in 2022 by socio-economic background. Economically active is defined as either being in work, or available for and actively looking for work. Parental social class is measured by the main wage earner’s occupation when the respondent was aged 14 years. For ethnicity, the estimated percentages and confidence intervals are derived from a logistic regression model on the likelihood of being economically active by ethnic group and SEB, controlling for sex. The model assumes that class effects are the same within each ethnic group. The estimated percentages are those for men. Percentages are shown only for those with lower working-class and higher professional-class backgrounds for illustrative purposes. For regional analysis, people are classified according to the area where they lived at age 14.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 25 to 29 in the UK. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years living in the UK.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer for the latest version

Intermediate outcome 3.2: Unemployment among young people

Definition: Percentage of young people aged 25 to 29 years in the UK who were unemployed by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: Proportions of people aged 25 to 29 years who were unemployed by socio-economic background. Unemployment refers to those without a job, who have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks. Parental social class is measured by the main wage earner’s occupation when the respondent was aged 14 years. For ethnicity, the estimated percentages and confidence intervals result from a logistic regression model on the likelihood of being unemployed by ethnic group and SEB, controlling for sex. The model assumes that class effects are the same within each ethnic group. The estimated percentages are those for men. Percentages are shown only for those with lower working-class and higher professional-class backgrounds for illustrative purposes. For regional analysis, people are classified according to the area where they lived at age 14.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 25 to 29 in the UK. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer for the latest version.

Intermediate outcome 3.3: Occupational level of young people

Definition: Percentage of young people aged 25 to 29 years in the UK in different occupational class positions by their socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: Figure: 2.19: 2022
Methodology: Proportions of people aged 25 to 29 years in different occupational class positions in 2022 by socio-economic background. Parental occupational class is measured by the main wage earner’s occupation when the respondent was aged 14 years. For ethnicity, because of small sample sizes, the outcome measure is whether the respondent has a professional occupation (either higher or lower professional). The estimated percentages and confidence intervals are derived from a logistic regression model on the likelihood of being in a professional occupation by SEB and ethnic group, controlling for sex. The model assumes that class effects are the same within each ethnic group. For regional analysis, people are classified according to the area where they lived at age 14.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 25 to 29 in the UK. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS person weights.
Figure(s): 2.19. See our online data explorer for additional intersectional analysis.

Intermediate outcome 3.4: Earnings of young people

Definition: Mean hourly earnings of young people aged 25 to 29 years in the UK by level of educational attainment
Unit of measurement: British Pounds (£)
Time period covered: Figure 2.20: 2022
Methodology: The frequency of earnings was chosen to be hourly earnings to avoid a possible skewing of earnings data resulting from people of one socio-economic background being more likely to work part-time than people of other backgrounds. Self-employed respondents and those without earnings are excluded. Parental occupational class is measured by the main wage earner’s occupation when the respondent was aged 14 years. For ethnicity, the estimated means and confidence intervals are derived from a linear regression model of log hourly earnings by SEB and ethnic group, controlling for sex. The model assumes that class effects are the same within each ethnic group. Means are shown only for men from lower working-class and higher professional-class backgrounds, but all SEBs and women were included in the sample. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). For regional analysis, people are classified according to the area where they lived at age 14.
Data source: Office for National Statistics, Labour Force Survey (LFS) 2022, respondents aged 25 to 29 years in the UK. For sex, ethnicity and disability breakdowns: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS income weights.
Figure(s): 2.20 See our online data explorer for additional intersectional analysis.

Intermediate outcome 3.5: Returns in earnings to education for young people

Definition: Percentage differences in hourly earnings of young people with different levels of highest qualification (aged 25 to 29 years in the UK) relative to those with lower level (below GCSE grade 1 or equivalent), controlling for socio-economic background (SEB), sex and age.
Unit of measurement: Percent
Time period covered: Figure 2.21: 2020 to 2022
Methodology: The percentage differences estimates are derived using a linear regression model. This model takes the log hourly pay as the dependent variable. The explanatory variables included in the model are: educational attainment, socio-economic background, sex and age. The returns by educational attainment and socio-economic background are derived from their respective coefficients for each 3-year period. The reference group to derive the estimates is men who were from a lower working-class background and had lower level (below CSE grade 1 or equivalent) qualifications. The data is pooled across 3 years to obtain more accurate estimates. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s.
For ethnicity, hourly earnings were estimated from a linear regression model of log hourly pay by ethnic group and SEB, controlling for educational level (defined as degree attainment) and age. The model assumes that class effects are the same within each ethnic group.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2020 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS income weights.
Figure(s): 2.21

Intermediate outcome 3.5: Returns in earnings to education for young people over time

Definition: Hourly earnings in pounds (£) of young people aged 25 to 29 years in the UK by highest qualification controlling for socio-economic background (SEB), sex and age.
Unit of measurement: British Pounds (£)
Time period covered: 3-year moving averages from 2014 to 2022.
Methodology: Hourly earnings were estimated from a linear regression model of log hourly pay, controlling for educational level, SEB, gender and age. The estimates shown refer to the hourly earnings of men who were from a lower working-class background. For sex, ethnicity and disability estimates are shown for people aged 27 years from lower working-class backgrounds. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s.
Data source: Office for National Statistics, Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK. For sex, ethnicity and disability: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS income weights.
Figure(s): 2.22; See our online data explorer for additional intersectional analysis.

Definition: Percentage differences in hourly earnings of young people from different socio-economic backgrounds (SEB) (aged 25 to 29 years in the UK) relative to those from lower working-class backgrounds, controlling for highest educational level, sex and age.
Unit of measurement: Percent
Time period covered: 2020 to 2022
Methodology: Percentage differences were estimated from a linear regression model of log hourly pay, controlling for educational level, socio-economic background, sex and age. The reference group is men who were from a lower working-class background and had lower-level qualifications (below CSE grade 1 or equivalent). We pool the data for years 2020 to 2022 in order to obtain more accurate estimates. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2020 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS income weights.
Figure(s): 2.23

Definition: Estimated mean hourly earnings of young people aged 25 to 29 years in the UK by socio-economic background (SEB), controlling for educational level and age.
Unit of measurement: British Pounds (£)
Time period covered: 3-year moving averages from 2014 to 2022.
Methodology: Hourly earnings were estimated from a linear regression model of log hourly pay, controlling for educational level, SEB, age. Estimates are shown for people with the lowest levels of education and aged 27 years. Interactions between sex and SEB, and ethnicity and SEB were not significant and have therefore not been included. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 29 years in the UK.
Notes: The data used is weighted using the LFS income weights.
Figure(s): 2.24. See our online data explorer for additional intersectional analysis.

Intermediate outcome 4.1: Further training and qualifications

Definition: Percentage of young people born in 1990 who had obtained degrees at age 25 (in 2015) and at age 32 (in 2022) in the UK, by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: 2015 and 2022
Methodology: This is derived by taking the percentage of young people born in 1990 who had obtained university degrees by age 25 (in 2015) and by age 32 (in 2022) respectively.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2015 and 2022.
Notes: The data used is weighted using the LFS person weights. Please note the LFS waves used are independent of each other meaning that the comparison is made between 2 independent surveys rather than following the same individuals from age 25 to 32.
Figure(s): This indicator is not in the report this year. See our online data explorer.

Intermediate outcome 4.2: Occupational progression

Definition: Probability of access to the professional classes for men in the UK by socio-economic background and age
Unit of measurement: Probability (ranging from 0 to 1)
Time period covered: 2014 to 2022
Methodology: This is derived by taking the average marginal effects from a logistic regression model of access to the professional occupational classes controlling for age, age squared (to account for the changing importance of age as people get older), survey year and social class background.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022, respondents aged 25 to 44 years in the UK in work at the time of the survey.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer.

Intermediate outcome 4.3: Income progression

Definition: Income by age and socio-economic background
Unit of measurement: British Pounds (£)
Time period covered: 2014 to 2022
Methodology: Estimates are derived from a linear regression of annual income controlling for age, age squared (to account for the changing importance of age as people get older), survey year, number of dependent children and socio-economic background. Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s.
Data source: Office for National Statistics, pooled Labour Force Survey (LFS) from 2014 to 2022 (pooled), respondents aged 25 to 44 years in the UK in paid employment.
Notes: The data used is weighted using the LFS person weights.
Figure(s): This indicator is not in the report this year. See our online data explorer.

Driver 1.1: Distribution of earnings

Definition: The gap in hourly earnings for all employees in the UK
Unit of measurement: Ratio of 90th percentile relative to 10th percentile
Time period covered: 1997 to 2023
Methodology: To calculate the 90th to 10th percentile ratio, values are taken from gross hourly earnings of all employees.
Data source: Office for National Statistics, Annual Survey of Hours and Earnings (ASHE) from 1997 to 2023.
Notes: Values are taken from ‘Earnings and hours worked, place of work by local authority: ASHE table 6.5a, Gross hourly pay for all employees from 1997 to 2023. The 2023 ratio is derived from provisional figures and may be subject to revision in a future update to table 6.5a. We have also updated our ratio for 2022 using the latest revision of table 6.5a.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 1.2: Childhood poverty

Definition: Percentage of children in relative poverty after housing costs in the UK by nation
Unit of measurement: Percent
Time period covered: 3-year moving averages from 1994 to 2023.
Methodology: Childhood relative poverty after housing costs is reported for the UK as a whole and for England, Scotland, Wales and Northern Ireland to allow for comparison across countries. Data are calculated using 3-year averages (including the current year and 2 preceding years). For example, the figure for 2022 represents the average of the financial years (FY) starting in 2020, 2021 and 2022. FY are reported by the year in which they start. For example, 2022 represents the financial year ending in 2023 (FY 2022 to 2023). A household is said to be in relative poverty if their equivalised income is below 60% of the median income. ‘Equivalised’ means adjusted for the number and ages of the people living in the household. Data after housing costs are not published by the DWP for ITL2 regions and therefore we will not have a breakdown by area this year.
Data source: Department for Work and Pensions (DWP), Households Below Average Income statistics from 1994 to 2023
Notes: Data used can be found in Table 4.16 of the data source.
Figure(s): 2.25

Driver 1.3: Distribution of parental education

Definition: Percentages of adults with each of a number of levels of education in families with dependent children in the UK
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The sample was established by selecting those respondents with dependent children (dependent children defined as those aged 0 to 15 years, and those aged 16 to 18 years who are in full-time education) in their household. Respondents who are aged less than 21 years are excluded and the median age of the included respondents is 40 years. The great majority of the selected respondents are likely to be the parents or carers of the dependent children. However, the dataset could include some adults who are living at home with parents who have dependent children. The data used is weighted using the LFS person weights. We also show the percentage of adults in families with dependent children with a higher education split by ITL2 region. People are classified in those regions according to their residence at the time of the survey.
Data source: Office for National Statistics, Labour Force Survey (LFS) from 2014 to 2022
Notes: Due to a change in the LFS methodology for the highest qualification attained variable, the proportion of people with below degree level comparisons in 2022 is not directly comparable to previous years. The data used is weighted using the LFS person weights.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 1.4: Distribution of parental occupation

Definition: Percentages of adults in each of a number of levels of occupation in families with dependent children in the UK by socio-economic background (SEB)
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The sample was established by selecting those respondents who had dependent children in their household (those aged 0 to 15 years and those aged 16 to 18 years who are in full-time education). Respondents who are aged less than or equal to 20 years are excluded and the median age of the included respondents is 40 years.
The great majority of the selected respondents are likely to be the parents or carers of the dependent children, but it could include some adults who are living at home with parents of dependent children.The data used is weighted using the LFS person weights. We also show the percentage of adults in families with dependent children in a higher professional and lower working occupation split by ITL2 region. People are classified in those regions according to their residence at the time of the survey.
Data source: Office for National Statistics, Labour Force Survey (LFS) from 2014 to 2022.
Notes: Due to rounding errors, in some instances the totals may not add up to 100%.The data used is weighted using the LFS person weights.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 2.1: Further education and training opportunities

Definition: Percentage of young people aged 16 to 18 years participating in education, training and employment in England
Unit of measurement: Percent
Time period covered: 2011 to 2022
Methodology: NEET includes anybody who is not in any forms of education or training and who is not in employment. This means that a person identified as NEET is either unemployed or economically inactive. Historically, there have been very small overlaps of students studying in further education and higher education and WBL at the same time. The total number of young people in training is calculated by omitting these overlaps. Of note, 16 to 17 year olds are required to remain in (at least part-time) education and training in England (but not in Wales, Scotland or Northern Ireland) following raising the participation age legislation in 2013.
Data source: Department for Education, participation in education, training and employment, 2011 to 2022.
Notes: Participation estimates for the 2020 and 2021 cohorts impacted by the COVID-19 pandemic may not fully reflect engagement and attendance. Due to rounding errors, in some instances the totals may not add up to 100%
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 2.2: Availability of high-quality school education

Definition: Average pupil attainment scores on PISA reading, maths, and science assessments over time in the UK and OECD member countries
Unit of measurement: PISA attainment scores
Time period covered: 2003 to 2022
Methodology: Pisa scores are used as a proxy measure of opportunities for high-quality school education. Assessment occurs every 3 years from 2003 to 2022. However, there is no available data for the science assessment in 2003. Due to small sample sizes in the UK, the OECD advises against comparisons between the UK and other countries for the year 2003.
Data source: Organisation for Economic Co-operation and Development (OECD)
Notes: Participation estimates for the 2020 and 2021 cohorts impacted by COVID-19 may not fully reflect engagement and attendance. PISA scores do not have a maximum or minimum, instead they are scaled so that the average score is around 500 points and one standard deviation is around 100 points.
Figure(s): 2.26

Driver 2.3: Access to higher education

Definition: Percentage of 19 year olds enrolled in secondary or tertiary education, UK and average of OECD member countries
Unit of measurement: Percent
Time period covered: 2010 to 2021
Methodology: Proxy measure of the participation rate relative to the number of young people aged 19 years in the population. Enrolment rates in secondary and tertiary education are expressed as net rates. These are calculated by dividing the number of students aged 19 years enrolled in these levels of education by the size of the population of 19 year olds. Generally, figures are based on headcounts and do not distinguish between full-time and part-time study. In some OECD countries, part-time education is only partially covered in the reported data.
Data source: Organisation for Economic Co-operation and Development (OECD)
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 3.1: Vacancy rate

Definition: Number of vacancies per unemployed person in the UK (seasonally adjusted)
Unit of measurement: Number of vacancies per unemployed person
Time period covered: 2001 to 2023
Methodology: A proxy for job opportunities is calculated by ONS as the ratio of the number of unemployed (as estimated from the LFS) relative to the number of vacancies (as estimated in the Vacancy Survey) and is published here as the reciprocal. Ratios were calculated using quarter 4 (October to December) from 2001 to 2023. A higher value indicates greater opportunities for job seekers. Respondents are aged 16 to 64 years.
Data source: Office for National Statistics (ONS),Vacancy Survey and Labour Force Survey (LFS)
Figure(s): 2.27

Driver 3.2: Youth unemployment

Definition: Percentage of young people aged 16 to 24 years in the UK, from 2014 to 2022, who were unemployed
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The LFS follows the internationally-agreed definition for unemployment recommended by the International Labour Organisation (ILO), a UN agency. Unemployed people are those without a job, who have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks; or are out of work, have found a job and are waiting to start it in the next 2 weeks. Those who are economically inactive are excluded from the calculations (for example in full-time education, looking after the home, or permanently sick and disabled). The data used is weighted using the LFS person weights. We also show the percentage of those who are unemployed split by ITL2 region on the online data explorer. People are classified in those regions according to their residence at the time of the survey.
Data source: Office for National Statistics, Labour Force Survey (LFS) from 2014 to 2022.
Notes: The data used is weighted using the LFS person weights.
Figure(s): 2.28

Driver 3.3: Type of employment opportunities for young people

Definition: Breakdown of occupational class of young people aged 22 to 29 years in the UK
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The 5 social classes distinguished here represent a shortened version of the ONS NS-SEC classification, which has 8 classes. We have grouped the ONS NS-SEC classes as shown in chapter 1 of the report.
Data source: Office for National Statistics (ONS), Labour Force Survey (LFS) from 2014 to 2022
Notes: The data used is weighted using the LFS person weights.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 3.4: Labour market earnings of young people

Definition: Median real hourly pay for people aged 22 to 29 years in the UK
Unit of measurement: British Pound (£)
Time period covered: 1997 to 2023
Methodology: Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner-occupiers’ housing costs (CPIH). Due to a change in the inflation base year, the results for this indicator are not directly comparable to last year’s. ASHE covers employee jobs in the UK. It does not include self-employed people or employees not paid during the reference period.
Data source: Annual Survey of Hours and Earnings (ASHE) from 1997 to 2023.
Notes: Data can be found in table 6.5a.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 4.1: Civic engagement

Definition: Percentage of adults who have engaged in democratic processes within the last 12 months in England
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The plot shows the percentages of adults who were civically engaged. This means engagement in democratic processes, both in person and online, including signing a petition or attending a public rally within the last 12 months. This does not include voting. Data is taken for the 9 financial years to March 2022. The 95% confidence intervals available for 2019 to 2020, 2020 to 2021, and 2021 to 2022 only.
Data source: Community Life Survey, Department for Culture Media and Sport from 2014 to 2022.
Figure(s): 2.29

Driver 4.2: Level of trust, fairness and helpfulness

Definition: Mean levels of trust, perceived fairness and helpfulness, 0 to 10 point scales, in the UK
Unit of measurement: Average on a 0 to 10 point scale
Time period covered: 2002 to 2020
Methodology: Fairness was measured on a scale running from 0 (indicating “most people try to take advantage of me”) to 10 (indicating “most people try to be fair”). Helpfulness was measured on a scale running from 0 (indicating “people mostly look out for themselves”) to 10 (indicating “people mostly try to be helpful”). Trust was measured on a scale running from 0 (indicating “you can’t be too careful”) to 10 (indicating “most people can be trusted”).
Data source: European Social Survey, data for the UK, rounds 1 to round 10 (from 2002 to 2020)
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 5.1: Broadband speed

Definition: The percentage of premises (including residential and business) that have Gigabit internet availability
Unit of measurement: Percent
Time period covered: 2020 to 2023
Methodology: In the past, we used Nesta data to look at Broadband speed, however, there are no current plans to update this dataset and therefore we have had to find another data source. This means that current findings are not comparable with the ones in the 2023 State of the Nation report. The new data represents the percentage of premises including both residential and business premises that have Gigabit capability in each of the UK nations. Data was collected in September each year. The online data explorer includes breakdowns of the data by each ITL2 region.
Data source: The Office of Communications (Ofcom), Connected Nations Report, 2023.
Figure(s): 2.30

Driver 5.2: Business expenditure on research and development

Definition: Total business expenditure on research and development
Unit of measurement: British pounds (£)
Time period covered: 2007 to 2018
Methodology: Earnings are adjusted for inflation by using 2022 as the base year and the Consumer Price Index including owner occupiers’ housing costs (CPIH). Due to slight revisions to the methodology and a change in the inflation base year, the results for this indicator are not directly comparable to last year’s. The National Endowment for Science, Technology and the Arts (Nesta) provides scores at the ITL2 regional level, but not a national average figure. Therefore, we take the total figure for all UK areas in order to track changes over time. We provide regional breakdowns at the ITL2 level in our online data explorer, these are adjusted for the population size by calculating the spend in millions of pounds per 100,000 people in each region.
Data source: The National Endowment for Science, Technology and the Arts (Nesta), Research and Development spatial data tool. 2007 to 2018.
Figure(s): This driver is not in the report this year. See our online data explorer.

Driver 5.3: University research students

Definition: Percentage of people aged 25 to 64 with a qualification above undergraduate degree level in the UK, from 2014 to 2022.
Unit of measurement: Percent
Time period covered: 2014 to 2022
Methodology: The percentages shown are the percentage of 25 to 64 year olds with a postgraduate degree. A postgraduate degree is defined as a qualification above undergraduate degree level or a higher degree. We provide a regional breakdown in our online data explorer, the regions are based on where the respondent was living at the time of survey.
Data source: Office for National Statistics, Labour Force Survey (LFS) from 2014 to 2022.
Notes: The data used is weighted using the LFS person weights
Figure(s): This driver is not in the report this year. See our online data explorer.

2.3 Confidence intervals and tests of significance

In the case of indicators derived from survey data, we calculate tests of statistical significance in order to check whether differences between the estimates for groups or over time are likely to be due to sampling error rather than represent real differences in the population. We show the 95% confidence intervals when making comparisons between percentages. A confidence interval is a range around a value that conveys how precise the measurement of the value is. A 95% confide nce interval shows that, if a sample survey of the given size were conducted 100 times, the true population value would lie within the confidence interval in 95 of the 100 samples, whereas in only 5 samples would the population value lie outside the confidence interval. In general, the smaller the size of the sample, the larger will be the confidence interval. Confidence intervals for estimates concerning small subgroups of a sample, such as some ethnic minorities or local areas, will tend to be large. In other words, there will be less precision in the estimates.

For most analyses of categorical data, such as the percentages experiencing absolute upwards mobility as in figure 2.7, we show the 95% confidence intervals. The standard formula for the 95% confidence interval of an estimated proportion is:

CI = p ± 1.96 √p(1-p)/N

Where p represents the estimated proportion of the group obtaining a given outcome, and N is the number of respondents in the group under consideration.

We frequently use confidence intervals in order to check whether rates of, for example, upward mobility differ between groups. A simple rule of thumb is that, if the confidence intervals do not overlap, then there is a statistically significant difference between the 2 groups. In other words, it is unlikely that a difference of that magnitude between the 2 groups could have occurred simply as a result of sampling error. Note however that a small overlap can also be significant at the 5% level and we therefore use a formal difference of proportions test in these cases. For our indicators derived from LFS data, our confidence intervals are calculated using the unweighted sample.

We also use a range of other formal tests of significance where different statistical techniques are employed.

2.4 Composite index methodology

The updated composite indices featured in this report are not comparable with the composite indices published in 2023 or in 2016 and 2017. That is because we have made a number of technical changes to improve the robustness of the index and to enable indices to be estimated at Local Authority level.

Indices at local authority level

SON 2023 reported 2 composite indices for intermediate outcomes and 3 composite indices for drivers showing how mobility chances and potentials varied across the UK between 41 broad areas at International Territorial Level 2 (ITL2). For SON 2024 we have developed a more granular index at Local Authority level, distinguishing just over 200 unitary and upper-tier local authorities. Unfortunately, our main data source, the LFS, does not enable us to distinguish local authorities within Northern Ireland, so we have to treat Northern Ireland as a single unit. The LFS also combines the separate local authorities of Cornwall and the Isles of Scilly into a single unit, and the separate local authorities of Camden and the City of London into a single unit. In addition, we have combined the newly created authorities of Cumberland and Westmorland and Furness into a single unit as the LFS coding does not allow us to distinguish the 2 accurately, and have also combined the 3 unitary authorities of Bath and North East Somerset, North Somerset and South Gloucestershire into a single unit.

This yields 203 geographical units covering the district councils of Scotland, the principal counties of Wales, the Metropolitan Districts of England, the London Boroughs, the Unitary authorities of England and the upper tier County Councils of England along with Northern Ireland as a single unit (but with the exceptions noted above). We do not include the lower-tier district authorities within the 2-tier County Councils as these tend to be relatively small in population (and hence sample size).

We should note that there are some very small local authorities, with correspondingly small sample sizes, in this dataset. In these cases, we cannot be confident about their position in the distribution. The local authorities 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 (Outer Hebrides) (27), Rutland (33), Orkney (19), Shetland (28) and Westminster (37).

Developing the composite indices

We first shrink the individual Local Authority estimates for each indicator using a random intercept multilevel model with 3 levels – individual, local authority, and ITL2. We do this in order to reduce the risk of ‘false positives’ and to ensure that more weight is given to more precisely measured estimates (that is, estimates that were generated from larger samples). This is the same procedure that we used for the composite indices of intermediate outcomes in SON 2023, but we now apply this procedure to the drivers based on the LFS as well because of the smaller sample sizes when constructing indices at LA level.

For the second stage of the analysis we change the methodology from that used for SON 2023. Following Breen and In’s (2024) work developing an overall mobility index for ITL3 areas,^{[footnote 7]} we move to a Principal Component Analysis (PCA) instead of the method which we used last year based on the Human Development Index (HDI) of the UN. However, in practice the results for each local authority are similar when derived via PCA or the HDI method from last year.

Principal Component Analysis (PCA) is a technique that takes a dataset with several correlated variables (as is the case with our intermediate outcomes). PCA then simplifies the data by identifying the single scale (dimension) associated with the largest amount of variation in the outcomes of interest. In this way, PCA reduces the complexity of the data. The process allows us to take into account several variables at once, but in a simple way that allows for geographical visualisation.

As previous work has shown, most LA areas have similar mobility chances for the people who grew up in them.^{[footnote 8]}^{[footnote 9]} In reporting the results therefore we focus on the 2 tails of the distributions of the local authority estimates. PCA enables us to calculate the Z-scores for each area. In essence the Z-scores tell us about the percentile position of each area in the overall distribution. Following the standard theory of normal distributions, we can say that an area with a Z-score >1.96 is in the top 2.5% of areas, while an area with a Z-score < -1.96 is in the bottom 2.5% of areas. A lower cut-off of 1.65 identifies the areas which fall into the top and bottom 5% of areas, and an even lower cut-off of 1.00 identifies the top and bottom 15.9% of areas. We treat those local authorities where the Z scores derived from the PCA are less than +/- 1.00 as belonging to a single ‘middle group’ category. This middle group category covers around two-thirds of local authorities. We do not attempt to make further distinctions within this middle group category (as we did in SON 2023), because of the imprecision of the estimates. However, since the actual distributions cannot be described as strictly normal (see further below), we must not place undue reliance on Z-scores.

A new composite index for the intermediate outcomes

Like the previous ‘Promising Prospects’ composite index, the new index is based on 3 intermediate outcomes – highest qualification, occupational level, and hourly earnings among young people in the UK. As is the case with all intermediate outcomes, we control for socio-economic background and assign respondents to the Local Authority area where they lived when aged 14. 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, therefore, as providing a measure of absolute mobility chances, not of relative mobility. In order to increase sample sizes and to improve the precision of the estimates, we have increased the age-range from 25-29 to 25-44 and added an extra year of LFS data, using the pooled LFS for 2018-22. We also combine the higher and lower professional classes into a single category, and likewise with the higher and lower working classes.

The PCA confirms last year’s conclusions that there are 2 dimensions underlying the intermediate outcomes. These correspond to the ‘promising prospects’ and ‘precarious positions’ composite indices. However, the PCA at local authority level showed that a working-class occupation belongs to the first dimension, not the second (to which unemployment and economic inactivity belong). Table 2 shows the loadings on the first 2 components.

Table 2: Loadings of the 6 indicators for intermediate outcomes on the first 2 principal components

	PC1	PC2
IN23	-0.44	0.18
IN31	0.11	0.70
IN32	-0.16	0.62
IN33a	-0.52	-0.16
IN33b	0.51	0.19
IN34	-0.50	0.17
Proportion of variance explained	0.52	0.24

We focus on this first dimension as it accounts for a larger proportion of the variation across areas and therefore construct our new composite index of Promising Prospects on the basis of the 4 items loading on PC1. The details of these 4 items are shown in Table 3.

Table 3 Summary of composite index for the intermediate outcomes this year (respondents aged 25-44, LFS 2018 to 2022).

Indicator	Correlation of the indicator with the index	LFS data used
IN2.3 Highest qualification (university degree)	0.77	Net levels of a university degree among people in each area aged 25-44, after controlling for socio-economic background (SEB)
IN3.3a Occupational level (professional occupation)	0.92	Net proportions of people aged 25-44 in professional-class jobs in each area after controlling for SEB
IN3.3b Occupational level (working-class occupation)	0.88	Net proportions of people aged 25-44 not in working-class jobs in each area after controlling for SEB
IN3.4 Hourly earnings	0.89	Mean hourly earnings among people aged 25-44 in each area after controlling for SEB

Both the PCA analysis and the HDI method that we used last year show that the distribution is skewed, with a longer ‘tail’ of local authorities with particularly promising prospects and a shorter ‘tail’ of local authorities with less promising prospects (see Figure 1). This is analogous to the shape of the distribution of individuals’ incomes, where there is a much longer tail of highly affluent individuals than of poorer individuals.

Figure 1: Histogram of the distribution of local authority scores on the composite index of promising prospects

New composite indices for the drivers

In addition to the new composite index of intermediate outcomes, we have developed 3 new composite indices at local authority level for the drivers. These new indices are based on much the same indicators as their equivalents in SON 2023, but we have made some changes in methodology and in the technical details in order to adapt them for use at local authority level. As in SON 2023, the indicators for drivers are based on area of current residence, not on the area where a person grew up. This is because the drivers are intended to provide a forward look, identifying areas which may provide more or less favourable contexts for mobility for young people currently growing up there.

The list of indicators for SON 2024 is shown in Table 4 below.

Table 4: Summary of composite indices for the drivers this year

Index	Correlation of indicator with the index	Indicator	Data used
Conditions of childhood	0.87	DR 1.2 Childhood poverty	% of children in relative poverty (DWP HBAI statistics, pooled years 2018 to 2022)
	-0.86	DR 1.3 Distribution of parental education	% of families (with a dependent child) containing a graduate parent/adult (Pooled LFS 2014 to 2022, 21 or over)
	-0.96	DR 1.4a Distribution of parental occupation (professional)	% of families with a professional parent or adult (Pooled LFS 2014 to 2022, age 21 or over)
	0.96	DR 1.4b Distribution of parental occupation (working class)	% of families with a working-class parent or adult (Pooled LFS 2014 to 2022, age 21 or over)
Labour market opportunities for young people	0.44	DR 3.2 Youth unemployment	% of young people aged 16-29 in employment (Pooled LFS 2014 to 2022)
	-0.96	DR 3.3a Type of employment opportunities for young people (professional)	% of young people aged 16-29 with a professional occupation (Pooled LFS 2014 to 2022)
	0.96	DR 3.3b Type of employment opportunities for young people (working class)	% of young people aged 16-29 with a working-class occupation (Pooled LFS 2014 to 2022)
Innovation and Growth	-0.64	DR 5.1 Broadband speed	% of premises with gigabit-capable broadband (Ofcom)
	-0.73	DR 5.2 Business expenditure on research and development	(log) Business expenditure per capita on research and development (NESTA)
	-0.69	DR 5.3 Postgraduate education	% of working-age (25 - 64) people with a postgraduate qualification (Pooled LFS 2014 to 2022)

For the first 2 indices (Conditions of Childhood, and Labour Market Opportunities for Young People), we have largely used the same indicators as for the equivalent composite index in SON 2023 but have made some detailed changes. First, we expanded the age ranges for some indicators in order to increase sample sizes and thereby improve the precision of the estimates at local authority level. 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.

As with the LA estimates for the composite index of intermediate outcomes, we first shrink the estimates based on the LFS in order to reduce the risk of false positives. This applies to all the indicators apart from DR1.2 (% children in relative poverty) which is based on the DWP’s HBAI dataset. We did not shrink the estimates last year at ITL2 level, as the sample sizes were substantially larger. However, at Local Authority level there are many small samples with a corresponding risk of ‘false positives’ when using the LFS.

As with the composite index of intermediate outcomes we then carried out Principal Component Analysis (PCA) of the 7 indicators. This generated 2 main components.

Table 5: Loadings of the 7 indicators for drivers on the first 2 principal components

	PC1	PC2
DR12	0.44	-0.09
DR13	-0.41	0.32
DR14a	-0.48	0.23
DR14b	0.48	-0.17
DR32	0.06	0.40
DR33a	-0.34	-0.51
DR33b	0.27	0.62
Proportion of variance explained	0.54	0.25

The first component (DR 1.2, 1.3, 1.4a and 1.4b) corresponds conceptually to a dimension measuring conditions of childhood, while the second component (DR 3.2, 3.3a and 3.3b) corresponds to a dimension measuring opportunities for young people in the labour market. These are close to but not identical to the 2 composite indices developed for SON 2023. One main difference is that there is a clearer distinction in the new analysis between conditions of childhood and labour market opportunities for young people. In the 2023 version, youth unemployment was assigned to the childhood poverty and disadvantage dimension whereas in the new version it is assigned to the labour market opportunities dimension, where it makes greater conceptual sense. In addition, youth opportunities for professional employment are now more clearly associated with other types of employment opportunities (or lack of them) and no longer go with a young person’s parental situation. There are a range of possible reasons for these differences from the 2023 indices: the most likely reason is that patterns can differ at different geographical levels, but changes in statistical technique or in the coverage of the data could also be relevant.

Figure 2 shows the histogram of the z-scores for the composite index of Conditions of Childhood and figure 3 for the composite index of Labour market Opportunities for young people. As we can see, both distributions are bell-shaped and are skewed with a longer tail of advantaged than disadvantaged areas.

Figure 2: Histogram of the distributions of Z-scores for the composite index of conditions of Childhood

Figure 3: Histogram of the distributions of Z-scores for the composite index of Labour Market Opportunities for Young People

Last year we introduced an experimental set of indicators to measure environments that potentially were favourable to innovation and growth and hence for future social mobility. We have changed the name of this composite index because it is more focussed on the conditions that can help economic growth and innovation rather than on 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, in order to increase sample sizes for local authorities, the postgraduate research indicator now focuses on the proportion of working-age 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 (a metric that Ofcom no longer publishes). Thirdly, we should note that Nesta (the data source for Business R and D expenditure) has not updated this metric, which is only available at ITL2 level. We have used 2018 data (the most recent available) and assigned each LA the value for the ITL2 area to which it belongs.

These 3 indicators do not have such strong correlations (at the LA level) as the indicators that make up the other composite indices, so this remains an experimental index which we would hope to improve in future years.

Figure 4: Histogram of the distributions of Z-scores for the composite index of Innovation and Growth

Table 6: The local authorities with the most favourable, favourable, unfavourable and most unfavourable scores on the index of Promising prospects. LAs with near-average outcomes are omitted.

Most favourable outcomes for Promising Prospects:

Barnet
Brent
Camden plus City of London
Ealing
Harrow
Hillingdon
Hounslow
Redbridge
Richmond upon Thames
Surrey CC

Favourable outcomes for Promising Prospects:

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

(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 4 intermediate outcomes for unitary and upper-tier local authorities, based on the local authority where respondents lived when they were aged 14 years. The suffix CC indicates that the authority is a 2-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.

Table 7: The local authorities with the most favourable, favourable, unfavourable and most unfavourable 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 CC
Richmond upon Thames
Surrey CC
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 CC
Hampshire CC
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: The Conditions of Childhood Index is a composite index covering 4 drivers for unitary and upper-tier local authorities (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. LAs with near-average outcomes are omitted.

Table 8: The local authorities with the most favourable, favourable, unfavourable and most unfavourable scores on the Index of Labour Market Opportunities for unitary and upper-tier LAs. LAs with near-average conditions are omitted.

Most favourable labour market opportunities:

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 CC
Hertfordshire CC
Kensington and Chelsea
Merton
Oxfordshire CC
Reading
Stockport
Surrey CC
Sutton
Trafford
West Berkshire
Westminster
Windsor and Maidenhead

(middle-ranked LAs not listed)

Unfavourable labour market opportunities:

Argyll and Bute Islands
Carmarthenshire CC
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 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.

Table 9: The local authorities with the most favourable, favourable, unfavourable and most unfavourable 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 CC
Cardiff
Cheshire East
Cheshire West and Chester
City of Edinburgh
Ealing
Hackney
Hertfordshire CC
Hounslow
Islington
Lambeth
Milton Keynes
Oxfordshire CC
Reading
Slough
Southampton
Warrington
West Berkshire
Windsor and Maidenhead
Wokingham

(middle-ranked LAs not listed)

Unfavourable for innovation and growth:

Barnsley
Caerphilly
Carmarthenshire CC
Ceredigion CC
Durham
East Ayrshire
Gwynedd
Isle of Anglesey
Lincolnshire CC
Merthyr Tydfil
Neath Port Talbot
North Ayrshire
Powys CC
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 CC
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.

Table 10: Z-scale groupings for composite indexes

Grouping	Conditions of childhood	Labour market opportunities for young people	Innovation and Growth	Promising prospects
Most favourable	Z >1.65	Z >1.96	Z > 1.96	Z ≥ 1.96
Favourable	1 < Z ≤ 1.65	1 < Z ≤ 1.96	1 < Z ≤ 1.96	1 < Z < 1.96
Middle Group	-1 ≤ Z ≤ 1	-1 ≤ Z ≤ 1	-1 ≤ Z ≤ 1	-1 ≤ Z ≤ 1
Unfavourable	-1 > Z ≥ -1.65	-1 > Z ≥ -1.5	-1 > Z ≥ -1.65	-1 > Z ≥ -1.4
Least Favourable	Z < -1.65	Z < -1.50	Z < -1.65	Z < -1.40

Question 3

3. Limitations of the data and analysis

Accepted Answer

We have included a list of limitations with our analysis below. Please note, this list is non-exhaustive. Some of these limitations have previously been referenced in our 2022 and 2023 State of the Nation reports.

3.1 Lack of harmonised educational data from administrative sources across the UK

Historically Scotland has had its own distinctive educational system and qualifications from the rest of the UK, and since devolution state education has become a separate responsibility of each of the 4 nations of the UK (the Department for Education (England), the Scottish Government through its executive agency, Education Scotland, the Department for Education and Skills (Wales) and the Department of Education (Northern Ireland)). As a result there are separate statistical series for each of the 4 territories’ administrative data on state education, reflecting local needs, with no UK-wide harmonised data.

3.2 Lack of coverage of the full population in the datasets

There are likely to be problems of under-coverage and of response bias both in the sample surveys and in administrative data that we use. For example, sample surveys such as the LFS sample private households but exclude residents of communal establishments (such as care homes). Similarly administrative data from the DfE cover children at maintained schools but may exclude those in private schools or home-schooled. In addition, among those who are in principle covered, there will be issues of non-response bias. Thus ‘hard to reach’ groups (such as undocumented residents) will tend to be under-represented both in administrative datasets and in sample surveys of the population. In the case of sample surveys, all our data sources use weighting techniques in order to mitigate known biases (although weighting does not necessarily correct for all sources of potential bias). Weighting is not, however, used in the case of administrative data.

3.3 Limitations of the variables available in the dataset

The set of indicators in the report are not exhaustive of the potential drivers and outcomes of mobility. In selecting drivers and indicators for inclusion, the main criterion has been whether quantitative data of sufficient quality are available over time for monitoring purposes, and whether there is sufficiently convincing evidence that the concept being measured is likely to have a causal (direct or indirect) influence on aggregate levels of social mobility. However, many factors that may be relevant for social mobility, such as social ties and different forms of social capital, are simply not available on a regular basis. Moreover, in some cases we have had to rely on proxy measures (such as FSM eligibility) rather than direct measures of the concept in question (for example SEB). As research in this area develops, and the evidence base improves, we expect that new concepts and measures will be added, and that existing ones could be dropped.

3.4 Small sample sizes and imprecision of estimates

While our main administrative and survey sources are large scale, the number of respondents in particular categories may sometimes be quite small, leading to imprecision due for example to sampling error. This is particularly likely to be the case for intersectional and disaggregated geographical analysis. Small sample sizes can also increase the risk that respondents will become identifiable. For this reason the ONS does not release estimates when there are concerns about imprecision and disclosure. Small sample sizes also limit the granularity of the data that can be analysed (as for example in the case of ethnic groups where highly simplified and heterogeneous categories have sometimes had to be used).

In some cases, we have had to report proportions that are estimated from a model, instead of the actual proportions we observed in the data. This is because the number of people in some subcategories is so low that the actual proportions may not be a very reliable indicator. In these cases, we have had to make simplifying assumptions, for example, that the SEB effect is similar across all ethnicities.

3.5 Measurement error

In addition to sampling error, data are also likely affected by other forms of measurement error. If this error is randomly distributed, it will tend to weaken the strength of the measured associations between variables (thus for example leading us to underestimate the strength of the association between SEB and mobility outcomes). However, not all measurement error is likely to be random and some types of non-random measurement error may lead to biases in the results. Problems of this kind are likely to be present with recall data on socio-economic background and on the geographical area where one grew up. For example, respondents whose families moved between areas, or whose parents changed jobs while they were growing up, may recall the most salient one from their youth rather than the one at age 14. A further complication with respect to local areas is that local government has been reorganised and hence respondents’ memories may well not correspond with current administrative areas.

3.6 Limitations of descriptive analysis

Descriptive monitoring data on its own cannot tell us why a problem has emerged or what interventions would be successful in mitigating the problem. Further work always needs to be carried out to improve our understanding of the underlying causal mechanisms. A major function of the index is to identify where deeper analysis is required in order to inform policy action.

3.7 Statistical analysis

The report and online data explorer tool mainly rely on relatively simple statistical techniques such as bivariate cross-tabulations. These have the advantage of staying close to the data. In some cases, however, we have used modelling techniques in order to obtain reasonably precise estimates for groups with small sample sizes such as ethnic minorities or local areas. These techniques (such as regression and multilevel modelling) borrow strength from the overall pattern of results in order to make estimates for the small group in question. These modelling techniques, however, invariably make assumptions which cannot always be rigorously verified (because for example of lack of statistical power). The results derived from these modelling techniques therefore need to be treated with caution.

3.8 Limitations of quantitative analysis

The measurement framework is designed to enable the SMC to fulfil its statutory duty of monitoring social mobility across the UK, and therefore has used quantitative data. However, in order to fully understand the processes at work, particularly for the most disadvantaged areas or groups, other sorts of data such as their cultural, social and institutional contexts may be needed. Ethnographic work can therefore be a valuable complement to statistical analysis.

Question 4

4. Local authorities in the UK

Accepted Answer

Table 11: The 203 upper-tier local authority areas of the UK in our analysis.

Number	Region
1	Camden and City of London
2	Westminster
3	Kensington and Chelsea
4	Hammersmith and Fulham
5	Wandsworth
6	Hackney
7	Newham
8	Tower Hamlets
9	Haringey
10	Islington
11	Lewisham
12	Southwark
13	Lambeth
14	Bexley
15	Greenwich
16	Barking and Dagenham
17	Havering
18	Redbridge
19	Waltham Forest
20	Enfield
21	Bromley
22	Croydon
23	Merton
24	Kingston upon Thames
25	Sutton
26	Barnet
27	Brent
28	Ealing
29	Harrow
30	Hillingdon
31	Hounslow
32	Richmond upon Thames
33	Manchester
34	Salford
35	Trafford
36	Stockport
37	Tameside
38	Bolton
39	Wigan
40	Bury
41	Oldham
42	Rochdale
43	Blackburn with Darwen
44	Blackpool
45	Lancashire CC
46	Cheshire East
47	Cheshire West and Chester
48	Warrington
49	Halton
50	Knowsley
51	St Helens
52	Liverpool
53	Sefton
54	Wirral
55	Durham
56	Hartlepool
57	Stockton-on-Tees
58	Middlesbrough
59	Redcar and Cleveland
60	Darlington
61	Northumberland
62	Newcastle upon Tyne
63	North Tyneside
64	South Tyneside
65	Gateshead
66	Sunderland
67	Cumbria
68	Hull
69	East Riding of Yorkshire
70	North East Lincolnshire
71	North Lincolnshire
72	City of York
73	Barnsley
74	Doncaster
75	Rotherham
76	Sheffield
77	Bradford
78	Leeds
79	Calderdale
80	Kirklees
81	Wakefield
82	Derby
83	Derbyshire CC
84	Nottinghamshire CC
85	Nottingham
86	Leicester
87	Rutland
88	Leicestershire CC
89	North Northamptonshire
90	West Northamptonshire
91	Lincolnshire CC
92	Herefordshire
93	Worcestershire CC
94	Warwickshire CC
95	Telford and Wrekin
96	Shropshire
97	Stoke-on-Trent
98	Staffordshire CC
99	Birmingham
100	Suffolk CC
101	Coventry
102	Norfolk CC
103	Solihull
104	Dudley
105	Sandwell
106	Luton
107	Walsall
108	Wolverhampton
109	Peterborough
110	Cambridgeshire CC
111	Hertfordshire CC
112	Bedford
113	Central Bedfordshire
114	Southend-on-Sea
115	Thurrock
116	Essex CC
117	Buckinghamshire
118	Bracknell Forest
119	West Berkshire
120	Reading
121	Slough
122	Windsor and Maidenhead
123	Wokingham
124	Milton Keynes
125	Oxfordshire CC
126	Brighton and Hove
127	East Sussex CC
128	Surrey CC
129	West Sussex CC
130	Portsmouth
131	Southampton
132	Isle of Wight
133	Hampshire CC
134	Medway
135	Kent CC
136	Bristol
137	Bath and North East Somerset^{[footnote 10]}
138	Plymouth
139	Torbay
140	Swindon
141	Bournemouth, Christchurch and Poole
142	Cornwall and Isles of Scilly
143	Devon CC
144	Dorset
145	Gloucestershire CC
146	Somerset CC
147	Wiltshire
148	North Yorkshire CC
149	Isle of Anglesey
150	Gwynedd
151	Conwy
152	Denbighshire CC
153	Flintshire CC
154	Wrexham
155	Powys CC
156	Ceredigion CC
157	Pembrokeshire CC
158	Carmarthenshire CC
159	Swansea
160	Neath Port Talbot
161	Bridgend
162	Vale of Glamorgan
163	Rhondda Cynon Taf
164	Merthyr Tydfil
165	Caerphilly
166	Blaenau Gwent
167	Torfaen
168	Monmouthshire CC
169	Newport
170	Cardiff
171	Aberdeen City
172	Aberdeenshire
173	Angus
174	Argyll and Bute Islands
175	Scottish Borders
176	Clackmannanshire
177	West Dunbartonshire
178	Dumfries and Galloway
179	Dundee City
180	East Ayrshire
181	East Dunbartonshire
182	East Lothian
183	East Renfrewshire
184	City of Edinburgh
185	Falkirk
186	Fife
187	Glasgow
188	Highland
189	Inverclyde
190	Midlothian
191	Moray
192	North Ayrshire
193	North Lanarkshire
194	Orkney Islands
195	Perth and Kinross
196	Renfrewshire
197	Shetland Islands
198	South Ayrshire
199	South Lanarkshire
200	Stirling
201	West Lothian
202	Na h-Eileanan Siar
203	Northern Ireland

Table 12: The 41 ITL2 regions of the UK in our analysis.

Where the data doesn’t allow us to break the UK down into smaller regions, we use the 41 ITL2 regions of the UK for our analysis.

Number	ITL2 region (and the ITL1 region that contains it)
1	Inner London – West (London)
2	Inner London – East (London)
3	Outer London – South (London)
4	Outer London – East and North East (London)
5	Outer London – West and North West (London)
6	Bedfordshire and Hertfordshire (East of England)
7	Berkshire, Buckinghamshire and Oxford (South East England)
8	Cheshire (North West, England)
9	Cornwall and Isles of Scilly, (South West England)
10	Cumbria (North West England)
11	Derbyshire and Nottinghamshire (East Midlands, England)
12	Devon (South West England)
13	Dorset and Somerset (South West England)
14	East Anglia (East of England)
15	East Yorkshire and Northern Lincolnshire (Yorkshire and the Humber, England)
16	Essex (East of England)
17	Gloucestershire, Wiltshire and Bristol and Bath area (South West England)
18	Greater Manchester (North West England)
19	Hampshire and Isle of Wight (South East England)
20	Herefordshire, Worcestershire and Warwickshire (West Midlands, England)
21	Kent (South East England)
22	Lancashire (North West England)
23	Leicestershire, Rutland and Northamptonshire (East Midlands, England)
24	Lincolnshire (East Midlands, England)
25	Merseyside (North West, England)
26	North Yorkshire (Yorkshire and the Humber, England)
27	Northern Ireland (Northern Ireland)
28	Northumberland and Tyne and Wear (North East England)
29	Shropshire and Staffordshire (West Midlands, England)
30	South Yorkshire (Yorkshire and the Humber, England)
31	Surrey, East and West Sussex (South East England)
32	Tees Valley and Durham (North East England)
33	West Midlands (West Midlands, England)
34	West Yorkshire (Yorkshire and the Humber)
35	West Wales and The Valleys (Wales)
36	East Wales (Wales)
37	Highlands and Islands (Scotland)
38	Eastern Scotland (Scotland)
39	West Central Scotland (Scotland)
40	Southern Scotland (Scotland)
41	North Eastern Scotland (Scotland)

Figure 5: The 203 LA levels of the UK included in our analysis.

Source: ONS Open Geography Portal, ITL geography hierarchy boundaries, January 2021.^{[footnote 11]}

Note: The map shows the 203 LA areas of the UK in our analysis. Our data did not allow us to divide Northern Ireland into smaller geographical areas.

Figure 5a: The 203 LA levels of the UK split by larger regions of the UK.

Figure 5b: The 203 LA levels of the UK split by larger regions of the UK in detail.

Source: ONS Open Geography Portal, ITL geography hierarchy boundaries, January 2021.^{[footnote 12]}

Figure 5c: The 203 LA levels of the UK split by larger regions of the UK in detail.

Source: ONS Open Geography Portal, ITL geography hierarchy boundaries, January 2021.^{[footnote 13]}

Note: Our data did not allow us to divide Northern Ireland into smaller geographical areas.

Figure 5d: The 203 LA levels of the UK split by larger regions of the UK in detail.

Source: ONS Open Geography Portal, ITL geography hierarchy boundaries, January 2021.^{[footnote 14]}

Figure 5e: The 203 LA levels of the UK split by larger regions of the UK in detail.

Source: ONS Open Geography Portal, ITL geography hierarchy boundaries, January 2021.^{[footnote 15]}

Question 5

5. Glossary

Accepted Answer

This is the idea that some people have different life outcomes from their parents.

Absolute mobility rates look at the proportion of the population who are in different positions (often occupational class or income) from their parents, and are usually given as a simple percentage.

For example, a person experiences upward absolute income mobility if their income is greater than their parents’ income. They experience upward absolute occupational mobility if their occupation class is higher than their parents’. Mobility can also be downward.

5.2 Apprenticeships

A work-based training system, where apprentices earn a qualification after completing a blended mix of study and work.

Apprentices must complete 20% of their training off the job, be paid the apprenticeship minimum wage (£4.81/hour for those aged 19 years and over) and pass an end point assessment.

5.3 Caterpillar plots

A chart which shows both an estimate of a data point and a confidence interval (see definition in this glossary), arranged according to the magnitude of the estimates. The confidence interval usually is represented by a line going through the point which itself represents the estimated value in the chart.

5.4 Composite index

An index consisting of multiple indicators. In our report they all consist of 3 different indicators. For example, our composite index on Promising Prospects consists of indicators on the highest level of qualification achieved, occupational class achieved and hourly earnings.

5.5 Confidence intervals

A range of values for which there is a defined probability (usually 95%) that an estimate lies within this range. For example if we have a confidence interval of 5 to 10 at the 95% level, we are 95% confident that our estimate lies within the range of values from 5 to 10.

5.6 Class pay gap

The difference in average pay between people who occupy the same type of job but come from different class backgrounds.

5.7 Drivers

These capture the background conditions that make social mobility easier. For example, the availability of good education is a driver, because it helps people to be upwardly mobile. So our measures of drivers tell us about these nationwide background conditions. They do not tell us what the UK’s rates of mobility have been, and they are not broken down by socio-economic background.

5.8 Early years foundation stage profile (EYFSP)

The early years foundation stage profile (EYFSP) sets standards in England for the learning, development and care of a child from birth to 5 years old. All schools and Ofsted-registered early years providers must follow the EYFSP, including childminders, pre-schools, nurseries and school reception classes. There are different early years standards in Scotland, Wales and Northern Ireland.

5.9 Economically inactive

Individuals that are out of work and not looking for a job. Reasons for this include sickness, looking after family, and being a student, among other reasons.

5.10 Free school meals (FSM)

In England, a free school meal (FSM) is a statutory benefit available to school-aged children from families who receive other qualifying benefits and who have been through the relevant registration process. FSM eligibility is often used as a proxy measure for disadvantage in school-aged children.

Free school meal eligibility is the only available measure of pupils’ social background in England, Wales and Northern Ireland. In Scotland, a completely different area-based measure of social background is used, the Scottish Index of Multiple Deprivation (SIMD).

FSM only includes those who have both applied for and been deemed eligible by the relevant local authority. It excludes an unknown number of people who might have been deemed eligible had they applied, and may also include some relatively affluent families.

5.11 Further education (FE)

Typically refers to classroom-based learning at further education (FE) colleges or providers. Students can start at age 14 or 16 years, depending on the college.

5.12 General Certificate of Secondary Education (GCSE)

The GCSE is an academic qualification taken in England, Wales and Northern Ireland, normally at age 16. State schools in Scotland use the Scottish Qualifications Certificate instead.

5.13 Higher education (HE)

Typically refers to post-secondary education, or tertiary education, leading to award of an academic degree. Higher education is an optional final stage of formal learning that occurs after completion of secondary education.

5.14 Income mobility

See social mobility, absolute social mobility and relative social mobility.

5.15 Intermediate (occupations)

See NS-SEC.

5.16 Intermediate outcomes

These capture the progress that people make from their starting point to an intermediate point, such as employment in their 20s, or educational attainment at 16. We break outcome measures down by people’s socio-economic background, so that we can see how different starting points affect progress to eventual end points.

5.17 International Territorial Level (ITL)

This is the internationally comparable regional geography for the UK. The regions that we use are part of a system developed by the Office for National Statistics (ONS). The level of the system we use, ITL2, divides the UK into 41 regions. Each region has between 800,000 and 3,000,000 inhabitants and contains about 4 upper-tier local authorities.

5.18 Key stage 2 (KS2)

The 4 years of schooling in England and Wales, described as year 3, year 4, year 5 and year 6, when pupils are aged between 7 and 11 years. Key stage 2 SATs (National Curriculum tests) are taken by pupils aged 11 years at the end of year 6, which is, usually, the final year in primary school.

5.19 Key stage 4 (KS4)

The 2 years of schooling known as year 10 and year 11, when pupils are aged between 14 and 16 years. Most pupils take their final general certificate of secondary education (GCSE) exams at the end of year 11.

5.20 Labour Force Survey (LFS)

The LFS is a large-scale nationally-representative government survey that covers England, Wales, Scotland and Northern Ireland. The survey is managed by the Office for National Statistics (ONS) in Great Britain, and the Northern Ireland Statistics and Research Agency (NISRA) in Northern Ireland.

Its purpose is to provide information on the UK labour market which can then be used to develop, manage, evaluate and report on labour market policies.

5.21 Multilevel Model

This is a statistical model which takes account of the clustering of individuals within higher-level units such as regions or schools.

5.22 Mobility outcomes

These capture the progress people have made compared to their parents at a later point in life (often in one’s 50s).

5.23 National minimum wage

The minimum wage that an employer must pay its workforce. For those aged over 23 years, it is currently set at £9.50, an amount known as the ‘national living wage’. There are lower national minimum wages amounts for younger people. In this report, this is referred to as the minimum wage.

5.24 Not in education, employment or training (NEET)

The annual publication of national participation figures of young people aged 16 to 18 years includes a measure of those who are NEET, or not in education, employment or training.

5.25 The National Statistics Socio-economic Classification (NS-SEC)

This is the best national measure to monitor occupational social mobility. We define an individual’s socio-economic background according to the occupation of their higher-earning parent. This year we use a 5 part grouping:

Higher professional: NS-SEC 1. Examples of which include CEOs, doctors and engineers.
Lower professional: NS-SEC 2. Examples of which include teachers, nurses and journalists.
Intermediate: NS-SEC 3 and 4. Examples of which include shopkeepers, taxi drivers and roofers.
Skilled working class: NS-SEC 5 and 6. Examples of which include mechanics, electricians and housekeepers.
Routine working class: NS-SEC 7 and 8. Examples of which include cleaners, porters and waiters.

5.26 Occupational mobility

See social mobility, absolute social mobility and relative social mobility

5.27 Odds ratio

The odds ratio is a statistic which can be used for comparing the mobility chances of people from different socio-economic backgrounds. It can be thought of as a way of assessing the outcome of a competition between people from 2 different backgrounds to achieve an advantaged outcome and to avoid a disadvantaged outcome.

5.28 The Organisation for Economic Co-operation and Development (OECD)

The OECD is an international organisation of 38 countries committed to democracy and the market economy. We use the 37 other member states as international comparators to the UK.

5.29 Pay As You Earn (PAYE)

Most people pay Income Tax through PAYE. This is the system the employer or pension provider uses to take Income Tax and National Insurance contributions before they pay wages or pension. Your tax code tells your employer how much to deduct.

5.30 Professional, professional/ managerial (occupations)

See NS-SEC.

5.31 Program for International Student Assessment (PISA)

PISA is the OECD’s Programme for International Student Assessment. PISA measures 15-year-olds’ ability to use their reading, mathematics and science knowledge and skills to meet real-life challenges.

5.32 Pupil Premium

A sum of money given by the UK government to schools in England to improve the attainment of disadvantaged children.

5.33 Quintile

20% of a population which is usually ordered in either ascending or descending order of some metric. After being ranked, the population is split into 5 equal sized groups, each of these groups is one quintile.

5.34 Random effects model

A type of statistical model in which one or more of the model parameters is a random variable.

5.35 Relative poverty

This is one measure of poverty. A household is in relative poverty if its income is below 60% of the average (median) net household income in the same year. In other words, the pound amount of the poverty line changes each year based on current median income in the country.

Relative mobility involves a comparison of the positions of people from different social backgrounds. For example, in the case of income, relative mobility tells us how strongly children’s ranking within their income distribution is associated with their parents’ ranking. Relative mobility is low if almost everyone ends up with a similar rank as their parents. For example, if parents in the bottom decile of earnings have children that mostly end up in the bottom decile of earnings.

While absolute and relative social mobility often go together, they are not the same concept. For example, if a society creates more professional jobs, absolute occupational mobility should improve. But if most of these professional jobs go to people from professional backgrounds, relative social mobility may remain static.

Social mobility refers to movement within a given stratification system. Social mobility can be either intergenerational (when children move away from their parents’ position) or intra-generational (when people move away from their own initial position). There are different dimensions of stratification; we focus on the key mobility dimension of occupational class, and add further dimensions like income, wealth, education and housing, as the data allows. See also the distinction between relative social mobility and absolute social mobility.

The Social Mobility Index is a long-term measurement framework for social mobility in the UK. It replaces the Commission’s original Social Mobility Index, launched in 2016. This new Index provides a critical starting point to improve the evidence base and goes well beyond solely reporting on the drivers of mobility.

We report on social mobility outcomes which show where people end up in comparison with where they started. This is across a range of outcomes of interest, including occupational class, income, education, and either at an earlier stage in their lives in their 20s and 30s (intermediate outcomes), or a later stage in their 40s and 50s (mobility outcomes).

5.39 Socio-economic classification, background

See NS-SEC.

5.40 Universities and Colleges Admissions Service (UCAS)

The Universities and Colleges Admissions Service – a nonprofit organisation which conducts the application process for UK universities.

5.41 Understanding Society, The UK Household Longitudinal Survey (UKHLS)

The UKHLS is a longitudinal survey of the members of approximately 40,000 households in the UK. The study is based at the Institute for Social and Economic Research at the University of Essex.

The purpose of UKHLS is to provide high-quality longitudinal data on subjects such as health, work, education, income, family, and social life. This helps to understand the long-term effects of social and economic change, as well as policy interventions designed to impact the general wellbeing of the UK population.

5.42 Unidiff parameters

The Unidiff parameter provides a single number to show whether the level of relative mobility (as measured by odds ratios) differs across the tables being compared. This allows us to compare levels of relative mobility across, for example, different years, or different ethnicities.

5.43 Wealth and Assets Survey (WAS)

A longitudinal survey of around 17,000 households conducted by ONS every 2 years. The purpose of the survey is to ‘measure the well-being of households and individuals in terms of their assets, savings, debt and planning for retirement’.

5.44 Working-class occupations

See NS-SEC.

Please see our technical annex for the 2023 State of the Nation report for the original publication. ↩
See the following ONS paper: ‘Non-response Weights for the UK Labour Force Survey? Results from the Census Non-response Link Study for comparisons based on the 2011 Census’ ↩
For further details of the changes, see the ONS Labour Force Survey Performance and quality monitoring report: April to June 2021. ↩
For further details see the LFS USER Guide, volume 1, section 10 ↩
Based on the methodology used by the International Labour Organisation. ↩
Some information about performance indicators and the reasons for discontinuation can be found on the HESA website. ↩
Breen, Richard and Jung In (2024). Regional Variation in Intergenerational Social Mobility in Britain. British Journal of Sociology. https://doi.org/10.1111/1468-4446.13095 ↩
Social Mobility Commission, ‘Long shadow of deprivation: Differences in opportunities across England’, 2020. Published on GOV.UK. ↩
Breen, Richard and Jung In (2024). Regional Variation in Intergenerational Social Mobility in Britain. British Journal of Sociology. https://doi.org/10.1111/1468-4446.13095 ↩
Bath and North East Somerset includes the 3 unitary authorities of Bath and North East Somerset, North Somerset and South Gloucestershire. ↩
ONS Geography, ‘ITL geography hierarchy boundaries’, 2021. Published on GEOPORTAL.STATISTICS.GOV.UK. ↩
ONS Geography, ‘ITL geography hierarchy boundaries’, 2021. Published on GEOPORTAL.STATISTICS.GOV.UK. ↩
ONS Geography, ‘ITL geography hierarchy boundaries’, 2021. Published on GEOPORTAL.STATISTICS.GOV.UK. ↩
ONS Geography, ‘ITL geography hierarchy boundaries’, 2021. Published on GEOPORTAL.STATISTICS.GOV.UK. ↩
ONS Geography, ‘ITL geography hierarchy boundaries’, 2021. Published on GEOPORTAL.STATISTICS.GOV.UK. ↩

Cookies on GOV.UK

1. Data sources

1.1 Office for National Statistics (ONS) Labour Force Survey

1.2 ONS Annual Survey of Hours and Earnings (ASHE)

1.3 ONS Vacancy Survey

1.4 Department for Business, Energy & Industrial Strategy (BEIS) / Nesta spatial data tool

1.5 The Department for Digital, Culture, Media & Sport (DCMS) Community Life Survey

1.6 DCMS Taking Part Survey

1.7 Department for Education (DfE) Early Years Foundation Stage Profile (EYFSP) results in England

1.8 Income Deprivation Affecting Children Index (IDACI)

1.9 DfE National Curriculum Assessments at KS2 in England

1.10 DfE National Curriculum Assessments at KS4 in England

1.11 DfE Participation in education, training and employment age 16-18 statistics

1.12 Department for Work and Pensions (DWP) Households Below Average Income (HBAI) statistics

1.13 Higher Education Statistics Agency (HESA) UK performance indicators

1.14 OECD online education database

1.15 OECD online education database and the Programme for International Student Assessment (PISA)

1.16 UK Household Longitudinal Study

1.17 European Social Survey (ESS)

1.18 Ofcom Connected Nations Report

2. Methodology and analysis

2.1 Indicators: Intermediate outcomes

Intermediate outcome 1.1: Level of development at age 5 years

Intermediate outcome 1.2: Attainment at age 11 years

Intermediate outcome 1.2: Disadvantage attainment gap index at key stage 2 (KS2)

Intermediate outcome 1.3: Attainment at age 16 years

Intermediate outcome 1.3: Disadvantage attainment gap index at key stage 4 (KS4)

Intermediate outcome 1.4: Skills at age 15

Intermediate outcome 2.1: Further education and training opportunities

Intermediate outcome 2.2: Entry of young people into higher education

Intermediate outcome 2.3: Highest qualification of young people

Intermediate outcome 3.1: Economic activity of young people

Intermediate outcome 3.2: Unemployment among young people

Intermediate outcome 3.3: Occupational level of young people

Intermediate outcome 3.4: Earnings of young people

Intermediate outcome 3.5: Returns in earnings to education for young people

Intermediate outcome 3.5: Returns in earnings to education for young people over time

Intermediate outcome 3.6: Direct effect of social origins on hourly earnings

Intermediate outcome 3.6: Direct effect of social origins on hourly earnings

Intermediate outcome 4.1: Further training and qualifications

Intermediate outcome 4.2: Occupational progression

Intermediate outcome 4.3: Income progression

2.2 Indicators: Drivers of social mobility

Driver 1.1: Distribution of earnings

Driver 1.2: Childhood poverty

Driver 1.3: Distribution of parental education

Driver 1.4: Distribution of parental occupation

Driver 2.1: Further education and training opportunities

Driver 2.2: Availability of high-quality school education

Driver 2.3: Access to higher education

Driver 3.1: Vacancy rate

Driver 3.2: Youth unemployment

Driver 3.3: Type of employment opportunities for young people

Driver 3.4: Labour market earnings of young people

Driver 4.1: Civic engagement

Driver 4.2: Level of trust, fairness and helpfulness

Driver 5.1: Broadband speed

Driver 5.2: Business expenditure on research and development

Driver 5.3: University research students

2.3 Confidence intervals and tests of significance

2.4 Composite index methodology

Indices at local authority level

Developing the composite indices

A new composite index for the intermediate outcomes

New composite indices for the drivers

3. Limitations of the data and analysis

3.1 Lack of harmonised educational data from administrative sources across the UK

3.2 Lack of coverage of the full population in the datasets

3.3 Limitations of the variables available in the dataset

3.4 Small sample sizes and imprecision of estimates

3.5 Measurement error

3.6 Limitations of descriptive analysis

3.7 Statistical analysis

3.8 Limitations of quantitative analysis

4. Local authorities in the UK

5. Glossary

5.1 Absolute social mobility

5.2 Apprenticeships

5.3 Caterpillar plots

5.4 Composite index