Policy paper

Chapter 2: Mobility since last year

Published 11 September 2024

Highlights

The attainment gap between pupils eligible for free school meals (FSM) and those not eligible remains largely unchanged from last year. For example, at age 5 years, there is a consistent gap of around 20 percentage points in the attainment of a ‘good level of development.’ However, in some cases it has widened, such as key stage 4 (KS4).

Among disadvantaged children, girls still do better than boys. For example, at age 11 years, 47% of disadvantaged girls reach the expected standard in reading, writing and maths, compared with 41% of boys.

FSM-eligible children from some ethnic backgrounds achieve very well. For example, FSM-eligible children of Chinese background perform better than the national average for non-FSM children at KS2 and KS4 (11 and 16 years). At age 11 years, 71% of FSM-eligible children of Chinese background reach the required standards. 

All the areas of London continue to do well in terms of educational attainment for FSM-eligible pupils at age 5, 11 and 16 years. 

The percentage of children living in relative poverty in the UK (after accounting for housing costs) has risen since 2012 and is at about 30%. It is still below the levels reached in the 1990s (when the percentage was closer to the mid-30s), but is much higher than historical levels from the 1960s and 70s.

The availability of high-quality education in the UK remains good. The UK has performed at or above the Organisation for Economic Co-operation and Development (OECD) average in the Programme for International Student Assessment (PISA) for mathematics, reading and science, but 2022 scores show decreases across the world.

We see that unemployment levels among young people are now the lowest they have been since 2014, at 11% in 2022. This means that far fewer young people are suffering the negative effects of unemployment. 

However, for those young people who are unemployed, finding a job could be more difficult, as job vacancy rates have fallen from 0.9 to 0.7 vacancies for every unemployed person between 2022 to 2023.

There appears to be a narrowing of the socio-economic background (SEB) gap in university enrolment between 2014 and 2022. In 2014, young people from higher professional backgrounds were 3.9 times more likely to be studying for a degree than those from lower working-class backgrounds. In 2022 they were only 2.2 times more likely. Data from the Department for Education (DfE), which goes back to 2006, suggests that this is an even longer-term trend.^{[footnote 1]}

Young people with low qualifications may have closed the earnings gap with their more qualified peers. For example, there has been a 16% increase in real hourly earnings for people with lower-level qualifications between 2014 to 2016, and 2020 to 2022. This is higher than the increases for all groups with higher-level qualifications.

Civic engagement – participating in democratic processes, such as signing a petition or attending a public rally – has decreased from 40% to 34%.

The percentage of premises with gigabit internet availability has increased sharply across the UK since 2020, potentially fostering better technical infrastructure for innovation and growth.

We have not included a full range of breakdowns by protected characteristics in this document.^{[footnote 2]} These breakdowns are instead published in full on our website, social-mobility.data.gov.uk.^{[footnote 3]} 

Introduction 

Social mobility has faced considerable challenges in recent years. COVID-19 caused prolonged disruption to education and employment, and real wage growth has been weak for more than a decade. The full effects of this will not be known until far into the future. However, we designed our Index to pick up early warning signs that future mobility may be affected, by looking at our intermediate outcomes and drivers.

Intermediate outcomes

An individual experiences intergenerational social mobility when their life outcomes, such as their type of occupation, differ from their parents. Change across generations, and the link between parents and children, are the core of social mobility. So our intermediate outcomes compare people’s starting point with an endpoint in their teens, 20s, or early 30s, as they move through education and into the labour market. These early (or intermediate) endpoints give an indication of what people’s later outcomes will be, because the skills, qualifications and experience of work that young people have will affect their social mobility. 

Understanding how these have been affected by recent events, such as the COVID-19 pandemic, is essential. We therefore report on them annually, since the experiences of each cohort of people leaving school and entering the labour market may change from year to year.

Outcomes split by background versus overall outcomes

To construct intermediate outcome measures, we need 2 elements. First, we need a measure of SEB – a starting point. And second, we need a socio-economic outcome – an ending point. The change between these 2 points is then the amount of mobility experienced.

However, it is also important to consider overall outcomes for the whole population, regardless of SEB, because these can be a good indicator of the level of opportunity available. For example, a high-performing education system indicates that there is a high level of opportunity to access good schooling. For this reason, our Index includes drivers, which are not split by SEB. 

Drivers

We include drivers to give a sense of how good conditions are likely to be for social mobility in the future. We therefore measure what is happening to the background conditions that make social mobility easier or harder. We call these conditions the ‘drivers’ of social mobility. 

Drivers, such as access to good-quality education and work opportunities, are included if there is a good reason for linking them to better overall rates of social mobility. This is different from what might benefit a particular individual. For example, it may be that going to grammar school will result in better outcomes for an individual, than if the same individual went to a non-selective school in the same area. But this is a distinct question from whether a grammar school system would result in higher mobility rates overall (not least because, by definition, most people cannot go to a grammar school). 

Finally, since the drivers are intended to show how much national or local circumstances help mobility, they aren’t broken down by SEB, and they can’t tell us the UK’s rate of social mobility. We also report on these annually. 

Indicators included in this report

In this report, we look at only a selection of indicators, for which we have seen important changes in recent years. The most up-to-date version of all indicators can be found in our online Data Explorer Tool. 

Intermediate outcomes

Compulsory school age (age 5 to 16 years)

The school years form a critical period in which children develop. These years build an important foundation for getting on in work and in life. Monitoring education and skills development is therefore important for understanding any early differences in outcomes by social background.

Social background measures and accountability systems vary across the UK. Therefore we only present the measures for England.^{[footnote 4]}

Free school meal eligibility as a measure of socio-economic background 

The only SEB measure available is eligibility for FSM. FSM is a binary measure (eligible or not eligible) that captures roughly the poorest 20% of students. Previous work has found considerable overlap between the incomes of families eligible for FSM and those who are not.^{[footnote 5]} While FSM eligibility is not ideal, it is the only SEB measure available in schools data.^{[footnote 6]} 

A more serious problem is that, due to the transitional protections covering FSM eligibility as we move from old-style multiple benefits to Universal Credit, there is a greatly increased number of children now eligible for FSM. This means that the average child on FSM today is probably not as disadvantaged as the average child on FSM 10 years ago. So results from more recent years will tend to understate any gap in achievement, compared with results from 10 years ago. In other words, the measured gap may have closed, even with no underlying change in the pattern of achievement.

Proportions and absolute numbers

In much of our analysis, we contrast the proportions of pupils from various groups who achieve a certain level of attainment. This allows us to meaningfully compare groups of different sizes. For example, there are many more non-Chinese pupils in UK schools than there are Chinese pupils. If we were to look only at the raw numbers of pupils from each group achieving the expected standard, we would see that far more non-Chinese pupils do so (precisely because there are far more non-Chinese pupils in the first place). But if we look at percentages, this reveals that the performance of an average Chinese pupil is far higher. 

Similarly, looking at percentages, we see that the performance of an average pupil classed as disadvantaged is lower than that of an average pupil who is not known to be disadvantaged. But it’s important to remember that there are far more pupils who are not known to be disadvantaged than there are disadvantaged pupils – roughly 3 times as many are not FSM-eligible. This in turn means that, in terms of raw numbers, more non-disadvantaged pupils are failing to meet the expected standard than disadvantaged pupils. As a result, any policy action that is targeted mainly on SEB (such as the Pupil Premium) will, by design, ignore the majority of pupils who fail to meet the expected standard. The only sure way of targeting help on low attainers is to look directly at attainment. 

Level of development at age 5 years

Starting with the youngest pupils, we look at ‘good level of development’, as defined in the early years foundation stage (EYFS) profile. The EYFS profile is intended to provide an accurate representation of each child’s development at the end of the EYFS to support their transition into year 1. It is made up of an assessment of the child’s outcomes in relation to 17 early learning goals (ELGs) across 7 areas of learning.^{[footnote 7]}

Children are defined as having a good level of development at the end of the EYFS if they are at the expected level for the 12 ELGs within the 5 areas of learning relating to: communication and language; personal, social and emotional development; physical development; literacy; and mathematics.^{[footnote 8]}

As with last year, due to the devolved nature of the education system, we can only monitor this measure for children in England. 

Additionally, data from the 2021 to 2022 and 2022 to 2023 academic years is not directly comparable with previous years, due to changes to the EYFS framework.

Figure 2 shows that, overall, the percentage of children achieving a ‘good’ level of development at the age of 5 years increased in the 7 school years ending in July 2019. No data was collected in the following 2 school years because of the pandemic. Data collection then resumed, but the results cannot be directly compared with the previous ones because of changes in methodology. However, there was an increase of 2 points between 2021 to 2022 (65%) and 2022 to 2023 (67%) in the percentage achieving a good level of development (around 416,000 children). For a more reliable look at overall educational performance, see Driver 2.2, ‘Availability of high-quality school education’.

This trend is consistent across both FSM-eligible and non-FSM eligible backgrounds, as we reported last year. Overall, 72% of children (around 353,000 children) not eligible for FSM had a good level of development, compared to only 52% of children eligible for FSM (around 56,000 children). This also means that 28% (around 141,000 children) of those not eligible for FSM did not have a good level of development, while 48% (around 53,000 children) of those eligible for FSM did not have a good level of development.^{[footnote 9]}

The gap of approximately 20 percentage points between FSM and non-FSM eligible pupils remains largely unchanged from last year (20.4% in 2021 to 2022 and 19.9% in 2022 to 2023). 

Figure 2: The gap in the percentage of children achieving a ‘good’ level of development between those eligible for FSM and those not eligible remains substantial.

Percentage of students achieving a ‘good level of development’ at age 5 years by eligibility for FSM in England, from the 2012 to 2013 academic year to the 2022 to 2023 academic year.

Explore and download the data: Level of development at age 5 years (State of the Nation data explorer).

Source: DfE. EYFS profile results from the 2022 to 2023 academic year, 2023.

Note: The grey line represents all children. The percentage ‘good level of development’ tracks development at age 5 years in England only. Children are defined as having a good level of development at the end of the EYFS if they are at the expected level for the 12 early learning goals (ELGs) within the 5 areas of learning relating to: communication and language; personal, social and emotional development; physical development; literacy; and mathematics. The EYFS was significantly revised in September 2021 which means we cannot directly compare the outcomes for 2021 to 2022 and 2022 to 2023 with earlier years. Data collection during the 2 school years ending in July 2021 was cancelled due to the COVID-19 pandemic. FSM eligibility is defined as collected in the school census which states whether a child’s family have claimed eligibility. Parents can claim FSM if they receive certain benefits.

We find a similar trend when we look at levels of income deprivation based on a child’s residence. The percentage of children with a good level of development is lowest for those who live in the 10% most deprived neighbourhoods of England and rises incrementally to being the highest for those who live in the 10% least deprived neighbourhoods. As with other relationships reported here, this is a correlation and a causal relationship cannot be assumed. 

Figure 2.1: Children living in more deprived areas tend to achieve a worse level of development at age 5 years than those living in less deprived areas.

Percentage of students achieving a ‘good level of development’ at age 5 years by their income deprivation affecting children index (IDACI) decile in England, in the 2022 to 2023 academic year.

Explore and download the data: Level of development at age 5 years (State of the Nation data explorer).

Source: DfE. EYFS profile results from the 2022 to 2023 academic year, 2023.

Note: Figures are based on where the child lives (in other words, the location of their residence). The IDACI deciles are calculated based on the percentage of children living in income-deprived households within a certain neighbourhood.^{[footnote 10]} These neighbourhoods are grouped into deciles so that the 10% of neighbourhoods with the highest scores (most deprived) make up decile 1, and the 10% of neighbourhoods with the lowest scores (least deprived) make up decile 10. The percentage ‘good’ level of development tracks development at age 5 years in England only. Children are defined as having a good level of development at the end of the EYFS if they are at the expected level for the 12 ELGs within the 5 areas of learning relating to: communication and language; personal, social and emotional development; physical development; literacy; and mathematics. 

Differences between boys and girls

Girls continue to outperform boys. In the 2022 to 2023 school year, more girls than boys had a good level of development (74.2% for girls compared to 60.6% boys overall), with the gap widening slightly compared with a year earlier (by 0.4 percentage points; 13.2% difference in 2021 to 2022 and 13.6% in 2022 to 2023). The gap between those eligible for FSM and those not eligible is also smaller for girls (21% for boys and 18% for girls).

Figure 2.2. There are substantial differences between boys and girls in achieving a good level of development at age 5 years. The gap by FSM eligibility is larger among boys.

Percentage of students achieving a ‘good level of development’ at age 5 years by eligibility for FSM and gender in England, in the 2022 to 2023 academic year.

Explore and download the data: Level of development at age 5 years (State of the Nation data explorer).

Source: DfE. EYFS profile results from the 2022 to 2023 academic year, 2023.

Note: The percentage ‘good level of development’ tracks development at age 5 years in England only. Children are defined as having a good level of development at the end of the EYFS if they are at the expected level for the 12 ELGs within the 5 areas of learning relating to: communication and language; personal, social and emotional development; physical development; literacy; and mathematics. FSM eligibility is defined as collected in the school census which states whether a child’s family have claimed eligibility. Parents can claim FSM if they receive certain benefits.

Differences among regions

We still see considerable variation in outcomes between different areas. Of the regions, in 2022 to 2023, parts of London, West Midlands and Lincolnshire had the highest percentage of children eligible for FSM with a good level of development while regions in the North West and Yorkshire and the Humber had the lowest. 

Figure 2.3. FSM-eligible pupils in parts of London, Lincolnshire and the West Midlands are the most likely to achieve a good level of development at age 5 years.

Percentage of FSM-eligible pupils reaching a good level of development at age 5 years by ITL2 regions in England, in the 2022 to 2023 academic year.

Explore and download the data: Level of development at age 5 years (State of the Nation data explorer).

Source: DfE. EYFS results in 2023.

Note: The DfE shows results for each LA in England. This data has been aggregated into ITL2 regions by weighting the LA results by the number of pupils in each authority.

Attainment at age 11 years

We now look at how children perform at the end of primary school. Meeting the expected standard in reading, writing and maths is important for success in secondary school and, ultimately, later mobility outcomes. 

Figure 2.4 shows the proportion of all pupils who meet the expected standard. In all of reading, writing and maths (combined), 60% of all pupils (around 400,000 pupils) met or exceeded the expected standard, up from 59% in the 2021 to 2022 academic year (around 390,000 pupils). However, this is still below attainment in the 2018 to 2019 academic year, where 65% of pupils (around 418,000 pupils) met the standard. This decline in absolute overall performance is discussed further in Driver 2.2, ‘Availability of high-quality school education.’ These statistics also show that in 2022 to 2023 around 268,000 pupils did not meet the expected standard, compared to around 226,000 pupils in 2018 to 2019. 

Considering the findings by disadvantaged status, we find that 44% of disadvantaged pupils (around 90,000 pupils) met the expected standard for reading, writing and maths in 2022 to 2023 compared to 66% of other pupils (around 310,000 pupils), a difference of 22 percentage points.^{[footnote 11][footnote 12]} This means around 114,000 disadvantaged pupils and 159,000 of other pupils did not meet the expected standard.^{[footnote 13]} 

Figure 2.4: Children from disadvantaged backgrounds are less likely to reach the expected standard in reading, writing and maths at KS2. This gap has widened slightly since before the pandemic.

Percentage of students reaching the expected standard in reading, writing and maths at KS2 by disadvantage status in England, from the 2015 to 2016 academic year to the 2022 to 2023 academic year. 

Explore and download the data: Attainment at age 11 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS2 in England, 2023.

Note: The grey line represents all children. Disadvantaged pupils are defined as those who were registered as eligible for FSM at any point in the last 6 years, and children looked after by a LA or who left LA care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order. 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. Between the academic years 2018 to 2019 and 2021 to 2022, there was a break in assessments due to the pandemic, though these last 2 data points are comparable.

Figure 2.5 shows the disadvantage gap index at KS2. This summarises the attainment gap between disadvantaged pupils and all other pupils.^{[footnote 14]} A disadvantage gap of zero would indicate that pupils from disadvantaged backgrounds perform as well as pupils from non-disadvantaged backgrounds.

The disadvantage gap index reduced between the 2010 to 2011 and 2018 to 2019 academic years, indicating that the attainment gap between disadvantaged pupils and their peers was becoming smaller. It remained at a similar level between 2018 and 2019 and increased in 2022 to the highest level since 2012. It remains largely unchanged from 3.23 in 2022 to 3.21 in 2023. 

Figure 2.5. The disadvantage gap index at KS2 (age 11 years) remains unchanged and substantial.

Disadvantage attainment gap index for England at KS2, from the 2010 to 2011 academic year to the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 11 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS2 in England, 2023.

Note: Comparisons are made by ordering pupil scores in reading and maths assessments at the end of KS2 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 an equal distribution of scores).^{[footnote 15]} Disadvantaged pupils are defined as those who were registered as eligible for FSM at any point in the last 6 years, and children looked after by a LA or who left LA care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order.

Differences between boys and girls

Consistent with last year, and previous years, girls continue to outperform boys at the expected standard in reading, writing and maths.

Although girls are more likely than boys to achieve the expected standard, the gap between those from disadvantaged and non-disadvantaged backgrounds is similar for boys and girls at around 22 to 23 percentage points. These patterns of gender differences at age 11 years are very similar to those shown for age 5 years.

Figure 2.6: In the 2022 to 2023 school year, girls were more likely than boys to reach the expected standard in reading, writing and maths at age 11 years.

Percentage of students reaching the expected standard in reading, writing and maths at KS2 by disadvantage status and gender in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 11 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS2 in England, 2023.

Note: Disadvantaged pupils are defined as those who were registered as eligible for FSM at any point in the last 6 years, and children looked after by a LA or who left LA care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order.

Differences between ethnic groups

In figure 2.7, we see a similar pattern in the overall achievement levels across different ethnicities as we did last year. FSM-eligible children of Chinese ethnicity represent the highest proportion achieving the expected standard (71%) and Gypsy or Roma ethnicity represent the lowest (14%). Given the uneven geographical distribution of children of different ethnicities, there may be regional effects contained in these different outcomes, for example, a ‘London effect’. One major factor in this effect is the presence of high numbers of pupils with an ethnic minority background in such large metropolitan areas. 

Figure 2.7: The percentage of FSM pupils reaching the expected standard by age 11 years varies greatly by ethnic background.

Percentage of FSM-eligible pupils reaching the expected standard in reading, writing and maths at KS2 by ethnicity in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 11 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS2 in England, 2023.

Note: FSM eligibility is defined as collected in the school census which states whether a child’s family have claimed eligibility. Parents can claim FSM if they receive certain benefits. Universal Infant Free School Meals and additional FSM provided in London are separate programmes and do not count towards these numbers. 

Differences among regions

Figure 2.8 shows that, among FSM-eligible students in England, there is a strong cluster of high performance in London, but not in the rest of the South East. Tees Valley and Durham also perform strongly. Some of this may be connected with the findings on ethnicity, as shown in figure 2.7, in the sense that the ethnicities that perform best may be more highly concentrated in these areas. However, this is unlikely to be the case for Tees Valley and Durham.

The pattern observed here is largely consistent with last year’s findings, and a good level of development at age 5 years. It is also consistent with what has often been termed ‘the London effect’. 

Figure 2.8: FSM-eligible pupils in London and Tees Valley and Durham are the most likely to achieve the expected standard at KS2.

Percentage of FSM-eligible pupils reaching the expected standard in reading, writing and maths at KS2 by ITL2 regions in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 11 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS2 in England, 2022 to 2023.

Note: DfE shows results for each LA in England. This data has been aggregated into ITL2 regions by weighting the LA results by the number of pupils in each authority.

Attainment at age 16 years

A young person’s educational outcomes at age 16 years help to shape their path onto higher or further education (HE or FE), training and employment. Good grades help to secure good jobs, and most importantly, options to follow a number of different routes after compulsory schooling. 

Figure 2.9 shows that, overall, 45% of pupils (around 275,000 pupils) achieved a grade 5 or higher in both English and maths (grey line). This is a decrease of 4.8 percentage points (from 49.8%) compared to 2021 to 2022, but still an increase of 1.8 percentage points (from 43.2%) in comparison with 2018 to 2019. However, some drop was expected due to changes in assessment methods.^{[footnote 16]} For a more reliable look at this decrease in performance, see Driver 2.2, “Availability of high-quality school education”.

To look at the connection between SEB and this early outcome, we consider the overall levels of attainment for disadvantaged pupils and all other pupils.^{[footnote 17]} In terms of group comparisons, 25% of disadvantaged pupils (around 40,000 pupils) achieved a grade 5 or above in both subjects, compared with 52% of all other pupils (around 235,000 pupils). This is a gap of 27.2 percentage points, which is similar to the previous 2 years when the gap was 27.4 and 27.5 percentage points. These results show that around 119,000 disadvantaged pupils and around 213,000 of all other pupils do not achieve a grade 5 or above in both subjects.^{[footnote 18]}

Figure 2.9: In the 2022 to 2023 school year, there was a drop in the proportion of pupils at KS4 achieving a grade 5 or above in GCSE English and maths, and the gap between disadvantaged and other pupils was similar to previous years.

Percentage of students achieving a pass (grade 5 or above) in both GCSE English and maths by disadvantage status in England, from the 2018 to 2019 academic year to the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 16 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS4 in England, 2023.

Note: 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 1 day or if they are recorded as having been adopted from care. Figures for the school years 2022 to 2023 are based on revised data. Figures for the 2018 to 2019 and 2021 to 2022 school years are based on final data. 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 and teacher-assessed grades).

We also report the KS4 disadvantage gap index for schools in England, in figure 2.10. The disadvantage gap index summarises the relative attainment in GCSE English and maths between disadvantaged pupils and all other pupils. It provides 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, where performance in a set of 8 GCSEs is measured).^{[footnote 19]} 

In 2022 as exams were re-introduced, the disadvantage gap index continued to widen and now stands at its highest level since 2011. As with the findings from last academic year, this widening likely reflects the effects of the disruptions to learning that many pupils experienced during the pandemic.

Figure 2.10: The disadvantage gap index at age 16 years has widened further, and is the largest gap since the 2010 to 2011 academic year.

The disadvantage attainment gap index for England at KS4, from the 2010 to 2011 academic year to the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 16 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS4 in England, 2023

Note: 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 0 would indicate that pupils from disadvantaged backgrounds perform as well as pupils from non-disadvantaged backgrounds. 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. Figures for the school years 2022 to 2023 are based on revised data. 

Differences between boys and girls

Figure 2.11 shows the proportion of pupils achieving a pass in both GCSE English and maths by sex and disadvantage status in the 2022 to 2023 school year. Overall both non-disadvantaged and disadvantaged girls have higher rates of passing GCSE English and maths than boys. 55% of non-disadvantaged girls passed both subjects, compared with 50% for boys. Similarly, 27% of disadvantaged girls passed both subjects compared with 24% of boys. At 28 percentage points, the disadvantage gap for girls is fairly similar to that for boys, who have a gap of 26 percentage points.

Figure 2.11: In the 2022 to 2023 school year, girls were more likely than boys to achieve a pass in both GCSE English and maths regardless of their disadvantaged status.

Percentage of pupils achieving a pass (grade 5 or above) in both GCSE English and maths by disadvantage status and gender in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 16 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS4 in England, 2023.

Note: 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. Figures for 2023 are based on revised data.

Differences between ethnic groups

Figure 2.12 shows the proportion of FSM-eligible pupils who achieve a pass in both GCSE English and maths. The figure shows substantial variation between the most disadvantaged ethnic group (Gypsy or Roma at 3%) and the top-performing ethnic group (Chinese at 75%). Overall, FSM-eligible pupils of South Asian ethnicities (such as Indian and Bangladeshi) have much higher rates of achieving a pass in both subjects compared with White British or Mixed White and Black Caribbean FSM-eligible pupils (18% and 19%, respectively).

Work by Professor Steve Strand for the Commission of Race and Ethnic Disparities examines this pattern of achievement.^{[footnote 20]} It suggests that some minority groups may see education as a way to escape poverty, while others are less optimistic, a distinction that may be connected to the minority’s ‘voluntary’ or ‘involuntary’ status. It may be that immigrant groups that have lived in the UK for a long time become gradually less optimistic. Patterns of migration from different countries are also different – for example, Indian migrants are often of a high socio-economic status in their country of origin. 

Although the outcomes of high-SEB children are not considered here, mainly due to data limitations, it is notable that Strand finds relative underperformance among Black Caribbean and Black African boys, and among Pakistani girls, from high SEBs.

Figure 2.12: There is great variation across ethnicities in the attainment of pupils eligible for FSM.

Percentage of FSM-eligible pupils achieving a strong pass (grade 5 or above) in both GCSE English and maths by ethnicity in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 16 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS4 in England, 2023.

Note: Figures for 2023 are based on revised data. FSM eligibility is defined as collected in the school census which states whether a child’s family have claimed eligibility. Parents can claim FSM if they receive certain benefits.^{[footnote 21]}

Differences among regions

Figure 2.13 shows a similar geographical pattern to figure 2.8, and to what was reported last year. In particular we see a similar ‘London effect’, with other densely populated urban areas also showing good results, like London and the West Midlands.

There is also a fairly clear and consistent pattern for lower percentages of FSM-eligible pupils in rural areas of England such as Cornwall and Cumbria achieving passes (grade 5 or higher) in English and maths. There is also a cluster of low performance in other areas of the North West, such as Lancashire and Cheshire. 

Figure 2.13: FSM-eligible pupils in London and the West Midlands are the most likely to achieve passes (grade 5 or above) in English and maths at GCSE.

Percentage of FSM-eligible pupils achieving a pass (grade 5 or above) in both GCSE English and maths by ITL2 region in England, in the 2022 to 2023 academic year.

Explore and download the data: Attainment at age 16 (State of the Nation data explorer).

Source: DfE. National curriculum assessments at KS4 in England, 2023.

Note: DfE shows results for each LA in England. This data has been aggregated into ITL2 regions by weighting the LA results by the number of pupils in each authority.

Skills at age 15 years

PISA aims 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. The tests “explore how well students can solve complex problems, think critically and communicate effectively” at age 15 years.^{[footnote 22]} “This gives insights into how well education systems are preparing students for real-life challenges and future success.”^{[footnote 23]}

We include parental education 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 parent. 

Figures 2.14, 2.15 and 2.16 show that students in the UK scored higher than the OECD average in mathematics, reading and science, on average. This is true across almost all levels of parental education. 

We also see a clear socio-economic gradient in scores for maths, reading and science, with pupils of higher-educated families (for example, master’s degrees) obtaining the highest scores, and those of the lowest educated families obtaining the lowest scores. Pupils from level 1 families in the UK scored significantly lower than those from level 7 families. For example, there is a 107 score point difference in mathematics between the most disadvantaged group and the least disadvantaged groups (levels 1 and 7). 

The OECD also calculates outcomes split by the SEB of pupils. It finds that, in the UK, “socio-economically advantaged students (the top 25% in terms of socio-economic status) outperformed disadvantaged students (the bottom 25%) by 86 score points in mathematics. This is similar to the average difference between the 2 groups (93 score points) across OECD countries”.

Case study: Leah Johns, 21, Belfast, Northern Ireland

At school, I got mostly Bs and Cs in my GCSEs. Then, at A-level, I did a Level 3 qualification in Health and Social Care which really piqued my interest. 

I did it through the pandemic. That was quite tough. I worked in a burger van 50 hours a week because I wanted to contribute to household expenses as I wasn’t able to eat school dinners. I was using my auntie’s really old laptop. It was quite hard because you didn’t have teachers pushing you. When you sit in the classroom you’re being told off if you’re talking or on your phone. But at home you’re alone on your laptop. 

Originally, I wasn’t going to go to university. I was going to stay home and get a job. But then I got the predicted grades I needed so on a whim I put in a late application and got five offers.

While I was doing my degree I worked as a carer, then in nurseries which I really enjoyed, then for the university as a student ambassador. I got the maximum student loan which was a massive help but in my first year it just covered my rent so I had to work to have the normal student experience. 

To start with I found it difficult getting used to being in a classroom again after the pandemic but the support from the tutors really helped. I have just graduated with a first-class degree in Health and Social Care. Now I’ve moved to Belfast and am hoping to get my master’s so I can apply for jobs in public health for a local authority. 

Where I’m from, there’s a sense that if your parents live there their whole lives then you will as well. I know a lot of people who go to university near where they live and don’t move away. I am the first person, even among all my cousins and aunties, to go to university. I love my family but wanted to do something out of the normal narrative.

Figure 2.14: On average, parental education is related to children’s mathematics scores at age 15 years.

Average pupil attainment scores on PISA mathematics assessments by highest level of education of parents (the International Standard Classification of Education (ISCED)), UK and OECD average, for 2022.^{[footnote 24]}

Explore and download the data: Skills at age 15 (State of the Nation data explorer).

Source: OECD, PISA, 2022 mathematics assessment.

Note: ISCED refers to the international classification for organising education programmes and related qualifications by levels. 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.^{[footnote 25]} For the UK only, the item response rate is below 85%. 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. 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 2.15: On average, parental education is related to children’s science scores at age 15 years.

Average pupil attainment scores on PISA science assessments by highest level of education of parents (ISCED), UK and OECD average, for 2022.

Explore and download the data: Skills at age 15 (State of the Nation data explorer).

Source: OECD, PISA, 2022 science assessment.

Note: ISCED is the reference international classification for organising education programmes and related qualifications by levels. 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.^{[footnote 26]} For the UK only, the item response rate is below 85%. 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. 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 2.16: On average, parental education is related to children’s reading scores at age 15 years.

Average pupil attainment scores on PISA reading assessments by highest level of education of parents (ISCED), UK and OECD average, for 2022.

Explore and download the data: Skills at age 15 (State of the Nation data explorer).

Source: OECD, PISA, 2022 reading assessment.

Note: ISCED is the reference international classification for organising education programmes and related qualifications by levels. 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.^{[footnote 27]} For the UK only, the item response rate is below 85%. Missing data has not been accounted for. The results for those with parents educated to level 8 (doctoral degree) have been omitted, as this is a small group. 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.

Routes into work (age 16 to 29 years)

Entry of young people into higher education 

Figure 2.17 shows the proportion of young people aged 18 to 20 years who began studying in HE by socio-economic background in 2022. 32% of all young people aged 18 to 20 years were studying in HE in 2022.

Overall, young people from a higher professional background (47%) still had significantly better chances of participating in HE than people from other SEBs (including those from a lower professional background). And, people from a lower working-class background had significantly lower chances (21%) even when compared with those from a higher working-class background (34%). However, from 2021 to 2022, the HE participation gap between those from the higher professional and the lower working classes has remained roughly the same, 30 percentage points in 2021 to 26 percentage-point difference in 2022. 

Figure 2.17: Young people from lower working, higher working, intermediate and lower professional backgrounds have lower HE entry rates than those from a higher professional background.

Percentage of young people aged 18 to 20 years in the UK studying for degree-level qualifications, 2022, by SEB.

Explore and download the data: Entry to higher education (State of the Nation data explorer).

Source: ONS, LFS 2022, respondents aged 18 to 20 years in the UK.

Note: Being in HE is defined as currently studying degree-level qualifications, this includes foundation degrees. The data refers to participation rates of young people aged 18 to 20 years. A formal test was conducted to test for differences in HE participation rates by SEB.^{[footnote 28]} The data used is weighted using the LFS person weights. The error bars show 95% confidence intervals. 

However, looking at longer-term trends, things are more positive. On average, enrolment rates are up. We also see that the gap between those from lower working-class and higher professional backgrounds has almost halved. In 2014, young people from higher professional backgrounds were 3.9 times more likely to be studying for a degree than those from lower working-class backgrounds. In 2022 they were only 2.2 times more likely (see figure 2.18). Figures from the Higher Education Statistics Agency (HESA) suggest a similar pattern. It has found that “the disparities in degree attainment between those from the most and least deprived areas (based on HESA’s deprivation measure) has narrowed by approximately 2 percentage points during COVID-19 (in other words, the academic years 2019 to 2020 and 2020 to 2021).”^{[footnote 29]}

The trend emerging from LFS and HESA data is confirmed by data from the DfE, which compares the participation in HE of students who were eligible for FSM at age 15 years.^{[footnote 30]} According to this data, the HE progression rate for FSM-eligible pupils has risen from 14.2% for the 2005 to 2006 cohort to 29.2% for the 2021 to 2022 cohort. Meanwhile, the progression rate for non-FSM eligible pupils has risen from 33.5% for the 2005 to 2006 cohort to 49.4% for the 2021 to 2022 cohort. This means that, among those turning 19 years in the 2005 to 2006 academic year, students who were not eligible for FSM were about 2.4 times more likely to progress to HE. For those turning 19 years in 2021 to 2022, they were only 1.7 times more likely. 

The narrowing of this gap is good news, in the sense that there is no longer such a tight link between a person’s SEB and their chances of going to university. But on top of the efforts that have been made to broaden HE access, we should also consider what happens to young people who do not attend university. The existence of good-quality, non-university paths to employment is very important. 

Figure 2.18: The HE enrolment gap between those from lower working-class and higher professional backgrounds has almost halved.

Ratio of the proportion of young people from  a higher professional background to those from a lower working background, aged 18 to 20 years, studying for degree-level qualification in 2014 versus 2022.

Explore and download the data: Entry to higher education (State of the Nation data explorer).

Source: ONS, LFS 2014 versus 2022, respondents aged 18 to 20 years in the UK.

Note: Being in HE is defined as currently studying degree-level qualifications, this includes foundation degrees. The data refers to participation rates of young people aged 18 to 20 years. The ratios were obtained by dividing the proportion of those from a higher professional background in HE by the proportion of those from a lower working background. 

Work in early adulthood (age 25 to 29 years)

Early stages of a person’s work experience can play an important role in shaping their career path. In this section, we look at differences in occupational levels and earnings by SEB to highlight how findings differ from last year. We then break down these findings further to understand the influence of education. We look first at differences in earnings by educational level controlling for SEB.^{[footnote 31]} That is, how earnings differ for people with the same SEB but different qualifications. We then flip this around and explore differences in earnings by SEB controlling for education. In other words, how earnings differ for people with the same educational level but different SEBs.

Earnings are not the only thing that matters. We capture the full range of early labour market outcomes in our data explorer tool, including unemployment, occupational level and economic activity. The measures cover ages 25 to 29 years to capture young people who have typically gone through HE or FE. We must emphasise that patterns of socio-economic differences vary when we look at different outcomes. The patterns shown for earnings here may be different to those for other measures like economic activity, for example. 

Occupational level of young people aged 25 to 29 years

Figure 2.19 shows clear SEB differences in the occupations taken by young people, but with no significant change from last year. SEB is strongly related to young people’s occupational class. One of the biggest proportional differences is in the percentage of young adults from a lower working-class background who are in lower working-class jobs (34%). It may be that young people from a lower working-class background are being held back, not only by lack of access to professional jobs, but also by the range of jobs above a routine manual level. 

Young adults from a higher professional-class background are more than 4 times more likely to be in a higher professional occupation (32%) than those from a lower working-class background (7%). And 34% of young people from a lower working-class background work in a lower working-class occupation, compared with only 12% of young people from higher professional backgrounds. For those from lower working-class backgrounds who do make it to a professional occupation, they are still 3 times as likely to be in a lower (rather than higher) professional occupation. 

These gaps between groups are not statistically different from last year. And, if we look over time, we see that more people are likely to be in professional jobs (see Driver 3.3 in our accompanying online tool) but we do not see huge improvements in terms of the gaps. In 2014, for example, young people from higher professional backgrounds were 4.3 times more likely to be in a higher professional job than young people from a lower working-class background. This has remained relatively unchanged at 4.5 times in 2022. 

Figure 2.19: Socio-economic background is strongly related to young people’s occupational class.

Percentage of young people aged 25 to 29 years in the UK in different occupational levels, 2022, by SEB.

Explore and download the data: Occupational level of young people aged 25 to 29 years (State of the Nation data explorer).

Source: ONS, pooled LFS 2022, respondents aged 25 to 29 years in the UK.

Note: Due to rounding errors, in some instances, the totals may not add up to 100%. Formal tests were conducted to test for differences in the chance of being in a higher professional occupation and for the likelihood of being in a lower working-class occupation.^{[footnote 32]} The data used is weighted using the LFS person weights.

Earnings of young people aged 25 to 29 years

Again this year, we see a fairly smooth relationship between SEB and young people’s earnings – the higher the background, the higher the earnings. Figure 2.20 shows that, on average, young people from lower working-class backgrounds earned £13 an hour, compared to £17.8 for those from higher professional backgrounds. However, young people from higher working-class backgrounds do not earn significantly more than those from lower working-class backgrounds. 

Figure 2.20: Socio-economic background is strongly related to the level of young people’s earnings.

Mean hourly earnings of young people aged 25 to 29 years in the UK, 2022, by SEB.

Explore and download the data: Earnings of young people aged 25 to 29 years (State of the Nation data explorer).

Source: ONS, LFS 2022, respondents aged 25 to 29 years in the UK.

Note: 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. Self-employed respondents and those without earnings are excluded. Earnings have been adjusted for inflation with a base year of 2022. A formal test was conducted to test for differences in earnings by SEB.^{[footnote 33]} The data used is weighted using the LFS person weights. The error bars show 95% confidence intervals. 

Case study: John Marston, 45, Walsall, West Midlands

I was a good student but I wasn’t very productive. I left school with only 3 Cs at GCSE or above and I got Ds for everything else. My parents divorced while I was going through my GCSEs and it distracted me a little bit.

My dad was an oil refiner, then he was a bailiff, then he was a prison officer, then he went into nursing. My mum worked for a contractor that worked for a bank. 

I did my NVQ in bricklaying as a hobby. When I was young, my uncle was a builder. I sometimes helped him at weekends and I loved the lifestyle of the self-employed builder. They seemed really relaxed and happy. I enjoyed the outside work and the pride of building things, but soon I had a son and I wasn’t earning what I should. Things were tight and I felt working for an employer would take the pressure off.

I applied for four jobs and got one in the water sector providing jetting services. From there, I moved to Severn Trent, working in different roles such as technical operator and maintenance. My first management job was maternity cover managing 14 people and all the pumping stations in Wolverhampton. Then I ended up moving to managing around 145 sites and £4.5m, double the budget. 

After a few more moves, I ended up being promoted to business lead for the south. The secret is mainly hard work and doing pretty much what I said I was going to. I’ve had a different career to most of the senior management team. I’ve had to gain credibility. If you do your GCSEs well and A-Levels that will give you more credibility earlier in your career because it shows you can apply yourself. What I’ve had to do throughout my career is compete with these people who on paper had a better chance than me. 

I get paid more than when I was a builder and it’s a lot more secure so that gives the family security and you can plan for things. I think the trade-off is you’re doing a few more hours so I try to enjoy the family time. 

The step up from managing 20 to 200 people is huge. It’s a different mindset and you have to have empathy.

Income returns to education: returns in earnings

Figure 2.21 illustrates the link between education and earnings, not the link between SEB and earnings. It can usefully be compared with figure 2.20, which illustrates the link between SEB and earnings for people with the same level of education.

Figure 2.21 shows the difference between what 2 different young people of the same SEB would earn on average, if one had no GCSEs and the other had a higher level of qualifications. If we consider 2 young people from the same SEB, we would expect young people with a higher degree – such as a master’s degree – to earn 58% more than those with no GCSEs. Similarly, we would expect young people with a first degree (an undergraduate degree) to earn 48% more. Those with qualifications at GCSE, A level or FE below degree level, earn approximately 3, 16 and 29% more than those with the lowest levels of education.

Figure 2.21: Young people with higher levels of education earn much more than those with lower levels of education.

Percentage differences in hourly earnings of young people aged 25 to 29 years in the UK, from 2020 to 2022 (combined), relative to those with lower level (below GCSE grade 1 or equivalent), controlling for SEB, sex and age.

Explore and download the data: Income returns to education (State of the Nation data explorer).

Source: ONS, pooled LFS from 2020 to 2022, respondents aged 25 to 29 years in the UK.

Note: 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. We adjusted earnings for inflation with a base year of 2022. We estimated the percentage differences from a linear regression model of log hourly earnings by educational level, controlling for, SEB, sex and age.^{[footnote 34]} The percentages shown are the differences in income for men aged 27 years from lower working-class backgrounds by level of qualification when compared to similar men with lower level qualifications (below GCSE grade 1 or equivalent). We pooled the data for the years 2020 to 2022 to obtain more accurate estimates. The data used is weighted using the LFS person weights.

Figure 2.22 shows that, after adjusting for inflation, the earnings have increased significantly over time for young people with all qualifications, apart from those with higher degrees and GCSEs or equivalent. For example, there has been a 16% increase in real hourly earnings for people with lower-level qualifications between 2014 to 2016 (first point on the chart), and 2020 to 2022 (last point on the chart). This is the highest of all groups over the same period.

We also see that the earnings gaps between young people with different levels of education have remained more or less constant since 2014 to 2016 (first point on the chart), and from 2020 to 2022 (last point on the chart). However, our most recent estimate from 2020 to 2022 shows that the gap in earnings between those obtaining higher and first degrees is significantly smaller than it was in 2014 to 2016. We also find that the earnings gap between those with O-level, GCSE or equivalent qualifications and lower-level qualifications is significantly smaller in the years 2020 to 2022 than in 2014 to 2016. 

Figure 2.22: Higher qualifications continue to be strongly associated with higher earnings, although the premium for higher degrees appears to have declined.

Real hourly earnings in pounds (£) of young people aged 25 to 29 years in the UK, 3-year moving averages from 2014 to 2016 until 2020 to 2022, by highest qualification controlling for SEB, sex and age.

Explore and download the data: Income returns to education (State of the Nation data explorer).

Source: ONS, pooled LFS from 2014 to 2022, respondents aged 25 to 29 years in the UK.

Note: 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. We adjusted earnings for inflation with a base year of 2022. Each year refers to the last year of the 3-year moving average, for example 2016 refers to the 2014 to 2016 period. We estimated hourly earnings from a linear regression model of log hourly pay, controlling for educational level, SEB, sex and age. Formal tests were conducted to test for differences in earnings by qualification over time.^{[footnote 35]} The estimates shown are the hourly earnings of men aged 27 years who were from a lower working-class background. The data used is weighted using the LFS person weights. The error bars show 95% confidence intervals. 

Direct effect of social origin on earnings

Now we look at how hourly earnings differ for people with the same educational level but different social origins. 

We find that the earning gap across SEBs holds true even when comparing young people with the same educational level (figure 2.23). Those from higher professional backgrounds earn 13% more than those from a lower working-class background with the same qualification level.

Figure 2.23: Young people from professional backgrounds earn significantly more than those from other backgrounds but with the same level of education.

Percentage differences in hourly earnings of young people aged 25 to 29 years in the UK, from 2020 to 2022 (combined), relative to those from lower working-class backgrounds, controlling for highest educational level, sex and age.

Explore and download the data: Direct effect of social origins on earnings (State of the Nation data explorer).

Source: ONS, pooled LFS from 2020 to 2022, respondents aged 25 to 29 years in the UK.

Note: 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. We adjusted earnings for inflation with a base year of 2022. We estimated percentage differences from a linear regression model of log hourly pay by SEB, controlling for educational level, sex and age.^{[footnote 36]} The reference group is men who are aged 27 years 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 to obtain more accurate estimates. The data used is weighted using the LFS person weights.

Figure 2.24 shows that real earnings have improved significantly from 2014 to 2016 (first point on the chart) to 2020 to 2022 (last point on the chart), particularly for those from lower working, higher working and intermediate SEBs. It also shows that the earnings gaps between young people from different SEBs but who have the same level of education have remained more or less constant since 2014 to 2016, and from 2020 to 2022. The difference from 2020 to 2022 is around £1.2 per hour. 

We do not see any convincing long-term trend in terms of the differences between young people from different SEBs. For example, the earnings gap between those from higher and lower working-class backgrounds is similar to what it was in 2016.

Figure 2.24: The earnings gaps across SEBs have remained roughly constant between 2014 to 2016, and 2020 to 2022.

Real hourly earnings in pounds (£) of young people aged 25 to 29 years in the UK, 3-year moving averages from 2014 to 2016 until 2020 to 2022, by SEB, controlling for highest qualification, sex and age.

Explore and download the data: Direct effect of social origins on earnings (State of the Nation data explorer).

Source: ONS, pooled LFS from 2014 to 2022, respondents aged 25 to 29 years in the UK.

Note: 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. We adjusted earnings for inflation with a base year of 2022. We estimated hourly earnings from a linear regression model of log hourly pay by SEB, controlling for education level, sex and age. Formal tests were conducted to test for differences in earnings by SEB over time.^{[footnote 37]} The reference group is men who were from a lower working-class background and had lower-level qualifications (below CSE grade 1 or equivalent). Estimates are shown for men aged 27 years. The data used is weighted using the LFS person weights. The error bars show 95% confidence intervals.

Case study: Naomi Spence, 20, Greenwich, London

When I was looking at my post-16 education options, I didn’t ever really think about attending a Further Education College - it just didn’t feature in my thought process at all. I think there’s a lot of stigma attached to FE colleges and there’s a false narrative that FE colleges are for less intelligent students or are an easy option, and that couldn’t be further from the truth.

After my GCSEs in 2020, I enrolled at a local sixth form, and had just completed a week on a media course when I realised that this was a subject I wanted to focus on in more depth. I’ve always enjoyed creating content, being on stage and performing, so media seemed like a good match for me. I searched out my options, and found that London South East Colleges, part of a group of FE colleges, could offer me a course that better suited my passions - a level 3 Creative Digital Media Production course.

College was a really big change from school, but from the very first day, I realised that the FE college and course I was on were meant for me. The environment felt very egalitarian and I was encouraged to be more independent. Being in a room of people who were really passionate about the subject and just enjoying my course made me feel really comfortable.

I have so many interests, and I’m a huge advocate for not pushing young people down just one path. I want to do many things in the future. I’m very entrepreneurial, and I think having the freedom to really explore my subject has given me more motivation to set up my own businesses – I provide oratory and public speaking services for organisations and I also work as a wellbeing workshop facilitator.

The thing that most don’t realise about FE colleges is the sheer diversity of the people they attract and the many options available. FE colleges are a home for students who want to follow different pathways. We had people in their 60s studying at our college, people who were starting over and looking for new opportunities, and the college was supporting them all to achieve their goals.

While I was there, alongside my course, I re-sat my maths GCSE. I think there’s a lot of pressure put on children to get their maths and English GCSEs, which can be damaging to young people’s mental health. I was really lucky with the support I had from the charity, Get Further, who helped me to prepare. They understood me and my learning style, adapting their approach to meet my needs. 

Getting my maths GCSE has made me more confident and it was a real relief when I passed – I was just so overjoyed.

I completed my course in 2022, and I took a gap year because I wanted to consider my options. I had a good offer from the police and managed to get some great overseas work experience, and also got the chance to work with different media companies. There are lots of great pathways out there, but I decided that for me, a degree in Marketing Management at university was the right way forward.

Neither of my parents went to university - my father worked on the railways and my mum in social work. My family highly valued education and my parents made significant sacrifices to invest in my education, providing me with numerous tutors over the years - they created a very nourishing environment for myself and my three siblings. My parents have always been my biggest cheerleaders and their encouragement has really helped me to believe in myself and has given me the confidence to go on to achieve everything I want to do in life.

Drivers

Conditions of childhood

A child’s chances for mobility are influenced by the availability of their families’ material and cultural resources. We look at the distribution of these resources by concentrating on childhood poverty. Proxies for cultural capital, including parental occupation and education, are featured in our online tool.^{[footnote 38]}

The percentage of children living in relative poverty in the UK (after accounting for housing costs) has increased since 2012.^{[footnote 39][footnote 40]} It is still below the levels reached in the late 1990s, but much higher than in the 1960s or 1970s, when the rate was roughly half of what it is today. 

Childhood poverty

Figure 2.25: In the past 5 years, the proportion of children living in relative poverty in the UK has remained around 30%.

Percentage of children living in relative poverty after housing costs, by country over time (UK, 1994 to 2023).

Explore and download the data: Childhood poverty (State of the Nation data explorer).

Source: Department for Work and Pensions, Households Below Average Income statistics, Table 4.16.^{[footnote 41]}

Note: Data is calculated using 3-year averages (including the current year and 2 previous 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 FY 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.

Availability of high-quality school education

Education is seen as one of the primary drivers of good social mobility outcomes, and equality of opportunity demands that everyone have access to a good-quality education regardless of their background, socio-economic status or any other personal characteristic. This helps to ensure that all students have a fair chance to succeed and achieve their full potential.

The PISA findings allow us to look at the UK as a whole (rather than just England), and understand how our students perform compared with similar countries. Figure 2.26 shows the latest trends. 

The UK has performed at or above the OECD average since 2006. And in 2022, students in the UK continued to score significantly above the OECD averages in maths (489 score points), reading (494 score points), and science (500 score points). In maths, the UK is 11th in the world out of 81 that took part. However, the UK’s ranking in reading and science has remained relatively unchanged. England’s performance is the best of the 4 home nations. The UK’s position therefore remains strong, with above average scores in all 3 subjects. Some have even noted that the UK stands out among European countries as a place where migrant pupils perform better than non-migrant pupils in maths and reading.^{[footnote 42]} 

But we do see some recent decreases in attainment over time, both in the UK and across the OECD. While average scores in maths and reading were increasing before the pandemic, they have declined in 2022. There were significant 13 and 10 point drops in maths and reading between 2018 and 2022. Similarly, average science scores have been falling slightly since 2012, but not significantly between 2018 and 2022. The OECD averages have also seen similar significant decreases over the same period. While some of this may be due to the pandemic, many of these trends started before this time.^{[footnote 43]}

Figure 2.26: The UK has performed at or above the OECD average in PISA for mathematics, reading and science, but 2022 scores have decreased across the world.

Average pupil attainment scores on PISA mathematics, reading and science assessments over years, UK and OECD average, from 2003 to 2022.

Explore and download the data: Availability of high-quality school education (State of the Nation data explorer).

Source: OECD, PISA, 2003 to 2022 mathematics, reading and science assessments.

Note: Red lines represent the UK average, and blue lines represent the OECD average. Assessment occurs every 3 years from 2003 to 2022. However, there is no available data for the 2003 science assessment. Average scores for young people aged 15 years on PISA’s assessments. Due to small sample sizes in the UK, the OECD advises against comparisons between the UK and other countries for the year 2003. 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.

Work opportunities for young people

Job vacancy rate

Figure 2.27 illustrates the number of vacancies per jobseeker over time. This ratio serves as a proxy measure of how easy it is for young people who are seeking work to find it.^{[footnote 44]} A higher ratio indicates that there are more vacancies per jobseeker and greater ease in finding a job for those seeking one. This trend provides estimates of the overall state of the labour market, as we cannot identify vacancies specifically aimed at young people.

Recently we have seen the highest number of vacancies for every jobseeker in the last 20 years. This year, however, we see a decrease from 0.9 to 0.7 vacancies for every unemployed person. This should be considered alongside lower youth unemployment levels (as shown in figure 2.27), but it means that for those young people who are unemployed, finding a job could be more difficult.

Figure 2.27: The estimated number of vacancies per jobseeker fell from 2022 to 2023.

Number of vacancies per unemployed person in the UK (seasonally adjusted), quarter 4 from 2001 to 2023.

Explore and download the data: Job vacancy rate (State of the Nation data explorer).

Source: ONS, Vacancy Survey and LFS (respondents aged 16 to 64 years).^{[footnote 45]}

Note: 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 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 jobseekers. 

Case study: Hafsa Anwar, 19, Manchester, North West England

I’m Hafsa, a first-year technology degree apprentice on a Tech Consulting pathway. I’m from Pakistani descent, and grew up in Birmingham until I moved to Manchester in September 2023 for this opportunity.

My cousin told me about the degree apprenticeship routes, so I did some research and found one in the industry I was most interested in. Not having to pay for my degree was a big motivator for me – especially with the cost of university fees becoming so high. Earning both a degree and a level 6 qualification, alongside 3 years of industry experience sounded too good to be true. This opportunity sets me up for the rest of my career and gives me a sense of security for my future. And earning a salary while learning allowed me to become financially independent at 18.

Now that I have experience in the tech industry I would love to progress my career and work as a tech consultant. My employer gives tech degree apprentices the opportunity to be part of a variety of different projects, allowing us to use and develop different skills. This really helps us figure out what we enjoy and what we’re good at, so we can start to plan where we would like to specialise as our career progresses.

Having a qualification and experience means that I’ve already started my career and learnt valuable skills I need and that employers look for. Working full time, studying and having financial responsibility, has meant that I have matured faster than some of my peers who perhaps don’t have that level of responsibility yet. 

I come from a working-class family, in a very low-income area and attended underfunded schools, and so growing up, I never thought opportunities like this would be open to me. I am passionate about giving other people the same opportunity that I have benefitted from, so I make the time to volunteer at events to educate and inform those from lower socioeconomic backgrounds on routes into roles like mine.

Youth unemployment

Youth unemployment has varied considerably over time, with spikes in levels following the 2008 financial crisis and COVID-19 pandemic. But, this year, we see clear improvements in terms of unemployment. Levels are now the lowest they have been since 2014, at 11% in 2022. This means that far fewer young people are suffering the negative effects of unemployment.^{[footnote 46]}

Figure 2.28: Youth unemployment rates are the lowest they have been since 2014.

Percentage of young people aged 16 to 24 years in the UK, from 2014 to 2022, who were unemployed.

Explore and download the data: Youth unemployment (State of the Nation data explorer).

Source: ONS, LFS, from 2014 to 2022, weighted data, economically active respondents aged 16 to 24 years, 95% confidence intervals. 

Note: The LFS follows the internationally agreed definition for unemployment recommended by the International Labour Organisation – 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.

Social capital and connections

Social capital refers to the social connections and the relationships that come from them, which enable a society to function well. Social capital’s role in social mobility is less well understood than that of education or work. However, it has been suggested that it can promote a more dynamic economy and society. 

Civic engagement

Civic participation is defined as engagement in democratic processes, both in person and online. This includes contacting a local official (such as a local councillor or MP), signing a petition, or attending a public rally (but not voting).

Our findings show that civic participation remained broadly stable from 2014 to 2021, despite decreases during the years most impacted by the COVID-19 pandemic. The most recent estimates, shown in figure 2.29, reveal another dip. Here 34% of respondents said they had engaged in some form of civic participation at least once in the last 12 months; a decrease compared to levels in 2020 to 2021 (41%).^{[footnote 47]}

Figure 2.29: Between 2014 and 2021, civic participation remained broadly stable, but decreased in 2022.

Percentage of adults who have engaged in democratic processes within the last 12 months in England, 9 years to March 2022.

Explore and download the data: Civic engagement (State of the Nation data explorer).

Source: Table C1, Community Life Survey 2021 to 2022, Department for Culture Media and Sport.^{[footnote 48]}

Note: 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. There are 95% confidence intervals available for 2019 to 2020, 2020 to 2021, and 2021 to 2022 only.

Environment favouring innovation and growth

Innovation and its commercial application have long been integral to national industrial strategies. A supportive educational, technical, and economic infrastructure can foster local economic growth, encouraging investment and broadening professional and business opportunities in the area, thereby offering pathways for upward mobility. The impact on social mobility tends to be indirect, but remains potentially significant. It is crucial to assess the innovation environment and determine whether a supportive environment can enhance growth and future upward mobility.

Three indicators – broadband speed, business research and development (R&D) expenditure, and postgraduate education – reflect different components of an environment conducive to innovation and growth:

1. Broadband speed indicates the technical infrastructure essential for firms in high-tech sectors. A lack of this infrastructure may deter investment and hamper productivity.

2. Business R&D expenditure represents investment in applying and implementing innovations, which is likely crucial for economic growth.

3. Postgraduate education  reflects the availability of advanced human capital that drives innovation. This indicator is not limited to STEM subjects, as the humanities may also play a significant role in the creative and media sectors.

These indicators represent various inputs that might enhance business activity, particularly in high-tech areas. Although these are experimental statistics and we cannot yet confirm a causal link between these indicators and an area’s potential for innovation, growth, and upward mobility, they could begin to illuminate the role of innovation in advancing social mobility in the UK.

Below, we present broadband speed, measured as the percentage of premises with gigabit internet availability since 2020.  

Broadband speed

As shown in figure 2.30, the percentage of premises with gigabit internet availability has increased sharply across the UK since 2020.

Figure 2.30: Across all UK nations, the percentage of premises with gigabit internet availability has increased sharply since 2020.

The percentage of premises (including residential and business) that have gigabit availability.

Explore and download the data: Broadband speed (State of the Nation data explorer).

Source: The Office of Communications (Ofcom), Connected Nations Report (2023).^{[footnote 49]}

Note: 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 of each year.

Case study: Carolyn Jay, 52, Surrey county council area, South East England

Community and place manager, Ringway

No-one wakes up in the morning and thinks ‘I know, I’ll go and work in highways.’ But it has great advantages. It’s very stable as our roads will always need fixing.

Ringway has a contract for 10 years in Surrey and need to make sure we’ve got people we can employ. There’s a national lack of groundworkers but especially so in the South East as it’s an area of high employment and high demand.

After doing some work in schools I realised there was a gap in the 16-to-18 space. You tend to lose a lot of teenagers at that point. When I was having a conversation with Surrey Careers Hub they said Ringway should set up a course. I had no idea this was possible!

She said I think Nescot College Surrey is your best partner, let’s set up a meeting. I got on a Teams call and found about 12 people from Nescot sat around a table. I could immediately see how meaningful this was to them.

I was insistent we didn’t rely on maths and English as a requirement. It forces some young people out of the system. Ringway wanted it to be a hands-on course aimed at hands-on people who don’t want to be in a classroom or behind a desk.

The model is going to be two year-long standalone courses delivered in Nescot’s construction faculty using an accredited construction curriculum which they’ve adapted to highways. Ringway will support with employer engagement. We’ll take the students to a live site and have a supervisor talking about what’s happening and giving them a chance to ask questions. They’ll get practical training on the course. They’ll be doing things like bricklaying and slab-laying and we want to give young people a sense of the exciting machinery we’re working with.

Nescot had the first recruitment day in April and one of the years is already full. The hope is we’ll be able to line people up for apprenticeships. Ringway’s goal is to employ people from the end of this course but there are fibre, water and gas companies who will be able to make use of qualifications like this, especially in Surrey where there’s a lot of construction and a lot of roads.

Ringway has guaranteed to interview anyone who completes year 13. In a year or two I would love to be taking on people who’ve done a qualification like this, and have a commitment to working in highways and a clear idea of what it involves.

Case study: Julie Kapsalis, 46, Surrey county council area, South East England

Principal and Chief Executive of Nescot (North East Surrey College of Technology) 

The Pathways to Highways programme is a great example of 3 organisations coming together to address an opportunity and a need: Nescot, as a FE college, Ringway as a major local employer and Surrey County Council, who contracts with Ringway for highways work and who also have a priority around social mobility.

What I often find happens in these situations is there’s a lot of great intentions and shared objectives but somehow it doesn’t quite get going. What I’m really proud of about this project is pretty quickly we’ve come together to get something off the ground and in a couple of months’ time we’ll have students on 2 new courses, a level 1 course and an entry level course in construction skills for highway maintenance.

Ringway has been brilliant at coming to open day events, bringing all kinds of incredible pieces of equipment and vehicles, because actually that does really inspire young people to understand if you are going to go on that programme this is the kind of work you’ll be doing.

We are also really lucky that through our work with careers organisations they’re able to go out through their networks to local schools. It’s about giving young people another pathway they might not have been considering.

Where we’re located in Surrey, one of the challenges is that young people who’ve been to school here are attracted by living and working in London or a big city. Of course, people will make all kinds of decisions based on a variety of personal circumstances but from the perspective of Surrey as a county we need people to stay in the region and fulfil jobs in all kinds of sectors.

One of the barriers is that Surrey is a very expensive county to live in and that’s often why we do sometimes lose people so when you have courses where there’s a progression pathway and an outcome into a job where you know what the salary will be, often that does help people who might be thinking: can I afford to stay here?

We’ve been fortunate that through the Local Skills Improvement Fund, Nescot has secured a significant investment in immersive suites which includes simulators so that young people on this course can get the basic skills and knowledge that are critical to these jobs before they can legally drive a car or specialist machinery. We want the learners to be really inspired because the best marketing channels will be the young people on the course themselves telling their friends and family about their experience.

1. In our reporting, a person’s SEB means the socio-economic status of their parents. For example, this might be the parents’ occupational class, income or education. So for instance, when we talk about someone with a “higher professional background”, we mean that at least one of their parents had a higher professional occupation when this person was a child. ↩

2. According to the Equality Act 2010, protected characteristics are age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, religion or belief, sex, sexual orientation, and race (including colour, nationality, and ethnic or national origin). It is against the law to discriminate directly against someone with any of these characteristics. ↩

3. Social Mobility Commission, ‘Data about social mobility in the UK’, 2023. Published on SOCIAL.MOBILITY.DATA.GOV.UK. ↩

4. The lack of harmonised education statistics across England, Wales, Scotland and Northern Ireland means that the only option at present is to have separate (non-comparable) measures for each of the 4 nations. If harmonised measures are not possible, we hope to present data for the separate nations in future years. However, the devolved nations do have similar examinations. Wales does GCSEs. Northern Ireland has the Nationals 4 and 5 and Scotland has Nationals 3, 4 and 5 and also has Highers.  ↩

5. Graham Hobbs and Anna Vignoles, ‘Is children’s free school meal ‘eligibility’ a good proxy for family income?’, 2013. Published on BERA-JOURNALS, ONLINELIBRARY.WILEY.COM. ↩

6. In the 2022 to 2023 academic year, 23.8% of pupils were eligible for FSM. This represents over 2 million pupils. GOV.UK, ‘School pupils and their characteristics’, 2023. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

7. GOV.UK, ‘Early years foundation stage profile handbook’, 2018. Published on GOV.UK.  ↩

8. GOV.UK, ‘Early years foundation stage profile results’, 2023. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

9. GOV.UK, ‘‘Headline measures by characteristics’ from ‘early years foundation stage profile results’’, 2024. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

10. GOV.UK, ‘Child characteristics’, 2023. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

11. Disadvantaged pupils are ordinarily defined as: those who were registered as eligible for FSM at any point in the last 6 years, children looked after by a LA or have left LA care in England and Wales through adoption, a special guardianship order, a residence order or a child arrangements order.  ↩

12. In 2023, 30% of pupils at the end of KS2 were considered disadvantaged. ↩

13. GOV.UK, ‘‘Attainment by pupil characteristics’ from ‘key stage 2 attainment’’, 2024. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

14. The gap index is more resilient to changes to assessment than attainment threshold measures and therefore offers greater comparability between years. The index ranks all pupils in the country and assesses the difference in the average position of disadvantaged pupils and others. A disadvantage gap of zero would indicate that there is no difference between the average performance of disadvantaged and non-disadvantaged pupils. We measure whether the disadvantage gap is getting larger or smaller over time. See the technical annex for further information.  ↩

15. GOV.UK, ‘Key stage 2 attainment’, 2023. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

16. Comparisons are made with 2022 and with 2019. The more meaningful comparison is with 2019, the last year that summer exams were taken before the pandemic, as 2023 saw a return to pre-pandemic grading. Caution is needed when considering comparisons over time, as they may not reflect changes in pupil performance alone. Differences in attainment may also reflect changes in the approach to grading over time. ↩

17. 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 1 day or if they are recorded as having been adopted from care.  ↩

18. GOV.UK, ‘‘National characteristics data’ from ‘key stage 4 performance’’, 2024. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

19. GOV.UK, ‘Key stage 4 performance revised’, 2024. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK. ↩

20. Steve Strand, ‘Ethnic, socio-economic and sex inequalities in educational achievement at age 16’, 2021. Published on GOV.UK.  ↩

21. See DfE guidance for more information on FSM eligibility, ‘Early years foundation stage profile results’, 2023. Published on GOV.UK. ↩

22. PISA assesses young people aged 15 years as this is the last point at which most children are still enrolled in formal education. ↩

23. PISA 2022 results: ‘Factsheets, UK’, 2023. Published on OECD.COM. ↩

24. UNESCO, Institute for Statistics, ‘The international standard classification of education’. Published on UIS.UNESCO.ORG. ↩

25. For an explanation of the classification for parents’ highest level of education see, PISA, ‘PISA 2022 results (volume II): learning during – and from – disruption’, 2022. Published on OECD.ILIBRARY.ORG. ↩

26.  For an explanation of the classification for parents’ highest level of education see, PISA, ‘PISA 2022 results (volume II): learning during – and from – disruption’, 2022. Published on OECD.ILIBRARY.ORG. ↩

27. For an explanation of the classification for parents’ highest level of education see, PISA, ‘PISA 2022 results (volume II): learning during – and from – disruption’, 2022. Published on OECD.ILIBRARY.ORG. ↩

28. A logistic regression model was used to test whether there were differences in the likelihood of being in HE by socio-economic background. The likelihood of being in HE was significantly lower for all SEBs when compared to the likelihood of those from higher professional backgrounds. Note, that the difference in the participation rates between those from lower professional backgrounds and higher professional backgrounds was only significant at the 10% level.  ↩

29. HESA, ‘Degree attainment by socioeconomic background: UK, 2017/18 to 2020/21’, 2023. Published on HESA.AC.UK. ↩

30. GOV.UK, ‘Widening participation in higher education, academic year 2021/22’, 2023. Published on EXPLORE-EDUCATION-STATISTICS.SERVICE.GOV.UK.  ↩

31. To control for a variable means that we remove its effect. So, for example, if we look at how different educational levels are associated with different levels of earnings, while controlling for SEB, it means we have removed the effect of SEB. We could also think of this as considering the earnings of people with different education levels but the same SEB.  ↩

32. A logistic regression on the likelihood of being in a higher professional occupation by SEB and year shows that compared to those from higher professional backgrounds, young people from all other SEBs are less likely to be in a higher professional occupation. A similar model on the likelihood of being in a working-class occupation shows that compared to those from a higher professional background, young people from all backgrounds except for a lower professional one have a significantly higher chance of being in a lower working-class occupation.  ↩

33. A logistic regression model of real hourly income by SEB shows that compared to young people from lower working-class backgrounds, those from all SEBs apart from higher working-class backgrounds have significantly higher earnings.  ↩

34. The model shows that when compared to lower-level qualifications, the estimated effect of a qualification on earnings is significantly higher for all qualifications with the exception of O level, GCSE and equivalents. ↩

35. A similar logistic regression model with a sample consisting of the years 2014 to 2016 and 2020 and to 2022 is used to test for differences in the effect of qualifications on earnings over time. The model finds that the gap between first degrees and higher degrees is significantly smaller in the years 2020 to 2022 when compared to 2014 to 2016. A similar model shows that the gap between low-level qualifications and O level, GCSE and equivalent qualifications is also significantly smaller in 2020 to 2022 when compared to 2014 to 2016.  ↩

36. The model shows that when compared to lower working class, the estimated effect of your SEB on earnings is significantly higher for higher and lower professional backgrounds. The effect on earnings for intermediate and higher working-class backgrounds is not significantly different to that of a lower working-class background.  ↩

37.   A similar logistic regression model with a sample consisting of the years 2014 to 2016 and 2020 to 2022 is used to test for differences in the effect for each SEB on earnings over time. These models show that the real hourly earnings for higher and lower professionals are not significantly different in the period 2020 to 2022 compared to 2014 to 2016. However during the same period, real hourly earnings significantly increased for both lower and higher working class and the intermediate class.  ↩

38. ‘Cultural capital’ loosely means the social and cultural knowledge that can help an individual to be socially mobile. ↩

39. Relative poverty covers families where ‘equivalised’ household income is less than 60% of the median. ‘Equivalised’ means adjusted for the number and ages of the people living in the household. ↩

40. The major advantage of the relative poverty measure used by the DWP is that it is updated annually and is available at a granular geographic level. It is very practical for monitoring purposes. While some of the measures developed by academics may have other strengths, they are unfortunately neither updated regularly nor available at a detailed geographical level. ↩

41. Department for Work and Pensions, ‘Households below average income (HBAI) statistics’, 2013. Published on GOV.UK. ↩

42. Economics Observatory, ‘What can the UK learn from the latest global data on pupil performance?’, 2024. Published on ECONOMICSOBSERVATORY.COM. ↩

43. Economics Observatory, ‘What can the UK learn from the latest global data on pupil performance?’, 2024. Published on ECONOMICSOBSERVATORY.COM. ↩

44. A proxy measure is a stand-in used to estimate or represent something else that is difficult to measure directly. ↩

45. Office for National Statistics, ‘Vacancy survey’, 2021. Published on ONS.GOV.UK. ↩

46. Recent experimental statistics (November 2023 to January 2024) from the ONS suggest that the unemployment rate is above estimates of a year ago (November 2022 to January 2023) but largely unchanged compared to the latest quarter. However these estimates account for all those aged 16 years and older, and not just young people aged 16 to 24 years. Additionally, they should be treated with extra caution, given the ongoing challenges with response rates to the LFS. ↩

47. The 95% confidence intervals are indicated by error bars on the charts. They show the range that we are 95% confident the true value for the population falls between. When there is no overlap between the error bars for 2 or more groups, we can be more confident that the differences between groups represent true differences between these groups in the population. For more information see Department for Culture, Media & Sport, ‘Community life survey 2021/22: civic engagement and social action’, 2023. Published on GOV.UK.  ↩

48. Department for Culture Media & Sport, ‘Community life survey 2021/22: civic engagement and social action’, 2023. Published on GOV.UK. ↩

49. Data extracted from Ofcom, ‘Connected nations 2023: interactive report’, 2024. Published on OFCOM.ORG.UK. ↩