Chapter 4: Drivers of social mobility

Question 1

Highlights

Accepted Answer

The drivers give a sense of how good conditions are for social mobility in the future. Drivers are included if evidence has linked them to better overall rates of social mobility.

Wage inequality, parental education, and parental occupation have all improved in recent years, but relative child poverty has worsened.

Slightly more 16 to 18 year olds are in education and employment than 10 years ago. Also, fewer are not in education, employment or training (NEET) – 6% in 2021, compared with 10% in 2011.

More 19 year olds are enrolled in education than ever before, and the UK has now surpassed the Organisation for Economic Co-operation and Development (OECD) average, with 63% of 19 year olds enrolled in secondary or tertiary education.

Access to higher-level jobs has also improved. 17% of young adults aged 22 to 29 years were in higher professional occupations in 2021, compared with 11% in 2014.

Looking at the UK region by region, we might have expected high levels of advantage to go together with low levels of disadvantage. But in reality, there are regions where significant advantage and disadvantage coexist.

Sociocultural advantage – university-educated and professional parents, and professional job opportunities for young people – is concentrated in London and surrounding areas. The Highlands, West Wales, Cornwall, Lincolnshire and areas of Yorkshire and the Humber (South and East Yorkshire) are the least advantaged areas by this measure.

In contrast, the metropolitan areas of Greater London, Greater Manchester and the West Midlands have some of the highest levels of childhood poverty, youth unemployment, and parents in lower working-class occupations. As with the intermediate outcomes, London has both extremes.

Most of London is in the best quintile for sociocultural advantage.^{[footnote 1]} Yet most of London is also in the worst quintile for childhood poverty and disadvantage. High levels of advantage and disadvantage can coexist in an area, so simply comparing average outcomes, or conditions, across areas is not enough.

Question 2

Introduction

Accepted Answer

Measuring social mobility outcomes has been likened to “looking in the rear-view mirror”.^{[footnote 2]} This is because the causes can lie decades ago – someone who is now 50 was a child about 40 years ago. But we would also like to look forward, to predict social mobility trends. To do this, we measure what is happening to the background conditions that make social mobility easier – the ‘drivers’ of social mobility.

In this section, we focus on the following drivers of social mobility:

Conditions of childhood.
Educational opportunities and quality of schooling.
Work opportunities for young people.
Social capital (the value of people’s social connections).
Research and development (R and D) environment.

We have included drivers where there are good grounds 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 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 socio-economic background, and they can’t tell us the UK’s rate of social mobility.^{[footnote 3]}

Where people live versus where they grew up

For the mobility and intermediate outcomes, when we gave statistics broken down by region, we were referring to where people grew up, not where they currently live. This is sometimes referred to as ‘adolescent geography’ (where someone lived while growing up), in contrast to ‘current geography’ (where they live now). This is because these outcomes show how well people are doing in comparison with their point of origin (whether socio-economic, geographical, or both).

However, for our drivers, we want to understand current levels of opportunity for mobility, so we report current geography – where children and young people are living now – not adolescent geography.

Where we have shown trends over time, the length of the period covered depends upon the availability of the data. Where we have shown maps, the regions in the map are divided into 5 equal groups (quintiles), ordered from best to worst. This is explained in more detail in chapter 1.

Question 3

Drivers by region: conditions for social mobility across the UK

Accepted Answer

Summary

Looking at the UK region by region, we might have expected regions with high levels of advantage to also have low levels of disadvantage. But in reality, there are regions where both significant advantage and disadvantage coexist.

Sociocultural advantage – university-educated and professional parents, and professional job opportunities for young people – is concentrated in London and surrounding areas. The Highlands, West Wales, Cornwall, and Lincolnshire and areas of Yorkshire and the Humber (South and East Yorkshire) are the least advantaged areas by this measure.

In contrast, the metropolitan areas of Greater London, Greater Manchester and the West Midlands have some of the highest levels of childhood poverty, youth unemployment, and parents in lower working-class occupations. As with the intermediate outcomes, London has both extremes.

Most of London is in the best quintile for sociocultural advantage. Yet most of London is also in the worst quintile for childhood poverty and disadvantage. High levels of advantage and disadvantage can coexist in an area, so simply comparing average outcomes across areas is not enough.

This year, we introduce 3 summary indices relating to the drivers of mobility. These indices summarise information from more than one dataset. The drivers refer to the conditions that are believed to be associated with upward mobility for the people who grew up in the area. We have called these 3 indices ‘childhood poverty and disadvantage’, ‘sociocultural advantage’ and ‘research and development environment’. Our drivers are not measures of mobility. They instead capture the background environment which allows social mobility at a population level. We do not break these indicators down by socio-economic background (SEB).

The new indices aim to provide summary measures of how different geographical areas of the UK compare in terms of their conditions for social mobility. We devised the indices by looking at how strongly the indicators were correlated and grouped together to pull out an underlying factor. Please see the technical annex for more information. The indicators included in the composites are featured in Table 4.0.

Table 4.0: Summary of composite indices for the drivers of social mobility.

Index	Indicator	Measurement
Sociocultural advantage	Driver (DR) 1.3 Parental education (university degree)	Percentages of parents in the area with a university degree
	DR1.4a Parental occupation (higher professional)	Percentages of parents in the area with a higher professional occupation
	DR3.3a Young people’s occupation (higher professional)	Percentages of young people in the area with a higher professional occupation
Childhood poverty and disadvantage	DR1.2 Childhood poverty	Data from the Department for Work and Pensions on Households Below Average Income
	DR1.4b Parental occupation (lower working)	Percentages of parents in the area with a lower working-class occupation
	DR3.2 Youth unemployment	Percentages of young people in the area who are unemployed
Research and development (R and D) environment	DR5.1 Broadband speed	The median broadband speed in the UK
	DR5.2 Business expenditure on R and D (logged)	The median business R and D expenditure in the UK
	DR5.3 University research students	Based on the median number of research students enrolled in the UK

Regional patterns in drivers

These indices reveal 2 important findings. First, some areas have both extremes, with higher-than-expected proportions both of advantaged and of disadvantaged families. London is a clear example, with some of the highest levels of poverty in inner London, alongside high levels of both graduate and of higher professional families. Greater Manchester also has a high level of poverty alongside a middling number of graduates and higher professionals.

Second, there are also some signs of a pattern in which there are differences between more centrally located areas (such as London) compared with those far away from the centre (such as Plymouth). The index of sociocultural advantage is significantly correlated with the index of the R and D environment. They both show this same centre-periphery pattern, with the most favourable environments for upward mobility being in London, and the least favourable being in more remote parts of the UK.

Sociocultural advantage

Sociocultural advantage is a complex but important background condition for social mobility. We try to capture a simplified measure of this by looking at drivers related to parents with degrees, and both parents and young people in higher professional occupations.^{[footnote 4]} Having highly-educated parents, parents in a higher professional occupation, or a good chance of attaining a higher professional occupation yourself, are all advantageous to upward mobility prospects. Of course, no measure could capture all relevant sociocultural factors, such as having parents who place a high value on education.

We see in figure 4.1 a pattern with higher levels of sociocultural advantage in the southern and central parts of the UK and lower levels on the periphery and Northern Ireland. Overall, Greater London and adjoining areas have the highest level of family advantage. The Highlands, West Wales, Cornwall, Lincolnshire and areas of Yorkshire and the Humber (South and East Yorkshire) are the least advantaged areas, but some former mining and industrial areas (such as Tees Valley and Durham, South Yorkshire) are also quite disadvantaged. In contrast, the northern areas of Cheshire and North Yorkshire are relatively advantaged. We also need to remember that there can be considerable geographical variations within, as well as between, these areas. The picture may well be even more complex than the figure suggests.

Figure 4.1: Sociocultural advantage tends to be higher in and around London, and appears to be lower on the periphery.

Index of sociocultural advantage.

Explore and download data on the index of sociocultural advantage on the State of the Nation data explorer.

Source: Data used from the following indicators: driver (DR) 1.3a, DR1.4a and DR3.3a.

Note: We follow the procedure used by the economists Sudhir Anand and Amartya Sen (1994) for constructing the UN’s Human Development Index (HDI). In order to ensure that all indicators are on a common scale, indicators are first rescaled, setting the best-performing area’s score on the indicator to 1 and the worst-performing area’s score to 0. For more information on how each area was scored, please see the technical annex.

Childhood poverty and disadvantage

The index of childhood poverty and disadvantage includes the drivers of childhood poverty, youth unemployment, and lower working-class parental occupations. All of these are disadvantageous to social mobility prospects.

We might expect to find high childhood poverty and disadvantage wherever there are low levels of sociocultural advantage. But in reality, there is only a weak correlation between the geographies of the 2. For example, Tees Valley and Durham have low scores on both indices, while neighbouring North Yorkshire scores quite well on both. In contrast, other areas score highly on one index but poorly on the other, examples being London, and the Highlands and Islands.

The metropolitan areas of Greater London, Greater Manchester and the West Midlands have some of the highest levels of childhood poverty and disadvantage (reflected mainly by youth unemployment). The geographical distribution of youth unemployment closely parallels that of childhood poverty.^{[footnote 5]}

Areas such as London have both high levels of advantage alongside high levels of childhood poverty, perhaps reflecting socio-economic inequality and polarisation. Inner London has some of the highest levels of childhood poverty in the UK and this may be due to the high housing costs.^{[footnote 6]} While average levels of material prosperity are highest in London, there is also a high level of variation around the average. In contrast, other areas with levels closer to the average may be less polarised.

Figure 4.2: Metropolitan areas tend to have high levels of poverty, with lower levels found in rural areas.^{[footnote 7]}

Index of childhood poverty and disadvantage.

Explore and download data on the index of childhood poverty and disadvantage on the State of the Nation data explorer.

Source: Data used from the following indicators: driver (DR)1.2, DR1.4b and DR3.2.

Note: We follow the procedure used by the economists Sudhir Anand and Amartya Sen (1994)) for constructing the UN’s Human Development Index (HDI). In order to ensure that all indicators are on a common scale, indicators are first rescaled, setting the best-performing area’s score on the indicator to 1 and the worst-performing area’s score to 0. For more information on how each area was scored, please see the technical annex.

Research and development environment

The final index for the drivers, R and D environment, comprises 3 new indicators for this year: broadband speed, business expenditure on R and D, and university research students. This index is less securely grounded in prior research than the 2 previous indices. However, we are interested in developing ways of measuring the link between economic opportunity, innovation and business vibrancy on social mobility. We hope to do more work on this in the future and consider how to capture more factors related to the wider business environment.

At this point, we note that it is possible that areas with a more favourable R and D environment will be among the more dynamic areas of the country in future decades, and so will provide favourable conditions for upward mobility. We plan to monitor whether this does in fact happen.

We see in figure 4.3 an arc from Bristol to inner West London that provides a favourable R and D environment, alongside the West Midlands, Derby and Nottingham, and Central Scotland. This pattern is broadly consistent with the locations of major universities and high-tech and major engineering firms, which are expected to be hubs of both innovation and implementation.

Figure 4.3: An arc from Bristol to inner West London provides a favourable environment, alongside the West Midlands, Derby and Nottingham, and Central Scotland.

Index of the research and development environment.

Explore and download data on the research and development environment index on the State of the Nation data explorer.

Source: Data used from the following indicators: driver (DR) 4.1, DR4.2 and DR4.3.

Note: We follow the procedure used by Sudhir Anand and Amartya Sen (1994) for constructing the UN’s Human Development Index (HDI). In order to ensure that all indicators are on a common scale, indicators are first rescaled, setting the best-performing area’s score on the indicator to 1 and the worst-performing area’s score to 0. For more information on how each area was scored, please see the technical annex.

Question 4

Driver 1: Conditions of childhood

Accepted Answer

Summary

Wage inequality, as measured by the 90:10 ratio, has declined in the last decade. Those at the 90th centile (high earners, and with most others below this point) now earn just over 3 times as much per hour as those at the 10th. In the late 1990s and the 2000s, they earned about 4 times as much.

The percentage of children living in relative poverty in the UK (after accounting for housing costs) has increased 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).^{[footnote 8]}

There has been a continuing increase in the proportion of families where the adults have higher levels of qualifications. Many more parents have degrees than ever before, with 41% in 2021 compared with 30% in 2014.

There has been a continuing increase in the proportion of families where the adults have professional and managerial occupations, with 46% in 2021 compared with 39% in 2014.

Family resources affect a child’s social mobility chances, and if resources are very unequal, then it may be difficult for those at the bottom to climb upwards. Yet these resources aren’t just economic: they include educational and cultural resources, such as having parents who can navigate the higher education system, or who have good parenting skills. Recognising the importance of these factors, our report includes drivers focusing on parental occupation and education. Parenting skills may also play an important role, but we currently do not have any measures to assess this. It is something we wish to investigate more in the future.

We use drivers 1.1 to 1.4 to show how these family resources have varied across the UK and over time.

The levels of inequality and average levels of parental education and occupation have been quite positive over recent years, but this is not reflected in levels of relative poverty. The percentage of children growing up in relative poverty has been increasing since 2012, reaching almost 30% in 2021. Given the association between relative poverty and poor social mobility chances for children, this means that major issues of inequality of opportunity may be likely to remain. Looking further back, the relative poverty rate was much lower in the 1960s and 1970s, rose sharply in the 1980s, and reached a peak in the mid-1990s.^{[footnote 9]}

We should note that there is no contradiction between stability in the level of relative poverty and making progress in parental education and occupation. The trends in parental education and occupation can be thought of as absolute trends, whereas the trend in relative poverty is, by definition, relative to the current average. Relative poverty covers families where ‘equivalised’ household income is less than 60% of the median.^{[footnote 10]} In contrast, absolute poverty is measured by comparing household income to a fixed level of income rather than to the current income of other households. Since economies tend to grow over time, incomes will increase, and absolute poverty will tend to decrease as more and more households surpass the fixed level.

We should also recognise that, within the broad group of those in relative poverty, there may be important differences in family circumstances which will make some particularly vulnerable to economic shocks such as rising inflation (general increases in prices). This warrants further investigation.^{[footnote 11]}

Driver 1.1: Distribution of earnings

To provide an overview of the distribution of earnings we compare how the earnings of those near the top (the 90th centile) and near the bottom (the 10th centile) compare over time. We refer to this as the 90:10 ratio, which is the income at the 90th centile (high earners) divided by the income at the 10th centile (low earners). For example, when the ratio is 2, this means that people at the 90th centile are earning twice as much.^{[footnote 12]} These are wages rather than total family income (which might include other income sources, such as benefits). However, the differences between relatively high and relatively low rates of pay can, on average, cause differences in the resources that families have. Higher (hourly) pay can also allow parents to spend more time on childcare, as they can work fewer hours. During their work life, people can move between better and worse-paying jobs. Therefore a person’s lifetime income could be made up of a mix of periods of high and low income. In the future, we will look at the importance of this income volatility over the life course.

Figure 4.4: There has been some decline in wage inequality (as measured by the 90:10 ratio) since 2010.

The gap in hourly earnings for full-time employees, calculated as the ratio between the 90th and 10th centiles in the UK, from 1997 to 2021. When the ratio equals 1, there is no gap in earnings.

Explore and download data on distribution of earnings on the State of the Nation data explorer.

Source: Office for National Statistics, Annual Survey of Hours and Earnings (ASHE).

Note: Values to calculate the 90:10 ratio are taken from ‘Earnings and hours worked, place of work by local authority: ASHE table 7, Gross hourly pay for full-time employees from 1997 to 2021. For 2022, ASHE Table 7 is yet to be updated, however, ASHE Table 6 provides a provisional estimate of the 90th and 10th centiles for full-time employees. We use these figures to calculate the ratio for 2022 and note this is a provisional figure only.

The ASHE is the most authoritative data available on inequality of earnings but the Office for National Statistics (ONS) also produces annual measures of household income inequality, based on the Household Finances Survey.

The 2021 ONS report suggested that household income inequality fell slightly in the financial year ending 2021, but remained in line with the average over the decade before the COVID-19 pandemic, having increased considerably in previous decades.^{[footnote 13]} ^{[footnote 14]} However, household income is a distinct concept from hourly earnings from employment, as it includes other sources of income such as state benefits and pensions.

The Institute for Fiscal Studies (IFS) also has looked at trends in household income inequality.^{[footnote 15]} When using the Gini coefficient, it found that there has been effectively no change in inequality over the 21st century.^{[footnote 16]} However, we should be aware that the Gini coefficient is a different statistic than the 90:10 ratio. It could, for example, be driven by changes at the extremes of the income distribution, which are not captured by the 90:10 ratio. The major advantage of the ASHE data over these other sources is that ASHE enables us to look at regional differences.

Driver 1.2: Childhood poverty

As shown in figure 4.5, the percentage of children living in relative poverty in the UK (after accounting for housing costs) has increased since 2012. It is still below the levels reached in the 1990s. But, in the past 5 years, the proportion in relative poverty has remained stable at 30%. Although we focus on the background conditions which allow social mobility, to make effective policy decisions we need to understand who is more at risk of long-term poverty.

Figure 4.5: The percentage of children living in relative poverty has risen slightly since 2012.

Percentage of children in relative poverty after housing costs in the UK and in England, Northern Ireland, Wales and Scotland, from financial years starting in 1994 to 2022 (see notes).

Explore and download data on childhood poverty on the State of the Nation data explorer.

Source: Department for Work and Pensions (DWP), Households Below Average Income statistics, Table 4.16.^{[footnote 17]}

Notes: Data is calculated using 3-year averages (including the current year and 2 preceding years). For example, the figure for 2021 represents the average of the financial years (FY) starting in 2019, 2020 and 2021. FY are reported by the year in which they start. For example, 2021 represents the financial year ending in 2022 (FY 2021 to 2022). 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.

Driver 1.3: Distribution of parental education

‘Cultural capital’ loosely means the social and cultural knowledge that can help someone be socially mobile. Here, as is common in sociological research, we use parental education as a proxy for that cultural capital, but education may also correlate with other family characteristics, such as composition and double incomes.^{[footnote 18]} ^{[footnote 19]} ^{[footnote 20]} There are also other factors that can contribute to cultural capital, such as the type of university attended. These and other factors may also be relevant to social mobility and we will look further into this in the future.

Figure 4.6 shows significant increases over time in the educational levels of adults in families with dependent children. Consistent with our reporting from last year, we find that the qualifications of young people’s parents have improved over time. Although the trends are similar to those shown last year, the levels are not strictly comparable because of the different methodologies and datasets used.^{[footnote 21]} This year we have used the Labour Force Survey (LFS) because of its much larger sample size than the UK Household Longitudinal Study (UKHLS) used previously. We can therefore undertake more granular geographical analyses. While more children are growing up in households educated to a degree level, a large number are in families where the highest level is GCSE or below. These children are of particular concern to the Social Mobility Commission and may be the focus of our future work.

Figure 4.6: There has been a continuing increase in the proportion of families where the adults have higher levels of qualifications.

Percentages of adults in families with dependent children having different levels of education in the UK, from 2014 to 2021.

Explore and download data on distribution of parental education on the State of the Nation data explorer.

Source: Office for National Statistics, Labour Force Survey from 2014 to 2021.

Note: The sample (N=160,238) was established by selecting those respondents with dependent children in their family (defined as those aged 0 to 15 years, and those aged 16 to 18 years who are in full-time education). Respondents who are aged less than 21 years are excluded and the median age of respondents is age 40 years. The great majority of the selected respondents are likely to be the parents or carers of the dependent children. However, the dataset could include some adults who are living at home with parents who have dependent children. The data used is weighted using the LFS probability weights. Due to rounding errors, in some instances the totals may not add up to 100%.

These figures suggest that more children are now in a position to benefit from the cultural capital gained by their parents during their parents’ post-school education. However, parental education only captures a part of cultural capital, and of course, other factors need to be considered. For example, the relative positioning of parents on the occupational ladder is another important consideration, and we examine the distribution of parental occupation in driver 1.4.

Driver 1.4: Distribution of parental occupation

The distribution of parental occupation is a new driver that shows how socio-economic backgrounds, based on occupation, have changed over time.

Figure 4.7 shows that there has been a significant increase in the proportion of higher-professional adults in families, from 14% in 2014 to 20% in 2021.^{[footnote 22]} ^{[footnote 23]} Overall in 2021, 46% of adults in families were in either a higher or lower professional occupation, compared with 39% in 2014. Conversely, the proportion of working-class adults in families has shrunk (higher working class: 19% in 2014 versus 13% in 2021; lower working class: 21% in 2014 versus 19% in 2021).

These trends are similar to the longer-term changes in the occupational structure in Great Britain between 1951 and 2011 and to the recent trends shown in Bukodi and Goldthorpe (2019)^{[footnote 24]} and our previous State of the Nation reports. ^{[footnote 25]}

Figure 4.7: There has been a continuing increase in the proportion of families in which the adults have professional and managerial occupations.

Percentages of adults in families with dependent children in different levels of occupation in the UK, from 2014 to 2021.

Explore and download data on distribution of parental occupation on the State of the Nation data explorer.

Source: Labour Force Survey (LFS) from 2014 to 2021.

Note: The sample (N=160238) was established by selecting those respondents who had dependent children in their family (those aged 0 to 15 years and those aged 16 to 18 years who are in full-time education). Respondents who are aged less than or equal to 20 years are excluded and the median age of the included respondents is 40 years. The great majority of the selected respondents are likely to be the parents or carers of the dependent children, but it could include some adults who are living at home with parents of dependent children.^{[footnote 26]} The data used is weighted using the LFS probability weights. Due to rounding errors, in some instances the totals may not add up to 100%.

Question 5

Driver 2: Educational opportunities and quality of schooling

Accepted Answer

Summary

Slightly more young people aged 16 to 18 years are in education and employment than 10 years ago, and fewer are NEET – 6% in 2021, compared with 10% in 2011.

The UK’s school system has performed at or above the OECD average in the Programme for International Student Assessment (PISA) since at least 2006.

More 19 year olds are enrolled in education than ever before and the UK has now surpassed the OECD average, with 63% enrolled in secondary or tertiary education.

Higher education dropout rates fell sharply in the most recent data.

The drivers in this section focus on the quality of education provided, and the opportunities for young people to access different forms of education after the age of 16. We focus mainly on data for England, as education is a devolved policy, and there is no harmonised data covering all 4 countries of the UK.

Driver 2.1: Further education and training opportunities

Since we are not currently able to measure opportunities directly, we illustrate this driver using the proxy measure of participation in education and training between age 16 and 18 years. The trends are shown in figure 4.8.

Figure 4.8: Slightly more 16 to 18 year olds are in education and employment than 10 years ago, and fewer are NEET.

Percentage of young people aged 16 to 18 years participating in education, training and employment in England, from 2011 to 2021.

Explore and download data on further education and training opportunities on the State of the Nation data explorer.

Source: Department for Education, participation in education, training and employment, 2022.

Note: Work-based learning (WBL); not in education, employment or training (NEET); NEET includes anybody who is not in any form of education, training or employment. This means that a person identified as NEET is either unemployed or economically inactive. Historically, there have been very small overlaps of students studying in further education and higher education and WBL at the same time. The total number of young people in training is calculated by omitting these overlaps. Since 2013, young people aged 16 to 17 years have been legally required to remain in (at least part-time) education and training in England, but not in Wales, Scotland or Northern Ireland. Participation estimates for 2020 and 2021 may be impacted by the COVID-19 pandemic and may not fully reflect engagement and attendance. Due to rounding errors, in some instances the totals may not add up to 100%.

Overall, the trends in the last 10 years seem to be rather modest but positive: participation in education and apprenticeships has slightly increased and the proportion of young people who are NEET has decreased since 2011. However, when comparing 2021 to 2020, we observe a small drop of 16 to 18 year olds in education and apprenticeships. The NEET rate for this age group has decreased and is still one of the lowest on record at the end of 2021.^{[footnote 27]}

Recent figures from the ONS show that the proportion of 16 to 24 year olds who are NEET increased sharply in the final quarter of 2022. Although our data does not yet cover this time period, this recent spike is something to monitor closely.^{[footnote 28]}

Driver 2.2: Availability of high-quality school education

With driver 2.2, we illustrate the quality of school education in the UK with the OECD’s PISA survey. This survey measures the performance of 15 year old school pupils in mathematics, science and reading. It is designed to evaluate education systems by measuring the performance of pupils at age 15 years, on a comparable basis, across the OECD and certain partner jurisdictions. Our findings are the same as reported last year, as there has been no update to PISA because of the pandemic.

PISA allows us to look at the UK as a whole (rather than just England), but also to see how our performance compares with similar countries’. Figure 4.9 shows that the UK has performed at or above the OECD average since the beginning of the programme in 2000 (although scores in 2000 and 2003 are thought to have low reliability and aren’t plotted in the figure). In 2018, students in the UK scored above the OECD averages in reading (504 score points), mathematics (502), and science (505).^{[footnote 29]} The UK’s reading and science scores have remained stable since 2006, with no significant change. In mathematics, there was a significant 9-point improvement between 2015 and 2018.^{[footnote 30]}

Figure 4.9: The UK has performed at or above the Organisation for Economic Cooperation and Development (OECD) average in the Programme for International Student Assessment (PISA) since at least 2006.

Average pupil attainment scores (out of 1,000) on PISA reading, maths, and science assessments, UK and OECD average, from 2006 to 2018.

Explore and download data on the availability of high-quality school education on the State of the Nation data explorer.

Source: OECD, PISA, 2006, 2009, 2012, 2015, and 2018 reading, mathematics and science assessments.

Note: Proxy measure of opportunities for high-quality school education. Average scores for young people aged 15 years on PISA’s overall reading, mathematics and science. The scale ranges from 0 to 1,000.

Driver 2.3: Access to higher education

Figure 4.10 shows the enrolment rate in secondary and tertiary education at age 19 years in the UK over time.

Overall, the enrolment rate has increased since 2011, reaching its peak in 2020. The most recent results from 2020 point to a 2% gap between the UK and international averages. The UK has not only reached but surpassed the OECD average. In future, we will also examine the role of other further study, such as technical qualifications.

Figure 4.10: More 19 year olds are enrolled in education than ever before and the UK has now surpassed the OECD average.

Percentage of 19 year olds enrolled in secondary or tertiary education, UK and international average, from 2010 to 2020.

Explore and download data on access to higher education on the State of the Nation data explorer.

Source: Organisation for Economic Cooperation and Development, Online Education Database: enrolment by age.

Note: Proxy measure of the participation rate relative to the number of young people aged 19 years in the population. Enrolment rates in secondary and tertiary education are expressed as net rates. These are calculated by dividing the number of students aged 19 years enrolled in these levels of education by the size of the population of 19 year olds. Generally, figures are based on headcounts and do not distinguish between full-time and part-time study. In some OECD countries, part-time education is only partially covered in the reported data.

A new measure released in January 2023 by the Department for Education called Cohort-based Higher Education Participation reinforces these findings – entrants to higher education (HE) continue to rise. In 2020 to 2021, 47% of people had entered HE by the age of 25, up from 45.2% for the previous year and the highest percentage on record.^{[footnote 31]}

Driver 2.4: Availability of high-quality higher education

With driver 2.4, we illustrate the retention and completion rates for those in HE. There has been no updated data release since our last report, and our findings therefore remain the same. Figure 4.11 shows that the proportion of UK students dropping out of university after the first year of their course hit a record low in the 2019 to 2020 academic year. Just 5.3% of full-time undergraduate students who started their course in the 2019 to 2020 academic year were no longer in HE at the start of their second year. This represents a fall of 1.4 percentage points on the previous year, and the lowest non-continuation rate observed since the statistics have been collected. However, we cannot be sure that this reflects an improvement in the quality of HE, as other factors such as the changing level of wages and job opportunities at the start of the COVID-19 pandemic could also be relevant to dropout rates.

Figure 4.11: Non-continuation (dropout) rates fell sharply in 2019 to 2020.

Non-continuation (dropout) rates of full-time entrants during their first year at a higher education provider, from 2014 to 2015 and 2019 to 2020.

Explore and download data on the availability of high-quality higher education on the State of the Nation data explorer.

Source: Higher Education Statistics Agency, non-continuation summary: UK performance indicators.

Note: Years represent the academic year of entry. Percentage of UK-domiciled full-time entrants who did not leave within 50 days of commencement and did not continue in higher education after their first year, academic years of entry 2014 to 2015 and 2019 to 2020.

Despite these increases in continuation, many questions remain. Understanding how the rise in participation rates relates to completion rates, and how HE relates to subsequent employment, forms important aspects of our future work.

A similar pattern is seen for the non-continuation rate for mature full-time, first degree entrants (aged 21 years and older). The number of students dropping out was 11.9% – down 1.6 percentage points from the previous year. Projected outcome statistics show that only 9.4% of full-time first degree entrants in the UK are projected to drop out of HE without a qualification. This is the lowest rate on record.^{[footnote 32]}

It is also important to note that HE may not be the most suitable education path for everyone. Although we monitor access to HE, we also want to think about access to other high-quality education and training routes which may enable people to make progress into the careers they desire. Establishing and further developing alternative pathways to HE is something the government has been focusing on in recent years by considering reform in both the higher and further education sectors.

Question 6

Driver 3: Work opportunities for young people

Accepted Answer

Summary

There are more vacancies available per jobseeker now than at any time in the last 20 years.

Youth unemployment has fallen back to pre-pandemic levels and was at 13% in the most recent data.

There is a long-term trend towards more professional employment opportunities for young people. 17% of people aged 22 to 29 years are in higher professional jobs now, compared with 11% in 2014.

Real hourly pay fell markedly after the 2008 financial crisis, recovered slowly to pre-crisis levels in 2021, but has fallen again.

To understand how prospects for social mobility change over time and across the UK, it is important to look at work opportunities. Drivers 3.1 to 3.4 look at these in detail. The metrics include job vacancy rates, youth unemployment, type of employment, and earnings.

The overall level of vacancies in the labour market has increased sharply over recent years. There has been a continued improvement in the proportion of professional employment for young people – up from 38% in 2014 to 44% in 2021. In contrast, the proportion of young people in working-class jobs has declined from 41% to 33%. In particular, the proportion entering skilled manual work has shrunk while the proportion entering low-skilled work remains high, and young people are still disproportionately likely to be unemployed. This suggests that there may be some polarisation in the opportunities for young people, with improving opportunities at the top end but no improvement, or even perhaps a decline, at the lower end.

Driver 3.1: Vacancy rate

Figure 4.12 illustrates the trend in vacancy rates, showing the number of vacancies per jobseeker over time. This ratio serves as a proxy for job opportunities. A higher ratio indicates that there are more vacancies per jobseeker, and so greater job opportunities.

Figure 4.12: There are more vacancies available per jobseeker now than at any time in the last 20 years.

Number of vacancies per unemployed person in the UK (seasonally adjusted), quarter 4 from 2001 to 2022.

Explore and download data on the job vacancy rate on the State of the Nation data explorer.

Source: Office for National Statistics (ONS), Vacancy Survey and Labour Force Survey (LFS) (respondents aged 16 to 64 years).^{[footnote 33]}

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 2021. A higher value indicates greater opportunities for job seekers.

Figure 4.12 shows that numbers of vacancies per unemployed person have varied considerably over time. The trend has fluctuated with shocks including the 2008 financial crisis and the pandemic. However, the labour market now appears to be very ‘tight’ with a high level of demand for labour relative to the numbers looking for work.

A more detailed analysis of the recent trends is provided by the Institute for Employment Studies (2022).^{[footnote 34]} It suggests the trend has been attributed in part to the decline in economic activity among older workers during the pandemic. They also highlight a decline in economic activity among young people, with a sharp increase in full-time participation in HE during the same time.

It is not currently possible to distinguish vacancies in entry-level jobs from other types of jobs, and so figure 4.12 provides estimates of the overall state of the labour market, not the opportunities specifically for young people entering the labour market. It is therefore necessary to supplement this information with data on youth unemployment and on the kinds of jobs that young people are taking (drivers 3.2 and 3.3).

Driver 3.2: Youth unemployment

To illustrate young people’s work opportunities, we show in figure 4.13 youth unemployment rates for the years 2014 to 2021. Unemployment is measured here as the proportion of the economically active respondents aged 16 to 24 years who are currently out of work but looking for a job. It does not include people who are in full-time education, looking after the home, or permanently sick and disabled.^{[footnote 35]}

Figure 4.13: In 2021, youth unemployment in the UK fell back to pre-pandemic levels.

Percentage of young people aged 16 to 24 years in the UK, from 2014 to 2021, who were unemployed.

Explore and download data on youth unemployment on the State of the Nation data explorer.

Source: Office for National Statistics, Labour Force Survey (LFS), from 2014 to 2021, 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 (ILO) – a UN agency. Unemployed people are those without a job, who have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks; or are out of work, have found a job and are waiting to start it in the next 2 weeks. Those who are economically inactive are excluded from the calculations (for example in full-time education, looking after the home, or permanently sick and disabled). The data used is weighted using the LFS probability weights.

Figure 4.13 indicates that youth unemployment has varied considerably over time. The trend has fluctuated with higher levels of youth unemployment after the 2008 financial crisis and the COVID-19 pandemic. However, youth unemployment has now returned to pre-pandemic levels.

The data has not been updated since publishing our report last year. So, the results presented here are the same as those shown in our State of the Nation 2022 report, figure 4.12.^{[footnote 36]} It is worth noting, as we did last year, that levels of youth unemployment are affected by levels of educational participation (see drivers 2.1 and 2.3).

Driver 3.3: Type of employment opportunities for young people

Next, we look at the percentage of young people taking up professional and managerial, intermediate and manual work. This approach allows us to look at the level of work available, not just the rate of employment. We expand on the findings from last year, looking at occupational levels using a 5-class instead of a 3-class grouping.

Figure 4.14 shows that there has been a gradual increase in the proportion of young people in professional and managerial work, up from 38% in 2014 to 44% in 2021. While the proportion in low-skilled work (the lower-working class) has remained roughly constant around 15%, there has been a long-term decline in skilled manual work (the higher-working class), down from 25% in 2014 to 17% in 2021. This decline may have negative implications for the chances of young people from working-class backgrounds of achieving upward mobility.

Figure 4.14: Longer-term trends are towards an increase in professional employment opportunities.

Type of occupation of young people aged 22 to 29 years in the UK, from 2014 to 2021.

Explore and download data on the type of employment opportunities for young people on the State of the Nation data explorer.

Source: Office for National Statistics (ONS), Labour Force Survey (LFS), from 2014 to 2021, respondents aged 22 to 29 years in employment.

Note: The 5 social classes distinguished here represent a shortened version of the ONS NS-SEC classification, which has 8 classes. We have grouped the ONS NS-SEC classes as shown in chapter 1. The data used is weighted using the LFS probability weights. A formal test shows that compared with 2014, access to the higher-professional class has become significantly different since 2018. Due to rounding errors, in some instances the totals may not add up to 100%.

These results are broadly the same as those shown in last year’s State of the Nation report, figure 4.13, although the classification used in the 2022 report did not enable us to see the marked decline in higher-working class jobs.^{[footnote 37]} At the time, we noted that major gender differences persist in the labour market, and we will be examining this in future work.

Driver 3.4: Labour market earnings of young people

The earnings that young people achieve through employment are another important driver of social mobility, reflecting the changing demand for labour. Figure 4.15 shows that real hourly earnings of young people dropped sharply after the 2008 to 2009 financial crisis but have recovered slowly and are now slightly above their pre-crisis levels. However, we observe a slight dip in real hourly earnings for 2022. This may be due to the high levels of inflation experienced during the cost of living crisis, as nominal pay may have risen at below the inflation rate.

Figure 4.15: Growth in real hourly pay for young people has been poor over the last 15 years, partly due to drops after 2008 (the financial crisis) and in 2022 (the cost-of-living crisis).

Median real hourly pay for people aged 22 to 29 years in the UK, from 1997 to 2021.

Explore and download data on labour market earnings of young people on the State of the Nation data explorer.

Source: Office for National Statistics (ONS), Annual Survey of Hours and Earnings (ASHE).

Notes: Values taken from earnings and hours worked by employees, place of work by local authority: ASHE table 6.5a. Hourly pay: gross from 1997 to 2022. Earnings are inflation-adjusted using the Consumer Price Index (base year = 2021) (see the technical annex for a complete explanation and sources). ASHE covers employee jobs in the UK. It does not include self-employed people or employees not paid during the reference period.

More detailed analysis by the ONS has compared the patterns for young people with those for other age groups.^{[footnote 38]} Somewhat surprisingly, the hourly earnings of those aged 30 to 39 years showed a smaller recovery than those of younger or older workers after the financial crisis.

Question 7

Driver 4: Social capital and connections

Accepted Answer

Summary

Civic engagement – participating in democratic processes, such as signing a petition or attending a public rally – has remained broadly stable since 2014, at around 40%.

Levels of social trust are low, but have not declined since 2002.

Another important factor to consider in understanding what helps or hinders social mobility is social capital. 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.^{[footnote 39]} ^{[footnote 40]} The following drivers broadly relate to social capital.

We measure this component with data on civic participation, using the government’s Community Life Survey. We build on the findings from last year with another measure of civic engagement, namely participation in democratic processes.

We then use another measure – social trust. This means how much people trust others, and how helpful and fair they think they are. The literature on entrepreneurship within ethnic minority groups suggests that social capital aids entrepreneurship.^{[footnote 41]} Social capital can lead to ‘generalised trust’ within a community. This trust, so the theory goes, reduces transaction costs and makes it easier for people to do business with each other.

We must note that these are experimental statistics. While social capital has been suggested by American researchers as an important driver of absolute upward mobility, there is a lack of evidence on its relationship with social mobility in the UK.^{[footnote 42]} A range of different measures of social capital have been suggested, and we show here 2 such measures – civic participation (England only) and social trust (UK).

However, more recent American work has suggested that the most important element of social capital for helping individuals achieve upward mobility is ‘linking social capital’, which involves connections with people from higher social classes.^{[footnote 43]} Unfortunately, to the best of our knowledge, direct measures of linking social capital are not available in the UK.

Driver 4.1: Civic engagement

Figure 4.16 shows that over 40% of people in England were civically engaged in the last 12 months. Civic engagement here is defined as participating in democratic processes, both in person and online, including signing a petition or attending a public rally within the last 12 months. It does not include voting.

Our findings show that civic participation has remained broadly stable since 2014, with slight increases in 2017 – the year following the Brexit referendum – and during 2020 to 2021, the years most impacted by the COVID-19 pandemic. However, we cannot tell from the data if these events were associated with these increases, and a cautious interpretation is required. Further work is needed to understand the volatility of the findings and any potential associations.

Figure 4.16: Between 2014 and 2021, civic participation remained broadly stable, with slight increases following the Brexit referendum and outbreak of the COVID-19 pandemic.

Percentage of adults who have engaged in democratic processes within the last 12 months in England, 8 years to March 2021.

Explore and download data on civic engagement on the State of the Nation data explorer.

Source: Community Life Survey.

Notes: 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 8 financial years to March 2021. 95% confidence intervals available for 2018 to 2019 and 2019 to 2020 only.

Looking at civic participation more broadly, State of the Nation 2022 showed a marked decreasing trend – a drop in participation in civic organisations over the period 1991 to 2017 (see figures 4.15 and 4.16 on pages 130 and 132).^{[footnote 44]} There was also no clear trend in volunteering over the 2010 to 2018 period (UKHLS data). Other research on civic participation in Great Britain has shown a gradual long-term decline over the period from 1959 to 2014.^{[footnote 45]} ^{[footnote 46]} This is consistent with US political scientist Robert Putnam’s seminal work on social capital, which also showed long-term declines in social capital in the US.^{[footnote 47]}

Driver 4.2: Level of trust, fairness and helpfulness

Figure 4.17 shows trends for the UK in 3 closely-related measures of trust and perceptions of interpersonal relations. While levels of trust are generally lower than the perceptions of fairness and helpfulness, there has been no significant decline since 2002.

Figure 4.17: Levels of social trust are relatively low but have not declined between 2002 and 2018.

Mean levels of trust, perceived fairness and helpfulness, 0 to 10 point scales, in the UK, from 2002 to 2018.

Explore and download data on levels of trust, fairness and helpfulness on the State of the Nation data explorer.

Source: European Social Survey, data for the UK, rounds 1 to round 9 (from 2002 to 2018).

Notes: Fairness was measured on a scale running from 0 (indicating “most people try to take advantage of me”) to 10 (indicating “most people try to be fair”). Helpfulness was measured on a scale running from 0 (indicating “people mostly look out for themselves”) to 10 (indicating “people mostly try to be helpful”). Trust was measured on a scale running from 0 (indicating “you can’t be too careful”) to 10 (indicating “most people can be trusted”).

Question 8

Driver 5: Environment favouring innovation and growth

Accepted Answer

Summary

Median broadband speed has tripled since 2014.

Business R and D spending fell between 2007 and 2011 but has been increasing since then.

There has been a slight increase in the number of research students in recent years.

Our measurement framework focuses on 4 main drivers of social mobility: conditions of childhood, opportunities and quality of education, young people’s opportunities in the labour market, and social capital and connections. There is a substantial body of theory and empirical research showing that these drivers are likely to have causal impacts on rates of absolute and relative mobility. New this year is a group of experimental drivers focused on an environment which favours innovation and growth.

Economic growth, particularly if it is concentrated in areas where growth in recent decades has been poor, is likely to improve absolute upward mobility. For example, it has been argued that renewing UK manufacturing could upgrade the class structure, creating more jobs with stability and good prospects, and reversing the relative decline of the UK’s former industrial areas.^{[footnote 48]}

Innovation and its commercial development has long been part of national industrial strategy. A favourable educational, technical and economic infrastructure can be expected to promote local economic growth, stimulating investment and expanding professional and business opportunities in the area. This provides opportunities for upward mobility. Conversely, areas with lower levels of what economists term ‘human capital’, a less favourable infrastructure and less investment are more likely to miss out on economic growth.^{[footnote 49]} The impact on social mobility will tend to be indirect, operating via local growth rates, but is nonetheless potentially important. It is of considerable interest to measure the innovation environment and to test whether a favourable environment promotes growth and upward mobility in the future.

The following 3 indicators – broadband speed, business R and D expenditure and the number of full-time research students – tap different potential components of an environment that is helpful for innovation and growth:

broadband speed is a potential indicator of the technical infrastructure necessary for firms operating in the high-tech area – lack of this technical infrastructure is likely to be a disincentive to investment and to inhibit productivity
the proportion of research students is a potential indicator of the human capital available at the forefront of knowledge and conducive to innovation – we do not limit this indicator to science, technology, engineering and mathematics subjects, as humanities may also be relevant to the creative and media sectors
business R and D expenditure is a potential indicator of investment in the application and implementation of innovations – this is likely to be important for economic growth

We can think of these as 3 indicators of different sorts of input which might then generate greater business activity, especially of a high-tech kind. These are experimental statistics and we cannot yet be sure that the chosen indicators are causally related to an area’s potential for innovation, growth and increased upward mobility chances. However, they could start to reveal the role of innovation in promoting social mobility in the UK.

For this new driver, we introduce 3 experimental indicators. We consider these drivers as experimental because further research is required to determine which factors are causally linked to social mobility. We will also need to consider the availability of the data for these factors to assess which indicators we can monitor on a regular basis. For now we monitor broadband speed, business expenditure on R and D and the number of research students. Taken together we hope to use these indicators as a proxy of the environment which enables and promotes innovation and growth. This helps set in place the foundations on which chances for upwards social mobility can improve in the future.

All 3 indicators show some degree of progress over the periods covered, with a very marked increase in median broadband speed. Of considerable interest is the geographical variation in these 3 indicators (see below), although we must emphasise that causal effects on mobility have yet to be securely established.

Driver 5.1: Broadband speed

In figure 4.18, we can see that median broadband speed in the UK has tripled between 2014 and 2019.

Figure 4.18: Broadband speed has tripled since 2014.

Ratio (relative to first available year) of the median broadband speed in the UK, from 2014 to 2019.

Explore and download data on broadband speed on the State of the Nation data explorer.

Source: Department for Business, Energy and Industrial Strategy and Nesta Research & Development spatial data tool, 2021.^{[footnote 50]}

Note: Nesta provides scores at the ITL2 regional level, but not a national average figure. So, we show here the figure for the median UK area to track changes over time.^{[footnote 51]}

Driver 5.2: Business expenditure on research and development

As we can see in figure 4.19, the UK median business enterprise spending on R and D has increased by 32% compared with 2007. Initially, spending went down by 22% between 2007 and 2011, but has increased since then. It reached its peak in 2017 and it has remained stable since then.

Figure 4.19: Business research and development (R and D) spending has been increasing since 2011, reaching its peak in 2017.

Ratio (relative to first available year) of the median business (R and D) expenditure in the UK, from 2007 to 2018.

Explore and download data on business spending on research and development on the State of the Nation data explorer.

Source: Department for Business, Energy and Industrial Strategy and Nesta Research & Development spatial data tool, 2021.^{[footnote 52]}

Note: Nesta provides scores at the ITL2 regional level, but not a national average figure. So, we show here the figure for the median UK area in order to track changes over time.

Driver 5.3: University research students

The number of university research students is the indicator that has seen the lowest progression among these new drivers on the environment favourable for innovation and growth. Figure 4.20 suggests that the median number of research students in the UK has increased only by 4% from 2015 to 2018.

Figure 4.20: Overall in the UK there has been a slight increase in the number of research students.

Ratio (relative to first available year) of the median number of full-time equivalent research students enrolled in universities in the UK, from 2015 to 2018.

Explore and download data on university research students on the State of the Nation data explorer.

Source: Department for Business, Energy and Industrial Strategy and Nesta Research & Development spatial data tool, 2021.^{[footnote 53]}

Note: Nesta provides scores at ITL2 region level, but not a national average figure. We therefore show here the figure for the median UK area to track changes over time.

Question 9

Conclusion

Accepted Answer

Despite the significant setbacks of the financial crisis and the COVID-19 pandemic, there are still encouraging signs for the future of social mobility in the UK.

More parents are educated to university level and working in professional occupations than before. More young people are in education, and fewer are NEET. Meanwhile, people in their 20s face a more favourable job market, with a much greater number of them working in professional jobs than just 10 years ago.

Against this, relative child poverty has slightly risen since 2012, while young people’s pay only recovered in 2021 to the levels seen before the financial crisis, before falling again in 2022. Levels of social trust in the UK are low and have been low for at least 20 years.

As with mobility outcomes and intermediate outcomes, understanding the way drivers are distributed across the country is important. There is no simple pattern of well-off and badly-off areas. In particular, London has high levels of both sociocultural advantage, and childhood poverty and disadvantage. So any area-based approach to tackling social mobility must take into account variation within areas, as well as variation among areas.

There is also much more work to be done to understand the role of industrial strategy and innovation in promoting social mobility. Disruptions to old patterns of industry have often produced opportunities for mobility in the past. There is a well-established link between innovation and economic growth, and between growth and absolute upward mobility.

Question 10

Case studies

Accepted Answer

Juraj Tancos, age 39 years, from West Yorkshire

I came to the UK from Slovakia in 2006. I’m from the Roma community and back in Slovakia I was facing terrible discrimination constantly.

When I was growing up people would say “you’re Roma, you’re only ever going to get manual cash-in-hand work, there’s no point going to school.” Because of that I didn’t even complete my primary school education because to be honest I didn’t see the point.

When I came to the UK I was so happy because I saw people of all different ethnic groups working all kinds of jobs and achieving. I said to my wife: “our children are going to have better lives here.

It was pretty hard getting started, I didn’t know any English so I used to go and mime with my hands at the job centre everyday asking for work. I actually ended up learning Polish before I learnt English, because when I finally got a job in a factory, all my colleagues were from Poland!

While I was working, my wife started going to Play and Learn at St Edmund’s Nursery School and Children’s Centre with our baby son. I was very very worried about this because back in Slovakia the authorities would discriminate against Roma people. My wife persuaded me to come along, and I was amazed by how kind and welcoming the staff were. I went to their parenting classes myself and eventually decided to give back and become a volunteer there.

St Edmund’s took me at face value, despite me having no education when I met them and they helped me to get my Child Care level 2 qualification, Parenting level 4 and Working with Families level 5. This meant I could get a paid full-time job in family centres, and after I’d worked for a bit, I completed a foundation degree and then a BA [bachelor of arts] in Integrated Working with Families and Children.

I contributed to research on why Roma parents don’t always put their kids in early years settings and school; I found out it’s all about fear of discrimination, social services and whether their kids will be treated differently to others.

It was all very hard but it really paid off for more than just my family but for the whole community of Roma people in Bradford. Roma people now trust putting their kids in our early education classes and trust our parenting courses – the fact they’re happy to leave their children with us in our care and go out to work is huge.

I’m so proud of how far I’ve come and proud of what it’ll mean for my kids. My life was: get married at 16 and work cash-in-hand. My kid’s life is: get an education, get a good job and then think about the family aspects.

If I met someone like me in 2006 who doesn’t trust school and authority I would say to them: “I’m from a Roma family, we’ve faced all kinds of discrimination and had everything taken from us but one thing no one can steal is education and it’s never too late to learn.”

Imran Sabir, Bradford

I work at New College Bradford. I’m the progression lead which means I spend most of my time focusing on our students’ futures. I came to this role from pastoral student care where I saw a need to get my students out there, show them the opportunities that are available and inspire them to make the most of things.

There aren’t loads of opportunities in Bradford, and most of our students don’t come from families that have been to uni or have really good jobs. This means I go out and find organisations to come give talks in college as well as arranging for our students to go out and get experiences, like work experience, that will inspire them and help build a good CV.

I’ve had students go on visits to unis across the country – including Durham and Newcastle and I invite local employers in to meet our students – like local law firms and Morrisons head office who offer brilliant work placements and degree apprenticeships to our students.

Some of our students don’t see the value of thinking about their future early and making a plan, so I give them frequent tutorials about all the things they can do with their life and assure them that they can succeed.

I’m from Bradford myself and I understand the challenges round here. That means I inspire students by telling them about the jobs they can go on to with different qualifications and I make a point of telling them the kinds of earnings they can make. I make it real for them by painting them a picture of how much easier life can be when you have a good job that you love and making it real like that inspires them to want to work hard!

It’s important to be honest with students about what different qualifications could bring them in their future so they can make an informed decision – having these honest conversations with the right info is so important here in Bradford because people don’t have a lot of money round here – and going to uni is therefore a massive choice and investment.

I’m really proud of the progress my college has made in transforming the futures of our local students. In 2019, the average grade in Bradford at post-16 was a D; it’s now a B and 83% of our students have gone to uni last year despite 45% coming to us from secondary schools that are rated inadequate or requiring improvement.

What we say here to our students is, ‘You cannot give up. This is about setting the tracks for the rest of your life – inside and outside of college.’ If we’re not striving to give our students the best knowledge and opportunities that they can use to make decisions about their future – then why do we bother?

Damien Anderson, age 37 years, from Coatbridge, Scotland

At 14, I got really ill from a pneumonia infection and then I developed chronic fatigue syndrome. It led to me being housebound, so school kind of ended at 14. My education was self-directed. I didn’t get any grades from high school.

When my dad’s business selling blinds went into bankruptcy, we went from fairly well off to struggling to afford to eat. My parents eventually separated and we ended up staying with my mum who was unemployed because of physical and mental health issues. We were having to plan how to spend every single penny and sometimes we had to skip meals. One year, it was coming up to Christmas and my mum realised she couldn’t afford to get me or my sister presents. I was feeling a bit better by then and realised I had to help.

I got a job at a call centre doing tech support which made me realise I wanted to try and do more, so I went to Coatbridge College and said I wanted to better myself. They were really helpful in guiding me and I ended up doing a National Qualification in digital media.

Eventually working and studying got too much with my ME so I decided to go fully in on my education. I did a Higher National Diploma in software development at City of Glasgow College then a BSc [bachelor of science] in software engineering at the University of Strathclyde. I stayed with my mum which helped tremendously because I didn’t have to pay for rent.

I instantly fell in love with university. In my third year I was fortunate enough to be accepted on an industrial placement at CERN [The European Organization for Nuclear Research], doing software engineering for the Large Hadron Collider. It was an amazing eye-opening experience and made me realise what else was out there.

In my final year I built an AI that could “play any video game on earth”, with some caveats! The project was very successful, I came third in the Young Software Engineer of the Year awards and my supervisor suggested I do a PhD [doctor of philosophy]. He recommended I apply for the Carnegie Trust Scholarship as we spent a lot of time working together and he knew my situation. You can’t apply without a first-class degree, so I realised I needed to up my game.

Doing postgraduate study was very hard, but also a fantastic time because while I was doing it I became utterly sure that this was the path for me. I never got burned out because I was so excited and passionate about what I was doing.

I’m now a research associate at the University of Strathclyde doing work on autonomous systems. The post-graduate qualification enabled me to do the job that I love but the work itself helped me find what I love. My mum is really proud and happy that what I went through when I was younger hasn’t held me back.

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Highlights

Introduction

Where people live versus where they grew up

Drivers by region: conditions for social mobility across the UK

Summary

Regional patterns in drivers

Sociocultural advantage

Childhood poverty and disadvantage

Research and development environment

Driver 1: Conditions of childhood

Summary

Driver 1.1: Distribution of earnings

Driver 1.2: Childhood poverty

Driver 1.3: Distribution of parental education

Driver 1.4: Distribution of parental occupation

Driver 2: Educational opportunities and quality of schooling

Summary

Driver 2.1: Further education and training opportunities

Driver 2.2: Availability of high-quality school education

Driver 2.3: Access to higher education

Driver 2.4: Availability of high-quality higher education

Driver 3: Work opportunities for young people

Summary

Driver 3.1: Vacancy rate

Driver 3.2: Youth unemployment

Driver 3.3: Type of employment opportunities for young people

Driver 3.4: Labour market earnings of young people

Driver 4: Social capital and connections

Summary

Driver 4.1: Civic engagement

Driver 4.2: Level of trust, fairness and helpfulness

Driver 5: Environment favouring innovation and growth

Summary

Driver 5.1: Broadband speed

Driver 5.2: Business expenditure on research and development

Driver 5.3: University research students

Conclusion

Case studies

Juraj Tancos, age 39 years, from West Yorkshire

Imran Sabir, Bradford

Damien Anderson, age 37 years, from Coatbridge, Scotland

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