About our analysis

Information about our measurement framework for social mobility – the Social Mobility Index – and an introduction to the main statistical concepts used in State of the Nation. For more information, see the full technical annex on GOV.UK.

The Social Mobility Commission (SMC) monitors social mobility across the UK. We aim to understand how many people are socially mobile, in what parts of the country, and whether a person’s background can limit their opportunity.

Monitoring social mobility is complex and difficult. Data can be hard to get or may not exist at all, such as income records linked across generations. Where data does exist, it can be very hard to interpret. We took the best available advice from academics, policymakers, and charities working in social mobility.

Our index presents a framework for monitoring social mobility in the UK. It consists of 3 types of measures: mobility outcomes, intermediate outcomes and drivers. Looking at these measures can inform us how social mobility has changed in the past and how it might develop in the future.

Mobility outcomes

Mobility outcomes are about making progress from your parents’ position to your own eventual position as an adult. For example, we might compare people’s incomes at around the age of 50 years. We look at:

occupational class
income
wealth
education
housing

Intermediate outcomes

Intermediate outcomes are similar, but we look at people’s progress from their parents’ position to their own position at an earlier point in life, such as employment in their 20s or educational attainment at age 16 years. This is important, because a person’s outcomes in their 20s can be a very good indicator of how their later life will turn out. It also means that we don’t have to wait 30 or 40 years for one’s full mobility outcomes before we have an indication of how their social mobility prospects fare.

Mobility and intermediate outcomes both break outcome measures down by people’s socio-economic background (SEB). This can show how different starting points might affect later progress.

Drivers are the background conditions that make social mobility easier or more challenging. We have included things as drivers if we think they may affect overall social mobility rates. For example, the availability of good schools is a driver, because it helps people who would not otherwise have had an opportunity to be upwardly mobile. Drivers tell us about these nationwide and regional background conditions.

However, drivers do not tell us what the UK’s rates of mobility currently are, and they are not broken down by SEB. The question of what is or is not a driver is also distinct from the question of what might help someone achieve upward mobility (for example, getting a good degree).

Composite indices

State of the Nation 2024 includes 4 ‘composite indices’, covering some of our drivers and intermediate outcomes. We call them composite indices because they summarise multiple drivers, or intermediate outcomes, in one score. They give us a summary of how different geographical areas of the UK compare on the main dimensions of mobility that we have identified from the data.

This is useful because the estimates for individual areas often involve sampling errors due to being based on sample surveys, and therefore need to be treated with caution. The sample surveys, such as the Labour Force Survey (LFS), contain relatively small samples within each area. Sample sizes become even smaller when we focus on results for specific age or ethnic groups, as with the intermediate outcomes.

The imprecision of the survey-based statistics means that we cannot confidently draw conclusions about the differences in outcomes between geographical areas. Indeed, very few areas prove to be significantly different from the national average when single indicators are used, such as unemployment or earnings.

The composite indices give a summary of how different geographical areas of the UK compare on the main dimensions of mobility identified from the data. This is useful because there is always a risk that differences between areas in respect of a single measure could be due to random sampling errors. But when multiple measures all give a similar picture, we can be more confident that there are real differences among the areas.

How the data was produced

There are many data sources used to produce our index. Some of these, such as the LFS, involve analysing raw individual level data before our indicators can be created.

Other sources, such as the educational attainment statistics from the Department of Education, have already been analysed, with the statistics readily available in the public domain.

See our full technical annex on GOV.UK for more details of our main data sources and the methodology of each of our indicators.

Socio-economic background

We define people’s socio-economic background according to the occupation of their highest-earning parent. Our occupational classes are based on the National Statistics Socio-Economic Classification (NS-SEC) which is the official socio-economic classification of the UK, as set by the Office for National Statistics (ONS).

To simplify our analysis, we have collapsed the 8 classes provided by the ONS into the following 5 categories:

higher professional (NS-SEC group 1) – examples include CEOs, doctors and engineers
lower professional (NS-SEC group 2) – examples include teachers, nurses and journalists
intermediate (NS-SEC groups 3 and 4) – examples include shopkeepers, taxi drivers and roofers
skilled working class (NS-SEC groups 5 and 6) – examples include mechanics, electricians and housekeepers
routine working class (NS-SEC groups 7 and 8) – examples include cleaners, porters and waiters

Intersectional analysis

For a more detailed picture of how certain groups of the population are experiencing social mobility, we split our mobility outcomes and intermediate outcomes by some protected characteristics (sex, ethnicity and disability status) and geography where possible.

Geographic breakdowns

We have improved our geographical breakdown of the UK for our 4 composite indices. We now have 203 ‘upper tier local authority areas’, compared with 41 regions in 2023. This increases both granularity and statistical power for investigating why areas differ in their mobility prospects. This type of breakdown also aligns more closely with policy responsibilities, since local authorities have responsibility for local services, including schools.

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

We continue to have 5 categories of performance. However, instead of the categories being of equal size (see ‘quintiles’, below), we have recognised that most local authorities are very near the average by making the middle category larger.

Our main data source, the LFS, does not allow us to distinguish between local authorities in Northern Ireland. Because of this, we have to treat Northern Ireland as a single unit. There are also some very small local authorities in the LFS, with small sample sizes. In these cases, we cannot be confident about their position in the distribution.

Quintiles

We use quintiles in the maps for some of our individual indicators only. A quintile represents a fifth (20%) of the population. This is calculated by ordering the regions from lowest to highest values (usually percentages) and then dividing them into 5 groups of equal size – such as from 1 (lowest) to 5 (highest). Each group is called a quintile.

The data shown in our maps is purely descriptive and we are not yet in a position to claim any causal effects of regions on outcomes. Because these statistics are based on sample surveys, they are affected by sampling error.

Confidence intervals

As the values we show are estimates in most cases, there is some uncertainty over how likely they are to reflect the true value in the population. We use confidence intervals to represent this degree of uncertainty.

According to the ONS: "A confidence interval gives an indication of the degree of uncertainty of an estimate and helps to decide how precise a sample estimate is. It specifies a range of values likely to contain the unknown population value. These values are defined by lower and upper limits. The width of the interval depends on the precision of the estimate and the confidence level used. A greater standard error will result in a wider interval; the wider the interval, the less precise the estimate is."

We use 95% confidence intervals in our analysis. According to the ONS: "This means that if we took 20 random samples and calculated a 95% confidence interval for each sample using the data in that sample, we would expect that, on average, 19 out of the 20 (95%) resulting confidence intervals would contain the true population value and 1 in 20 (5%) would not. If we increased the confidence level to 99%, wider intervals would be obtained."

Read more about confidence intervals on the ONS website: Uncertainty and how we measure it for our surveys.

Rounding rules

We follow a general rule of rounding upwards to the nearest whole number. In some cases, we provide 1 or 2 decimal places when the numbers are small, when the decimal place might make a difference to the interpretation, or when we are presenting financial data, such as hourly earnings.

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