Guidance

Simplifying how employers measure socio-economic background: An accompanying report to new guidance

Updated 21 May 2021

Report originally released: November 2020

About the Commission

The Social Mobility Commission is an independent advisory non-departmental public body established under the Life Chances Act 2010 as modified by the Welfare Reform and Work Act 2016. It has a duty to assess progress in improving social mobility in the UK and to promote social mobility in England.

The Commission board comprises:

• Sandra Wallace, Interim Co-Chair, Joint Managing Director Europe at DLA Piper
• Steven Cooper, Interim Co-Chair, Chief Executive Officer, C. Hoare & Co
• Alastair da Costa, Chair of Capital City College Group
• Farrah Storr, Editor-in-chief, Elle
• Harvey Matthewson, Aviation Activity Officer at Aerobility and Volunteer
• Jessica Oghenegweke, Presenter, BBC Earth Kids
• Jody Walker, Senior Vice President at TJX Europe (TK Maxx and Home Sense in the UK)
• Liz Williams, Chief Executive Officer of Futuredotnow
• Pippa Dunn, Founder of Broody, helping entrepreneurs and start-ups
• Saeed Atcha, Chief Executive Officer of Youth Leads UK
• Sam Friedman, Associate Professor in Sociology at London School of Economics
• Sammy Wright, Vice Principal of Southmoor Academy, Sunderland

About the employers’ programme

The employers’ programme aims to improve socio-economic diversity and inclusion in the UK workforce and, in doing so, improve social mobility.

The programme provides advice to employers across England on improving socio-economic diversity in these six key pillars: data measurement, culture and leadership, outreach, hiring, progression and advocacy.

We conduct research and analysis and translates this into actionable toolkits and masterclasses. We work closely with other social mobility charities, academics, trade groups, membership bodies, think tanks and others to craft high quality advice to employers. Visit our website for more information.

Executive summary

This report accompanies new guidance released by the Commission on 24 November 2020 on measuring the socio-economic background (SEB) of employers’ workforces. It was amended in May 2021 with updated national benchmarks. The new guidance involves a revision of the four questions we recommend employers ask on SEB as outlined below:

1. Former question: parental occupation at age 14 with 4 sub-questions a to d

   Revised question: parental occupation at age 14 without sub-questions b to d and changes to response options

2. Former question: type of school attended at age 11 to 16

   Revised question: one response option added

3. Former question: free school meal eligibility

   Revised question: no changes

4. Former question: highest parental qualification

   Revised question: removed and replaced with an optional question aimed at graduate hires

This report explains what changes (if any) have been made to each of these questions and why, and what the limitations are. First, however, the report provides a brief introduction as to what drove the review of these questions, who was involved and what we sought to achieve.

Please note that this report is not intended to be a step-by-step guide on what questions to ask on SEB and how to analyse, interpret and benchmarks results. This guide can be found on our microsite.

Introduction

This review aimed to help employers understand the composition of their workforce and use that knowledge to underpin interventions to boost socio-economic diversity and inclusion initiatives. This fits with the Commission’s wider mission to improve social mobility in the UK.

This introductory section will outline:

• the questions we previously recommended employers ask on SEB

• the reasons why we reviewed the questions

• what we did and who we engaged as part of the review

• the purpose and structure of the report

Previous questions recommended to employers

Prior to November 2020, we recommended employers ask applicants and employees four questions on SEB, as per advice from the Cabinet Office. These included questions on:

• parental occupation at age 14
• type of school attended at age 11 to 16
• free school meal eligibility
• highest parental qualification

Each question elicited different data and as such, provided different ways of understanding SEB. Take for example:

• parental occupation, which provides a distribution of different SEB groups
• type of school attended, which shows extreme economic and cultural advantage
• free school meals eligibility, which shows extreme economic disadvantage
• highest parental qualification, which shows educational advantage

However, as measures of SEB, each question had its respective strengths and weaknesses with regards to clarity and accessibility, accuracy. See summary below:

Question: parental occupation at age 14

Strengths: parental occupation is the most accurate measure available to assess socio-economic background. It is widely used and highly endorsed by academics due to its ability to produce a distribution of socio-economic background. Parental occupation also typically gets the highest response rates and is accessible to those from all nationalities.

Weaknesses: answering four separate questions creates respondent burden and makes it difficult for employers to analyse this measure.

Question: type of school attended

Strengths: the type of the main secondary school that an individual mainly attended between the ages of 11 to 16 is a commonly used measure of advantage, given the high proportion of independent school educated individuals at top universities and across elite professions. Moreover, it is easy to comprehend advantage and disadvantage from the results.

Weaknesses: definitional issues can present problems – some respondents may not feel the categories reflected their type of school (for example different types of funding for independent schools or if they went to a grammar school). Moreover, it is not an accurate measurement of SEB – one could attend a state school and be privileged, or one could go to fee-paying school and be less privileged (that is, had a bursary to attend). International comparison can also be an issue for workforces with non-nationals.

Question: free school meal eligibility

Strengths: receipt of free school meals is a common measure of disadvantage. It is easy to understand. It is also used across a large portion of studies, policy and research, which allows for tracking outcomes across a range of life stages.

Weaknesses: prior to 1980 there was universal entitlement to free school meals and there have been various policy changes over time, making comparison across generations challenging. Therefore, this may not be useful measure for the workforce as a whole and may be more appropriate for recent recruits or younger workforces. There are also disclosure issues (potential for perceived stigma to reduce disclosure) and awareness of eligibility (not all who are eligible for FSM apply, and not all children may realise they were on FSM). There are also no international comparisons available if employers have high rates of non-nationals.

Question: highest parental qualification

Strengths: there is good evidence on the enduring importance of parental qualifications on life outcomes. It is also easy to understand and collect.

Weaknesses: parental qualification may not reflect that the parent had a bursary to attend higher education or that they got the qualification later in life. It produces a stratification of results, which can be erroneously interpreted as proxies for SEB. It is also difficult to operationalise or benchmark results as it requires analysing the results against respondents’ age. Parental qualification also does not guarantee labour market success. Some employers used this measure to target interventions to those who were first in family to go to university, despite the measure not explicitly measuring this.

Reasons why we reviewed the questions

Our review of the four SEB questions was driven by insights and/or issues we gathered through our engagement with employers and industry groups. These included, for example, that:

• employers were having difficulty measuring and analysing SEB, including how to interpret results and how to use benchmarks
• employers and industry groups were hesitant to advocate for data measurement as it was perceived to be too difficult
• employers were favouring questions with lower accuracy rates. As per the 2020 Social Mobility Employer Index (SMEI):
  • 58% of organisations asked about whether their current employees were the first in the generation to attend university (a deviation of the highest parental qualification question)
  • 56% asked about the type of school they attended
  • 38% asked about free school meal eligibility
  • 37% asked about parental occupation

In essence, employers and industry groups expressed that measuring the SEB of their workforce was not straightforward and that this was limiting both uptake of the agenda and assessing outcomes of existing interventions. We therefore recognised the need to simplify our guidance to better assist employers and increase the volume of employers asking questions on SEB.

What we did as part of the review and who we engaged

We undertook a number of steps to ensure we achieved simpler guidance for employers while maintaining analytical rigour.

We first convened a Data Review Panel made up of academic experts, employer representative bodies, government officials, social mobility charities and individual employers. We consulted the panel on proposals to revise the four SEB questions and co-designed the new guidance.

Dr Sam Friedman, SMC Commissioner, convened a roundtable of academic experts to resolve more complex issues.

These were the following organisations and experts who participated in the consultation process:

• Bridge Group

• Business in the Community

• Chartered Institute of Personnel and Development

• Cabinet Office

• City of London

• Dr Dave O’Brien, Chancellor’s Fellow, Cultural and Creative Industries at University of Edinburgh

• Dr Eric Harrison, Senior Research Fellow at City, University of London

• Dr Louise Ashley, Senior Lecturer, Royal Holloway, University of London

• Dr Sam Friedman, Commissioner at the Social Mobility Commission and Associate Professor in Sociology at London School of Economics

• HMRC

• KPMG

• Penguin Random House

• PwC

• Social Mobility Foundation (SMF)

• Social Mobility Pledge

• Sutton Trust

Question 1: Parental occupation

Summary of key changes and reasons why

A number of changes have been made to the four-part parental occupation question.

1. Removed sub-questions b to d (and thus simplified the analytical process to arrive at a one-part measure of socio-economic background)
2. Added ‘small business owners’ and ‘large business owners’ as response options (to reduce the estimated accuracy error in removing sub-questions b to d)
3. Collapsed 13 response categories to 9 (to reduce and simplify the response options)
4. Coded ‘long-term unemployed’ as lower-socio economic background (to reflect disadvantage experienced by those who are long-term unemployed)

Issues with former question

Looking back at the original question

1a. What was the occupation of your main household earner when you were about aged 14?

• modern professional such as: teacher, nurse, physiotherapist, social worker, musician, police officer (sergeant or above), software designer

• clerical and intermediate occupations such as: secretary, personal assistant, call centre agent, clerical worker, nursery nurse. senior managers or administrators (usually responsible for planning, organising and co-ordinating work, and for finance) such as: finance manager, chief executive

• technical and craft occupations such as: motor mechanic, plumber, printer, electrician, gardener, train driver

• semi-routine manual and service occupations such as: postal worker, machine operative, security guard, caretaker, farm worker, catering assistant, sales assistant

• routine manual and service occupations such as: HGV driver, cleaner, porter, packer, labourer, waiter or waitress, bar staff

• middle or junior managers such as: office manager, retail manager, bank manager, restaurant manager, warehouse manager

• traditional professional occupations: accountant, solicitor, medical practitioner, scientist, civil or mechanical engineer

• long-term unemployed (claimed Jobseeker’s Allowance or earlier unemployment benefit for more than a year)

• retired

• this question does not apply to me

• I don’t know

• I prefer not to say

1b. At age 14, did the main household earner in your house work as an employee or were they self-employed?

• employee

• self-employed with employees

• self-employed or freelance without employees (go to question 4d)

• not working

• I don’t know

• prefer not to answer questions about parental occupation (skip remaining questions)

1c. Where 1b is an employee: How many people worked for your main household earner’s employer

Where 1b is self-employed with employees: How many people did your main household earner employ at this time? Move to question 1d when you have completed this question.

• 1 to 24
• 25 or more
• I don’t know

1d. Did they supervise employees?

• yes
• no
• I don’t know

If employers are to ask one question, we recommended they ask the four-part parental occupation question. This is because it is the most accurate way to produce a distribution of SEB.

Despite being the primary question we recommend (of the four SEB questions), only 37% of employers on the SMEI ask this question.^{[footnote 1]} Whereas 56% and 58% of employers on the SMEI ask questions on type of school attended and if respondents were the first in generation to attend university respectively.

This difference can be primarily attributed to the difficulty experienced by employers in analysing the data and in the extra length of the question. There are four sub-questions that make up the parental occupation question, which means employers have to add more questions to surveys and analyse a large amount of data.

Steps taken to address issues

The complexity of the question and the coding process was linked to the use of sub-questions b to d which concern whether the main household earner was self-employed (sub-question b), how many people worked for them (sub-question c) and how many employees they supervised (sub-question d). This required a 3-step coding process as shown below.

Coding the former version of the question

Step 1: Select an employment code from the below

Image replicated from Sutton Trust, Social mobility in the workplace: an employer’s guide, 2020

Step 2: Using the code from step 1 and the answer provided in part A, determine the group

Answer to Part A	Code 1	Code 2	Code 3	Code 4	Code 5	Code 6	Code 7
1. Modern Professional occupations	1	1	1	1	1	1	1
2. Clerical and intermediate occupations	1	3	3	1	1	1	2
3. Senior managers or administrators	1	3	3	1	1	1	1
4. Technical and craft operations	1	3	3	1	1	4	4
5. Semi-routine manual and service operations	1	3	3	1	1	4	5
6. Routine manual and service occupations	1	3	3	1	1	4	5
7. Middle or junior managers	1	3	3	1	1	1	1
8. Traditional professional occupations	1	1	1	1	1	1	1

Step 3: Assign parental occupation groups:

Group	Parental occupation groups
1	Higher managerial, administrative and professional occupations
2	Intermediate operations
3	Small employers and account holders
4	Lower supervisory and technical occupations
5	Semi-routine and routine occupations

These groups could then be simplified as follows:

• 1: professional background
• 2 and 3: intermediate background
• 4, 5 and long term unemployed: working class background

We have reduced the complexity of this 3-step coding process by removing sub-questions b to d and replacing it with a 1-step coding process. Under the 1-step coding process, employers can simply use the code attached to each parental occupation response option, as shown below.

The updated question

Note: bracketed text defines the codes employers should categorise responses into and should not be included in surveys.

Question: What was the occupation of your main household earner when you were aged about 14?

• modern professional and traditional professional occupations such as: teacher, nurse, physiotherapist, social worker, musician, police officer (sergeant or above), software designer, accountant, solicitor, medical practitioner, scientist, civil or mechanical engineer. (code = professional background)
• senior, middle or junior managers or administrators such as: finance manager, chief executive, large business owner, office manager, retail manager, bank manager, restaurant manager, warehouse manager. (code = professional background)
• clerical and intermediate occupations such as: secretary, personal assistant, call centre agent, clerical worker, nursery nurse. (code = intermediate background)
• technical and craft occupations such as: motor mechanic, plumber, printer, electrician, gardener, train driver. (code = lower socio-economic background)
• routine, semi-routine manual and service occupations such as: postal worker, machine operative, security guard, caretaker, farm worker, catering assistant, sales assistant, HGV driver, cleaner, porter, packer, labourer, waiter or waitress, bar staff. (code = lower socio-economic background)
• long-term unemployed (claimed Jobseeker’s Allowance or earlier unemployment benefit for more than a year) (code = lower socio-economic background)
• small business owners who employed less than 25 people such as: corner shop owners, small plumbing companies, retail shop owner, single restaurant or cafe owner, taxi owner, garage owner (code = intermediate)
• other such as: retired, this question does not apply to me, I don’t know (code = exclude)
• I prefer not to say (code = exclude)

The codes used in this 1-step process are re-worded versions of the three-class version of the NS-SEC. That is, our methodology is to have respondents self-code to the three-class version of NS-SEC. As follows:

NS-SEC three-class version	SMC classification of SEB
Higher managerial, administrative and professional occupations	Professional background or higher socio-economic background
Intermediate occupations	Intermediate background
Routine and manual occupations	Working class background or lower socio-economic background

For reference, there are two other versions of the NS-SEC including the eight-class version and the five-class version (see step 2 and step 3 for these respective classes).

Simplifying the coding process will save employers time and effort in analysing the data they gather. It will also allow employers to more easily use the question on supplemental employee surveys where a 4-part question is too long.

We also collapsed the number of response options from 13 to 9 to simplify and reduce the length of the survey question.

For full details on the methodology used, please refer to the annex in this document: Analysis of workforce in England by socio-economic background.

Limitations associated with changes and how they are being addressed

Accuracy error

It is important to note that it is unlikely any self-reported question will be completely accurate all of the time. The only way to achieve full accuracy is for respondents to input their exact job and for an analyst to code it using SOC codes (as the ONS does for the Labour Force Survey). This, however, is unsuitable method for employers and as such, a simplified self-coded question is necessary. Managing this inaccuracy is important to sustaining a valid measurement.

Removing questions b to d raises the risk of respondents being coded to an incorrect SEB group. In other words, accuracy is reduced.

The group at most risk of being coded to the wrong SEB group as a result of our changes are those who are self-employed as questions b to d distinguish those who are self-employed by the number of staff they employ and supervise. This helps to distinguish those whose parent, for example:

• owned a café and had 5 people working for them (code = intermediate background) as opposed to those who owned a large company and had thousands of people working for them (code = professional background)
• owned a plumbing business and had 2 people working for them (code = intermediate background) as opposed to someone who worked as a contracted plumber (code = lower SEB)

The first example demonstrates how removal of questions b to d may affect who would have otherwise been coded to either the intermediate and/or professional background. The second example demonstrates how removal of questions b to d may affect who would have otherwise been coded to the intermediate background.

There are two sources we have used to roughly estimate the accuracy error linked to not asking sub-questions b to d. These are intended to give a guide and not to be exact measures. The first source is a report by the ONS, which calculates the accuracy of using three different methods to derive NS-SEC including the full, reduced and simplified methods. The simplified method is closest to our recommended approach as it also derives NS-SEC without using sub-questions b to d. The key difference however, is that the simplified method uses the four-digit unit group code of SOC2010 and the eight class version of the NS-SEC. Notwithstanding these technical differences, the report by the ONS (see 88% figure on p. 20, point 12.2) found that the simplified method correctly allocated 88% of cases. This provides us with an indicative inaccuracy figure of 12% if sub-questions b to d are removed.

The second source that we have used to estimate the accuracy error linked to not asking sub-questions questions b to d is self-employment rates in the UK. This rate is relevant given that the inaccuracy error is driven by sub-questions b to d, which concern self-employment. According to the ONS, 15.3% of the UK labour market is represented by those who are self-employed. This figure, together with the indicative 12% inaccuracy figure calculated via the simplified method, allows us to estimate an indicative accuracy error of around 12 to 15% in removing sub-questions b to d.

This introduces risks for employers, namely that:

• the error makes comparison to benchmarks impossible, which would be a significant reputational risk for employers and reduce the effectiveness of data to drive strategy and targets
• the error manifests in such a way as to risk employers making appropriate policy decisions. That is, it systematically overestimates the proportion of employees from lower SEB meaning employers decide they do not need to take action on their recruitment policies, for example

To minimise this accuracy error and therefore these risks, we worked with the panel of experts to update the response categories. A new category was added for ‘small business owners’ and clarification on ‘large business owner’ was added to the responses options as per below:

• senior, middle or junior managers or administrators such as: finance manager, chief executive, large business owner, office manager, retail manager, bank manager, restaurant manager, warehouse manager
• small business owners who employed less than 25 people such as: corner shop owners, small plumbing companies, retail shop owner, single restaurant or cafe owner, taxi owner, garage owner

This was intended to address the issues described in the two hypothetical examples. We used SOC codes and consultation with academic experts to derive examples of small business owners. We also described small business owners as those who employ less than 25 people as per the ONS definition for NS-SEC.

We expect that these new response options will reduce the accuracy error and associated risks to an acceptable level.^{[footnote 2]} We do not expect that this new measure will be exactly accurate.

Overall, we felt this trade off (that is, the accuracy error) was justified to simplify the coding process for employers and encourage them to ask this important question. In other words, we considered it a lesser risk than employers not engaging with the agenda due to a method that is too complex to be implemented or sustained in practice.

Please note that this 1-step coding process is only recommended for employer workforce purposes and not for national datasets (for example Labour Force Survey) which should retain their current methodology. We also advise that employers who are already using the original four-part method continue to do so if it is operationally feasible.

Validity of our measurement method

We consulted a number of academic experts on the validity of our measurement method. As a result of this expert review process, we do not think that the validity of our measurement method is undermined by – (a) our question structure and response coding or (b) the risk of the measurement error.

Comparability to benchmarks

Using the 1-step coding process could affect the ability to compare results to benchmarking data from the LFS, which uses a more precise approach. However, as previously highlighted, our SEB classification is a re-worded version of the ONS three-class NS-SEC. That is, our methodology is to have respondents self-code to the three-class version of NS-SEC. As such, outputs produced through our approach, in structural terms, are comparable with LFS NS-SEC outputs. We emphasise structural because there remain significant differences in collection and calculation.

Other changes

We have also recommended for ‘long-term unemployed’ to be coded as lower SEB, which was previously coded as ‘other’. We acknowledge the size of this effect – according to analysis we conducted to understand this impact, approximately 4% of the population in England would be coded as lower SEB. See analysis in Annex 3.

We acknowledge the ambiguity around ‘long-term unemployed’ as a group that could include people of extreme wealth. However, we think it is important to code this group as lower SEB to capture long-term scarring from stretches of unemployment, as a form of extreme disadvantage. Moreover, we think employers would unhelpfully disregard long-term unemployed if it was an ‘other’ category.

Ultimately, though, this trade off was deemed appropriate both from a sociological and operational perspective.

Question 2: Type of school attended

Summary of key changes

One change has been made to the type of school attended question.

The former question:

Which type of school did you attend for the most time between the ages of 11 and 16?

• a state-run or state-funded school
• independent or fee-paying school
• attended school outside the UK
• I do not know
• prefer not to say

The new question:

Which type of school did you attend for the most time between the ages of 11 and 16?

• a state-run or state-funded school
• independent or fee-paying school
• independent or fee-paying school, where I received a bursary covering 90% or more of my tuition
• attended school outside the UK
• I do not know
• prefer not to say

Summary of key changes and reasons why

Added ‘Independent or fee-paying school, where I received a bursary covering 90% or more of my tuition’ to list of response options (to distinguish those who are disadvantaged)

Issues with previous response options

The response options, ‘independent or fee-paying school’ and ‘state-run or state-funded school’ are ambiguous and can lead to issues in accurately interpreting socio-economic background. As it stands, someone who attends an ‘independent or fee-paying school’ is coded as advantaged, while someone who attends a ‘state-run or state-funded school’ is coded as disadvantaged. However, the problem with this is as follows:

• someone who is disadvantaged may have attended an independent or fee-paying school
• someone who is advantaged may have attended a state-run or state-funded school

This therefore affects the accuracy of results. For employers, it means that they may not have an accurate picture of the SEB of their workforce.

Steps taken to address issue

The ambiguity surrounding the response option, ‘independent or fee-paying school’ can easily be resolved. Students who receive a bursary covering 90% or more of their tuition, which could be used as a marker of disadvantage. We therefore added the following response option:

• independent or fee-paying school, where I received a bursary covering 90% or more of my tuition

This will allow employers to interpret results more accurately and have a clearer picture of their workforce.

Other

Our partners at the SMF suggest advanced employers (and law firms, who are required by the SRA) include ‘Selective state school’ and ‘Non-selective state school’ in the response categories for this question to get an even clearer picture of the type of school respondents attended. They suggest that selective state schools are typically more socially privileged and that selective schools can offer a greater level of cultural capital and support to students attending.

We have not created a new response option to address those who are advantaged and attended a state-run or state-funded school. It would be difficult (and problematic) to derive a single marker of advantage (for example a certain income level). To achieve this consultation’s aim of simplifying measurement, we have likewise chosen not to formally include a selective vs. non-selective response category. However, employers who can follow SMF’s advice will capture more nuance around the types of state schools they are admitting in their hiring practices.

Question 3: Free school meal eligiblity

No changes

No changes have been made to this question. See existing question below:

If you finished school after 1980, were you eligible for free school meals at any point during your school years?

• yes
• no
• not applicable (finished school before 1980 or went to school overseas)
• I do not know
• prefer not to say

Why no changes have been made

Free school meal eligibility remains a good measure of economic disadvantage due to its narrow eligibility criteria. It is also easy to understand and is widely used by academics and policy makers. Many employers have been asking this question for years, allowing them to review longitudinal data on their workforce. There are still some issues surrounding this measure as outlined in the introductory chapter (that is its applicability to different age groups, people’s limited awareness of their eligibility and lack of international comparisons). These are issues that employers with an older or international workforce need to be particularly aware of but are not significant enough risks to warrant removal of the measure. Employers should view this measure alongside the other recommended measures, to contextualise and enrich their understanding of their socio-economic diversity.

Question 4: Highest parental qualification

Summary of key changes

We have removed this as a recommended question for employers and replaced it with an optional question for employers who have graduate schemes.

See the former question below:

What is the highest level of qualification achieved by either of your parent(s) or guardian(s) by the time you were 18?

• above degree level (for example MA, MSc, MPhil, PhD)
• degree or equivalent (for example first or higher degrees, postgraduate diplomas, NVQ/SVQ Level 4 or 5
• below degree level (for example A level, SCE Higher, GCSE, O level, SCE Standard or Ordinary, NVQ/SVQ, BTEC)
• no qualifications
• I do not know
• prefer not to say
• not applicable

See the new optional question below:

Did either of your parents attend university by the time you were 18?

• no, neither of my parents attended university
• yes, one or both of my parents attended university
• do not know or not sure
• prefer not to say

Summary of key changes and reasons why

1. Removed original question (due to challenges in analysing, comprehension and benchmarking results)
2. Replaced it with an optional question for employers who have graduate schemes (to provide additional lens to view diversity of new graduate hires)

Issues with former question

The previous question was intended to measure whether someone is from an advantaged or disadvantaged background based on their parent’s educational background. This is problematic however, as education varies over time and space. Participation in higher education has widened significantly over time, for example. It is therefore difficult for employers to interpret results without also breaking down their workforce by age group, then benchmarking to higher education participation in each decade over the past fifty years. This was deemed not feasible for most employers. Comprehension is low for this question; employers can confuse the gradient it produces as a gradient of SEB, which is instead produced by the parental occupation question. For this reason, the question on parental occupation is more insightful, while still capturing the same students targeted by the highest parental occupation question. Moreover, the other three SEB questions we recommend more accurately capture advantage and disadvantage, albeit from different angles.

The question on highest parental qualification was thus not practical for employers given the challenges in analysing, comprehension and benchmarking results.

Steps taken to address issue

We have removed the previous question on highest parental occupation as a recommended question for employers based on the issues identified. This also forms part of our wider efforts to influence employers to ask questions on SEB by making it simpler. In this instance, we have removed an otherwise time-consuming and difficult question for employers to analyse, interpret and benchmark.

We have replaced the previous question with a new optional one for employers who have a graduate scheme (see question on previous page). This question is relevant to ask as being the ‘first in family’ to attend university is an acute form of disadvantage, signals a lowered potential for cultural capital and correlates to other outcomes, such as lower attainment. It signals a lack of support to navigate university and entry into the graduate workforce. It otherwise provides employers with an additional lens to view the diversity of their new graduate hires and can help employers target recruitment at institutions who are successfully achieving widening participation aims.

Employers who work with delivery partners who use this measure should continue to do so, to ensure their programme runs within their experience of best practice.

Annex A: Updates using 4-digit SOC code in 2021

This Annex summarises a technical update implemented to the national benchmark of the UK working population in May 2021, as the result of additional analysis done by the Social Mobility Commission, the Creative Industries Policy and Evidence Centre (PEC) and the Office for National Statistics (ONS).

What is the national benchmark?

The benchmark sets out what percentages of each of the socio-economic background categories (professional, intermediate and lower socio-economic background) are found in the working population (age 16 and over) and in different sectors. Employers can use these benchmarks to see how their own diversity and inclusion of people from different socio-economic backgrounds compares to the national and sector socio-economic background benchmarks. SMC can also use these benchmarks when monitoring social mobility trends over time.

See table 1 below for a comparison between the updated national socio-economic benchmarks and the previous national socio-economic benchmarks.

Table 1: National benchmarks for all industries, previous and new method

Background	Previous method using 3-digit SOC code	New method using 4-digit SOC code
Professional background	34%	37%
Intermediate background	24%	24%
Working class background	42%	39%

What has been updated? Key methodology changes:

• following analysis, a change in the use of Standard Occupation Classification (SOC) is required to improve the accuracy of the national benchmark. The benchmark will use the 4-digit SOC codes in place of the 3-digit codes SOC codes. The 4-digit codes provide additional detail and allow for a more accurate allocation of jobs to socio-economic profession
• socio-economic classifications are allocated as previously by mapping the SOC code onto the Labour Force Survey (LFS) to the professional, intermediate and working class occupations. This is the same method used when calculating the previous benchmarks
• using the 4-digit SOC codes has increased accuracy for the benchmarks and resulted in slight changes to them. The national benchmark for working class occupations is now 39%, compared to 42%. Intermediate remains the same and professional backgrounds have increased slightly from 34% to 37%. See figure chart below
• data for 2019 is used as the most up to date benchmark due to the uncertainty of the 2020 data as a result of the pandemic. This is in place of pooled 2017 to 2019 data as used previously
• these benchmarks will be reviewed and updated every few years, changes will not be made on an annual basis

The national benchmark - Socio-economic background of the overall UK workforce aged 16 and over (May 2021)	Percentage
Professional Backgrounds	37%
Intermediate	24%
Working Class	39%

Data source: Labour Forces Survey, 2019

Context

As outlined in this report the national benchmark demarcates the national and sector workforce according to socioeconomic background (SEB), or the occupation of the survey respondent’s main wage earner when they were 14^{[footnote 3]}, it breaks the workforce into percentages, showing how much is made up of people from: professional, intermediate and lower socio-economic background. These categories are created using the National Statistics Socio-economic Classification (NS-SEC) information on the Labour Force Survey (LFS).

This was previously done by mapping the 3-digit Standard Occupation Classification (SOC) of the main wage earner in the LFS onto NS-SEC categories. The 3-digit codes have previously been used because the 4-digit SOC codes were not accessible in the publicly available LFS data, it has been standard practice to use the 3-digit codes. To our knowledge a comparison of the impact of using the 3-digit and 4-digit codes has not been done before. In collaboration with the Creative Industries Policy and Evidence Centre (PEC) and the Office for National Statistics (ONS) we have gained access to the 4-digit codes and conducted some comparative analysis. This has found that the previous benchmark calculations overestimated the proportion of the workforce from lower socio-economic backgrounds and underestimated the proportion from professional backgrounds.

What is the Standard Occupation Classification (SOC)?

The Standard Occupational Classification (SOC) is a common classification of occupational information for the UK and is maintained by the Office for National Statistics (ONS). It is a coding framework used to classify occupations, allowing for comparisons to be made of occupations across different datasets. It assigns all jobs a code based on the skills and qualifications needed for the job.

There are four tiers of the Standard Occupation Classification (SOC). The 3-digit code provides classification of occupation into minor groups, for example finance professionals, whereas the 4-digit code provides classification into the lowest, most detailed definition of occupation, such as chartered and certified accountants. The 4-digit codes therefore provide additional detail and allow for a more accurate assessment.

Following analysis, we have found that there are instances where the 3-digit SOC codes conceal 4-digit codes underneath them, resulting in some occupations being allocated to a different socio-economic classification (NS-SEC). As a result, the benchmarks are slightly different using the more accurate 4-digit codes.

Updated benchmark now based on 2019 data

The previous methodology using the 3-digit SOC codes averaged the benchmarks across pooled 2017 to 2019 data using data from the July to September quarters of the Labour Force Survey (LFS). However, the aggregating of data across years can create challenges when weighting the data to ensure it represents the wider population. Also, the sample sizes are large enough to not use aggregated data here. The 4-digit analysis has produced benchmarks for each year 2014 to 2020 inclusive, however due to the pandemic there is uncertainty in the ongoing stability of the 2020 data. Additional years of data will be required to understand the composition of the labour force as a result of the pandemic.

Therefore, 2019 data is being used in this latest version of the benchmark. The National benchmark will likely be updated every few years to reflect the latest labour market structure.

Additionally, the population captured in these benchmarks remains consistent and includes the working population age 16 and over. Those who ‘never worked’ or were ‘long-time unemployed’ continue to be included in our definition of those who are from lower socio-economic backgrounds.

Parameters of the changes

The changes outlined here only apply to how jobs are allocated to the NS-SEC categories (professional, intermediate and lower socio-economic background). The methodology to do this has not changed, other than the use of 4-digit Standard Occupation Classification (SOC) codes in place of the previous 3-digit SOC codes (see below for more details on these codes).

There is no change in how employers should collect data on the socio-economic backgrounds of their workforce. They should continue to do this by asking their workforce the recommended question on parental occupation. A dataset based on this question will give employers a cross comparable data set against the benchmarks in this tool. Additionally, the analysis performed by employers to understand the make-up of their workforce remains unchanged.

Impact

These latest benchmarks, released by the Social Mobility Commission in partnership with the Creative Industries Policy and Evidence Centre (PEC) and the Office for National Statistics (ONS) are the most accurate figures to use when needing socio-economic breakdowns for the national workforce and for sectors. Having these benchmarks, and the national benchmark tool, is critical for employers who are looking to set socio-economic diversity and inclusion targets and measuring their own workforce data against these representative figures at the national and sector levels. They can also be used by researchers doing socio-economic analysis on workforces. You can find out more about the benchmarks, as well as our guidance for employers when it comes to their social mobility strategies on our website.

Annex B: Analysis of workforce in England by socio-economic background

When conducting the consultation in November 2020, the Commission used the July to September quarters of the LFS from the years 2017 to 2019 to determine the breakdown of the workforce in England by SEB.^{[footnote 4]} It compared results using five and three NS-SEC classes of SEB as highlighted in the respective graphs below. Further information on each data set can be found in the grey text underneath each graph. Following a review in May 2021 these benchmarks now use 4-digit SOC codes using 2019 data (see appendix A).

Analysis using five socio-economic background categories

Percentage of workforce by NSSEC category of the main wage earner when respondent was 14	Percentage
Higher managerial and professional	34%
Intermediate occupations	24%
Routine and manual occupations	37%
No one was earning	4%
Not living with family	1%

Data sourced from the July to September quarters of the LFS from the years 2017 to 2019

The NSSEC category of the main wage earner when the survey respondent was 14 was generated by mapping the 3-digit SOC categories( occupational groups) of the main wage earner in the LFS to NSSEC categories. This was done using the existing mapping of 4-digit SOC codes to NSSEC categories by the ONS. Where not all 4-digit SOC codes within a 3-digit SOC category mapped to the same NSSEC category, the most common NSSEC category for that 3 digit SOC code was chosen

Analysis using three socio-economic background categories

Percentage of workforce by NSSEC category of the main wage earner when respondent was 14	Percentage
Higher managerial and professional	34%
Intermediate occupations	24%
Routine and manual occupations	42%

Data sourced from the July to September quarters of the LFS from the years 2017 to 2019

The NSSEC category of the main wage earner when the survey respondent was 14 was generated by mapping the 3-digit SOC categories( occupational groups) of the main wage earner in the LFS to NSSEC categories. This was done using the existing mapping of 4-digit SOC codes to NSSEC categories by the ONS. Where not all 4-digit SOC codes within a 3-digit SOC category mapped to the same NSSEC category, the most common NSSEC category for that 3 digit SOC code was chosen.

1. Social Mobility Foundation. Employer Index Report 2020. ↩

2. We are unable to say by precisely how much, as it was determined the risk was too low to justify A/B testing in the field, and thus poor value for money to complete. ↩

3. This corresponds to the variable SMSOC103 in the Labour Force Survey (LFS). Respondents whose main wage earners were not working when they were 14 were also included in the routine and manual occupations socio-economic background (SEB) category to capture the effects of long-term scarring caused by stretches of unemployment, as a form of extreme disadvantage. ↩

4. The Commission monitors progress towards improving social mobility in the UK, and promotes social mobility in England. This analysis was thus was exclusively completed on the workforce in England as per the Commission’s remit to undertake advocacy work in England. ↩