House of Lords

Office for National Statistics follow-up written evidence to the Lords Economic Affairs Committee’s inquiry on growth and productivity: statistical methdology

Dear Lord Bridges,

When giving evidence to you and your Committee on 18 April, Grant and I promised to follow-up on a number of points with various members of the Committee.

Firstly, we promised to send the Office for National Statistics (ONS) and OECD joint study, International comparisons of the measurement of non-market output during the COVID-19 pandemic, which compared methodologies used by national statistical institutes; and Lord King also asked if we could share our Public service productivity, healthcare, England bulletin.

You asked if we were able to conduct a comparison of other countries that do use the direct measurement approach for healthcare and how they fared during the pandemic. The following charts relate to international comparisons of countries that use the direct volume output measurement approach to healthcare: the use of activity data.

International comparisons of healthcare specifically are limited partly as many countries do not publish data by individual industries such as healthcare. Figure 1 shows the Gross Value Added (GVA) of a broader group of industries: human health, education and public administration. This industry grouping is reported to the OECD and so available for a wider group of countries. Only countries that measure health care on a direct volume output basis are included in Figure 1.

Figure 1 shows a greater fall in the UK than other countries in these three industries combined. This will partly be due to differences in the measurement and output of education services over the pandemic, but healthcare will also be a factor. Gross value added for healthcare for the UK is in part supressed because of the large increase in intermediate consumption – the goods and services used as inputs, such as personal protective equipment. While the GVA of healthcare is now lower than it was before the pandemic, the ONS measures of the volume output of government in healthcare show this to be higher than before the pandemic.

Figure 1: Gross value added from health care, education and public administration combined for countries that use the direct volume output method for health care

Figure 1 shows the Gross value added from health care, education and public administration combined for countries that use the direct volume output method for health care

Source: Office for National Statistics For a more accessible version, please visit our accessibility policy.

Figure 2 shows the output of government services provided to individuals (such as healthcare and education) unadjusted for intermediate consumption. Again, this measure is an imperfect comparison, particularly as the proportion of healthcare that is government-funded varies greatly between countries. However, for the UK, the coverage of this measure and the three industries included in Figure 1 substantially overlap. Figure 2 shows less of a difference between the UK and other countries, with the total output of government-funded services at a similar level to before the pandemic for the UK.

Figure 2: Individual consumption expenditure of general government, chained volume estimates for countries that use the direct volume output method for health care

Figure 2 shows the Individual consumption expenditure of general government, chained volume estimates for countries that use the direct volume output method for health care

Source: Office for National Statistics For a more accessible version, please visit our accessibility policy.

In addition, while discussing healthcare, Lord Davies asked if we ensure that the two sets of figures between private and public provision make sense. For healthcare, we have separate data sources for public and private provision, and for private provision we collect data via a survey where respondents can add comments. For example, during the recent nurses’ strike, there were stronger numbers for the private healthcare section which made reference to increased numbers of people deciding to attend private healthcare. Ultimately, if public healthcare provision is doing well, with good outcomes and short waiting lists, then people have less incentive to use private provision and there is only a finite amount of healthcare needed so the two will offset each other to some degree.

Lord Rooker discussed life expectancy, specifically if has stalled for the first time in 120 years and whether this has had an impact on business (particularly the funeral industry). We agree that in the most recent period of 2018-20, compared with 2015-17, male life expectancy at birth was 7 weeks shorter and for females there was almost no change. Since 2010-12 life expectancy has grown by 0.3 years for males and females, compared with an increase of 2.5 years for males and 1.7 years for females between 2000-02 and 2008-10.

In terms of the impact on the funeral industry, funeral care is included within a more general heading of ‘other personal care’, so the data are not separately identified. As this industry includes hairdressers (who were severely impacted by the pandemic) we did not see large growth overall.

While discussing measurement of the digital economy, you asked how other countries gather statistics on how people spend their time. The UK is particularly active in the UN-led process around updating the System of National Accounts (SNA) in the area of digitalisation and the treatment of free digital products as described by Lord King in his example. UK officials led, with the US Bureau of Economic Analysis, the development of guidance on how to treat free digital products in a satellite account, potentially as part of the unpaid work satellite account and were recently asked to take on the lead editorship of a manual providing guidance to national statistics institutes on the correct treatment of these products, recognising that the production costs and income derived from their production is already included in the national accounts.

The UK also chairs the development of guidance on the treatment of unpaid household service work with the SNA. Within those guidance notes recommendations is a proposition to include the time spent in paid work, unpaid work and leisure time within a supplementary table. This would then allow users to better consider factors like leisure time which have an impact on personal well-being when also evaluating changes in the value of the economy, while including or excluding the value of unpaid household services in their appraisals. We hope that the next SNA will therefore allow a better understanding of how households are experiencing their time, while also complementing existing measures of GDP with more complete measures of output including more traditional unpaid household services, and those which are digitally mediated, as captured by the new UK Time Use Survey.

The ONS ran a pilot online Time Use Survey in March 2020 which has since evolved over five successive surveys, with the latest undertaken in March 2023. The survey not only records all the activities carried out in a respondent’s day, but also captures how much they enjoyed the activities and the extent to which they were using smart devices to engage in them.

As a result of the Covid-19 pandemic, time use analyses produced by the ONS throughout 2020 to 2022 were focused on meeting policy interests in how the public were spending their time and changing behaviours during periods of lockdown and periods where restrictions were eased.

However, moving on from the pandemic, we are now refocused on using this source in measuring the digital economy, and are reviewing existing methodologies for the measurement of unpaid household production activities (as outlined in the UNECE Guide on Valuing Unpaid Household Service Work), including those used in compiling the UK Household Satellite Accounts, last published in 2018. We endeavour to produce new estimates up to 2020 during summer 2023 and then aim to start developing new methods utilising the time use data collected since 2020 for more recent years immediately afterwards.

From 2023/24 onwards, the Time Use Survey will be run twice per year as part of the Economic Statistics Centre of Excellence (ESCoE), and we will be collaborating with academic experts in producing new experimental estimates of unpaid household production work, with an ambition to produce quarterly estimates in the future which would coincide with quarterly GDP estimates.

Finally, Lord Turnbull asked for information directly after the session on a) what proportion of health and education sector are non-market outputs, and b) how treated GPs, including those working in the NHS – i.e. private sector or non-market healthcare output, and c) likewise for dentists. In the Blue Book 2022, we provide rounded figures of the splits for health, social care and education: health is split 85/15 public/private, social care is 50/50 public/private (we publish human health and social care) and education is 69/8/23 public/private/other (this includes higher and further education). If there is market activity (i.e. private sector activity within our health data) we treat this as a P11 transaction, which is classed as market output and is deducted from our final consumption exemption figure for Government output. This would apply for GPs and dentists.

Please do let us know if any other questions, and if we can help the Committee further on either this topic or any of its other inquiries.

Yours sincerely,

Mike Keoghan

Deputy National Statistician for Economic, Social and Environmental Statistics

Office for National Statistics oral evidence to the Lords Economic Affairs Committee’s inquiry on growth and productivity: statistical methodology

On Tuesday 18 April, Mike Keoghan, Deputy National Statistician for Economic, Social and Environmental Statistics and Grant Fitzner, Chief Economist and Director for Macroeconomic and Environment Statistics and Analysis at the Office for National Statistics, gave evidence to the Lords Economic Affairs Committee for a one-off session on growth and productivity: statistical growth.

A transcript of which has been published on the UK Parliament website.

Office for National Statistics response to the Lords Economic Affairs Committee report, “Where have all the workers gone?”

Dear Lord Bridges,

I write following the Economic Affairs Committee’s report, ‘Where have all the workers gone?’, particularly the conclusion that “more timely high-quality information on the wealth holdings of early retirees, such as that which will ultimately be available through the Wealth Assets Survey or the English Longitudinal Study of Ageing, would be very helpful in assessing the financial resilience of people who have recently retired. (Paragraph 84)”

As the Committee is aware, the Office for National Statistics (ONS) produces a range of statistics showing the income, spending and wealth of British households. These statistics are primarily based on three household surveys, through which 25,000 households are asked about the income they receive (including earnings from work and income from benefits), the money they spend and the assets they own, such as property and pensions. These statistics are a vital source of information for policymaker’s understanding people’s financial well-being, including the effects from the rising cost of living.

We believe that by combining the current surveys into a single survey in conjunction with alternative data sources it will be possible to deliver higher quality, more timely and in-depth analysis of households’ financial well-being. With this in mind, we are holding a consultation that seeks feedback from users on:

the need for a single set of data on income, spending and wealth
the requirements for more inclusive spending data
the value of more timely indicators ahead of detailed estimates of income, spending and wealth
the value of longitudinal data on household financial well-being

Depending on the outcome of the consultation, we plan to test and roll out the newly combined survey with its various modules over the next three years. Our aim is to ensure that the household financial statistics and analysis we produce continue to meet the evolving needs of policy makers, citizens and other data users, and your conclusion illustrates that we are right to consult on this topic. Our goal is that our statistics and analysis should provide inclusive, coherent, timely and granular insights into wide aspects of the financial wellbeing of households with improved coverage and accuracy.

We also have a longer-term aim to make much greater use of other existing UK Government data sources, known as administrative data. Our ambition is to put these at the heart of our income statistics, supported by data from our surveys, which continue to be fundamental for measuring aspects of household finances not covered in other sources. This proposed approach essentially constitutes a shift from predominantly survey-based estimates supported by administrative data to the converse position. This aligns with our broader plans and ambitions for population and social statistics and the Government Statistical Service (GSS) work programme on the coherence of income and earnings.

We would be happy to keep the Committee regularly updated on this work.

Yours sincerely,

Mike Keoghan

Office for National Statistics response to the Environment and Climate Change Committee’s report, ‘In our hands: behaviour change for climate and environmental goals’

Dear Lady Parminter,

I write in response to the Committee’s report, ‘In our hands: behaviour change for climate and environmental goals’. We welcome the report and its positive comments about the UK Climate Change Statistics Portal, and would be happy to contribute to fulfilling the Committee’s recommendation, that:

“The BEIS Public Attitudes Tracker or the Office for National Statistics (ONS) UK Climate Change Statistics Portal should regularly monitor whether people would like to or are making changes in how they travel, use energy at home and what they eat and buy, and the reasons behind people’s willingness to change. (Paragraph 63)”

Having launched a prototype in October 2021 ahead of the UN COP26 climate conference, we successfully launched a new version of the UK Climate Change Statistics Portal on 27 October, ahead of COP27 in November 2022.

A cross-government project led by the ONS, the Portal provides a valuable resource for policymakers and the public. As the Committee suggests, it also offers a useful place to bring existing and new behavioural change statistics and data together, an enable monitoring of changes.

Since our December 2021 evidence submission,[1] the ONS now collects and publishes statistics on individuals’ climate change concern and actions on a regular basis through our Opinions & Lifestyle Survey (OPN). This complements and supplements the larger quarterly BEIS Public Attitudes Tracker, and we regularly engage with BEIS on topics and questions for OPN.

On 28 October 2022, we published a synthesis of individuals’ climate change worries and actions, drawing on the several waves of OPN data that we have now built up and also Public Attitudes Tracker. Some of the key findings are:

74% of adults in Great Britain reported feeling (very or somewhat) worried about climate change, a similar figure to around a year ago (75%), with women more likely to report such worries (77%) than men (71%);
75% expected rising UK temperatures to affect them by 2030, up from 62% six months previously; and
75% reported making lifestyle changes to help tackle climate change, lower than the level found around a year ago (81%).

We are also using OPN statistics, including on behaviour change, in our new quarterly Climate Change Insights, which brings together a range of climate change-related official statistics. For example, the latest edition was published on 11 November 2022 and explains that 34% of adults in Great Britain reported reducing their meat and dairy consumption to help tackle climate change in the last 12 months. Additionally, August 2022’s Insights has a focus on homes and families.

The Committee’s report also looks at businesses. The ONS is now regularly gathering similar data as above for businesses, using our Business Insights & Conditions Survey (BICS).

The Climate Change Insights published on 6 October reports on respondents’ concerns about the impact of climate change on their business. The accompanying dataset contains several waves of data on business actions taken to protect the environment, including whether they have net zero or other emissions targets, climate change strategies and/or monitor climate risks, and whether they have sustainability reports. In December 2022, we are also planning to publish a similar output for businesses to that above for individuals, drawing on multiple waves of BICS data.

We already use the Portal’s “related links” section to refer to the individuals synthesis article and Climate Change Insights publications, and we will look to add the business synthesis article once published. We have also added some of the detailed OPN data to the Portal’s data store. As we further develop the Portal, we will seek to add further relevant OPN and BICS data in the future, as a way to improve access and use of these data.

We would be happy to consider further use of OPN, BICS and other ONS surveys and tools, such as potential innovative data sources to further inform policymakers and the public about behaviour change in the key areas which the Committee’s report identifies, including what people purchase and consume.

We would be happy to keep the Committee regularly updated on our work in this area.

Yours sincerely,

Grant Fitzner

Office for National Statistics written evidence to the Lords Economic Affairs Committee’s inquiry on UK labour supply

Dear Lord Bridges,

Thank you for inviting David Freeman and I to give evidence for the Committee’s inquiry on UK Labour Supply. During that session on 6 September, we promised to follow-up with the Committee on several topics.

Over-50s Lifestyle Survey

The latest iteration of the Over-50s Lifestyle Survey was discussed at many points during the session, and I am pleased to confirm it was published on 27 September 2022. In the period from 10 to 29 August 2022, based on adults aged 50 to 65 years in Great Britain (GB) who have left or lost their job since the start of the coronavirus (COVID-19) pandemic and not returned to work:

The majority (66%) owned their homes outright and were more likely to be debt free (61%) compared with those who left their job since the pandemic and returned to work (42% debt free).
Financial resilience varied by age: those aged 50 to 54 years were significantly less likely to be debt free, excluding a mortgage (49%), compared with those aged 60 to 65 years (62%), and more likely to have credit card debt (39%, compared with 24%).
Age was also a factor when considering whether to return to work; the younger cohort were more likely to say that they would consider returning to work (86% for those aged 50 to 54 years, 65% for those aged 55 to 59 years and 44% for those 60 to 65 years).
Adults aged 50 to 59 years were more likely to report mental health reasons (8%) and disability (8%) as a reason for not returning to work when compared with those aged 60 to 65 years (3% and 3%, respectively).
Adults aged 50 to 59 years (14%) were also more likely to be currently looking for paid work, compared with adults aged 60 to 65 (6%).
Among those who would consider returning to work (58%), the most important factors when choosing a paid job were flexible working hours (32%), good pay (23%), and being able to work from home (12%).
Around 1 in 5 (18%) said they were currently on an NHS waiting list for medical treatment; this rose to 35% for those who left their previous job for a health-related condition.

The Over-50s Lifestyle Survey is a cross-sectional survey. This means that different respondents have answered each wave of the questionnaire and therefore changes are not directly comparable.

Older people in the labour market

Separately, we released a publication on older people in the labour market using Labour Force Survey (LFS) data on 12 September, which found that from April to June 2022, people aged 65 and over in employment increased by a record 173,000 to 1.468 million. However, this increase was driven by rises in part-time work, and therefore the average hours worked for those aged 65 and over fell in the same time period.

Long-term sickness

We considered the increases in long-term sickness among the economically inactive, and the guidance we give field officers working on the LFS when asking this question. To explain further, the issue of long-term or chronic illness is addressed in two different parts of the LFS.

There is a section on health, that looks at whether people have health conditions or illnesses lasting or expecting to last 12 months or more. However, this section is looking generally at disabilities and health conditions, rather than being directly related to labour market status.

The long-term sickness series that was referenced during our discussion relates to people who give long-term sickness as the main reason why they are neither in work (employed) or searching for work (unemployed).

Once establishing that someone is neither in work nor searching for work, they are asked:

May I just check, what were the reasons you did not look for work (in the last 4 weeks)?

Waiting for the results of an application for a job/being assessed by a training agent
Student
Looking after the family/home
Temporarily sick or injured
Long-term sick or disabled
Believe(s) no jobs available
Not yet started looking
Do(does) not need employment
Retired from paid work
Any other reason

Following this, they are asked which of these is their main reason for not looking for work, which is used for classifying them within the labour market framework. The only further guidance the respondent may be given relating to sickness is if they are unsure whether to classify their sickness as temporary or long-term. The interviewer would indicate that long-term would be one that has lasted, or is expected to last, 12 months or more.

The long-term time series for long-term sickness in the labour market can be found on our website. Figure 1 shows that the number of people who are economically inactive mainly due to long-term sickness has risen to a record high, but not a record proportion of the population aged 16 to 64 years.

Figure 1: The number of people who are economically inactive mainly due to long-term sickness

Graph showing the number of people who are economically inactive mainly due to long-term sickness

Source: Office for National Statistics – Labour Force Survey For a more accessible version, please visit our accessibility policy.

Due to sample size limitations, we do not routinely publish estimates of long-term sickness at a regional level from the LFS. However, using the Annual Population Survey (APS), we do publish estimates of long-term sick as the main reason for economic inactivity at a regional level. This dataset allows you to access estimates of economic inactivity by region by sex by main reason for inactivity.

Trends in self-employed

During the session, we discussed the movement of those who are self-employed to employed during the pandemic, and whether these were real changes in employment status rather than reclassification. It is difficult to put a figure on the numbers that fall into different reasons for changing from self-employed to employee status during the pandemic.

Even prior to the pandemic a significant number of respondents would report that they were an employee in one period and self-employed in another period, despite also reporting that they hadn’t changed jobs. In general, the net flow of this has been around 20-30,000 per quarter. Similarly, those who reported that they had changed jobs between interviews had a general net flow of around 10-20,000 per quarter prior to the pandemic.

During the pandemic, both flows shifted from a net flow from employee to self-employed, to people changing their status from self-employed to employee. In the case of those who hadn’t changed jobs, there was a net flow of around 350,000 from self-employed to employee for people who had not changed jobs, compared with around 100,000 for those who had.

We published an article in July looking at changes in self-employment in the UK from January 2020-March 2022, which noted that large increases in the number of self-employed workers remaining in the same job but reclassifying their labour market status to “employee” were observed between April and September 2020 (coinciding with the introduction of the furlough scheme), most commonly among business directors and partners, and those in high-skilled occupations. Self-employment had fallen across all industries, most notably in construction (although construction remained the largest self-employment industry).

Quality of employment

David mentioned in the session that we had previously completed analysis looking at quality of jobs, which we did by using APS data for 2018. Work is currently underway on a 2021 ‘Quality Jobs’ publication, expanding on the previous methodology to include more dimensions of job quality. For time series purposes, this year’s publication will reproduce the 2018 analysis with 2021 APS data (i.e., low pay, satisfactory hours & desired contract). It will also add new dimensions on career progression, employee involvement, overtime and zero-hour contracts.

In future, we plan to produce this report annually and make this a regular output. The current expected date for the 2021 publication is December 2022 and we will share with the Committee once published.

We also have information which reflects labour market status by highest level of qualification. It shows a positive relationship between qualification and likelihood of being employed, with those educated to degree level having a much higher employment rate than any other category and those with no qualifications having a much lower employment rate than any other category. Between those two extremes, there is still a positive correlation with higher level of qualification, but the spread is less marked than those two extremes.

Vacancies

Unfortunately, we do not produce estimates of the number of vacancies in the UK broken down into the public and private sectors. The closest approximation we can make is to look at estimates by industry, making assumptions around their public/private splits based on employment statistics which are available with a sector breakdown.

Table 1 summarises the overall position for the UK and for relevant industries both for the latest movements, as well as change since the pre-pandemic period.

	UK	Public Admin	Education	Health
Public Sector Share of Employees (BRES)	18%	99%	57%	48%
Vacancies in Jun-Aug 2022	1.266m	41,000	74,000	219,000
Quarterly Change	-34,000 -2.6%	+3,000 +9%	+3,000 +3.6%	+7,000 +3.4%
Change since Jan-Mar 2020	+470,000 +59%	+19,000 +86.4%	+25,000 +51.0%	+83,000 +61.0%

For Public Administration and Defence, 99% of employees were in the public sector therefore we can be confident that overall vacancy trends for this industry will also be in the public sector. In the latest period, June-August 2022, there were 41,000 vacancies in this industry, an increase of 3,000 (9%) on the quarter. The number of vacancies in this industry was 19,000 (86%) above its pre-pandemic level (Jan-Mar 2020).

The other two sectors worth exploring are Human Health and Social Work activities and Education, which respectively are comprised of 48% and 57% public sector employees. The more even public/private split within these industries makes it more difficult to interpret whether vacancy trends are more or less concentrated in one sector the other.

Focusing firstly on Education, the number of vacancies in Jun-Aug 2022 was 74,000, an increase of 3,000 (3.6%) on the quarter and 25,000 (51%) when compared with Jan-Mar 2020. Looking then at Health and Social Work there were 219,000 vacancies in the latest period, an increase of 7,000 (3.4%) on the quarter, and 83,000 (61%) compared with Jan-Mar 2020.

What can be seen across all three industries is that the latest quarterly change saw an increase in the number of vacancies, moving in the opposite direction to the overall UK picture and to the majority of other industries. Looking at the pre-pandemic comparisons, if we exclude the smallest industries which can often show large proportional changes, only Accommodation and food service activities had a larger percentage increase in the number of vacancies (97.6%) than Public Administration. The changes for Education and Health were more in line with the national average, though as noted above recent movements were not.

EU Exit impact

The Committee were interested in understanding the sectors that have had migration impacts. The fall in employment seen since 2016 have been largely driven by UK nationals as shown in the Changing Trends and Recent Shortages in the Labour Market publication¹. In the 12 months to September 2020, the number of EU workers increased by 119,000 when compared with the same period in 2016. The change on the year from October to September 2020 to October to September 2021 saw a fall of 91,000 EU workers, suggesting a possible pandemic effect.

Payrolled employment counts from HMRC showed the same signal of a fall in EU workers, indeed, the magnitude was higher using this measure, though comparing a longer time period. Between June 2019 and June 2021, payrolled employments held by EU nationals fell by 6% (171,100). This was offset by non-EU nationals, which increased by 9% (186,300) in the same period. There is a lot of variation at industry level, meaning changes in the makeup of migration could be affecting some industries more than others.

In the same period, the largest decline in total payrolled employments was seen in Accommodation and food services; this was driven by a 25% (98,400) fall in payrolled employments of EU nationals during the two years up to June 2021. There were also large falls by EU employments in Agriculture, forestry and fishing and Arts, entertainment and recreation. These sectors also saw falls in non-EU employments. Indeed, the three sectors that saw the largest growth of EU and non-EU employments were the same: Construction, Transportation and storage and construction and Health and social work.

According to the Business Insights and Impact on the UK Economy Survey, looking at why businesses are experiencing vacancies, a year ago (23 August to 5 September 2021) a quarter of businesses who were experiencing difficulties recruiting cited reduced numbers of EU applicants. This has gradually declined to 12 per cent (as of June 2022) as EU migrants have returned to the workforce.

International comparisons

During the session we discussed international comparisons. The latest data for this show that the UK has maintained relatively strong employment within the G7 throughout 2021 and into 2022. However, like the United States, the UK employment rate is still below pre-pandemic rates. We publish a monthly update on our website.

I hope this is helpful to the Committee, and please do let me know if we can assist further as the inquiry progresses.

Yours sincerely,

Mike Keoghan

Office for National Statistics oral evidence to the Lords Economic Affairs Committee’s inquiry on UK labour supply

On Tuesday 6 September, Mike Keoghan, Deputy National Statistician for Economic, Social and Environmental Statistics, Office for National Statistics, and David Freeman, Head of Labour Market and Households, Office for National Statistics, gave evidence to the Economic Affairs Committee for its inquiry, ‘the UK labour supply’.

A transcript of which has been published on the UK Parliament website.

Office for National Statistics follow-up written evidence to the Lords Youth Unemployment Committee

Dear Lord Shipley,

I write in response to the Youth Unemployment Committee’s report, “skills for every young person”. I am also following up on the Office for National Statistics (ONS) ongoing work to measure youth labour market status against economic background.

Recommendation: The Government must work with the ONS to improve the quality and quantity of employment data collected on specific groups of young people, in particular those from disadvantaged (such as FSM-eligible) and ethnic minority backgrounds. This data must be published at more regular intervals than is presently the case so that it can be interrogated by policymakers.

The ONS currently publishes data on employment status by ethnicity and by disability every quarter. These data are available from the Labour Force Survey (LFS), a quarterly run household survey of the employment circumstances of the UK population. Although specific statistics of young people could be produced from this source, due to sample sizes, they would not be of sufficient quality to reliably measure the labour market conditions for this level of granularity.

Currently we use an expanded version of the LFS, the Annual Population Survey (APS) for such granular estimates. This survey uses a larger sample and is available for 12-month periods four times a year i.e. January to December, April to March, July to June and October to September. Again the limiting factor would be sample size which affects the quality of very granular analysis.

To improve the granularity of these data, we are currently developing an “online first” survey called the Labour Market Survey (LMS). The LMS is a mixed-mode survey that focuses on the core data collection requirements needed to produce labour market estimates. As the LMS is designed to have a larger sample size it will improve the quality and quantity of estimates available for specific groups of young people. It should be noted however, that given the potential number of characteristics and the small size of some of the population groups, we will still need to be mindful of statistical quality where analysis becomes particularly granular.

Additionally, the increased sample size of the LMS will give us options around the balance between regularity and quality of our estimates. We will work with key users to take advantage of those options to tailor our output strategy to best suit their needs.

Measuring youth labour market status against economic background

As outlined in my letter from October 2021, I promised to follow up with the Committee on the measurement of youth unemployment by economic background. The Committee were interested in knowing whether we could produce analysis on this using the ONS LFS.

We have assessed whether this is possible and have concluded that it is.

We have looked at the social mobility data in the LFS, which are collected annually between July and September. These data collect information on an individual’s socio-economic background when they were 14 years old:

Where the respondent lived
Household composition (i.e., with parents, with other family, not living with family)
Main wage earner in the household
Occupation of main wage earner (i.e., a parent/guardian, joint-earners, or no earners)
Whether main wage earner is an employee or self-employed

We have assessed the possibility of producing analysis using these data, and their quality. Our main concern was small counts, given the small sample sizes of the young unemployed when broken down by socioeconomic background. We have looked at individual counts, non-responses, and proxy responses. We have investigated potential issues specific to the young, such as, likelihood of being unemployed or underemployed at some point in their life. We have also assessed whether we can breakdown the data by protected characteristics or by other characteristics of interest (e.g., regions).

We have concluded that the best way to present a breakdown of labour market status by economic background is to produce a measure of socioeconomic background using the National Statistics Socio-Economic Classification (NS-SEC), using occupation[1]. Using this method, we can derive the NS-SEC of an individual when they were 14 years old using the occupation of the main wage earner in the household at that time. This also covers individuals living in households where no one is in work. We believe that this is the best measure of socioeconomic background we can produce using the social mobility data in the LFS. The NS-SEC is considered to be one of the most accurate measures of socioeconomic status and is widely used in academia.

We will analyse both the unemployed and those who are not in work (unemployed and economically inactive) when it is possible and there are no sample size limitations.

We will present these data on the young people ages 16 to 24 broken down by smaller age groups when possible, i.e. 16-17, 18-21 and 22-24. This is because these three age groups are at a very different stage of their working life: the 16–17 age group are in mandatory education, the 18–21 age group are either in higher education or entering the labour market, while most young people in the 22–24 age group are already in the labour market. We will look at sex and disability, but not other protected characteristics due to the small sample. We also plan to compare our results to other age groups, for example those aged 25-34 or 35-49.

We expect to produce an article summarising our proposed analysis and findings by the end of April 2022, which will be shared with the Committee.

Please do not hesitate to contact me if I can be of any further assistance.

Yours sincerely,

Darren Morgan

Director, Economic Statistics Production and Analysis

[1]It is not possible to use the full method to derive the NS-SEC when an individual was 14 years old because LFS does not collect some information needed to derive it.

Office for National Statistics response to the Lords Youth Unemployment Committee report on “skills for every young person”