Lords Economic Affairs Committee

Office for National Statistics follow up correspondence to the Lords Economic Affairs Committee on labour market statistics

Dear Lord Bridges,

Firstly, David and I wanted to thank you for the opportunity to discuss labour market statistics with the Committee on 23 April. During the session we promised to follow-up on several areas of interest to the Committee.

How household composition has changed since before COVID-19

During our discussion, we said that we would revert with further information we have on how household composition has changed since the start of the COVID-19 pandemic. This was in response to a point raised by Lord Blackwell who asked if there is any evidence that more young people are living at home with their parents since the start of the pandemic.

The ONS produces a report called Families and Households in the UK. This release includes tables looking at the prevalence of different types of households, including the number of households with dependent children and households that have non-dependent children only. This report does not show a significant change in the mix of households by type or size since the start of the pandemic. There has been a recent change in family type between couples being married or not married when there are no dependent children in the family, with unmarried cohabiting couples slowly on the increase when there are no dependent children. However, this does not affect the household composition relating to non-dependent children. Longer term trends can also be found in analysis comparing the 2021 Census with the 2011 Census.

Economic inactivity in Scotland

Whilst discussing how inactivity varied between the nations of the UK, Baroness Liddell asked for some further information on the economic inactivity picture in Scotland specifically.

The ONS publishes information on the comparative labour market situation in each of the regions and countries of the UK in its monthly report, Labour market in the regions of the UK. The proportion of people aged 16 to 64 years in Scotland who are economically inactive, tends to be one or two percentage points higher than the UK average, but generally moves in the same ways.

Of those who are economically inactive, Scotland tends to have a higher proportion than the UK of those who consider their main reason for inactivity to be long-term sickness or disability. Conversely, Scotland tends to have a lower proportion economically inactive because they are looking after the family or home. Scotland also has a higher proportion of the economically inactive who consider themselves to be retired. Scotland also has a lower proportion of men who are economically inactive due to being students than the UK as a whole, although the proportion of women is comparable.

International comparisons of economic inactivity

Baroness Wolf raised whether the United Kingdom could be considered an “international outlier” when it comes to economic inactivity. I wanted to provide some further detail on this point.

Even though the UK maintained relatively strong employment throughout the pandemic, the UK is the only country within the G7 with employment rates below pre-pandemic levels and economic inactivity rates above pre-pandemic levels.

The economic inactivity rates for France, Germany and Italy are well below pre-pandemic levels as well as those of Canada and the US. Further information is contained in the annex to this letter.

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

ANNEX

International Summary

The UK maintains relatively strong employment within the G7 and all countries in the OECD dataset throughout 2021 and 2022. However, the UK remains the only G7 country with employment rates still below pre-pandemic levels (down 1.1%).

While the UK has seen unemployment rates above pre-pandemic levels for prolonged periods, it is now largely unchanged compared with pre-pandemic levels. Recently, the UK had seen the highest rise in unemployment since before the pandemic out of the G7 countries, however now we are down in 4^th, with Japan, the United States and Canada all above their pre-pandemic levels. The other G7 countries are below their pre-pandemic unemployment rates.

Pre-pandemic, the UK had relatively low economic inactivity rates compared with other countries. It is now the only G7 country where the economic inactivity rate is still above pre-pandemic levels (1.1 percentage points above pre-pandemic rates).

Looking at it more broadly, the OECD members collectively have generally seen a growth in employment rate, and a decrease in unemployment and inactivity rates since the pandemic; with the UK performing in mostly the opposite direction to its fellow members.

OECD employment, unemployment and economic inactivity rates

	Employment			Unemployment			Economic Inactivity
Country	Pre-pandemic rate (Q4 2019)	Q1 2024*	Change	Pre-pandemic Rate (Q4 2019)	Q1 2024*	Change	Pre-pandemic Rate (Q4 2019)	Q1 2024*	Change
Canada	74.6	75.0	0.5	5.7	5.9	0.2	20.8	20.0*	-0.8
France	66.7	68.3*	1.6	8.2	7.5*	-0.7	27.3	26.0	-1.3
Germany	75.8	77.3*	1.6	3.2	3.1*	-0.1	22.1	20.1*	-1.9
Italy	59.0	62.1*	3.1	9.6	7.3*	-2.3	34.6	32.9*	-1.7
Japan	78.1	79.1	1.0	2.3	2.5	0.2	20.0	18.7	-1.3
United Kingdom	76.1	75.0*	-1.1	3.9	3.8*	0.0	20.8	21.9*	1.1
United States	71.7	72.0	0.3	3.6	3.8	0.2	25.6	25.1	-0.5
EU Average	68.0	70.6*	2.6	6.6	6.0*	-0.6	27.0	24.8*	-2.3

*Where Q1 2024 figures are unavailable, Q4 2023 figures have been used.

For comparability UK data shown here are sourced from the OECD and may differ from ONS published data as OECD do their own seasonal adjustment

Data are sourced from OECD – Employment is 15-64 (UK/USA is 16-64) and Unemployment is 15+ (UK/USA is 16+)

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 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 written evidence to the Lords’ Economic Affairs Committee’s inquiry on employment and COVID-19

Dear Lord Forsyth,

I write in response to the Economic Affairs Committee’s call for evidence for its inquiry, Employment and coronavirus (COVID-19).

As the Committee will be aware, the Office for National Statistics (ONS) is the UK’s National Statistical Institute, and largest producer of official statistics. We aim to provide a firm evidence base for sound decisions and develop the role of official statistics in democratic debate.

We have focused our evidence on the impact of COVID-19 on the labour market and what our initial statistics on industries affected, hours worked and changes to working practices are illustrating.

We anticipate that our analysis will track the long-term effect of the coronavirus pandemic on the Labour Market for some time and would be happy to continue to keep the Committee updated.

I hope this evidence is helpful to the Committee. Please do not hesitate to contact me if I can be of any further assistance.

Yours sincerely,

Jonathan Athow

Office for National Statistics written evidence: Employment and COVID-19

Executive Summary

Early estimates for September 2020 suggest that there is little change in the number of payroll employees in the UK; up 20,000 compared with August 2020. Since March 2020, the number of payroll employees has fallen by 673,000; however, the larger falls were seen at the start of the coronavirus pandemic.
Data from our Labour Force Survey show the employment rate has been decreasing since the start of the coronavirus pandemic, while the unemployment rate and the level of redundancies have been increasing in recent periods.
Total hours worked, while still low, show signs of recovering and had a record increase on the latest quarter. There are also fewer people temporarily away from work.
Vacancies also show signs of a recovery. After record-low vacancies in April to June 2020, there has been an estimated record quarterly increase of vacancies in July to September 2020, but they remain below the pre-coronavirus pandemic levels.
Annual growth in employee pay strengthened in August 2020 as employees continued to return to work from furlough; this followed strong falls in months since April when growth was affected by lower pay for furloughed employees, and reduced bonuses.
The Claimant Count increased in September 2020, reaching 2.7 million; this includes both those working with low income or hours and those who are not working.
The ability to work from home varies by occupation, with occupations requiring higher qualifications and experience more likely to provide the opportunity to homework than manual occupations. 69.6% of professional occupations did some working from home in April 2020, compared with 18.9% of skilled trade occupations.
The amount of homeworking undertaken also varies significantly between regions. Working from home during the coronavirus pandemic is more common in London, where 57.2% reported home working, than in the West Midlands, where just over one-third (35.3%) did some homeworking.

Unemployment before pandemic

Unemployment measures people without a job who have been actively seeking work within the last four weeks and are available to start work within the next two weeks. The unemployment rate is not the proportion of the total population who are unemployed. It is the proportion of the economically active population (those in work plus those seeking and available to work) who are unemployed.

The UK unemployment rate for the period June to August 2020 was estimated at 4.5%. This is 0.6 percentage points higher than a year earlier and 0.4 percentage points higher than the previous quarter.

Estimated unemployment rates for both men and women aged 16 years and over have generally been falling since late 2013 but have increased over recent periods.

For men during the period June to August 2020, the unemployment rate was 4.9%, 0.8 percentage points higher than a year earlier and 0.7 percentage points higher than the previous quarter. For women, it was 4.0%, 0.3 percentage points higher than a year earlier and 0.1 percentage points higher than the previous quarter.

The annual increase in unemployment was driven by unemployed people aged under 25 years (up 87,000).

Figure 1: Unemployment in the UK by age (aged 16 years and over), seasonally adjusted, between June to August 2015 and June to August 2020

Graph showing unemployment rates in the UK for different age groups, from June to August 2015, to June to August 2020

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

The immediate impact on the Labour Market due to COVID-19

The ONS statistics on the labour market include both detailed and less timely survey data from the Labour Force Survey, and more up to date indicators, including administrative data from HM Revenue and Customs and the Department for Work and Pensions.

Early indicators for September 2020 indicate that the number of payroll employees fell by 2.3% compared with March 2020. In September, 673,000 fewer people were in paid employment than in March 2020 and 20,000 fewer than in August 2020. The largest falls were seen at the start of the pandemic and while the number of payroll employees is still falling the decline is slowing. Flows analysis suggests that the falls in June to August are largely due to fewer people moving into payrolled employment.

Figures for June to August 2020 show an increase in the unemployment rate and the number of redundancies continue to increase, while the employment rate continues to fall. Over the quarter, there has been a large decrease in the number of young people in employment, while unemployment for young people has increased. While redundancies were still historically low, the annual changes are the largest seen since 2009 and the quarterly change is the largest on record.

The number of people who are estimated to be temporarily away from work includes furloughed workers, those on maternity or paternity leave and annual leave. Prior to the coronavirus pandemic there was on average 2 to 2.5 million people temporarily away from work. The number of people temporarily away from work rose to almost 7.3 million people in April to June 2020 but has fallen to 6.4 million people in June to August 2020.

Meanwhile, the n umber of vacancies available have increased, showing a record quarterly increase of 144,000, in the latest period, driven by the smaller businesses, some of which are reporting taking on additional staff to meet COVID-19 guidelines. However, vacancies remain around 40% lower than the pre-pandemic level.

Annual growth in employee pay strengthened in August 2020 as employees continued to return to work from furlough; this followed strong falls in months since April when growth was affected by lower pay for furloughed employees, and reduced bonuses. For the sectors of wholesaling, retailing, hotels and restaurants, and construction, where the highest percentages of employees returned to work from furlough, there was improvement in pay growth for August 2020, but growth remains negative.

Labour Force Survey (LFS) estimates of self-employment shows a sharp fall over the quarter, which is not reflected in employees. There was a decrease of 240,000 on the quarter to 4.56 million people, the number of employees in employment continues to increase by 92,000 on the quarter to 27.90 million for June to August 2020.

Regional Labour Market

Looking at regional breakdowns, for the three months ending August 2020, the highest employment rate estimate in the UK was in the South East (79.1%) and the lowest was in Northern Ireland (70.6%). The UK region with the highest unemployment rate estimate for June to August 2020 was the North East at 6.6%. The region with the lowest estimated unemployment rate was Northern Ireland at 3.7%. This was followed by Wales at 3.8%.

Figure 2: Unemployment rates by UK region, seasonally adjusted, June to August 2020.

Graph showing unemployment rates by UK Region

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

Hours Worked

Total actual hours worked in the UK remain low, down 158.2 million, or 15.1%, on the year, showing the impact of furloughing on the labour market. However, the latest figures show some signs of recovery, between March to May 2020 and June to August 2020, total actual weekly hours worked in the UK saw a record increase of 20.0 million, or 2.3%, to 891.0 million hours. Looking at average actual weekly hours, this fell by 4.8 hours on the year to 26.3 hours. This is a recovery from the record low of 25.8 in April to June 2020.

When looking at our experimental weekly data, please note these are based on old weighting methodology therefore results are likely to be revised when figures are next updated on 10 November. We saw the largest falls in average actual weekly hours during the week commencing 23 March, which is the week in which lockdown measures were introduced. This fall was most evident for those who work part-time and for the self-employed. The fall in average actual weekly hours continued throughout the weeks in April, however since May we have seen hours for all groups start to increase slowly, although we are yet to see any group reach their pre-lockdown level. Self-employed hours have been more volatile than employee hours throughout the lockdown period and is not yet back in line with employee hours as was seen pre-lockdown.

Average hours per worker from the LFS have fallen significantly when compared to the same period in 2019, which isn’t surprising given the impact of the pandemic. However, imputation used for the LFS was not designed to deal with the changes experienced in the labour market in recent months. Experimental work with adjusted methodology suggests that during the early stages of lockdown we were understating the full extent of the reduction in hours. However, now that hours are increasing, this has reversed so that the experimental methodology now suggests the actual number of hours are approximately 2.2% higher than stated.

Figure 3, based on data for May to July 2020 and using the old weighting methodology (latest data available), shows the industries that experienced the largest reduction in hours because of the coronavirus pandemic are also those where this reduction is most understated. For example, using this adjusted imputation methodology, the hours worked in accommodation and food service activities decrease by a further 2.1 hours compared with the original imputation method, to an average of 11.4 hours a week in May to July 2020.

Figure 3: Average actual weekly hours worked by industry (people aged 16 years and over), not seasonally adjusted, between May to July 2019 and May to July 2020

Graph showing which industries have experienced the largest reduction in hours worked, from May to July 2019, to May to July 2020

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

Impact on sectors

Industrial breakdown of the labour market can be seen across several of our labour market sources which all show that the impact of COVID-19 varied by sector. Figure 4 looks at the latest vacancies series where the “Arts, entertainment and recreation” sector struggled the most during the pandemic, with vacancies in August 2020 around 78.2% lower in July to September compared to January to March 2020. Accommodation and food services” also saw a large fall of 90.4% between January to March 2020 to April to June 2020, however it has seen a stronger recovery in the latest quarter than “arts, entertainment and recreation” and estimated vacancies are 61.9% lower than January to March 2020. “Construction” and “transport and storage” sectors are showing the biggest signs of recovery, both saw large quarterly falls in vacancies at the start of the pandemic in April to June 220 (71.7% and 70.7% respectively). Construction in particular has recovered well with estimated vacancies in the latest quarter 18.2% lower than January to March 2020.

Figure 4: Three-month average vacancies in the UK, seasonally adjusted, between January to March 2020 and July to September 2020; index January to March 2020=100, difference in percentage points compared with January to March 2020

Graph showing three-month average vacancies in the UK by industry

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

Figure 5: Changes in the number of jobs in the UK, seasonally adjusted, March 2020 to June 2020

Graph showing changes in the number of jobs in the UK by industries, March 2020 to June 2020

Source: Office for National Statistics – Monthly Wages and Salaries Survey. Acronyms used in chart: workforce jobs (WFJ); employee jobs (EJ); self-employed jobs (SEJ); Government Supported Trainers (GST); and HM Forces (HMF). For a more accessible version, please visit our accessibility policy.

Average weekly earnings estimates are based on the pay period including the last week of each month. Pay estimates are based on all employees on company payrolls, including those who have been furloughed under the Coronavirus Job Retention Scheme (CJRS).

Between June to August 2019 and June to August 2020, average pay growth varied by industry sector (Figure 6). The public sector saw the highest estimated growth, at 4.1% for regular pay. Negative growth was seen in the construction sector, estimated at negative 5.3%, the wholesaling, retailing, hotels and restaurants sector, estimated at negative 1.8%, and the manufacturing sector, estimated at negative 0.9%. This is, however, an improvement on the growth rates during May to July 2020.

Figure 6 also includes estimates of annual growth in regular pay for the single month of August 2020. For the construction, manufacturing, and the wholesaling, retailing, hotels and restaurants sectors, the August 2020 estimate of annual growth is notably higher than for the three-month average June to August 2020.

This pattern of pay growth is closely linked to the proportion of employees who are furloughed and the extent to which employers have topped up payments received for these employees under the CJRS. The ONS has published estimates of approximately 12% of the workforce on partial or full furlough leave during 24 August to 6 September 2020, with the arts, entertainment and recreation sector, and the accommodation and food service activities sector having the highest proportions of furloughed workers, at 41% and 29% respectively. These industries also showed 10% of the workforce that were still on partial or full furlough leave returned from leave in the last two weeks.

Figure 6: Annual growth in Great Britain nominal average weekly earnings excluding bonuses by sector, seasonally adjusted, May to July 2020 compared with June to August 2020 and August 2020

Graph showing annual growth in regular pay, by industry

Source: Office for National Statistics – Monthly Wages and Salaries Survey. For a more accessible version, please visit our accessibility policy.

Figure 7 shows, by lower-level industry, average weekly employee pay in May 2020 and the percentage change in pay compared with May 2019. Figure 7 indicates that the three lowest-paid industries: accommodation and food service activities, the retail trade and repairs industry, and the arts, entertainment and recreation industry, all saw falls in pay compared with May 2019.

This is closely linked to differing numbers of employees being furloughed across industries (as indicated by HMRC data published on 11 June and ONS estimates published fortnightly), affecting the numbers of hours worked, as shown by LFS estimates. The decline in pay received by employees, especially those in lower- paid jobs, may contribute to increases in benefits claims caused by decreased household income.

Figure 7: Average weekly earnings excluding bonuses and annual percentage pay growth in Great Britain by industry, May 2020

Graph showing average weekly earnings and annual percentage change in regular pay, by industry

Source: Office for National Statistics – Monthly Wages and Salaries Survey. For a more accessible version, please visit our accessibility policy.

People who could potentially move into unemployment

Increases in unemployment are matched by decreases in numbers in other groups of people who are out of work and could potentially be seeking employment

These groups include:

employees who, because of the impact of the coronavirus pandemic, have reported that they are temporarily away from work and not getting paid
self-employed people who are temporarily away from work but not eligible for the Self-Employment Income Support Scheme (SEISS)
people who are economically inactive as they are not currently looking for work but may look for work in the future if circumstances change.

Please note that the figures below are based on the old weighting methodology of the LFS, they will be updated on 10 November. Between April to June 2020 and May to July 2020, the number of people in these groups decreased from 2.13 million to 2.03 million (Figure 8). This decrease in the number of people who are around the fringes of unemployment coupled with the observed increase in unemployment suggests that some of the people who could have potentially been seeking employment in the previous period (April to June 2020) are seeking employment in May to July 2020.

Figure 8: Economically inactive who may seek employment, those away from work because of the pandemic and not getting paid, and all unemployed (aged 16 years and over), not seasonally adjusted, April to June 2020 and May to July 2020

Graph showing the number of economically active people and their employment status as a result of Covid-19

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

Changes to Working Practices

Homeworking during coronavirus pandemic

The ONS has published analysis of Labour Market Survey data from April 2020 on homeworking patterns in the UK, broken down by sex, age, region and ethnicity.

In April 2020, nearly half (46.6%) of people in employment did some of their work from home, with the vast majority (86.0%) of these homeworkers stating that this was because of the coronavirus pandemic. Of those who did some work from home, around one-third worked fewer hours than usual (34.4%), and around one-third worked more hours than usual (30.3%).

There are regional variations for those doing some of their work at home. More than half of people living in London (57.2%) did some work at home, while just over one-third of workers living in the West Midlands (35.3%) and Yorkshire and The Humber (37.6%) did some of their work from home.

Wales, Scotland, and Northern Ireland saw broadly similar proportions of homeworkers (approximately 40%).

Of those residents of London who did some work at home, 91.6% cited the coronavirus pandemic as their main reason for doing so. Conversely, the North East (76.6%) and the South West (79.1%) were the two regions where respondents were least likely to cite the coronavirus pandemic as the main reason for homeworking.

Figure 9: Homeworking rates, by region, of those in employment (aged 16 years and over), UK, April 2020

A homeworker refers to a person who did anything working from home in the reference week. Source: Office for National Statistics, Labour Market Survey. For a more accessible version, please visit our accessibility policy.

Occupations requiring higher qualifications and experience are more likely to provide homeworking opportunities than elementary and manual occupations. The first four major occupations all saw over half of their workers doing some amount of homeworking. Over two-thirds (69.6%) of the professional occupations did some work at home.

Conversely, the last five major occupations (except “Elementary Occupations” which has been excluded because of small sample sizes) all saw under 20% of their workers doing some amount of homeworking. The skilled trade occupations saw 18.9% of their workers home working

Those working in associate professional and technical occupations were most likely to cite the coronavirus pandemic as the main reason for homeworking (91.1%), while those in skilled trades occupations were least likely to do so (65.0%).

Figure 10: Homeworking rates, by occupation, of those in employment (aged 16 years and over), UK, April 2020

Graph showing homeworking rates by occupation for those in employment

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

Additional analysis published in July 2020 examined the ability to work from home by occupation, considering location, interaction intensity, exposure to hazards, physical activity and use of tools/equipment to develop a measure of the ability to work from home. This analysis was conducted using data from the Occupational Information Network (O*NET) database. One additional element – not fully reflected by the measure – is the access to technology and extent to which the workplace is digitalised.

The analysis showed that professional occupations such as actuaries, economists and statisticians are most likely to be able to be done from home. Occupations such as these, alongside management, technical and administrative jobs, involve relatively little face-to-face contact, physical activity or use of tools or equipment.

Applying data from the Annual Survey of Hours and Earnings showed employees who earn higher hourly wages are more likely to be able to work from home. Employees in the 20% of the workforce most likely to be able to work from home had median earnings of around £19.00 per hour, compared with around £11.00 per hour for workers in the 20% of the workforce in jobs least likely to be adaptable to home working.

The gender split of the 20% of the workforce most likely to be able to work from home is fairly representative of the workforce as a whole: 49% are women. Around 75% of employees in the 20% of the workforce least likely to be adaptable to work from home are men.

Skill level of Employees Working from Home

According to the Standard Occupational Classification (SOC) manual, occupations can be classified into four skill levels defined with respect to the duration of training and/or work experience normally required to perform the job competently and efficiently.

The first skill level equates with competence associated with a general education, usually acquired by the time a person completes compulsory education and signaled via a satisfactory set of school-leaving examination grades. Competent performance of jobs classified at this level will also involve knowledge of appropriate health and safety regulations and may require short periods of work-related training. Examples of occupations defined at this skill level within the SOC2010 include postal workers, and catering assistants.

The second skill level covers a large group of occupations, all of which require the knowledge provided via a good general education as for occupations at the first skill level, but which typically have a longer period of work-related training or work experience. Occupations classified at this level include machine operation and caring occupations.

The third skill level applies to occupations that normally require a body of knowledge associated with a period of post-compulsory education but not normally to degree level. A number of technical occupations fall into this category, as do a variety of trades occupations and proprietors of small businesses. In the latter case, educational qualifications at sub-degree level or a lengthy period of vocational training may not be a necessary prerequisite for competent performance of tasks, but a significant period of work experience is typical.

The fourth skill level relates to what are termed ‘professional’ occupations and high-level managerial positions in corporate enterprises or national/local government. Occupations at this level normally require a degree or equivalent period of relevant work experience

Using the published data for occupation groups on the level of homeworking, one can sum occupations into skill categories and calculate a rate of homeworking for each skill level.

In 2019, the highest skill level showed the highest proportion of individuals homeworking, with the lowest skill level showing the least. While these data do not reflect the COVID-19 period, they do provide an insight into the skill group previously most likely to homework.

Figure 11: Percentage of employed reporting to have worked from home by skill group in 2019

Graph showing the percentage of employed reporting homeworking in 2019, by skill group.

Skill levels taken from SOC 2010. Source: Office for National Statistics – Annual Population Survey. For a more accessible version, please visit our accessibility policy.

Homeworking by Industry

This section includes information from the Business Impact of Coronavirus Survey for the period 7 September to 20 September 2020.

Across all industries, of businesses not permanently stopped trading, 9% of the workforce were on partial or full furlough leave (compared with 30% in early June), 59% were working at their normal place of work and 28% were working remotely.

The arts, entertainment and recreation industry and the accommodation and food service activities industry had the highest proportions of their workforce on partial or full furlough leave under the terms of the UK government’s Coronavirus Job Retention Scheme (CJRS), at 32% and 27% respectively

The information and communication industry and the professional, scientific and technical activities industry had the highest proportions of their workforce working remotely instead of at their normal place of work, at 74% and 62% respectively.

Figure 12: Working arrangements, businesses who have not permanently stopped trading, broken down by industry, weighted, UK, 7 September to 20 September 2020

Graph showing working practises for businesses still trading in UK, 7 September to 10 September 2020

Source: Office for National Statistics – Business Impact of Coronavirus. For a more accessible version, please visit our accessibility policy.

UK Statistics Authority response to the Lords Economic Affairs Committee report on their use of RPI inquiry

Dear Lord Forsyth,

Following the Committee’s recent report on Measuring Inflation, I write with the UK Statistics Authority’s response to your recommendations.

As your report made clear, the question faced by the Authority in 2012 was whether to make substantive changes to the construction of the Retail Prices Index (RPI). The decision made by the then National Statistician, one widely supported in the consultation at the time, was to leave the RPI unchanged. This decision gave rise in turn to the conclusion that the RPI should be treated as a legacy measure, with no future substantive changes to its construction and methods. That position was endorsed by an independent review of consumer prices led by Paul Johnson, which reported in 2015. In the period since, the Office for National Statistics (ONS) has developed alternative measures of inflation, and the Authority has urged users to move away from the RPI.

Nonetheless, the RPI continues in widespread use. This – along with new advice from ONS on the flaws of the RPI, new advice from the National Statistician’s Advisory Panels, and the urgings of your Committee – convinced the Board that further action was necessary. The then National Statistician put options for the future of the RPI to the UK Statistics Authority’s Board on 26 February 2019.

After receiving this advice, Sir David Norgrove, Chair of the UK Statistics Authority, wrote on behalf of the Board to the previous Chancellor of the Exchequer on 4 March 2019 with the following recommendations:

that the publication of the RPI be stopped at a point in future; and
in the interim, the shortcomings of the RPI should be addressed by bringing the methods of the CPIH into it.

Today the Chancellor has announced his intention to consult on whether to bring the methods in CPIH into RPI between 2025 and 2030, effectively aligning the measures.

The proposals made by the Authority address many of the recommendations made by the Committee in its report. More detailed responses to the other recommendations are set out in the attached Annex.
Yours sincerely,

Sir David Norgrove

Related Links:

ONS oral evidence – (2018)

UKSA oral evidence – (2018)

UKSA follow up written evidence – (2018)

Annex: Detailed Response to Specific Recommendations

We heard evidence that the Carli formula, as used in the RPI, produces an upward bias. But expert opinion on the shortcomings of the RPI differs. (Paragraph 99)
There is however broad agreement that the widening of the range of clothing for which prices were collected has produced price data which, when combined with the Carli formula, have led to a substantial increase in the annual rate of growth of RPI. (Paragraph 100)
We are not in a position to reach a conclusion on the question of whether the Carli formula is problematic in areas other than clothing. Given the properties of the Carli formula that may lead to upward bias have long been evident, yet expert opinion still differs, it may be a perpetual debate. (Paragraph 101)

The Authority agrees that there is never likely to be unanimity on the issue of the elementary indices (e.g. Carli, Jevons or Dutot) used in inflation measurement. There is no single universally agreed set of criteria against which to judge them and there are specific examples where each index can be shown to produce either plausible or implausible results. A judgement therefore needs to be taken in the round.

Our view is that the Carli is not generally a good index. A thorough exploration of the issues related to the Carli index was set out in both Chapter 10 of the independent review of consumer prices by Paul Johnson and the 2012 review of UK consumer price statistics conducted by Erwin Diewert, a leading authority on index numbers.

This view is supported by international practice and the National Statistician’s Technical Advisory Panel for Consumer Prices. Many technical manuals and academic papers also highlight the undesirable properties of the Carli index. Regulations on the production of the Harmonised Index of Consumer Prices go further and state that the Carli should not be used unless it can be demonstrated to behave in a similar way to the Jevons or Dutot.

We agree that the interaction between the Carli index and the collection of clothing prices created an increase in the rate of RPI inflation in 2010. It was this event that led ONS and the Authority to put in place a programme of work that led to the 2012 consultation on the future of RPI.

4. Given its widespread use, it is surprising that the UK Statistics Authority is treating RPI as a ‘legacy measure’. The programme of periodic methodological improvements should be resumed. (Paragraph 116)

5. We are unconvinced by the National Statistician’s suggestion that in publishing statistics that serve the public good, the interests of those who may be affected negatively by any change should be taken into account. It is not clear from section 7 of the Statistics and Registration Service Act 2007 that this is a relevant consideration for the statistical authorities to be taking into account when they are producing and publishing statistics. (Paragraph 117)

6. What is clear from section 7 is that the UK Statistics Authority has to promote and safeguard the quality of official statistics, which includes their impartiality, accuracy and relevance, and coherence with other statistics. In publishing an index which it admits is flawed but refuses to fix, the Authority could be accused of failing in its statutory duties. (Paragraph 118)

7. We believe section 7 requires the Authority to attempt to fix the issue with clothing prices. Section 21 may require the Authority to consult the Bank of England over the change and obtain the consent of the Chancellor of the Exchequer, however this provision cannot be cited as a reason for not requesting the change in the first place. (Paragraph 119)

8. If the Authority requests the change, the Chancellor of the Exchequer should consent to it. It is untenable for an official statistic, that is used widely, to continue to be published with flaws that are admitted openly. (Paragraph 120)

The announcements by the UK Statistics Authority and HM Treasury on 4 September deal with this substantive issue raised in these recommendations, and are summarised in the covering letter to this response.

9. While we accept the arguments that consumer price indices have different purposes, we do not believe this warrants the production of multiple indices for government use. Two different measures of inflation allow a government to engage in ‘inflation shopping’. (Paragraph 134)

10. The Government should address the imbalance in its use of consumer price indices. It risks undermining public confidence in economic statistics. It is encouraging to see that the present Government is taking some steps to address the imbalance, for example with the change to uprating business rates by CPI and recent discussions around rail fares. (Paragraph 135)

11. In future there should be one measure of general inflation that is used by the Government for all purposes. This would be simpler and easier for the public to understand. But the UK Statistics Authority should also continue to develop the Household Cost Indices, discussed below. (Paragraph 136)

We welcome the Committee’s recommendation that the Household Cost Indices should continue to be developed. On 28 June 2019, the National Statistician outlined the next steps in the development of these Indices.

12. We disagree with the UK Statistics Authority that RPI does not have the potential to become a good measure of inflation. With the improvements to RPI that we set out in the previous chapter, and a better method of capturing owner-occupier housing costs as discussed below, we believe RPI would be a viable candidate for the single general measure of inflation. (Paragraph 139)

13. We are not convinced by the use of rental equivalence in CPIH to impute owneroccupier housing costs. The UK Statistics Authority, together with its stakeholder and technical advisory panels and a consultation of a wide range of interested parties, should agree on the best method for capturing owner-occupier housing costs in a consumer price index. (Paragraph 153)

14. Once a method of capturing owner-occupier housing costs has been agreed, the UK Statistics Authority, after consulting the stakeholder and technical panels, should decide which index to recommend as the Government’s single general measure of inflation. The Government should have adopted the preferred candidate as its single general measure of inflation within five years. (Paragraph 154)

Owner occupiers’ housing (OOH) costs are one of the most challenging aspects of inflation to measure. There is also no single approach that will be correct in all circumstances, as the choice will depend on the purpose of the index and also practical issues around data availability. In light of this, ONS has spent the last 10 years developing and consulting on its approaches to owner occupiers’ housing costs.

The development of an OOH measure for CPI was first considered in 2009 by the Consumer Prices Advisory Committee (CPAC). The committee then spent the next three years investigating different approaches to measuring OOH costs. In September 2010 it narrowed down the options to two – net acquisitions and rental equivalence – which it evaluated in detail against the five dimensions of statistical quality defined by the European Statistical System. The Committee finally agreed on rental equivalence in April 2012, giving consideration to both conceptual appropriateness and how well the index could be calculated in practice.

A first consultation was launched in the summer of 2012, in which users were asked about rental equivalence. The responses were fairly evenly split between support for rental equivalence, net acquisitions and neither approach. The National Statistician chose rental equivalence reflecting the quality of the underlying data available and whether asset prices were appropriately treated. The process is described in more detail in Appendix A of the CPIH Compendium.

Paul Johnson’s review of consumer prices was published in January 2015. This looked again at CPAC’s recommendation to use the rental equivalence method. It concluded the underlying assumptions are reasonable in a UK context and that the measure is based on a large, detailed source of underlying data. Therefore, the Review recommended that ONS should continue to use the rental equivalence measure.

A further consultation was conducted on the findings of the Johnson Review. Responses to the review on CPIH and OOH were again mixed, highlighting that users are unlikely to come to an agreement on the most appropriate choice for measuring OOH costs.

The Office for Statistics Regulation’s 2016 re-assessment of CPIH as a National Statistic noted that ‘there is some disagreement among users about the concepts and methods…’ Work to address these recommendations resulted in a wide ranging process of user engagement on CPIH, and the publication of numerous supporting materials such as the CPIH Compendium, which articulates the rationale for ONS’s choice of rental equivalence alongside the pros and cons of each approach, an ongoing published comparison of alternative OOH measures, and documentation on the various users and uses of our consumer price inflation statistics.

ONS have also looked at international practice where they found widespread use of the rental equivalence measure. The approach taken by different countries is summarised in the CPIH Compendium. Of the 40 countries considered, the most common approach is rental equivalence (12 countries) if discounting those that exclude OOH altogether (15 countries). It is also worth noting that the method requires a reasonably large rental market to work, and so many countries may be constrained in their choice by the availability of data. The countries that use rental equivalence include the United States, Germany, Norway and the Netherlands.

In light of the 10 years of development and consultation, ONS are not minded to undertake any further engagement with users and experts specifically on rental equivalence and owner-occupier housing costs. There is never likely to be agreement on a single approach. ONS views rental equivalence as the correct approach conceptually for an economic measure of inflation, and one where sufficient data is available to make it practical. Of course, they remain committed to ongoing monitoring and development of the CPIH and the Household Cost Indices.

15.Our recommendations will not however solve the issue of index or inflation shopping immediately. The Government will need to take action in the interim to address this. (Paragraph 155)

16.While the single general measure is being determined, the Government should switch to CPI for uprating purposes in all areas where it is not bound by contract to use RPI (except for the interest rate on student loans which, as we recommended in our Treating Students Fairly report, should be set at the ten year gilt rate thus reflecting the Government’s cost of borrowing). (Paragraph 156)

17.The Government should begin to issue CPI-linked gilts and stop issuing RPI-linked gilts. We heard evidence to suggest there was sufficient demand to make a viable market. (Paragraph 170)

18.Once the long-term single official measure of inflation has been agreed, gilts should begin to be issued that are linked to that index. The prospectuses for new issuances of index-linked gilts should be clear that the inflation index will change to the Government’s single general measure of inflation once it has been agreed. (Paragraph 171)

Recommendations (15) to (18) are primarily directed at HM Government and the Authority has nothing to say on those issues. We continue to urge the Government to cease to use the RPI for its own purposes where practical.

19. Once the single general measure of inflation has been introduced, the UK Statistics Authority and the Government should decide whether RPI should continue to be published in its existing form for the purposes of existing RPI-linked contracts, or whether a programme of adjustments should be made to the RPI so that it converges on the single general measure. (Paragraph 194)

20. To avoid disruption, we envisage any programme of convergence would take place gradually, over a sufficiently long time, and that the plan for that should be published at the outset. (Paragraph 195)

21. We note that the consent of the Chancellor of the Exchequer to changes to RPI that cause material detriment to index-linked gilts holders is no longer required after the last issuance to which that clause relates to expires in 2030. (Paragraph 196)

We strongly agree that any changes to the RPI or stopping the publication of RPI needs to be carefully planned. The Authority and ONS have been discussing the mechanics of any changes with the Government in the run up to the 4 September announcement.

Office for National Statistics response to the Lords Economic Affairs Committee’s report on economics of higher, further and technical education

Dear Lord Forsyth,

Further to the National Statistician’s comments to the Committee on 17 July, I am writing to offer the Office for National Statistics (ONS) response to the Economic Affairs Committee report on Treating Students Fairly: The Economics of Post-School Education.

The Committee made one recommendation relating to ONS:

“Most student loans will not be repaid in full: some will be paid in full, some not at all, and a lot only partially repaid. The expected write-offs should be shown in the deficit when the loan is issued. The true cost of funding higher education would then be immediately apparent. This would allow for a better discussion as to where funding in the higher education system should be allocated. (Paragraph 42 and 388)”

In our written evidence, submitted to the Committee January 2018, we explained how student loans are currently treated in the National Accounts and public finance statistics, specifically that student loans are treated as any other loan following the approach mandated in the European System of Accounts and the United Nations System of National Accounts.

However, as you mention in your report, there are certain features of UK student loans that appear to distinguish them from other loans. They have a high level of contingency, both as they are based on a student’s subsequent income and as there are a number of scenarios under which the loans, or the portion of the loans not yet repaid, will be written off.

Reflecting further on the contingent nature of student loans and the issues raised by the Committee, we recognise that there is a need to establish whether student loans should be treated within national accounts as loan assets for government, or whether they should in part, or in total, be viewed as contingent assets.

In April we announced that we had begun a review into how best to record student loans within national accounts and the public sector finances. This is not an easy issue to tackle and one which has implications wider than the UK given the use of income contingent loans in other countries. We have therefore been discussing with international agencies and other countries the relevant issues and examples with a view to identifying the appropriate statistical treatment, and from there to develop relevant guidance.

On 17 July we published information on the main accounting options we have been considering as part of the student loan review, along with the pros and cons and implications for each option. There is still work to do on evaluating the different options but we are continuing to engage with our international partners and are hopeful that we will reach internationally an agreed treatment by the end of the year. It is important to note, however, that implementation within the fiscal statistics and national accounts is likely to take longer given the complex nature of many of the options under consideration. I hope the Committee will welcome this work.

Yours sincerely,

Jonathan Athow Deputy National Statistician and Director General, Economic Statistics Office for National Statistics

Office for National Statistics oral evidence to the Lords Economic Affairs Committee’s inquiry on the use of RPI

On 17 July 2018, John Pullinger CB, UK National Statistician, Head of the Government Statistical Service and Chief Executive of the UK Statistics Authority, gave evidence to the Economic Affairs Committee’s inquiry on the use of RPI.

Office for National Statistics written evidence: Employment and COVID-19

Executive Summary

Unemployment before pandemic

The immediate impact on the Labour Market due to COVID-19

Regional Labour Market

Hours Worked

Impact on sectors

People who could potentially move into unemployment

Changes to Working Practices

Homeworking during coronavirus pandemic

Skill level of Employees Working from Home

Homeworking by Industry

Annex: Detailed Response to Specific Recommendations

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