Office for National Statistics written evidence to the Public Administration and Constitutional Affairs Committee’s inquiry on data transparency and accountability: COVID-19

Dear Mr Wragg,

Thank you for your letter of 2 February requesting an update on COVID-19 data, following the evidence I gave to the Committee in May and September 2020 for the inquiry data transparency and accountability: COVID-19. I have addressed the specific questions from your letter below. I also wanted to use this opportunity to formally update the Committee on Census 2021 preparations and highlight the new Government Statistical Service (GSS) user engagement strategy, that had been part of a recommendation from this Committees’ governance of official statistics report.

Data transparency and accountability: COVID-19

Key areas of progress since the start of the pandemic

The pace of progress made with COVID-19 data and analysis is truly remarkable, and testament to the hard work of colleagues in the Office for National Statistics (ONS) and across the GSS. You ask what key data do we have now that we did not have in March/April 2020: the answer is really an extraordinary amount. I have focused on the new surveys we set up early on in the pandemic and looked specifically at their progress since I gave evidence in September.

COVID-19 Infection Survey (CIS)

When I gave evidence in September, we spoke about the continuing roll-out of the CIS to the devolved administrations. As of 23 October 2020, the CIS has been publishing as a UK wide survey. This enables the devolved administrations to make better informed decisions using data that are directly relevant to their populations and giving a much deeper picture of the UK as a whole. We can now provide critical evidence on community infections which is tailored to the specificities of each country and enables decision makers to respond with bespoke approaches where appropriate.

For England, in addition to the regional estimates, we are now also producing sub-regional estimates which gives us a much richer picture of infection rates in the community.

In response to the emergence of the new variant, we can estimate the proportion of cases which are compatible with this new variant across the UK and within the regions in England. This means that we have a better understanding of its impact on positivity rates in different regions and can more closely track and understand its spread across England.

Opinions and Lifestyle Survey (OPN)

The OPN has increased sample size, which has allowed us to do granular breakdowns by different demographics such as sex, age, ethnicity, income and disability, and more regular breakdowns, such as impacts of tiers and lockdowns on behaviours. It covers key compliance measures to help stop the spread of coronavirus such as washing hands, avoiding physical contact, wearing face coverings, social distancing and reasons for leaving home. The survey also captures personal wellbeing and perceptions of the future. Questions regarding attitudes to COVID-19 vaccination have recently been added, with data on the likelihood of having the vaccine and reasons for being unlikely to have it recently published, and there are plans to continue monitoring this as the vaccination rollout continues.

Business Impact of COVID-19 Survey (BICS)

BICs captures businesses’ views on financial performance, workforce, prices, trade, and business resilience. It is well placed to quickly respond to changing events by rapidly introducing new questions to meet policy needs; such as the impact of the end of the transition period on businesses which was incorporated into the survey towards the end of 2020 and for early 2021.

Weekly mortality release

We continue to provide weekly and monthly COVID-19 analysis using our mortality data. This is supported by ad hoc analyses such as occupational risk and regional variation. However, the main way we have been able to develop our mortality analysis is through the acquisition and integration of other data sources, which I discuss in more depth when discussing mortality demographics.

Other areas of progress

Other relevant COVID-19 linked surveys are also ongoing. For example, the second wave of the shielding survey will be published mid-February, which looks at clinically extremely vulnerable people (the shielding population) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

We set up the COVID-19 Schools Infection Survey to assess the role of schools in coronavirus      (COVID-19) transmission and how transmission within and from school settings can be minimised. Round one of this survey was published in December 2020, with round two due to be published in March.

And the Student COVID-19 Insights Survey includes information on the behaviours, plans, opinions and well-being of higher education students in the context of guidance on the coronavirus (COVID-19) pandemic, with the second wave of this published in January.

Localised data

As mentioned previously, we are now producing, from the CIS, estimates at a sub-regional level in England. This enables us to identify areas in England where cases are increasing, or positivity is particularly high. Tracking these trends is useful to help understand how and where COVID is spreading in the community and where action should be taken. Such estimates allow us to capture where the virus may be spreading at a lower level which would not largely be captured in regional or national estimates. We are continuing to work on improvements around our methodology for this analysis.

More broadly, a significant improvement since I gave evidence in May has been the setting up of the Joint Biosecurity Centre which provides localised data, and we work closely with them to collaborate on data and analytical needs.

Data to understand mortality demographics

Following my evidence in September, we have made progress in several areas to understand mortality demographics further, reacting to user needs, while continuing to publish our regular weekly and monthly publications using our mortality data.

In October we published an update to our ethnicity analysis and using linked Hospital Episodes data were also able to investigate the impact of pre-existing conditions on the risk of death from COVID- 19 on ethnic groups. A further follow-up piece will be published in March.

We have been working across government partners to provide better understanding on the impact of the pandemic on care homes, and to that effect we now provide in our weekly deaths publication deaths of care home residence alongside the existing series on deaths in care homes.

Using our mortality data, we have undertaken experimental cluster analysis of COVID-19, published in January. The analysis determined geographic clusters of raised COVID-19 mortality, and suggested that the known risk factors of age, population density, ethnicity and socioeconomic deprivation only partly explain the distribution of deaths across England and Wales. We also published an update on deaths by occupation in January (including teaching professions).

On 11 February we will be publishing updated analysis on COVID-19 mortality for disabled people. This will be first time we are able to use additional health data from Hospital Episodes and GP data to investigate the impact of underlying health conditions on the risk of death from COVID-19 of disabled people. We will also be including analysis on learning disabilities for the first time.

Increased data access from across Government is a big factor in what analysis we can produce. For example, we are now receiving monthly hospital data for England from NHS Digital and have also received a 20-year extract of primary care data for England. We have combined these with our census 2011 and mortality data and used this new linked data to produce several outputs looking at inequalities in COVID-19 mortality.

Using newly available data from the CIS we have been able to produce new experimental estimates on the prevalence of long COVID symptom, published in January. We are working to further refine the methods for producing the estimate and new questions will be asked on the CIS to provide a more direct measure.

Finally, the ONS has just started to receive vaccination data. We plan to combine this with our linked Public Health Data Asset and will then allow us to investigate areas such as risk of death from COVID following vaccination, the demographic breakdown or people who refuse the vaccine.

Lessons learnt for vaccine data

The ONS and the Government have been on the front foot when it comes to vaccination data collection. ONS surveys collect data that will provide wide-ranging information relating to vaccines. We have recently started asking questions around vaccinations in the CIS including the type of vaccine, the number of doses and when the latest dose was received. We are also taking monthly blood samples in 20% of the sample to monitor seroconversion. This will enable us to understand the impact vaccinations has on transmission within the population and antibody response.

The OPN has already started reporting on attitudes to vaccination, and in due course will report on vaccination uptake, refusal and compliance following vaccination. Other surveys are being scoped to further improve the evidence base on people’s attitudes and behaviours relating to vaccination. The ONS is also conducting linkage of NHS vaccination data with survey data, as well as administrative data on mortality and hospitalisation.

We have developed a comprehensive question bank to standardise data collection and maximise comparability of data across government. The question bank includes the topic of vaccines, with questions on the likelihood of having a vaccine when offered and reasons for vaccine hesitancy. Other topics available include following government guidance, leaving home and satisfaction with the testing programme.

We have developed a vaccine plan on a page which crystallises the key vaccine research questions currently. These are focused on vaccine effectiveness, vaccine refusals, attitudes to vaccines, impact of vaccinations on behaviours and adverse effects. This has been circulated across government groups including the 4 Nations Vaccine Group and the SAGE Vaccine Subgroup. The plan captures ongoing work that can answer those questions and more importantly identify where new data collection and analysis needs to happen.

In terms of vaccination data presentation and analysis, the ONS have made themselves available to provide advice at the beginning of the process, as we did for testing data following correspondence from Sir David Norgrove to the Secretary of State for Health and Social Care.

Data gaps and next steps

The COVID-19 pandemic has been a catalyst for incredible improvements across the GSS. The ONS has been a major part of those improvements, working with Government on their analytical priorities, rapidly setting up surveys to meet user needs, and advising on presentation and communication of data and analysis: I mentioned in my previous evidence that we embedded statisticians within No10 and Cabinet Office to provide statistical expertise.

Some of that has been through the Analysis Function, which I head up. It has been agile in its support of professions and departments in developing new products and insights in this novel policy area. For example, the Function responded quickly to support DHSC through short-term redeployment of analytical staff from across government, to produce high-profile daily reporting of cases, deaths and testing, which met both Government and public need.

Throughout this letter I have also highlighted the importance of fast and secure data sharing and collaboration between departments to produce cross-cutting analysis. The ONS is leading on the Integrated Data Platform (IDP), which will be a key enabler for enhancing the capability of sharing data across Government, and ultimately allow faster evidence-based policy decisions to be made. Positive engagement and timely sharing of data will be critical to the success of the IDP.

Census 2021

As set out in our statement of 22 January, Census 2021 is coming at a critical point. It will be fundamental to our understanding of the impact the coronavirus (COVID-19) pandemic has had on different communities and how we all live. Information from the 2011 Census has been crucial in our understanding of mortality for different groups during the pandemic, as set out in this letter already, and is the only source of local-level information on occupation and household composition. With fresh data from 2021, we will be able to update this analysis and use it alongside new data sources to give us the richest data we have ever had.

This will be a digital-first census and we will be encouraging people to respond online if they can on their mobile phones, laptops, PCs or tablets. We have seen that through the pandemic many households and services have successfully switched to online only. We will also be providing a comprehensive range of support for those who are not able to complete the census online, including paper questionnaires for around 10% of households, where the take-up of the online option is likely to be relatively low. They can either fill it in or use the code it includes to complete online.

The main census field operation will begin only after Census Day (21 March). The primary role of our field officers is to give help and encouragement to those who have not yet filled in their census questionnaire online or on paper, and to direct them to the support services they need. Our field  staff will never enter people’s houses; they will be supplied with PPE, will always be socially distanced and will work in line with all government guidance. They will be operating in the same way as a postal or food delivery visit.

The quality of census outputs relies on optimising response rates, which is at the heart of the design and collection operation. We have designed Census 2021 to be simple, straightforward and safe to complete, and the Government is confident following our advice that Census 2021 will be a success.

GSS User Engagement Strategy

The GSS user engagement strategy is a four-year strategy which has been developed in collaboration with a wide range of users and producers of statistics. It will be officially launched on 22 February 2021. The strategy presents a plan of action for building a more meaningful and sustained dialogue between producers, users and potential users of statistics. It sets out a vision for user engagement to be: “second nature for all producers of statistics, embedded into an organisation’s wider engagement activities and actively implemented throughout the statistical development, production and review cycle.” This vision is supported by three ambitious goals which promote collaboration, build capability and strengthen our culture of engagement.

The strategy will be delivered in phases to ensure sustainable change can be achieved. Producers of statistics will be able to draw on the help and expertise of the User Support and Engagement Resource (USER) hub to support the delivery of the strategy.

Please do let me know if I can help the Committee further with this inquiry, or more broadly. Yours sincerely,

Professor Sir Ian Diamond

Related links:

UK Statistics Authority and Office for Statistics Regulation written evidence to the Public Administration and Constitutional Affairs Committee’s inquiry on data transparency and accountability: COVID-19

Office for National Statistics follow-up written evidence to the Treasury Committee’s inquiry on the economic impact of coronavirus

Dear Mr Stride,

Thank you for inviting me to give evidence for the Committee’s inquiry on the economic impact of coronavirus on 20 January. While speaking to the Committee, I promised to provide further information on the comparability of GDP figures between countries; more specifically on approaches for measuring the output of the public sector.

In response to the increased interest in UK GDP compared to other countries, we have published an article today that looks at international comparisons of GDP. This explores, in full, the different changes in GDP between the UK and the other G7 countries.

The article is quite lengthy, but following my comments at the Committee, there are two particular comparisons I would draw you to. The first focuses on the difference between the ‘real’ and ‘nominal’ measure of GDP. GDP captures the value added through the production of goods and services in a country in a given period of time. GDP is recorded in current prices, often referred to as ‘nominal’, and in volume terms, often called ‘real’. The current price estimates simply record the value of output, income and expenditure. If we consider output, current price GDP can rise because we produce more goods and services, or because the prices of those goods and services are rising. The volume estimate of GDP takes out the effect of price rises and tends to be the headline estimate, and so typically is the focus for international comparisons.

The difference in practices for recording public sector output between countries only affects comparability of the headline volume or ‘real’ estimates of GDP. Current price or ‘nominal’ estimates of GDP are not affected and therefore more internationally comparable, but such comparisons do not always capture all the features of government services provided in the volume estimates. Figure 1 shows that while the UK’s performance on the volume measure is the weakest, the current price measure puts the UK in a much more comparable position.
The second comparison, at figure 2, looks at countries’ performance in terms of volume or ‘real’ GDP, if we remove all Government expenditure from the measure of GDP. While this also removes the effect of different practices for recording public sector output, it obviously means an important part of the economy is ignored. As the chart below shows, while this approach makes little difference to the fall in UK GDP, for other countries it makes the falls in their GDP substantially larger therefore narrowing the gap between the UK and other countries. The ‘volume GDP w/ GGFCE’ includes Government spending, while the ‘w/o GGFCE’ series excludes Government spending.

Figure 1: International comparisons of GDP highlight how the UK has been hit relatively worse than other advanced economies
Current price and volume G7 GDP, Quarter 4 2019 to Quarter 3 2020

Graph showing international comparisons of GDP which highlight how the UK has been hit relatively worse than other advanced economies

Source: Office for National Statistics, Organisation for Economic Co-operation and Development

Figure 2: The recording of volume estimates of government consumption expenditure has an impact on the size of the shortfall in GDP for other G7 countries
Volume G7 GDP, Quarter 4 2019 to Quarter 3 2020

Graph showing the recording of volume estimates of government consumption expenditure has an impact on the size of the shortfall in GDP for other G7 countries

Source: Office for National Statistics, Organisation for Economic Co-operation and Development

 

I also mentioned that we would share the direct and indirect impacts of COVID-19 on excess deaths and morbidity December 2020 update as soon as it was published, which we did on 29 January. Please do let me know if I can be of any further assistance to the Committee.

Yours sincerely,

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

Office for National Statistics written evidence to the Treasury Committee’s inquiry on economic crime

Dear Mr Stride,

I write in response to the Treasury Committee’s call for evidence for its inquiry into economic crime.

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.

As we explained when we wrote to the Committee for their last inquiry into economic crime, the ONS has collected information on the extent and nature of economic crime and how it affects consumers since 2012, having taken responsibility from the Home Office. In the years following, significant progress has been made in developing the evidence base on fraud.

The ONS publishes statistics on fraud, mainly based on the Crime Survey for England and Wales (CSEW), a victimisation survey of the resident population which is used to measure the extent and nature of crime and was expanded to cover fraud offences in 2015.

We have focussed our evidence on the scale of different forms of fraud, trends in fraud, the extent of financial loss to individuals, the emotional impact on the victims of fraud and reporting of fraud incidents.

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,

Iain Bell

Office for National Statistics written evidence: Economic Crime

Introduction

Until March 2020, CSEW data was collected through face-to-face interviews. However, the CSEW was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. The Telephone-operated Crime Survey for England and Wales (TCSEW) was specifically designed to allow the ONS to continue measuring crime during this period. TCSEW data collection started on 20 May 2020. The survey continues to ask residents of households about their experiences of a range of crimes in the 12 months prior to the interview. However, the smaller sample size for this survey means that there is more uncertainty surrounding crime estimates.

The TCSEW operation closely replicates that of the face-to-face CSEW, however, because of restrictions on interview length and sensitivities around the topic, the TCSEW contains a reduced number of questions. Because of the change in data collection mode and sample design, CSEW and TCSEW estimates are not directly comparable and there is a break in the data time series. Further research will be conducted to explore the comparability of the TCSEW and face-to-face CSEW and the impact changes to survey mode have on understanding long-term trends.

TCSEW estimates are presented as Experimental Statistics. The Office for Statistics Regulation, on behalf of the UK Statistics Authority, has reviewed these statistics against several important aspects of the Code of Practice for Statistics and regards them as consistent with the Code’s pillars of Trustworthiness, Quality and Value.

The ONS has also expanded its use of other data sources to help build a fuller picture of the extent and nature of fraud. The official statistics include information on crimes reported to the authorities as well as offences referred to industry bodies representing businesses and other organisations. In particular, these sources can help provide some insights into trends in fraud.

Data sources and what they cover

There are four main sources of data used in ONS statistics on fraud.

  • The face-to-face CSEW: a large household survey collecting information on crimes directly affecting the resident adult population of England and Wales provides estimates up to year ending March 2020.
  • The telephone operated TCSEW: set up to measure crime whilst the CSEW is suspended and allows investigation of trends during the coronavirus pandemic.
  • National Fraud Intelligence Bureau (NFIB) data on the number of incidents of fraud referred to them by Action Fraud (the national fraud and cybercrime reporting centre). This also includes referrals of fraud incidents by two industry bodies, Cifas and Financial Fraud Action UK (FFA UK, a constituent part of UK Finance), who report instances of fraud where their member organisations have been a victim.
  • Bank and credit account fraud data from UK Finance: these include crimes not referred to the NFIB and provides an important insight into trends in these types of fraud.

The CSEW encompasses a broad range of frauds, including attempts as well as completed offences involving a loss; Annex A gives further information on the types of fraud covered by the survey. One of the main strengths of the CSEW is that it captures incidents that are not reported to the authorities. Unlike administrative sources it is not affected by changes in recording practices or reporting rates to official bodies.

The CSEW is a household survey and does not cover crimes against businesses. Given the emphasis of the Committee’s inquiry on consumer fraud, the CSEW provides the most appropriate measure. It is also important to note when interpreting the figures that the survey counts the individual as a victim, even in incidents where they have been fully reimbursed (e.g. by their bank).

TCSEW data collection started on 20 May 2020 and the sample size is not yet large enough to provide a detailed breakdown of the different types of fraud or the nature of fraud during the coronavirus pandemic. Therefore, only estimates of overall fraud offences are currently available. These are not directly comparable with CSEW estimates and are surrounded by more uncertainty due to the smaller sample size.

Incidents of fraud referred to the NFIB by Action Fraud, Cifas and UK Finance will include reports from businesses and other organisations as well as members of the public. They will also tend to mostly be focused on cases at the more serious end of the spectrum. This is because, by definition, they will only include crimes that the victim considers serious enough to report to the authorities or where there are viable lines of investigation.

Findings from the CSEW indicate that only a relatively small proportion of fraud incidents (including those where a loss was suffered) either came to the attention of the police or were reported to Action Fraud: 14% of incidents in the year ending March 2020 CSEW. This low reporting rate means that NFIB recorded fraud data provide only a partial picture of the extent of fraud. However, they do provide an indication of emerging trends in more serious fraud offences, where the financial loss to the victim is greater, as reporting rates for these offences tend to be higher.

Most of the figures used in this paper are sourced from the CSEW as the survey provides the best indication of the volume of fraud offences directly experienced by individuals in England and Wales.

Estimates of the scale of fraud

The latest published finding from the CSEW refer to the survey year ending March 2020. These figures show an estimated 3.7 million fraud incidents experienced by adults in England and Wales. Table 1 shows these latest estimates broken down into separate offence groups.

Table 1: Estimated number of incidents of fraud, year ending March 2020 CSEW

England and Wales 
Offence groupApril 2019 to March 2020
Number of incidents (thousands)
Fraud3,675
Bank and credit account fraud2,474
Consumer and retail fraud914
Advance fee fraud60
Other fraud227

Source: Crime Survey for England and Wales, Office for National Statistics

 

Around two-thirds (67%) of incidents were bank and credit account fraud which usually involve falsely obtaining or using personal bank or payment card details to carry out fraudulent transactions. Consumer and retail fraud was the next most commonly occurring form of fraud; this includes crimes such as fraudulent sales, bogus callers, ticketing fraud and computer software service fraud.

These estimates show that fraud offences are among the most prevalent crimes in England and Wales. For example, fraud offences accounted for around a third (36%) of all CSEW crime. In addition, adults were six times more likely to be a victim of bank and credit account fraud than theft from the person (i.e. pickpocketing or theft of items being carried on the person) in the year ending March 2020.

Estimates from the TCSEW showed that there were 4.3 million fraud offences in the year ending June 2020. Although not directly comparable with estimates from the CSEW, this estimate lies within the range of those reported in recent years.

The nature of fraud

The CSEW also provides information on the nature of fraud incidents. Almost three-quarters of fraud incidents involved loss of money or goods to the victim (74%), independent of any reimbursement received; this equates to an estimated 2.7 million offences, compared with one million incidents of fraud involving no loss. The proportion of incidents resulting in loss varied by type of fraud, with 81% of bank and credit account fraud victims experiencing loss compared with 68% of consumer and retail fraud victims (Table 2). However, the large majority of bank and credit account victims received full reimbursement for their loss, while reimbursement was less common in cases of consumer and retail fraud where less than half of those of experiencing loss were fully reimbursed (data for year ending March 2019; Annex B Table A1).

Table 2: Financial loss suffered by victims of fraud by offence group, year ending March 2020 CSEW

England and Wales  
Offence groupApril 2019 to March 2020
Financial loss
(%)
Unweighted base -
number of incidents
Fraud74.02,283
Bank and credit account fraud74.01,526
Consumer and retail fraud68.1575

Source: Crime Survey for England and Wales, Office for National Statistics

Where money was taken from victims of fraud, in over a third (40%) of incidents the victim lost less than £100 and in over half (62%) of incidents the loss was less than £250. CSEW estimates indicate that losses of larger amounts of money were comparatively rare. For example, in 14% of incidents involving loss, the amount was greater than £1000 (Table 3).

Table 3: Financial loss suffered by victims of fraud, year ending March 2020 CSEW

England and Wales  
FraudApril 2019 to March 2020
Total frauds (%)Frauds involving loss (%)
No financial loss26.0-
Less than £10029.840.3
£100 to £24916.121.8
£250 to £99917.423.6
£1000 or more10.614.4
Unweighted base – number of incidents2,2831,673

Source: Crime Survey for England and Wales, Office for National Statistics.

New data on Authorised Push Payment (APP) scams have been collected by UK Finance since January 2017. These involve cases where victims are tricked into sending money directly from their account to an account which the fraudster controls. The new data show that in the year ending June 2020, there were 131,135 cases of APP fraud reported to UK Finance, an increase of 21% compared to year ending June 2019 (Annex B Table A3).

APP fraud can often involve significant sums of money and have adverse financial and emotional consequences for the victim. Unlike most other frauds, victims of APP fraud authorise the payment themselves and this means that they have no legal protection to cover them for losses. UK Finance reported that £207.8 million was lost through such scams in the first six months of 2020, a similar level to losses over the same period in 2019. Banks and other finance providers were able to return £73.1 million of the overall losses from APP fraud to victims, an 86% increase on the sum returned in the same period in 2019. These increases are likely to be a result of the introduction of the APP voluntary code in May 2019.

The CSEW also collects further data on the emotional impact of fraud victimisation more generally. Tables showing the latest estimates are presented in Annex B (Tables A2). The majority (74%) of victims were emotionally affected by the fraud. Common with other types of acquisitive crime the most common emotional reactions were annoyance, anger and shock.

Trends in fraud

There are limited data sources that give us information of how the extent and nature of fraud has changed over time. As the CSEW has only included estimates of fraud since year ending March 2017 it does not yet provide a reliable picture of trends. There was no change in fraud for year ending March 2020 compared to the previous year but there was a 13% increase compared to year ending March 2018. However, it is important to keep in mind that these comparisons over four data points provide limited insight into trends. As more data are compiled the CSEW will provide more robust trend data.

Other sources of data do provide some insights into trends, particularly in banking and credit account fraud. While data on frauds referred to the NFIB only give a partial picture (and provide a valuable source of reported fraud and demands placed on the police), separate data collated by UK Finance (via their CAMIS system) provide a broader range of bank account and plastic card frauds. These data include card fraud not reported to the police for investigation. They therefore offer a better picture of the scale of bank account and plastic card fraud identified by financial institutions in the UK and can give a valuable indication of trends in these types of crime.

In comparison with offences reported to the NFIB, most of the additional offences covered in this broader UK Finance dataset fall into the category of remote purchase fraud (where card details have been fraudulently obtained and used to undertake fraudulent purchases over the internet, phone or by mail order) and fraudulent incidents involving lost or stolen cards. Collectively these account for a high proportion of plastic card fraud not included in the NFIB figures.

In the year ending June 2020, UK Finance data showed 2.8 million cases of frauds (excluding Authorised Push Payments) involving UK-issued payment cards, remote banking and cheques (Annex B Table A3). This is broadly stable from the previous year. Over the eight years for which these data have been available the general trend indicates a rise in payment card and banking fraud reported to UK Finance.

These UK Finance figures also indicate that remote purchase fraud has consistently accounted for around three-quarters of all plastic card fraud reported to them. In the latest year, there was a 58% increase in “remote banking” fraud (to 55,058 incidents). This increase reflects the greater number of people now regularly using internet, telephone and mobile banking, and the attempts by fraudsters to take advantage of this.

Coronavirus and fraud

The TCSEW estimated that there was not a significant change in the number of victims of fraud between April to June 2020, the period most affected by coronavirus (COVID-19) restrictions, and January to March 2020. The impact of the coronavirus pandemic on fraud will be explored further explored once sample size is sufficient to provide a breakdown of fraud types.

The coronavirus pandemic is likely to have had differential effects on trends in fraud. For example, Action Fraud reported that the increase in “online shopping and auctions” fraud (to 70,403 offences) could be accounted for by the increase in online shopping whilst the decrease in “other advance fee” fraud (to 25,418 offences) could be attributed to reduction in holiday fraud figures as fewer holidays were booked. However, it is too early to say whether this is evidence of a change to longer-term patterns. Further information on trends in payment industry fraud based on industry data collated by UK Finance is available in Fraud the Facts 2020.

Annex A – CSEW Fraud categories

Bank and credit account fraud: Comprises fraudulent access to bank, building society or credit card accounts or fraudulent use of plastic card details.

Advance fee fraud: Comprises incidents where the respondent has received a communication soliciting money, mainly for a variety of emotive reasons, for example, lottery scams, romance fraud and inheritance fraud.

Consumer and retail fraud: Comprises cases where the respondent has generally engaged with the fraudster in some way, usually to make a purchase that is subsequently found to be fraudulent, for example, online shopping, bogus callers, ticketing fraud, phone scams and computer software service fraud.

Other fraud: Comprises all other types of fraud against individuals not recorded elsewhere, for example, investment fraud or charity fraud.

Annex B: Additional data tables

Table A1: Fraud and computer misuse by loss (of money or property) – number and rate of incidents and number and percentage of victims, year ending March 2019 CSEW

Table A1: Fraud and computer misuse by loss (of money or property) - number and rate of incidents and number and percentage of victims, year ending March 2019 CSEW

England and Wales   Adults aged 16 and over
Offence groupNumber of incidents (thousands)Rate per 1,000 adultsNumber of victims (thousandsPercentage victims once or more
FRAUD 3,809813,1766.8
With loss, no or only partial reimbursement692156381.4
With loss, fully reimbursed2,232481,8974.1
Without loss885197581.6
Bank and credit account fraud2,579552,1714.5
With loss, no or only partial reimbursement25152130.5
With loss, fully reimbursed1,86240,5693.4
Without loss466104190.9
Consumer and retail fraud 1,019229382.0
With loss, no or only partial reimbursement39283800.8
With loss, fully reimbursed34773240.7
Without loss28062430.5
All other fraud21151770.4
With loss, no or only partial reimbursement491480.1
With loss, fully reimbursed241210.0
Without loss13831070.2
COMPUTER MISUSE966218471.8
Computer virus44493850.8
With loss, no or only partial reimbursement14231340.3
With loss, fully reimbursed0000.0
Without loss30362510.5
Unauthorised access to personal information (including hacking)522114671.0
Unweighted base - number of adults34,163

Source: Crime Survey for England and Wales, Office for National Statistics.

Notes:

  • Data for sub-categories will not sum to totals shown for headline categories because people can be victims of more than one crime.
  • The ‘With loss’ categories relating to fraud refer to financial loss, including money stolen and additional charges or costs incurred, as well as loss of property or goods.
  • ‘All other fraud’ refers to ‘Advance fee fraud’ and ‘Other fraud’ combined.
  • In the large majority of cases of loss relating to ‘Advance fee fraud’ and ‘Other fraud’, victims received no or only partial reimbursement, as the nature of such frauds makes full reimbursement less likely.
  • Loss through computer viruses is mainly associated with additional charges or costs incurred as a result of the virus (e.g. repair/replacement costs), which are less likely to be fully reimbursed.

Table A2:  Emotional impact of incidents of fraud, year ending March 2020 CSEW

England and Wales   Incidents
FraudBank and credit card fraudConsumer and retail fraudAll other fraud
Respondent was emotionally affected74747378
Very much871013
Quite a lot20202027
Just a little45474338
Respondent was not emotionally affected26262722
Unweighted base - number of incidents2,3531,561602190
Types of emotional response experienced
Annoyance72727273
Anger51485849
Shock31332734
Loss of confidence or feeling vulnerable20182324
Anxiety or panic attacks10101017
Fear109913
Difficulty Sleeping54611
Depression5555
Crying/tears66510
Other4377
Unweighted base - number of incidents1,7641,152461151

Source: Crime Survey for England and Wales, Office for National Statistics.

Notes:

  • 1In March 2018 the new CSEW estimates on fraud and computer misuse were assessed by the Office for Statistics Regulation against the Code of Practice for Statistics and were awarded National Statistics status.
  • Within this table, ‘All other fraud’ refers to advance fee fraud and other fraud combined.
  • Figures are based on incidents in which the victim reported that they were emotionally affected by the incident.
  • Figures may not sum to100 as more than one response is possible.

Table A3Volume of fraud incidents on all payment types, UK Finance CAMIS database, year ending March 2017 to year ending June 2020, and percentage change

UK       
Apr '16 to
Mar '17
Apr '17 to
Mar '18
Apr '18 to
Mar '19
Apr '19 to
Mar '20
Jul '18 to
Jun '19
Jul '19 to
Jun '20
Jul '19 to Jun '20 compared with previous year:
Change (%)
Plastic Card Fraud1,817,0391,979,2252.773,7252,745,6392,801,3182,747,141-2
Lost and Stolen246,503382,706454,458455,346460,856416,469-10
Card not Received11,19210,4599,8327,7749,2987,398-20
Counterfeit Card103,13275,56260,23966,89461,50765,3686
Remote Purchase Fraud1,423,7831,479,4412,180,3032,163,6602,199,2532,210,5961
Account Take Over32,43931,05768,89351,96570,40447,310-33
Cheque Fraud2,7631,5412,3702,6102,8532,046-28
Remote Banking Fraud33,76732,45733,69250,12834,74155,05858
Authorised Push Payment8,72449,84398392124,913108,044131,13521
Total (excluding APP)1,853,5692,013,2232,8097872,798,3772,838,9122,804,245-1
Total(including APP)1,8622942,063,0662,908,1792,923,9562,949,9562,935,3800

Source: UK Finance.

Notes:

  • Fraud data are not designated as National Statistics.
  • All offences are classed under HOCR as NFIB5A, cheque, plastic card and online bank accounts (non-PSP). The categories they have been split into are UK Finance’s breakdowns.
  • The total number of offences here is including all offences that are also included in the FISS dataset.
  • Remote purchase fraud includes telephone, internet and mail order fraud.
  • Account takeover has been renamed to Card ID theft to more accurately reflect the data captured in this category which includes third party application fraud and account takeover. Figures presented in this table continue to be comparable with previously published figures.
  • Remote banking fraud includes telephone and internet banking.
  • The increase in the Remote Banking Fraud reflects the greater number of people now regularly using internet, telephone and mobile banking, and the attempts by fraudsters to take advantage of this.
  • UK Finance began collecting data on authorised push payment scams (also known as APP or authorised bank transfer scams) in January 2017, therefore the figure for the year ending March 2017 is based only on one quarter of data. This explains the increase in APP in the year ending March 2018.Improved reporting has also contributed to increases in subsequent years.

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 number 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

Office for National Statistics written evidence to the Lords' Economic Affairs Committee's inquiry on employment and COVID-19

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

Office for National Statistics written evidence to the Lords' Economic Affairs Committee's inquiry on employment and COVID-19

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.

Office for National Statistics oral evidence to the Science and Technology Committee and the Health and Social Care Committee’s joint inquiry on Coronavirus: lessons learnt (use of statistics and modelling)

On 21 October 2020 Professor Sir Ian Diamond, National Statistician, gave evidence to the Science and Technology Committee and the Health and Social Care Committee’s joint inquiry on Coronavirus: lessons learnt, specifically considering the use of statistics and modelling.

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

Office for National Statistics follow-up written evidence to the Public Administration and Constitutional Affairs Committee’s inquiry on data transparency and accountability: COVID-19

 

Dear Mr Wragg,

While providing evidence to the Committee on 22 September for the inquiry Data Accountability and Transparency: COVID-19, I promised to provide further information on sex differences in antibodies.

On Monday we published our latest analysis on antibodies from the COVID-19 Infection Survey. Blood samples taken between 26 April and 02 September show that females are less likely than males to have ever tested positive for COVID-19 antibodies, although the upper confidence interval shows that this difference may be very small.

The NHS blood transfer service are collecting convalescent plasma from individuals who have had the COVID-19 virus, for use in trials looking at possible treatments for COVID-19. The trials are investigating whether convalescent plasma transfusions could improve a COVID-19 patient’s speed of recovery and chances of survival.

Please do let me know if I can help the Committee further with this inquiry.

 

Yours sincerely,
Professor Sir Ian Diamond

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on post-pandemic economic growth: levelling up local and regional structures and the delivery of economic growth

Dear Mr Jones,

I write in response to the Committee’s call for evidence for its inquiry on Post-pandemic economic growth: Levelling up – local and regional structures and the delivery of economic growth.

The Office for National Statistics (ONS) is the principal producer of regional economic statistics in the UK and it is our responsibility, in partnership with the National Records of Scotland and the Northern Ireland Statistics and Research Agency, and other interested parties, to provide the data that regional policy makers need to carry out their function. The ONS engages with devolved and regional policy makers at all levels of administrative geography to identify those needs and their relative priorities, and to respond with appropriate development programmes.

Over the past decade we have made significant improvements to the range and depth of regional economic statistics available but recognise that there is still more to be done. We are also aware that greater devolution to local administrative bodies is increasing the need for regional data on a range of topics.

The 2016 review of economic statistics by Sir Charles Bean highlighted the need for better provision of regional statistics and made three main recommendations: for more timely regional economic data; for greater flexibility in the range of geographic areas covered; and for greater use of administrative data in the production of regional economic statistics. These priorities have guided our development programme.

In our evidence to the Committee, we have focused on describing the range of regional economic data that are currently available as an evidence base for regional policy making, and what we know about gaps in the provision of regional data and our plans to address them. We have also provided evidence of the use of our statistics in informing regional and local industrial strategies from the engagement we have had with these stakeholders.

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 – Post-pandemic economic growth: Levelling up local and regional structures and the delivery of economic growth

Executive Summary

  1. The ONS produces several measures of economic activity at a regional level. These are: annual regional gross value added, annual regional gross domestic product and quarterly regional gross domestic product. The ONS also works with experts who produce ‘nowcasts’ which provide early views of economic activity, produced soon after quarterly UK GDP figures are available.
  2. The coronavirus (COVID-19) pandemic has highlighted the need for faster indicators of economic activity. Surveys launched in response to the pandemic have produced high frequency, detailed economic statistics at national and regional levels for use across the UK Government and devolved administrations, and by the media, academia, businesses and the public.
  3. There is a wide range of labour market statistics produced by the ONS at both regional and sub-regional levels. Generally, headline measures of labour market status for regions are taken from the Labour Force Survey and published monthly on a rolling three-monthly basis, alongside the headline UK labour market measures. More detailed measures, for regions and below region level, are taken from the Annual Population Survey and published quarterly on a rolling twelve-monthly basis.
  4. The ONS takes two approaches to measuring regional productivity: a whole-economy approach using regional gross value-added data and an approach based on examining firm-level productivities using ONS microdata derived from sources, such as the Annual Business Survey. It is important to be aware of the external factors associated with the location of the firm that impact productivity when taking the second approach.
  5. We are aware of a need to calculate subnational estimates of international trade in services, and the first estimates of this were published in 2016. These estimates were at a country and regional level. Further estimates broken down further, to a county, district or unitary authority level, were published in 2020.
  6. We produce experimental public sector revenue and expenditure statistics for each country and region of the UK. The purpose of these statistics is to provide users with information on what public sector spending has occurred and what public sector revenues have been raised.
  7. The ONS provides a regional version of the household sector account, which measures the finances of all residents in a region. From this, the gross disposable house income can be ascertained, which is a measure of all sources of income into households, and provided at several regional levels, from countries and regions, to local authority districts and city regions. This measure is a reliable way to compare different areas on a consistent basis.
  8. The ONS is aware of the gaps in regional economic data, such as a need for more flexible geographical statistics and for more information provided by supply and use tables. There is ongoing work in collaboration with external organisations to assess the practical and theoretical issues around expanding the data in these areas.

Measures of economic activity available as an evidence base for regional policy making

Annual regional gross value added
  1. The standard measure of economic activity is gross domestic product (GDP). For regional purposes we have traditionally measured gross value added (GVA), which differs from GDP only in excluding taxes on products, such as VAT. This is because taxes on products are difficult to measure on a regional basis because they are often paid by consumers, rather than producers. In the short term, movements in GVA are generally considered a good proxy for movements in GDP. Paragraphs six and seven, however, sets out how we have also recently developed annual regional GDP estimates.
  2. Turning first, though to GVA, the principal measure at a regional level is the balanced measure of regional GVA produced by the ONS and published each December, denoted as GVA(B). This is an annual National Statistic, formed by combining two independent measures of regional GVA, known as the income and production approaches. It is provided as a time series from 1998 to the year prior to publication.
  3. GVA(B) is provided in current prices, which include the effect of inflation, and in “real terms” as chained volume measures with inflation removed. Each of these is provided as a total for each region, and for a set of detailed industries (defined using the Standard Industrial Classification (SIC) 2007) and intermediate aggregates.
  4. GVA(B) is provided for a range of different geographic areas. Some of these are defined by the EU Nomenclature of Units for Territorial Statistics (NUTS) classification, but others have been developed more recently in response to the needs of new bodies with devolved responsibility. To date we have only provided areas that can be constituted from whole local authority districts or Scottish Council areas. These include combined authorities with elected mayors, other city regions, growth deal areas, local enterprise partnerships, and other economic and enterprise regions of interest to users.
  5. We need to safeguard the confidentiality of information relating to any individual person or company and therefore the industry detail we can provide diminishes as regional geography becomes smaller. Thus, we provide 81 industries at the NUTS1 level of countries and regions; 72 industries for NUTS2 sub-regions; 48 industries for NUTS3 local areas; and 34 industries for local authorities and any area built from them.
Annual regional gross domestic product
  1. Owing to developments in regional measures of public sector finances, we now have more data available to us to inform the regional allocation of taxes on products. In December 2019, we published the first annual estimates of regional GDP, building on the established GVA(B) data and using, where available, the Country and Regional Public Sector Finances data to allocate taxes to NUTS1 level regions of the UK.
  2. The GDP estimates are only provided as a total for each area, with no industry breakdown. They are provided in current prices, as a total and on a per-person basis, and as chained volume measures with inflation removed.
Quarterly regional gross domestic product
  1. Annual GVA(B) provides a lot of useful information for regional policy makers, but it cannot provide a view of what is going on right now in regional economies. To meet the need for more timely statistics we have been developing a quarterly measure of regional GDP for the nine NUTS1 regions of England and for Wales. This new experimental statistic was published for the first time in September 2019 and is regularly updated as new data become available. This release uses the VAT HMRC administrative dataset to reduce burden on business. Industries that are not covered by the VAT dataset are supplied by multiple external data sources from other government departments/other departments within the ONS.
  2. Quarterly regional GDP is published in real terms, with an industry breakdown that matches the corresponding UK GDP publication. As the key administrative data sources have a slightly longer lag than monthly survey data; regular publication is around six months after the end of the quarter. We are investigating the feasibility of speeding up the publication.
  3. The devolved administrations of Scotland and Northern Ireland already produce and publish quarterly measures for those countries. Our new development of regional GDP completes the coverage of the UK at the NUTS1 level. All three of the devolved administrations were involved in the development of the new statistics, to ensure that they are consistent and coherent with the other measures that are available, both nationally and regionally. The ONS has regular contact with the devolved administrations to share data sources and discuss methods.
Regional nowcasting
  1. The ONS has never produced economic forecasts; our focus is on measuring what has happened. However, we recognise the interest in this area. A recent project undertaken by a research team in the Economic Statistics Centre of Excellence (ESCoE), which was established by the ONS to harness academic research in the production of economic statistics, has developed a model-based approach to provide early views of regional economic activity, known as “nowcasts”. These estimates are produced soon after the UK quarterly GDP figures are available and are currently published by ESCoE. They cover the NUTS1 countries and regions of the UK.
  2. The ONS is working closely with ESCoE academics and plans to feed data from the new quarterly regional GDP measure into the ESCoE model, to improve the accuracy of its estimates. It is hoped that this work will provide a “flash” estimate similar to the UK’s early estimate of GDP.
Faster indicators
  1. The overall aim of faster indicators is to develop and co-ordinate a single, cross-government, publicly visible data hub for high frequency economic statistics used in policy making at multiple geographies, developed through regular and thorough consultation with other government departments and across society to proactively identify new data sources.
  2. The publication has been developing over time to include more regional breakdowns of the different indicators, including NUTS1 breakdowns of online job adverts and Energy Performance Certificates. It contains traffic camera data for selected cities and regions, and shortly will also include regional footfall data at retail locations. In addition to these existing regional indicators, our aim is to expand the number and detail of regional faster indicators even further, to continue to meet the needs of local decision makers, the devolved administration and other government departments.
  3. The fortnightly Business Impact into Coronavirus Survey (BICS) was set up to understand the impact of the pandemic on businesses. As of 1 September, it has been running for 12 return periods, known as ‘waves’, and currently has a sample size of 25,000 with an average response rate of 25%. Our rapid approach to BICS development has allowed us to update questions and collect data on new and emerging issues, such as the take up of new government schemes and initiatives, and enabling insights into businesses’ future, such as risk of insolvency and how quickly businesses can recover.
  4. BICS outputs are used extensively across the devolved administration, all levels of government, media and academia. As part of our detailed set of outputs, we provide national and regional breakdowns for the different variables and industries. The ONS has continued to work closely with the devolved administration in understanding their needs and requirements. Our recent user consultation also highlighted the need for BICS to continue, particularly to help with regional analysis for localised impacts, over time we will consider how to reshape this survey to provide timely insight beyond the pandemic

Labour market statistics

    1. The ONS provides a wide range of labour market statistics at both the regional and sub-regional levels. The Labour Force Survey and its derivative the Annual Population Survey are large household surveys that provide the main sources of statistics on people and their interactions with the labour market. The surveys look at the labour market status of individuals, that is whether employed, unemployed or economically inactive.
    2. For those who are employed, information is collected on the nature of the employment; industry, occupation, sector, full-time/part-time, self-employed/employee, nature of contract, hours of work, earnings etc. For those who are unemployed, information collected includes the duration of unemployment. For those who are economically inactive, people are asked about their reasons for not working or looking for work.
    3. In addition to information on their labour market situation, a wide range of personal characteristics are collected, including geographic information, sex, age, ethnicity, nationality, country of birth, religion, marital status, health, disabilities, and qualifications. This allows these concepts to be cross tabulated in numerous ways, with the main limitation on outputs being the size of the sample available. This means that while it is possible to produce statistics for small domains, the resulting quality is questionable due to high sampling variability.
    4. Generally, headline measures of labour market status for regions are taken from the Labour Market Survey and published monthly on a rolling three-monthly basis, alongside the headline UK labour market measures. More detailed measures, for regions and below region level, are taken from the Annual Population Survey and published quarterly on a rolling twelve-monthly basis.
    5. In addition to looking at the supply side of the labour market, business surveys look at the demand for labour. These surveys look at the number of jobs and vacancies that businesses have, along with rates of pay. Like the supply side, for many of these concepts there are short-term estimates that have less granularity available, while annual surveys provide more detail. For jobs the short-term output is Workforce Jobs, which provides estimates of the number of jobs that businesses have filled at a regional level by a broad industry breakdown. The annual equivalent, the Business Register and Employment Survey, supplies a more detailed breakdown of employment and employees at geographic and industry breakdowns.
    6. Again, for earnings the short-term survey provides the Average Weekly Earnings estimates available each month. These are only available at a GB level and are broken down by broad industry and type of earnings.
    7. More detailed earnings estimates come from the Annual Survey of Hours and Earnings. This larger survey collects information on the various earnings components and hours of work, along with information on the industry and occupations of employee jobs. This provides breakdowns for these various concepts at regional and sub-regional levels. The survey is the basis for detailed information on the rates of pay and hours worked in various jobs, and also for estimating the gender pay gap and numbers paid below pay thresholds such as the National Living Wage.
    8. The one notable exception to regional coverage of labour market statistics is vacancies. The ONS Vacancy Survey only provides estimates of the number of vacancies at UK level, split by industry and size of business. Work is ongoing to look at the possibility of producing estimates of vacancies with some geographic aspects, based on techniques such as web-scraping. Some initial results by country and region of the UK are being published as part of the ONS faster indicators suite.

Productivity

  1. In economic terms, productivity is the level of output per unit of input. Labour productivity, therefore, is defined as the quantity of goods and services produced per unit of labour input, for example, per hour worked or per filled job. Productivity matters because increasing productivity is critical to increasing the standard of living in an economy. A more productive economy can produce more goods and services, not by increasing inputs such as labour hours, but by making production more efficient.
  2. The preferred measure of labour productivity is GVA per hour worked, because GVA and hours are measured on a workplace basis and is the best metric for assessing the economic performance of workplaces in a region or subregion. GVA per job filled can also be used, although it is not quite as good a measure as it doesn’t account for different working patterns across areas.
  3. It should be noted that we do not recommend using GVA per head as a measure of productivity. The reason for this is that the productivity measures (GVA per hour worked or GVA per job filled) provide a direct comparison between the level of economic output and the direct labour input of those who produced that output. This is not the case, however, for GVA per head, as the GVA per head measure includes people not in the workforce (including children, pensioners and others not economically active) in the calculation and can also be very heavily biased by commuting flows. This is because if an area has a large number of in-commuters, the output these commuters produce is captured in the estimate of GVA, but the commuters are not captured in the estimate of residential population. In this situation, a GVA per head measure would be artificially high if used as a proxy for economic performance or welfare of a region.
  4. There are two different approaches adopted by the ONS for examining regional labour productivity. The first approach provides a whole economy approach utilising the published regional gross value added (GVA) data. These economic output data are compared with labour input data to produce GVA per hour worked and GVA per filled jobs estimates for a range of different subnational geographies.
  5. A second approach is based on examining firm-level productivities using ONS microdata such as the Annual Business Survey. This approach excludes the public sector and the agriculture and financial sectors of the economy. However, for the rest of the business economy this approach can provide a rich source of information on distributions of firm-level productivity and the opportunity to analyse sources and drivers of productivity.
  6. Using micro-data firm-level analysis we have explored the reasons behind productivity differences between areas We have found that differences in firm-level productivity within industries are a bigger determinant of the geographical differences in productivity than the different industry structures of the areas.
  7. External factors associated with the location of a firm, such as differing local labour markets, existence of agglomeration benefits, and levels of local consumer spending and factors internal to firms, such as whether a firm-trades internationally, its management practices, and its ownership; age and size of a firm can all have an influence on firm productivity.

Trade statistics

  1. Following engagement with sub-national government organisations and users, the ONS Centre for Subnational Analysis identified a need to calculate subnational estimates of international trade in services. This directly contributed toward delivery of the UK trade development plan, meeting needs of other government departments including the Department for International Trade and HM Treasury, and meeting the data needs of regional and local bodies for understanding their local economies.
  2. The first publication of estimates of exports of services at NUTS1 geographic level were published in 2016. With funding as part of the ONS Devolution programme, further developments have been undertaken to create geographic breakdowns to the NUTS3 level and “joint authority” geographies across Britain, and exploratory analysis of destination countries receiving British exports from each NUTS1 area. In September 2019, we published further developments including alignment with national-level statistics, and an improved allocation of exports split by industry.
  3. The first publication of estimates of exports and imports of services and trade in services balances at geographic breakdowns to the NUTS3 level and “joint authority” geographies across Britain were published in September 2020, providing a picture of international trade in services by subnational areas together for the first time. The ONS has worked with HMRC in development of these statistics, which in part led to changes in the production of HMRC’s statistics on subnational trade in goods.
  4. Information on imports and exports of goods at the subnational level can be found in Her Majesty’s Revenue and Customs’ Regional Trade Statistics.

Public sector finances

  1. From May 2017, in response to a user consultation, we began producing experimental public sector revenue and expenditure statistics for each NUTS1 country and region of the UK – known as the Country and regional public sector finances. The aim of the statistics is to provide users with information on what public sector expenditure has occurred, for the benefit of residents or enterprises, in each country or region of the UK; and what public sector revenues have been raised in each country or region – including the balance between them.
  2. Public sector expenditure is the total capital and current expenditure (mainly wages and salaries, goods and services, expenditure on fixed capital, but also subsidies, social benefits, and other transfers) of central and local government bodies, as well as public corporations. Public sector revenue is the total current receipts (mainly taxes, but also social contributions, interest, dividends, gross operating surplus and transfers) received by central and local government as well as public corporations.
  3. Net fiscal balance is the gap between total spending, which is current expenditure plus net capital expenditure, and revenue raised, which at the UK level is equivalent to public sector net borrowing.
  4. Under the current constitutional arrangements, most aspects of fiscal policy are controlled by the UK Government. More recently, however, certain fiscal powers have been delegated to the devolved administrations. The statistics presented in the country and regional public sector finances are neither reflective of the annual devolved budget settlements nor are these data used when calculating devolved budget settlements. Furthermore, they do not provide information on the spending and revenue of individual country or regional bodies such as the Greater London Authority.
  5. The most recently available statistics present data for the period from the financial year ending (FYE) 2000, up to and including FYE 2019. Three main aggregates are presented in the statistics – public sector revenue, public sector expenditure and net fiscal balance. These aggregates are presented in absolute terms for each country and region as well as on a per head basis to account for population differences.
  6. Net fiscal balance does not represent borrowing powers of any country or region in the UK. A negative fiscal balance figure represents a surplus; and a positive net fiscal balance represents a deficit.

Household income and expenditure

  1. The ONS provides a regional version of the household sector account, which measures the finances of all people resident in a region, whether they live in conventional households or in communal establishments. The principal National Statistic from this account is gross disposable household income (GDHI), which is the amount of money people in an area have available for spending or saving.
  2. The compilation of GDHI involves measuring all the sources of income that come into households, such as wages and salaries, income from self-employment, rental and investment income, social security benefits and pensions. From these are subtracted money going out, such as taxes on income and wealth, social and pension contributions, and mortgage interest payments. All these components are also published alongside GDHI, giving users a wide range of information about households’ finances.
  3. GDHI and its components are provided for the same geographic areas for which we provide GVA(B): NUTS1 countries and regions; NUTS2 sub-regions; NUTS3 local areas; local authority districts (and Scottish Councils); combined authorities; city regions; growth deal areas; local enterprise partnerships; and other economic and enterprise regions of interest to users.
  4. GVA is a workplace measure, and so per head can be distorted by commuting and demographic variation. GDHI relates to the resident population of an area. This means GDHI per head is a reliable way to compare different areas on a consistent basis to give a measure of relative prosperity. GDHI is only available in current prices, including the effect of inflation, and is only available as an annual time series with a considerable time lag (around 16 to 17 months after the latest year). GDHI also doesn’t consider variation in the cost of living between different parts of the UK. For that we have developed an experimental measure of regional household final consumption expenditure (HFCE), often called household expenditure.
  5. Regional HFCE can be measured in two ways, known as the domestic concept and the national concept. The domestic concept provides a measure of how much is spent in a region, regardless of where the people spending have come from. The national concept provides a measure of how much all the people resident in an area have spent, regardless of where they are when they are spending. Both concepts provide spending on a range of goods and services, classified according to the UN Classification of Individual Consumption According to Purpose (COICOP).
  6. The national concept can also be provided on a per head basis for direct comparison across different regions. Furthermore, when used in conjunction with regional GDHI, the national concept allows us to complete the story of household finances and derive a measure of gross saving and the households’ saving ratio: the proportion of total resources left over for saving.
  7. In our first publication in September 2018 we provided HFCE for the NUTS1 countries and regions of the UK, in annual time series going back to 2009. In July 2020 we published an update including both NUTS1 and NUTS2 sub-regions, which also covers several other contiguous areas such as some of the mayoral combined authorities and local enterprise partnerships. In time we plan to develop other areas, at least down to local authority level, but for this we will need to obtain additional administrative or commercial data not currently available to us.
  8. It should be noted that these household expenditure figures are experimental and should therefore be interpreted with a degree of caution.

Other data sources

Consumer and producer prices

  1. The ONS publishes regional inflation estimates for the housing market, however, there is currently no regional breakdown for the suite of headline consumer price indices.
  2. Both the UK House Price Index (HPI) and the Index of Private Housing Rental Prices (IPHRP) are published at a regional level. In the case of the HPI, consistent sub-regional data are published each month supplemented by quarterly estimates at a Lower Super Output Area level.The IPHRP is currently only published at a regional level, although plans are in place to develop this output over the next 12-18 months to produce comparable estimates at a sub-regional level.
  3. The development of regional Consumer Price Indices (CPI) has been considered previously. However, the suitability of the existing price sample, which is designed for national estimates, for regional price indices has been questioned due to several concerns such as the difference between national and regional baskets.
  4. In 2017, at the request of the ONS, Southampton University carried out research to assess the suitability of using current price data in the calculation of regional consumer price indices, to quantify the limitations of using this current CPI data and identify the ongoing requirements to allow for the production of regular regional CPI. The research report, along with a rudimentary set of regional indices was published in November 2017.
  5. This was followed by with a further period of research in 2018/19[5], again led by Southampton University, to look at modelling approaches that could potentially stabilise some of the volatility, particularly in the regional weights, seen in the regional indices published in the initial Southampton Report. This work will be followed up in 2019/20 by further research that considers how the CPI price sample can be improved, either through modelling or by utilising alternative data sources, to improve the estimates of regional CPI that can be produced. This research is expected to be published by the end of 2020.

Towns

  1. In July 2019, we published the first in a series of outputs which will provide new data and analysis on towns in England and Wales, alongside existing data for cities and rural areas[6]. The data on towns will allow for more understanding of the similarities and differences between city, town and rural areas as well as providing much needed evidence on which towns are growing and which towns are struggling. The next outputs in the series are scheduled for the final calendar quarter of 2020, with further outputs to follow in 2021.
  2. Overall, our initial analysis does not suggest that towns are more deprived than the rest of the country or performing worse economically. However, the analysis does show very different outcomes across different towns, with some growing very strongly in terms of employment and population growth and some declining on these same metrics. For example, between 2009 and 2017, employment declined in 26% of towns, most commonly in towns with existing higher levels of income deprivation, but employment increased by over double the England/Wales average in 32% of towns.

High Streets

  1. In addition to publishing new data and outputs on towns, the ONS has also produced new data and analysis on the topic of high streets. To do this, we worked collaboratively with Ordnance Survey (OS) to help digitally identify the physical geography of high streets in Great Britain and then to use these geographic extents to produce two experimental analysis articles using a range of ONS and OS data. This innovative project was used as a case study by the Geospatial Commission in its recently published UK Geospatial Strategy, and further developments to the project are planned by the ONS and OS to further enhance the identification of retail geographies and the production of associated data and analysis.

Data gaps and our plans to address them

Flexible geography

  1. One of the priorities identified by the Bean Review was to provide more flexible geographic statistics to meet the needs of new and emerging regional bodies with responsibility for non-standard geographic areas. In response to this we set up a Flexible Geography project, which aims to develop economic statistics for any user-defined area of interest.
  2. To date the project has delivered expansions to the range of areas for which we provide GVA and GDHI statistics, providing estimates for local authority districts and a range of other areas that can be constructed from whole local authorities.
  3. In the future we aim to provide these statistics for even smaller areas, drawing upon the wealth of data becoming available to us from administrative sources within government. For GVA, we plan to use VAT administrative data to break the data down to very small areas; most likely lower and middle super output areas, the former as a single GVA total and the latter with a broad industry sector breakdown.
  4. For GDHI, we will need to utilise more sources of administrative data, including PAYE and self-assessment data from HMRC, and benefits data from the Department for Work and Pensions. Our ultimate aim is to break the data down to output areas, the smallest geographic area for which household statistics can be provided without the disclosure of confidential information.
  5. In both cases we aim to produce the smallest possible building blocks, from which we can construct any area of interest to users. Full implementation of this project will take a few years and will involve the development of a dissemination tool capable of handling the enormous amount of data and constructing the outputs wanted by users.

Regional supply and use tables

  1. In our discussions with users, particularly those with devolved responsibility, it has become clear that there is a growing demand for the information provided by regional supply and use tables. Regional supply and use enable a rich picture to be created of all the goods and services produced or imported in an area and their ultimate use, and provide the building blocks for the modelling of the economic effects of various interventions or other changes in supply and demand.
  2. We already have many of the pieces needed to populate regional supply and use tables, but there are some important pieces missing. Many of these relate to expenditure measures, which are less well developed on a regional basis. We also currently lack regional prices information. However, the most challenging gap is the need to measure trade flows between regions, something that doesn’t exist at a national level but without which regional supply and use is simply impossible.
  3. We have set out a programme of work needed to achieve regional supply and use tables, which involves several streams to develop the parts needed to complete the framework. We do not yet have the resources or funding to carry out this work, but we have plans ready to begin as soon as appropriate funding can be secured.
  4. In preparation we have commissioned a project by ESCoE to carry out research into the practical and theoretical issues around regional supply and use, with the aim of developing guidance we can use to ensure we are able to produce good quality tables at a regional level.

Gross fixed capital formation

  1. We already produce regional estimates of gross fixed capital formation, otherwise known as capital expenditure. We compile estimates on an annual basis for NUTS1 and NUTS2 level regions and sub-regions.
  2. However, we are aware the quality of these regional statistics is not very high. The principal data source for regional capital expenditure is the ONS Annual Business Survey, which provides regional estimates through an apportionment model. While this model works well for most economic variables, it is not a good way to allocate capital expenditure, which tends to contain very large expenses linked to specific sites, such as new building construction.
  3. We are investigating utilising Corporation Tax records held by HMRC, which contain a lot of information on companies’ expenditure, both operating and capital. We hope that this administrative source will provide better coverage of capital expenditure, enabling an improvement to the quality of our regional gross fixed capital formation If successful this will be followed by full domestic publication, and quite possibly the development of additional geographic areas.

Ongoing stakeholder engagement

  1. In addition to our well established and comprehensive user engagement, with additional funding secured during the 2015 Spending Review, and in support of recommendations from the Bean Review, we created a new Centre for Subnational Analysis. This includes both existing priorities around subnational economic analysis and spatial analysis, as well as an expansion of our ability to engage directly with stakeholders and to deliver additional analysis at lower geographic levels. Through 2018 and 2019, we conducted workshops with the new Combined Authorities, as well as engagement with other City Region stakeholders, membership organisations, and individual local authorities. These activities aimed to introduce the data the ONS has available, to gather feedback and requirements those authorities had of statistical outputs, and to build working relationships with analysts across the country. It has also enabled closer engagement on projects such as the Local Industrial Strategies, developments with data science, and work in support of the Stronger Towns Fund.

The ONS and Local Industrial Strategies

  1. The development of the UK Industrial Strategy in 2018 has been followed by development of individual Local Industrial Strategies for Combined Authorities and Local Enterprise Partnerships, to promote the coordination of local economic policy and national funding streams and establish new ways of working between national and local government, and the public and private sectors.
  2. While the strategies are high-level policy documents, the evidence bases feeding into them draw on many of the sources mentioned in previous sections of this document. Even when the evidence bases are developed by intermediaries or consultancies, they will usually draw upon ONS data as the primary source for much of the evidence provided.
  3. Key datasets for the industrial strategies tend to include Regional GDP and GVA (balanced measure), the Labour Force Survey or Annual Population Survey, the Annual Survey of Hours and Earnings and the Business Register Employment Survey, among others. Analysis produced by both the ONS and consultancies from our micro-datasets on topics such as sub-regional productivity have also helped inform the Local Industrial Strategies.

 

Office for National Statistics and Office for Statistics Regulation oral evidence to the Public Administration and Constitutional Affairs Committee’s inquiry on data transparency and accountability: COVID-19

On 22 September 2020 Professor Sir Ian Diamond, National Statistician and Ed Humpherson, Director General for Regulation, gave evidence to the Public Administration and Constitutional Affairs Committee’s inquiry on data transparency and accountability: COVID-19.

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

Office for National Statistics written evidence to the Work and Pensions Committee’s inquiry on preparations for changes in the world of work

Dear Mr Timms,

I write in response to the Work and Pensions Committee’s call for evidence for its inquiry on the Department for Work and Pensions’ preparations for changes in the world of work.

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 immediate impact COVID-19 has had on the Labour Market and what our initial statistics on industries affected, hours worked and changes to working practices are illustrating. We have also considered the impact of automation on the Labour Market, which may have been accelerated or delayed due to the recent pandemic. We anticipate that our analysis will track the long-term effect of the COVID-19 pandemic on the Labour Market for some time, alongside the potentially more gradual move to automation, 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: Department for Work and Pensions’ preparations for changes in the world of work inquiry

 

Executive Summary

  • Analysis of the immediate impact of COVID-19 on the labour market shows that while we haven’t seen a significant change in headline employment and unemployment figures, the largest changes are seen in the number of people temporarily away from work, including furloughed workers.
  • Occupations see variety in the probability of automation in the future. The occupations with the highest probability of automation are low skilled or routine occupations, such as waiters and waitresses (72.1%) and shelf fillers (71.70%). Conversely, high-skilled occupations, such as medical practitioners and higher education teaching professionals have a much lower probability of automation at 18.11% and 20.27% respectively.
  • The risk of job loss due to automation is higher for young people and women. When looking at jobs with a higher risk of automation, women account for 70.2% of employees in those jobs, compared with 42.6% of employees in jobs at low risk of automation. Of those aged 20 to 24 years who are employed, 15.7% were in jobs at high risk of automation. Just 1.3% of people aged between 35 and 39 are at a high risk of automation.
  • Young people are more likely to work in low-skilled occupations, putting them at a higher risk of job loss through automation, or being furloughed during the COVID-19 pandemic, as lower-skilled occupations are also less likely to have the ability to work from home.
  • The ability to work from home also 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 to 18.9% of skilled trade occupations.
  • The amount of homeworking undertaken also varies significantly between regions. Working from home during the COVID-19 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.