Treasury Committee

UK Statistics Authority correspondence to the Treasury Select Committee on revisions within Blue Book 2023

Dear Ms Baldwin,

Thank you for your letter of 14 September 2023 regarding revisions within Blue Book 2023. To take your four points in turn:

An overview of the main drivers of these revisions, and whether there were particular circumstances (including those arising from the pandemic) in 2020 and 2021 that made early estimates of GDP especially uncertain.

As I outlined in the Financial Times recently, The UK’s official economic statistics are rightly seen as among the world’s best. This includes the recent upgrade of our official estimates for economic growth in the pandemic years of 2020 and 2021.

It is certainly true that the large shifts in activity, and the means of delivering that activity in many cases, made it harder for all statistical agencies to measure economic activity during the pandemic. But it is equally true that the larger revisions we have seen for our 2020 and 2021 GDP estimates are proportionally in line with the much larger declines and growths seen over these periods as well.

The main drivers of revision in our 2020 and 2021 GDP estimates come from these changes in activity. For example, the health service had increased costs to deliver a reduced amount of output (e.g. protective equipment, and extra staff) during 2020 which increased the intermediate consumption and decreased the value added of the health sector. During 2021 these intermediate consumption costs continued to rise, but more slowly, while output volumes saw a massive increase from the return of mainstream health activities such as elective surgeries but also from the COVID vaccination programme and so value added then grew strongly.

Secondly retailers and wholesalers also changed the way they operated with specialist stores being forced to close or be limited to click and collect, and a much larger proportion of transactions were completed on-line. This again changed the retail and wholesale margins element in 2020 and then this partially swung back the other way in 2021 as retailers, especially those selling clothing and textiles saw a strong recovery in 2021.

The third driver of revisions was inventories data, where our annual, more complete, data sources gave information that businesses undertook more stock building that previously thought at the start of the pandemic when restrictions were quickly introduced. For more detail, please see our article on the 1^st September 2023.

An explanation of what has been learnt from these revisions about what may have been wrong with the earlier estimates, and what improvements the ONS will implement from what it has learnt.

Our early monthly and quarterly estimates for GDP followed the standard ONS procedures using the available ONS data sources. The challenge was the sheer scale of fundamental change in the economy in such a small space of time. The ratio of intermediate consumption to final output is usually very stable, and as a result ONS did not have any data sources for changes to this ratio for periods beyond the latest supply and use balanced year, which was 2018 at the time the pandemic started.

We have now sourced intermediate consumption data on a more timely basis for the health service with quarterly and annual data available within a month or two of the reference period. We are also investigating the use of administrative tax data (VAT) on purchases by businesses as a means of identifying changes in the intermediate consumption ratio more quickly across industry.

We have welcomed the recently announced review by the Office for Statistics Regulation, and look forward to their recommendations as one of the themes relates to “Potential improvements to early estimates of GDP enabled through enhanced access to data”.

An outline of whether the ONS expects similarly large revisions to GDP data for 2022, in either direction, and more broadly whether the ONS sees revisions of this size as exceptional or typical.

The revisions profile of GDP estimates for 2022 and for the first half of 2023 were published on 29 September in the Quarterly National Accounts. There was little to no revision to previously published GDP from 2022 onwards, and we saw only 1 out of the last 6 quarters have been revised. The quarterly growth rate of GDP across all of 2022 was unrevised, while growth in 2023 Q1 was revised up 0.2 percentage points and 2023 Q2 was unrevised. With this release, we observed that revisions for that period are more typical of the pre-pandemic era.

As part of our continual improvement, we have already implemented the new health intermediate consumption data to reduce the potential for revision in this large sector of the economy. While other work looking at wider intermediate consumption continues, we have proactively reviewed areas such as rail transport and air transport to ensure that the intermediate consumption ratio of 2021 does not apply directly to 2022 as well, where we can see clear evidence of a recovery in those sectors. As part of the OSR review of GDP, ONS has committed to provide additional revision analysis of our GDP estimates in October 2023.

Given the ONS notes that it has completed its revisions to GDP using a Supply and Use Table framework ahead of many other countries, what it expects may happen in comparator countries when they undertake their own similar analysis.

Each country will follow different revision policies and practices, which can result in their estimates being revised at a later date according to their own needs. The timing and impact of revision changes will depend on data availability and magnitude, with large annual structural surveys being the data source needed to make detailed product and industry changes. These annual data sources come with lags on timeliness, often being available up to 2 or 3 years later.

We have now seen revisions to GDP estimates published by other countries. As we previously announced, the 2021 GDP estimates for the UK were revised to 8.7 percent growth from our initial estimate of 7.6 percent growth, a revision of +1.1 percentage points. The Spanish Statistical Agency has now published 6.4 percent growth in GDP for 2021, compared with the previous estimate of 5.5 percent, a revision of +1.1 percentage points. The Netherlands have now published 6.2 percent growth for 2021, revised from an initial estimate of 4.9 percent, a revision of +1.3 percentage points. Italy, have now published 8.3 percent growth for 2021, revised from an initial estimate of 7.0 percent, a +1.3 percentage point revision. All are a similar magnitude of upwards revision for 2021 as observed in the UK context. Conversely, the United States have now published 5.8 percent growth for 2021, compared to a previous estimate of 5.9 percent growth, a revision of -0.1 percentage points [ONS own calculations based on published US data from www.bea.gov]. This highlights that revisions can differ across countries.

Please do let me know if you have any further questions about this topic or if I can be of assistance to the Committee on any other matter.

I am copying this letter to Rt Hon Greg Clark MP, Chair of the Science, Innovation and Technology Committee, and William Wragg MP, Chair of the Public Administration and Constitutional Affairs Committee.

Yours sincerely,

Professor Sir Ian Diamond

Office for National Statistics correspondence to the Treasury Select Committee on insurance industry inflation

Dear Ms Baldwin,

Thank you for your letter of 8 June 2023 regarding insurance industry inflation. To take your questions in turn:

How the ONS calculates insurance inflation for the purposes of the Consumer Price Index, and whether differences in methodology account for the differences between ONS and ABI measures of inflation.

Our data sources and methods are publicly available on our website, and you may find our Consumer Prices Indices Technical Manual helpful. As a general point, our inflation measures are designed to capture inflationary pressures only, thus we follow a fixed basket approach. This means we compare like for like each month, and any changes that affect the quality of a good or service are adjusted out. So, for example, in the case of insurance, if a consumer amends a policy to (for example) remove an optional extra then this would be treated as a change in quality, rather than a change in price. This is consistent with international best practice.

The manual explains that the Office for National Statistics (ONS) measures two major types of insurance: home contents insurance and car insurance. The car insurance price index is a combination of two separate indices, one for fully comprehensive insurance and the other for third party, fire, and theft insurance.

Each of these is split further into unpublished price indices for specific car insurance companies. Expenditure data is used to weight these indices together and to ensure that a representative sample of insurers is selected. Each index is constructed from actual insurance price quotes for new policies provided by a third-party company. These quotes are returned for a database of customer profiles. For car insurance, the customers in question cover a wide range of ages, driving experience, regions, and vehicles. For the home contents insurance index customer profiles cover a wide range of regions, the material used for the construction of their house or flat, the number of rooms, the number of occupants and many other attributes.

We strive to continuously improve our statistics and are undertaking a programme of transformation by identifying new data sources, improving our methods, and developing our systems to ensure CPI and CPIH are of the highest possible quality. We are currently working on an ambitious programme to improve prices for used cars, rents and groceries using new, more comprehensive data sources.

Insurance data is one of several areas we are considering for inclusion in future development work. We are in discussions with the Association of British Insurers (ABI) who have provided historical aggregate data. This data is a simple average price of all policies. While the data is more comprehensive as it covers all policies (both new policies and renewals) it does not control for changes in the mix of products bought over time, which means we cannot determine if changes in the average price reflect true price changes or changes in the level/type of cover. This may explain some of the differences between ONS and ABI data and is an area we are looking to explore further.

Whether it is true that ONS insurance inflation is based on quotes, not actual prices paid. If so, what is the justification for using that method?

The ONS insurance inflation is based on quotes. The international guidance explains that it would be ideal for transacted prices to be used while acknowledging that this is not always possible, meaning that using the advertised prices of products offered for sale is often admissible. As such, it is not uncommon for the ONS to collect advertised prices, in fact we use prices listed on shop shelves, websites or quoted in a call for a wide array of goods and services. The use of price quotes should only distort measures of inflation if the relationship between quotes and prices changes significantly; even if quotes are typically higher or lower then final prices paid, there would be no effect on inflation as long as that level difference was consistent over time. We are not aware of any evidence that the relationship between quotes and prices has changed.

An additional consideration for us is the necessity to have timely prices in order for us to be able to calculate monthly indices. Consumer price indices are published within a month of the reference period and are not revised so this places a high bar on timeliness for potential data sources. We have so far been unable to source timely insurance data using prices paid. We are working with the ABI to identify whether there are timely data sources that we could use to calculate CPI .

Your assessment of whether the ONS may be materially overstating the level of insurance inflation actually experienced by consumers.

Both the ABI and ONS data have advantages and disadvantages. While our methodology controls for changes in the quality or type of product bought in a way that the ABI data does not. Whereas the ABI data covers renewals – a segment of the market not covered by the ONS sample. It’s possible that prices in this part of the sector may have fallen following regulatory intervention in 2022, meaning our insurance index may have overstated inflation at the time the change was made. This is less likely to explain divergences in more recent data for 2023. More recent differences between the ABI and ONS measure may reflect changes in the type of insurance products bought by households (where the ONS methodology would give a clearer steer on underlying price changes). However, we cannot draw any firm conclusions at this stage and are seeking more granular data from ABI to help provide us with further insights.

The confidence intervals for each of your categories of insurance inflation.

We are currently sponsoring research to provide robust confidence intervals for consumer prices inflation. This work is still ongoing, but early analysis indicates that the standard deviation for annual inflation for ‘miscellaneous goods and services’, which includes insurance, is a few tenths of a percentage point. However, this is early partial analysis that may understate the true confidence intervals for these statistics.

Please do not hesitate to contact us if there are any further questions.

Yours sincerely,

Mike Keoghan

Deputy National Statistician for Economic, Social and Environmental Statistics

Office for National Statistics follow-up written evidence to the Treasury Committee’s inquiry on an Equal Recovery

Dear Mr Stride,

Thank you for inviting me to give evidence to your Committee on 15 September for the inquiry ‘An Equal Recovery’. During that evidence session, I promised to follow up on several points, and to answer some questions that members did not have time to ask.

Impact of the pandemic on lower-paid workers

In May 2021 we published analysis that found that those in the lowest income bracket (up to £10,000 per annum) continued to be more likely to report negative impacts to personal well-being in comparison with those in higher income brackets. These negative impacts included the pandemic making their mental health worse (18%) and feeling stressed or anxious (32%).

We have also explored the likelihood of experiencing some form of depression. In our coronavirus and depression in adults in Great Britain publication, we found that around 3 in 10 (29%) adults who reported being unable to afford an unexpected but necessary expense of £850 experienced some form of depression, compared with 11% of adults who reported being able to afford this expense. In addition, for working age adults aged 16 to 64 years, rates of moderate to severe depressive symptoms generally decreased as income increased. Around 3 in 10 (29%) working adults with a personal income of less than £10,000 a year experienced some form of depression; this was four times greater than working adults with a personal income of £50,000 or more (7%).

In our analysis of COVID-19 deaths by occupation we found that men who worked in elementary occupations (699 deaths) or caring, leisure and other service occupations (258 deaths) had the highest rates of death involving COVID-19, with 66.3 and 64.1 deaths per 100,000 males respectively. For women, process, plant and machine operatives (57 deaths) and caring, leisure and other service occupations (460 deaths) had the highest rates of death involving COVID-19 when looking at broad occupational groups, with 33.7 and 27.3 deaths per 100,000 females, respectively. Please note that this analysis does not prove conclusively that the observed rates of death involving COVID-19 are necessarily caused by differences in occupational exposure; we adjusted for age, but not other factors such as ethnic group and place of residence.

Impact of the pandemic on people with disabilities

During the evidence session, I was asked if the ONS could support TUC written evidence that said six out of 10 people who died from COVID-19 were disabled. This figure was taken from our release looking at COVID-19 related deaths by disability status in England, published in February 2021, which found that disabled people made up six in 10 (59.5%) of all deaths involving the coronavirus (COVID-19) for the period to 20 November 2020 (30,296 of 50,888 deaths). For comparison, disabled people made up 17.2% of the study population, suggesting that disabled people have been disproportionately impacted by the COVID-19 pandemic.

We have also looked at outcomes for disabled people in the UK across all areas: education, employment, social participation, housing, crime and wellbeing. Within this we found disabled people’s (aged 16 to 64 years) average well-being ratings in the UK were poorer than those for non-disabled people for happiness, worthwhile and life satisfaction measures; average anxiety levels were higher for disabled people at 4.47 out of 10, compared with 2.91 out of 10 for non-disabled people (year ending June 2020).

Impact of the pandemic by ethnicity

In terms of the differences in mortality by ethnicity, during the first wave of the coronavirus (COVID-19) pandemic (24 January 2020 to 11 September 2020), people from all ethnic minority groups (except for women in the Chinese or “White Other” ethnic groups) had higher rates of death involving the coronavirus compared with the White British population. Differences were less pronounced in the second wave, but higher rates were notable in Bangladeshi and Pakistani ethnicities. Adjusting for location, measures of disadvantage, occupation, living arrangements and pre-existing health conditions accounted for a large proportion of the excess COVID-19 mortality risk in most ethnic minority groups; however, most Black and South Asian groups remained at higher risk than White British people in the second wave even after adjustments.

In December 2020, we published an overview of the social impacts of COVID-19 pandemic on different ethnic groups in the UK. This highlighted that in April 2020 in the UK, over a quarter (27%) of those from Black, African, Caribbean or Black British ethnic groups reported finding it very or quite difficult to get by financially, significantly more than those from White Irish (6%), Other White (7%), Indian (8%) and Pakistani or Bangladeshi (13%) ethnic groups. In addition, this publication explored mental health and found that it deteriorated across most ethnic groups during the first lockdown period, but also outlined that those in the Indian ethnic group may have been particularly affected.

The relationship between economic growth and inequality

The ONS produces a range of statistics on both economic growth and inequality, and others such as the OECD and the IMF have produced analysis of the link between the two. Our data allows for an examination of broad trends in the two concepts across several years. UK National Accounts data show that real GDP grew by 19% over the ten years to 2019/20, while median household income also rose (by around 6.9%). However, over a similar period measures of income inequality stayed broadly stable, and the level of wealth inequality rose slightly (wealth data are currently only available biennially up to April 2018).

The Committee might also be interested to note that the ONS is developing wider measures that go beyond GDP, such as looking at natural capital and how much work people do for free in their own homes.

Relationship between home ownership and wealth inequalities, including the role of inheritance

The latest data we have for total wealth in Great Britain, including net property wealth, is for April 2016 to March 2018. Increases in net property wealth were largely associated with rising house prices, as well as an increasing share of homeowners who own their property outright rather than with a mortgage.

We can use Wealth and Assets Survey data to consider the relationship between home ownership and wealth. For April 2016 to March 2018, these tables show that those households where the main home is rented have median household total wealth of £33,000; those buying with a mortgage have more than 10 times this value, £353,000, and those that own outright have median wealth more than 20 times bigger than renters at £685,000.

In February 2020 we published analysis on housing tenure which showed that almost three quarters of people aged 65 and over own their home outright, where younger people are less likely to own their own home than in the past. Half of people in their mid-30s to mid-40s had a mortgage in 2017, compared with two-thirds 20 years earlier.

We also previously looked at inheritances and intergenerational transfers, which found that between July 2014 and June 2016 the median inheritance received by those in the top personal wealth quintile was £35,000, compared with £3,000 for those in the lowest wealth quintile. However, while those in the lower wealth and income quintiles were likely to receive less than those in the higher quintiles, the inheritances they received made up a far higher proportion of their total wealth. Inheritances for those in the top wealth quintile were equivalent to 5% of their net wealth on average, while in the bottom wealth quintile this proportion was 44%.

We will publish the latest (April 2018-March 2020) Wealth and Assets Survey data and analysis toward the end of this year, including modelling the impact a range of demographics such as age, sex, disability, region, ethnicity, and education level have on wealth, and analysis of living standards across generations. We will of course make the Committee aware when these are published.

Inclusive Data Taskforce recommendations

The Committee may be interested to note that in October 2020, the National Statistician established an independent Inclusive Data Taskforce of senior academics and civil society leaders with expertise in a range of equalities areas. Their task was to develop recommendations on how to improve UK data and evidence to better reflect the diversity of UK society, in particular focusing on those in protected characteristics groups and others at greater risk of disadvantage.

The Taskforce have now published their recommendations, which were developed in the context of the pandemic and acknowledge the essential role of better data in monitoring its impacts. The National Statistician has provided an initial response, including details of early work that is underway to address some of their recommendations.

Over the coming months, the ONS will be working with a range of stakeholders to develop a detailed action plan to take forward these recommendations, to ensure that we have the data that we all need, including the data and evidence we need to effectively monitor the impacts of the pandemic. This action plan will be published in January 2022.

Please do let me know if I can be of any further assistance to the Committee.

Yours sincerely,

Liz McKeown, Director of Public Policy Analysis
Office for National Statistics

Office for National Statistics oral evidence to the Treasury Committee’s inquiry on an Equal Recovery

On 15 September 2021, Liz McKeown, Director for Public Policy Analysis at the Office for National Statistics, gave evidence to the Treasury Committee for its inquiry on an Equal Recovery.

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

Office for National Statistics written evidence to the Treasury Committee’s inquiry on an Equal Recovery

Dear Mr Stride,

I write in response to the Treasury Committee’s call for evidence for its inquiry into ‘An Equal Recovery’.

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

Our submission focuses on the following questions in the terms of reference:

What are recent trends in income and wealth inequality in the face of the pandemic?
What are the trends in intergenerational inequality, and how has the crisis affected them?
How has the economic impact of the crisis affected disability, gender, and race inequality?
How has the crisis impacted on regional inequality?
Are certain regions or sectors likely to recover more slowly or have longer term economic damage and greater scarring?

I hope this is useful to the Committee, and please do let me know if we can follow-up on any further specific questions for this inquiry.

Yours sincerely,

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

Office for National Statistics written evidence: An Equal Recovery, July 2021

Summary

Overall, the COVID-19 pandemic has affected the economy in a variety of ways, which has translated into differing effects on the population. The Office for National Statistics (ONS) has reacted to user demand and published analysis throughout the pandemic which illustrates that:

Throughout the pandemic, those at the lower end of the income distribution were more likely to have felt negative financial impacts.
Households of younger people were less likely to be able to sustain levels of spending and the 18 to 24 years age-group had the highest proportion of those who spend more than their income.
This translated into significant differences between the financial effects on young and older people during the pandemic: older people (60 and over) were significantly more likely to say they expected the financial situation of their household to stay the same during the next 12 months from April/May 2020.
In addition, in the labour market, young people’s (16-24) employment rate saw a very large decline in 2020 compared with 2019 and relative to other age groups.
The pandemic negatively impacted the wellbeing of disabled people, with some financial impacts worse as well.
Women and men had different experiences of the pandemic too, though there were differences between the formal labour market and wider household activities. While the employment rate for men fell more than for women, women continued to deliver the most childcare, and were more likely to report worse personal well-being impacts.
Early on in the pandemic we published analysis that showed that over a quarter (27%) of Black, African, Caribbean or Black British ethnic groups reported finding it difficult to get by financially, significantly more than any other ethnic group.
Using quarterly regional GDP, we saw London and the East of England impacted more than other regions and nations due in part to their reliance on services industries in Quarter 2 and 3 of 2020 (the latest data available).
The travel and tourism and hospitality sectors were especially affected by the pandemic, and we published specific analysis looking at various linked impacts.

The following evidence submission picks up on these points in greater detail.

Moreover, as highlighted throughout this submission, the ONS are using new surveys and administrative data to provide the best evidence to understand the impact of COVID-19 on the economy and society, and to track the UK’s recovery from the pandemic. We continue to work to address the gaps in the data that remain whilst improving the granularity and timeliness of analysis we produce. We are working across Government to do so, including bringing together administrative data using the Integrated Data System as a key enabler. We will also look to seek funding to improve our survey portfolio where appropriate.

What are recent trends in income and wealth inequality in the face of the pandemic?

Early in the COVID-19 pandemic, the Office for National Statistics (ONS) set up the Opinions and Lifestyle Survey (OPN), which was created to understand the impact of the pandemic on society. This complemented our regular statistics bringing together personal (subjective) and economic well-being. The latest data was published on May 2021.

These statistics illustrates that throughout the pandemic those at the lower end of the income distribution were more likely to have felt a loss of income than those at the top end of the income distribution, as well as other negative financial impacts, such as being more likely to need to use savings to cover living costs, not being able to save for the year ahead, and having to borrow money due to COVID-19.

Using Quarterly Labour Force Survey and Longitudinal Labour Force Survey data with a focus on 2020 and up to March 2021 we can also see the impact on young people (those aged 16-24 years) in the labour market has been notable when compared with other age groups.

Young people’s employment rate saw a large decline in 2020 compared with 2019, while their unemployment and economic inactivity rates increased. After an initial fall in young people in full-time education in the first few months of the pandemic, the proportion of young people in full-time education increased in the second half of 2020, reaching a new high of 47.2% in Quarter 3 (July to Sept) 2020 and remaining high since, with the challenging labour market likely to be a contributing factor.

The number of young people employed in the accommodation and food services industry who moved to unemployment or economic inactivity increased by more than 50% in Quarter 2 (April to June) 2020 compared with Quarter 2 2019. Young people who worked part-time moved from employment to economic inactivity at a faster rate than they moved to unemployment in 2020, so in short, more likely to stop looking for work. Moreover, their labour mobility (job-to-job moves) also declined more during the pandemic than for older age groups.

What are the trends in intergenerational inequality, and how has the crisis affected them?

We looked at the social impacts of coronavirus on younger people (aged 16 to 29) and older people (aged 60 and over) at the start of the pandemic (3 April to 10 May 2020). While young people were mainly worried about the impact of coronavirus on their education, work and household finances, among older people, their main concerns were around being unable to make plans in general and personal travel plans such as holidays.

A significantly higher proportion of young people (30%) reported that the coronavirus was affecting their household finances, than older people (13%), with 84% of young people who had reported this, saying they had experienced a reduction in income and 38% saying they were unable to save. The proportion of 16 to 29-year-olds reporting these impacts were significantly higher than for those aged 60 years and over. Older people were also significantly more likely to say that they expected the financial situation of their household to stay the same over the next 12 months than younger people; 56% of those aged 60 years and over expressed this compared with only 40% of those aged 16 to 29.

As may be expected when comparing with older people, many of whom are retired, younger people were also significantly more likely to report that the pandemic had affected their work, with 21% of those aged 16 to 29 who reported this saying they had experienced a reduction in hours worked. Data from other sources for the period leading up to the pandemic also showed that young people were the most likely to report their working arrangement as a zero-hours contract.

How has the economic impact of the crisis affected disability, gender, and race inequality?

Disability

Before the pandemic, between April 2018 to March 2020, younger people and those who were unemployed, or sick and disabled, were more likely to report that they (a) would not be able to make ends meet for one week without their main source of income, (b) regularly run out of money before the end of the week/month and (c) would not be able to find money to cover a large, unexpected expense.

During the pandemic, disabled people have been more likely to report worse personal well-being impacts, such as being stressed or anxious, worsening mental health, and feeling worried about the future. They have been less able to work from home, and while they were as likely to report having reduced household income than those without a disability, their financial impacts have been worse in some respects, such as being more likely to report borrowing money due to COVID-19, using savings to cover living costs and not being able to save for the year ahead.

In the first quarter of the pandemic, employment rates of people with disabilities fell a little more sharply than for those without disabilities, with a more pronounced increase in economic inactivity, however they have also recovered more since those early days.

Gender

We published a release looking at the different effects of the pandemic on men and women in the UK from March 2020 to February 2021. For example, women were more slightly likely than men to be furloughed: 2.91m to 2.72m for men on 1 July 2020. Over time, the difference decreased, with preliminary data suggesting that on 31 December 1.88m women and 1.85m men were furloughed.

In the labour market, the employment rate for men aged 16 to 64 fell 2.7 percentage points between the three months ending February 2020 and its lowest point during the pandemic, but has recovered slightly to 2.4 percentage points below by the three months ending May 2021. Meanwhile the rate for women fell to 1.2 percentage points below its pre-pandemic level, before recovering to 1.1. percentage points below. This may be largely due to the bigger effect of the pandemic on the self-employed, which has a higher representation from men, and the lower impact on public administration, education and health during the pandemic, which have higher representation from women.

The effects of the pandemic were, however, much wider than just the formal labour market. A greater proportion of women (67%) than men (52%) home-schooled a school-age child in their home in late January and early February 2021, and a greater proportion of women reported that home schooling was affecting their well-being than men (34% compared with 20% for men) during the first lockdown (April and early May 2020). By late January and early February 2021, it was taking a greater toll on both women (53%) and men (45%).

We also looked specifically at parenting in lockdown and the effects of this on work life balance, which illustrates a gendered impact. In the first lockdown, men took on a larger share of the childcare than previously, but women were still delivering an average of 3 hours and 18 minutes of childcare, which includes time spent supervising children, while men contributed 2 hours.

The shares were more evenly split between men and women for developmental childcare such as reading to children or helping them with their homework and home-schooling. However, women picked up a much larger share of ‘non-developmental’ time, such as washing, feeding and dressing children and supervision of children. This is reflected particularly for parents of children under 5, where we see the biggest difference in time spent on childcare with women spending 4 hours 25 minutes on average while men spent 2 hours 29 minutes on it on average per day.

Overall, women have been more likely to report worse personal well-being impacts such as being stressed or anxious, the pandemic making their mental health worse, and feeling worried about the future. Women’s financial impacts have been worse in some respects, such as being more likely to report borrowing money due to COVID-19, using savings to cover living costs and not being able to save for the year ahead. While men were slightly more likely to say they were not able to save as usual, women were as likely to report having reduced household income as men.

Ethnicity

We used the UK Household Longitudinal Study to look at the impacts of coronavirus on different ethnic groups in the early part (April 2020) of the pandemic, finding that over a quarter (27%) of those from Black, African, Caribbean or Black British ethnic groups reported finding it very or quite difficult to get by financially, significantly more than those from White Irish (6%), Other White (7%), Indian (8%) and Pakistani or Bangladeshi (13%) ethnic groups. A quarter of people (25%) from Black, African, Caribbean or Black British ethnic groups reported being behind on bills and 22% reported being less able to keep up with housing payments, with significantly higher proportions reporting struggling in these ways than respondents in other ethnic groups.

Looking at the labour market by ethnic group, the picture is not clear, with groups performing better or worse than others on different metrics.

How has the crisis impacted on regional inequality?

The latest release of quarterly regional GDP is for Quarter 3 (July to Sep) 2020 and is compiled mainly from administrative data sources, principally HMRC’s VAT turnover data. Lags in the data arising from the reporting requirements on businesses means the timeliness of our regional GDP is two quarters behind the first estimate of GDP.

To give context, in Quarter 2 2020, out of the 9 English regions and Wales, the East of England saw the largest fall (-21.1%) and Wales saw the smallest fall (-15.2%). Of the 9 English regions London saw the smallest fall (-17.2%) but provided the largest contribution to the UK fall.

In the East of England, for Quarter 2 2020, the services industries provided the largest contribution to the fall (–18.1%). Within this main sector, we can break this down by the wholesale and retail (-19.8%), accommodation and food services (-75.7%) and education (-30.4%). In addition, manufacturing (-27%) and construction (-42.7%) fell. By Quarter 3, all these industries bounced back from the fall apart from manufacturing (23.6%) and education (20.3%). Four of the 14 services industries recovered the growth lost in Q2 2020.

In Wales, for Quarter 2 2020, the largest falls were seen in manufacturing (-15.2%), accommodation (-76.3%), wholesale and retail (-17.8%) and construction (-32.7%). By Quarter 3, all these industries bounced back from the fall apart from construction, with a rise of 30.9%. In London, for Quarter 2 2020, total services fell by 16%, with the largest contribution to the fall from accommodation and food services (-75.8%), administrative support services (-31%) and transport and storage (-38.1%).

Figure 1 shows GDP growth for the UK and its countries from Quarter 3 (July to Sept) 2018 to Quarter 3 (July to Sept) 2020.

Figure 1: All four countries in the UK had positive growth for Quarter 3 (July to Sept) 2020

Seasonally adjusted quarter on quarter GDP growth for the UK and its countries, Quarter 3 (July to Sept) 2018 to Quarter 3 (July to Sept) 2020

In Quarter 3 2020, the services industry has not fully rebounded, with a rise of 11.7% and only six of the 14 sub-industries recovering from their fall in growth in Quarter 2 2020. The transport and storage; arts, entertainment and recreation, and administrative and support service activities and education show the weakest recovery in growth following their decline in Quarter 2 2020. London experienced the largest fall in financial services out of all UK regions in Quarter 2 2020, with a fall of 8.8% although rebounded a little with growth of 3.2% in Quarter 3 2020.

In Quarter 3 2020, only three of the English regions recovered the growth lost in Quarter 2 2020: Yorkshire & Humber, East Midlands and the South West. For all three regions, accommodation and food services provided the largest contribution to the growth along with wholesale and retail, manufacturing and construction.

The regions that saw the smallest recovery in Quarter 3 2020 were London (13.3%) and West Midlands (16.8%). In the West Midlands for Quarter 3 2020, manufacturing has not recovered from the fall in Quarter 2, and only four of the fourteen services industries have recovered in growth. Figure 2 shows quarter on quarter growth for the regions of England and countries of the UK for Quarter 2 (Apr to June) 2020 and Quarter 3 (July to Sept) 2020.

Figure 2: The region with the largest positive GDP growth in Quarter 3 (July to Sept) 2020 was the South West which increased by 19.9%

Seasonally adjusted quarter on quarter GDP growth for the regions of England and countries of the UK for Quarter 2 (Apr to June) 2020 and Quarter 3 (July to Sept) 2020

Are certain regions or sectors likely to recover more slowly or have longer term economic damage and greater scarring?

Another survey set up at the beginning of the pandemic was the Business Insights and Conditions Survey (BICS), which helps us understand business impacts at the national and sub-national level on the UK economy. Using microdata from this survey we can understand the business impacts of local and national restrictions.

For example, we can see that multi-site businesses (tend to be larger companies) have a consistently higher proportion of businesses currently trading than single-site businesses (smaller companies) from November 2020 to July 2021, though the gap is narrowing. Meanwhile, the percentage of businesses experiencing a decrease in turnover is similar for both single-site and multi-site businesses with both seeing a steady improvement over time.

Wales had the highest percentage of single-site businesses currently trading in early July 2021, at 98%. And Scotland has consistently had the highest proportion of its workforce on furlough leave since early November 2020, but is now at similar level to both England and Wales.

Based on information from HM Revenue and Customs Real Time Information on the number of payroll employees, in June 2021 four regions were above pre-pandemic levels: North East, North West, East Midlands and Northern Ireland. Most other regions were within around 1% of pre-pandemic levels. The exception was London, which fell by a higher percentage than other regions and still had over 3% fewer payroll employees in June than pre-pandemic.

We have also looked at particular sectors that have been heavily impacted by the pandemic, such as hospitality (published July 2021) and travel and tourism (published February 2021). For example, these highlighted the uneven impact on hospitality, and lack of confidence in business survival compared to the all sector level. Job vacancies in the hospitality sector have seen large increases and are higher than pre-pandemic levels; however, in June 2021, the number of employees within the sector remained 11% below February 2020 levels.

For travel and tourism, the impact was immediate: monthly air passenger arrivals to the UK fell from 6,804,900 in February 2020 to 112,300 in April 2020, a fall of 98.3%. It had a regional impact, as Greater London saw the largest fall in room occupancy of any English region from 2019 to 2020, with just 20% of rooms occupied in July 2020 compared with 90% in the same month in 2019. In the three months to June 2020, employment in accommodation for visitors fell by 21.5% compared with the same three months of 2019.

Accommodation and travel agency businesses saw the sharpest decline in turnover during the first national lockdown, falling to 9.3% of their February levels in May 2020. In travel and tourism industries overall, the number of people aged 16 to 24 years saw the largest fall in employment of any age group between Quarter 3 (July to Sept) 2019 and Quarter 3 2020.

Office for National Statistics
July 2021

Office for National Statistics written evidence to the Treasury Committee’s inquiry on jobs, growth and productivity after coronavirus

Dear Mr Stride,

I write in response to the Treasury Committee’s call for evidence for its inquiry into Jobs, growth and productivity after coronavirus.

Our submission focuses on the following questions in the terms of reference:

What are the causes of the gap in the UK’s level of productivity compared to other advanced economies, and why has productivity growth been persistently weak in the aftermath of the 2007-09 financial crisis?
Do economic statistics adequately capture growth in the modern economy, and what lessons can be learned from the pandemic about the measurement of economic activity?

I hope this is useful to the Committee, and please do let me know if we can provide any further assistance to this inquiry.

Yours sincerely,

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

Office for National Statistics written evidence: Jobs, growth, and productivity after coronavirus inquiry, May 2021

What are the causes of the gap in the UK’s level of productivity compared to other advanced economies, and why has productivity growth been persistently weak in the aftermath of the 2007-09 financial crisis?

The UK’s productivity performance

Productivity in the UK has grown slowly since the 2008/09 economic downturn, a phenomenon often referred to as “the productivity puzzle”. Before this, productivity growth was typically around 2% per year for the economy as a whole in the UK, although productivity levels were slightly lower than other comparable developed economies. The slowdown in growth since 2008/09 is not unique to the UK, but among the sharpest of developed countries.

During 2020, UK productivity (using the headline measure, output per hour worked) increased slightly, by 0.4% on 2019. Many industries experienced a fall in productivity because of the coronavirus pandemic, as ‘furlough’ and lockdown made operations more difficult and costly. However, those industries most affected by the pandemic tended to be lower-productivity industries, such as the accommodation and food services and entertainment industries. As a result, a larger share of the economy in 2020 was in higher productivity industries, and this increased aggregate average productivity – known as a positive ‘re-allocation effect’ – even though many industries did not see significant growth in their productivity.

The Office for National Statistics (ONS) publish a range of statistics on productivity, with industrial and regional breakdowns. The simplest measures are ‘labour productivity’ – the amount of output (measured by gross value added, GVA) produced per unit of labour employed. Labour measures are typically either the number of workers (including the self-employed), or the number of hours worked. The output per hour worked measure is preferred since it accounts for changes in working patterns (e.g. changes in rates of part-time working). Labour productivity measures are published quarterly by the ONS, with an early estimate around 45 days after the end of the reference quarter, and a revised and more detailed estimate around 100 days after the end of the reference quarter.

More complex measures include multi-factor productivity (MFP), which accounts for both labour and capital inputs. Capital assets are those used in production for a year or more, such as buildings, machinery, computer hardware, and some intangible assets such as software. The preferred measure of capital input for productivity statistics is capital services, which is complex to measure, but accounts for the contribution a capital asset makes to the production process. Labour input measures for MFP account for the skills of the workforce as well as their hours worked. MFP estimates produced by the ONS are published quarterly around 100 days after the end of the reference quarter – amongst the most timely in the world. They cover the market sector (excluding government and the charity sector) with a 19 industry breakdown.

The ONS also publish productivity estimates for public services, compiled in a specialist framework given the nature of public service delivery. These are published quarterly in line with other productivity statistics as detailed above, and annually with an approximate 2-year lag. The annual estimates account for changes in the quality of service provision, which requires more data to be gathered.

During the pandemic, challenges in how we measured productivity have emerged. First, measures of output per hour worked and output per worker have diverged substantially, which is unusual historically. This has been driven by workers on furlough, who are still counted as employed but work no hours – as such, they are included in output per worker, but not within output per hour worked. Adjusting for furloughed workers increases the level of output per worker during 2020, evidencing that furlough has been disproportionately used in less productive industries. The result is similar to the increase in output per hour worked already described.

The productivity puzzle

The causes of the “productivity puzzle” are complex and disputed – the very reason it is known as a puzzle. The academic literature has proposed a range of potential causes or contributing factors, none of which have been found to completely explain the slowdown in growth. A range of popular theories have been summarised in an article by the ONS.

It is beyond the scope of this response to set out all the theories fully, but a summary of popular theories is as follows:

Structural arguments – the decline of the North Sea oil and gas industry, or changes in financial regulation since the global financial crisis.
Labour hoarding – businesses held onto workers due to their investments in training and skills, and the costs of staff turnover, reducing reallocation of resources and increasing misallocation of resources.
‘Zombie’ firms – low interest rates have enabled low productivity businesses to survive, weighing down aggregate productivity.
Mis-measurement – the growth of the digital economy may have made it harder to measure goods and service in GDP and increased the amount of economic activity outside of GDP.
Low levels of capital investment – as a result of reduced bank lending in response to the 2008/09 financial crisis, banks’ inability to lend against intangible assets which were becoming increasingly important, large pension deficits reducing available funds for investment, or persistent business and macroeconomic uncertainty.
Reduced technology diffusion and an increase in productivity dispersion – a slowdown in the rate of ‘catch up’ by businesses at the bottom end of the productivity distribution, relative to the rest of the business population.
A longer-term slowdown associated with slower technological progress – modern innovations are less substantial than in the past, and/or the rate of adoption of new technologies is slower than before.
Weak demand – due to higher levels of uncertainty, or for some other reasons, economic demand has been lower and grown slower than in the past, limiting the scope for productivity growth.

The ONS publishes regular analysis on productivity and the productivity puzzle and has invested in data collection to help shed light on these issues. We have run a new wave of the Management and Expectations Survey, which collects valuable data on management practices and business uncertainty. In addition, research using microdata has enabled analyses of “productivity laggards“, business dynamism, and firm-level productivity.

Looking forward, the impact of EU Exit and recovering from the 2020 recession may alter our understanding or perspective of the productivity puzzle.

International comparisons of productivity

International comparisons of all manner of economic statistics are challenging because of differences in data sources, methods and statistical infrastructure across countries. International comparisons of productivity are no different, and indeed perhaps even more challenging since they require production of comparable statistics on output (GDP or gross value added), labour inputs (employment or hours worked), and currency conversions.

When last published in 2018, ONS statistics showed the UK around 30% less productive than the US, Germany and France on an ‘hours worked’ basis. The UK was also around 10% less productive than Italy, at a similar level to Canada, and about 10% more productive than Japan. The degree of dispersion among developed countries that these estimates imply seems implausibly large – suggesting Japan is 40% less productive than the USA – so the ONS has suspended publication of these statistics since then.

The OECD, part-funded by the ONS, published a report on the comparability of hours worked measures used in productivity statistics across countries. They found that different methods, data sources and adjustments made in different countries led to substantial variation in estimates of hours worked – with implied average working weeks as long as 34 hours in the US (equivalent to an average working day of 6.8 hours), and as short as 27 hours in Germany (equivalent to an average working day of 5.4 hours).

After attempting to align approaches, the differences in estimates of hours worked between countries were generally smaller. The alternate measure of hours worked for the UK was lower, and hence output per hour worked higher, closing the gap between the UK and comparator countries. While these estimates remain uncertain, more work is required to ensure comparable measures are needed to compare variation in levels of productivity between similar economies.

The ONS intends to re-commence publication of statistics on international comparisons of productivity later this year, using a format that better reflects the inherent uncertainty in the comparability of the measures. We are also reviewing our methods for measuring hours worked for productivity calculations and will look to make any necessary changes to improve their quality in due course. Longer term, a collaboration between national statistical institutes and international bodies such as the OECD may help to improve comparability of productivity statistics.

Despite improved measurement, the UK is still thought to be less productive than comparable developed economies. This could be for a range of reasons, including differences in industry composition across countries, low investment levels, the quality of infrastructure, differences in international trade exposure and foreign direct investment, or differences in labour market flexibility or education. Experimental analysis by the ONS for 2014 suggests that the UK exhibits lower productivity than other developed countries in many industries. Different experimental ONS analysis also suggests that many regions of the UK are less productive than the majority of regions in other developed European countries, with London and the South East the only exceptions.

Do economic statistics adequately capture growth in the modern economy, and what lessons can be learned from the pandemic about the measurement of economic activity?

Capturing growth in the modern economy

Developments in the modern economy present two separate but related problems for measurements of economic growth. Firstly, ensuring our National Accounts sources and methods are equipped to both capture and reveal the sources of economic growth within the well-established boundaries of what constitutes “the economy” within the National Accounts, and secondly, re-examining the extent to which recent developments have fundamentally re-shaped what “the economy” is, such that a substantial portion of what people think of as “the economy” falls outside the traditional scope of National Accounts.

A key feature of the modern economy which lies within the boundaries of GDP is the proliferation of digital goods and services, known as the “Digital Economy”. The ONS are engaged in international efforts, such as the Digital Economy Task Force and the OECD’s Working Party for Measuring and Analysing the Digital Economy, to both better define as well as measure the digital economy. Definitionally, what is thought of as the “digital economy” presents an issue for statistical offices as it is often contextual and evolves quickly over time. There is a broad consensus in the internal community (e.g. exemplified by the Roadmap Toward a Common Framework for Measuring the Digital Economy) that any definition needs to support a framework of digital-economy measurements, which can then be used to tailor relevant domestic measures of the digital economy. These dimensions include whether products are digital in nature, use digital products in their production process, or are sold via Digital Intermediary Platforms. The central framework to deliver this tailored approach is through a Digital Supply-Use Table (DSUT), which extends traditional Supply-Use tables by breaking them down further by these additional dimensions. We are engaging both with the international work to develop the framework of these tables, as well as research into the feasibility of their application within the UK.

Moving beyond, but still building upon, the current international framework for measuring the economy (the System of National Accounts 2008), the ONS are undertaking research into additional Intellectual Property Products – commonly referred to as intangible capital. Investment in intangible capital is often theorised as a key source of economic growth for high income, service-led economies. The current status of intangible assets for which we have produced estimates, as well as whether these are currently included within National Accounts, are summarised in the table below.

Broad Category	Type of Intangible Asset	Capitalised in the National Accounts?
Computerised Information	Software and databases	Yes
	Research and development	Yes
	Mineral exploration and evaluation	Yes
Innovative Property	Entertainment, literary and artistic originals	Yes
	Design	No
	Financial product innovation	No
Economic Competencies	Branding	No
	Organisational capital	No
	Firm-specific training	No

Estimates for investment in these intangible assets (capitalised and uncapitalised) over the past decade suggest they may constitute around half of all investment in the UK, and so could play a central role in understanding and capturing growth in the modern economy.

In addition to these categories of intangibles for which we have been able to produce estimates, we are also engaged in an international discussion around the definition and possible measurement of data within the National Accounts. While the proliferation of data and its importance within the modern economy is well documented (e.g. in the recent OECD paper Perspectives on the Value of Data and Data Flows), national statistics institutes and international organisations are working in collaboration to identify how data, as an asset, can be included in a National Accounting framework. Some of the key questions under review are whether data are produced assets (i.e. fixed capital) or whether they are non-produced (i.e. not capital), whether data (e.g. the recorded temperature outside, one’s GPS co-ordinates, an online movie review) can be sufficiently distinguished from the ‘observable phenomenon’ (e.g. the actual temperature outside, one’s location, or one’s opinion about a movie) they are recording, and how these can be valued. Given the increasing prominence of data in production and consumption processes across the economy, the outcome of these questions may have an important impact on the measurement of economic growth in the future.

The ONS also have several workstreams looking beyond GDP to broader measures of what constitutes ‘the economy’, and what a modern approach to measuring it should look like. Our Spectrum framework seeks to place GDP as just one point on a continuum of what constitutes ‘the economy’ – from a restrictive definition including just the market economy on one side, to an expansive definition including household production, additional intangible investment, environmental asset degradation, and more. We plan to release a paper soon which provides estimates for these different definitions of ‘the economy’, their trends over time, and as a result shed light on the extent to which the extent of growth over the past decade depends on which of these definitions we choose.

We are currently seeking funding for research into the measurement of inclusive wealth, as recommended by the Dasgupta Review. Using this as a primary measure of the economy would shift focus away from flows of income, production, and expenditure in a particular year – as is the case for GDP – to the stock of resources available to the UK in a particular year, and how this may have changed. According to the report, this shift in perspective may place greater emphasis on the sustainability of the economy. Inclusive wealth includes within it the productive capital already measured as part of the National Accounts and adds Natural Capital and Human Capital. The ONS already publishes estimates for both. However, the Dasgupta Review proposes extending beyond the traditional National Accounts approach of measuring using market prices, instead advocating ’accounting prices’, which would include the value of externalities associated with different capitals. Researching this approach to measuring the economy may help understand the extent to which previous economic growth. as defined by GDP, may have been founded on unsustainable practices. As a result, this could help indicate whether shifting production and use of produced, natural, and human capital could cause traditional, GDP-like measures of the economy to suggest a slowdown in economic growth, while the improved sustainability of those practices could show an improvement to the economy as measured by inclusive wealth.

Measurement of economic activity during the pandemic

The first key lesson from the pandemic is that headline volume or “real” estimates of GDP may be less comparable between countries than usual. Whilst all countries comply with the same international statistical guidance, differences in the methods used to estimate non-market output such as healthcare and education can significantly impact relative GDP performance. Understanding the relative impacts of these methodological differences, as described in our February 2021 analysis, is important for measures of productivity as these National Account measures define the measure of output used in productivity calculations.

The second key lesson has been around the need to consider the degree to which capital is utilised. A machine in a factory which employees cannot enter due to furlough will still exist but will no longer be contributing towards the production of output. We therefore need to discount the quantity of capital services from capital items which are not being utilised. This acts to reduce the contribution from capital and commensurately increase the contribution from multi-factor productivity, which is often considered akin to the recipe by which capital and labour are combined to produce output. The best way to undertake this utilisation adjustment is a subject currently under review. Whilst we have implemented a simple method using weighting for hours worked, our researchers will shortly publish further work into this question via the Economic Statistics Centre of Excellence (ESCoE), which we can send to the Committee.

The third lesson relates to public service productivity. This series accounts for government output adjusted for the quality of the outcome achieved. We quality adjust to account for the fact that when a service is free at the point of delivery, there is no price to reveal whether consumer’s valuation of the product has changed in the light of improvements in quality*. Using methods developed following the Atkinson Review and described in a recent ONS journal article, the UK is a world-leader in quality adjusting public services. The key question the pandemic asks us is whether the measures we use to quality-adjust services in normal times are equally applicable to the 2020-21 pandemic, or should be revised to reflect those unique circumstances and the changes in public services offered in this period. We are in the process of establishing an academic working group to support ONS development in this area.

Office for National Statistics
May 2021

*An example we may illustrate this problem looks at the three methods to value public services: assuming inputs equal outputs, directly measuring outputs or adjusting measures of output for quality change. Taking an example of 100 operations delivered for £100 each in year 1, where all the patients show health improvements, let’s imagine that in year 2, 200 operations are delivered, each costing only £50, but all patients experience extremely negative health impacts. If one just measures inputs, total spend remains the same, so there is no output growth. If one measures the output (number of operations), output has doubled, but if one quality adjusts for the health impacts, one can see that output has collapsed. In this example, the three methods give radically different results which may distort how one views the service.

Office for Statistics Regulation written evidence to the Treasury Committee’s inquiry on jobs, growth and productivity after coronavirus

Dear Mr Stride,

I write in response to the Treasury Committee’s call for evidence for the inquiry considering Jobs, growth, and productivity after coronavirus.

The Office for Statistics Regulation (OSR) is the independent regulatory arm of the UK Statistics Authority. We provide independent regulation of all official statistics produced in the UK, including those in Devolved Nations and the NHS. Our regulatory work is underpinned by the Statistics and Registration Service Act 2007.

We set the standards official statistics must meet through the statutory Code of Practice for Statistics. We ensure that producers of official statistics uphold these standards by conducting assessments against the Code. Those which meet the standards are given National Statistics status, indicating that they meet the highest standards of trustworthiness, quality, and value. We also report publicly on system-wide issues and on the way statistics are being used, celebrating when the standards are upheld and challenging publicly when they are not.

Within this evidence, we have examined the adequacy of economic statistics for capturing growth in the modern economy. Particularly, we have considered the lessons to be learned by statistical producers from the pandemic about the measurement of economic activity.
I look forward to seeing the conclusions of your inquiry. Please do not hesitate to contact me if I can be of any further assistance.

Your sincerely,

Ed Humpherson

Director General for Regulation

Office for Statistics Regulation (OSR) written evidence: Jobs, growth and productivity after coronavirus, May 2021

Overview

1. Strong statistical leadership in a system willing and prepared to be flexible and innovative in the face of new data demands is essential for ensuring government statistics meet user needs and serve the public good. The Office for National Statistics (ONS) should aim to build on its good performance through the pandemic and ensure that the same agile and innovative use of alternative data sources and improvements to timeliness of its outputs is carried through to its post-pandemic work.

2. There is a want and need from users for a measurement of growth beyond GDP that considers other assets, as it does not account for the depreciation of assets, such as the natural environment. As our primary measure of economic success, GDP is useful as a short-term measure but many are asking for more comprehensive measures of growth which help policymakers to make longer-term decisions and investments.

3. A network of regional statistical agents, akin to the Bank of England’s regional agents, can help provide the ONS and others with better insight into regional economic issues that should be considered to enrich regional data.

4. In adopting international best practice, users should exercise caution in making international comparisons of real GDP growth currently. ONS has recommended that nominal GDP estimates are currently more internationally comparable, as they are not affected by the differences in public sector output measurement. Whilst ONS is a leader in measuring GDP, there are still known areas which need to be addressed.

5. There is debate about what is seen as productive and not seen as productive in the UK economy – ‘the production boundary’. There are several factors contributing to the ‘productivity puzzle’, but one element may lay in the way digital technologies make activity at home part of the productive economy.

Statistical Leadership

6. Strong statistical leadership is essential to ensure that statistics serve the public good, which in the context of the pandemic required statistical producers to make data and analysis available to policymakers quickly to address the immediate and the longer term impacts on the economy. ONS responded well to this challenge, engaging with their users, and producing the data they required.

7. ONS published the first of its faster indicators of economic activity publications in April 2020, one month after the UK first went into lockdown. The publication contained a series of economic indicators, which assisted policymakers with understanding the industrial impact of the pandemic and gauging the level of overall economic activity through the analysis of traffic flow data.

8. As outlined to users, the statistics were considered as experimental indicators in line with OSR’s COVID-19 guidance to statistical producers. These new indicators reflected the trade-off between their prompt availability versus the corresponding impact on accuracy, a balance that users told us they appreciated to ensure they had readily available information for decision making.

9. During May 2020, ONS proactively published information for its users on the work it was doing to ensure that its headline economic indicators, including Consumer Price Inflation, Gross Domestic Product and Labour Market Statistics, continued to be produced, ensuring that policymakers got this vital information to respond to the economic impacts of the pandemic.

10. As well as meeting the needs of policymakers, ONS has sought to communicate the story of how the economy and labour market have been impacted by the pandemic more widely. It has released a series of blogs which draw out the key messages and explain the significance of changes in the data, in a way which can be understood by less expert users.

Alternative Data Sources

11. Assessing the scale of economic change, when the usual sources of data were impaired by COVID-19, presented a major challenge to statistical producers. However, intensive use of alternative data sources including administrative and new survey data helped ONS enhance the timeliness of statistical production throughout the pandemic. Many of the statistics contained in the ONS’s faster indicators publication were produced using administrative data, including indicators on businesses’ turnover using HMRC VAT returns improving their timeliness and availability to users. These indicators helped policymakers understand the immediate impacts of the pandemic on the performance of business.

12. These statistics were complemented by data gathered using new surveys and existing surveys augmented to take account of the COVID-19 pandemic. The Business Impact of COVID-19 Survey (BICS), launched in March 2020, provided users with data on the impact of the pandemic on business operations, including the status of employees following the introduction of the UK Government furlough scheme.

13. The economic impact of the pandemic has also been captured via the ONS’s Management and Expectations Survey, the results of which help to examine the economic impact of COVID-19 through its coverage of business inputs, outputs, and process. The challenge of sourcing data which would usually be through face-to-face surveys has given the ONS the push to accelerate its plans to use new sources of data for its labour market statistics.

14. ONS has worked closely with HMRC to produce Pay As You Earn (PAYE) Real Time Information (RTI) experimental estimates of earnings and employment, which provide a timely snapshot of payrolled employees using admin data. ONS has also drawn on new innovative data sources to address known data gaps, such as vacancy data in which it has used data from a job search engine – Adzuna – to develop a set of experimental online job advert estimates covering the UK job market.

Flexibility and Innovation

15. Throughout the pandemic statistical producers needed to innovate quickly and to change their business operations to meet the information demands of users and economic policymakers. ONS was successful in meeting this short-term need and will now need to ensure that this same agility and innovation is introduced to its longer term measures of economic performance to improve the timeliness of these statistics, given the significant structural impacts of the pandemic on issues such as business operations, investment, productivity and employment.

16. Greater use of administrative data and consideration of other existing data sources, in line with ONS’s five-year business plan, will not only remove burden from businesses, but enable policymakers to address these structural issues in a timelier manner. The ONS has been successful this year in improving the timeliness and frequency of longer term measures of economic performance, such as business births and deaths, through more intensive use of VAT administrative data. This innovation has been crucial for policymakers concerned with understanding the impact of the pandemic on different industries and the design of policy to minimise the displacement of economic capital and labour across industrial sectors. Here again, users were content to accept the improvement in timeliness of these data at the cost of degrees of accuracy.

17. ONS should aspire to improve the timeliness of the outputs of its annual surveys including, for example, the Annual Business Survey, to enable policymakers to more readily evaluate the structural economic impacts of the pandemic. The ONS has also used the pandemic as an opportunity to bring forward its transformation plans for labour market statistics, through the development of its online Labour Market Survey.

18. Reducing the survey burden on business will also free up ONS capacity to address the granular data needs of local economies.

Regional Economic Agents

19. We expressed to the Treasury Select Committee in our evidence as part of its inquiry into Regional Imbalances that it may be worth considering a network of regional statistical observatories, akin to the Bank of England’s regional agents, that can help provide the ONS and others with better insight into regional economic issues. The Bank makes its regional agencies work successfully with just a couple of people in each region. ONS and other UK official statistical producers could tap into these local sources to enrich its official statistics.

Capturing growth in the modern economy

20. Economic statistics in the UK could more adequately capture growth in the modern economy. However, it is not surprising that such statistics sometimes struggle to reflect the pace of change: almost all National Statistical Institutes (NSIs) are running to catch up with the developments in their economies. However, statistics from the ONS as the NSI and other statistical producers are more reflective of the modern economy now five years on from Sir Charles Bean’s Independent Review of Economic Statistics in 2016.

21. What has become clearer since the Bean Report is that there is a much wider concept of growth that people are interested in measuring. Since the publication of Beyond GDP: Measuring What Counts for Economic and Social Performance, there is even greater awareness that people want to measure growth beyond GDP, which is needed for short-run macroeconomic analysis and management. However, GDP is a very narrow measure of growth and for example, does not account for the depreciation of assets, including the natural environment. As our primary measure of economic success, GDP is useful as a short-term measure of economic growth but many are asking for more comprehensive measures of growth to help policymakers make longer term decisions.

22. The COVID-19 pandemic has, by necessity, shifted priorities and producers have had to shelve some of the work that they might have accomplished to capture the modern economy more completely. Unsurprisingly, policymakers are more focused on the short-term as described above. From the policy-making perspective, economic statistics need to capture growth in economic welfare over more than the short-run.

A sustainable economy

23. Professor Partha Dasgupta’s report The Economics of Biodiversity makes it clear that our economies, livelihoods, and well-being all depend on our most precious asset: nature. Professor Dasgupta in his report says our unsustainable engagement with nature is endangering the prosperity of current and future generations. The solution starts with understanding and accepting a simple truth: our economies are embedded within nature, not external to it. We need to change how we think, act and measure success, and change our measures of economic success to guide us on a more sustainable path. The question posed by the Committee talks about growth and it is important that an appropriately broad interpretation is adopted.

24. The Dasgupta Review shows that to judge whether economic development is sustainable, an inclusive measure of wealth is needed. By measuring our wealth in terms of all assets, including natural assets, ‘inclusive wealth’ provides a clear and coherent measure that corresponds directly with the well-being of current and future generations. This approach accounts for the benefits from investing in natural assets and illuminates the trade-offs and interactions between investments in different assets.

25. The OSR set out in our review of the public value of devolved public finance statistics our view that data should be expected to present a fuller picture of sustainability of public finances by taking an intergenerational view. And in our review of ONS’s Natural Capital Accounts we found many positives in these statistics, particularly in their breadth, which enhance their value and relevance to a wider range of users. We also found several ways we considered ONS could further enhance the trustworthiness and value of these statistics which ONS is expected to address.

Inclusive Growth

26. The Inclusive Growth Commission (2017) proposed four key ways for putting inclusive growth at the heart of public policy and finance for making inclusive growth a working definition of economic success which meant modernising involving statistics, including establishing inclusive growth as a regular official statistic by publishing a quarterly national measure of inclusive growth alongside GDP. Realising this ambition will require investment in mining new data sources, data gathering and in data analysis.

27. In our letter to the Committee as part of its inquiry into regional imbalances we identified statistics on inclusive growth as among the data gaps we saw at this level. We will continue to work with data suppliers, statistics producers and statistics users to assess the extent to which these statistics serve the public good and how they can be developed to meet unmet needs going forward.

Measuring GDP

28. Whilst the ONS is a leader in measuring GDP there are still known areas which need to be addressed. Below we have set out areas that our recent work has highlighted. This is not intended to be a comprehensive list but illustrative of some of the areas that are still being worked on to provide better estimates of short-term economic growth in the UK.

International Comparability

29. In some correspondence with a statistics user in January 2021 we noted that ONS is adopting international best practice, particularly in respect to estimating real terms non-market output (mainly in the public sector) when many advanced economies have not done so. ONS suggests users exercise caution in making international comparisons of real GDP growth currently. ONS has recommended that nominal GDP estimates are currently more internationally comparable, as they are not affected by the differences in public sector output measurement.

Making better distinctions between final output and intermediate output

30. One of the key determinants of growth is making the distinction between intermediate and final output. The ONS relies on many sources to make this distinction and is heavily reliant on business surveys among such sources. One area where measurement of business investment might be improved is in own-account software. Own-account software forms part of Gross Fixed Capital Formation (GFCF). As software developed for own-final use is not traded on the market, a purchaser’s price cannot be established, and the recommended method is therefore to estimate by costs of production. The proportion of own-account software expenditure reported by businesses to ONS to intermediate consumption (around half of the total expenditure) is suspect. There are tax benefits in writing off expenditure on own account software rather than booking it as investment. Given the importance of own-account software to economic growth statistics as it accounts for around 10% of business investment, around 7% of GFCF and around 1% of GDP, it is an example of where economic growth may be being mismeasured.

Research & Development

31. It has been reported that the UK underperforms on traditional innovation indicators and Governments have deployed policy and structures to remedy this. For example, statistics suggest that the UK performs poorly on business expenditure on research and development (R&D) and on the production of patents. As a result, policymakers across the UK have sought to drive improvements in these areas, and have focused on incentives for scientific and technological R&D, support for high-tech manufacturing firms, increasing public investment in the science base and improving links between universities and industry.

32. However, traditional indicators such as business expenditure on R&D ignore major sectors of the UK economy like financial services and oil and gas production. Many commentators believe that R&D spend is a poor indicator of innovation for UK business. There are methodological issues with these data and the Organisation for Economic Co-operation and Development (OECD) suggests that caution should be exercised when using them.

Productivity – what’s not seen as productive in our economy

33. There is debate about what is seen as productive and not seen as productive in the UK economy – ‘the production boundary’. In principle it is clear: anything that is marketed is inside, including activities mediated through the public sector, and anything not marketed is outside. For example, when your child goes to school the learning in school is seen as productive and counted in GDP. However, if your child is home-schooled that is not seen as productive.

34. The modern economy is often measured in statistics using transactions-based data founded on a financial accounting system from the 14th century. The modern economy is far less organised around transactions. Diane Coyle’s research for the Economic Statistics Centre of Excellence (ESCoE) sets out a taxonomy of the range of potential measurement artefacts arising from digital innovations. It also specifically considers digitally enabled substitutions in activity across the production boundary. She argues that these, along with other substitutions occurring within the production boundary, may be making a meaningful contribution to the productivity puzzle as measured using existing statistical definitions and transactions-based data.

35. There are many examples in our economy of disintermediation where work that previously used to be in the transactional economy is now carried out in the household. These include holiday bookings previously carried out and paid to travel agents, insurance previously brokered by insurance brokers and photograph development.

36. Each of these in themselves might be small in their effects but combined with other types of substitution inside the production boundary might potentially add up to a significant measurement impact on the GDP deflator and real GDP. There are several factors contributing to the puzzle, but perhaps one element lies in the way digital technologies are making activity at home part of the productive economy in a way they have not been since the Industrial Revolution. Measurement is one of the issues at the heart of the productivity puzzle – for example, in the contrast between GDP per hour worked in nominal and real terms; the growth of the former has slowed since 2008, while the latter has flat-lined.

37. This implies that an explanation of the ‘puzzle’ needs to explore the price/volume split to examine the clear change in the behaviour of the deflator in the past decade. Digital activities and business models are affecting the measurement of GDP, on existing national accounts definitions, in multiple ways.

38. On 17 May 2021, we published our assessment of some of ONS’s suite of productivity statistics in which we required ONS to enhance these statistics to better answer questions about the UK’s performance in improving productivity. We also develop a rolling programme of regulatory interventions designed to monitor and assess statistics measuring economic growth at the different levels of the UK. This helps us in our role of championing the improvement of these statistics to meet ever higher demands made upon them by decision makers and the public.

OFFICE FOR STATISTICS REGULATION, MAY 2021

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

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

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 oral evidence to the Treasury Committee’s inquiry on the economic impact of coronavirus

On 20 January 2021, Jonathan Athow, Deputy National Statistician for Economic Statistics at the Office for National Statistics, gave evidence to the Treasury Committee for its inquiry on the economic impact of coronavirus.

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

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 group	April 2019 to March 2020
	Number of incidents (thousands)
Fraud	3,675
Bank and credit account fraud	2,474
Consumer and retail fraud	914
Advance fee fraud	60
Other fraud	227

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 group	April 2019 to March 2020
	Financial loss (%)	Unweighted base - number of incidents
Fraud	74.0	2,283
Bank and credit account fraud	74.0	1,526
Consumer and retail fraud	68.1	575

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
Fraud	April 2019 to March 2020
	Total frauds (%)	Frauds involving loss (%)
No financial loss	26.0	-
Less than £100	29.8	40.3
£100 to £249	16.1	21.8
£250 to £999	17.4	23.6
£1000 or more	10.6	14.4

Unweighted base – number of incidents	2,283	1,673

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 group	Number of incidents (thousands)	Rate per 1,000 adults	Number of victims (thousands	Percentage victims once or more
FRAUD	3,809	81	3,176	6.8
With loss, no or only partial reimbursement	692	15	638	1.4
With loss, fully reimbursed	2,232	48	1,897	4.1
Without loss	885	19	758	1.6
Bank and credit account fraud	2,579	55	2,171	4.5
With loss, no or only partial reimbursement	251	5	213	0.5
With loss, fully reimbursed	1,862	40	,569	3.4
Without loss	466	10	419	0.9
Consumer and retail fraud	1,019	22	938	2.0
With loss, no or only partial reimbursement	392	8	380	0.8
With loss, fully reimbursed	347	7	324	0.7
Without loss	280	6	243	0.5
All other fraud	211	5	177	0.4
With loss, no or only partial reimbursement	49	1	48	0.1
With loss, fully reimbursed	24	1	21	0.0
Without loss	138	3	107	0.2
COMPUTER MISUSE	966	21	847	1.8
Computer virus	444	9	385	0.8
With loss, no or only partial reimbursement	142	3	134	0.3
With loss, fully reimbursed	0	0	0	0.0
Without loss	303	6	251	0.5
Unauthorised access to personal information (including hacking)	522	11	467	1.0
Unweighted base - number of adults	34,163

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
	Fraud	Bank and credit card fraud	Consumer and retail fraud	All other fraud
Respondent was emotionally affected	74	74	73	78
Very much	8	7	10	13
Quite a lot	20	20	20	27
Just a little	45	47	43	38
Respondent was not emotionally affected	26	26	27	22
Unweighted base - number of incidents	2,353	1,561	602	190
Types of emotional response experienced
Annoyance	72	72	72	73
Anger	51	48	58	49
Shock	31	33	27	34
Loss of confidence or feeling vulnerable	20	18	23	24
Anxiety or panic attacks	10	10	10	17
Fear	10	9	9	13
Difficulty Sleeping	5	4	6	11
Depression	5	5	5	5
Crying/tears	6	6	5	10
Other	4	3	7	7

Unweighted base - number of incidents	1,764	1,152	461	151

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 A3: Volume 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 Fraud	1,817,039	1,979,225	2.773,725	2,745,639	2,801,318	2,747,141	-2
Lost and Stolen	246,503	382,706	454,458	455,346	460,856	416,469	-10
Card not Received	11,192	10,459	9,832	7,774	9,298	7,398	-20
Counterfeit Card	103,132	75,562	60,239	66,894	61,507	65,368	6
Remote Purchase Fraud	1,423,783	1,479,441	2,180,303	2,163,660	2,199,253	2,210,596	1
Account Take Over	32,439	31,057	68,893	51,965	70,404	47,310	-33

Cheque Fraud	2,763	1,541	2,370	2,610	2,853	2,046	-28
Remote Banking Fraud	33,767	32,457	33,692	50,128	34,741	55,058	58
Authorised Push Payment	8,724	49,843	98392	124,913	108,044	131,135	21

Total (excluding APP)	1,853,569	2,013,223	2,809787	2,798,377	2,838,912	2,804,245	-1
Total(including APP)	1,862294	2,063,066	2,908,179	2,923,956	2,949,956	2,935,380	0

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.