Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Dear Chair,

I write in response to the Business, Energy and Industrial Strategy Committee’s call for evidence for its inquiry on the impact of coronavirus on businesses and workers.

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 decision makers and develop the role of official statistics in democratic debate. To do this during the coronavirus (COVID-19) pandemic, we are regularly publishing detailed commentary on, and analysis of, the impacts of COVID-19 on the UK economy and society. Alongside our regular publications, a suite of COVID-19 related statistics are now available on the ONS website. These include faster indicators, social impacts , economic impacts, and furloughing of workers across UK businesses.

We have focused our evidence on the new analysis being published to highlight the immediate impacts of the pandemic on businesses and workers, and what the initial results of this analysis are.

We published the first of a new weekly series of faster indicators in response to COVID-19 on 2 April. The indicators use data from a variety of sources, including a new ONS Business Impact of Coronavirus Survey (BICS), which collects information on the financial and operational performance of businesses during the COVID-19 outbreak. The survey is voluntary, and therefore we caveat its results by noting that it may only reflect the characteristics of those businesses who responded. We have also introduced the Opinions and Lifestyle (OPN) Survey to help understand the impact of the COVID-19 pandemic on people, households, and communities in Great Britain. Together, these surveys provide a well-rounded view of the impact of the pandemic on both our businesses and our population. We have continued to add to the list of measures that are published as part of the faster indicators, reflecting the changing impacts of the pandemic as well as our ability to ring new data sources online and provide new and innovative analysis.

Economic activity

Gross domestic product (GDP) fell by 2.0% in the three months to March 2020 (Q1), signalling the first direct impacts of the coronavirus (COVID-19) on the economy. All the headline sectors provided a negative contribution to growth. The services sector fell by 1.9%, production by 2.1%, construction by 2.6%, and agriculture by 0.2%. The impacts of COVID-19 were seen right across the economy, with nearly all subsectors falling in the three months to March.

Monthly GDP fell by 5.8% in March 2020, the biggest monthly fall since the series began in 1997. Services and construction also saw record falls in the most recent month. This reflects the first government advice on social distancing, published on 12 March 2020, and introduction of restrictions in movement across the UK, which began on 23 March 2020. It should be noted that monthly GDP is volatile and should therefore be used with caution and alongside other measures.

Figure 1. Index of services: Rolling three-month on three-month index, January to March 2019 until January to March 2020 (January to March 2019 = 100)

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – GDP monthly estimate

Analysis of our Monthly Business Survey (MBS) returns and external data, including comments from over 10,000 businesses, demonstrated that the arrival of the coronavirus (COVID-19) pandemic had a significant and broad-based negative impact on output during March 2020, though some industries did see a positive impact. This was caused by a complex mix of factors, including the effects of social distancing, which led to a fall in consumer demand, business and factory closures and supply chain disruptions. The bulletin contains detailed industry analysis. To give one example, following a steady decline in growth from January 2008 to February 2020, COVID-19 had a significant negative effect on travel and tourism in March 2020.

Figure 2. Index of production: Rolling three-month on three-month index, January to March 2019 until January to March 2020 (January to March 2019 = 100)

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – GDP monthly estimate

Business Impact of Coronavirus (COVID-19) Survey (BICS)

The BICS was stood up within the first two weeks of lockdown and is sent out to a large sample of UK businesses each fortnight. We call each return period a wave. We have changed a number of the questions on the survey over that time, to reflect that business impacts are changing and adapting in different ways. This new online survey provides a timely and useful snapshot of the impact of COVID-10 on business conditions and sentiment; we anticipate continuing with the survey, and refining it, for some time.

Business operations

Initial results from Wave 4 of BICS (the period from 20 April 2020 to 3 May 2020) showed that over a fifth (22%) of businesses that responded had temporarily closed or paused trading, while less than 1% had permanently ceased trading.

Of the businesses that responded, 77% reported that they were continuing to trade during this period. Of those, only 6% responded that they had started trading again during the reference period. Of those who had paused trading, 99% reported that they had done so prior to 20 April.

Of all business trading during the period, 61% reported that their turnover had decreased to some extent when compared with normal. A quarter of trading businesses reported their turnover decreased by more than 50%, while 32% reported that turnover was within the normal range.

International trade

Businesses exporting goods and services reported that the most common challenges faced in exporting during the period were COVID-19-related transport restrictions (44%), followed by reported increases in transportation costs (28%). However, almost two-fifths (39%) of exporting businesses reported they did not experience any challenges in exporting.

Transport restrictions due to COVID-19 were also the most cited challenges for importing business (50%), followed by increasing costs for transportation (29%). Similarly to exporting businesses, 33% of importing businesses reported they did not experience any challenges in importing.

Figure 3. Businesses (exporting/importing goods or services) continuing to trade and with financial performance outside of normal expectations, UK, 20 April to 3 May 2020

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – Business Impact of Coronavirus Survey

Figure 3 refers only to businesses continuing to trade, who reported their financial performance was outside normal expectations between 20 April and 3 May and were continuing to export or import. It does not include businesses whose financial performance was within normal expectations. 72% of exporting businesses reported that their business was still exporting but less than normal, while 59% of importing businesses said they were importing less than normal. ( Initial results, Wave 4 of ONS Business Impact of Coronavirus (COVID-19) Survey. (Exports: n = 701 Imports: n =927))

Government support schemes

For the businesses that responded to BICS Wave 4, the two most popular government support schemes to apply for among businesses that had not permanently ceased trading were the coronavirus Job Retention Scheme (CJRS) (76%) and the Deferring VAT Payments Scheme (59%), (Figure 4).

Around 91% of business who had paused trading applied for the Coronavirus Job Retention Scheme, compared with 72% of businesses who were still trading.

Figure 4. Percentage of all government schemes applied for, businesses continuing to trade and paused trading, UK, 20 April to 3 May 2020

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – Business Impact of Coronavirus Survey
Bars will not sum to 100% as businesses are able to select more than one government scheme
‘Initial results, Wave 4 of ONS Business Impact of Coronavirus (COVID-19) Survey that are either continuing to trade or who have temporarily paused or ceased trading.

Workforce impact

Employees of businesses that are still trading or have paused trading will experience different impacts, whether furloughing staff, working as normal, or operating in other scenarios. Table 1 identifies the proportion of employees within businesses that have been furloughed, been made redundant, or are continuing to work, broken down by industry and apportioned by employment size.

Estimates of workforce proportions for each industry were based on the employment recorded for that reporting unit on the Inter-Departmental Business Register (IDBR). While this method is likely to provide broadly accurate industry estimates, they cannot be grossed up to provide representative UK-wide estimates.

Table 1: Proportion of the workforce that had been furloughed, made redundant, are continuing to work or any other reason, for responding businesses that are continuing to trade or temporarily paused trading, apportioned by employment size, UK, 6 April to 19 April 2020

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

  • The apportionment of workforce methodology used for these data does not involve grossing for UK wide estimation.
  • This table of data represents the proportion of responses to each question from businesses. This is apportioned using the employment recorded for each Reporting Unit on the Interdepartmental Business Register (IDBR).
  • Real Estate Services, Other Services and Mining and Quarrying have been removed due to their low response rate, but their totals are included in ‘All industries’.
  • Final results, Wave 3 of ONS Business Impact of Coronavirus (COVID-19) Survey that are continuing to trade and
  • temporarily paused trading, apportioned by employment size.
  • The percentages in this chart may not sum to 100% due to rounding
  •  Businesses who responded as temporarily pausing trade, were not asked to report levels of staff sickness or selfisolation, whilst Businesses who responded as continuing to trade were. To enable comparison between businesses who have paused trading and who have continued trading, the categories “Other” and “Off sick or in self-isolation due to coronavirus (COVID-19) with statutory or company pay” have been summed together into “Other (including sick pay and self-isolation)”.

In the reference period 6 April to 19 April 2020, 19% of the workforce had been furloughed across businesses continuing to trade, compared to 81% of those who had temporarily closed or paused trading. Less than 1% of the workforce had been made redundant across responding businesses.

The proportion of the workforce that had been furloughed across responding businesses varied substantially between industries, and it depended on whether the business employing them was still trading or had temporarily paused its activities.

Figure 5 shows that the highest proportions of workforce being furloughed, of those businesses continuing to trade or having temporarily paused trading combined, were recorded in the  accommodation and food service activities industry (73%) and in the art, entertainment, and recreation industry (70%).

Figure 5. Rates of businesses on furlough leave (under the terms of the UK Government’s Coronavirus Job Retention Scheme), 6 April to 19 April 2020

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – Business Impact of Coronavirus Survey

Business cash flow

Businesses that had not permanently ceased trading were also asked how long they thought their cash reserves would last in Wave 4. Initial results were that businesses which had temporarily closed or had paused trading were much more likely to report having less than six months’ cash reserves (59%) than more than six months (11%). For businesses continuing to trade, two-fifths (40%) reported they had less than six months’ cash reserves, while around two-thirds (32%) said they had more than six months. Around a quarter of responding businesses were unsure how long their cash reserves would last. (These are initial results and may be revised. Final results for Wave 4, with more detailed breakdowns, will be published in Coronavirus, the UK economy and society, faster indicators)

Figure 6. Cash reserves, businesses continuing to trade and paused trading, broken down by trading status, UK, 20 April to 3 May 2020

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – Business Impact of Coronavirus Survey

Labour Market Statistics

The ONS statistics on the labour market include both detailed but less timely survey data from the Labour Force Survey, and more up to date indicators including administrative data from HM Revenue and Customs and the Department for Work and Pensions. We have also rolled out a new on-line Labour Market Survey, and initial results from that are due to be published in the coming weeks.

In March, there was little sign of significant change to employment or unemployment. However, we are able to see how the COVID-19 restrictions affected the labour market using some new and experimental singleweek data from the Labour Force Survey. These data, which only cover the first few weeks of lockdown restrictions, show hours worked fell by around 25 per cent in the last week of March compared to usual, as workers were either furloughed or saw their hours reduced.

Throughout April there were signs of falling employment as real-time tax data show the number of employees on companies’ payrolls fell by around 450,000 compared to March. There was also a large rise in the ‘claimant count’ though care needs to be taken with this figure as it is possible to still be working and included in the claimant count. We also saw vacancies fall sharply in April.

The Opinions and Lifestyle Survey (OPN)

The Opinions and Lifestyle Survey (OPN) is a regular omnibus survey. In response to the coronavirus (COVID-19) pandemic, we have adapted the OPN to become a weekly survey used to collect data on the impact of the coronavirus on day-to-day life in Great Britain. The survey results are weighted to be a nationally representative sample for Great Britain, and data are primarily collected using an online selfcompletion questionnaire.

Working from home

Final results for Wave 6 of the OPN (covering period 24 April to 3 May 2020) showed the same proportion of adults in employment saying they had worked from home at some point this week (44%) compared with the previous week.

This consisted of 36% of adults who had only worked from home, and 9% who had both worked from homeand travelled to work (both key workers and non-key workers). A further 26% of adults In employment said they had travelled to work in the last seven days and had not worked from home.

Key workers

The ONS recently published analysis giving an indication of the number of people who were employed in 2019 in key worker occupations and key worker industries. The key worker occupations and industries are based on an interpretation of UK government guidance that defines who is eligible for childcare places. Key workers are also defined in Department of Health and Social Care guidance on testing eligibility. The ONS’ analysis is based on various sources: The Annual Population Survey, the Labour Force Survey and the Annual Survey of Hours and Earnings.

In 2019, around 10.6 million of those employed (33% of the total workforce) were in key worker occupations and industries. The largest group of those employed in key worker occupations worked in health and social care (31%). Figure 7 shows the number of key workers by occupation group.

Figure 7: The largest group of key workers worked in health and social care.

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on the impact of coronavirus on businesses and workers

Source: Office for National Statistics – Annual Population Survey

Among key workers, 58% were women and 42% were men. These proportions differ to that of women and men in non-key worker roles (42% and 58% respectively). However, the gender split was very different within different occupation groups. Women were most represented in education and childcare (81%), and health and social care (79%). Conversely, the majority of workers in transport occupations were male (90%).

The majority of key workers were of White ethnicity (86%), with 14% belonging to an ethnic minority. The ethnic minority categories included Black/African, Asian, mixed and other. Of these  categories, Asian and Black/African had the highest proportions of key workers at 8% and 4% respectively. Key workers who were of an ethnic minority were most represented in health and social care (16%).

When surveyed in the OPN, 75% of all key workers said they are very or somewhat worried about the effect the coronavirus is having on their life. The most common issue affecting key workers was the impact on their work, with 46% saying this was the case, and 35% saying concerns with their health and safety were a
reason for this. The most cited reasons for concerns around health and safety were difficulty in following
social distancing advice (86%) and a limited amount of or no protective clothing being available (41%).

Deaths related to COVID-19

The ONS has published additional analysis looking at how deaths in England and Wales related to COVID19 vary by occupation, and also on the occupations in the UK that have the highest potential exposure to COVID-19. The two articles show that, generally, occupations with the most frequent and close interaction with others have greater exposure to disease and some of these occupations also have high rates of COVID-19 deaths.

There was a total of 2,494 deaths involving the coronavirus (COVID-19) in the working age population (those aged 20 to 64 years) of England and Wales were registered up to and including 20 April 2020. Nearly two-thirds of these deaths were among men (1,612 deaths), with the rate of death involving COVID19 being statistically higher in males, with 9.9 deaths per 100,000 compared with 5.2 deaths per 100,000 females (882 deaths).

Compared with the rate among people of the same sex and age in England and Wales, men working in the lowest skilled occupations had the highest rate of death involving COVID-19, with 21.4 deaths per 100,000 males (225 deaths); men working as security guards had one of the highest rates, with 45.7 deaths per 100,000 (63 deaths).

Men and women working in social care, a group including care workers and home carers, both had significantly raised rates of death involving COVID-19, with rates of 23.4 deaths per 100,000 males (45 deaths) and 9.6 deaths per 100,000 females (86 deaths).

Healthcare workers, including those with jobs such as doctors and nurses, were not found to have higher rates of death involving COVID-19 when compared with the rate among those whose death involved COVID-19 of the same age and sex in the general population.

Among men, a number of other specific occupations were found to have raised rates of death involving COVID-19, including: taxi drivers and chauffeurs (36.4 deaths per 100,000); bus and coach drivers (26.4 deaths per 100,000); chefs (35.9 deaths per 100,000); and sales and retail assistants (19.8 deaths per 100,000).

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 for other factors such as ethnic group and place of residence.

Potential exposure to COVID-19 by occupation

We have obtained an estimate of exposure to disease (generally) and physical proximity for UK occupations based on US analysis of these factors, using 2019 data. While working practices and
conditions may be slightly different in the US for similar occupations, these estimates offer valuable insight into occupations that involve working in close proximity with others and those that are regularly exposed to diseases. This is a useful indication of which roles may be more likely to come into contact with people with COVID-19, based on what these roles normally entail.

There is a clear correlation between exposure to disease, and physical proximity to others across all occupations. Healthcare workers such as nurses and dental practitioners unsurprisingly both involve being exposed to disease on a daily basis, and they require close contact with others, though during the pandemic they are more likely to be using PPE.

Our analysis also looks at the characteristics of workers in occupations which are more likely to be in close contact with people and also frequently exposed to disease. Three in four workers in these roles are women. One in five of those working in these occupations are 55 or older, the same as in the working population generally. However, around half of those employed as care escorts are 55 or over. Also, one in five workers in these occupations are from black and minority ethnic groups, compared with just over one in 10 of the working population.

Challenges for economic statistics

The disruption from COVID-19 has made for challenges in measuring the economic and compiling many of  our regular economic statistics. These challenges broadly fall into three main categories:

• Conceptual challenges; how should the various phenomena we are observing be accounted for in our economic statistics?
• Data collection problems; how do we keep collecting data when some companies are not trading or when we cannot send people to interview households or record prices from shops?
• Methodological concerns; how do we adjust our raw data, given the way our economy functions has changed so significantly?

Conceptual challenges

There are multiple conceptual challenges that the ONS has needed to consider separately to produce meaningful statistics. These include:

• Determining the correct treatment of the Coronavirus Job Retention Scheme in the National Accounts. After considering the Scheme and National Accounts guidance, we have decided to count the scheme as a subsidy to business, netting it off the income measure of GDP, as the furloughed employees will continue to count as employed and the payments they receive from their employer as wages and salaries.
• How to measure education output when children are not in school? Our approach is to calculate output including a new measure of remote learning, with appropriate adjustments for teacher input and parental support.

• Measuring inflation when some goods and services are not available, or where the number of prices collected is small. We will be using methods such as assuming their prices would have moved in line with the average movement for related goods and services, or the overall index. This is the simplest approach that comes as close as possible to reflecting that the supply of certain goods and services has been interrupted.

Data collection challenge

Many economic statistics produced by the ONS are underpinned by business and household surveys. For example, we survey firms to measure GDP and to collect prices to measure inflation, and survey individuals to understand their employment status. A number of our surveys have been understandably disrupted due to businesses temporarily closing or having employees work from their homes. For shops and services that are closed or under restricted operation, we can no longer send people there to collect their prices. All of this means we are relying more on remote-data collection, over the phone or online. Such sudden changes can result in a lower response rate.

Methodological challenges

In general, one of the most common issues we deal with is when firms or businesses do not respond to our survey, or where data are late for other reasons. When that happens, we must fill in gaps in our data collection, technically known as imputation. Normally, we can do this by using historical relationships between different data sources. But those historical relationships may not hold given the current crisis.

Addressing the challenges

To address these issues, we have been looking to develop new data sources that shed light on specific economic issues, such as how businesses are changing their employment practices. They can also help us cross-check our core economic data and inform the judgements we need to make. As discussed above, we have created new surveys that can help us fill the gaps and are using administrative data such as information from HMRC on employees being paid through ‘real time information’. We also continue to work with businesses to gain access to valuable economic data.

Secondly, we have been drawing on expertise within the ONS, international statistical bodies and the academic community. There are skilled methodologists in the ONS who are helping us develop approaches to dealing with missing data. We also have, for example, an expert technical panel which supports us on inflation measurement and can look to international guidance and practice to inform decisions.

Lastly, we are being as transparent as possible about the issues and how we are addressing them. We have published detailed articles laying out how we will continue to produce GDP, labour market, and prices data to ensure transparency in these processes. These are unprecedented times and there is scope for more revisions than normal. We have made some changes to our publication schedule to account for the challenges we are facing, and these are included in the articles too.

As the ONS continues to publish analysis of the impact of COVID-19 on businesses and workers, we would be happy to keep the Committee informed. Please do not hesitate to contact me if I can be of any further assistance.

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

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on supporting regional investment and growth

Dear Ms Reeves,

I write in response to the Business, Energy and Industrial Strategy Committee’s call for evidence for its inquiry Supporting Regional Investment and Growth.

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

The range of statistics and analyses published by ONS on regional and sub-regional productivity allows us to explore the differences in economic performance between different regions in the UK.

This note summarises some key findings that have emerged from analysis of existing evidence.

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

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

Supporting Regional Investment and Growth

Executive Summary

1. Labour productivity is unevenly distributed across the UK, with significant differences across and within different regions. The majority of high productivity areas are in London and the South East region, while predominantly rural areas in England and Wales are among the areas with the lowest productivity levels.

2. Observed average aggregate productivity in an area can be derived from the presence of a range of industries, or differences in firm productivity within the same industry. Analyses from the past three years suggest that differences in firm-level productivity is the main determinant of regional productivity differences.

3. Spatial concentration of employees within Great Britain is seen most prominently within knowledge-intensive industries such as information and communication; and professional, scientific  and technical activities. The manufacturing industry displays a more varied spatial distribution of jobs.

4. London had the highest public sector expenditure in the FYE 2018 and Northern Ireland had the lowest. However, on a per-head basis, Northern Ireland had the highest public sector spending of all regions, and the South East had the lowest.

5. Public sector revenue for the FYE 2018 on a per-head basis was highest in London and lowest in wales. London also raised the highest revenue on a population-share basis and on a geographic-share basis.

6. London and the South East region have the lowest net fiscal balance for FYE 2018, and the North West region has the highest. This is a continued trend from FYE 2016.

Regional and sub-regional productivity

7. ONS regional and sub-regional productivity estimates show that economic performance in terms of labour productivity, which is measured as Gross Value Added (GVA) per hour worked, GVA per job or GVA per employment, is not evenly spread across the UK. There are significant differences in aggregate average labour productivity between and within different regions and countries of the UK. For example, productivity in Tower Hamlets in London is around 2.5 times higher than productivity in the rural area of Powys in Wales.

8. However, there is a skewed distribution of labour productivity across the nation. Figure 1 below shows that 15 out of 17 very high productivity areas are located either in London or west of London along the M4 corridor in the South East region, while predominantly rural areas in England and Wales were among the areas with the lowest levels of labour productivity in 2017. The data also highlight that many subregions (at Nomenclature of Territorial Units for Statistics (NUTS) 3 level) of the UK have very similar productivity levels to each other.

9. Geographical differences in labour productivity in the UK have been persistent over the last 13 years. Data show that spread in average productivity was increasing slightly as average  productivity differences between the areas were widening before the financial crisis of 2007. However, after 2007, the spatial productivity differences at NUTS 3 level decreased slightly, mainly due to lower productivity growth rates in the high-productivity areas of London. Therefore, overall while productivity differences continue to exist between areas, these differences have not increased over the past decade. It is also the case that there has not been significant changes in the relative rankings of areas through the period.

Figure 1: Labour productivity (GVA per hour worked) distribution in the UK: standard deviation of the UK average GVA per hour work for NUTS 3 subregions, 2017

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on supporting regional investment and growth

Drivers of regional productivity differences

10. Observed average aggregate productivity in an area derives from two main sources:
• the areas can have a different industry mix. Therefore, a relatively high aggregate productivity in a region may sometimes be a reflection of a relatively large share of more productive industries (e.g., knowledge intensive service industries or advance manufacturing) in that location.
• within the same industries, the firm productivities in one area can differ from those in the same industry in other areas.

11. Our analyses from 2017, 2018 and 2019 show that differences in firm-level productivity within industries are a bigger determinant of the geographical differences in productivity than the different industry structures of the areas. Even within single industries we can observe large differences in average productivity levels between different parts of the country, particularly in services industries. Differences in productivity within service sectors of the economy between London and other areas of the country are a particularly important source of the difference in productivity levels. For Scotland, London and the South East, their industry compositions also have some positive impact on their overall average productivity levels, however, the higher average productivity of the firms in these regions play a more significant role than their industry structures on their overall average aggregate productivity levels.

12. ONS also investigated the factors that are associated with firm-level productivity in more detail. Particularly focussing on examining and contrasting factors that are internal to firms with those that are external and associated with location. While obtaining conclusive evidence is difficult, the available evidence suggests that while there are some important internal factors influencing firm-level productivities, such as the ownership of a firm and whether it trades internationally, it is also the case that to understand the larger geographical differences, for example, between London and other areas of the UK, focus also must be placed on external geographical factors.

13. Such a focus on external factors recognises that each firm operates in its own locale with, for example, a specific mix of local product and factor markets, local infrastructure, agglomeration benefits, firm competition, consumer tastes and local spending power. These factors can affect firm-level productivities and ultimately average productivity in an area.

Planned and possible future work on productivity:

14. In response to policy interest, we aim to continue further work in this area to increase our understanding of the observed spatial labour productivity differences in the UK. In particular we would like to investigate in more detail the external location-based factors that influence spatial productivity differences.

Spatial distribution of industries

15. Within Great Britain, a spatial concentration of employees is seen particularly in some of the relatively high productivity knowledge-intensive service industries such as information and communication; and professional, scientific and technical activities (also see: The spatial distribution of industries). Employee jobs in these industries are particularly concentrated in London, South East and East of England

16. The scientific research and development sector is a particularly important sector within the professional, scientific and technical activities industry. Employee jobs in this sector are highly  concentrated in Berkshire, Buckinghamshire and Oxfordshire in the South East, and East Anglia in the East of England. In both areas the share of employee jobs in scientific research and development was three and a half times greater than their share of all employee jobs in Great Britain. Concentration of employee jobs in scientific research and development can also be seen around Eastern Scotland and North Eastern Scotland, as well as Tees Valley and Durham, and North Yorkshire NUTS 2 subregions.

17. London has a low relative share of employee jobs in scientific research and development. However, it has relatively high concentrations of employee jobs in other knowledge intensive services sectors, in particular the activities of head offices, advertising and market research and financial service activities. Aside from a high concentration in central London, Eastern Scotland and West Yorkshire NUTS 2 subregions also have a relatively high share of employee jobs in the financial service activities sector.

18. The manufacturing industry has a more mixed spatial distribution of jobs. Employee jobs in manufacturing are concentrated in the East Midlands, West Midlands, Wales and in the northern
regions of England but virtually absent from London.

19. Within the manufacturing industry, employee jobs in the manufacture of motor vehicles sector are highly concentrated in the West Midlands region, which accounted for a third of all  eployment in this sector nationally. In particular, Herefordshire, Worcestershire and Warwickshire NUTS 2 subregion has a relatively high concentration of employee jobs in the manufacture of motor vehicles, with four point six times its share of employment in this industry than its share of total employee jobs in Great Britain. The North East, Northumberland and Tyne and Wear NUTS2 subregions within the North East region also has a relatively high concentration of employees in the manufacture of motor vehicles.

20. The manufacture of chemicals displays a different spatial distribution to the manufacture of motor vehicles. Employee jobs in the manufacture of chemicals are mainly concentrated in the north of England, particularly in Tees Valley and Durham in the North East. Four of the five NUTS 2 areas in the North West are also among the NUTS 2 areas with the highest concentration of jobs in this sector. In the south of England, the NUTS 2 areas with relative concentrations in the manufacture of chemicals were Kent, East Anglia, and Dorset and Somerset.

Country and regional public sector finances

21. The country and regional public sector finances are published annually by ONS, aiming to provide users with information on what public sector expenditure has occurred, for the benefit of
residents or enterprises, in each country or region of the UK; and what public sector revenues have been raised in each country or region – as well as the balance between them. The country and regional public sector finances are consistent with the UK public sector finances.

22. The statistics are neither reflective of the annual devolved budget settlements nor are these data used when calculating devolved budget settlements. Furthermore, they do not provide information on the spending and revenue of individual country or regional bodies such as the Greater London Authority.

Expenditure

23. Public sector expenditure is the total capital and current expenditure (mainly wages and salaries, goods and services, expenditure on fixed capital, but also subsidies, social benefits and other transfers) of central and local government bodies, as well as public corporations.

Statistics published in May 2019 show:

• London had the highest public sector expenditure at £115.8 billion in the financial year ending (FYE) 2018; the total for the UK was £794.8 billion.
• The lowest total public sector expenditure in the same year occurred in Northern Ireland at £26.5 billion.
• Figure 2 shows public sector expenditure for all NUTS1 countries and regions from FYE 2016 to FYE 2018.

24. When taking into account the population of each country and region, a different picture can be seen for the FYE 2018:

• Public sector expenditure on a per-head basis in Northern Ireland was £14,195, the highest of all regions.
• While the lowest per head expenditure occurred in the East Midlands and the South East at £11,146 and £11,169 respectively.
• Figure 3 shows public sector total expenditure on a per head basis for each NUTS1 country and region.

25. The underlying data source for expenditure in the country and regional public sector finances is HM Treasury’s (HMT) Country and Regional Analysis. These data are presented on the basis
of the United Nation’s Classifications of Functions of Government, as are the expenditure data in the ONS’ country and regional public sector finances.

26. Though no definition of expenditure on ‘investment’ exists in this framework, some categories of expenditure can be considered to be related to investment, such as economic affairs and
housing.

27. Table 1 shows data from FYE 2016 to FYE 2018 for expenditure on economic affairs and housing for each NUTS1 country and region – spending in value terms and per head. While most expenditure for both categories in value terms occurs in London and the least in areas such as Northern Ireland and the North East, this is not the case when considering the population of each region. On a per head basis, the lowest expenditure generally occurs in Yorkshire and the Humber for economic affairs, and the South East for housing. Further breakdowns of data are available from the Country and Regional Analysis conducted by HMT.

Figure 2: NUTS1 public sector expenditure, FYE 2016 to FYE 2018
NUTS1 public sector expenditure, FYE 2016 to FYE 2018
Source: Office for National Statistics
Figure 3: NUTS1 public sector expenditure, per head, FYE 2016 to FYE 2018

NUTS1 public sector expenditure, per head, FYE 2016 to FYE 2018

Source: Office for National Statistics

Expenditure on economic affairs and housing, FYE 2016 to FYE 2018, by NUTS1 countries and regionsExpenditure on economic affairs and housing, FYE 2016 to FYE 2018, by NUTS1 countries and regions

Source: Office for National Statistics

28. For all regions, most expenditure occurs in relation to social protection, namely expenditure on pensions, but also on social benefits. Further information on expenditure for other categories are published alongside the bulletin.

Revenue

29. Public sector revenue is the total current receipts received by central and local government as well as public corporations. These receipts predominantly relate to taxes, but also social
contributions, interest, dividends, gross operating surplus and transfers.

30. In the country and regional public sector finances, total public sector revenue in each NUTS1 country and region is presented, including North Sea revenue, on a population-share basis and
a geographic-share basis. Under the geographic-share basis, a greater share of North Sea revenue is allocated to Scotland.

31. In the financial year ending (FYE) 2018:
• The most public sector revenue was raised in London at £150.2 billion on a populationshare basis and £150.3 billion on a geographic-share basis.
• The least revenue was raised in Northern Ireland at £17.3 billion on both bases of North Sea revenue.
• On a per-head basis, London raised £17,110, the highest of all regions; while the lowest per-head revenue was raised in Wales at £8,710.
• Table 2 shows these figures for FYE 2016 to FYE 2018.

Net fiscal balance

32. Net fiscal balance is the gap between total spending and revenue raised. At the UK level, this is equivalent to public sector net borrowing. A negative net fiscal balance figure represents a
surplus, meaning that a country or region is receiving in revenue more than is being spent for the benefit of residents or enterprises in that country or region. A positive net fiscal balance represents a deficit, meaning a country or region is attracting more expenditure for the benefit of its residents or enterprises than it is receiving in revenue. Figure 4 shows the net fiscal balance
for each NUTS1 country and region for the FYE 2018. Table 3 shows these figures for FYE 2016 to 2018.

Figure 4: Net fiscal balance for FYE 2018, by NUTS1 countries and region
Net fiscal balance for FYE 2018, by NUTS1 countries and region
Source: Office for National Statistics
Table 2: Public sector revenue, FYE 2016 to FYE 2018, by NUTS1 countries and regions

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on supporting regional investment and growth

Source: Office for National Statistics
Table 3: Net fiscal balance, FYE 2016 to FYE 2018, by NUTS1 countries and regions

Office for National Statistics written evidence to the Business, Energy and Industrial Strategy Committee’s inquiry on supporting regional investment and growth

Source: Office for National Statistics

Engagement through Devolution Programme

33. Following the Spending Review in 2015, and the Independent Review of UK Economic Statistics by Sir Charles Bean, ONS has conducted several projects under its Devolution programme.
These projects had explicit aims of producing lower geographic breakdowns of statistics, including developments to Regional Accounts; Public Sector Finances; Sub-regional Productivity; estimates of exports of services; and engagement with local area users of statistics. Much of this work has now been completed and published on the ONS website.

34. In support of this programme, ONS created a Centre for Subnational Analysis which has been increasingly engaging with users of statistics. Such engagement included discovery workshops
conducted with the new Mayoral Combined Authorities; meetings with City Regions and City Growth Deals across the UK; presentations at conferences such as the Economic Forums; and the instigation of a new Combined Authorities Liaison Group where we bring users together to show and discuss statistical developments. It has also included direct involvement in the production of Local Industrial Strategies in collaboration with the Department for Business, Energy and Industrial Strategy, meetings with the What Works centres, and engagement with partner organisations including the Local Government Association, Centre for Cities and Core Cities.

35. This engagement has been supported by a continuous programme of work to improve current channels of dissemination, create new insights through forthcoming publications and meet the
specific needs of authorities through targeted support. Bespoke projects have included analysis of productivity to feed into Greater Manchester’s Independent Prosperity Review and of the impacts of building the Midlands Metro, in collaboration with Ordnance Survey. We are currently working to produce analytical articles and ad-hoc data tables which we expect to publish over the coming months in direct response to the needs identified by City Regions through our workshops. Those needs specifically included more information on economic growth, skills, the labour market, productivity and low pay, among other social and economic topics.

Office for National Statistics, June 2019