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

Dear Mr Jones,

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

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

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

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

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

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

Yours sincerely,

Jonathan Athow

Office for National Statistics written evidence – Post-pandemic economic growth: Levelling up local and regional structures and the delivery of economic growth

Executive Summary

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

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

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

Labour market statistics

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

Productivity

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

Trade statistics

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

Public sector finances

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

Household income and expenditure

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

Other data sources

Consumer and producer prices

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

Towns

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

High Streets

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

Data gaps and our plans to address them

Flexible geography

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

Regional supply and use tables

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

Gross fixed capital formation

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

Ongoing stakeholder engagement

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

The ONS and Local Industrial Strategies

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

 

Office for National Statistics 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