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Economic Forum Webinar 2 November 2020

The document summarizes research on the relationship between household inflation and income inequality in the UK. Real and nominal income series were created using Household Cost Index data to deflate incomes. A real Gini coefficient was calculated from these series annually from 2005 to 2018. The analysis found that income inequality nominally improved over this period but inequality in real terms showed little change once inflation is accounted for. The impacts of COVID-19 on consumer spending and inequality are also briefly discussed.

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ONS Economic Forum Deputy Chief Economist Office for National Statistics Chair – Ed Palmer #economicforum@ONSfocus2 November 2020
Agenda 10:00 – 10:05 Introduction – Ed Palmer, Deputy Chief Economist 10:05 – 10:25 Blue Book double-deflated Gross Value Added – Rhys Lewis 10:25 – 10:30 Q&A 10:30 – 10:40 Household Inflation and Income Inequality in the UK – George Clarke 10:40 – 10:45 Q&A 10:45 – 10:55 Business Dynamism in the UK – Silvia Lui 10:55 – 11:00 Q&A 11:00 – 11:10 Foreign Direct Investment in Digital Industries – Andrew Jowett 11:10 – 11:15 Q&A 11:15 – 11:25 Overview on the state of the economy – Grant Fitzner, Chief Economist 11:25 – 11:30 Close #economicforum@ONSfocus
Rob Kent-Smith Rhys Lewis Deputy Director, National Accounts Double Deflation lead @Rob_KS_ONS Double Deflation 2 November 2020 @ONSfocus #economicforum
GDP – the production approach Nominal GDP Output Input (of goods & services) output / production approach Real GDP Nominal GDP Prices Improving the production approach Improving Deflation
Double Deflation GVA~GDP volume/real Nominal Output Nominal inputs Output Price GVA~GDP volume/real Nominal Output Nominal inputs Output Price Input Price OFFICIAL EXPERIMENTAL Output/Production Inputs / Intermediate consumption
Compiling GDP: Pre BB19 GDP(P) Goods and services produced minus the inputs used GDP(E) The sum of all final expenditures within an economy GDP(I) The sum of all factor incomes within an economy GDP(O) Turnover as a proxy for GDP(P) Balance Nominal GDP in ‘SU’ Framework Remove inflation using GDP(E) Headline volume GDP Deflated turnover from monthly business surveys Structural annual business surveys Industry GDP (GVA) aligned to heading via adjustments to services
Compiling GDP: Experimental GDP(P) Goods and services produced minus the inputs used GDP(E) The sum of all final expenditures within an economy GDP(I) The sum of all factor incomes within an economy GDP(O) Turnover as a proxy for GDP(P) Balance Real and Nominal GDP in ‘SU’ Framework Remove inflation using GDP(E) deflators Headline volume GDP Deflated turnover from monthly business surveys Structural annual business surveys Monthly GDP(O) benchmarked to GDP(P) by industry Remove inflation using GDP(P) deflators
Sources of change 1) Better Quality Current Price data • Current volume series is based on long run CP MBS time series, which can move differently to supply and use 2) Double Deflation • Deflating output and intermediate consumption separately 3) Reconciliation across the accounts at a product level • Reconciling the volume balance at a 114 product x 114 industry level in the SU framework in volume terms, previously adjustments were all allocated onto services and production was left untouched. 4) Deflator improvements • Improved Telecommunication services deflator and changes to clothing deflator (pre-2010)
Headline GVA~GDP volume
Contribution to GVA(~GDP) annual growth
Services breakdown
Mining and Quarrying- deflators GVA~GDP volume/real Nominal Output Nominal inputs Output Price GVA~GDP volume/real Nominal Output Nominal inputs Output Price Input Price OFFICIAL EXPERIMENTAL
Mining and Quarrying - GVA volume growth Output Price Input Price
Manufacturing- GVA volume Product x industry volume reconciliation Better quality current price and Double Deflation
Telecommunication services deflator Information and communication Deflator improvements Increased coverage of deflator; • Broadband and mobile data • Business and consumer transactions Improvement in the handling of access charges
Impact to GVA volume growth Information and communication Output volume Nominal Prices GVA volume Output volume Inputs volume
Next steps • Feedback blue.book.coordination@ons.gov.uk • Blue Book 2021 • DD Methodology article – early 2021 • BB21 impact article – Spring/Summer 2021 • Blue Book and Pink Book – Autumn 2021
Rob Kent-Smith Deputy Director | National Accounts Coordination @Rob_KS_ONS robert.kent-smith@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
George Clarke Assistant Economist | Prices Household inflation and income inequality 2nd November 2020 #economicforum@ONSfocus
Scope of the analysis • To investigate the relationship between household inflation and income inequality in the UK • The Household Costs Indices are used to deflate our income time series, which are used to create a “real Gini coefficient” • The un-inflated income series is also used to create a nominal Gini coefficient as a means of comparison, to illustrate the effect of inflation on income inequality
Household Cost Indices background
The Household Cost Indices (HCIs) • The HCIs aim to reflect UK households’ experience of changing prices and costs • Previous analysis of the HCIs suggests that different household groups experience different levels of inflation • The HCIs are a key part of the Prices landscape • The HCIs complement our other measures of price change: the Consumer Prices Index including owner-occupiers housing costs (CPIH), the Consumer Prices Index (CPI) and the Retail Prices Index (RPI)
The HCIs vs CPIH 1. The HCIs adopt a “democratic” weighting approach 2. The HCIs measure directly the payments that owner occupiers’ make to consume housing services, whereas the CPIH measures owner occupiers’ housing costs (OOH) using a rental equivalence approach 3. The HCIs also use a payments approach for higher education 4. The HCIs include a measure of interest costs on credit card debt, as they impact a households’ budget 5. Difference in the measurement of insurance premiums
The HCIs for low- and high-income households’ • We divide the population of UK households into income deciles • We choose the second lowest (2nd) and second highest (9th) income deciles to represent low- and high-income households, respectively • The lowest (1st) and highest (10th) income deciles are expected to share the unusual composition as described in the CPIH-subgroups, and may display unusual spending patterns that could obscure the underlying trends
0 10 20 30 40 50 60 70 80 90 100 1.Foodandnon- alcoholicbeverages 2.Alcoholic beveragesand tabacco 3.Clothingand Footwear 4.Housing,OOH, water,electricity, gasandotherfuels 5.Furniture, household equipmentand… 6.Health 7.Transport 8.Communication 9.Recreationand Culture 10.Education 11.Restaurantsand Hotels 12.Miscellaneous goodsandservices AverageSpending(%) COICOP Division Expenditure Shares for the HCIs of low- and high-income households by COICOP¹ division, UK, 2005 to 2019 Low-income households High-income households ¹COICOP = Classification of individual consumption by purpose
-3 -2 -1 0 1 2 3 4 5 6 7 200601 200605 200609 200701 200705 200709 200801 200805 200809 200901 200905 200909 201001 201005 201009 201101 201105 201109 201201 201205 201209 201301 201305 201309 201401 201405 201409 201501 201505 201509 201601 201605 201609 201701 201705 201709 201801 201805 201809 201901 201905 201909 12MGrowthRate(%) 12-Month HCI (2005 = 100) Growth Rates for low- and high-income households, UK, 2006-2019 Low-income households High-income households
Income inequality and inflation in the UK
Methodology – real income • To illustrate households experience of income and changing prices, we have created a real income series for both low- and high-income households • The income series used is mean equivalised household disposable income of decile groups (£ per year, not inflated) • To achieve the real (HCI deflated) mean income series we deflated each income by their respective income decile, using the following formula: 𝑅𝑒𝑎𝑙 𝐼𝑛𝑐𝑜𝑚𝑒 𝑑0,𝑡 = 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝐼𝑛𝑐𝑜𝑚𝑒 𝑑0,𝑡 𝐻𝐶𝐼 𝑑0,𝑡 × 100
£- £10,000.00 £20,000.00 £30,000.00 £40,000.00 £50,000.00 £60,000.00 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Averageincome(£,2005prices) Nominal and real average income for low- and high-income households, UK, 2005-2018 Nominal income, low-income households (£) Nominal income, high-income households (£) Real income, low-income households (£) Real income, high-income households (£)
Methodology – Gini coefficient • The real and nominal income series were then used to calculate an annual Gini coefficient from 2005 to 2018 • The Gini coefficient is a measure of income inequality which explains the distribution of household income amongst a select population • To calculate the Gini coefficient, we use the following formula: 𝐺𝑖𝑛𝑖 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 = 𝐴 𝐴 + 𝐵
31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 GiniCoefficient(0=perfectequality,100=perfect inequality) Year Nominal and real (HCI deflated) Gini coefficient, UK, 2005-2018 Nominal Gini coefficient Real (HCI deflated) Gini coefficient
-1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 12Mgrowthrate(%) 12-month growth rate, Low-income households 12-month growth rate, High-income households 0.33 0.34 0.34 0.35 0.35 0.36 0.36 0.37 0.37 0.38 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 RealGinicoefficient(0=perfect equality,100=perfectinequality) Real Gini coefficient and 12 month growth rate for low- and high-income households, UK, 2006-2018 Real Gini coefficient
Comments on the effects of COVID-19 • Studies show that pandemics lead to a persistent and significant increase in the net Gini measure of inequality. • Consumer expenditure trends have shifted during lockdown. • In more recent months as lockdown measures have begun to ease, we have seen falling prices • Effects of COVID-19 are not uniform across the income distribution
Conclusions • This research aimed to use the HCIs to analyse the relationship between household inflation and income inequality in the UK • By computing a real (HCI deflated) Gini coefficient, we can illustrate the relationship between income inequality and inflation • Our analysis shows that generally, income inequality in the UK is improving over the time period, but in real terms is not improving as quickly • This finding provides evidence towards the positive relationship between inflation and income inequality found in academic literature
George Clarke Assistant Economist | Prices George.Clarke@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
Business Dynamism in the UK: New Findings Using a Novel Dataset Presenter: Silvia Lui ONS Economic Forum November 2020 Authors: Silvia Lui, Russell Black, Josefa Lavandero-Mason and Mohammad Shafat
Business Dynamism  Business dynamism is a study of birth, growth and decline of businesses and its impact on employment o A steady rate of business birth and death contributes to economic growth in the long run o Growing interest from academics and policy-makers  What motivates us to look at business dynamism? o Between 2012 to 2016 : the IDBR workforce expanded by 3.2 million jobs o Only 1.8 million jobs would have been added if job creation and destruction rate had remained as they were before 2008 => Highlights the importance of understanding business dynamism to identify the origin of employment growth
• Measures employment dynamics of UK businesses from 1999 to 2019 • Building on microdata from the Inter-Departmental Business Register (IDBR) to examine dynamics at a quarterly frequency o Improves existing databases in its ability to capture within-quarter changes • Use IDBR PAYE information to derive employment measure • Derive a consistent firm age from the new activity, entry and exit measures What we do in our paper
• Use two consecutive IDBR snapshots to build one reference quarter o identify active enterprises throughout the entire period • More suitable for analysing dynamism • Higher frequency than existing databases o Quarterly active enterprise population • Potential to alleviate issues of non-match when further linking our dataset to alternative data that provides information over a period of time Data innovations
Main finding (I) 1999to 2007 2011to 2019 Change 5.12 4.82 -0.30 New businesses 1.31 1.12 -0.19 Incumbents, growing 3.81 3.70 -0.11 4.71 4.37 -0.34 Closing businesses 1.36 0.74 -0.62 Incumbents, shrinking 3.35 3.63 0.28 Net effect 0.41 0.45 0.04 Net effect incumbents 0.46 0.07 -0.39 Source: Office for National Statistics –Inter-Departmental Business Register (IDBR) Employment contributions (%) Job creation Job destruction Employment contributions over the active total IDBRworkforce (including public sector), UK, 1999to 2007and 2011to 2019  In the decade to 2019, business dynamism (the rate of entry and exit of businesses) has declined in the UK, in particular through reduced job destruction
Main finding (II) -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0 to 9 10 to 49 50 to 249 250 and over Job creation by new businesses Job destruction by closing businesses  Micro businesses (0-9 employees) are primarily responsible for the decline in job destruction rate from exit  Large businesses (250+ employees) have experienced a substantial decline in both job creation and destruction The difference in quarterly average contributions by new and closing businesses by size, UK, from 1999 to 2007 and 2011 to 2019 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR)
Main finding (III)  Job destruction rate from firm exits has fallen in all industries -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 Construction Finance Government education and healthcare Retail food and accommodation Professional services technology and media Distribution transport and wholesale Production Other services Job destruction by closing businesses Job creation by new businesses The change in job destruction by closing businesses and job creation by new businesses, UK, from 1999 to 2007 and 2011 to 2019 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR)
Main finding (IV)  Age has an important role in UK’s business dynamism both in terms of job creation by entry and destruction by exit 1999 to 2007 2011 to 2019 Net effect 0.75 0.76 Job creation¹ 1.30 1.11 Job destruction 0.55 0.35 Net effect -0.19 -0.11 Job destruction 0.19 0.12 Net effect -0.24 -0.08 Job destruction 0.25 0.08 Net effect -0.12 -0.05 Job destruction 0.14 0.05 Net effect -0.24 -0.15 Job destruction 0.24 0.15 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR) Notes 1. 15 and over Job creation rates from new businesses and job destruction rates from closing businesses by different age bands as a proportion of the active total IDBR While technically we are calculating the reactivations of older firms as job creation from entrants, the effect is small and almost zero. Employment contribution (%) 0 to 1 2 to 4 5 to 9 10 to 14
International comparison • The trends are similar. Although the UK figures are larger in magnitude Gross job flows by intensive (incumbents) and extensive (entry-exit) margins, periods between 2001 and 2010 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR), OECD -- DynEmp
Potentials • Our method to construct our dataset capture activity and changes for enterprises over a period of time • Enrich the dataset by linking it to all other IDBR business units to establish a longitudinal identity spines • Provide a framework to link survey data and administrative data for the same firm consistently over time • Possibility to construct different linked datasets to suit different research and analysis purposes • Enable us to further explore the use of administrative data and to conduct sub-national level analysis • To track dynamics of firms and their establishments • Establish consistent measures of firm entry, exit and growth
Silvia Lui Office for National Statistics Silvia.Lui@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
Andrew Jowett Senior Economist | International Analysis @ONS Foreign direct investment in digital industries #economicforum@ONSfocus2 November 2020
Overview • What is foreign direct investment (FDI)? • FDI in digital industries • Geography links for FDI in digital industries
What is foreign direct investment (FDI)?
What is FDI? (1) • Direct investments made with the intention to form a lasting interest in the host economy:  Outward: UK-resident companies controlling affiliates outside the UK.  Inward: UK resident companies that are controlled by a foreign parent company • FDI positions: the value of the stock of investment held at a point in time.
What is FDI? (2) • Can be presented using the industry of the UK parent company or UK-based affiliate. • Digital industries from the FDI Survey are:  Manufacture of computers and electronics  Publishing  Film, TV, video, radio and music  Telecommunications  Computer services  Information services
FDI in digital industries
FDI in digital industries (1) 0 200 400 600 800 1,000 1,200 1,400 1,600 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 Outward Inward £ billion Digital Rest Source: Foreign direct investment in digital industries, UK trends and analysis, September 2020, ONS Foreign direct investment position of companies with digital industries compared with all other FDI companies, outward and inward, 2014 to 2018
FDI in digital industries (2) Outward foreign direct investment positions with digital sub- industries, 2014 to 2018 0 20,000 40,000 60,000 80,000 100,000 120,000 2014 2015 2016 2017 2018 £ million Manuelec Publ Film Telecom Compserv Infoserv
FDI in digital industries (3) Inward foreign direct investment positions with digital sub- industries, 2014 to 2018 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 2014 2015 2016 2017 2018 £ million Manuelec Publ Film Telecom Compserv Infoserv
Geography links for FDI in digital industries
Geography links: FDI in digital (1) Ranking of UK outward FDI digital positions by country, top 10 in descending position value order in 2018 Luxembourg United States Spain Netherlands Switzerland Jersey France Ireland Germany South Africa 1 2 3 4 5 6 7 8 9 10 2014 2015 2016 2017 2018
Geography links: FDI in digital (2) Ranking of UK inward FDI digital positions by country, top 10 in descending position value order in 2018 United States Hong Kong Netherlands Jersey Luxembourg Spain France Barbados Canada Japan 1 2 3 4 5 6 7 8 9 10 2014 2015 2016 2017 2018
Geography links: FDI in digital (3) Outward FDI positions in digital industries by country, ten highest values in 2018 and rest of the world -5,000 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Luxembourg United States Spain Netherlands Switzerland Jersey France Ireland Germany South Africa Rest £ million2014 2015 2016 2017 2018
Geography links: FDI in digital (4) Inward FDI positions in digital industries by country, ten highest values in 2018 and rest of the world 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 United States Hong Kong Netherlands Jersey Luxembourg Spain France Barbados Canada Japan Rest £ million2014 2015 2016 2017 2018
Conclusions (1) • Digital industries accounted for 10.4% of the UK’s inward FDI position, and 8.6% of the outward FDI position. • Highest values in telecommunications sub-industry in both directions. • Over four-fifths of the outward FDI digital position was in five economies – Luxembourg, US, Spain, Netherlands and Switzerland – in 2018.
Conclusions (2) • Highest outward FDI position in digital industries with Luxembourg between 2014 and 2018. • Value of inward digital position with the US increased considerably between 2014 and 2018; becoming highest position value in 2016, 2017 and 2018. • Around three-quarters of the inward FDI digital position was with five economies – US, Hong Kong, Netherlands, Jersey and Luxembourg – in 2018.
For queries or suggestions for other FDI-related analysis topics: fdi@ons.gov.uk Thank you for your participation
Andrew Jowett Senior Economist | International Analysis andrew.jowett@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
Grant Fitzner Chief Economist | Director, Macroeconomic Statistics and Analysis Economic Forum Overview of the UK economy 2 November 2020
Where we are Economic Forum, November 2020
Past UK recessions… Source: ONS GDP quarterly national accounts Economic Forum, November 2020 The path of UK economic downturns and recovery; index: 100 = last quarter before onset of recession 90 92 94 96 98 100 102 0 1 2 3 4 Years since pre-recession peak Q2 1973 peak Q4 1979 peak Q2 1990 peak Q1 2008 peak
Past UK recessions… and this one Source: ONS GDP quarterly national accounts Economic Forum, November 2020 The path of UK economic downturns and recovery; index: 100 = last quarter before onset of recession 70 75 80 85 90 95 100 105 0 1 2 3 4 Years since pre-recession peak Q2 1973 peak Q4 1979 peak Q2 1990 peak Q1 2008 peak Jan 2020 peak
COVID-19 pandemic entering a second wave Source: European Centre for Disease Prevention and Control (ECDC), ONS Coronavirus (COVID-19) Infection Survey Note: The number of confirmed cases is lower than the number of actual cases, due to limited testing. Economic Forum, November 2020 Daily new confirmed COVID-19 cases per million, 7 day average Incidence rate per 10,000 people per day, England Estimated percentage of the population testing positive
Uncertainty remains elevated, confidence weak Source: https://www.policyuncertainty.com/ and EU Business and consumer surveys Economic Forum, November 2020 Daily UK Economic Policy Uncertainty Index, 2020 UK consumer confidence and economic sentiment index 0 200 400 600 800 1000 1200 1400 1600 Jan 2020 Feb 2020 Mar 2020 Apr 2020 May 2020 Jun 2020 Jul 2020 Aug 2020 Sep 2020 Oct 2020 50 60 70 80 90 100 110 120 -30 -25 -20 -15 -10 -5 0 UK consumer confidence UK economic sentiment indicator
Economic activity Economic Forum, November 2020
Source: ONS GDP quarterly national accounts Note: Index is referenced to 2019 Q4 = 100. Economic Forum, November 2020 Real GDP fell by 19.8% in Quarter 2 2020, the largest quarterly contraction on record 75 80 85 90 95 100 105 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 2015Q1 2015Q4 2016Q3 2017Q2 2018Q1 2018Q4 2019Q3 2020Q2 Diamond Jubilee Original Brexit deadline Financial Crisis COVID Current GDP is level with 2003 Q2 figures Olympics
Source: ONS Monthly Business Survey – Retail Sales Inquiry Economic Forum, November 2020 Volume sales, seasonally adjusted, Great Britain, September 2018 to September 2020 Retail sales have seen a V-shaped bounce 70 75 80 85 90 95 100 105 110 Jan 2018 Jul 2018 Jan 2019 Jul 2019 Jan 2020 Jul 2020
Source: ONS GDP monthly estimate Note: GDP estimates for August 2020 are subject to more uncertainty than usual. Economic Forum, November 2020 Monthly GDP, UK, February 2020 = 100 But monthly GDP remains 9.2% below its February level 50 60 70 80 90 100 50 60 70 80 90 100 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Monthly GDP Services Manufacturing Construction
Labour market Economic Forum, November 2020
Source: Labour Force Survey, UK Note: Estimates have been reweighted to account for the impact of the pandemic on survey interviewing methods. Economic Forum, November 2020 UK employment, unemployment and economic inactivity rates, seasonally adjusted, June to August 2005 to June to August 2020 The unemployment rate and economic inactivity rates are rising, while the employment rate is falling
PAYE employee payrolls decline has slowed Source: HM Revenue and Customs – Pay As You Earn Real Time Information Note: The latest period is based on early data and therefore is more likely to be subject to slightly more significant revisions. Economic Forum, November 2020 Monthly RTI payroll flows, UK -1,000,000 -800,000 -600,000 -400,000 -200,000 0 200,000 400,000 600,000 800,000 January2017 March2017 May2017 July2017 September2017 November2017 January2018 March2018 May2018 July2018 September2018 November2018 January2019 March2019 May2019 July2019 September2019 November2019 January2020 March2020 May2020 July2020 September2020 Inflows Outflows (inverted) Change in payrolled employees Payrolled employees, seasonally adjusted, UK, to September 2020 27.0m 27.5m 28.0m 28.5m 29.0m 29.5m Jan2016 Apr2016 Jul2016 Oct2016 Jan2017 Apr2017 Jul2017 Oct2017 Jan2018 Apr2018 Jul2018 Oct2018 Jan2019 Apr2019 Jul2019 Oct2019 Jan2020 Apr2020 Jul2020
Mixed picture on job vacancies by industry Source: ONS Vacancy Survey Economic Forum, November 2020 Percentage point changes in monthly job vacancies by industry, n.s.a. compared to February 2020 levels -100 -80 -60 -40 -20 0 20 40 60 80 Feb to May May to Aug Feb to Aug
Source: ONS Business Impact of Coronavirus (COVID-19) Survey, HMRC Economic Forum, November 2020 Estimates of employments furloughed, million 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 HMRC: Employments furloughed Unweighted BICS: Employments furloughed estimate Weighted BICS: Employments furloughed estimate Furloughed jobs heading lower
Where now? Economic Forum, November 2020
Economic Forum, November 2020 Source: ONS Business Impact of Coronavirus (COVID-19) Survey (BICS), GDP monthly estimates BICS turnover net balances, for businesses currently trading, compared with GDP monthly estimates, UK, February to August 2020 BICS versus monthly GDP
Source: Springboard and the Department for Business, Energy and Industrial Strategy Economic Forum, November 2020 Volume of footfall, percentage change from the same day the previous year, UK, 1 March to 25 October 2020 Overall footfall around two-thirds of last year’s level
Summing up Economic Forum, November 2020
Economic Forum, November 2020 Any questions?
Closing remarks Deputy Chief Economist Office for National Statistics Ed Palmer 2 November 2020 #economicforum@ONSfocus
Upcoming Events ESCoE COVID-19, Economic Measurement webinars • The Impact of COVID-19 on Productivity – 5 November • Necessity is the mother of invention: the Bank’s use of “faster indicators” of economic activity since the COVID-19 outbreak – 12 November • Free goods and economic welfare – 26 November • Mapping ‘career causeways’ for workers displaced by automation and COVID-19 – 10 December To book on these events please visit - www.escoe.ac.uk Dates for your diary Economic Forum – End of January 2021 (TBC) ESCoE Conference on Economic Measurement 2021, 11-13 May 2021 – via virtual platform Further information on the conference will be shared in the next few weeks via www.escoe.ac.uk and @ESCoEorg #economicforum@ONSfocus

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Economic Forum Webinar 2 November 2020

  • 1.
    ONS Economic Forum DeputyChief Economist Office for National Statistics Chair – Ed Palmer #economicforum@ONSfocus2 November 2020
  • 2.
    Agenda 10:00 – 10:05Introduction – Ed Palmer, Deputy Chief Economist 10:05 – 10:25 Blue Book double-deflated Gross Value Added – Rhys Lewis 10:25 – 10:30 Q&A 10:30 – 10:40 Household Inflation and Income Inequality in the UK – George Clarke 10:40 – 10:45 Q&A 10:45 – 10:55 Business Dynamism in the UK – Silvia Lui 10:55 – 11:00 Q&A 11:00 – 11:10 Foreign Direct Investment in Digital Industries – Andrew Jowett 11:10 – 11:15 Q&A 11:15 – 11:25 Overview on the state of the economy – Grant Fitzner, Chief Economist 11:25 – 11:30 Close #economicforum@ONSfocus
  • 3.
    Rob Kent-Smith RhysLewis Deputy Director, National Accounts Double Deflation lead @Rob_KS_ONS Double Deflation 2 November 2020 @ONSfocus #economicforum
  • 4.
    GDP – theproduction approach Nominal GDP Output Input (of goods & services) output / production approach Real GDP Nominal GDP Prices Improving the production approach Improving Deflation
  • 5.
  • 6.
    Compiling GDP: PreBB19 GDP(P) Goods and services produced minus the inputs used GDP(E) The sum of all final expenditures within an economy GDP(I) The sum of all factor incomes within an economy GDP(O) Turnover as a proxy for GDP(P) Balance Nominal GDP in ‘SU’ Framework Remove inflation using GDP(E) Headline volume GDP Deflated turnover from monthly business surveys Structural annual business surveys Industry GDP (GVA) aligned to heading via adjustments to services
  • 7.
    Compiling GDP: Experimental GDP(P) Goodsand services produced minus the inputs used GDP(E) The sum of all final expenditures within an economy GDP(I) The sum of all factor incomes within an economy GDP(O) Turnover as a proxy for GDP(P) Balance Real and Nominal GDP in ‘SU’ Framework Remove inflation using GDP(E) deflators Headline volume GDP Deflated turnover from monthly business surveys Structural annual business surveys Monthly GDP(O) benchmarked to GDP(P) by industry Remove inflation using GDP(P) deflators
  • 8.
    Sources of change 1)Better Quality Current Price data • Current volume series is based on long run CP MBS time series, which can move differently to supply and use 2) Double Deflation • Deflating output and intermediate consumption separately 3) Reconciliation across the accounts at a product level • Reconciling the volume balance at a 114 product x 114 industry level in the SU framework in volume terms, previously adjustments were all allocated onto services and production was left untouched. 4) Deflator improvements • Improved Telecommunication services deflator and changes to clothing deflator (pre-2010)
  • 9.
  • 10.
  • 11.
  • 12.
    Mining and Quarrying-deflators GVA~GDP volume/real Nominal Output Nominal inputs Output Price GVA~GDP volume/real Nominal Output Nominal inputs Output Price Input Price OFFICIAL EXPERIMENTAL
  • 13.
    Mining and Quarrying- GVA volume growth Output Price Input Price
  • 14.
    Manufacturing- GVA volume Productx industry volume reconciliation Better quality current price and Double Deflation
  • 15.
    Telecommunication services deflator Informationand communication Deflator improvements Increased coverage of deflator; • Broadband and mobile data • Business and consumer transactions Improvement in the handling of access charges
  • 16.
    Impact to GVAvolume growth Information and communication Output volume Nominal Prices GVA volume Output volume Inputs volume
  • 17.
    Next steps • Feedbackblue.book.coordination@ons.gov.uk • Blue Book 2021 • DD Methodology article – early 2021 • BB21 impact article – Spring/Summer 2021 • Blue Book and Pink Book – Autumn 2021
  • 18.
    Rob Kent-Smith DeputyDirector | National Accounts Coordination @Rob_KS_ONS robert.kent-smith@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
  • 19.
    George Clarke Assistant Economist| Prices Household inflation and income inequality 2nd November 2020 #economicforum@ONSfocus
  • 20.
    Scope of theanalysis • To investigate the relationship between household inflation and income inequality in the UK • The Household Costs Indices are used to deflate our income time series, which are used to create a “real Gini coefficient” • The un-inflated income series is also used to create a nominal Gini coefficient as a means of comparison, to illustrate the effect of inflation on income inequality
  • 21.
  • 22.
    The Household CostIndices (HCIs) • The HCIs aim to reflect UK households’ experience of changing prices and costs • Previous analysis of the HCIs suggests that different household groups experience different levels of inflation • The HCIs are a key part of the Prices landscape • The HCIs complement our other measures of price change: the Consumer Prices Index including owner-occupiers housing costs (CPIH), the Consumer Prices Index (CPI) and the Retail Prices Index (RPI)
  • 23.
    The HCIs vsCPIH 1. The HCIs adopt a “democratic” weighting approach 2. The HCIs measure directly the payments that owner occupiers’ make to consume housing services, whereas the CPIH measures owner occupiers’ housing costs (OOH) using a rental equivalence approach 3. The HCIs also use a payments approach for higher education 4. The HCIs include a measure of interest costs on credit card debt, as they impact a households’ budget 5. Difference in the measurement of insurance premiums
  • 24.
    The HCIs forlow- and high-income households’ • We divide the population of UK households into income deciles • We choose the second lowest (2nd) and second highest (9th) income deciles to represent low- and high-income households, respectively • The lowest (1st) and highest (10th) income deciles are expected to share the unusual composition as described in the CPIH-subgroups, and may display unusual spending patterns that could obscure the underlying trends
  • 25.
  • 26.
  • 27.
  • 28.
    Methodology – realincome • To illustrate households experience of income and changing prices, we have created a real income series for both low- and high-income households • The income series used is mean equivalised household disposable income of decile groups (£ per year, not inflated) • To achieve the real (HCI deflated) mean income series we deflated each income by their respective income decile, using the following formula: 𝑅𝑒𝑎𝑙 𝐼𝑛𝑐𝑜𝑚𝑒 𝑑0,𝑡 = 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝐼𝑛𝑐𝑜𝑚𝑒 𝑑0,𝑡 𝐻𝐶𝐼 𝑑0,𝑡 × 100
  • 29.
    £- £10,000.00 £20,000.00 £30,000.00 £40,000.00 £50,000.00 £60,000.00 2005 2006 20072008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Averageincome(£,2005prices) Nominal and real average income for low- and high-income households, UK, 2005-2018 Nominal income, low-income households (£) Nominal income, high-income households (£) Real income, low-income households (£) Real income, high-income households (£)
  • 30.
    Methodology – Ginicoefficient • The real and nominal income series were then used to calculate an annual Gini coefficient from 2005 to 2018 • The Gini coefficient is a measure of income inequality which explains the distribution of household income amongst a select population • To calculate the Gini coefficient, we use the following formula: 𝐺𝑖𝑛𝑖 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 = 𝐴 𝐴 + 𝐵
  • 31.
    31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 2005 2006 20072008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 GiniCoefficient(0=perfectequality,100=perfect inequality) Year Nominal and real (HCI deflated) Gini coefficient, UK, 2005-2018 Nominal Gini coefficient Real (HCI deflated) Gini coefficient
  • 32.
    -1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 2006 2007 20082009 2010 2011 2012 2013 2014 2015 2016 2017 2018 12Mgrowthrate(%) 12-month growth rate, Low-income households 12-month growth rate, High-income households 0.33 0.34 0.34 0.35 0.35 0.36 0.36 0.37 0.37 0.38 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 RealGinicoefficient(0=perfect equality,100=perfectinequality) Real Gini coefficient and 12 month growth rate for low- and high-income households, UK, 2006-2018 Real Gini coefficient
  • 33.
    Comments on theeffects of COVID-19 • Studies show that pandemics lead to a persistent and significant increase in the net Gini measure of inequality. • Consumer expenditure trends have shifted during lockdown. • In more recent months as lockdown measures have begun to ease, we have seen falling prices • Effects of COVID-19 are not uniform across the income distribution
  • 34.
    Conclusions • This researchaimed to use the HCIs to analyse the relationship between household inflation and income inequality in the UK • By computing a real (HCI deflated) Gini coefficient, we can illustrate the relationship between income inequality and inflation • Our analysis shows that generally, income inequality in the UK is improving over the time period, but in real terms is not improving as quickly • This finding provides evidence towards the positive relationship between inflation and income inequality found in academic literature
  • 35.
    George Clarke Assistant Economist| Prices George.Clarke@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
  • 36.
    Business Dynamism inthe UK: New Findings Using a Novel Dataset Presenter: Silvia Lui ONS Economic Forum November 2020 Authors: Silvia Lui, Russell Black, Josefa Lavandero-Mason and Mohammad Shafat
  • 37.
    Business Dynamism  Businessdynamism is a study of birth, growth and decline of businesses and its impact on employment o A steady rate of business birth and death contributes to economic growth in the long run o Growing interest from academics and policy-makers  What motivates us to look at business dynamism? o Between 2012 to 2016 : the IDBR workforce expanded by 3.2 million jobs o Only 1.8 million jobs would have been added if job creation and destruction rate had remained as they were before 2008 => Highlights the importance of understanding business dynamism to identify the origin of employment growth
  • 38.
    • Measures employmentdynamics of UK businesses from 1999 to 2019 • Building on microdata from the Inter-Departmental Business Register (IDBR) to examine dynamics at a quarterly frequency o Improves existing databases in its ability to capture within-quarter changes • Use IDBR PAYE information to derive employment measure • Derive a consistent firm age from the new activity, entry and exit measures What we do in our paper
  • 39.
    • Use twoconsecutive IDBR snapshots to build one reference quarter o identify active enterprises throughout the entire period • More suitable for analysing dynamism • Higher frequency than existing databases o Quarterly active enterprise population • Potential to alleviate issues of non-match when further linking our dataset to alternative data that provides information over a period of time Data innovations
  • 40.
    Main finding (I) 1999to2007 2011to 2019 Change 5.12 4.82 -0.30 New businesses 1.31 1.12 -0.19 Incumbents, growing 3.81 3.70 -0.11 4.71 4.37 -0.34 Closing businesses 1.36 0.74 -0.62 Incumbents, shrinking 3.35 3.63 0.28 Net effect 0.41 0.45 0.04 Net effect incumbents 0.46 0.07 -0.39 Source: Office for National Statistics –Inter-Departmental Business Register (IDBR) Employment contributions (%) Job creation Job destruction Employment contributions over the active total IDBRworkforce (including public sector), UK, 1999to 2007and 2011to 2019  In the decade to 2019, business dynamism (the rate of entry and exit of businesses) has declined in the UK, in particular through reduced job destruction
  • 41.
    Main finding (II) -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0to 9 10 to 49 50 to 249 250 and over Job creation by new businesses Job destruction by closing businesses  Micro businesses (0-9 employees) are primarily responsible for the decline in job destruction rate from exit  Large businesses (250+ employees) have experienced a substantial decline in both job creation and destruction The difference in quarterly average contributions by new and closing businesses by size, UK, from 1999 to 2007 and 2011 to 2019 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR)
  • 42.
    Main finding (III) Job destruction rate from firm exits has fallen in all industries -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 Construction Finance Government education and healthcare Retail food and accommodation Professional services technology and media Distribution transport and wholesale Production Other services Job destruction by closing businesses Job creation by new businesses The change in job destruction by closing businesses and job creation by new businesses, UK, from 1999 to 2007 and 2011 to 2019 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR)
  • 43.
    Main finding (IV) Age has an important role in UK’s business dynamism both in terms of job creation by entry and destruction by exit 1999 to 2007 2011 to 2019 Net effect 0.75 0.76 Job creation¹ 1.30 1.11 Job destruction 0.55 0.35 Net effect -0.19 -0.11 Job destruction 0.19 0.12 Net effect -0.24 -0.08 Job destruction 0.25 0.08 Net effect -0.12 -0.05 Job destruction 0.14 0.05 Net effect -0.24 -0.15 Job destruction 0.24 0.15 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR) Notes 1. 15 and over Job creation rates from new businesses and job destruction rates from closing businesses by different age bands as a proportion of the active total IDBR While technically we are calculating the reactivations of older firms as job creation from entrants, the effect is small and almost zero. Employment contribution (%) 0 to 1 2 to 4 5 to 9 10 to 14
  • 44.
    International comparison • The trendsare similar. Although the UK figures are larger in magnitude Gross job flows by intensive (incumbents) and extensive (entry-exit) margins, periods between 2001 and 2010 Source: Office for National Statistics – Inter-Departmental Business Register (IDBR), OECD -- DynEmp
  • 45.
    Potentials • Our methodto construct our dataset capture activity and changes for enterprises over a period of time • Enrich the dataset by linking it to all other IDBR business units to establish a longitudinal identity spines • Provide a framework to link survey data and administrative data for the same firm consistently over time • Possibility to construct different linked datasets to suit different research and analysis purposes • Enable us to further explore the use of administrative data and to conduct sub-national level analysis • To track dynamics of firms and their establishments • Establish consistent measures of firm entry, exit and growth
  • 46.
    Silvia Lui Office forNational Statistics Silvia.Lui@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
  • 47.
    Andrew Jowett Senior Economist| International Analysis @ONS Foreign direct investment in digital industries #economicforum@ONSfocus2 November 2020
  • 48.
    Overview • What isforeign direct investment (FDI)? • FDI in digital industries • Geography links for FDI in digital industries
  • 49.
    What is foreigndirect investment (FDI)?
  • 50.
    What is FDI?(1) • Direct investments made with the intention to form a lasting interest in the host economy:  Outward: UK-resident companies controlling affiliates outside the UK.  Inward: UK resident companies that are controlled by a foreign parent company • FDI positions: the value of the stock of investment held at a point in time.
  • 51.
    What is FDI?(2) • Can be presented using the industry of the UK parent company or UK-based affiliate. • Digital industries from the FDI Survey are:  Manufacture of computers and electronics  Publishing  Film, TV, video, radio and music  Telecommunications  Computer services  Information services
  • 52.
    FDI in digitalindustries
  • 53.
    FDI in digitalindustries (1) 0 200 400 600 800 1,000 1,200 1,400 1,600 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 Outward Inward £ billion Digital Rest Source: Foreign direct investment in digital industries, UK trends and analysis, September 2020, ONS Foreign direct investment position of companies with digital industries compared with all other FDI companies, outward and inward, 2014 to 2018
  • 54.
    FDI in digitalindustries (2) Outward foreign direct investment positions with digital sub- industries, 2014 to 2018 0 20,000 40,000 60,000 80,000 100,000 120,000 2014 2015 2016 2017 2018 £ million Manuelec Publ Film Telecom Compserv Infoserv
  • 55.
    FDI in digitalindustries (3) Inward foreign direct investment positions with digital sub- industries, 2014 to 2018 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 2014 2015 2016 2017 2018 £ million Manuelec Publ Film Telecom Compserv Infoserv
  • 56.
    Geography links forFDI in digital industries
  • 57.
    Geography links: FDIin digital (1) Ranking of UK outward FDI digital positions by country, top 10 in descending position value order in 2018 Luxembourg United States Spain Netherlands Switzerland Jersey France Ireland Germany South Africa 1 2 3 4 5 6 7 8 9 10 2014 2015 2016 2017 2018
  • 58.
    Geography links: FDIin digital (2) Ranking of UK inward FDI digital positions by country, top 10 in descending position value order in 2018 United States Hong Kong Netherlands Jersey Luxembourg Spain France Barbados Canada Japan 1 2 3 4 5 6 7 8 9 10 2014 2015 2016 2017 2018
  • 59.
    Geography links: FDIin digital (3) Outward FDI positions in digital industries by country, ten highest values in 2018 and rest of the world -5,000 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Luxembourg United States Spain Netherlands Switzerland Jersey France Ireland Germany South Africa Rest £ million2014 2015 2016 2017 2018
  • 60.
    Geography links: FDIin digital (4) Inward FDI positions in digital industries by country, ten highest values in 2018 and rest of the world 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 United States Hong Kong Netherlands Jersey Luxembourg Spain France Barbados Canada Japan Rest £ million2014 2015 2016 2017 2018
  • 61.
    Conclusions (1) • Digitalindustries accounted for 10.4% of the UK’s inward FDI position, and 8.6% of the outward FDI position. • Highest values in telecommunications sub-industry in both directions. • Over four-fifths of the outward FDI digital position was in five economies – Luxembourg, US, Spain, Netherlands and Switzerland – in 2018.
  • 62.
    Conclusions (2) • Highestoutward FDI position in digital industries with Luxembourg between 2014 and 2018. • Value of inward digital position with the US increased considerably between 2014 and 2018; becoming highest position value in 2016, 2017 and 2018. • Around three-quarters of the inward FDI digital position was with five economies – US, Hong Kong, Netherlands, Jersey and Luxembourg – in 2018.
  • 63.
    For queries orsuggestions for other FDI-related analysis topics: fdi@ons.gov.uk Thank you for your participation
  • 64.
    Andrew Jowett Senior Economist| International Analysis andrew.jowett@ons.gov.uk 2 November 2020 @ONSfocus #economicforum
  • 65.
    Grant Fitzner Chief Economist| Director, Macroeconomic Statistics and Analysis Economic Forum Overview of the UK economy 2 November 2020
  • 66.
    Where we are EconomicForum, November 2020
  • 67.
    Past UK recessions… Source:ONS GDP quarterly national accounts Economic Forum, November 2020 The path of UK economic downturns and recovery; index: 100 = last quarter before onset of recession 90 92 94 96 98 100 102 0 1 2 3 4 Years since pre-recession peak Q2 1973 peak Q4 1979 peak Q2 1990 peak Q1 2008 peak
  • 68.
    Past UK recessions…and this one Source: ONS GDP quarterly national accounts Economic Forum, November 2020 The path of UK economic downturns and recovery; index: 100 = last quarter before onset of recession 70 75 80 85 90 95 100 105 0 1 2 3 4 Years since pre-recession peak Q2 1973 peak Q4 1979 peak Q2 1990 peak Q1 2008 peak Jan 2020 peak
  • 69.
    COVID-19 pandemic enteringa second wave Source: European Centre for Disease Prevention and Control (ECDC), ONS Coronavirus (COVID-19) Infection Survey Note: The number of confirmed cases is lower than the number of actual cases, due to limited testing. Economic Forum, November 2020 Daily new confirmed COVID-19 cases per million, 7 day average Incidence rate per 10,000 people per day, England Estimated percentage of the population testing positive
  • 70.
    Uncertainty remains elevated,confidence weak Source: https://www.policyuncertainty.com/ and EU Business and consumer surveys Economic Forum, November 2020 Daily UK Economic Policy Uncertainty Index, 2020 UK consumer confidence and economic sentiment index 0 200 400 600 800 1000 1200 1400 1600 Jan 2020 Feb 2020 Mar 2020 Apr 2020 May 2020 Jun 2020 Jul 2020 Aug 2020 Sep 2020 Oct 2020 50 60 70 80 90 100 110 120 -30 -25 -20 -15 -10 -5 0 UK consumer confidence UK economic sentiment indicator
  • 71.
  • 72.
    Source: ONS GDPquarterly national accounts Note: Index is referenced to 2019 Q4 = 100. Economic Forum, November 2020 Real GDP fell by 19.8% in Quarter 2 2020, the largest quarterly contraction on record 75 80 85 90 95 100 105 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 2015Q1 2015Q4 2016Q3 2017Q2 2018Q1 2018Q4 2019Q3 2020Q2 Diamond Jubilee Original Brexit deadline Financial Crisis COVID Current GDP is level with 2003 Q2 figures Olympics
  • 73.
    Source: ONS MonthlyBusiness Survey – Retail Sales Inquiry Economic Forum, November 2020 Volume sales, seasonally adjusted, Great Britain, September 2018 to September 2020 Retail sales have seen a V-shaped bounce 70 75 80 85 90 95 100 105 110 Jan 2018 Jul 2018 Jan 2019 Jul 2019 Jan 2020 Jul 2020
  • 74.
    Source: ONS GDPmonthly estimate Note: GDP estimates for August 2020 are subject to more uncertainty than usual. Economic Forum, November 2020 Monthly GDP, UK, February 2020 = 100 But monthly GDP remains 9.2% below its February level 50 60 70 80 90 100 50 60 70 80 90 100 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Monthly GDP Services Manufacturing Construction
  • 75.
  • 76.
    Source: Labour ForceSurvey, UK Note: Estimates have been reweighted to account for the impact of the pandemic on survey interviewing methods. Economic Forum, November 2020 UK employment, unemployment and economic inactivity rates, seasonally adjusted, June to August 2005 to June to August 2020 The unemployment rate and economic inactivity rates are rising, while the employment rate is falling
  • 77.
    PAYE employee payrollsdecline has slowed Source: HM Revenue and Customs – Pay As You Earn Real Time Information Note: The latest period is based on early data and therefore is more likely to be subject to slightly more significant revisions. Economic Forum, November 2020 Monthly RTI payroll flows, UK -1,000,000 -800,000 -600,000 -400,000 -200,000 0 200,000 400,000 600,000 800,000 January2017 March2017 May2017 July2017 September2017 November2017 January2018 March2018 May2018 July2018 September2018 November2018 January2019 March2019 May2019 July2019 September2019 November2019 January2020 March2020 May2020 July2020 September2020 Inflows Outflows (inverted) Change in payrolled employees Payrolled employees, seasonally adjusted, UK, to September 2020 27.0m 27.5m 28.0m 28.5m 29.0m 29.5m Jan2016 Apr2016 Jul2016 Oct2016 Jan2017 Apr2017 Jul2017 Oct2017 Jan2018 Apr2018 Jul2018 Oct2018 Jan2019 Apr2019 Jul2019 Oct2019 Jan2020 Apr2020 Jul2020
  • 78.
    Mixed picture onjob vacancies by industry Source: ONS Vacancy Survey Economic Forum, November 2020 Percentage point changes in monthly job vacancies by industry, n.s.a. compared to February 2020 levels -100 -80 -60 -40 -20 0 20 40 60 80 Feb to May May to Aug Feb to Aug
  • 79.
    Source: ONS BusinessImpact of Coronavirus (COVID-19) Survey, HMRC Economic Forum, November 2020 Estimates of employments furloughed, million 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Aug 20 Sep 20 HMRC: Employments furloughed Unweighted BICS: Employments furloughed estimate Weighted BICS: Employments furloughed estimate Furloughed jobs heading lower
  • 80.
  • 81.
    Economic Forum, November2020 Source: ONS Business Impact of Coronavirus (COVID-19) Survey (BICS), GDP monthly estimates BICS turnover net balances, for businesses currently trading, compared with GDP monthly estimates, UK, February to August 2020 BICS versus monthly GDP
  • 82.
    Source: Springboard andthe Department for Business, Energy and Industrial Strategy Economic Forum, November 2020 Volume of footfall, percentage change from the same day the previous year, UK, 1 March to 25 October 2020 Overall footfall around two-thirds of last year’s level
  • 83.
  • 84.
    Economic Forum, November2020 Any questions?
  • 85.
    Closing remarks Deputy ChiefEconomist Office for National Statistics Ed Palmer 2 November 2020 #economicforum@ONSfocus
  • 86.
    Upcoming Events ESCoE COVID-19,Economic Measurement webinars • The Impact of COVID-19 on Productivity – 5 November • Necessity is the mother of invention: the Bank’s use of “faster indicators” of economic activity since the COVID-19 outbreak – 12 November • Free goods and economic welfare – 26 November • Mapping ‘career causeways’ for workers displaced by automation and COVID-19 – 10 December To book on these events please visit - www.escoe.ac.uk Dates for your diary Economic Forum – End of January 2021 (TBC) ESCoE Conference on Economic Measurement 2021, 11-13 May 2021 – via virtual platform Further information on the conference will be shared in the next few weeks via www.escoe.ac.uk and @ESCoEorg #economicforum@ONSfocus

Editor's Notes

  • #47 Thank you very much for listening. My email address is on the slide now if you would like to get in contact. Otherwise, are there any questions?