Economic Statistics TransformationPete Benton

Director of Data Collection, ONS

Nick VaughanDirector and Chief Economic Adviser, ONS

Frankie KayDirector for Economic Statistics Transformation, ONS

Chair:Ed Humpherson

Director General for Regulation, UK Statistics Authority

Transforming ONS Data Collection

Pete BentonDirector of Data Collection, ONS

Integrated data collection vision

80 business survey datasets

Current

10 social survey datasets

Administrative datasets

10 business survey datasets

5 social survey datasets

Administrative / regulatory / 3rd party datasets

10 sets of social outputs

Integrated data store

80 sets of business outputs

Future

(More) Integrated suite of outputs

admin data outputs

Interim datasets End product ANALYSIS

Interview or paper based

Online and modular

Topics by no. surveys / business contacts

0 5 10 15 20 No. surveys

700

600

500

400

300

200

100

0

Turnover

Employment

Stocks

Income

Prices / costsR&D

No. business contacts (‘000s)

The challenge

Image thanks to arcticglaciers.org

60m census records

Census outputs

20,000 FRS

records

incomeoutputs

5,000 LCF

records

incomeoutputs

linkage

Multivarate, income outputs

90m HMRC

records

Earnings outputs

80m DWP

records

Benefits outputs

Modelled income variable

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Data integration: Cross–referenced frames

Anonymised Statistical Population Dataset

Business frame

Addressframe

8

“Frame-referenced” data sourcesAnonymised Statistical

Population Dataset

Business frame

Addressframe

Census data

Administrative / big data

Survey data

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Transforming ONS Data Collection

Pete BentonDirector of Data Collection, ONS

Transforming National Accounts

Nick Vaughan

Director and Chief Economic Adviser, ONS

Key opportunities

• The output measure of GDP: The use of data on turnover from VAT returns

• The income measure of GDP: The use of data from PAYE returns

• Measuring the service sector: a new survey as a complement to ‘PRODCOM’

The VAT data

• Gross Value Added (GVA) = Output less intermediate consumption, broadly turnover less purchases

• So the output measure of GDP(O) driven in the short term by measures of turnover from the Monthly Business Survey (MBS)

• MBS single question each month to 45k firms: ‘What was the value of your turnover excluding VAT’

• All firms registered for VAT i.e. with turnover above £82k must return ‘Total value of sales and all other outputs excluding VAT’

• But VAT data is 2m returns each quarter, less timely but exhaustive

• So this administrative dataset a near exact substitute for survey data

VAT data: GVA not just output

• ‘Total value of purchases and all other inputs excluding any VAT’

• Purchases ex. investment ≈ intermediate consumption , and certainly no worse than short-term assumptions given annual purchases survey

• Hence VAT data offers potential to supply robust estimates of rolling 3m nominal GVA in near real time e.g. within 4-6 months.

• This would provide assurance over turning points and the strength of nominal demand

• Does not supply detail of purchases so VAT data would inform industry benchmarks and purchases survey key source for detail.

• NB data could provide unprecedented precision and detail by industry and geography on a timely basis

VAT data: development

• Challenging pace of development, more sophisticated approach to lead with survey in short term and transition to admin data as it matures

• Challenges in apportionment between large/complex firms and small/simple; fully sample large firms and use admin data for the rest

• Unlike new survey have historical VAT data to undertake analysis

• Intend to use growth rates first in selected industries, and develop levels in due course to inform balancing against other sources

• Lead candidate industries include construction where 174k small firms with 0-4 employment account for 20% turnover - 8k survey and 216k VAT returns

• By contrast in Retail 1,353 firms account for 75% of turnover - 5k survey and 164k VAT returns.

PAYE data: The labour market

• Workforce jobs measure of employment driven by monthly surveys

• Average earnings derived from Monthly Wages & Salaries Survey (AWE)

• Wages and Salaries (W&S) in National Accounts ≈ AWE * LFS employment

• After some considerable lag HMRC data benchmarks labour income

• Within 2 weeks PAYE data could potentially supply employment , jobs and persons, along with W&S and hence AWE

• As with VAT data near exhaustive so unprecedented precision in estimates by industry cf AEI review and geography

• This administrative data would dominate any survey measure of employees in employment for precision and timeliness

PAYE data: Income measure of GDP

• GDP = Compensation of Employees (CoE) + Operating Surplus (OS) but formally OS derived by residual with gross trading profits a component.

• GVA ≈ GDP so OS = GVA (from VAT) – CoE but PAYE data (potentially) includes employers’ social contributions e.g. NICs and pension contributions.

• This means the PAYE data could pin down the income measure of GDP and large parts of the household income account in near real time.

• Minimise large revisions from current HMRC benchmark data and ensure congruence between labour market and National Accounts.

• Much earlier stage of development so looking to develop this data source against existing measures of employment and earnings.

• Monthly measures by fine geography and industry

Admin data: stylised precision

• Standard error in sample surveys ≈ 1 / square root of sample size

• So for VAT 2.2m returns compared with 135k survey is a 16-fold increase in sample size and a standard error that is 4 times smaller

• The gains will be much smaller in aggregate because samples are optimised e.g. 500 firms account for roughly half of turnover.

• The loss of precision in surveys is compounded when the sample is subdivided in multiple dimensions e.g. industry and geography - 2 dimensions but 1/4

• The real precision is that the admin data is not so much a sample as a census accounting for 95%+ of the variables in question.

• For many detailed industries and geographies the survey data is effectively unusable whereas the admin data provides all there is.

Measuring services: SERVCOM

• To reconcile Supply and Use across the economy requires not only measures of output/turnover but also products.

• 114 x 114 matrix of industry sales by product supply for Supply tables, and industry intermediate consumption by product demand for Use tables

• Historically economy dominated by industrial production and goods, reflected in detail of surveys for industrial production for EU e.g. PRODCOM

• Economy has changed: services now 80% of GVA of which general government roughly a quarter – with industrial production 10% of GVA

• Key recommendation of Bean review to address measurement of the service sector and disproportionate detail e.g. deflators and quality etc.

• So propose counterpart to PRODCOM of SERVCOM

SERVCOM: Industry and Product

No product factors

available/applied

Production Industries Services Industries

Production Products

Historical product factors

applied to ABS question

“Sales of non-industrial

services”

ITIS product factors and historical source product factors applied to

ABS total industry sales

Services Products

PRODCOM sales data product factors applied to

ABS sales of goods and industrial services

Servcom

Measuring services

• Servcom will cover the whole service sector (excluding banks and government) with a sample of 40,000 covering 274 industries

• Will ask for a breakdown of turnover into the service products from which it is generated, irrespective of classified industry

• Will complement new data from the purchases survey and measures of turnover and intermediate consumption from VAT returns.

• In particular Servcom will enable improved deflation and hence volume measures of output

• In the round new data sources will provide a profound improvement to Supply-Use and hence measurement of GDP more broadly.

• In the future extend coverage to ALLCOM.

Transforming National Accounts

Nick Vaughan

Director and Chief Economic Adviser, ONS

Transforming Economic Statistics

Frankie Kay

Director for Economic Statistics Transformation

ONS Transformation

• Data is central to the decisions which affect our lives

• Data is now available from previously unimaginable sources

• We treat personal data confidentially and make sense of numbers for the public good

Economic Statistics Transformation Drivers

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• Users’ needs are changing• Economy evolving quickly• Opportunities from new data

sources• ONS needs to be more open

▪Bean

▪ Independent Review of UK Economic Statistics 2016

▪Johnson

• UK Consumer Prices Stats – A Review 2015

▪Barker / Ridgeway

• National Statistics Quality Review 2014

▪Best Practice

• European System of Accounts 2010

Economic Statistics and Analysis Strategy

Better measurement of the modern economy -

the digital revolution

Better measurement of services sector activities

Better measurement of Gross Domestic Product

Better measurement of Trade

Better understanding of the productivity puzzle

Better measurement of the Labour market

Better measurement of prices

Exploitation, interrogation and understanding of

administrative data and other large datasets

Better information below whole economy level

Transforming economic statistics – Enhanced Financial Accounts Case Study

The Enhanced Financial Accounts

A collaborative project between ONS and the Bank of England

to improve the quality, coverage and granularity of the UK’s financial

statistics, including whom-to-whom information

• How can we better understand the UK’s financial stability?

• How big is the UK hedge fund sector?

• Who owns government debt? Who is financing the current deficit?

• What type of credit is fuelling the growth in consumer debt?

• What is the size of the UK’s unsecured loans marked?

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UK has large financial balance sheets

30source: OECD

Total asset levels as proportion of nominal GDP, by G7 country

UK whom-to-whom statistics

• UK flow of funds visualisation

• see Economic Statistics Transformation Programme:

UK flow of funds experimental balance sheet

statistics, 1997 to 2015

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What do we want

to do?

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•more sector granularity•which sectors are at risk?•detailed financial sector breakdown•industry and size breakdown

•more instrument detail•how is debt being financed?•type of credit - bank loan, car finance, pay-day loans etc.•currency and maturity of debt

•whom-to-whom statistics•who holds the risk?•identify the unknown counterparties•richer information on the rest of the world

enhance surveys with new data sources: commercial, regulatory, administrative

Non-financial corporations

Public corporationsPrivate corporations Non-commercial real estateSME

LargeCommercial real estate

Financial corporations Monetary Financial Institutions (MFI)

Central Bank Other monetary financial

institutionsOther deposit taking-corporations

Ring-fenced Other UK-owned Foreign-owned Money market funds (MMF) Financial corporations

except MFIs and Insurance corporations and pension funds (ICPFs)

Non-MMF investment funds

Collective investment schemes excl. hedge funds

Institutional Open-ended Leveraged

Unleveraged Closed-ended Leveraged Unleveraged Retail Open-ended Leveraged Unleveraged Closed-ended Leveraged Unleveraged Exchange traded funds Hedge funds Private equity

fundsBuyout

Other Other financial

intermediaries, except MFIs and ICPFs

Financial vehicle corporations engaged in securitisation transactions Security and derivative dealers Financial

corporations engaged in lending

Include a split of type of lending e.g. mortgages, auto, consumer credit, business)

Specialised financial corporations (incl. central counterparties) Financial auxiliaries

Captive financial institutions and money lenders

Insurance companies and pension funds

Insurance companies Life insurance General insurance Pension funds Defined benefit Defined contributionGeneral government Central government

Local governmentHouseholds and NPISH Households

Non-profit institutions serving households (NPISH)

currently published

to be published 2017

enhanced financial accounts proposal

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Timetable for delivery

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data acquisition experimental statistics

National Accounts implementation

2017commercial, regulatory, administrative data

initial working papers and experimental statistics

2018 methods and data recommendations

2019full flow of funds experimental statistics

2020

International Monetary Fund requirements delivered

2021full implementation into Blue Book 2021

For more information

UK Flow of Funds – understanding the UK’s financial flows

ONS Economic Forum special event, in collaboration with the Bank of England

Introduction by Andy Haldane, Chief Economist, Bank of England

Monday 6 March 2017, 9:30-12:30

Threadneedle Street Conference Centre, Bank of England

register on eventbrite

– email: flowoffundsdevelopment@ons.gsi.co.uk

– search: ‘flow of funds’

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Transforming Economic Statistics

Frankie Kay

Director for Economic Statistics Transformation

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