Economic Forum 25 February 2020

ONS Economic Forum
Chief Economist
Office for National Statistics
Chair – Grant Fitzner
#economicforum@ONSfocus25 February 2020

Agenda
09:30 – 10:00 Registration with tea and coffee
10:00 – 10:05 Welcome and introduction – Grant Fitzner, Chief Economist (ONS)
10:05 – 10:20 Measuring household disposable income: Using tax data to improve the measurement of top incomes
– Richard Tonkin (ONS)
10:20 – 10:35 Child poverty and education outcomes by ethnicity – Rebecca Williams/Nadyne Dunkley (Race
Disparity Unit)
10:35 – 10:50 Question and answer session – Grant Fitzner, Chief Economist (ONS)
10:50 – 11:00 What’s next for UK economic statistics? – Grant Fitzner, Chief Economist (ONS)
11:00 – 11:20 Refreshment break
11:20 – 11:35 ONS’ Centre for Equalities and Inclusion – Dawn Snape (ONS)
11:35 – 11:50 Analysing regional economic and wellbeing trends – Amina Syed/Ben O’Sullivan (ONS)
11:50 – 12:00 Question and answer session – Ed Palmer, Deputy Chief Economist (ONS)
12:00 Closing remarks – Ed Palmer, Deputy Chief Economist (ONS)
#economicforum@ONSfocus

Welcome and introduction
Chief Economist
25 February 2020
Grant Fitzner

Measuring household
disposable income:
Using tax data to improve the
measurement of top incomes
Public Policy Analysis Directorate
@richt2
Martin Shine, Dominic Webber, Richard Tonkin & Ozer Beha
25 February 2020 #economicforum@ONSfocus

The need for a top income adjustment
Background

ONS Household income statistics
Effects of Taxes and Benefits (ETB)
• Produced since 1961 - shows impact of taxes (direct & indirect)
and benefits (cash & in-kind) on income inequality
• Currently based on the Living Costs and Food Survey
Other main UK source of household income statistics:
• Households Below Average Income (HBAI) produced by DWP
& based on the Family Resources Survey

ETB shows how UK income inequality
has changed since 1970s
Source: Household Income Inequality,
2017/18, ONS
20
25
30
35
40
45
50
55
Gini coefficients for original, gross and disposable income, 1997-2017/18, UK
Original Gross Disposable
%

Issues measuring incomes of top
earners
• Well-recognised that surveys do not fully capture
incomes of very richest – particularly the ‘1%’
• Several potential reasons:
• Non-response – high income individuals don’t respond to
survey at all, or don’t report all their income sources
• Under-reporting – levels of income received intentionally
or unintentionally underreported
• Sparseness – very few households with high incomes in
data

Source: Office for National Statistics
0.8
1.0
1.2
1.4
1.6
1.8
2.0
90 90.5 91 91.5 92 92.5 93 93.5 94 94.5 95 95.5 96 96.5 97 97.5 98 98.5 99 99.5
Quantile
2013/14 2014/15 2015/16
Ratio of
gross
income
in tax
data to
survey
data
Those with highest incomes under-report their
income in surveys

The need for an adjustment
• Use of surveys provides several important benefits
compared to use of admin data alone.
• Adjustment to survey data needed to:
• Address potential under-estimation of inequality and
measures of topic incomes due to nonresponse and
under-reporting
• Reduce volatility in inequality estimates due to
sparseness of data
• Research undertaken jointly with DWP

Criteria for selecting approach
1) methodologically sound, based on academic standards
2) transparent and understandable by users
3) combines admin & survey data
4) adjustment made on underlying microdata rather than
aggregates
Decision to develop new methods building on Burkhauser et al. (2018)
& DWP use of HMRC SPI data
• SPI (Survey of Personal Incomes) is an administrative dataset
containing sample of individuals potentially liable to UK tax

How it works – part 1
1) Rank ETB and SPI data by gross income
2) Decide a threshold, and size of quantile groups above
threshold, e.g. 98% threshold, 1% quantile groups
3) Calculate
income
boundaries for
quantile
groups in SPI
4) Create bands in
ETB using these
boundaries
Effects of taxes and benefits data
Survey of personal income data
10th decile9th decile
86th 87th 88th 89th 90th80th 81st 82nd 83rd 84th 85th 96th 97th 98th 99th91st 92nd 93rd 94th 95th
9th decile 10th decile
80th 81st 82nd 83rd 84th 85th 86th 87th 94th 95th 96th 97th 98th 99th88th 89th 90th 91st 92nd 93rd
1 2
80th 81st 82nd 83rd 84th 85th 86th 87th 94th 95th 96th 97th 98th 99th88th 89th 90th 91st 92nd 93rd87th 88th 89th 90th 91st81st 82nd 83rd 84th 85th 86th 97th 98th 99th 100th92nd 93rd 94th 95th 96th
87th 88th 89th 90th 91st81st 82nd 83rd 84th 85th 86th 97th 98th 99th 100th92nd 93rd 94th 95th 96th

6) Reweight
ETB bands,
so that their
weights are
the same as
the SPI
quantiles
8) Recalculate individuals’ taxes based on new income & aggregate
to household level
How it works –part 2
7) Reweight rest of dataset to population totals
80th 81st 82nd 83rd 84th 85th 86th 87th 94th 95th 96th 97th 98th 99th88th 89th 90th 91st 92nd 93rd87th 88th 89th 90th 91st81st 82nd 83rd 84th 85th 86th 97th 98th 99th 100th92nd 93rd 94th 95th 96th
1 2
1 2
1 2
5) Impute the mean average for each SPI quantile group onto individuals in
equivalent survey bands

Questions addressed by research
1. Which variant of the SPI adjustment methodology should be
chosen?
2. Should the richest retired and non-retired people be adjusted
separately?
3. How low should the threshold be?
• Thresholds from 95% to 99% tested
4. How granular should the adjustment be?
• 1%, 0.5% & 0.25% quantile bands tested
5. Should estimates be revised once final SPI data is available?
• Final SPI data released around 2 years after reference period
therefore need to use projections

The impact of applying top income adjustment to ONS’
data
Effect on income
estimates

Adjustment leads to large increase in average
income of richest 10%
£70,000
£80,000
£90,000
£100,000
£110,000
£120,000
Unadjusted Adjusted

Adjusted data also show higher levels of
income inequality
28%
30%
32%
34%
36%
38%Ginicoefficient
Unadjusted Adjusted

Adjusting allows new analysis – income share of
top 1%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%

What’s next?
• Latest Household Income Inequality / Average Household Income
releases out 5th March
• Updating provisional estimates for 2018/19 published July 2019
• Top income adjustment will be included in headline figures
• Research underway to extend SPI adjusted time series to cover
1977 – 2001/02
• Initial research outputs on linked survey/admin data planned for late
2020

Child poverty and education
outcomes by ethnicity
Nadyne Dunkley and Rebecca Williams
Race Disparity Unit ethnicity-facts-figures.service.gov.uk

How we categorise ethnicity
White
English/Welsh/Scottish/Northern Irish/British
Irish
Gypsy or Irish Traveller
Any other White background
Mixed/Multiple ethnic groups
White and Black Caribbean
White and Black African
White and Asian
Any other Mixed/Multiple ethnic background
Asian/Asian British
Indian
Pakistani
Bangladeshi
Chinese
Any other Asian background
Black/African/Caribbean/Black British
African
Caribbean
Any other Black/African/Caribbean background
Other ethnic group
Arab
Any other ethnic group

Child poverty
Education
Child poverty and education

Race Disparity Unit
Low income
£22,200 or below for a couple with two
children
47% of children in Pakistani households
and 41% of children in Bangladeshi
households lived in low income
White British and Indian children were
the least likely to live in low income
households out of all ethnic groups, at
17%
ethnicity-facts-figures.service.gov.uk
Figure 1. Percentage of children living in households in low income, by ethnicity
(UK, three-year average, financial year ending 2016 to financial year ending 2018)
Households Below Average Income: 2017/18, DWP

Race Disparity Unit
Low income
over time
Children in Pakistani and Bangladeshi
households were the most likely to live in low
income across the two time periods
The largest decrease was among children
living in Indian households; from 23% to 17%
The largest increase was among children
living in households from the Other White
ethnic group; from 16% to 23%
Figure 2. Percentage of children living in households in low income, by ethnicity over
time (UK, three-year averages, financial year ending 2012 to financial year ending 2018)

Race Disparity Unit
Persistent low income
Figure 3. Percentage of children living in households in persistent low
income by ethnicity (2013 to 2017, UK)
27% of children in Asian households
lived in persistent low income
Children in Mixed ethnicity households
were least likely to live in persistent
low income
Income Dynamics: 2013 to 2017, DWP

Race Disparity Unit
Low income and material deprivation
29% of children in Bangladeshi
households lived in low income and
material deprivation
Indian households were the least
likely to be in material deprivation and
low income (5%) out of all ethnic
groups
Figure 4. Percentage of children living in households in low income and material
deprivation (UK, three-year average, financial year ending 2016 to financial year
ending 2018)

Race Disparity Unit
Progress 8
Progress 8 measures how well pupils progress at secondary school, by
comparing students’ KS4 (GCSE) results to the national average KS4 results
of students who had similar prior attainment at KS2

Race Disparity Unit
Progress 8 score
Figure 5. Average Progress 8 score, by ethnicity (England, academic year
ending July 2019)
Key stage 4 performance 2019 (revised), DfE
Chinese and Indian pupils achieved
the highest Progress 8 scores, at
0.86 and 0.71 respectively
Traveller of Irish Heritage and
Gypsy/Roma pupils made the least
progress, achieving scores of -1.05
and -0.81 respectively

Race Disparity Unit
Eligibility for free school meals
(FSM)
The Chinese and Indian ethnic groups had the lowest percentages of
students who were eligible for FSM, at 7%
The highest percentages of FSM-eligibility were seen in Traveller of Irish
Heritage pupils (56%) and Gypsy/Roma pupils (39%)
26% of Bangladeshi and 20% of Pakistani pupils were eligible for FSM

Race Disparity Unit
Progress 8 score and
eligibility for FSM
Figure 6. Average Progress 8 score, by ethnicity and eligibility for
free school meals (England, academic year ending July 2019)
Key stage 4 performance 2019 (revised), DfE
Pupils eligible for FSM made less progress (-0.53)
than those not eligible (0.06)
Among FSM-eligible pupils:
● those from the Chinese ethnic group made
the most progress (0.66)
● Bangladeshi and Pakistani pupils progressed
higher than the average
● White minority groups made the least
progress
● scores varied within the Black ethnic group

Race Disparity Unit
Progress 8 score
and geography
Suggested
North/Midlands/South variation
when looking at average
Progress 8 and poverty scores
separately
Moderate negative correlation
(-0.55) between IDACI and
average Progress 8 scores for
White pupils
Figure 7. Average Income Deprivation Affecting Children Index
(IDACI) score, by local authority (England, 2019)
IMD 2019

Race Disparity Unit
Conclusion
Certain ethnic groups stand out for their prevalence of poverty, across multiple
measures of poverty
The relationship between child poverty and the progress pupils makes between
KS2 and KS4 is not straightforward and it varies by ethnic group
Child poverty appears to impact the progress a child makes but it does not
necessarily prevent progress or, in fact, pupils making more progress than the
national average

Race Disparity Unit
Find out more and get involved:
https://www.ethnicity-facts-figures.service.gov.uk/
ethnicity@cabinetoffice.gov.uk
#racedisparityaudit
Sign-up to our monthly Ethnicity Facts and Figures newsletter

Q & A session
© Photo by Vicky Gu on Unsplash
Grant Fitzner
Chief Economist, Office for National Statistics

Grant Fitzner
Chief Economist and Director of Macroeconomic Statistics and Analysis
Office for National Statistics @grantfitzner
What’s next for UK
economic statistics?

New developments in
economic statistics

Improving our National Accounts
What we’re doing
• Chain linking of business price statistics
• Experimental double deflated estimates
• Improvements to measurement of financial services
Things to look out for
• Summer 2020 – Methods and Impact articles
• September 2020 – Blue & Pink Book consistent quarterly releases
• October 2020 – Blue and Pink Book annual publications

Transforming UK consumer price statistics
What we’re doing
Improving the coverage and accuracy of consumer price statistics
using new alternative data sources from retailers’ tills and websites,
researching new methods and developing new production systems
• 2020/2021: Bi-annual research publications and user events
• 2022: Quarterly experimental release of transformed statistics
• 2023: New data and methods included in our headline measures

Transforming trade statistics
What we’re doing
Improving trade data, statistics and analysis to meet the challenges
of a new era in UK trading relationships
• March 2020 – Services Trade by Business Characteristics [first publication]
• April 2020 – Publish roadmap for specific improvements to trade in services
• Summer 2020 – Goods Trade by Business Characteristics [first publication]
• October 2020 – Introduce better data sources for Transport services
• March 2021 – Better sources for other service accounts (incl. country breakdowns)
• March 2021 – Investigate and identify improvements to subnational trade data

Foreign Direct Investment
What we’re doing
Redeveloping FDI sources to provide users with detailed insight of
investment within the UK and ability to make informed policy decisions
• March 2020 – Improved FDI sample via Bureau Van Dijk Orbis data
• Throughout 2020 – Experimental insights using BvD & survey data
(e.g. regional FDI, alternative measures of productivity, network FDI)
• March 2021 – Transformed FDI quarterly survey using electronic
data capture and providing more detail (e.g. type of FDI)

Financial accounts and corporations
What we’re doing
• Re-initiating Enhanced Financial Accounts (EFA) project; high-
level planning to 2025, iterative implementation into Blue Books
• New data sources being introduced as they become available
• March 2020 – historical data for households and NPISH sectors
• April 2020 (tbc) – EFA external stakeholder engagement event
• September 2020 – estimates from new Financial Survey of Pension
Funds incorporated in National Accounts

Retail Prices Index
consultation

RPI Consultation
• The UK Statistics Authority are running a joint consultation with HMT
about addressing the shortcomings of the Retail Prices Index
• UKSA is consulting on how to make the proposed methodological
changes to the RPI in a way that follows best statistical practice
• HMT is consulting on the appropriate timing for the proposed
changes to the RPI to take place
• Consultation dates: 11 March to 22 April. Further information on the
consultation will be published on the UKSA website
• RPI will be discussed at our next Economic Forum (16 April) and
Regional Economic Forums in Manchester, Birmingham, and Belfast
RPI Consultation

Refreshment break

Dawn Snape
Assistant Director | Sustainability and Inequalities Division
The Centre for Equalities
and Inclusion

A multi-disciplinary convening centre bringing
together people from across the equalities world, to
understand equalities in the UK and inform action.
The Centre

Our objectives
• Increase impact on public debate and policy using a
range of resources tailored for different audiences
• Improve engagement with audiences
• Improve the coherence and accessibility of
equalities data and analysis
• Develop a strong network of equalities stakeholders

Our network
Other
ONS
teams
Centre for
Equalities &
Inclusion
Other
Government
Departments
Civil
society
Academia
International
organisations
Equalities
bodies

Our work
Data
• Increasing data
accessibility
• Making better
use of existing
data
• Developing new
data sources to
address gaps
Analysis
• Relevant and
timely
publications
• Dissemination of
existing analysis
• New and
insightful
analysis
Methods
• Working with
others to
develop new
methods
• Disseminating
best practice
• Scoping,
feasibility and
piloting work

Making the invisible,
visible: recent findings

Deaths of homeless people
• New experimental
estimates based on
death registration
records
• 2018 had the highest
year to year increase
(22%) since the time
series began
• Most of the deaths
were among men
(88% of the total)
There were more than seven times as
many male deaths compared with
females in 2018
Source: ONS, Death registrations

Child abuse
• Experimental statistics
collating data from
multiple sources
published for the first
time in January 2020
• Adults with a disability
were significantly more
likely to have
experienced abuse
before age 16 than those
without a disability (32%
vs 19%)
Adults with a disability were around
twice as likely to have experienced any
type of abuse before the age of 16
years

Suicide
• Research shows
poverty and financial
insecurity can
increase suicide risk.
• For some age groups,
suicide rates were
double or more in the
most deprived
neighbourhoods
compared to the most
affluent.
There were more suicides among the most
deprived communities in most age groups

Children and young people’s
experiences of loneliness
• Based on survey results
and in-depth interviews
• Transitioning through
different life stages is
linked to loneliness
• 11.3% of 10-15 year-olds
and 9.8% of 16-24 year-
olds reported “often”
feeling lonely
Reported frequency of loneliness by
free school meal receipt, GB
Source: Good Childhood Index Survey, Children’s Society

Forthcoming
• Religion and equalities in England & Wales
• Exploring the potential of new data sources, e.g. linked
data for children
• Data navigator tool making equalities data easier to
find
• Approaches to measuring social exclusion (UNECE)
• Regular newsletter with a round-up of equalities
findings from ONS and partners

25th February 2020
Analysing regional
economic and
wellbeing trends
Economic Advisor | Placement Student
Dr Amina Syed | Ben O’Sullivan
#econstats@ONSfocus

Contents
1. Macroeconomic variables:
 Productivity (output per hour)
 Wages (median hourly earnings from ASHE)
 Human capital
 Household costs
 Household income (before and after housing costs)
2. Economic and personal wellbeing indicators
 Wealth inequality
 Life satisfaction
 Feeling worthwhile
 Happiness
 Anxiety

London v other NUTS1
regions of the UK
Main Points

London v other NUTS1
regions of the UK
Main Points
• London outperforms other regions and
countries of the UK in macroeconomic
indicators, such as productivity, wages,
human capital.
• The opposite is seen for the socio-
economic variables, such as life
satisfaction, feeling worthwhile,
happiness, and anxiety.
• There is little indication of the other
regions catching up.

Macroeconomic Variables
Estimates of productivity (output per hour)
Average (median) earnings
An indicator of human capital
Household costs

• Highest in London and the South East
• Lowest in Wales and Northern Ireland
Output per Hour and Median Hourly Earnings
↑ labour
productivity
↑ earnings

Productivity and earnings are the highest in London and the South East, outstripping all other NUTS1 regions
80 90 100 110 120 130 140
Wales
Yorkshire & Humberside
Northern Ireland
East Midlands
North East
West Midlands
South West
North West
East
Scotland
South East
London
Median Hourly Earnings, Indexed UK=100 Output per hour (All Industries), Indexed UK=100

The gap between the regions for productivity has stayed steady over time

The gap between the regions for wages has stayed steady over time

Human Capital
• London showed the highest and Northern Ireland the
lowest human capital for all years measured.
• Possible reasons:
• London’s specialisation in finance and technology
• Mobility of skilled labour
• Age

London has the highest real human capital per head
60 70 80 90 100 110 120 130 140
Northern Ireland
North East
Wales
East Midlands
South West
North West
Scotland
West Midlands
East
South East
London
2018 2004

London has the greatest stock of real human capital per head for degree holders
£0 £200,000 £400,000 £600,000 £800,000 £1,000,000
Wales
North East
Northern Ireland
South West
Scotland
North West
East Midlands
West Midlands
East
South East
London
2018 2004

Housing Costs
London residents spend a higher proportion of their household income on housing costs
0 5 10 15 20 25 30 35 40 45 50
Northern Ireland
Scotland
North East
Wales
Yorkshire & the Humber
East Midlands
North West
West Midlands
East of England
South West
South East
London
3 year average 2014/15-2016/17
%
Source: Department for Work and Pensions

Household income
• Before housing costs: London has
the highest median weekly
household income.
• After housing costs: London is only
slightly above the UK average
household income, with South East,
East, Scotland, and South West
performing much better.
• Northern Ireland, Yorkshire and the
Humber, North West, West
Midlands, Wales, and North East
remain below the UK average even
after taking housing costs into
consideration.
• The difference between household
income before and after housing
costs has worsened for London
between the two periods.
Source: Department for Work and Pensions

Economic and Personal
well-being indicators

Wealth Inequality
• Least unequal region: South East
Gini Coefficient Aggregate Total Wealth
South east Lowest Highest
North East 2nd Highest Lowest
London Highest 2nd highest

Gini coefficients for total wealth, NUTS 1 regions, July 2006 to June 2008, and April 2016 to March 2018
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
South East
East of England
South West
Wales
West Midlands
East Midlands
North West
Scotland
Yorks and the Humber
North East
London
April 2016 to March 2018 July 2006 to June 2008

Wellbeing
Indicators:
• Life satisfaction
• Worthwhile
• Happiness
• Anxiety

• Performance of all indicators has improved between 2012 and
2019
• London underperforms on all the chosen personal well-being
indicators
• Northern Ireland and the South West ranked highest for
satisfaction, happiness and worthwhileness.
Wellbeing

Conclusion
• London and the South East outperform other UK regions and countries in
macroeconomic indicators such as:
 Productivity,
 Human capital, and
 Wages.
• There is little indication of the other regions catching up.
• However, once housing costs are considered London is only slightly above the
UK average level for household income, while South East has the highest.

Conclusion
• London tends to underperform on socio-economic variables such as:
 Cost of living,
 Wealth inequality, and
 Personal wellbeing indicators
• South East is the least unequal region of UK in terms of within region wealth
inequality using the Gini coefficient.
• South West and Northern Ireland rank the highest for personal wellbeing
indicators such as life satisfaction, feeling worthwhile and happiness.

Q & A session
Ed Palmer
Deputy Chief Economist, Office for National Statistics

Closing remarks
Deputy Chief Economist
Ed Palmer

ONS Events
ONS Economic Forum, on the Road:
24 March 2020, Manchester
27 March 2020, Birmingham
Economic Statistics Working Group (ESWG) Gender Pay Gap Seminar
30 March 2020, London School of Economics (LSE), London
Getting Under the surface of Business Statistics
31 March, County Hall, London
ONS Economic Forum
16 April 2020, Kings College London
Further details on all the above events can be found at: ons.gov.uk/economicevents

The Economic Statistics Centre of Excellence (ESCoE) will hold its annual conference, organised in
partnership with the UK Office for National Statistics (ONS), at King’s Business School, King’s College
London, 20 – 22 May 2020.
Keynote speakers include:
Anil Arora (Statistics Canada)
John Van Reenen (London School of Economics)
Anna Vignoles (University of Cambridge)
The conference is a meeting for researchers working in academia and in national and international
institutions to discuss recent research advances in economic statistics.
Registration opens 13 March 2020. Details will become available closer to the time.
ESCoE Conference on Economic Measurement 2020

Economic Forum 25 February 2020

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