1. Transforming household finance statistics in the UK
Matthew Minifie
Research Officer, Household Income and Expenditure Analysis

2. Presentation overview
Current state of UK household finance statistics (micro-level datasets)
Harmonised ‘income core’ on surveys to be used for income statistical outputs
Lengthen longitudinal EU-SILC from 4 to 6 waves
Use of administrative data
Online surveys

3. Household finance statistics – current state of affairs (1/4)
UK has a number of social surveys collecting data on household finances:
Family Resources Survey (FRS)
Labour Force Survey (LFS)
Living Costs and Food Survey (LCF)
Survey on Living Conditions (SLC)
Wealth and Assets Survey (WAS)

4. Household finance statistics – current state of affairs (2/4)
Current uses:
SLC
Until 2016, follow-up survey to the Family Resources Survey (FRS) and used exclusively for producing longitudinal EU-SILC
From 2017, used to provide both cross-sectional and longitudinal EU-SILC
FRS
Until 2016, used to produce cross-sectional EU-SILC
Households Below Average Income (HBAI)
LCF
Household Disposable Income and Inequality (HDII) etins/householddisposableincomeandinequality/financialyearending2016
Effects of Taxes and Benefits (ETB) etins/theeffectsoftaxesandbenefitsonhouseholdincome/financialyearending2016
Nowcasting household income
WAS
Estimates of wealth and wealth inequality; Exposure to debt; attitudes to saving / debt; monitoring pensions up-take

5. Household finance statistics – current state of affairs (3/4)
Living Costs & Food Survey (LCF)
Survey on Living Conditions (SLC)
Wealth and Assets Survey (WAS)
Unit: Survey sample of private households – representative of UK (WAS GB)
Mode: Face-to-face Computer Assisted Personal Interviewing
Sample: Stratified 2 stage sampling off PAF:
postcode sectors selected as primary sampling unit (PSU) - clusters
Addresses within sectors/clusters selected as secondary unit
Content: Income / tax (employment, property, investments, benefits, pensions)
Housing (accommodation, tenure, mortgages, costs (except WAS))
Economic status, occupation, industry, hours worked
Basic demographics, education, health
Pension contributions
Exclusive:
Detailed expenditure
Rotating module;
Longitudinal 6 waves (annual)
Detailed wealth & debt, financial planning;
Longitudinal (biennial)

6. Household finance statistics – current state of affairs (4/4)
HBAI
Surveys
Outputs
LCF
SLC
FRS
Nowcasting, HDII, ETB
Longitudinal EU-SILC
X-SEC EU-SILC
Wealth in GB
WAS
Summary:
Lack of coherence: large number of sources and outputs so confusing for users
Lack of granularity: multiple surveys with varying sample sizes so currently unable to produce reliable estimates at lower levels of geography
Inefficient:
duplication of effort in data processing due to multiple sources and systems
reliance on expensive face-to-face surveys with falling response rates

7. The future – a new Household Finance Statistics (HFS) datatset
Surveys
Outputs
Benefits
Admin data Co r e
Expenditure
Income distribution / inequalities
Fully coherent income statistics
Increased quality income statistics at lower levels of geography (through larger sample/admin data)
Small core – options for electronic data collection
More efficient collection of data enabling improved timeliness
Capable of meeting wide range of user requirements
Wealth
Low income / poverty (inc. EU-SILC)
Material deprivation & EU SILC modules
Income, consumption & wealth
Micro-simulation models
Other policy needs

8. Initial developments – survey data (1/7)
Initial work has focussed on ‘combining’ the Living Costs and Food Survey (LCF) and the Survey on Living Conditions (SLC) to create a larger Household Finance Statistics (HFS) dataset
Common sampling strategy
Harmonised core of questions on household income
Creation of different income statistical outputs from the combined HFS using common conceptions, definitions and derivation of household income

9. Initial developments – survey data (2/7)
Integrated / joint sample
Questionnaire harmonisation on key economic activity, income and living conditions questions
Working towards single outputs from LCF/SLC combined datasets
Household Finance Survey:
Joint / single outputs for household finance statistics
Large sample for precise UK and Regional statistics
Common methods
Living Costs and Food Survey
Survey on Living Conditions
Exploring joint sampling with LCF/SLC
Questionnaire change proposals to harmonise with LCF/SLC concepts
Wealth and Assets Survey
Exploring improvements to sampling by using AddressBase, Council Tax data
Exploring improvements to estimation by using combined samples, LFS and admin sources

10. Initial developments – survey data (3/7)
Sampling
Common sample adopts a two stage probability sampling design with stratification by NUTS2 representing major strata
Postcode sectors are the primary sampling units (PSUs) and sorted by various Census factors
PSUs selected jointly within each major stratum using systematic random sampling
Representative sample by region and month for combined sample as well as each component survey
Sample balanced monthly so can produce consistent rolling annual statistics (e.g., financial year, calendar year)

11. Initial developments – survey data (4/7)
Questionnaire harmonisation
Questions on income, material deprivation and work intensity harmonised across surveys
Question wording on other household income surveys taken into account
Variables on relevant administrative data sources taken into account

12. Initial developments – survey data (5/7)
Editing and imputation
At present markedly different editing and imputation strategies are implemented on data collected from the Living Costs and Food Survey (LCF) and the Survey on Living Conditions (SLC)
Currently work is in progress to implement a harmonised approach to editing and imputation that is the most appropriate for producing household income statistics

13. Initial developments – survey data (6/7)
Production of 2017 EU-SILC and UK Household Disposable Income and Inequality (HDII)
LCF
SLC W1
SLC W2
SLC W3
SLC W4
SLC W5
SLC W6 W6
Longitudinal EU-SILC W5 W5 W4 W4 W4
HDII and
cross-sectional
EU-SILC W3 W3 W3 W3 W2 W2 W2 W2 W2 W1 W1 W1 W1 W1 W1
Year
Y-5
Year
Y-4
Year
Y-3
Year
Y-2
Year
Y-1
Year Y
Year Y

14. Initial developments – survey data (7/7)
Production of 2017 EU-SILC and UK Household Disposable Income and Inequality (HDII)
Achieved sample size expected to be ~17,000 households annually
Can meet new NUTS2 precision requirements
Integrated cross-sectional and longitudinal EU-SILC
12 months collection to reduce seasonal effects
Increased alignment and coherence of national and EU-SILC household income statistics

15. Future developments – survey data
Production of EU-SILC and UK Household Disposable Income and Inequality (HDII)
Future plans include the inclusion of the Wealth and Assets Survey (WAS) data into the HFS dataset
This should increase the achieved sample size by ~10,000 households annually
WAS is a longitudinal study and so will increase numbers of households available for longitudinal analysis
WAS also provides an insight into the link between household income and wealth

16. Lengthening of EU-SILC to 6 waves
At present in the UK the longitudinal duration of EU- SILC is 4 waves
In 2017 a 5th wave was added to the Survey on Living Conditions (SLC)
6th wave will be included for the first time in the SLC in 2018
Increases the sample size available for deriving estimates of persistent poverty
Provides a longer time period for which to analyse transitions in household income and living conditions and the causes of these transitions

17. Use of administrative data (1/4)
The UK is committed to making use of administrative data in the production of household income statistics
Administrative data could be utilised to improve sampling, monitor quality and shorten questionnaires by replacing questions and providing variables for direct use in the production of statistical outputs
Initial work has focused on exploring the feasibility and potential for using administrative data in the Household Finance Statistics (HFS) dataset

18. Use of administrative data (2/4)
Using administrative data has the potential to provide a a number of advantages:
Reduce burden on respondents
Reduce undercoverage and under-reporting issues
Reduce time spent on editing and imputation
Improve accuracy and quality of statistical outputs
Reduce bias and improve precision
Reduce collection and processing costs
Improve coherence of government statistics as the same sources of data will be used

19. Use of administrative data (3/4)
Number of potential sources:
RTI
Real time information
Owned by Her Majesty’s Revenue and Customs (HMRC)
Contains information on employee pay and pension payments
Timely – uploaded 2 days after submission of information
Big data - ~ 65 million submissions per month SA
Self Assessment
Contains information on self-employed, foreign and investment income including high income earners (£100,000+ pa)
Time lag up to 10 months between income reference period and submission deadline
NBD
Owned by Department for Work and Pensions (DWP)
Contains information on a number of benefits including Job Seekers’ Allowance, Disability Living Allowance, State Pension)
Information at individual benefit claim level (including information on claimant, other household members, component of claim

20. Use of administrative data (4/4)
Summary:
Huge potential in using administrative data
Number of sources which could be of value to the Household Finance Statistics dataset
New UK legislation, Digital Economy Act, should act as enabler
A number of issues still have to be overcome

21. Online capability
ONS currently testing electronic data collection on a number of its surveys
Planned testing of online data collection for the Survey on Living Conditions (SLC) in 2019
There is ongoing research into utilising a “digital diary” for the expenditure element of the Living Costs and Food Survey (LCF)
Ongoing engagement with research community on potential and impact of online data collection

22. Summary (1/2) Desire for change and transformation
Aim to have a common core to different social surveys with harmonised collection of household income data whilst the component surveys collect information on different topics (e.g. wealth and consumption)
Integrated sampling design for all survey components feeding into the Household Financial Statistics (HFS) dataset

23. Summary (2/2)
HFS used to produce income statistical outputs including EU-SILC
Large sample size of HFS in order to meet national and regional precision requirements
Increased duration of longitudinal element of HFS
Aim to use administrative data in the near future to improve sampling design and question replacement
Testing the potential for online surveys relating to household finances and living conditions

24. Any questions?