ONS presentation at RSS South Wales poverty & inequality stats event
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The document provides an update on poverty statistics and research initiatives in Wales, including details on the transformation of data collection methods by ONS to enhance accuracy and reduce reliance on surveys. It outlines current and future plans for household finance surveys, aiming for annual estimates of poverty indicators and improved data integration using administrative sources. The development efforts focus on maximizing online data collection and utilizing diverse data sets to satisfy user needs while minimizing costs and improving response rates.
ONS presentation at RSS South Wales poverty & inequality stats event
- 1. Update on ONS data for poverty statistics and research Poverty & Inequality in Wales: Statistics for Action 28th Sept 2016 Richard Tonkin [email protected] @richt2
- 2. Aims • To update on some of the latest ONS poverty-related data and analysis developments • To provide information on the ONS Data Collection Transformation Programme • To outline plans for ONS’s household finance surveys and statistics
- 3. Relative income poverty in Wales 0 5 10 15 20 25 % of individuals in households with equivalised income (BHC) less than 60% of median Wales GB/UK Source: DWP Households Below Average Income, 1994/95 - 2014/15
- 4. Small area estimation • Model based estimates produced using a combination of: • survey data for indicators of interest • Area-specific auxiliary data (admin data and/or Census) • At risk of poverty or social exclusion (AROPE) • Europe 2020 headline target • 3 components: i. relative low income (equivalised disposable income below 60% of median) ii. severe material deprivation (enforced lack of 4+ out of 9 items) iii. low work intensity (adults in household worked less than 20% of potential in previous year) • Research to produce estimates at NUTS 2 level using SAE techniques • Survey source: EU Statistics on Income & Living Conditions (EU-SILC) • Auxiliary data: 2011 Census, data on receipt of benefits (DWP) and energy consumption (DECC/BEIS)
- 5. At risk of poverty or social exclusion 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% At Risk of Poverty or Social Exclusion rate, UK NUTS 2 regions, 2013 • Development ongoing • Intention to make NUTS 2 level estimates of AROPE and 3 component indicators available annually from 2017 onwards Source: ONS
- 6. MSOA level small area poverty estimates • % of households with equivalised income below 60% of median (AHC) • Estimates at MSOA level • Experimental Statistics • Most recent data currently available 2007/08 (England & Wales) • However… • 2011/12 estimates to be published 16 December 2016 • 2013/14 estimates planned for Spring 2017 • 2015/16 estimates planned for Autumn 2017 • Mailing list & further info: [email protected]
- 7. Small area poverty estimates Source: ONS Small Area Poverty Estimates, 2007/08
- 8. Census Transformation Programme: Admin data research outputs on income • Estimates of income distribution at local authority level for England & Wales • Published 16 December 2016 • Produced solely from administrative data sources • Using PAYE data from HMRC and benefits data from DWP • Outputs of ongoing research – not Official Statistics • Incomplete measure of gross annual income(i.e. before direct taxes) for individuals • Limited or no data on certain income components e.g. self- employment, property/investment income • Looking for feedback from users to inform development
- 9. ONS Data Collection Transformation Programme
- 10. Context – Environment for Transformation • Wealth of information held by govt departments and other public bodies • Legislation in place to secure access to (some) data (although limitations) • Commercial and Big Data Sources – new opportunities? Data Sources • Digital Age – ‘digital by default’ • New technology methods, systems, capabilities and continuous developments • Risks with existing systems and methods Technology • more mobile, diverse population • Lifestyle changes • Less willing to engage with government ? Less willing to participate in surveys • Expectation of digital methods but security, privacy, concerns Societal Changes
- 11. Goals - a future social statistics system will…… • Exploit the potential of non-survey data sources Wherever possible, replace survey collection with non-survey sources Use data from non-survey sources to improve survey design (e.g. precision, covariates) Use non-survey data to enhance and extend outputs (e.g. data on supplementary topics or in development of model-based estimates) • Maximise the take-up of online collection A redesigned survey portfolio taking into account availability of non-survey data and online capability Implement online self-completion as the default mode of collection, where appropriate, within mixed mode operation Implement a ‘new’ organisational structure and field collection model to deliver value for money in supporting the future approach • Implement systems to support the future statistical system Redevelop IT systems under a service oriented architecture approach exploiting opportunities for re-use of Census Transformation Systems wherever possible Ensure that the business has available the capability, skills and tools to implement the future statistical system
- 12. Vision for Future social statistics system Administrative sources DATA COLLECTION Commercial Sources / Big Data User / output needs Survey Sources Integrated social data sources Statistical Methods Social statistics + Future Census? Social statistics outputs Registers
- 13. Plans for ONS’s household finance surveys and statistics
- 14. Current uses and outputs Surveys Uses / Outputs LCF SLC •Effect of Taxes and Benefits (ETB), HDII, Nowcasting •Input into IGOTM •Household consumption data for National Accounts •Informs “basket of good” and weights for inflation indices •Estimates of food consumption and nutrient intake •EU Household Budget Survey •Longitudinal EU-SILC (FRS provides X-sectional data) •Estimates of persistent at risk of poverty •Analysis of transitions in and out of employment / poverty •Estimates of wealth and wealth inequality •Monitoring pensions up-take •Exposure to debt WAS
- 15. Current survey designs LCF SLC WAS Computer Assisted Personal Interviewing Postcode sectors / address selected from PAF (private HHs) Clustered by postcode sector Stratified by Region Implicit stratification by Census indicators (which differ across surveys) 5K HHs achieved (annual UK) 7K HHs achieved (annual – all waves UK) 20K HHs achieved each wave (10K annual) High – GB Over samples wealthy Cross-sectional Longitudinal (follow up to FRS subsample) 4 yearly rotational design - Individuals followed Longitudinal: panel survey with annual boosts - Individuals followed 2 week diary for expenditure CATI (telephone) Keep in Touch Exercise (KITE) between waves CATI KITE between waves
- 16. Current survey content – topic level SLC LCF WAS • Basic demographics and education • Tenure and accommodation, Mortgages • Economic status, occupation, industry, hours worked • Employment income • Benefits and tax credits (receipt and amounts) • Pensions • Income from property rental / pensions / assets • Health • Childcare • Material Deprivation • EUSILC secondary modules • Detailed expenditure data • Wealth – financial and physical assets • financial planning • Savings and debt • Value of pensions
- 17. The drivers for change – Household Financial Surveys Coherence: Responding to UK Statistics Authority monitoring review Informing policy: Meeting needs of UK policy makers & IESS regulation Transformation: Delivering ONS Data Collection Transformation programme Efficiency: Minimising cost & burden of statistical production • Large number of sources & outputs – difficult for users to know where to look • Outputs largely based on sources rather than themes • Range of surveys makes responding quickly to changing policy needs more difficult • Difficulties in meeting user requirements on timeliness and regional data • 4-year longitudinal dataset considered insufficient • Data collection relies on expensive face- to-face surveys with diminishing response rates •Survey based estimates prevent effective examination of top/bottom of distribution •Duplication of effort in data processing due to multiple sources & systems
- 18. Where we want to be Core (including labour, income, housing, material deprivation, work intensity etc) Expenditure Admindata Wealth Other user needs (e.g. EU-SILC modules) Dataavailablelongitudinally • Greater coherence / thematic approach • Joint analysis of income, consumption and wealth possible • Best use of administrative data • High quality data for analysis of income distributions (including top and bottom) • Responsive to user requirements • Precise regional estimates • Timely estimates • Regulatory requirements met • Make use of new technology and mixed mode data collection • Reduced costs and respondent burden
- 19. Developments in 2016 and 2017 • Integration of the SLC and LCF - harmonised methods for sampling, collection (income data) and processing - Potential to improve sampling designs and therefore precision of UK and regional estimates - Supports a larger sample for key survey estimate - Common methods for collection and data processing, drawing on best practice • Expansion of the Survey on Living Conditions (SLC) to a 6 wave longitudinal design - Larger sample for regional and longitudinal analysis - SLC will meet the full EU-SILC requirement
- 20. Developments in 2016 and 2017 (cont) • Assessment of how administrative and other non-survey data could improve the surveys (initial focus on DWP, HMRC, VOA data – including income, tax and benefits data) - Could replace survey data, thus reducing questionnaire length. This provides greater opportunity for online collection - Potential for use in sampling designs, to improve coverage and precision - Possible use in the editing, imputation, estimation processes to improve data quality • Responding to the LCF National Statistics Quality Review - Improvements to income data - Greater use of other data sources - Electronic data collection (diary)
- 21. Longer term development • Incorporating administrative data into the statistical system • Integration of wealth data into the data collection model • Mixed mode collection