ONS progress update on Transforming Population and Migration Statistics System in England and Wales Pete Jones Admin Data Census Project Office for National Statistics

Presentation Outline Background to ONS research with admin data Conceptual model for admin based statistical outputs Estimating size of population Wider population statistical system – including migration Admin Based Income Statistics (ABIS) Research plans 2018 and 2022

Census Transformation Programme Background - ONS Admin Data Research for Census and Population Statistics Beyond 2011 Programme (2011-2014) Census Transformation Programme Introduce ONS Administrative Data Census Replicate census outputs – “Research Outputs” Aiming for a ‘parallel run’ in 2021 Transforming the Population and Migration Statistics System Admin data at the core of migration statistics: Autumn 2019 Admin data at the core of population statistics: Spring 2020 Need to ensure that the Statistical Design and Processing of 2021 Census is coherently aligned with the transformation of this system

Conceptual model Data sources Transformation process Statistical register Outputs Registers Linkage and transformation methods Other admin data Statistical Registers: People Addresses Businesses Activity Census surveys Question marks around whether to include big data on the framework A number of statistical transformations are needed derive sound statistical outputs from multiple data sources. Once data has been received into the NSO, standard processes include: Reformatting to standard Coding input data to statistical classifications Editing data (identifying missing values and internal inconsistencies) Imputation of missing values Derivation of statistical variables Applying weighting, or estimation models Existing/ new surveys Other data?* Quality measurement / assurance *eg. Big, commercial

Statistical Population Dataset (SPD) V2.0 – data source coverage English and Welsh School Census DWP and HMRC Customer Information System (CIS) NHS Patient Register (PR) Included in SPD Higher Education Statistics Agency (HESA) data Personal Demographic Service (PDS) ‘address moves’ – used to resolve address conflicts DWP benefits data – used to resolve address conflicts SPD population estimates

Comparison of Statistical Population Dataset V2 Comparison of Statistical Population Dataset V2.0 with the official mid-year estimates by five-year age group and sex, England and Wales, 2016 The percentage difference by five-year age groups between the MYE and SPD V2.0 is most prevalent for males of working age where our SPD is higher. This is most apparent between the ages of 30 and 54 years, where each five-year age group has a percentage difference of over 4.0%. A likely reason may be people who are no longer resident in England and Wales still appearing on administrative sources. For example, they may not inform the Department for Work and Pensions (DWP) or HM Revenue and Customs (HMRC) of their departure from the country. Another area of interest is the age group of 20 to 24 years where the SPD V2.0 is 2.9% lower for males but 3.1% higher for females when compared with the MYE. A possible cause of the difference for the male population may be a lack of interaction with different administrative sources. For example, males may be less likely than females to access NHS services. In the case of male immigrants, this may mean they are less likely to register with a GP (and less likely to appear on the NHS Patient Register). The difference in the female population may be caused by the difficulty in removing people from administrative data sources who have since emigrated. For those aged 70 and over, the SPD V2.0 is consistently lower than the MYE for both males and females. The age group 90 and over shows the greatest difference, 6.9% lower for males and 4.6% lower for females when compared with the MYE. This discrepancy between the SPD V2.0 and the MYE has a number of possible causes. The process of creating the MYE involves “rolling” the census estimates forward, allowing for ageing, births, deaths and migration (the cohort component method). Therefore an over estimation of the elderly population in the 2011 Census or an under estimation of net international migration for this age group may have caused this difference. Another possible reason for this difference is error with the SPD V2.0 estimates caused by issues with matching records with missing or incorrect information on administrative sources. Furthermore

Coverage Surveys and Dual System Estimation Even by removing suspected erroneous records in this way to produce the active SPD, for most age-sex groups the estimates after DSE is applied over-estimate the 2011 Census population and SPD V2.0 without a coverage adjustment. 20-24 year olds have the largest bias. Estimates for females aged 35 to 54 are closer to the census than the SPD on its own. Suggests there is still an issue with over-coverage.

Integrating Admin Data Coverage Survey with Labour Market Survey LMS Master Wave (200k HH sample) Coverage boost (150k) Under-coverage methodology established Over-coverage more complicated to design for - Dependent interviewing using admin data - Alternative sources of data to measure overcoverage - Model based approaches Address Register Population Spine ‘Boost’ optimises allocation of sample to areas with higher coverage error Equal probability sampling for LFS waves and other ONS surveys Labour force survey wave 2 Expenditure Opinions Labour force survey wave 3 Annual Population Survey Labour force survey wave 4 Labour force survey wave 5

The Population Statistics System Migration Stats Economic Stats system Census - Up until now been working on an Admin Data Census as a programme of work that runs in parallel to 2021 Census preparations - Since we last met, increasing priority for ONS to use admin data at the core of migration statistics - Recognising that we have 3 major streams of work – lots of engagement with stakeholders , but need to start integrating a coherent set of plans that integrate all three - Admin Data Census (future censuses based on admin data) - 2021 Census transformation (online / enhanced use of admin data) - Migration stats (admin at the core) These need to be coherently brought together to effectively transform Population Statistics System - A lot of restructuring at ONS around this. 1st IDD created (PPP remit), now widened to SDR incorporate Statistical Design for Census. Much closer working with MSD - Diagram conceptulises how our remit has changed. Pop Stats Social Stats

Wider Population Statistics System

Combining a stock based and flows based approach to produce population stocks and flows ONS Population Spine 2015 Apply rules Usually Resident Population 2015 Components of change from admin data are added into the 2015 stock: New to the population (births and immigrants) Leaving the population (deaths and emigrants) Special populations Components of change derived from differencing 2015 and 2016 usually resident populations 1. Stocks Approach 2. Flows Approach 3. Evaluate differences between components of change and the usually resident population estimates they inform Hybrid Approach Model Triangulate the estimates Usually Resident Population 2016 derived from stocks and flows (hybrid)

Stocks: age-group source hierarchies (under development) First hierarchy Second hierarchy Third hierarchy Age Source Rationale 0-4 year olds Birth registrations Covers all E&W births Some risk of outward migration since birth Personal Demographic Service High chance of registering non-UK born children Will only qualify with evidence of active adult family member at same address … …. 5-15 year olds English and Welsh School Censuses State school pupils represent large proportion of population Annual submissions reduce risk of over-coverage Child Benefit Extract High take-up to account for children not in state schools Child benefit unlikely to paid for children resident abroad 16-64 CIS linked to: PAYE NBD SHBE Child Benefit Looking for evidence of economic activity Periodic benefit payments on or near reference date Annual earnings exceeding income threshold Evidence of low income longitudinally HESA Will cover large proportion adult population not economically active with DWP/HMRC Identify and remove short term foreign students (using longitudinal analysis / nationality) Our findings from PDS-MWS work show evidence of lags in registration particularly for EU. Although do find presence of migrants on neither source (so PDS fills some gaps) 65+ Likely to be pensions and health registrations Doing research to establish ‘top of hierarchy’ sources for each age group Aim is to account for majority of population at top of hierarchy with minimal over-coverage

Sources for migration statistics Travel Events data – Exit Checks (Non EEA) Semaphore (All countries) Arr Dep Arr Dep Arr Dep VISA START DATE VISA END DATE VALID VISA PERIOD (visa length) First arrival Last departure One approach would be to use the first arrival and last departure and the time spent in the UK variable derived by the Home Office However this does not say much about the time spent in the country between these points Some caveats, such as if you arrive 1 hour before your visa is due to start, this does not count as your first arrival (as it is not within the visa period) In this case, whenever you then departed and re-arrived would be counted as your first arrival. We are building on the approach and making it more specific by looking at the individual arrival and departure events. We avoid using information on visas as much as possible to allow us to use the same approach for Semaphore (adapted from : Report on international migration data sources – July 2018)

Identifying Potential Migrants and Longitudinal Evidence 2013 2014 2015 2016 2017 2018 LONGITUDINAL DATA - PDS LONGITUDINAL DATA - SC LONGITUDINAL DATA - BIDS LONGITUDINAL DATA - PAYE LONGITUDINAL DATA - HESA Potential Migrants MWS HESA PR/PDS Hannah

ONS plans for income from admin data Pre-publication slides ONS plans for income from admin data Administrative outputs Integrated sources Output quality 2017 2018 2019 Increased use of administrative data More detailed PAYE & Self Assessment data Direct estimates from administrative data to produce individual and household estimates Incorporate Child Benefit data More developed income concept and definitions – net income and income deciles Impute Winter Fuel Payment and Christmas Bonus LSOA OA? geographical detail Greater Increased coverage of income components Incorporate Self Assessment and more detailed PAYE data *Research Outputs *Experimental Stats

Availability of administrative data on the components of income Pre-publication slides What’s missing from income: Self-employment Investments Universal credit Personal independence payments Some other benefits e.g. Industrial injuries disablement benefit Some non-UK pensions Current transfers from non-profit institutions and other households (e.g. child maintenance and parental contributions) Anything which doesn’t go through the PAYE system (may be because it is below the tax threshold) Same as last year – although have added Winter Fuel Payment and Christmas Bonus data not huge change in availability of admin data on income Still no access to Self Assessment data for this work – check with Vic how to explain why this is the case

Admin Based Income Statistics Pre-publication slides Admin Based Income Statistics This year’s publication will be “Admin Based Income Statistics” (ABIS) We are building on the Income Research Outputs previously published This year the ABIS will be experimental statistics Still based on the 2015/16 tax year We have responded to user feedback to include: Both individual and household income Gross and net income estimates

Percentage of the population with PAYE and benefits income of £20,000 or below by local authority Tax year 2013/14 Males and females, aged 16 and over This map shows the percentage of the population that had an annual income of £20,000 or below in 2013/14 for each local authority in England and Wales.   LAs shaded darker indicate areas where a greater percentage of the population had an income of £20,000 or below. As expected LAs with lower percentages of their population with an income of £20,000 or below were clustered around London and the South East of England. Data Source: PAYE employment and pension data and Tax Credits data from HM Revenue & Customs and benefits data from the Department for Work and Pensions

PAYE and benefits income distribution for males by age, England and Wales Tax year 2013/14 Males, aged 16 and over Finally moving on to look at the distributions by age and sex. Here is the distribution of the male population by five-year age group across income bands.   Over 50 per cent of 16 to 19 year olds fell within the ‘no income information’ category. This was a much higher percentage than for any other age group. In reality many of these individuals probably fall within the £0.00 or lower income band. The population aged 25 to 64 had the highest percentage of individuals in the higher income bands. Data Source: PAYE employment and pension data and Tax Credits data from HM Revenue & Customs and benefits data from the Department for Work and Pensions

ONS Pop Stats Transformation: 2018 to 2022 Ref year Decision point 2018 2019 2020 2021 2022 (onwards) Stock Estimate? Stock Estimate? Stock estimate Stock + flow estimates Flows estimates Flow estimate Flow estimate Stock + flow estimates Admin-based stock? National stock estimates (PCS + Admin) Census + CCS (+ admin) PCS + Admin (+ Census) B Births B B B B D Deaths D D D D I Internal Migration I I I I Int International Migration Int Int Int Int IPS + Admin data V small test PCS* (c2k) IPS + Admin data Small PCS* (c50k) IPS + Admin data Small PCS* (c50k) IPS + Admin data PCS (c350k) Admin Survey *Integrated with LMS Admin at core of migration statistics Admin at core of population statistics “No surprises”

Questions? peter.jones@ons.gov.uk