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Migration statistics: what the data tell us

getstats in Parliament event organised by Royal Statistical Society House of Commons Library All Party Parliamentary Group on Statistics

Migration statistics: what the data tell us

• Jakub Bijak, Southampton University
• Sin Yi Cheung, Cardiff University
• David Coleman, Oxford University
• Ian Cope, ONS
• Jonathan Portes, NIESR
• Hetan Shah, Royal Statistical Society

(chair)

2

Ian Cope, ONS

3

International Migration Statistics

Ian Cope, Director Population & Demography December 2014

Population and migrations statistics overview

Calculating LTIM estimates

Long-Term International Migration

Source: Long-Term International Migration (LTIM), ONS

583,000 323,000 260,000

Net Migration – EU/Non-EU/British

Source: Long-Term International Migration (LTIM), ONS

• 150
• 100
• 50

50 100 150 200 250 300

1975 1980 1985 1990 1995 2000 2005 2010 2013 q1 q2

Calendar Year

Net Migration (thousands)

YE = Year Ending q1 = YE March q2 = YE June

Non-EU Citizens EU Citizens British Citizens

2014

Net migration

260,000 168,000 142,000

• 50,000

EU migrants living in UK (2011)

Source: 2011 Census UK – passports held, or country of birth

100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

Source: 2011 Census

Economic Activity

Source: 2011 Census

Strengths and Limitations of sources

IPS Frequent collection More information than Semaphore Limited sample size – robust nationally but less robust for smaller sub groups LFS/APS Frequent collection Smaller sample than census Greater respondent burden Visas Counts not estimates Published with 2 month lag to reference period Mainly non-EU citizens Includes short term migrants NINos Counts not estimates Published with 2 month lag to reference period Includes short term migrants Migration for work only Lags before registering Patient Register Can analyse people registering with previous address abroad Does not record nationality Not registering or de- registering at all, or with a lag Census Rich data source including ethnicity, language, labour market, etc Once every 10 years

Impact of migration on population

• ONS calculate long-term international migration based on

the UN definition:

“A person who moves to a country other than that of his or her usual residence for a period of at least a year (12 months), so that the country of destination effectively becomes his or her new country of usual residence”

• ONS calculate 54% of population growth between mid-2001

and mid-2013 due to migration.

• Migrants tend to be young adults – with higher fertility rates
• 60% of projected increase in population mid-2012 to mid-

2037 is attributable to future migration

• either directly attributable to future migration (43%),or
• indirectly attributable to the effect of fertility and mortality on these

future migrants (17%).

International Migration Statistics

For more information:

http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Migration Email: migstatsunit@ons.gov.uk

Sin Yi Cheung, Cardiff University

15

Getstats ¡in ¡Parliament: ¡

Beyond ¡Net ¡Migration ¡ Statistics ¡ ¡

4 ¡December ¡2014, ¡Houses ¡of ¡Parliament ¡ ¡ Sin ¡Yi ¡Cheung ¡

School ¡of ¡Social ¡Sciences ¡ Cardiff ¡University ¡

¡ cheungsy@cardiff.ac.uk ¡

¡

What ¡do ¡migration ¡statistics ¡tell ¡us? ¡

Labour ¡market ¡integra/on ¡

• Evidence ¡of ¡ethnic ¡penalty ¡in ¡the ¡BriFsh ¡labour ¡

market ¡(Heath ¡and ¡Cheung ¡2007), ¡parFcularly ¡in ¡the ¡ private ¡sector ¡(Heath ¡and ¡Cheung, ¡2006) ¡using ¡data ¡ from: ¡

• Quarterly ¡Labour ¡Force ¡Survey ¡(ethnicity, ¡labour ¡

force ¡parFcipaFon, ¡unemployment, ¡occupaFon) ¡

• General ¡Household ¡Survey ¡(Fll ¡2001) ¡– ¡parents’ ¡

country ¡of ¡birth, ¡own ¡COB, ¡year ¡of ¡arrival ¡

• Individual ¡Sample ¡of ¡Annoymised ¡Records ¡SARs ¡

2001 ¡– ¡1.8m ¡cases ¡

Labour ¡Market ¡Integration ¡

• Clear ¡evidence ¡of ¡language ¡skills ¡contribuFng ¡to ¡

labour ¡market ¡success ¡for ¡the ¡second ¡ generaFon’s ¡ethno-­‑religious ¡minoriFes ¡(Cheung ¡ 2014) ¡

• Ethnic ¡Minority ¡BriCsh ¡ElecCon ¡Study ¡(EMBES) ¡2010 ¡
• Refugees ¡IntegraFon ¡-­‑ ¡Social ¡Network, ¡language ¡

and ¡employment ¡(Cheung ¡and ¡Phillimore ¡2013) ¡

• Survey ¡of ¡New ¡Refugees ¡(SNR) ¡2005-­‑07: ¡data ¡on ¡

social ¡networks: ¡co-­‑ethnic, ¡religious, ¡relaFves, ¡ friends, ¡formal ¡organisaFons; ¡also ¡on ¡health, ¡ housing, ¡and ¡language ¡training ¡

Signi<icant ¡improvement ¡for ¡the ¡second ¡ generation ¡(EMBES ¡2010) ¡

Sample ¡size ¡of ¡Survey ¡of ¡New ¡Refugees ¡

Refugee’s ¡social ¡network ¡pro<ile ¡at ¡ baseline ¡survey ¡

Recent ¡advance ¡in ¡migration ¡data ¡

• Census ¡2011 ¡
• New ¡quesFons ¡on ¡migraFon: ¡temporary ¡

residence, ¡English ¡language ¡ability, ¡ passport ¡held ¡

• SARs ¡– ¡10% ¡individual ¡Samples ¡of ¡

Annoymised ¡Records ¡with ¡full ¡occupaFon ¡ and ¡COB ¡coding ¡

• Labour ¡Force ¡Survey ¡
• WhyUK ¡since ¡2010 ¡

Policy ¡questions ¡cannot ¡be ¡ answered ¡without ¡robust ¡data ¡

• Migrants’ ¡own ¡views ¡on ¡integraFon ¡meaning ¡and ¡

prioriFes ¡

• Longitudinal ¡survey ¡not ¡just ¡of ¡refugees ¡but ¡all ¡

migrant ¡groups ¡

• Consistent ¡quesFons ¡in ¡repeated ¡cross-­‑secFonal ¡and ¡

longitudinal ¡surveys ¡

• Without ¡Parents’ ¡country ¡of ¡birth, ¡impossible ¡to ¡

idenFfy ¡(white/other ¡white) ¡second ¡or ¡any ¡third ¡ generaFon, ¡in ¡order ¡to ¡study ¡intergeneraFonal ¡ change/mobility. ¡

¡

Policy ¡questions ¡cannot ¡be ¡ answered ¡without ¡robust ¡data ¡ ¡

• First ¡language ¡at ¡home ¡quesFon ¡needed ¡every ¡

year ¡

• Language ¡skills ¡at ¡arrival ¡and ¡language ¡acquisiFon ¡
• ver ¡Fme ¡
• Pre-­‑migraFon ¡socio-­‑economic ¡characterisFcs: ¡

educaFon, ¡employment ¡(only ¡in ¡SNR) ¡

• Lessons ¡from ¡other ¡immigraFon ¡desFnaFons ¡– ¡

cross-­‑naFonal ¡comparison ¡crucial ¡to ¡see ¡how ¡we ¡ fare ¡

¡

David Coleman, Oxford University

1

Migration statistics: what the data tell us RSS / House of Commons Library Briefing. Or what the data cannot tell us?

David Coleman david.coleman@spi.ox.ac.uk

Structure of UK migration data

Home Office Control of Immigration data – inflow only. Entries classified according to Immigration Rules (2500 page handbook) PBS, visas, asylum etc. Nothing on stock: e.g. of resident migrants under each category by nationality and by visa type with which they entered the country (e.g. number

• f Indian migrants with ILR who came in as students).

Many potential migrant flows not fully captured - over 200,000 family visit visas issued each year. How many actually went home? Data not easily related to: International Passenger Survey (plus tweaks: asylum, switchers etc) Unique direct measure of inflow and outflow. But small voluntary sample only (80% response rate, interviews 2620 in, 1824 out in 2011., +/- 35,000 confidence interval). Inadequate for detailed analysis. No data on many individual countries of interest (e.g. Syria, Brazil). Requires frequent, often substantial revision (+67000 for 2006). New annual National Insurance Number allocations Stock data from decennial census, Annual Population Survey etc

What the data cannot tell us

Reliable, complete numbers of persons entering and leaving the United Kingdom and their characteristics. Timely exact information on number of immigrants resident in the UK and their whereabouts. Consequent uncertainty on national population estimates Whether persons admitted on time-limits have actually left the UK and when. e.g. how many in UK have indefinite leave to remain? How many as students, etc? Abundant data and analysis, not always easy to find. Don’t know what we don’t know ‘None of the data sources used, while offering the best data currently available, are specifically designed to capture information solely on long-term international migration’. (ONS 2014)

Difficult data – an example

Question: Which is the correct number for net international migration in 2007? 209000;? 233000? 273000?

Difficult data – the answers

Answer: All are correct (insofar as any are). 209000 is the IPS number, the only one that can be used for most statistical analyses (by age, purpose of journey, citizenship etc) 233000 was the LTIM number (IPS plus asylum etc) before the 2011 census result revision, the difference between inflow and outflow. 273000 is the revised net number which the ONS and HO now use following adjustment of the undercount of net migration revealed by the 2011 census However, the census revision cannot determine the gross flows in and out. The difference between the LTIM gross inflow and outflow gives 233, not 273. 233 is correct to make sense of the gross flows. Simples! (similar variations exist for other years).

What is to be done?

Heroic efforts by ONS to improve / patch up data - Migration Statistics Improvement Programme since 2002 Therefore repeated revisions of figures. More data and detail has been provided. E-borders failure. ‘Semaphore’ cannot provide better in / out data. But however ingeniously the IPS is patched up, it’s still the IPS.

Some small ways forward 1

Wider synthesis of data relating to migration under a common hub: IPS, HO (crime as well as migration), Population Surveys, Census , NiNos, HIPE and NHS, etc. Intermediate menu-driven step in ONS website between headline data and the vast store of information

Some ways forward 2

Join the rest of Europe in the 21st Century by abandoning the 18th century census concept Develop a Population Register for residents and immigrants (ID card not needed, but desirable). Already much of the way there with administrative data. Political will and understanding lacking At least a Feasibility study urgently needed

A blast from the past

‘……. I heard from several distinguished persons that there was a general complain to the imperfection of elementary population documents in this country… It is indeed a subject of wonder to every intelligent stranger, that in a country so intelligent as England, with so many illustrious persons occupied in statistical enquiries, and where the state of the population is the constant subject of public interest, that the very basis on which all good legislation must be grounded had never been prepared; foreigners can hardly believe that such a state of things could exist in a country so wealthy, wise and great.’ (Adolphe Quetelet, 1835).

Jakub Bijak, Southampton University

11

Uncertain migration statistics: How much can we know?

RSS/GetStats in Parliament event on Migration Statistics Houses of Parliament, London, 4 December 2014

ESRC Centre for Population Change

Jakub Bijak University of Southampton

With thanks to: George N Disney, Jonathan J Forster, Sarah Lubman, James Raymer, Peter W F Smith and Arkadiusz Wiśniowski

• Migration statistics: A paradox of plenty?
• Issues: definitions, coverage, accuracy…

The Challenge

Source 1 Source 2 Migration flows according to a benchmark definition Source 3

               



   



  

Option one: Multiple sources

• Model-based reconciliation of evidence

Source: Disney (2014) courtesy of the author

Option two: Multiple countries

• Model for a whole EU migration system:

Integrated Model of European Migration

Raymer et al. (2014) JASA 108, 801–819.

Example: UK

Option three: Multiple models

Source: Wiśniowski (2014), courtesy of the author

• IMEM + LFS-based model for short- and

long-term Polish migration to the UK

• Cornucopia of data
• Reconciliation via statistical models
• Uncertainty becomes a key feature
• Future: micro-level data linkages,
• ther sources (e.g. Semaphore)

Key Messages

Thank you!

j.bijak@soton.ac.uk

Jonathan Portes, NIESR

No slides, spoke from notes

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House of Commons Library

House of Commons Library Standard Notes available:

• Migration Statistics
• Asylum Statistics

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Migration statistics: what the data tell us

getstats in Parliament event organised by Royal Statistical Society House of Commons Library All Party Parliamentary Group on Statistics