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The European Commission’s science and knowledge service
Joint Research Centre
The Challenge of Migration Data
Michele Vespe, Marlene Alvarez Alvarez
Knowledge Centre on Migration and Demography (KCMD), European Commission
6-8 September 2017, Senec, Slovakia
Outline
• Introduction
• Data on International Migration – why good data are needed?
• Data gaps
• Data integration
• Case studies
• Innovative data sources & Big Data
Introduction
Knowledge
Uptake
Partner- ships
Better data & analytics
Making sense of information
Anticipation & foresight
Tailor-made evidence
New knowledge
The need
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
The European Commission's Knowledge Centre for Migration and Demography
EU The European Agenda on Migration explicitly highlights the need for more and better use of information in the areas of smuggling, return, root causes of irregular migration, border management and job matching.
UN Agenda 2030 for sustainable development
NY Declaration “We recognize the importance of improved data collection, particularly by national authorities, and will enhance international cooperation to this end, including through capacity-building, financial support and technical assistance. Such data should be disaggregated by sex and age and include information on regular and irregular flows, the economic impacts of migration and refugee movements, human trafficking, the needs of refugees, migrants and host communities and other issues.“
Global Compacts
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Migration data - policy context
Types of migration data
1. Census
2. Administrative data
3. Surveys
4. Operational data
5. Research
Frequency
10y
Annual
Ad-hoc
Monthly; daily
Ad-hoc
Aggregation/features/reliability/…
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Limitations of existing migration data
Working with the data
Accessibility/visibility
Confidentiality
Integration
Understanding the data
Uncertainty
Collection methodology
Representativeness
Consistency
Improving the data
Disaggregation (age, sex, education,
status…)
Timeliness/readiness
Refresh rate
Spatial Resolution
Coverage (space-time)
Missing data?
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
KCMD Initiatives
To make data available in a single place, and help making sense of them enabling:
• Better awareness of the existing data and identification of gaps* by theme (migration flows, minors, socio-economics, sub-national)
• Improved understanding of the current situation on migration through the analysis of the dynamics of variables and their correlation
• Analysis of the potential impact of proven determinants of migration at national and regional levels, and anticipate future migration trends through better evidence based scenarios
• Providing scientific evidence e.g. for the better targeting of aid funds for migration and related policies
*Gaps: datasets discovery, quality, missing and fragmented data, analysis capacity
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
KCMD Initiatives
Fragmented &
scattered data
Discovered, filtered &
quality checked
catalogue of data
sources
Pre-processed
data access &
visual analytics
KCMD DATA CATALOGUE
KCMD DYNAMIC DATA HUB
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
KCMD Data Catalogue
To help policy makers, researchers and stakeholders to discover and
use data on migration https://bluehub.jrc.ec.europa.eu/catalogue
~120 datasets
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
https://bluehub.jrc.ec.europa.eu/migration/ap
p/index.html
- Recommended browser: Google Chrome
- No need to login, the Hub is open
- Read and accept disclaimer
This symbol throughout the slides invites you to explore and interact with
the data using the Dynamic Data Hub KCMD Dynamic Data Hub
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Data on International Migration why good data are needed?
World:
3.3% in 2015
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Global Migration Trend:
is this a period of unprecedented migration?
Sources: - migration stocks, UNDESA - population, World Bank
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Global Migration Trend:
is this a period of unprecedented migration?
• The highest number of migrants are Asians (99.5 million), moving mostly within Asia (59.4 million)
• About 50% of African migrants (32.6 million) move within Africa (16.4 million)
• The highest migration rates are intra-Europe (3.9% from EU-28 to EU-28; and 4.3% from rest-Europe to rest-Europe) and from Latin America to Northern America (3.9%)
Source: UNDESA Produced by the KCMD
Migration stocks vs total population
Sources: - migration stocks, Eurostat - population, Eurostat
EU28:
10.7% in 2016 including intra-EU mobility
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Immigrant as % of total population
Sources: - migration stocks, Eurostat - population, Eurostat
EU28:
6.9% in 2016 Third Country Nationals only
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Where do the majority of immigrants come from?
Explore other countries using the Dynamic Data Hub in 2015
Source: - migration stocks, UNDESA
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
The countries of origin vary amongst European countries
Sourc
e:
UN
DESA
Pro
duced b
y t
he K
CM
D
Where do the majority of immigrants come from?
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
How % of immigrants population has changed in the past years?
Sources: - migration stocks, UNDESA
% annual increase immigrant population between 2010 and 2015
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Data Gaps
Data Mapping
Coverage expressed as number of origin-destination pairs
As an example, stocks of migrants by country of birth in Spain (sources Eurostat, OECD, UNDESA, and World Bank) highlight temporal gaps the different datasets
→
Data on migration are fragmented, incomplete and scattered.
Knowledge and awareness of availability, geographic and temporal coverage of the datasets is a preliminary and fundamental step to provide solid evidence.
←Example of migration stocks and flows data map
[produced using the Dynamic Data Hub]
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Irregular Migration Data
Data source Description Frequency Coverage
Frontex1 Detections of irregular border crossings Monthly EU land and sea ext.
borders
International organization for
Migration (IOM)2 Mixed migration flows in the Mediterranean and beyond Monthly EU land and sea routes
UNHCR3 UNHCR refugees operational data portal Monthly Mediterranean situation
Eurostat – asylum applications4
Asylum and first-time asylum applications, by citizenship,
age and sex, including unaccompanied minors
(migr_asyapp)
Monthly EU–European Free Trade
Association (EFTA)
Eurostat – asylum decisions Decisions by citizenship, age, sex and type of status
(migr_asydec) Yearly EU–EFTA
Eurostat – recognition rate statistics5 First-instance decisions by outcome and recognition rates Quarterly EU–EFTA
Eurostat – enforcement of
immigration legislation
Third-country nationals refused entry at the external
borders (migr_eirfs), found to be illegally present
(migr_eipre) and ordered to leave (migr_eiord)
Yearly EU–EFTA
[1] See http://frontex.europa.eu/trends-and-routes/migratory-routes-map/ [2] See http://migration.iom.int/europe/ [3] See https://data2.unhcr.org/en/situations [4] See http://ec.europa.eu/eurostat/data/database [5] See http://ec.europa.eu/eurostat/statistics-explained/index.php/Asylum_quarterly_report
There are no official datasets that measure directly irregular migration and irregular migrants in the EU However, there are several datasets that can be used as proxies to provide estimates
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Irregular Migration Data
0
50000
100000
150000
200000
2010-12 2011-06 2011-12 2012-06 2012-12 2013-06 2013-12 2014-06 2014-12 2015-06 2015-12 2016-06 2016-12
Central Mediterranean route
Eastern Mediterranean route
Western Mediterranean route
Irregular Border Crossing (IBC) by sea following the Central, Western and Eastern Mediterranean routes Source: IBCs data, Frontex
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Irregular Migration Data
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
Number of uniform Schengen visas in 2016 by main countries where consulates issuing the visas are located The data do not necessarily reflect the country of origin of the people receiving the uniform Schengen visa Source: Schengen Visa Statistics, European Commission
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Irregular Migration Data
-200,000
300,000
800,000
1,300,000
1,800,000
2,300,000
2008 2009 2010 2011 2012 2013 2014 2015 2016
EU 28 TCNs refused entry at the external
borders
EU 28 TCNs found illegally present
EU 28 TCNs ordered to leave
EU 28 TCNs returned following an order to
leave
Data on Enforcement of Immigration Legislation. The high values for TCNs found illegally present in 2015 and 2016 may be attributed to the inclusion of Irregular Border Crossings for several countries
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Source: Enforcement of Immigration Legislation, Eurostat
Irregular Migration Data
0
200000
400000
600000
800000
1000000
1200000
1400000
2008 2009 2010 2011 2012 2013 2014 2015 2016
1st time asylum applications
Total 1st instance decisions
Negative 1st instance decisions
First time asylum applications, total number of first instance decisions and negative first instance decisions First instance rejections data is a proxy of irregular migration geographic and status flows
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Sources: asylum and managed migration, Eurostat
Irregular Migration Data
Main limitations: • aggregation at the EU level is prone to double-counting and variable coverage; • each data set refers to time periods that are not aligned and capture different stages of administrative process; and • most of the data collected refer to detected irregular migrants and migration while the real stock of irregular migration remains unknown.
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Migration Policy Practice – Volume II, Nr 2, April-September 2017
Data integration
Migration & Unemployment • Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Spain: how migration might follow boom (construction sector) & bust economic (2007) cycles
Sources: - immigration, OECD - unemployment, World Bank
Migration & Unemployment
Explore migration flows to Spain from 2006-2011
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Source: immigration, OECD
IDPs, Refugees, Arrivals
EU irregular arrivals by sea [FRONTEX]
Stock of Refugees [UNHCR]
Internally Displaced Persons conflicts-related [IDMC]
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Visualising data of different nature and from multiple sources in this case helps understanding that the number of arrivals to the EU by sea (blue) are significantly smaller than the stocks of Internally Displaced Persons and refugees This is particularly clear in the case of Syria (see the inset), where the increase of IDPs in 2012 is followed by the one of people leaving the country and obtaining refugee status
Data on conflicts related IDPs (source: Internal Displacement Monitoring Centre - IDMC), origin countries of refugees (source: UNHCR) and origin countries of EU irregular border crossings through the Mediterranean Sea (source: Frontex) in 2015
Case Studies
Case study I • Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Which route did Syrian migrants and refugees follow in 2013, 2014 and 2015? How did this change?
Source: IBCs, Frontex
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Case study II
What is the trend of the % of migrant stocks (non-EU-28 and total) versus the total population in EU-28 during 2014-2016?
Source: migration stocks, Eurostat
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Case study III
Nigeria: arrivals, asylum applications, residence permits
Sources: - residence permits, Eurostat - asylum applications, Eurostat - IBCs, Frontex
Refugees: where are located? Where do they come from?
Case study IV • Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Source: - population of concern, UNHCR
Refugees: where are located? Where do they come from? Example: Sudan / Ethiopia
Case study V • Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Source: - population of concern, UNHCR
How many children applied for asylum in 2016 in EU28?
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Case study VI
Source: asylum, Eurostat
Innovative Data Sources and Big Data
Level of concentration of migrants from Philippines (orange), China (yellow), Egypt (green), Peru (red) in Milan. Source: Italian Census Data
City Centre
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
KCMD Maps of migrants in EU cities Spatial Segregation
Density of mobile phone traffic with China and areas of highest concentration of migrants from 2011 Census Sources: - Italian Census Data & - Call Detail Records, Telecom
Italia
Mobile phones data Activity-based Spatial Segregation
• Introduction • Data on International Migration • Data gaps • Data integration • Case studies • Innovative data sources & Big Data
Conclusions
• Data can help balancing the public debate around an often misperceived theme such as migration
• Policy advice and knowledge management for migration require full awareness of data opportunities and limitations
• Data limitations need to be addressed through research and alternative methods which, among others, need to cover
• International mobility versus migration
• local dimension of migration (diversity)
Any questions? You can find us at @M_Vespe @Marlene_aa [email protected] [email protected] https://ec.europa.eu/jrc/en/migration-and-demography