1. How to combine data from multiple sources
Frans Willekens, NIDI
NTTS2017 Satellite Event ‘Measuring migration’ Brussels, 13 March 2017

2. Content Framework Data sources Census and sample survey
Administrative data, incl. big data
How to combine data from different sources?
Conclusion

3. Migration statistics <- data on individual and location
Identify individual: personal characteristics and proof of identity
Approach person and ask (or ask proxy)
Person approaches authorities and self-report (e.g. registration)
Biometric identifiers (biometric authentication)
Electronic identification (authentication): e-Verify
Electronic ID card (eID) / Personal Identity Verification Card (e.g. LincPass, USA)
RFID implant (Radio Frequency Identification)

4. Migration statistics <- data on individual and location
Determine location of individual (and relocation): proof of residence
Location ≠ residence
Actual and usual residence (de facto / de jure)
IP address
GPS tracking (device ≠ person)
In document
Implanted microchip with GPS
Info on individual and relocation
Personal attributes
Date of relocation
Forced / Voluntary
Reason for relocation
Is relocation authorized by authorities?

5. Data source: census and sample survey
Location
Place of birth
Place of previous residence
Place of residence at given prior date
Date
Date of birth
Date of census or survey
Data reliability: good, but
Natives and immigrants only (no emigrants except if proxy respondents are interviewed)
Census: coverage ok, but few personal attributes
Sample survey: few migrants unless migrants oversampled

6. Data source: Administrative data A. Civil registration
Location : Address
Date: date of birth and date of change of address /date of registration
Data reliability:
Self reporting: notify authorities of arrival and departure
Registration: new address (arrival / immigration)
Deregistration: old address (departure / emigration)
Response: international cooperation
Nordic countries
Romania, Italy, Spain (Pisicã, 2016)
USA-Canada

7. Data source: Administrative data B
Data source: Administrative data B. Entry visa / residence permit / blue (green) card
Authorization of stay
Location: no address
Date: starting date (visa issuing date) and ending data (visa expiration date or status expiration date (USA))
Other info: reason for entry; citizenship; location issued
Data reliability
Unauthorized (illegal) entry
Visa overstay

8. Responses to visa overstays: e-borders
Assumes eID / e-Passport
A. Travel Authorisation System
USA:
Visa Waiver Program: Electronic System for Travel Authorization (ESTA)
EU: EU Smart Border Initiative
European Travel Information and Authorisation System (ETIAS)
Visa Information System (VIS) (EU)
Collects biographic and biometric info
Identify prior to arrival if a traveller poses a security or migratory risk an exchange data with other countries

9. B. Verfication system USA EU
NSEERS (National Security Entry/Exit System) (initiated in 2002 for citizens from ‘high-risk’ countries) (ended by Obama in 2016)
EU-Visit (since 2004)
Trump Executive Order 27/1/2017:
Biometric Entry-Exit Tracking System for all travelers to the United States
Visa Interview Security: “extreme vetting” (= ideological screening incl. pw social media) EU
Entry/Exit System (EES): replaces manual stamping of passports
Stores biographic and biometric info, including
Date and place of entry and exit
Four fingerprints and the facial image)
Comparison of info in EES and VIS (EES and VIS connected)

10. Data sources: social networks and Google
Location: geo-locator
IP address ≠ residence (accurate identification of country)
GPS location ≠ residence (accurate identification of country)
Account holder
Internet Service Provider (IPS) may give name and address
Data not representative for general population
Google: searches may signal outmigration intentions
Review: Hughes et al. (2016) Inferring Migrations: Traditional Methods and New Approaches based on Mobile Phone, Social Media, and other Big Data. Feasibility study on Inferring (labour) mobility and migration in the European Union from big data and social media data. European Commission project #VT/2014/093

11. Data issues Definition of migration Coverage
Usual residence
Duration threshold
Coverage
Underreporting / undercount
Accuracy of data collection

12. How to Combine data from different sources
How to Combine data from different sources? Answer: use a model of the true migration flows Consequence: estimates or synthetic data

13. Data Synthetic data Model (Measurements) (Estimates)
Census and Survey
Administrative data
Big data
Data
(Measurements)
Synthetic data
(Estimates)
Model
Other relevant information
quantitative
qualitative
Distinguish between observations (data) and ’true’ migration flows
Raymer, J., Wiśniowski, A., Forster, J.J., Smith, P.W.F., and Bijak, J. (2013). Integrated modeling of European migration. Journal of the American Statistical Association, 108(503):

14. Approach: Model the true migration flows
True flows are stochastic
Model the stochastic proces: counting process
Example: Poisson process
Observations are realisations of the stochastic process
Use all available data: quantitative and qualitative

15. Data generating process: counting process
Count data: number of migrations n(t)
Stochastic process {N(t), t ≥ 0}, with N(t) the ’true’ migration flows
Simplest counting process: Poisson process
One parameter, but varies by migrant category
Unobserved heterogeneity: mover-stayer model
Direction of migration: Origin - destination
E(N) = λ
var(N) = λ
λ = 𝜇t

16. How to estimate model of true flows from data?
Theory of counting processes
Number of migrations (occurrences) by origin, destination, and attributes of migrants and stayers
Exposure:
Number of persons exposed
Duration of exposure
Parameter of model: migration rate

17. Use all relevant info: data and prior info
Types of prior information Quantitative: primary data and auxiliary data Qualitative: expert opinions How to add prior information? Information as probability distributions Bayesian rule
Prior

18. Observed vs ‘true’ migrations
Migration model predicts ‘true’ number of migrations Measurement model quantifies difference between observed flows and ‘true’ flows (separate for origin and destination country)
True flow = Observed flow * Correction factor

19. Conclusion Combine data from different sources Meta data are essential
Use statistical models:
Migration model
Measurement model
For missing data: use proxy respondents
Emigration data

20. thank you Willlekens@nidi.nl