Statistical Modelling for Official Migration Statistics ... · Outline Background State of the Art Applications Examples Perspectives Background Art. 9 “scientifically based and

Statistical Modelling for Official Migration Statistics:

State of the Art and Perspectives

DIME/ITDG Plenary | Eurostat | Luxembourg | 14 February 2017

Jakub Bijak [email protected]

William Davies, in The Guardian, 19 January 2017

Outline

• Background and policy needs

• Current state of the art

• Promising applications

• Selected examples

• Perspectives

Outline Background State of the Art Applications Examples Perspectives


Fot: Mstyslav Chernov [CC BY-SA 4.0], via Wikimedia Commons

Importance


Background

Art. 9 “scientifically based and well documented statistical estimation methods may be used” for official statistics on migration and asylum

Methods

Design-based Model-based

Chiefly frequentist Chiefly Bayesian

Strength: Model formulation

Strength: Inference given model

Challenge: Coherent inference

Challenge: Model can be wrong


After: R J Little (2006) American Statistician, 60(3): 213–223.

Policy needs

Usefulness

Readiness Timeliness


Policy needs

• Trade-offs: generally two out of three

–Administrative data: timely and ready

–Big data, potentially: useful and timely

–Design-based surveys: ready and useful

• Can model-based approaches help?


State of the art • Sources of data on flows in EU/EFTA countries

Nordic registers

Other registers

Mostly surveys


Map: Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Europe_political_chart_complete_blank.svg, CC-BY-SA 3.0 Meta-data: Eurostat, http://ec.europa.eu/eurostat/cache/metadata/en/migr_immi_esms.htm

State of the art

• Migration estimates mostly administrative or design-based

• Harmonisation of definitions envisaged in Regulation 862/2007 – UN ‘Gold standard’

• ‘Statistical mainstreaming of migration’ by Eurostat

– Example: LFS migration modules and questions

• Model-based work mainly academic


State of the art

• Discrepancies despite harmonisation efforts

Examples from the Eurostat database for 2014: good, moderate, and problematic alignment


Receiving country data Sending country data

To: From:

ES IT UK To: From:

ES IT UK

ES - 3,427 33,325 ES - 9,477 33,851

IT 14,781 - 17,587 IT 4,701 - 14,991

UK 17,747 3,740 - UK 18,002 : -

State of the art

• Different stakeholders – different categories

• Need for a flexible reconciliation of data


Migrants

3 months

Asylum seekers

Refugees

6 months

1 year

5 years

Applications

Areas crucial for migration statistics:

• Reconciliation of data sources

• Small-domain estimation

• Complexity and uncertainty

• ‘Big Data’

• Privacy and disclosure control


Reconciliation of sources

• Harmonisation of definitions

• Flexibility

• Administrative sources

• Survey data

• ‘Big data’


Small domains

• Small geographic units

– Random variability in survey data ( design)

– Non-random errors in other sources ( models)

• Short time series

– Migration projections/forecasts ( models)

• Other domains

– Cross-classification of migration and other variables ( design and models)


Complexity and uncertainty

• Migration is complex and uncertain

• Need to reflect that in official statistics

• Design-based approaches alone cannot achieve that:

– Complexity requires modelling

– Uncertainty far beyond sampling error

• Borrowing of strength through modelling


‘Big Data’

Key challenges:

• Biases in data

• Ethical issues

• The ‘4V’ volume, velocity variety, veracity See also: E Zagheni and I Weber (2012),

WebSci 2012, 52(5): 1627–50.


Privacy and disclosure control

• Issues especially with individual-level and linked data

• But: explicit trade-offs between disclosure risk and benefits of data


After: S E Fienberg (2011) Statistical Science, 26(2): 212–226.

Example: Reconciliation

• Bayesian hierarchical model for 31 countries


Source: J Raymer et al. (2013) Journal of the American Statistical Association, 108 (503): 804 & 811.

Example: Reconciliation

• Similar model – one country, many sources


Source: NG Disney (2015) PhD thesis, University of Southampton.

Example: Small areas • Design-and-model-based estimates

Source: Office for National Statistics, via www.ons.gov.uk


Example: Uncertainty


Source: Fig. 1 from JJ Azose and AE Raftery (2015), Demography, 52(5): 1627–50.

• Migration projections for the UN WPP

Example: Flows from stocks


Source: Guy Abel, via https://gjabel.wordpress.com See also: GJ Abel and N Sander (2014). Science, 343 (6178).

Perspectives

• Within easy reach

– Micro-level data integration

– Macro-level integration and modelling

– Inference on linked data

• Longer-term agenda

– Non-traditional sources

– Early warning systems


A

C B

Early warnings


After: J Bijak (2016) http://gmdac.iom.int/gmdac-data-briefing-6

Syrian refugees (UNHCR)

ABC of current challenges

Administrative sources

Big data

Complex problems


Specific issues

• New ethical and privacy concerns in Big data

• Assurance of ‘scientific validity’ of estimates

• Communication of model-based results

• Uncertainty of estimation

...but all of them are already recognised and are not unsurmountable


New methods

• Methodological innovation e.g. Roderick J Little’s ‘Calibrated Bayes’


After: R J Little (2012) Journal of Official Statistics, 28(3): 309–334.

Model: inference

Design: calibration

Calibrated Bayes

Group effort

Different levels of collaboration

• Between European countries

• Between data producers and users

• Between official statistics and academia

• Between survey statisticians and modellers


Final points

• Modelling can help utilise the data better

• Design and models are not contradictory: design can lead to models

• Harmonisation still needs attention

• New challenges for the future


John Pullinger UK National Statistician

“We are far from ‘leaving behind the age of statistics’ ... Quite the reverse. This is the moment when we can make our greatest contribution to society by providing the better statistics that allow for better decisions.”

Statistical Modelling for Official Migration Statistics:

State of the Art and Perspectives

Jakub Bijak [email protected]

DIME/ITDG Plenary | Eurostat | Luxembourg | 14 February 2017

With credit to: GJ Abel, JJ Azose, E Dodd, JJ Forster, JD Hilton, T King, SL Nurse, AE Raftery, J Raymer, PWF Smith, A Wiśniowski

Statistical Modelling for Official Migration Statistics ... · Outline Background State of the Art Applications Examples Perspectives Background Art. 9 “scientifically based and

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