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Piecing together the puzzle: new UNECE guidance on data integration for migration

Measuring international migration is a complex task. To paint a complete picture we need to know not only how many people enter and leave a country—which in itself is challenging enough—but also the characteristics of those people and the ways in which they integrate into and impact societies.

The data we need can rarely be captured from a single source, so the integration of data from various sources is a fundamental part of producing migration statistics.

UNECE’s newly-released Guidance on Data Integration for Migration offers support for countries wishing to draw on multiple sources to measure stocks, flows and characteristics of international migrants. With 13 country case studies, it demonstrates the breadth of current practice, while recommending ways to improve these practices through, among other things, enhancing international comparison and exchange, increasing transparency of integration techniques, and fostering support in legislation for better access to administrative data for statistical purposes.

One particular challenge in measuring international migration is accounting for emigration. Often people have no incentive or requirement to report leaving the country. So how do we even know they have left? Data integration techniques can be used to create indices of ‘presence signals’—clues that suggest a person remains in the country, such as tax and social security records, use of health or education services, electricity or gas consumption, as well as data from a wide range of other government services. Statistical offices apply algorithms to these clues to determine the likelihood that a person has left the country, based on their appearance or absence in these administrative sources. For example, Spain used administrative and survey data after the 2011 census to determine that almost 500,000 foreign nationals who were listed on the population register no longer resided in Spain.

There are two sides to every migration event, giving statisticians the opportunity to capture moves from the point of view of both the origin and destination country. There have been efforts to improve migration statistics by using ‘mirror statistics’, in which emigration data collected by one country is compared with other countries’ immigration data. For example, since the country of destination for most Canadian emigrants is the United States, Canada makes use of American immigration data to improve their measurement of emigration. This form of data exchange usually occurs at the aggregate level, although the new Guidance argues that the benefits of data exchange could be even greater if applied at the individual level.

Ultimately, data integration leads to better measurement of migration, a key issue cutting across the 2030 Agenda for Sustainable Development. Most of the 17 Sustainable Development Goals contain targets and indicators relevant to migration or mobility. At a time of unprecedented migratory flows and the policy debates that surround them, accurate measurement is more important than ever. 

The Guidance is available at:

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