Title (eng)
What are good quality data on a phenomenon that is hard to measure?
Description (eng)
Key points
• Irregular migration is difficult to measure, and the data that exist are often limited,
inconsistent, or outdated. This chapter introduces a practical framework to help users
assess the quality and credibility of such data, rather than taking estimates at face value.
• It distinguishes between key data types—stocks vs flows, estimates vs indicators—and
highlights how conceptual ambiguity, observational gaps, and poor documentation can
undermine how irregular migration data are interpreted and used.
• When applied to over 250 estimates across 14 countries, the framework reveals significant
variation in quality. While some countries produce relatively robust and transparent figures,
many rely on outdated, methodically weak or poorly documented estimates. Still, pockets of
good practice exist across North America and Europe, which can be built on.
• The chapter argues that responsible use of irregular migration data depends not only on
improving data systems, but also on the ability of users to critically assess what data mean,
how they were produced, and whether they are fit for purpose.
Keywords (eng)
Data qualityIrregular migrationmeasurement uncertaintyindicatorsestimatescritical data assessmentFAIR data principles
Type (eng)
Language
[eng]
Persistent identifier
Is contained in
Title
Handbook on Irregular Migration Data. Concepts, Methods and Practices.
ISBN
978-3-903470-24-8
Editor
Denis Kierans
Albert Kraler
Publication
University of Krems Press
From page
55
To page
64
Date issued
2025-09-30
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