How fit is the available data on irregular migration for policymaking?

This policy brief is aimed at those engaged in evidence-based policymaking on irregular migration. It provides an assessment and tips for using available data on irregular migration. 

Evidence-based policymaking is often considered a laudable goal – but applied to the field of irregular migration, there arise some principal challenges. Irregular migration is notoriously hard to measure as it occurs outside of regulatory norms and may often go undetected, while robust data and estimates are often non-existent, outdated or incomplete. Nevertheless, data are cited in policymaking in this area, where ‘illegal border crossings’ are used as a substitute, or outdated maximum estimates are used as a ‘low bar’ by future standards. New policies are put forward that aim to fill the gap in information, for example on ‘overstayers’ or ‘secondary movements’. This policy brief gives a 360- degree overview on what we currently know about the extent of irregular migration across the EU.

Based on MIrreM’s analysis and aggregation of estimates available for 12 European countries (including the UK) in the period 2016 to 2023, the irregular migrant population can be estimated at between 2.6 and 3.2 million. The previous Clandestino estimate was 1.8 to 3.8 million for the same 12 countries in 2008. This suggests that the number of the irregular migrant population has not substantially changed at the aggregate level for these countries – although there have been important changes in individual countries. In addition, the policy brief also surveys what information is available and still lacking in regard to trends and patterns of irregular movement, including what information we will have on ‘overstayers’, a key proportion of the irregular migrant population identified under the Clandestino project. We also discuss innovative methods that offer promising new ways to estimating the broader irregular migrant population. Throughout, we signpost where policymakers should treat data with care, and understand the limitations of the data they may use.