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Learning objectives
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Summary
Whereas chapters 1 and 2 of Part II deal with migration concepts measured exclusively based on the spatial (e.g., change in residence) and temporal (e.g., duration of residence) dimensions, this chapter will focus on measuring concepts that also consider the reasons for (e.g., employment, conflict, disasters) and nature of (e.g., forced vs. voluntary, regular vs. irregular) migration. More specifically, this chapter will provide an overview of relevant methods for collecting data on labour migration, forced displacement, irregular migration and environmental migration, all of which are relevant to policymakers around the world.
Some data sources are more relevant to specific sub-types of migration, for example, work permit data, establishment surveys and social security data capture labour migration, operational data such as humanitarian needs assessments and registers of applications for international protection capture forced displacement and administrative data on forced returns, irregular stays and refusals of entry capture irregular migration. However, data on these sub-types might also be generated by including the “reasons for move”, with answer categories applicable to the national context, in population censuses and household surveys. Optimizing the design of censuses and surveys to collect data on labour migration, forced displacement or environmental migration also facilitates comparisons of the host population with migrants in vulnerable situations. A key consideration for such comparisons is the inclusion of socio-economic items and indicators across data sources, in line with international measurement standards (e.g., ISIC, ISCO, ISCED, ISCE, BPM6). Furthermore, the disaggregation of data on labour migration, forced displacement and environmental migration by “income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts”, as emphasized by SDG Target 17.18., would allow for intersectional analyses highlighting the overlapping vulnerabilities of certain migrant groups.
Finally, several ‘cost-effective’ strategies might be pursued to generate knowledge on the different migration sub-types:
- using censuses and surveys to design a sampling frame for targeted surveys;
- adding a migration module to a multi-topic survey;
- adding relevant socio-economic indicators to administrative data sources;
- integrating different kinds of individual- and contextual-level data;
- linking records from statistical and administrative data sources based on a unique PIN.