One of the persistent data gaps is that data on stocks and flows of migrants are not always disaggregated by relevant socio-demographic makers such as, but not limited to, age, gender, ethnicity or other risk categories such as persons with disabilities or unaccompanied and separated children (UASC). Disaggregating data means unpacking larger population samples in order to make comparisons between multiple sub-groups of the population by also recording information on their relevant socio-demographic markers. While disaggregating by sex is fairly common, disaggregating by an individual’s gender identity, which may or may not correspond with their sex assigned at birth or the gender attributed to them by society, is less common. Gender impacts on opportunities to migrate, labour market integration, exposure to violence and exploitation and access to rights and services. Therefore, disaggregating by gender would enable us to identify the inequalities faced by women and persons with diverse sex characteristics or sexual orientations (IOM, 2021b). A second consideration with regards to disaggregation is the level or granularity of disaggregation, for example, disaggregating by ten-year age groups instead of by five-year age groups or year of birth might not facilitate the identification of minors or other policy-relevant age categories. A third consideration is the need for an intersectional approach, or the recognition that different socio-demographic markers may interact with each other creating a unique set of challenges (Bastia et al., 2023). Intersectionality implies that simultaneously disaggregating migration data by two or more socio-demographic markers, e.g., gender and ethnicity, can reveal inequalities that are not observed when disaggregating by only one marker.
In addition to the broader concern of providing disaggregated migration data, the Centre for Global Development (2009) provided a list of policy questions on the causes and effects of movement as well as the effects of policies on shaping movement that existing data cannot conclusively provide answers to (CGD, 2009). The questions, presented in the table below, summarize the limits of the conclusions that can be reached using the data that are currently available.
Table 1: Major policy questions that existing migration data cannot answer
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Causes of movement |
How much return migration and temporary migration is there? |
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Should destinations adopt measures to encourage return migration and how? |
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What common traits are shared among emigrants, return migrants & circular migrants? |
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How many irregular migrants are there and what are their characteristics? |
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How will climate change shape migration patterns? |
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Effects of movement |
Are return migrants more productive due to their migration experience? Do they bring back skills, technology, and entrepreneurship as well as money? |
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What are the effects of guest worker programs on migrants and their countries of origin? |
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How does high-skilled emigration affect education decisions in origin countries? |
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How policy shapes movement? |
Which policies encourage permanent migration and which encourage temporary migration, and to what degree? |
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How can destination country policies leverage remittances for development? |
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How is irregular migration shaped by immigration policy? |
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How can management of temporary and permanent migration flows help country navigate demographic change? |
As shown in the figure below, there is generally more data available on the ratification of international conventions, migrant stocks, international students, remittances and trafficking in persons (IOM, 2017). It should be noted, however, that data on hard-to-reach migrant groups such as missing migrants, irregular migrants and victims of human trafficking or migrant smuggling tend to be biased towards the experiences of beneficiaries identified by relevant stakeholders (IOM, 2015). Future efforts should therefore be geared towards collecting more data on migratory flows (as opposed to only stocks), temporary migration, internal migration, and hard-to-reach migrant groups.
Figure 3:
