Attachments
SUMMARY OF KEY POINTS
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The viability and long-term sustainability of sponsorship and complementary pathways programmes depend, in part, on achieving a good fit between the characteristics and preferences of arriving beneficiaries (whether refugees or other people in need of international protection) and the supports and services available in the communities where they settle, including those provided by their sponsors, hosts, or employers.
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Some new approaches to matching in Europe and North America rely on the idea that, to the extent possible, integrating individual beneficiaries’ preferences into decisions about their placement in a specific city, local community, or with a certain sponsor group will produce better programme and integration outcomes.
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Responses to humanitarian crises in Afghanistan and Ukraine suggest the importance of establishing multi-stakeholder coalitions that include sponsor groups, civil society, and refugee-led organisations, and that tapping their experience and expertise can result in more effective matching criteria and sustainable procedures. Such coalitions can also be a way to engage transnational affinity groups present in host societies (e.g., LGBTQI+ communities, human rights defenders, ethnic or conational organisations) in order to expand protection opportunities and ensure more targeted matching of specific refugee groups.
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Digital tools can help optimise match quality, reduce barriers to scaling programmes (by speeding up matching and making the process less staff-time intensive), and ensure accountability through digital records. To date, most matching is still done by hand, though technology may be used to create databases that store and sort data on potential beneficiaries and that can be searched for potential matches. However, some sophisticated initiatives use preference-matching algorithms to help allocate beneficiaries to localities where they are more likely to find employment and integrate successfully.
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Scaling the use of digital tools, particularly those based on algorithmic models, would require a programme’s implementing actors to establish guiding principles to ensure transparency and accountability and set clear obligations to maintain high-quality data and guarantee data protection.
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Embedding well-designed monitoring into the design and implementation of matching mechanisms is key to evaluating their operations and improving match quality. In addition, systematic data collection in the context of matching procedures—including of participants’ baseline data and administrative data (e.g., on local employment rates and housing availability)—offers an opportunity to strengthen the evidence base on the longer-term impacts of sponsorship and complementary pathways programmes.