Skills-based mobility remains the cornerstone of attracting third-country nationals and unlocking Europe’s growth and innovation potential. Incorporating algorithmic matching to account for a wider range of considerations—beyond just skills—can enhance international talent retention, reduce barriers for employers, and broaden destination options throughout Europe.
The European Union, along with its Member States, municipalities, and a wide range of employers, is grappling with significant labour shortages across sectors and skill levels, eroding Europe’s global competitiveness. Unmet demand has more than doubled over the past decade, yet third country nationals (TCNs) in Europe remain under-represented across critical sectors. Stakeholders recognise the urgent need to identify, attract, and recruit TCNs to fill workforce gaps left by aging populations and support emerging strategic sectors, particularly in the digital and green economies. Europe sorely needs more workers, and skills-based mobility schemes will be the central avenue for attracting them.
National differences in priorities and entry requirements make universal solutions challenging. Moreover, the immediate need to match candidates with jobs often overshadows long-term retention considerations, i.e., goodness of fit with local communities—a key factor to ensuring they stay. Retention considerations are further complicated by different national and local contexts favouring temporary, circular, or permanent mobility. And while EU-level initiatives play a crucial role in framing and supporting policy innovation, national and local priorities will ultimately drive solutions due to varying demand-side needs and context-specific preferences.
This commentary argues that Europe’s economic, social, and political diversity can be reframed as a strategic advantage, enabling better alignment between TCN preferences, profiles, and receiving communities. Stakeholders at all levels should consider holistic, data-driven approaches to optimise matches for employers, candidates, and their families. At Pairity, our work with algorithmic matching offers valuable insights into TCN labour mobility, incorporating skills as one of several factors—helping to diversify destinations, lower barriers for employers, and bolster retention.
Considering retention for Europe’s skilled migration future
Taking a step back, ICMPD’s recent scoping project, Re-Thinking Approaches to Labour Migration, identifies 294 migration pathways to Europe across 27 EU Member States. It describes a patchwork of national schemes focused on different market needs, candidate types, and balances between temporary versus long-term programmes.
Identifying and attracting talent is only half of the equation—stakeholders must also anticipate secondary migration within Europe or beyond. Retention is not merely about return on investment; it requires building social, political, and business consensus about the potential for labour migration to meet Europe’s diverse needs and boost competitiveness.
While European research and public discourse often focus on empowering governments and employers to find the right talent, immigrant retention studies underscore the need to foster inclusive communities, address language barriers, and reduce bureaucratic and legal hurdles to support longer-term stays. Europe’s attraction and retention policies must now also consider technical alignment with emerging mobility frameworks.
International lessons on retention and the global race for talent
Comparative examples help us understand why policy frameworks alone cannot ensure retention. Despite Canada’s long-standing success in attracting skilled migrants, new research shows that economic immigrants leave Canada at a higher rate than any other immigrant category—20 per cent eventually depart, with 34 per cent of these leaving within the first five years.
Like Europe, Canada has major regional differences that affect retention. Where skilled immigrants settle can determine whether they stay, with city size and subnational dynamics playing a significant role. Long-standing provincial and regional programmes, along with pilot initiatives addressing labour market shortages in rural, northern, and Atlantic regions—as well as efforts targeting Francophone immigrants—operate within a broader framework that considers not only job market fit but also long-term integration outcomes. These programmes run parallel to temporary foreign worker schemes, which require labour market needs assessments similar to those used by many EU Member States, particularly in lower wage sectors like agriculture, services, and tourism.
Even tailored programmes that directly engage employers struggle with retention. Across Canada, many immigrants who arrive with job offers still decide to leave. Those in major urban centres in populous regions (for example Toronto or Vancouver) are more likely to relocate than those in mid-sized cities in less populous regions. Canada’s Atlantic provinces, which face similar demographic challenges to many European regions, have both the lowest overall attraction rates and the highest proportion of secondary migration.
The reasons are complex. A candidate may be an excellent fit for a job, but long-term retention depends on more than employment. Family life, access to services, and community all play a role. Likewise, while skills-matching and national policies matter, immigrants with high human capital or in-demand skills have greater mobility options as countries compete for their talent.
Technical considerations for holistic labour market matching
The upshot for this discussion is that the quality of matches will impact the success of European labour mobility schemes. While the EU has robust data on labour market shortages, the patchwork of national and local contexts, third country mobility partnerships, and immigration programmes call for an overarching yet flexible approach to sorting and matching candidates with destinations.
The EU’s Talent Partnerships and Talent Pool provide promising frameworks and digital tools for connecting employers with foreign candidates. Both are built around “matching” – but significant questions remain about how they will assess goodness of fit and address data management complexity across TCN candidate pools, Member States, and employers.
For example, while the Talent Pool will offer an employer-accessible job matching platform, it has yet to address competition for potentially scarce talent or retention. A more comprehensive, data-driven approach to matching could help mitigate these challenges. Below, we highlight four key considerations to help diversify destinations and proactively support retention.
Increased access for SMEs and new destinations
First, a more holistic matching system could lower entry barriers for small and medium-sized enterprises (SMEs) by broadening the scope of locality data, particularly in non-traditional destinations. For example, TCN candidates may be eligible for jobs across Europe, especially in competitive sectors. Meanwhile, SMEs in smaller locations may be disadvantaged in the case of multiple job offers. Smaller regions or municipalities may lack diaspora communities or higher education institutions compared to larger cities. However, they sometimes offer more affordable housing, school availability, or employment opportunities for spouses and adult children.
Pairity’s Re:Match project, in collaboration with the Berlin Governance Platform, matched displaced Ukrainians to German municipalities of varying population sizes, services, and labour markets. While many participants initially indicated a preference for larger cities, they were matched with smaller localities based on accommodation availability and other expressed preferences. Over time, satisfaction with smaller cities increased, largely due to strong alignment on other matching variables.
Broadening candidate preferences and aspirations
Second, more holistic matching requires collecting candidate preferences and household characteristics. Job matching has traditionally considered only the skill profile and eligibility of prospective candidates, and excluded those of family members who may accompany them—despite the fact that they play an important role in decisions to stay or move on. Existing bilateral and regional mobility partnerships provide an ideal testing ground for broader data collection.
Pairity’s matching systems incorporate a range of preferences—including city size, services, housing, cultural supports, education, and diaspora presence—to optimise resource allocation. Crucially, participants rank the relative importance of each factor, which helps balance preferences, mitigate competition, and reduce dissatisfaction with destinations.
Across multiple international projects, we have found that many candidates prioritise family-level preferences over immediate work or housing considerations. In our Re:Match project, retention rates were higher than average among displaced Ukrainians, as measured by larger surveys of over 11,000 Ukrainians, signalling that more holistic matching can improve long-term outcomes—even when city-size preferences are not met.
Aligning candidate pools with mobility pathways
Third, a more holistic approach can streamline the process of matching candidates with the right national immigration schemes, reducing complexity for governments, employers, and job seekers navigating Europe’s fragmented mobility pathways.
One key advantage of algorithmic matching is its ability to flexibly filter candidates based on different inclusion and exclusion criteria. For example, while some skilled TCNs seek circular mobility, others may prefer long-term residency with a path to eventual family reunification. Capturing these preferences in conjunction with skill profiles would help reduce barriers for employers, ensuring that SMEs and other businesses with limited resources are connected only with candidates who are likely to accept positions and stay.
Measuring outcomes for iteration and scaling
Fourth, expanding the scope of matching considerations also creates opportunities for better measuring outcomes. The factors influencing retention and secondary migration are complex and challenging to disentangle. Matching along a range of variables and collecting baseline data would allow for comparable, granular outcome analysis. It would also provide valuable insights for refining policies and improving retention strategies, recognising that Europe’s migration future is a long-term project requiring continuous optimisation.
Piloting holistic matching in Europe’s mobility partnerships
Data-driven matching is effective because it accounts for variation in needs and preferences. It can match skills to jobs, distinguish between temporary and permanent immigration programmes, filter shortage occupations, factor in candidates’ household preferences, and account for destination characteristics. Municipalities and regions can partner with employers to maximise return on initial investments and pilot holistic matching programmes. Doing so would generate more evidence-based outcomes, with an eye to scaling, policy transfer, and interoperability across Europe.
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Craig Damian Smith, PhD is Pairity’s VP of Policy & Social Impact. Craig leads Pairity’s work helping stakeholders integrate technical interventions with policy frameworks, with an emphasis on outcome measurements and scaling.
Radboud Reijn is Pairity’s VP of Sales and leads its work in Europe. Rad brings over 15 years of experience in public affairs and governmental relations to Pairity’s work providing technical and data solutions for pressing policy issues.
Opinions expressed in this publication are those of the author(s) alone.