Platform Details

Pairity’s end-to-end platform covers the lifecycle of activities from programme development, to recruitment, to monitoring and evaluation. We developed Pairity over several years of consultation with settlement organizations, volunteers, refugee newcomers, donors, and governments. It prioritizes community empowerment and evidence-based policy. 

The core of the platform is our matching system. Demographic and preference-ranking surveys are administered to volunteer groups and refugee newcomer households. After meeting inclusion and exclusion criteria, our algorithm uses scoring and randomization to make matches. Our dashboard offers implementing partners an easy-to-use interface for programme management and to understand matching.  

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Consultation & Training

Pairity is designed for scaling and quick adaptation to different contexts. We offer consultations with Canadian and European experts in programming for volunteer engagement with refugee newcomers.

We also offer training curriculum for volunteers, consultations on volunteer recruitment, programming timelines, and training staff.

Volunteer & Newcomer Recruitment

Pairity has low barriers to participation. It works with volunteers and newcomers who are already in the same location, giving people the chance to make an immediate difference with no commitment beyond their time. Pairity works with already established groups of volunteers. these can be friends, family members, coworkers, neighbours, or any group of people who want to be involved.



Pairity matches based on objective criteria. It accounts for the preferences and characteristics of both volunteers and refugees, and is designed to be flexible for local contexts.

Pairity’s comprehensive preference-ranking and demographic surveys were developed over two years, with both volunteers and refugee newcomers. They cover the range of information and preferences of both groups. 

Surveys are ready to translate to any language, and adaptable to different contexts depending on the needs of the implementing partner.

Volunteer surveys are conducted online and take roughly 35 minutes. Newcomer surveys are conducted in person by trained field researchers and take roughly 60 minutes.


Pairity’s algorithm is the core of our matching process. It works directly with the online and in-person surveys through our back-end data architecture. 

The algorithm has two purposes: to make the best possible matches, and to provide baselines for outcome analysis. It matches based on six variables.

  • Geographic distance
  • Household composition
  • Newcomer vulnerability & volunteer capacity
  • Labour market experience
  • Language and culture
  • Hobbies and interests


Data Dashboard

Pairity’s data-visualization dashboard offers implementing partners an easy-to-use interface for understanding the characteristics of both volunteers and newcomers.

It features an easy map function, contact information, household composition, and measurements including a self-sufficiency matrix for newcomers. It also includes notes from volunteers and field researchers so implementing partners understand the particulars of the different matches.

Outcome Analysis

Pairity is backed by robust academic research. It was designed by an interdisciplinary team from the University of Toronto, Columbia University, and the University of Mannheim.

We provide monitoring and evaluation for implementing partners and funders, as well as generating a data-set for a broader research program.

The research program is designed to generate a growing dataset for comparative academic research, context-specific evidence about how social networks affect integration and social cohesion, and to use data-driven analytics to optimize outcomes and evidence-based policy decisions.


Integration Outcomes

Pairity’s end-to-end platform ensures we can measure the real impact of social networks on outcomes including labour market participation, language skills, and community integration. 

Analysis includes monitoring & evaluation activities for local implementing partners, donors, and governments, contributes to a growing scholarly dataset, and is used to optimize the matching algorithm.