Building a Stronger Evidence Base for Skills: Using Linked Data to Measure Long-Term Outcomes

Most program evaluations capture short-term results, leaving long-term benefits and the return on public investment unclear. A new partnership between Blueprint, Statistics Canada, the Future Skills Centre, and Employment and Social Development Canada links administrative data to track long-term outcomes, value for money, and returns to Canadians.

Strengthening programs through better evidence

Since 2021, Blueprint and the Future Skills Centre’s Scaling Up Skills Development portfolio—funded by Employment and Social Development Canada—has been expanding and rigorously evaluating high-potential training and employment programs across the country. This represents a unique opportunity to understand how different training and employment supports help participants build skills, enter the labour market, and progress in their careers.

As part of this effort, Blueprint is conducting randomized controlled trials (RCTs) of three programs in the portfolio: the Canadian Career Development Foundation’s In Motion & Momentum+ (IM&M+), the Diversity Institute’s Advanced Digital and Professional Training (ADaPT), and NPower Canada’s IT training programs. RCTs are a cornerstone of the portfolio’s evidence strategy because they allow us to measure whether impact can be attributed to the program intervention or not.  

But to understand how those impacts unfold over time, we need long-term, reliable outcome data.

The evidence gap: Why we need better long-term measurement

To understand impact on individuals, program evaluations often rely on surveys administered to participants at various points during and after a program. These sources of information are important, but they can be time-intensive to administer because they require distribution, outreach to participants, and repeated follow-ups. Post-program surveys can also suffer from low response rates and capture only the earliest stages of people’s employment journeys (periods when employment and earnings may be especially volatile). Some people may be more likely to respond to the surveys than others, resulting in sample bias.

International evidence reflects this pattern: in other RCTs of skills programs, it is common to see small or no effects among participants in the first two years after program participation. Many people need time to complete training, secure stable work, and progress in their careers to achieve higher earnings. For example, MDRC’s new 10-year study found that short-duration training in the United States can generate real earnings gains, but these impacts took years to appear. If we look only at the short-term impacts (when people immediately complete a program), we may be missing the longer-term impact of a given intervention.

Without stronger long-term evidence, governments and service providers may lack the clarity needed to identify which approaches deliver lasting results and the greatest return for people and public investments. This information is essential to ensure resources are directed toward programs that generate sustained labour-market gains.

A new approach: Linking program data with national administrative data

To address this evidence gap, Blueprint, FSC, and ESDC have established a groundbreaking partnership with Statistics Canada (StatCan) to link participant data from several projects to StatCan’s Social Data Linkage Environment (SDLE). The SDLE is StatCan’s secure data linkage platform that connects information from multiple administrative sources, such as tax, employment, and education records, for research and statistical purposes. 

Under the Statistics Act, StatCan is the only organization authorized to acquire and link these datasets at a national scale. When participants enrolled in training programs, they consented for FSC and Blueprint to use their data anonymously to conduct longitudinal research. Blueprint securely submits to StatsCan only the basic information needed for linkage, such as name and date of birth, under StatCan’s established intake procedures. StatCan then separates personal identifiers from analytical data using its linkable data file system, encrypts and removes identifiers during linkage, and provides researchers with access only to anonymized, linked datasets in secure research environments. This process meets Canada’s highest privacy, security, and confidentiality standards.

By connecting program participation records to national administrative data, researchers can follow participants for many years after they complete training and observe when earnings gains begin to emerge, how stable those gains are, how different groups of participants benefit, and how programs compare in terms of long-term value for Canadians. Administrative data cover nearly the entire population, are collected consistently each year, and are not affected by recall error or survey non-response. When combined with rigorous evaluations like RCTs, linked data allow for accurate, reliable measurements of long-term program impacts.

Building a stronger evidence infrastructure: The Results for Canada initiative

This StatCan-led, privacy-protected process is a critical foundation for building a more robust skills evidence system in Canada. The Results for Canada initiative is testing how to make this capability accessible to community-based organizations nationwide. The project is developing practical models for structuring data, securing consent, and streamlining linkage with StatCan, laying the foundation for a standardized, efficient, privacy-protected evidence system that serves all partners.

Pairing secure data linkage with rigorous evaluations can help governments and training providers determine whether programs work, how they work, and for whom they create lasting results. This infrastructure can also extend to measuring broader outcomes such as education, health, housing stability, and other indicators of economic and social well-being. 

As the initiative progresses through 2026, Blueprint will share case studies on how the Results for Canada model has been mobilized to support community-based organizations to better understand their impact and improve their services. In addition to these case studies, we will share a toolkit for data linkage across the sector, providing guidance to service delivery organizations on how to prepare and structure their data for linkage, what analytical questions they can address with linkage, and how to engage StatCan to make linkage possible. These case studies and toolkits will provide a basis for further scaling the model to leverage administrative data across the country and support an efficient, evidence-driven service delivery ecosystem.

As this approach expands, it can reshape how Canada’s skills ecosystem measures success. It reduces survey burden, generates consistent evidence across programs, and provides a shared foundation for assessing outcomes for people and value for money for funders.

The views, thoughts and opinions expressed here are the author’s own and do not necessarily reflect the viewpoint, official policy or position of the Future Skills Centre or any of its staff members or consortium partners.