IRPP study proposes new algorithms to identify the best employment and training options
Montreal — Whether it’s due to COVID-19, ongoing automation or an aging workforce, Canada’s economy has in recent years seen many sectors lose jobs while others scramble to find workers. The constantly evolving labour market has made one thing clear: workers’ resilience to change depends more than ever on their ability to take advantage of emerging employment opportunities. To better equip workers for finding the jobs that are right for them, the authors of this study from the Institute for Research on Public Policy propose a new skills-based approach to career guidance.
In their IRPP study, Matthias Oschinski and Thanh Nguyen put forward two algorithms. The first one, which they call the “proximity algorithm,” identifies occupations in which there is a lot of overlap with an individual’s current occupation in terms of competencies, general work activities and interests. This helps identify the most suitable employment alternatives for this individual.
The second algorithm determines the skills gaps that need to be addressed to make these employment transitions possible and serves as a basis for making training recommendations.
“Our algorithm is flexible enough to select the employment alternatives that minimize retraining requirements – thus ensuring faster transitions ─ and offer better pay and employment prospects than workers’ current jobs. As a result, individuals would have a variety of feasible employment pathways to choose from and would be better able to find the one that suits them best,” says Oschinski.
To illustrate their method, the authors look at employment alternatives for occupations that are most affected by the pandemic. For instance, they find that retail salespersons — who are at high risk of viral transmission and automation— could, with relatively moderate effort and appropriate training, become insurance sales agents. Integrating the methods proposed in this IRPP study with a career information system that points workers to the right training programs and relevant job postings would help make their career-change dreams a reality.
As the world of work constantly evolves, workers’ ability to effectively navigate employment and training options is becoming increasingly important. The algorithms Oschinski and Nguyen propose can provide tailored employment options for those looking for new opportunities and inform them about the skills gaps that need to be addressed to undertake these job transitions.
Finding the Right Job: A Skills-Based Approach to Career Planning by Matthias Oschinski and Thanh Nguyen can be downloaded from the IRPP’s website (irpp.org).
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