Why it's Important to Correctly Classify Workers in Canada’s Growing Gig Economy

Gigs might have been associated with musicians back in the day, but now the modern economy is being shaped by the rising gig economy, where freelance workers are upending the traditional job market historically centered on full-time workers. This shift is crucial for employers and HR managers to recognize as a mainstay in how companies do business given the important, and potentially costly, tax ramifications that can arise by incorrectly classifying a worker.

According to media reports, around 1.9 million Canadians dub themselves self-employed while another 2.3 million are classified as temporary employees. Taken together, these two groups represent approximately 21.5 percent of Canada's overall workforce.

A 2017 study from Intuit Canada estimates nearly half of Canada’s job market will consist of freelancers, independent contracts and on-demand workers by 2020.

Web designers, consultants, writers, photographers, filmmakers and architects, among others, make up this new class of workers, whose very definition of “work” is flexible. Their offices may be coffee shops or work-sharing spaces. Such telecommuting allows them to tackle their projects on weeknights and weekends, if they like. They might juggle several clients at once, or work for one company over an extended period of time. If you’re in the workforce today, chances are you work alongside one of these individuals.

But if you’re an employer, misclassifying an employee’s status can be a risky road to go down. Such a mistake has critical tax and legal considerations for workers and employers.

As we noted in this blog post, amendments to Ontario’s Employment Standards Act, 2000 (“ESA”) came into force on November 27, 2017, and include criminal prosecution for hirers who misclassify their workers under certain circumstances. We also stressed that “there is a rebuttable presumption that a worker is an employee and the hirer has the burden of proving otherwise.”

With these changes, the number of misclassification cases brought against employers will likely surge, and these cases should progress more quickly with the increase in enforcement officers, this post highlights.

A few examples of the risks hirers face if they make this type of error include liability for overtime pay, lawsuits stemming from vicarious liability or lack of reasonable notice when ending the working relationship and damage to reputation. Employees risk being deprived of their rights under relevant worker legislation and shouldering responsibilities and risks that they might not be best suited to bear.

Employers who violate the ESA could face prosecution and, if convicted, fines can range between $50,000 and $500,000 depending on whether the conviction applies to an individual or corporation, and if it is a first, second or third offence.

Finding enough information to help with worker classification can be onerous. As this blog by Fasken points out: “Proving that a worker is not an employee can be particularly challenging for an employer who may not have access to information about the contractor's business, such as whether the contractor has other sources of income, or what tools the contractor uses to perform the work.”

It was this challenge that led to the creation of Blue J Legal’s Worker Classifier. The tool cuts through the ambiguity of this complex legal area, quickly and plainly:

• Showing how likely it is that the courts would rule one way or another on the issue
• Providing an explanation for the prediction, which includes a list of past decisions that are most similar to the issue
• Predicting the outcome with an average accuracy of 90%

Within minutes, you can find out if the worker in question is an employee or an independent contractor.

In a recent test expanded upon here, Employment Foresight correctly predicted the correct outcome in 36 of the 40 cases, reaching an accuracy level of 90%. These included three cases where the worker was found to be a Dependent Contractor.

Even more impressive was the confidence the Employment Foresight algorithm expressed in its predictions. In 31 of the 36 correctly predicted cases, our program was at least 80% confident in its result. In 25 of those, Employment Foresight was over 90% confident.