Thank you, Mr. Chair and honourable members of this committee, for the invitation to share the inconvenient and unfortunate truths about the gig industry, specifically ride-share technologies operating in Canada.

My name is George Wedge, and I am the president-at-large of the Rideshare Drivers Association of Ontario, commonly known as RDAO. RDAO is an organizing, advocacy and lobbyist association. Our mission is to help governments of all levels understand how the ride-share industry is negatively impacting the lives of citizens, whether they are consumers or service providers, and to help drivers unite for the purpose of helping all levels of government see their way to bylaws, regulations and bills to prevent a monopolistic industry from driving the average wages of blue-collar workers down to third-world levels.

In October of this year, Canadian HR Reporter wrote:

More than one in five (22%) Canadians are participating in gig work of various kinds across the country. That equates to about 7.3 million adults finding work outside of regular employment.

Specific to ride-share, here in Ontario there are well in excess of 120,000 drivers. The number of ride-share drivers in Ontario pales in comparison to the number of delivery drivers in Ontario who depend on one of the fastest-growing industries in Canada: the gig industry. RDAO estimates that 25% of ride-share drivers are full-time drivers. They rely on their net proceeds to feed, clothe and house their families. This is the group of drivers most damaged by the unchecked, unregulated, monopolistic and exploitive tech giants. An overwhelmingly large percentage of ride-share drivers in Ontario are new Canadians, having come here to find a better life, many from third-world and conflict countries.

The big question—after generations of labour movements helped establish workers' rights and minimum wages—is this: How is it possible that more than one million Canadians are forced to accept hourly earnings well below the standard? The simple answer is a lack of transparency. The median hourly wage after expenses and vehicle depreciation for ride-share drivers in Canada is as low as $6.37 per hour, as reported in the peer-reviewed report entitled “Legislated Poverty”.

Uber and Lyft use marketing campaigns and cash incentives to lure unsuspecting drivers into the ride-share driving and gig work industry with vague promises of earnings that simply do not materialize. Recently in the U.S., Lyft settled a civil fine with the FTC for $2.1 million U.S. for misleading prospective drivers. RDAO can't imagine that Uber is immune on this issue, and we speculate their day is already circled on the FTC calendar.

Today was a very important day for RDAO. In the city of Toronto, a report was submitted to the city council executive committee based on one and a half years' worth of study of the vehicle-for-hire industry. In that report, we expected to see recommendations that would make the vehicle-for-hire industry in Toronto, specifically ride-share, taxi and limousine drivers.... We expected to see a road map in there that would create sustainability and wages that could support blue-collar workers. Unfortunately, it didn't materialize exactly the way we expected. We saw two of the three pillars required for the road map, but the third one is currently missing. There's a lot for us to do in that area.

On October 8 of this year, Uber activated its newest feature, which is called “upfront fares”.

Typically in the industry, there's a rate card that estimates a fare for a rider and the payout for a driver based on distance and time. This is what Uber has used since their installation in Ontario some nine years ago. On October 8, Uber activated something called “upfront fares”. There is no rate card. Instead, upfront fares are a three-part feature. The first part of the feature is where the driver gets all of the information for the ride that he expects to see: who he's picking up, where they're going and how long it's going to take them. The artificial part of this now is that they're using AI that looks at the rider, looks at all of their information and all of their history, and decides the maximum amount they can be charged. Then it looks at the driver and does exactly the opposite: What's the minimum amount they'll accept?