In most cases, workers are given only two choices for each job they’re offered on a platform — accept or decline — and they have no power to negotiate their rates. With the asymmetric information advantage all on their side, companies are able to use the data they’ve gathered to “calculate the exact wage rates necessary to incentivize desired behaviors.”
One of those desired behaviors is staying on the road as long as possible, so workers might be available to meet the always-fluctuating levels of demand. As such, Dubal writes, the companies are motivated “to elongate the time between sending fares to any one driver” — just as long as they don’t get so impatient waiting for a ride they end their shift. Remember, Uber drivers are not paid for any time they are not “engaged,” which is often as much as 40% of a shift, and they have no say in when they get offered rides, either. “The company’s machine-learning technologies may even predict the amount of time a specific driver is willing to wait for a fare,” Dubal writes.
If the algorithm can predict that one worker in the region with a higher acceptance rate will take that sushi delivery for $4 instead of $5 — they’ve been waiting for what seems like forever at this point — it may, according to the research, offer them a lower rate. If the algorithm can predict that a given worker will keep going until he or she hits a daily goal of $200, Dubal says, it might lower rates on offer, making that goal harder to hit, to keep them working longer.