The Achilles Heel of the Gig Economy is that Workers Can’t Make Enough Money

New Orleans   Uber is the canary in the coal mine. After years of listening to the reports that held up Uber as the herald of the future, creating a new business model where an application would substitute for an employer, the accounting is finally coming due. Its drivers were touted as the vanguard of the gig economy, complete with claims that this was what the “new” worker really wanted from employment. Now it turns out Uber may be the canary dying in that coal mine because the Achilles heel of the gig economy is increasingly revealed: it’s not sustainable. No matter what Uber and others want to call them, they depend on workers, and workers are voting with their feet that they can’t make it on temporary work, so they have to keep moving, and that means working for another company. The gig economy doesn’t work when people can’t make a living on Uber and similar gigs.

Uber has lost $4 billion over the past 18 months for lots of reasons, but largely because it can’t make its workforce either happy or stable. They are like a bait-and-switch operation offering incentives, prizes, tips, and extra bonuses, but increasingly hitting the brick wall where their drivers are realizing they still are barely making minimum wages per hour. In fact the Wall Street Journal reported that Uber cooperated with a study done by a New York University professor that,

“found that no matter which directions fares go, drivers invariably take home about the same earnings over time…[because] When there is a fare cut, drivers’ pay per trip falls but riders flood the service, offering more business. A price rise eventually lures more drivers than Uber needs and scares away riders. The changes are short-lived as an equilibrium is reached after about eight weeks, and drivers’ average pay comes out the same.”

This means that Uber, the harbinger of the future, “must lean heavily on pricey incentive payments – cash for completely a certain number of rides a week, say – to bring driver earnings above what typically amounts to around minimum wage.”

Wow! I’ll guarantee you, because I know many once upon a time Uber and Lyft drivers that join on the promise of higher wages, and they leave when they finally realize that paying for gas, their car, insurance, and then looking at their pay, it just doesn’t add up. Uber is stuck on a business model that is based on exploitation of workers, that business model, like most of the vaunted “gig economy” is unsustainable, because workers fooled at first, are not fooled forever when it comes to the empty pay envelope.

Uber and the rest of these companies are not a new model, but an old one. They are labor contractors trying to sweat workers with a new tool, but an old scam. This is a piece rate scheme. Some workers can make it, but most can’t. Worse, all of these companies are pushing off their responsibilities as employers to provide social security, unemployment and even bare bones benefits, but making the workers who are their lifeblood into subcontract labor. In Europe and some US cities, that part of the hustle is also falling apart as Uber is increasingly declared an employer.

Workers are being gigged by this model. The canary is dying in red ink. A business model that depends on exploiting workers is doomed, even if it takes some time to die.

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Discrimination by Math

5399389a5e1ae61cf1eda5d0e84ef070Seattle   Having spent a week in Juneau, Alaska working with men and women dealing daily with the stigma and discrimination that comes with mental health challenges and disabilities, I should have been prepared for Cathy O’Neil’s Weapons of Math Destruction and its warnings of the pervasive, powerful, and often destructive and discriminating role that Big Data and the algorithms it is fueling are having on all of our lives. I wasn’t. But, I also wasn’t surprised.

One of the issues I heard about from the members of MCAN included being fired from jobs in violation of the Americans with Disabilities Act (ADA). They didn’t know the half of it! O’Neil detailed the way that huge employers including lower wage service establishments like McDonalds and others are using personality tests with data driven questions that sort out people with any kind of mental health issue. A lawyer in Tennessee watched his son, a super student with two years at Vanderbilt University who had dropped out for a couple of semesters to deal with depression successfully, somehow failed to land any minimum wage jobs as a janitor, burger flipper, and so forth from a number of companies using the same blunt instrument of a personality test. He filed a ADA class action suit that is still pending. Even that may be only the tip of the iceberg since data driven, resume reader machines are also discarding applications with a few misspellings, bad typos, and other trivialities.

These WMD’s, as O’Neill cleverly calls them, are perhaps most destructive when it comes to the way too many of them from police and crime statistics to loan applications to even the efforts to get insurance or an apartment from a landlord are discriminating, often invisibly, based on the zip codes identifying where someone lives. The question may never say race or risk, but the zip code identifying the neighborhood plots the Big Data odds, and they do not stack up in your favor. Stop and frisk programs, common under New York mayors Guilliani and Bloomberg and now touted by Trump, under analysis revealed huge racial profiling and targeting of African-Americans and Latinos because of misapplied and understood algorithms.

It was also disconcerting, given our long experience in the United States and Canada in providing service at citizen wealth centers for low-and-moderate income families to find that algorithms employed by payday lenders, diploma mills, and other shyster, predatory operations that are datamining names and contact information from people who are going online to ask for information and access to programs to provide them advice or assistance. I shouldn’t have been surprised. I can remember complaining to our tech people years ago when we used Google Ads about the fact that I could be writing a Chief Organizer Report on our fights against payday lenders and find, embarrassingly, ads running alongside my blog for some of the same blood sucking, scammers I was calling into account in the paragraphs next to their ads. Duh!

It goes on and on. O’Neill cautions that there are dangers here, and they need to be regulated not just for privacy along the European opt-in system, but for transparency. If you ever thought, even for a second, that some of the “value-added” tests for teacher evaluations that many states have employed were valid or about the meaning of things like body-math-indexes and wellness, your application for McDonald’s would also probably be rejected.

She does argue that it is not the math’s fault, as much as the way the math is being used. With a different objective some of the same algorithms could be pointing people in the right direction, connecting them with resources, getting them out of prison, rather than in, and into a job rather than out on the street.

There seems to be no mathematical formula on when that miracle might happen.

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