The 'gig economy' was all the rage a few years ago. And then things started to go bad in 2106 .
"Participation in the “sharing” or “gig” economy was once touted as the future of work in America. But the new data from the JPMorgan Chase Institute suggests that isn’t the case. Instead, wages for workers have gotten worse as many of these companies—Uber and Lyft, to pick two examples—have cut pay rates to make prices more attractive to consumers. And the jobs themselves appear to have served as stop-gap measures for people who were unemployed or had fallen on hard times during and after the recession." (Source: link)
Key to this new awareness was the gap between the so-called 'capital' and 'labour' platforms. As the article says "there’s a big difference in the kind of extra money that people earn on platforms like Airbnb versus platforms like Uber. It illustrates the classic divide between capital and labor. A new study (link) from the JPMorgan Chase Institute, finds that people who rent out assets on “capital” platforms like Airbnb or car-sharing site Turo are bringing in supplemental income. That’s starkly different from people who sign up for ”labor” platforms like Uber or TaskRabbit. They’re typically working to offset shortfalls in their monthly earnings." (Source: link).
No other platform has come under the spotlight for this more than Uber. And rightfully so: "Because of this tremendous scale, Uber is the most important test case for the gig economy, the new economic arrangement where contract workers are arranged into a cohesive labor force by software. There are many companies that share Uber’s controversial approach to doling out work, but none has amassed 3 million people who use the service to try to make money. Never before has an app’s design been so important to so many people." (Source: link)
Uber responded by launching a new app for drivers (link) with over 40 new features that "improve the driving experience." Improvements include helping drivers: track progress toward their goals, upcoming earning opportunities, feedback from their riders, and information about their account. All of which is good stuff.
But the real way forward may need a deeper understanding of the motivations and behaviours driving a global shift to the on-demand / gig economy.
"According to drivers, one main advantage of working for a rideshare platform was the flexibility that the system affords in terms of where and when to work, and the low level of commitment that is required by signing up. Some individuals drove full-time, but many also drove for fun, out of curiosity, or on a part-time basis. Many drivers used the ridesharing app in collaboration with their own daily routine to earn extra income, turning the driver app on for the daily commute for example, or doing chores around the house while waiting for a ride request to come in. In addition to the added financial flexibility that rideshare work affords, many drivers we interviewed mentioned social motivations for rideshare driving. Several drivers, for example, weighed the fun of meeting and having conversations with new people and the desire to help out the community as greater than or equal to their motivation to earn extra income."
This perspective is mirrored in a recent article by philosopher and sociologist Hanzi Freinacht on what he calls the 'Triple H' Phenomena (thanks Gilad Gome!) These 'Hackers, Hipsters & Hippies' ”form a complex but united front against the capitalist society in which they take part, a subtle revolution of cultural capital.” (Source: link)
"Somewhat strange bed-fellows, these three. What, then, unites the triple-H population? One thing is that all three groups share an alternative relationship to work and the market: they are all driven by what psychologists of work call intrinsic motivation and self-realization, rather than extrinsic motivation, such as monetary rewards, consumption and security." (Source: link)
And that is the key shift - from extrinsic to intrinsic motivation - is what gig economy platforms, whether capital or labour driven, but probably more so the latter, need to connect to.
As the Carnegie Mellon study says on 'designing algorithmic information support': "Supply-demand control algorithms were originally designed to solve mathematical optimization problems that involve non-human entities. In Uber and Lyft, however, they are used to motivate and control human behaviors. This causes problems, as the supply-demand control algorithm does not consider the pace at which drivers work. Consistent with previous research on a smart agent that tried to encourage sustainable behaviors, the algorithm failed to account for feelings of inequity people had toward surge pricing, and ignored the social and altruistic motivations of drivers. This highlights the importance of making algorithmic management accommodate: a) the speed and the way humans work, b) diverse types of motivations rather than only economic ones, and c) emotions that people feel about the decisions that algorithms make. In addition, some drivers did not trust the surge-priced areas as they trusted in their experiences more. Transparency in how the surgepriced area was computed in real-time could improve workers’ trust toward the algorithmic information."
Gig economy jobs typically feature low barriers to participation (in terms of assets, skills and time), as well low switching costs. This makes them very vulnerable to churn and consequent volatility.
If dominant platforms such as Uber don't find ways to engage with the deeper motivations of their workforce, they might see erosion of worker market share to way more interesting, more personally rewarding gig platforms.
Such as, for example: dog walking!
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