The JP Morgan Chase Institute released their 2018 Online Platform Economy in 2018 study this week.
It generated a lot of media attention, most of which focused on the data showing the average rideshare driver's earnings per month declined 53% between 2013 and 2017 (click on the chart to enlarge).
This, of course, led to a rash of headlines proclaiming earnings for gig workers have fallen substantially. These include:
Recode: Uber drivers and other gig economy workers are earning half what they did five years ago
Wall Street Journal: When the Supply of Uber and Lyft Drivers Rises, Their Earnings Fall
MarketWatch: Drivers for Uber, Lyft are earning less than half of what they did four years ago, study finds
The only problem is that's not exactly what the study found.
Yes, the study did find that the monthly earnings for the average gig worker driving people or goods decline by 53% between 2013 and 2017.
But as Uber nicely summarizes in their response to the study, the study does not include any data on how many hours per month the drivers worked.
So if the average driver worked fewer hours in 2017 than in 2013 - which Uber says is true for their drivers - then their monthly total income from driving would of course go down. This is true even if their hourly wage stayed the same or increased.
Our research shows, like Uber claims, that on average those working via the online gig platforms are working fewer hours per month than in the past.
A key reason is more people are taking advantage of the low friction, highly flexible work offered by these platforms to work when they have small amounts of spare time or gaps in their schedules.
This includes students, care givers, those with illnesses, those with full-time jobs looking for supplemental income and others who need high levels of flexibility.
Our research also shows only a slight decline in gig worker hourly earnings over the study's time frame. This data supports the thesis that the average platform worker is working fewer hours.
The study, BTW, is excellent and has a lot interesting data. It also has a "big data" methodology that knocks your socks off (or at least it knocked our socks off. And yes, we do need to get out more).
See the report for details on the methodology.
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