Paying people more to cover undesirable shifts means that people with more flexibility or who choose to make more personal sacrifices will get paid more. I remember something about... pharmacists I think it was? and how these factors lined up with some of the traditional "group X gets paid more than group Y" groupings.
This is key because it’s close to the thing that made me go “ah ha” reading the intro: modern surveillance and algorithmically-managed pricing (so, cheaply modified at arbitrarily-fine resolution based on arbitrarily-many quantifiable factors) open up the possibility of pushing this exact effect from groups of workers to individual workers.
You can avoid (at least to some degree not previously achievable) addressing a pool of workers and instead only clear the rate for a particular worker. No human management input needed per-decision or in any part of the broader offer-decision process and surrounding data gathering and measuring, which is what makes it possible.
This is a topic my friends and I have been talking about since at least 2018, and much of this review just feels affirming. It's good to know 4 bozos in Wisconsin aren't the only ones realizing life sucks for Uber drivers. All I can really do about it is vote.
The thing that stood out to me the most is the transparency argument. When you get paid hourly, you're told upfront how much extra you get paid for overtime, for odd hours, etc. But with rideshare apps, a number just shows up on your screen. You have no knowledge of why that number is what it is.
There's also the social side of the transparency issue. Back when I worked foodservice, I could just ask my coworker what they got paid per hour. As a rideshare driver, you never see your coworkers. Even if you did, you have nothing to compare. There's no "per hour," and "per mile" is rife with caveats, assuming the app even shows you that. With no way to compare to each other, how do rideshare drivers collectively discern what is or isn't fair pay?
Great app idea for the bright minds of HN: an app that lets a rideshare driver easily record hour + mileage when working, combines it with the daily earnings (to get $/hr and $/mile) and let's them share with fellow workers.
In dumb people terms, corporate gig economy players are using algorithms to artificially depress wages of random workers for work that is very similar in scope, if not the exact same (they work similar hours, in a similar area, with similar vehicles and in some cases they are even getting offers for the exact same job with wildly varying wages)
It’s like how a normal person who’s worked with a couple of solo independent contractors (say, for some work on their house) and has a sense of how they both price things from past bids, might offer a job they need done by a certain time to both of them—exact same job—at different rates, all else being equal (the homeowner rates their work as comparable), just because they know one of them historically bids lower. They’d prefer the cheaper one, but are willing to pay either rate, and need the job done by time X, so offer it to both, first to accept gets it.
Now throw in a bunch more factors than just prior bid history, thousands of workers in the dataset instead of two, and such high volume and pace of work that you can afford to periodically experiment by setting, say, a random 10% of each set of offer-receivers lower than your formula would usually suggest, to see if anything’s changed and maybe they’re more-desperate for some reason (you don’t even need to know why… though, imagine if you could spot reasons some workers might be more desperate! Hm…)
Now (maybe) extrapolate to similar, slower-paced efforts in less-marginal areas of work. Interesting (yikes) possibilities.
Comments
This is key because it’s close to the thing that made me go “ah ha” reading the intro: modern surveillance and algorithmically-managed pricing (so, cheaply modified at arbitrarily-fine resolution based on arbitrarily-many quantifiable factors) open up the possibility of pushing this exact effect from groups of workers to individual workers.
You can avoid (at least to some degree not previously achievable) addressing a pool of workers and instead only clear the rate for a particular worker. No human management input needed per-decision or in any part of the broader offer-decision process and surrounding data gathering and measuring, which is what makes it possible.
This is a topic my friends and I have been talking about since at least 2018, and much of this review just feels affirming. It's good to know 4 bozos in Wisconsin aren't the only ones realizing life sucks for Uber drivers. All I can really do about it is vote.
The thing that stood out to me the most is the transparency argument. When you get paid hourly, you're told upfront how much extra you get paid for overtime, for odd hours, etc. But with rideshare apps, a number just shows up on your screen. You have no knowledge of why that number is what it is.
There's also the social side of the transparency issue. Back when I worked foodservice, I could just ask my coworker what they got paid per hour. As a rideshare driver, you never see your coworkers. Even if you did, you have nothing to compare. There's no "per hour," and "per mile" is rife with caveats, assuming the app even shows you that. With no way to compare to each other, how do rideshare drivers collectively discern what is or isn't fair pay?
Great app idea for the bright minds of HN: an app that lets a rideshare driver easily record hour + mileage when working, combines it with the daily earnings (to get $/hr and $/mile) and let's them share with fellow workers.
In dumb people terms, corporate gig economy players are using algorithms to artificially depress wages of random workers for work that is very similar in scope, if not the exact same (they work similar hours, in a similar area, with similar vehicles and in some cases they are even getting offers for the exact same job with wildly varying wages)
It’s like how a normal person who’s worked with a couple of solo independent contractors (say, for some work on their house) and has a sense of how they both price things from past bids, might offer a job they need done by a certain time to both of them—exact same job—at different rates, all else being equal (the homeowner rates their work as comparable), just because they know one of them historically bids lower. They’d prefer the cheaper one, but are willing to pay either rate, and need the job done by time X, so offer it to both, first to accept gets it.
Now throw in a bunch more factors than just prior bid history, thousands of workers in the dataset instead of two, and such high volume and pace of work that you can afford to periodically experiment by setting, say, a random 10% of each set of offer-receivers lower than your formula would usually suggest, to see if anything’s changed and maybe they’re more-desperate for some reason (you don’t even need to know why… though, imagine if you could spot reasons some workers might be more desperate! Hm…)
Now (maybe) extrapolate to similar, slower-paced efforts in less-marginal areas of work. Interesting (yikes) possibilities.