Eine Studie hat untersucht, wie es bei dem „Taxikonkurrenzunternehmen“ Uber zu einem Gender Pay Gap kommt. Uber ist im wesentlichen eine App, bei der sich beliebige Leute als Fahrer anmelden können, über die App Fahrten annehmen können, die nach einem von Uber festgelegten und für alle Fahrer gleichen Satz abgerechnet werden
Der Abstract der Studie:
The growth of the „gig“ economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7% gender earnings gap amongst drivers. The uniqueness of our data—knowing exactly the production and compensation functions—permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors:
- experience on the platform (learning-by-doing),
- preferences over where/when to work, and
- preferences for driving speed.
This suggests that, as the gig economy grows and brings more flexibility in employment, women’s relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination
Quelle: The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers
(Auflistung zum besseren Überblick ergänzt)
In einem interessanten Interview wird das Ergebnis näher besprochen:
Erst einmal zu Uber:
DUBNER: Now, for people who don’t know what Uber is or haven’t used it, just give us a quick explanation of how it works and kind of what it is.
DIAMOND: Uber is a platform that connects drivers to riders, so people want a ride from A to B, and you put that into the Uber app, and it will match you with a driver who will happily give you that ride.
LIST: When Uber receives a call from a customer like me, what they simply do is they put out a dispatch to drivers that are nearby and ask them to accept the trip.
HALL: And then a driver who is on the system can decide whether or not to accept your request. And he or she can come pick you up and take you where you need to go, so we ultimately coordinate drivers and riders.
Zu der Frage, wie man Benachteiligungen ausgeschlossen hat:
HALL: But that does not guarantee that the platform couldn’t facilitate some kind of nefarious discrimination.
LIST: There are two kinds of discrimination that might actually occur on Uber’s platform. The first is from the dispatches or from setting the wages. And that’s what Uber’s job is. There’s no discrimination there. But on the other hand, there could be customer-side discrimination. It could be the case that we as riders prefer men or prefer women as our drivers.
DUBNER: So did you find discrimination on behalf of the users of Uber? Did riders tend to prefer male drivers to women?
LIST: No, we find no evidence of discrimination on the customer side, meaning that riders don’t prefer men to women or women to men. They view men and women the same when it comes to being their driver.
DIAMOND: That’s right. And we don’t see overall differences in rejection rates between male and female drivers. And if you were to put that in the regression, it doesn’t contribute to a gender gap.
Uber setzt also die Preise fest, für alle gleich, und Frauen werden nicht häufiger abgelehnt als Männer, wenn es um Fahrten geht, so dass es in der Tat schwierig ist, da noch eine Diskriminierung auszumachen.
Wie kommt es dann zu dem Unterschied?:
Der erste Faktor, der gefunden worden ist, wären die Orte und die Zeit zu der Fahrten gemacht werden:
LIST: So after reaching the dead end of discrimination doesn’t seem to be a determinant, we then decided to ask, “Well, what about where and when?” So what I’m thinking about here is time of day, day of week, and where in Chicago they actually drive. And here, we had some success. So what we find is that after you explore the where’s and when’s, we find that we can explain roughly 20 percent of the gender pay gap by choices over where to drive and when to drive.
(…)
LIST: So what is more important than when you drive, is exactly which trips or routes do you tend to focus on. So one particularly salient example here is that airport trips tend to be the most profitable trips on the Uber platform. So what you have is that men tend to complete more airport trips than women complete.
DIAMOND: So we find that where people pick up is more important for contributing to the gender gap than the when. There are differences between when men and women drive. Men are much more likely to drive the graveyard overnight shift, which could have more people coming home from bars or whatnot. But women are actually dramatically more likely to drive the Sunday afternoon shift, and that is also a very lucrative driving time. So it’s not so much that there aren’t differences about when men and women drive. It just doesn’t seem to be super-related to driving a pay gap, because they’re both driving at lucrative times, they’re just different times. I mean, Sunday afternoon, that’s when football is on. Maybe women are more willing to go drive for Uber then.
DUBNER: And why — why is Sunday afternoon a more lucrative time to drive? It it because so many male drivers are watching football, so they’re not flooding the market with supply of drivers and therefore the price goes up?
Also Männer und Frauen suchen sicher verschiedene Fahrtzeiten und Ziele aus, Männer, wie auch sonst häufig im Berufsleben, Ziele weiter weg von zuhause, eher nachts, dafür nicht, wenn große Sportereignisse laufen.
Als nächster Faktor wurde Erfahrung benannt:
LIST: I think that’s right. But now after looking at time and location, that analysis actually hinted at a deeper effect, which I will call driver experience.
HALL: Yeah. So there are pretty large returns to what we call experience, which is literally the number of trips that you have done. This is an area that’s pretty well-studied in economics, and it’s learning-by-doing. We estimate that the more trips you do as a driver, the more you learn about how to make money on the platform.
DIAMOND: So obviously this is not getting a raise from Uber, in the sense that the formula of pay is changing. Drivers are just getting better at figuring out when and where to drive, a little bit about how fast to drive, and also, how to strategically accept or cancel rides that they think are a bad match.
HALL: And we estimate that men and women learn identically quickly in trips. So a man or a woman in the data who have done the same number of trips will have accumulated the same amount of learning. However …
LIST: When you look at the experience of our drivers or the average tenure, this is heavily tilted in men’s direction. Men are far more likely to have been driving on Uber for over two years. Women are likely to have just joined in recent months, and this is because women leave the platform much more often than men.
(…)
DIAMOND: So at any given day or time, the men driving for Uber have a higher level of experience under their belt than women, and that plays an important role in compensation.
HALL: And that explains about 30 percent of the pay gap that we measure.
Ich vermute mal, dass Frauen eher überbrücken, um etwas dazu zu verdienen, wenn es gerade passt. Das würde auch gut zum Sonntag passen. Zudem bleiben sie vielleicht auch lieber in der Nähe der Kinder und nehmen deswegen weniger gern lange Fahrten an und arbeiten auch weniger gern nachts. Männer hingegen sehen es nicht als Überbrückung, sondern als zusätzliche Verdienstquelle. Gerade dann kann eine Nachtschicht am Wochenende besonders lukrativ sein.
Und schließlich die Fahrgeschwindigkeit:
HALL: Yeah. So the third factor, which explains the remaining 50 percent of the gap, is speed.
DIAMOND: So men happen to just drive a little bit faster, and because driving a little bit faster gets you to finish your trips that much quicker, and get on to the next trip, you can fit more trips in an hour, and you end up with a higher amount of pay.
DUBNER: Now how did these Uber driver data for male/female speed compare to male/female driver speed generally? Do we know for a fact that men generally drive faster than women?
LIST: Yeah, what you find is that in the general population men actually drive faster than women.
DUBNER: Okay, so male Uber drivers drive faster than female Uber drivers, and therefore that helps them make more money. Is that, however, more dangerous, the faster driving?
DIAMOND: So the gap is small — men drive about 2 percent faster than women. So it doesn’t suggest that that’s leading to big differences in risk
Männer fahren also auch noch schneller und können daher in der gleichen Zeit auch mehr Fahrten erledigen. Sie fahren dabei nur ein klein wenig schneller, aber das macht immerhin 50% des Unterschieds aus.
Interessanterweise sind das alles Faktoren, die bei einer typischen Überprüfung des Gender Pay Gaps gar nicht geprüft werden können. Denn dort werden meist nur Statistiken, die vom Arbeitgeber übermittelt werden, ausgewertet. Ob die Männer beispielsweise auch dort eher effektiver arbeiten oder nicht, ob sie sich bestimmte Jobs zusätzlich auflasten, die etwa in unbezahlten Überstunden geleistet werden oder die eher zu unbequemen Zeiten stattfinden oder bei denen mal eher die Gefahr besteht, dass man Dienstreisen macht oder etwas flexibler mit der Zeit sein muss, dass alles wird noch nicht einmal getestet.
Dennoch entsteht ein relativ großer Unterschied in einem ansonsten sehr fairen Markt, der keine Diskriminierung erkennen lässt. Ich denke ich werde noch häufiger auf die Studie zurückkommen.
Einen interessanten Aspekt habe ich auch noch hier gefunden:
Frauen bekommen üblicherweise mehr Trinkgeld. Es könnte gut sein, dass sich für sie kürzere Strecken mehr lohnen, wenn sie dafür häufiger mehr Trinkgeld bekommen oder sie eben darüber den Nachteil ausgleichen ohne Nachtschichten zu fahren.
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