by The gender wage gap Uber has conducted a study of internal pay differentials between men and women, which they describe as “gender blind” … The study found a 7% pay gap in favor of men. They present their findings as proof that there are issues unrelated to gender that impact driver pay. They quantify the reasons for the gap as follows: Where: 20% is due to where people choose to drive (routes/neighborhoods). Experience: 30% is due to experience … Speed: 50% was due to speed, they claim that men drive slightly faster, so complete more trips per hour … The company’s reputation has been affected by its sexist and unprofessional corporate culture, and its continued lack of gender balance won’t help. Nor, I suspect, will its insistence, with research
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Lars Pålsson Syll considers the following as important: Economics
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The gender wage gap
Uber has conducted a study of internal pay differentials between men and women, which they describe as “gender blind” … The study found a 7% pay gap in favor of men. They present their findings as proof that there are issues unrelated to gender that impact driver pay. They quantify the reasons for the gap as follows:
Where: 20% is due to where people choose to drive (routes/neighborhoods).
Experience: 30% is due to experience …
Speed: 50% was due to speed, they claim that men drive slightly faster, so complete more trips per hour …
The company’s reputation has been affected by its sexist and unprofessional corporate culture, and its continued lack of gender balance won’t help. Nor, I suspect, will its insistence, with research conducted by its own staff to prove it, that the pay gap is fair. This simply adds insult to obnoxiousness.
But then, why would we have expected any different? The Uber case study’s conclusions may actually be almost the opposite of what they were trying to prove. Rather than showing that the pay gap is a natural consequence of our gendered differences, they have actually shown that systems designed to insistently ignore differences tend to become normed to the preferences of those who create them.
Spending a couple of hours going through a JEL survey of modern research on the gender wage gap, yours truly was struck almost immediately by how little that research really has accomplished in terms of explaining gender wage discrimination. With all the heavy regression and econometric alchemy used, wage discrimination is somehow more or less conjured away …
Trying to reduce the risk of having established only ‘spurious relations’ when dealing with observational data, statisticians and econometricians standardly add control variables. The hope is that one thereby will be able to make more reliable causal inferences. But if you do not manage to get hold of all potential confounding factors, the model risks producing estimates of the variable of interest that are even worse than models without any control variables at all. Conclusion: think twice before you simply include ‘control variables’ in your models!
That women are working in different areas than men, and have other educations than men, etc., etc., are not only the result of ‘free choices’ causing a gender wage gap, but actually to a large degree itself the consequence of discrimination.
The gender pay gap is a fact that, sad to say, to a non-negligible extent is the result of discrimination. And even though many women are not deliberately discriminated against, but rather ‘self-select’ (sic!) into lower-wage jobs, this in no way magically explains away the discrimination gap. As decades of socialization research has shown, women may be ‘structural’ victims of impersonal social mechanisms that in different ways aggrieve them.
Looking at wage discrimination from a graph theoretical point of view one could arguably identify three paths between gender discrimination (D) and wages (W):
- D => W
- D => OCC => W
- D => OCC <= A => W
where occupation (OCC) is a mediator variable and unobserved ability (A) is a variable that affects both occupational choice and wages. The usual way to find out the effect of discrimination on wages is to perform a regression “controlling” for OCC to get what one considers a “meaningful” estimate of real gender wage discrimination:
W = a + bD + cOCC
The problem with this procedure is that conditioning on OCC not only closes the mediation path (2), but — since OCC is a “collider” — opens up the backdoor path (3) and creates a spurious and biased estimate. Forgetting that may even result in the gender discrimination effect being positively related to wages! So if we want to go down the standard path (controlling for OCC) we certainly also have to control for A if we want to have a chance of identifying the causal effect of gender discrimination on wages. And that may, of course, be tough going, since A often (as here) is unobserved and perhaps even unobservable …