Once again we find the wage gap is due to mommy track

[Tom Sawyer]

That should be grammar, sentence construction and learning to write a coherent paragraph. In simple series with three or more items, the comma should be used between all but the last two items.

And between each is acceptable.

It adds an overly redundant amount of redundancy to the sentence and makes what you're saying sound redundant.

You also shouldn't start a sentence with "And".

 

[Loren Pechtel]

These researchers are assuming that the only way the gender would impact age and experience is via non-sexist factors like desire to be a stay at home mom. Only if that untested assumption is true, is there approach valid. And there is evidence that it isn't true. Women quit due to sexist treatment, and that would make those still on the job at any timepoint younger and less experienced than people who don't quit due to sexism, men.

True, you can't prove they didn't quit due to sexist treatment. I find it very hard to believe that most quit for such reasons, though!

- - - Updated - - -

If society wants to continue it should pay women more if they have children, not less.

It should reward all productive work. Not just productive work for the master.

Then society should pay them. No off-the-books welfare schemes!

- - - Updated - - -

It also said in the article that internal promotions between men and women is generally equal; however, men being offered higher positions with other companies is much more common than women being given the same opportunities. That said, internal promotions compared to external promotions statistically produce lower earnings.

You're missing the fact that the women were making more until they disappeared from the data.

 

[whichphilosophy]

You need women to have children. It is an imperative.

If most women did not have children, society would continue and be better off.
Societal continuation only needs a subset of women to have a single kid, or an even smaller subset to have multiple kids.
All evidence suggests that even with "punishments" for child bearing, too many women are having kids than is good for the future.
Thus, we sure as hell do not need or want to be giving women any kind of reward for child bearing.

Then we would end up like Japan an China with a high level of aging populations.

 

[Toni]

And between each is acceptable.

It adds an overly redundant amount of redundancy to the sentence and makes what you're saying sound redundant.

You also shouldn't start a sentence with "And".

Lol. You win.

 

[bigfield]

If society wants to continue it should pay women more if they have children, not less.

It should reward all productive work. Not just productive work for the master.

There are tax breaks and subsidies for mothers who aren't already wealthy (such as executives). Society doesn't need to go any farther than that.

 

[ronburgundy]

Can you expand on that?

Your post is poorly written and the reasoning is worse. There isn't a coherent argument to be found in the entire post. You fail to make your point altogether, assuming that your point is something other than you don't like Schools of Business and Economics. Even if we assume that is your intent, your argument fails.

This is not new from you. Instead of being a reasoned post pointing out weaknesses in the linked article, you seem to be airing an ill conceived and poorly reasoned personal grudge against economists. I don't know what your problem is, but if I were you, I would start with grammar and sentence construction and learning to write a coherent paragraph. It helps with reasoning and logic.

IOW, you cannot point to a fault with the actual reasoning and logic, but blame me for your own incapacity to understand statistics and causal modeling with correlational data. That would explain why you never post any valid evidence for anything, just blind citing of other people's invalid conclusions that you defend against detailed critiques with appeals to authority and "credentials", and why you never have specific arguments against other people's evidence, just blind dismissal. Ignorance promotes faith, and your posts are full of both.

My grudge against Schools of Business is that they constantly churn out so called "research" like the OP where large numbers of correlated variables are blindly stuffed into a multivariate analysis without consideration for the reliability of each measurement or plausible theoretical causal models of human behavior that should dictate how the analyses are performed so competing plausible models can be tested against each other instead of testing one preferred theory against implausible strawmen notions that no one would argue for.

 

[laughing dog]

IOW, you cannot point to a fault with the actual reasoning and logic, but blame me for your own incapacity to understand statistics and causal modeling with correlational data.....

Your "analysis" usually are chocked full of bombast, preening and ignorance about statistical methods and econometrics . No empirical study in any discipline is above reproach. It is always possible to disagree or to criticize the approach, data quality or methodology. Yet, you appear to reserve your diatribes to studies from particular disciplines. However, the intemperate use of rhetoric make your "analysis" appear less like reasoned and disinterested analysis from a knowledgeable party and more like butthurt bloviation.

BTW, the economics profession, as a whole, deserves some derision and skepticism based on its historical superiority complex vis-a-vis the rest of the social sciences. But the proliferation of less than excellent published studies is not the unique achievement of economics in the social sciences. There is plenty of "junk" and mediocre work done throughout the various disciplines in social science.

 

[Toni]

Your post is poorly written and the reasoning is worse. There isn't a coherent argument to be found in the entire post. You fail to make your point altogether, assuming that your point is something other than you don't like Schools of Business and Economics. Even if we assume that is your intent, your argument fails.

This is not new from you. Instead of being a reasoned post pointing out weaknesses in the linked article, you seem to be airing an ill conceived and poorly reasoned personal grudge against economists. I don't know what your problem is, but if I were you, I would start with grammar and sentence construction and learning to write a coherent paragraph. It helps with reasoning and logic.

IOW, you cannot point to a fault with the actual reasoning and logic, but blame me for your own incapacity to understand statistics and causal modeling with correlational data. That would explain why you never post any valid evidence for anything, just blind citing of other people's invalid conclusions that you defend against detailed critiques with appeals to authority and "credentials", and why you never have specific arguments against other people's evidence, just blind dismissal. Ignorance promotes faith, and your posts are full of both.

My grudge against Schools of Business is that they constantly churn out so called "research" like the OP where large numbers of correlated variables are blindly stuffed into a multivariate analysis without consideration for the reliability of each measurement or plausible theoretical causal models of human behavior that should dictate how the analyses are performed so competing plausible models can be tested against each other instead of testing one preferred theory against implausible strawmen notions that no one would argue for.

You engaged in absolutely no statistical analysis or reasonong and demonstrated a marked lack of understanding of the terms and issues raised in the paper, instead substituting your own ill reasoned opinions. Your dismissive attitude towards the term 'credentials' says it all. You don't know what you are talking about. As usual.

Plus what Laughing dog said.

 

[ronburgundy]

True, you can't prove they didn't quit due to sexist treatment. I find it very hard to believe that most quit for such reasons, though!

"Most" don't need to. Merely some quitting for gender-related mistreatment is sufficient to produce their results, leaving sexism the causal mechanism that is responsible for both the pay and promotion differences, and for the attrition, experience, and age differences that they are "controlling" for.

It also said in the article that internal promotions between men and women is generally equal; however, men being offered higher positions with other companies is much more common than women being given the same opportunities. That said, internal promotions compared to external promotions statistically produce lower earnings.

You're missing the fact that the women were making more until they disappeared from the data.

No women weren't paid more before they left. The results show higher pay for women only "After controlling for the background variables". That doesn't mean any women were paid more than men of equal rank. It means that if you eliminate those aspects of being a woman that relate to why women quit or move to another company more often and why the young employees are disproportionately women, then the remaining aspects of being a women are predicted to (not observed to) have a positive impact on salary.

The reason that such analyses are called "Inferential Statistics" is because they do not describe what is actually true about the observed data or observed relationships. They provide estimates of what is predicted to be true in a hypothetical universe in which various pathways between variables do not exists and thus the measured variables are not what they actually are but only those sub-dimensions of the variables that are not related to those eliminated pathways.

Multiple regression coefficients can reflect nonsensical values that don't or would never actually exist. For example regression coefficients also tell you what would be true of Y when at a given level of X, when a third variable Z is at a value that isn't even possible in the real world. They can tell you the salary of a person who is negative 1000 years old.

In the present case, suppose that due to sexism young hot women start out with slightly higher pay than men. Maybe the company wants female underlings but not as many in higher ranks, so it pays them higher than men at the start, but they do not get equal raises to men as they get older and have more job experience. So, the men pass them by in pay and the older women get pissed and leave. The result would be just what their data show. If you run a regression controlling for age and experience, it will take the higher starting pay of the women and assume that age and experience impact raises equally for men and women, extrapolating what each group's salary would be if they had the same age and experience. But again, age and experience differences could easily be effects of attrition due to differential pay raises over time, so the assumption the analysis is making is invalid.

The results do not say what women who don't leave are getting paid compared to men. They predict what the people categorized as "female" would get paid in a world where female's do not differ from males in any way that is related to quitting, transferring, and thus age and experience are eliminated.
Only if that hypothetical non-existent universe is the same as the real one is all important theoretical ways is that a meaningful result.

Another way to think about this is that adding a control variable changes what the main variables actually represent, especially when you are not measuring a singular, isolated physical property of the thing like in the natural sciences. Control variables transform the main predictors into a new variable that reflects only the portion of the variance in the main variable that has no relationship to the control variables. If the critical aspects of that main variable are related to the control variables, then you have changed the main variable into something qualitatively lacking the core features of what you are trying to measure. In our example, there may be central features of gender that trigger sexism which then impacts attrition and thus avg age and experience of women on the job. By controlling for age and experience, the gender variable changes into those things correlated with gender that have no impact on or relation to anything that might impact attrition, such as sexism.

An analogy would be if your main predictor was self-reported gender (like in this study), but then you controlled for whether a person has a Y chromosome. You would likely alter how the gender variable is related to almost everything, because you have altered what its remaining variance represents, which in this case would be almost nothing about gender in general, and only variance tied to whether a person has a gender identity that differs from their sex as indicated by their chromosomes.
Obviously, that is the extreme case to illustrate the problem, but a milder version of that problem occurs every time a control variable is used that could be causally related to much of what your trying to capture with your main variable.

In sum, the general problem, and a common one in the Social Sciences, is a naive notion that entering control variables show you the "true" relationship between variables. This is bullshit. The true relationship is the simply two-variable correlation. That is the observed relationship. Everything else is presumption filled inference. There are 2 general uses of control variables that are valid. The first is to show that the observed relationship (simple correlation) is estimated to be largely the same even in hypothetical universes where all pathways related to the control variables don't exist. That supports the inference that the observed relationship is via some other pathways. The second valid use is to show that the observed relationship does change in that hypothetical universe without those pathways. That supports the inference that this eliminated pathway is the meaningful one in the real world, driving the actual relationship between the variables. Notice that in both cases the conclusions are focused on why the actual observed relationship exists, NOT on assuming that the estimated relationship in the hypothetical universe is meaningful because it isn't. Its fiction. Its variables don't have some of the often key dimensions that they do in the real world, and it doesn't allow pathways that actually exist and matter for outcomes.

A red flag for statistical bullshit is when multivariate analyses do not start by showing you the actual two-way correlations between the variables, and then use the regression coefficients not as meaningful in themselves but as evidence for interpreting the nature of the observed relationship, which is the simple aggregate correlation.

 

[ronburgundy]

IOW, you cannot point to a fault with the actual reasoning and logic, but blame me for your own incapacity to understand statistics and causal modeling with correlational data. That would explain why you never post any valid evidence for anything, just blind citing of other people's invalid conclusions that you defend against detailed critiques with appeals to authority and "credentials", and why you never have specific arguments against other people's evidence, just blind dismissal. Ignorance promotes faith, and your posts are full of both.

My grudge against Schools of Business is that they constantly churn out so called "research" like the OP where large numbers of correlated variables are blindly stuffed into a multivariate analysis without consideration for the reliability of each measurement or plausible theoretical causal models of human behavior that should dictate how the analyses are performed so competing plausible models can be tested against each other instead of testing one preferred theory against implausible strawmen notions that no one would argue for.

You engaged in absolutely no statistical analysis or reasonong and demonstrated a marked lack of understanding of the terms and issues raised in the paper, instead substituting your own ill reasoned opinions. Your dismissive attitude towards the term 'credentials' says it all. You don't know what you are talking about. As usual.

Plus what Laughing dog said.

I don't dismiss credentials, I just don't blindly bow to them, and my own credentials are greater than that of the authors of this paper.
Once again, you fail to even attempt to point to a single flaw in my argument, because you cannot. You haven't the slightest clue how to actually understand their data, thus cannot understand my critique of it.

As for laughing dog, his scientific illiteracy and political dogmatism are well established.

Oh, and it turns out that buried near the end of the conclusions is an admission that confirms my analysis.

"It is most implausible to suggest that giving birth and caring for young children is the predominant reason why female executive managers, who average 50 years old in our sample, quit. Other unobserved factors leading managers to attrit could include more un-pleasantness, indignities, and tougher unrewarding assignments at work"

IOW, even though they make no mention in the abstract or introduction where they focus on child rearing as the cause of attrition (thus seemingly supporting Loren's interpretation that it's parenting choices and not sexism responsible, they do make brief acknowledgement that a key mechanism often presumed to be causally central to gender pay differences might have been indirectly eliminated from showing up in their results by controlling for attrition, because it can be a causal mediator between gender and attrition. Unfortunately, they do not point out that their other controls of age and experience are both inherently influenced by attrition and thus controlling for them also eliminate a major pathway by which sexism would impact compensation and promotion. They also don't point out that this fact they just acknowledged makes all their analyses meaningless in terms of understanding how or to what extent gender bias plays a role in compensation and promotion.

It leaves my alternative account that sexism causes attrition (and thus attrition, age, and experience are not reasonable control variables) as not just plausible, but far more plausible than the parenting hypothesis, which is ruled out by the post 50 age of most the women who quit.

 

[Loren Pechtel]

"Most" don't need to. Merely some quitting for gender-related mistreatment is sufficient to produce their results, leaving sexism the causal mechanism that is responsible for both the pay and promotion differences, and for the attrition, experience, and age differences that they are "controlling" for.

The paper said "most".

No women weren't paid more before they left. The results show higher pay for women only "After controlling for the background variables". That doesn't mean any women were paid more than men of equal rank. It means that if you eliminate those aspects of being a woman that relate to why women quit or move to another company more often and why the young employees are disproportionately women, then the remaining aspects of being a women are predicted to (not observed to) have a positive impact on salary.

Being female isn't a relevant background variable here, you don't get to weasel out of it this way.

Multiple regression coefficients can reflect nonsensical values that don't or would never actually exist. For example regression coefficients also tell you what would be true of Y when at a given level of X, when a third variable Z is at a value that isn't even possible in the real world. They can tell you the salary of a person who is negative 1000 years old.

Of course statistics can be abused. You haven't shown that this report abuses statistics, though.

In the present case, suppose that due to sexism young hot women start out with slightly higher pay than men. Maybe the company wants female underlings but not as many in higher ranks, so it pays them higher than men at the start, but they do not get equal raises to men as they get older and have more job experience. So, the men pass them by in pay and the older women get pissed and leave. The result would be just what their data show. If you run a regression controlling for age and experience, it will take the higher starting pay of the women and assume that age and experience impact raises equally for men and women, extrapolating what each group's salary would be if they had the same age and experience. But again, age and experience differences could easily be effects of attrition due to differential pay raises over time, so the assumption the analysis is making is invalid.

But why would this case attrition rather than just slower progress??

 

[barbos]

Well, original claim was that women get paid less for doing the same work. OP article demonstrated that this is not the case at least in the case of executives.
The fact that may drop out of lucrative executive workforce due to sexism is separate question. As long as they stay they are paid the same.

 

[laughing dog]

Oh, and it turns out that buried near the end of the conclusions is an admission that confirms my analysis.

"It is most implausible to suggest that giving birth and caring for young children is the predominant reason why female executive managers, who average 50 years old in our sample, quit. Other unobserved factors leading managers to attrit could include more un-pleasantness, indignities, and tougher unrewarding assignments at work"

IOW, even though they make no mention in the abstract or introduction where they focus on child rearing as the cause of attrition (thus seemingly supporting Loren's interpretation that it's parenting choices and not sexism responsible,....

The authors are being intellectually honest. They look for quantifiable measures to explain the wage gap. Using well understand techniques, they find from their data set that attrition is the major factor. They do not claim nor try to explain the causes of the differential attrition rates. Their results are consistent with sexism as an underlying cause (major or minor) and with sexism having no effect. Unless you can point to unambiguous quantifiable measures of unambiguous sexism, your critique and pique are aimed at the OP's misinterpretation of the results instead of the study.

 

[ksen]

"Most" don't need to. Merely some quitting for gender-related mistreatment is sufficient to produce their results, leaving sexism the causal mechanism that is responsible for both the pay and promotion differences, and for the attrition, experience, and age differences that they are "controlling" for.

It also said in the article that internal promotions between men and women is generally equal; however, men being offered higher positions with other companies is much more common than women being given the same opportunities. That said, internal promotions compared to external promotions statistically produce lower earnings.

You're missing the fact that the women were making more until they disappeared from the data.

No women weren't paid more before they left. The results show higher pay for women only "After controlling for the background variables". That doesn't mean any women were paid more than men of equal rank. It means that if you eliminate those aspects of being a woman that relate to why women quit or move to another company more often and why the young employees are disproportionately women, then the remaining aspects of being a women are predicted to (not observed to) have a positive impact on salary.

The reason that such analyses are called "Inferential Statistics" is because they do not describe what is actually true about the observed data or observed relationships. They provide estimates of what is predicted to be true in a hypothetical universe in which various pathways between variables do not exists and thus the measured variables are not what they actually are but only those sub-dimensions of the variables that are not related to those eliminated pathways.

Multiple regression coefficients can reflect nonsensical values that don't or would never actually exist. For example regression coefficients also tell you what would be true of Y when at a given level of X, when a third variable Z is at a value that isn't even possible in the real world. They can tell you the salary of a person who is negative 1000 years old.

In the present case, suppose that due to sexism young hot women start out with slightly higher pay than men. Maybe the company wants female underlings but not as many in higher ranks, so it pays them higher than men at the start, but they do not get equal raises to men as they get older and have more job experience. So, the men pass them by in pay and the older women get pissed and leave. The result would be just what their data show. If you run a regression controlling for age and experience, it will take the higher starting pay of the women and assume that age and experience impact raises equally for men and women, extrapolating what each group's salary would be if they had the same age and experience. But again, age and experience differences could easily be effects of attrition due to differential pay raises over time, so the assumption the analysis is making is invalid.

The results do not say what women who don't leave are getting paid compared to men. They predict what the people categorized as "female" would get paid in a world where female's do not differ from males in any way that is related to quitting, transferring, and thus age and experience are eliminated.
Only if that hypothetical non-existent universe is the same as the real one is all important theoretical ways is that a meaningful result.

Another way to think about this is that adding a control variable changes what the main variables actually represent, especially when you are not measuring a singular, isolated physical property of the thing like in the natural sciences. Control variables transform the main predictors into a new variable that reflects only the portion of the variance in the main variable that has no relationship to the control variables. If the critical aspects of that main variable are related to the control variables, then you have changed the main variable into something qualitatively lacking the core features of what you are trying to measure. In our example, there may be central features of gender that trigger sexism which then impacts attrition and thus avg age and experience of women on the job. By controlling for age and experience, the gender variable changes into those things correlated with gender that have no impact on or relation to anything that might impact attrition, such as sexism.

An analogy would be if your main predictor was self-reported gender (like in this study), but then you controlled for whether a person has a Y chromosome. You would likely alter how the gender variable is related to almost everything, because you have altered what its remaining variance represents, which in this case would be almost nothing about gender in general, and only variance tied to whether a person has a gender identity that differs from their sex as indicated by their chromosomes.
Obviously, that is the extreme case to illustrate the problem, but a milder version of that problem occurs every time a control variable is used that could be causally related to much of what your trying to capture with your main variable.

In sum, the general problem, and a common one in the Social Sciences, is a naive notion that entering control variables show you the "true" relationship between variables. This is bullshit. The true relationship is the simply two-variable correlation. That is the observed relationship. Everything else is presumption filled inference. There are 2 general uses of control variables that are valid. The first is to show that the observed relationship (simple correlation) is estimated to be largely the same even in hypothetical universes where all pathways related to the control variables don't exist. That supports the inference that the observed relationship is via some other pathways. The second valid use is to show that the observed relationship does change in that hypothetical universe without those pathways. That supports the inference that this eliminated pathway is the meaningful one in the real world, driving the actual relationship between the variables. Notice that in both cases the conclusions are focused on why the actual observed relationship exists, NOT on assuming that the estimated relationship in the hypothetical universe is meaningful because it isn't. Its fiction. Its variables don't have some of the often key dimensions that they do in the real world, and it doesn't allow pathways that actually exist and matter for outcomes.

A red flag for statistical bullshit is when multivariate analyses do not start by showing you the actual two-way correlations between the variables, and then use the regression coefficients not as meaningful in themselves but as evidence for interpreting the nature of the observed relationship, which is the simple aggregate correlation.

yeah, loren ain't reading all of that

 

[ronburgundy]

The paper said "most".

The paper said "most" what? If some women quit after being fed up with sexist mistreatment, this would account for the results they report. And we know that they are not quitting due to child-rearing, because they are quitting at after age 50.

No women weren't paid more before they left. The results show higher pay for women only "After controlling for the background variables". That doesn't mean any women were paid more than men of equal rank. It means that if you eliminate those aspects of being a woman that relate to why women quit or move to another company more often and why the young employees are disproportionately women, then the remaining aspects of being a women are predicted to (not observed to) have a positive impact on salary.

Being female isn't a relevant background variable here, you don't get to weasel out of it this way.

You are making false claims. I am not weaseling out of anything. Show me the data (actual numbers) where women are paid more. They don't report such data, because it isn't true. They report a regression coefficient that emerges, after dozens of other variables (including attrition, age, and years with that company) have removed most of the shared variance between gender and pay, leaving something which does not actually exist but is only an exstimate of what would exist in a hypothetical world where gender and pay were unrelated to all those other variables.

Multiple regression coefficients can reflect nonsensical values that don't or would never actually exist. For example regression coefficients also tell you what would be true of Y when at a given level of X, when a third variable Z is at a value that isn't even possible in the real world. They can tell you the salary of a person who is negative 1000 years old.

Of course statistics can be abused. You haven't shown that this report abuses statistics, though.

It isn't a matter of abusing statistics, but a matter of making false inferences from the stats produced by your inferential analyses. The key there is "inferential" because they don't tell you what is true, they give you information about how the covariance is partitioned from which you need to apply reason and theory to interpret and determine the relative plausibility of theories that can account for that covariance pattern.
They report a positive gender coefficient that emerges after numerous other variables (some of them obvious mediating mechanisms between gender and compensation) are eliminated. You wrongly interpret those results as if they were not multiple regression estimates, but rather descriptions of something that could actually be observed without need for any inferential stats, namely that women are paid more than men.

In the present case, suppose that due to sexism young hot women start out with slightly higher pay than men. Maybe the company wants female underlings but not as many in higher ranks, so it pays them higher than men at the start, but they do not get equal raises to men as they get older and have more job experience. So, the men pass them by in pay and the older women get pissed and leave. The result would be just what their data show. If you run a regression controlling for age and experience, it will take the higher starting pay of the women and assume that age and experience impact raises equally for men and women, extrapolating what each group's salary would be if they had the same age and experience. But again, age and experience differences could easily be effects of attrition due to differential pay raises over time, so the assumption the analysis is making is invalid.

But why would this case attrition rather than just slower progress??

I said why in the now highlighted part above. It is very simple. Mistreated workers are going to be more likely to leave that company.

 

[ronburgundy]

Well, original claim was that women get paid less for doing the same work. OP article demonstrated that this is not the case at least in the case of executives.
The fact that may drop out of lucrative executive workforce due to sexism is separate question. As long as they stay they are paid the same.

The article demonstrated that it is not true that "women get paid less for doing the same work"?

Can you show me where they actually measured the quantity, difficulty, type, and quality of the work performed by each employee?
They didn't, and they must in order to show anything about whether gender impact equal pay for equal work. Note that experience and job Rank are not measures of these aspects of actual work performed, whether they are is precisely the assumption in question that must be tested, so to treat them as such is to presume a priori what you are claiming to demonstrate empirically (question begging).

In fact, they don't even appear to ever consider or test whether their "experience" variable interacts with gender, meaning whether females and males get equal pay gains per unit of experience. To that requires entering an gender X experience interaction term into the regression model, which Table 8 shows they did not do. Instead, their approach presumes equally increases compensation for the same experience. IOW, even with experience as a measure of "quantity of work" their analysis presumes without testing the thing you are claiming their results show. If that assumption is false, then it greatly throws off the regression estimates for the gender variable that they are using to conclude that females get more pay after controlling for background variables and attrition. Their estimates assume (and do not test) that if women worked there as long they would get the same raises. It uses that assumption to estimate what the avg female compensation would be (not what it is), in an assumed world where they had equal experience and got equal raises for it.

 

[ronburgundy]

Oh, and it turns out that buried near the end of the conclusions is an admission that confirms my analysis.



IOW, even though they make no mention in the abstract or introduction where they focus on child rearing as the cause of attrition (thus seemingly supporting Loren's interpretation that it's parenting choices and not sexism responsible,....

The authors are being intellectually honest. They look for quantifiable measures to explain the wage gap. Using well understand techniques, they find from their data set that attrition is the major factor. They do not claim nor try to explain the causes of the differential attrition rates. Their results are consistent with sexism as an underlying cause (major or minor) and with sexism having no effect. Unless you can point to unambiguous quantifiable measures of unambiguous sexism, your critique and pique are aimed at the OP's misinterpretation of the results instead of the study.

So, at very best, these authors published an article on the causes of the wage gap that has zero bearing on the question of whether all, part, or none of that wage gap is related to how male and female employees are treated or compensated in their workplace due to their gender.
That isn't much of a defense for it, especially given how recklessly misleading to the general public they would have known their paper was, given that virtually every discussion about the wage gap in both the research literature and political discourse is centered on the question of whether gender bias is responsible and women leaving for reasons of choice is the most common alternative explanation.

Also, they mischaracterize their analyses as "controlling for the background variables" when they only actually control for simple linear and additive effects of those background variables, which leads to results without clear interpretations. The model's assumption of only additive effects and failure to test for non-additive effects of experience and age is a glaring omission that promotes invalid conclusions like barbos made above that women and men are getting equally paid for the same work and experience levels. They don't show that. They show that if you assume a priori that women get the same raises for the same experience, etc.., then they are predicted to get the same salary in a world where none of the other factors entered as controls are in any way related to gender.

It could be the case that a small % of the most experienced females execs are much higher paid than their equally experienced male counterparts, but that the majority of female execs are paid less than males with the same experience. And this latter fact could be a cause of the former. For example, most women see men getting higher raises for their same experience and eventually some of them leave, resulting in a selection bias where only only a unique special subset of women execs stick around. That special subset could be those women who were the exception to the rule and got equal or even higher pay than males because they were much better than equally experienced males at their job. This exceptional talent is noticed and rewarded with higher pay compared to men with same experience but who did not need as much talent to stick around because they got rewarded more than women early on just for being average.

The observed result of this would be just would their data show, a seeming positive coefficient for being female when experience is "controlled", despite the majority of women getting less pay for equal experience and only a small % getting much more pay precisely because their extreme talent and quality led to pay that made them stick around despite having to work harder than other people getting that pay. It would have been so easy to examine this possibility by just adding interaction terms for gender X experience and gender X attrition, rather than claiming they controlled for these variables when the only controlled for their simple additive effects which produce results that are less interpretable and more misleading than not controlling for them at all.

In their abstract, their final conclusion is a causal one: "Hence the gender pay gap and job rank differences are primarily attributable to female executives attriting at higher rates than males in an occupation where survival is rewarded with promotion and higher compensation."

That very clearly suggests that lack of attrition (aka "survival") is the cause of higher compensation and promotion. They have no data to support that, and its just as likely that higher compensation for men early on is what causes attrition and lack of "survival" among women, thus causing a selection bias where women more talented than their male counterparts survive who are then highly rewarded for that talent and not survival itself. Bottom line is their main conclusion is no more supported than its opposite, but had they thought more carefully about their analyses and examined interactive effects, they could have produced results that would have tested their casual assumption against this and other alternatives.

 

[laughing dog]

The authors are being intellectually honest. They look for quantifiable measures to explain the wage gap. Using well understand techniques, they find from their data set that attrition is the major factor. They do not claim nor try to explain the causes of the differential attrition rates. Their results are consistent with sexism as an underlying cause (major or minor) and with sexism having no effect. Unless you can point to unambiguous quantifiable measures of unambiguous sexism, your critique and pique are aimed at the OP's misinterpretation of the results instead of the study.

So, at very best, these authors published an article on the causes of the wage gap that has zero bearing on the question of whether all, part, or none of that wage gap is related to how male and female employees are treated or compensated in their workplace due to their gender. ...

No. They do not pretend to investigate whether sexism is the underlying cause.

 

[ronburgundy]

So, at very best, these authors published an article on the causes of the wage gap that has zero bearing on the question of whether all, part, or none of that wage gap is related to how male and female employees are treated or compensated in their workplace due to their gender. ...

No. They do not pretend to investigate whether sexism is the underlying cause.

They do not investigate anything that explains why women have lower pay. Attrition is just as or more likely to be another outcome of whatever causes pay differences rather than a mediating mechanism as they baselessly conclude without doing any analyses that favor that conclusion they explicitly make in their abstract.

 

[laughing dog]

No. They do not pretend to investigate whether sexism is the underlying cause.

They do not investigate anything that explains why women have lower pay. Attrition is just as or more likely to be another outcome of whatever causes pay differences rather than a mediating mechanism as they baselessly conclude without doing any analyses that favor that conclusion they explicitly make in their abstract.

Regardless of your view of the study's efficacy, it is abundantly clear that this study
1) does not pretend to show what the OP claims,
2) does not show what the OP claims, and
3) cannot possibly show what the OP claims.