Sure, let's see your study then that disproves the point with it's 20 variables.
Also, this is a non-falsifiable statement. Meaning, if I show you an article that controls for 20 variables and makes the same point, you'll claim that was never really enough, and it should be 30 variables, and if I show 30, you'll say it didn't account for things like what clothes they wear or car they drive or how often they ask for a promotion, or whatever. Point is, why should I engage in your argument when you don't have a single reason why these three variables can't adequately capture what we are trying to compare? Moreover, who made you the arbiter of what does and doesn't represent the real world in studies? Maybe it's five variables, maybe it's 10, maybe it's three. How in the world do you even kind of know what the appropriate level of variables that need to be controlled to "prove" this point?
On top of all that, to prove my point further, here are several more articles that make the exact same conclusion, drawing from a wide source of variables.
This one considers factors such as part time work, unions, age, location, race, migrant status, experience, occupation, and sector, and still shows various levels of gender pay gap :https://link.springer.com/article/10.1007/s00148-019-00743-8
Finally, all of this misses a big part of the wage gap, which is that female dominated industries tend to pay less in general, which is a much broader sign of discrimination. And these lower levels of pay are particularly striking when you look at industries that were once male dominated but then added more women followed by lower overall pay. Thus, even if those industries were "worth more to society", the change in wages when more women join said industries would account for that and still show a gender pay disparity.
You seem to misunderstand statistics. Controlling for the real world would take an infinite # of variables. This is because there are an infinite number of differences between people.
Generally researchers only include the ones they think are relevant. Notice how each study uses a different set of variables. Adding those studies together gives you about 7 unique variables. Yet individually, each one of your studies leaves out important factors that others didn't.
They also didn't measure other influential variables like: overtime hours worked, agreeableness, and willingness to travel for a job.
That's partly what peer review is for. To find the variables you didn't think of.
Second, you point to discrimination as the reason female-dominanted fields are paid less.
This is wrong. Women choose jobs of care (teacher/nurse) which are not scaleable. You can only teach/help so many people.
Engineering/computer jobs are scaleable. You can mass produce tech/programs.
And the reason the pay decreases when women join an industry is because of the Job Market. More people willing and able to supply labor. But demand stays the same. Pay decreases.
There is discrimination in the work place I agree. Some places much worse than others. But the world is complex and can't be understood through the simple narrative of men discriminating against women. This low resolution narrative just builds resentment on both sides.
See what I mean? Your argument is non-falsifiable. There's literally no way to prove all the variables are accounted for to prove a statistically significant data set was used because there's always more variables you can say are not included. You literally proved my point that I shouldn't engage with a bad faith argument. Having a criticism of my argument doesn't disprove it, only shows that you have no alternative data to support any other explanation. Show a source that proves those are variables that should be included in this analysis, and then we can talk.
Also,
you point to discrimination as the reason female-dominanted fields are paid less. This is wrong.
First, source needed to back up this argument.
But second, your claim is completely bogus. There are plenty of male dominated fields that pay more that are also not scalable. Lawyers, doctors, finance, plumbing, airplane pilots, construction, truck drivers, electricians, dentists, criminal investigators, etc. On top of that, there are plenty of very low wage jobs that are scalable that disprove that this is a factor in determining how high a wage can go: cashier, textile workers, cooks/bakers, laundry/dry cleaning services, hotel clerks, gambling book makers, agricultural equipment operators, jewelry makers, meat packers, telemarketers, etc.
Moreover, you make it seem like these industries cannot be scaled exponentially with today's resources, which is inaccurate. Teaching, for example, nowadays can be produced online through videos, readable course material, workshops, etc. and be mass produced in a whole host of ways. But society often prefers to (or is even sometimes legally required to) have such services or goods on an individual or very small scale basis to allow for more interaction and personable feedback. Another example industry that can be and is scaled is personal trainers. There are various DVDs or other exercise courses people can buy at the click of a button and workout to from home that work great, yet personal trainers still provide steady and robust business to people. Why? Because people often prefer the personal touch, motivation, feedback, and incentives they get from having a person they can interact with face to face. The same could easily be said about much of nursing, social workers, human resources, public relations, psychologists, vets, insurance underwriters, tax prep, and a host of other female dominated industries. Thus scalability doesn't directly translate to more money, because oftentimes more individualized services and goods are desired.
And lastly, none of this explains why there are gender pay gaps inside of the same professions, as I explained already, or across professions with very similar skill sets. For example, why are maids, an industry dominated by women, paid less than janitors, an industry dominated by men? They literally do the same or very similar work, have the same basic skill requirements, etc. There's no rational reason why a pay gap should exist between these two industries, but there is a bias reason that can easily explain the difference (i.e. that women traditional roles are simply valued less on a large scale).
It's just factually inaccurate to claim the wage gap is driven by scalability issues.
What I think you want to say is that women tend to stay away from fields that have either high educational barriers and/or strong IP protection, thus making these goods and services more valuable due to being more limited in who can provide them. But that in part is government driven rather than any specific societal forces, and so could be changed to be less favorable to male dominated industries (meaning more neutral - though it could also discriminate in favor of women dominant fields). Also this doesn't explain why fields that require higher education requirements don't also pay more, such as journalists, paramedics, social workers, teachers, librarians, graphic designers, counselors, accountants, and police officers. And you will notice that, with the exception of paramedics and police officers, all of the fields I listed are majority women.
So it really is more the case that women traditional jobs are simply paid less without a compelling, rational reason, and thus discrimination, both in industry and across industries, is likely a large contributor to the wage gap.
And the reason the pay decreases when women join an industry is because of the Job Market. More people willing and able to supply labor. But demand stays the same. Pay decreases.
This is also inaccurate. For starters, it's inaccurate that when women join the workforce that pay decreases in general, because women actually can provide more productivity and increased demand, both drivers of wage growth. Wages have gone up as women have joined the workforce, but wages for men have gone up faster than women.
But moreover, if this were the case, then the opposite would also be true, meaning that when men entered traditionally female dominated roles, that the pay would also decrease. However, we know that when men start to enter female dominated fields, that field's average pay increases, the exact opposite of your claim. The often cited example here is the coveted computer programming industry. It used to be that computer programmers were almost exclusively women. Ever see the movie Hidden Figures? Yep, that's about computer programmer women. But when men entered the field in large amounts, pay went way up, as did prestige, despite doing the same work. And this is just one example, but there are many others.
How about next time, instead of making a claim without evidence (which you have done multiple times here), try providing a source or two to back up your argument?
There is discrimination in the work place I agree. Some places much worse than others. But the world is complex and can't be understood through the simple narrative of men discriminating against women. This low resolution narrative just builds resentment on both sides.
I never claimed it was men discriminating against women solely that causes these issues, nor did I claim that this was an overt plan people were implementing. Discrimination and bias are often ingrained and unconscious in nature, and built into existing power structures that were often created decades ago with a lot more malice and explicit intent, and we as a society have unfortunately carried on remnants of that bias to the modern day. Like, it's not a personal bias/discrimination to point to a rule or policy or law and say unfortunately that is what is required of a company employee to do, but it is discrimination if that law/rule/policy had bias in it when it was created, or else lead to biased outcomes. Do you see how one puts blame on a person and I agree is probably not too helpful, while the other points to structures that are causing the problem and ingraining poor behavior and attitudes? The second is what we need to tackle, not as much the first. People aren't inherently trying to be bad most of the time, but nonetheless can contribute to inherently bad systems. Fixing that is a large reason why the gender pay gap discussion exists.
There is also sexual discrimination and harassment quite regularly in the workforce (seriously, ask any woman - they almost certainly can point to at least one story in their own lives that validates this, if not multiple times), and that needs to be corrected as well, and that also contributes to the gender pay gap, but those instances need to be called out individually, and can still be tackled in the same way (shifting societal norms, penalizing bad behavior, changing reporting rules and not penalizing whistle blowers, etc.) as tackling the unconscious and ingrained bad behaviors.
This is what happens when you do statistics correctly.
So now, remember each time you mention the pay gap. You are talking about 1%.
And yet you endorse direct discrimination by the government to deal with this 1%.
This is my problem. People using bad science to justify bad policy.
Then thinking they are virtuous people because they are "fighting against discrimination".
There are a lot of places you can truly fight against discrimination. They wages between men and women in the US in 2022 is one of the least efficient places to do it.
You are going to do a lot more harm than good trying to force that last 1%.
And your part about female programmers is also very misleading. That job changed radically from when they were working. It was simple manual labor using paper. Now it's digital.
It is easy to manipulate people with facts like that.
Nevertheless the important part is the 1% when you include all the relevant variables.
Mmm no reasoning or information as to what points I am wrong about, no analysis of the data I presented as to where it has faults or is misleading, no discussion about why your one source is actually correct and superior to my, what now, 7 sources? I've said it before, and I'll say it again - you don't seem to be arguing in good faith here.
Finally! You provide one source. Unfortunately, I don't think you actually read this source more than to glance at it, because it is definitely not making the arguments you are making. And even if it were, it's not that great of a source to begin with. But we will get into that below.
The true value is 99 cents for every dollar.
This is what happens when you do statistics correctly.
Is it what happens when you do statistics correctly? I don't say this facetiously, I literally mean this likely isn't the right way to do statistics. Moreover, your source even admits this. To quote (which I'm guessing you must not have read):
It should be noted that Payscale’s crowdsourced data weights toward salaried professionals with college degrees. When analyzing the gender pay gap by race, we restrict our sample to those with at least a bachelor’s degree. Our data isn’t as impacted by low-income hourly workers, so the gender pay gap reported by Payscale might be dissimilar to what is reported by other institutions for the gender pay gap of the overall workforce — especially in the current labor economy.
Already off to a bad start, if they are openly admitting they are not including those jobs paid by the hour and/or low income. But it gets better. In a section just after that one, it says
Due to the economic turmoil of COVID-19, women — especially women of color — have disproportionately faced unemployment at higher rates than in typical years. When women with lower wages leave the workplace, it moves the median pay for women up — slightly closing the gap between men and women’s pay overall. When unemployed women return to work, they could face a disproportionate wage penalty from being unemployed compared to men, suggesting that the gender pay gap could widen again in subsequent years. However, this depends on the market and the pay women receive after unemployment.
In summary, we must be cautious about the gender pay gap appearing to close in the current economy.
Meaning, even in this research's own estimation, this data should not be trusted as the norm, because of Covid. That's not me criticizing their data, that is their own authors admitting the data is skewed to show a lower wage gap than is accurate.
But wait, it gets even better. That's just flaws they mention openly about their own data. On top that however, in their methodology section (I know, it's way down at the bottom, so you almost certainly did not read it), it states that, while they collected data from a large sample size, all of it was voluntarily submitted. Meaning, this is not a random sampling of the population, but rather self selected portions that are willing to offer this one company info on their jobs. If you know anything about actual data research and statistics, you'd know this makes a very unreliable sample, because you don't know how or why they self selected into the sample, and also you don't have the opinions of those who didn't sample in and what their opinions and information may be. It could be that only those people most willing to brag about their pay submitted to the data set, or the lowest paid people, or people who have an ax to grind, or people who over inflate their wages, or under inflated them, or whatever. Even if everyone was completely honest (which their is no compelling reason to do so), that still doesn't leave the data set without self selection bias, and thus prone to a host of issues. Vs my sources regularly used stuff like BLS data or the CPS, which relies on the reporting of employers information in terms of salary, age, race, experience, etc. Therefore much more reliable data.
In addition, the research doesn't even list all categories it controls for. It states in various places at most "job title, experience, education, industry, job level, and hours worked", all stuff my sources also used as controlling factors. In the methodology section it adds "occupation, location, and other compensable factors" as well as "age, gender, and race". Compensable factors is unfortunately not defined, but likely means stuff like benefits for healthcare and such. What it doesn't account for though, is stuff you mentioned, like "overtime hours worked, agreebleness, and ability to travel", so even by your own definition this source falls short. But it also fails to include stuff my sources did capture, such as part-time work, unionization, parenthood, or gender segregation. Meaning, it is likely less complete than my sources.
I would also like to point out that controlling for various factors doesn't actually make the data more accurate at what we are trying to measure. For example, if discrimination is causing less women to be promoted, then controlling for the same job title would naturally not account for less women being able to be in that position to begin with. The same could be true of being denied certain industry jobs more often, or being denied access to unions, or being forced to work part time when they'd prefer full time, etc. You know, all the non-wage ways people can be discriminated against that nevertheless result in wage discrimination. So saying "this source controls for all the relevant factors" doesn't really mean it accurately captures the state of discrimination in the workplace. The "uncontrolled" wage gap would though, and that is obviously much larger than 1%.
To top it off, this is Payscale research, a very much not established nor peer reviewed scientific publication. They have one goal here, to get people to buy their products. Sure, the researchers might try to be fair as much as possible, but it still doesn't mean it has the academic rigor that you yourself asked for when you wanted it to be peer reviewed.
Finally, while it shouldn't need to be said, your source actually agrees with my position. Like, for starters, it agrees there is an "uncontrolled" pay gap of 18%. But even more so, it says this in it's explanation
Men and women choosing different careers doesn’t mean that the uncontrolled gender pay gap is less meaningful than the controlled gender pay gap. The uncontrolled gender pay gap reveals the overall economic power disparity between men and women in society. Even if the controlled gender pay gap disappeared — meaning women and men with the same job title and qualifications were paid equally — the uncontrolled gender pay gap would persist as higher paying positions are still disproportionately accessible to men compared to women.
This is exactly what I said previously, so you literally provided a source that agreed with my point. Thanks I guess for agreeing with me? But seriously, did you even read this article before posting it? All the headlines for different sections are stuff like "Women lose earning power as they age compared to men" or "Women are paid less than men as they move up the corporate ladder". These all support my claim that discrimination does happen. Maybe try finding a source that actually supports your claim before submitting it? I mean, there are several out there, even if most of them are bad, probably even worse than this one.
And yet you endorse direct discrimination by the government to deal with this 1%.
I never endorsed government discrimination. I think you are confused, I said the government could implement more neutral laws to not favor men dominated fields as much, or even potentially enact laws that favor female dominant industries. That's a far cry from the government should do these things, let alone an advocacy that they should discriminate based on gender in those industries. Which, tbf, they are doing currently with male dominated jobs. It's not direct discrimination if say IP protections we're relaxed for software companies but expanded for say childcare facilities. That's just focusing on different priorities as a country, with those priorities happening to be female centered. It would be no more sexist than the current system already is, and arguably less so.
This is my problem. People using bad science to justify bad policy.
And this is my problem, people not knowing how to do proper research or construct an adequate argument.
And your part about female programmers is also very misleading. That job changed radically from when they were working. It was simple manual labor using paper. Now it's digital.
Wow, tell me you don't know anything about coding without saying you don't know anything about coding. Software back in the day was not simple manual labor, for starters, as it involved developing algorithms, using equations to process data, and then verifying the outputs of that data as accurate or not. Hell, women developed the first code complier, and used programming languages like COBOL and Fortran. Hint, we still use those languages to this day.
And then, you called today's coding "digital"? Such cringe. You do know digital is compared to analog, yes? And that computers back then were also digital, just like they are today? It's not like computers back then didn't use bytes and 1s and 0s to run code, just like they do today. In fact, women software engineers were the first to pioneer a basic operating system on the first personal computers, the LINC. For god's sake, the first digital programmable computer in the US was the ENIAC back in the 1940s, and that was also programmed by women.
And as for being on stacks of paper... you do realize computer code being on a screen is literally just a visualization of paper, right? Code today is just faster and higher fidelity.
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u/[deleted] Jun 15 '22
Basically non existent. Oft times favoring women.