Why Meritocracy Doesn’t Work

Reading the mystery Googler’s anti-diversity manifesto really pissed me off. The least of the problems was his terrible use of footnotes.

Inclusive Diverse Team

It’s easy to have a knee-jerk reaction, so I’m not going to go into all the bad arguments because frankly there are too many. If someone can’t identify the issues with citing biological differences as a basis for discrimination, it’s not likely that logic or research is going to sway their mind.

I would like to have a serious discussion about diversity, but I’m not going to treat racist and sexist claims as if they qualify for serious discussion.

So if we’re going to seriously discuss the actual intellectual arguments that genuinely smart people seem to actually believe in, I’ll try and dust off my philosophy brain. I strongly encourage anyone to find the holes in this argument and then build it up to make it better as I’m going to skip over or simplify some things.

This may invite a lot of trolls, but so be it. The one upside to the recent sexual harassment scandals in venture capital and the mystery Googler’s anti-diversity manifesto is that now we can see the issue in broad daylight.

I would rather see the issue and speak out against it than be in the dark or be silent.

I will focus on one claim that seems to be the general undercurrent of all anti-diversity arguments in the valley: Meritocracy is the best form of governance.

Faith in Meritocracy

There is an absolute insistence and faith in meritocracy from many, many people in Silicon Valley.[1] In case you’re not familiar, a meritocracy is a political system where power is allocated to those with the most ability. In the case of Silicon Valley, this equates to “the best ideas win and the best people get promoted.”

Best ideas win, best people get promoted

The idea that Silicon Valley is a functioning meritocracy is then used as a basis for saying that policies like affirmative action and diversity quotas are bad. I am not agreeing with this argument, just making sure the argument against diversity policies is clear.

The basic argument is:

  • This is a meritocracy.
  • Meritocracy is good.
  • In a meritocracy, only ability is considered for advancement.
  • Diversity policies require factors outside of ability to be considered for advancement.
  • Therefore diversity policies undermine meritocracy.
  • Therefore diversity policies are bad.

Diversity at Euclid Co.

To make this simpler and more concrete, let’s say we have a company called Euclid Co. with a population of 60 blue squares and 40 red triangles. We have 10 job openings for senior polygon, and only 10 percent of the population is qualified for this position. This means 6 blue squares qualify and 4 red triangles qualify. Let’s assume that the population outside of Euclid Co. is 50 percent blue squares and 50 percent red triangles.6 blues to 4 reds

The meritocracy argument says that any diversity quota that dictates that the jobs be given to a 50-50 split to match the population would be unfair. This is because only 4 red triangles qualify, so the sixth blue square is going to be shut out of its dream job because a red triangle is going to be randomly allocated to the senior polygon position that they don’t deserve.

The blue square is sad.

Sad Blue Square

How Meritocracies Really Work

This argument equates any diversity policy designed to combat discrimination with discrimination against the majority. It flips the anti-discrimination argument like this:

  • Discrimination is bad.
  • Diversity policies are discriminatory.
  • Therefore diversity policies are bad.

Essentially it challenges anyone campaigning against discrimination to justify why their diversity policy isn’t discriminatory and regards any such policy as an undeserved handout.

To put it even simpler, the meritocracy argument says that diversity policies tilt the level playing field in favor of the undeserving.

Bias in Big Company Hierarchies

Let’s reset the scenario at Euclid Co. and see what happens when we introduce bias. We’ll make the company bigger so the math is easier. There are 60,000 blue squares and 40,000 red triangles. There are 10,000 senior polygon positions open, and 10 percent of the population is qualified: 6,000 blue squares and 4,000 red triangles.

Unfortunately, there is a small bias in the promotion process. In a 100 question skill assessment, one of the questions is, “Why are 90 degree angles the most perfect type of angle?”

Red triangles, of course, don’t understand the question because they enjoy all sorts of angles. They are triangles, after all, and come in all sorts of angles, whereas squares only have 90 degree angles. So the triangles get the question wrong, and the squares get it right.

Confused Red Triangle from 1 percent bias

However, believing that a 90 degree angle is perfect doesn’t have much impact on the job performance of a senior polygon and there are lots of other questions that don’t predict job performance. So let’s imagine that this question or another similar factor has introduced a minor 1 percent bias.

This small 1 percent bias means that 11 percent of blue squares are considered for promotion and only 9 percent of red triangles are considered. That’s 6,600 “qualified” blue squares and 3,600 “qualified” red triangles —10,200 candidates total.

So if there are only 10,000 positions open and they are divided evenly among the pool of candidates, we will get 6,471 senior blue squares and 3,529 senior red triangles.

This small 1 percent bias has already resulted in a loss of representation. From 40 percent to 35.29 percent.

Continuing this math with the same small 1 percent bias, we will have only 30.86 percent red triangles represented at the VP level, 26.75 percent represented at the senior VP level, and down to 23 percent at the C level.

More levels of seniority will compound biased representation. In other words, hierarchies amplify any bias in the system.

(For those wondering, even a “small” company of only 2000 people can have 9 levels of seniority. Also, yes…the lack of diversity in the U.S. Congress probably has something to do with this phenomenon.)

The Bias of a Growing Company

Some will argue, “We don’t have hierarchies. We don’t even have titles!”

First, get real. Even when we remove titles, people sort themselves into a pecking order. People use other ways of indicating social status, including who talks first and last in meetings. But okay, let’s just assume that Euclid Co. has no hierarchies.

Will this lack of hierarchy eliminate the problem? Maybe, but only if that company doesn’t hire anyone.

Let’s say that Euclid Co. starts with a small population of 5 red triangles and 5 blue squares, and there are no promotions. Euclid Co. is going to grow from 10 people to about 10,000 — every Silicon Valley company’s dream.

As Euclid Co. starts to grow, there is a slight 1 percent bias in the hiring process that gives new blue square hires an 11 percent chance of getting the job and red triangles a 9 percent chance of getting the job.Time Clock

In the beginning, this makes very little difference. By the time Euclid Co. is ~20 people it will be ~51.58 percent blue and ~48.42 percent red. But things quickly escalate.

By the time the company is ~10,000 people, it will be 69.69 percent blue and 30.31 percent red. Again a pretty radical problem in representation introduced by a relatively small bias over time just by a bad hiring process.

It’s true that we don’t hire fractional people so the math here is off. However, rounding the hiring bias makes the situation worse. With standard rounding, the company will be 76.19 percent blue at just 21 people and 99.95 percent blue at ~10,000 people.

Time will amplify any bias inherent in the system. Click To Tweet


Meritocracy by Democracy

Of course, some will say, “Our hiring process has no biases.” The variant of this that I have heard is, “Our hiring process has no biases because it’s peer-to-peer and we vote.”

This is a kind of magical “democracy beats racism” in a meritocracy argument. Unfortunately, in a democracy, discrimination can go viral.

Let’s say blue squares are really biased and only hire other blue squares. Red triangles hire only red triangles. Clearly no problem. Everyone is equally biased and the representation will remain proportional.

However, if only one part of the population is biased, problems show up quickly. Let’s say that blue squares only hire blue squares while red squares hire equally.

If the population starts at 50 red and 50 blue, in just one round of hiring, blue will hire another 50 blues. Red will meanwhile hire 25 reds and 25 blues. So in just one round, there will be 62.50 percent blue and 37.5 percent red.

Democracy Can Actually Help

It’s worth digging out a spreadsheet and playing with the numbers in many of these scenarios because a relatively fair-minded population can erase a lot of biases over a sufficient length of time by voting. If you’re more technically minded, try playing with genetic algorithms to see the same thing.

It gets very interesting when constraints are put on the population, such as a limited number of senior polygon positions, limited venture capital funding, limited housing, and so forth. But that’s too complicated for a Sunday night essay.

The end result is that if blue is only slightly biased, this effect may be relatively insignificant and the bias in representation can approach a limit. However, that’s the point. If people make great decisions, democracy is great.

This greatness requires two things:

  1. All people are perfectly rational.
  2. All people have access to perfect information.

Neither of these are true.

Even if the counterargument is, “In our company we make perfectly rational decisions,” it doesn’t matter. There just has to be some bias somewhere in the system for things to quickly get out of whack.

Evidence for Being Perfectly Rational

Perfect information is interesting, so let’s go back to Euclid Co. and ask ourselves, “How do we know which red triangles and blue squares are qualified to be senior polygons?”

The unfortunate answer is, “We can tell they are qualified because they were promoted.”

In other words, the fact that they were promoted is sometimes considered proof that they were promoted for the right reason.

This is an argument we’ve heard in venture capital. Successful venture capitalists claim that they have “pattern matching” powers. The evidence for this is that they have been successful.

This is an overly simplistic representation of that argument, but it’s sadly common. (You can read just some of why there aren’t enough women in VC or why minorities can’t get VC funding.)

I am not saying that there are no VCs or angels with good judgment.

Angel Investor

I am saying that this is a terrible argument. It has the same intellectual rigor of a casino lounge lizard explaining their magical rabbit’s foot. “I haven’t lost yet!”

Everyone is lucky until they lose. Click To Tweet

A good argument to prove that the “pattern matching” system actually works would be to predict which companies would be successful and track the results without tipping the scales by actually investing in them.

Alternatively, the minimal amount of rigor would be to create a control group and actually track companies that did not match the pattern. In other words, predict failure and be falsifiable.

Sadly, few research scientists have the budget to compete against VCs!

Again, before every VC gets furious with me (too late), I’m not saying there aren’t good and bad VCs, just that the argument of “I can prove my system is successful by looking at my selective sample set of data that I am helping out with money and connections with no control group to hold me accountable” is a terrible argument to prove an investor has a good or bad system.

It’s also a terrible argument to prove that a compensation system is unbiased.

Information Flow in a System

The best way to prove that a system is flawed is, of course, to simply wait. A VC who has just been lucky will eventually start losing. Those red triangles or blue squares that are unfairly promoted will get fired if they can’t perform.

So the counterargument is, “unfair promotions will eventually be weeded out.”

This would be true in a closed system with perfect information. If a senior blue square is promoted for unfair reasons and then underperforms, they will leave the company.

Sad blue box gets fired

Sadly, that’s not how it works, and I think that everyone knows at least one idiot of every race, gender, and ethnic background who consistently manages to avoid being fired despite gross incompetence. However, let’s just assume Euclid Co. is amazing and they figure it out eventually. Then from the perspective of Euclid Co., meritocracy is eventually reinstated.

However, when that underperforming blue square from Euclid Co. goes to get a new job at Riemann Inc., it applies for a senior polygon position based on its resume at Euclid Co. showing a senior polygon position. Euclid Co. has a great reputation for only promoting the best people, so this counts as a strong point in Mr. Square’s favor in the application process.

This would be impossible if there was 100 percent information flow from Euclid Co. to Riemann Inc. Riemann Inc. would reject the blue square on the basis of a bad reference.

So, out of all our personal experiences both hiring and being hired, does anyone really believe that the interviewer did a perfect reference check? Have you, me, or anyone else you know ever pushed the interviewer to contact the most favorable reference? Will the great brand value of Euclid Co. introduce a subtle 1 percent bias at a different company?

As it turns out, switching companies is a faster career path for most people and generally results in higher salary.

To get this system to work, Euclid Co. needs to be a perfect meritocracy, automatically correct for any bias, and broadcast this information to the rest of the world.

Even a perfect meritocracy may unintentionally introduce subtle biases in other parts of the system.

Assumptions for Meritocracy

We’ve already seen how even a small 1 percent bias will have a large effect on the representation of Euclid Co. In order for meritocracy to fix this, there are some fundamental requirements:

  1. The evaluation process is perfect.
  2. There is perfect information flow.
  3. It’s a closed system.

Number 3 is obviously wrong in Silicon Valley (and everywhere). Those people that are inclined to follow this path are likely busy looking for John Galt and not reading this.

Number 2 is also something I have never heard anyone seriously claim (even the Googler).

Number 1 is the unassailable self-doubt that the meritocratic argument must fall back on. “Our evaluation process is perfect.”

Or more specifically, “I am infallible.”

Infallible Pope

I honestly have no counterargument here, and that is where the heart of my frustration lies.

We All Make Mistakes

I have made some pretty brutal mistakes in my life. I have said sexist things and been blissfully unaware of them. (Thank you to those that have appropriately called me on it.)

I’m sure I’ve made even more mistakes that I’m not aware of.

I’m also a consultant, which means I get paid to sound very confident, even when figuring things out on the fly.

But even in my most arrogant moments, such as when I’ve just said something very clever and been pat on the back, I cannot imagine that I don’t make mistakes or hold biases.

There is no way for me to reconcile that argument or even have a discussion with those that believe that they do not have any biases and that there is a “level playing field.”

I look at the distorted representation and it tells me there is not a level playing field. That same data tells others that there are “biological differences” because it’s a meritocracy.

Perhaps I’m stupid for even writing this argument out longhand. But I somehow hope that we might all take a moment and consider, “Hey, maybe this isn’t a meritocracy. Maybe there is a slight chance that I have a 1 percent bias.”

If so, maybe we should introduce something to correct that bias.


The argument that meritocracy is the best form of governance isn’t wrong so much as it is naïve. A true meritocracy would be wonderful. A “level playing field” would be amazing.

But we do not live in a level playing field. People are biased. People are always biased. People will likely continue being biased. Even chatbots sometimes turn out to be racist.

It is frankly astonishing that it’s even necessary to convince people that after several hundred (or several thousand) years of institutionalized racism, sexism, and general insanity that there might still be an element of bias in the world.

Shockingly, we can’t just ignore history and start the slate clean as of now. Electing Obama didn’t fix that. This is not a post-racial world.

Not a level playing field

The common argument for meritocracy is really backwards. When people claim that a minority group isn’t getting their share of the pie because they don’t have what it takes, it’s nonsense. What they are really saying is this:

  • This is a meritocracy.
  • In a meritocracy, only ability should be considered for advancement.
  • “Those people” aren’t doing well.
  • Therefore “those people” don’t have much ability.

They are using the lack of performance of some group or individual to justify a discriminatory bias because they have utter faith in their own judgment and think they live in a meritocracy.

So this article is for those that say, “Level playing field vs. affirmative action is harder to argue.”

I understand the point, but I disagree. It’s easy to argue that there is no level playing field. It only took me a couple hours on a Sunday night and I’m neither the smartest person in the room nor am I the most informed on this subject.

The default assumption when asked, “Are we biased?” should not be, “I don’t know.” We know enough. The default assumption is, “Yes. We are probably biased.”

There are a lot of great arguments for diversity initiatives and how they help companies innovate. (I was 90 percent done with a post on that when I got distracted by the manifesto.) But here is the argument I wish we would consider together:

  1. We have many biases, cognitive and otherwise.
  2. Therefore we are not perfect decision-makers.
  3. Therefore we are not in a perfect meritocracy.
  4. Therefore diversity initiatives, however imperfect, might just tilt the playing field back to being level.

Because however unfair diversity initiatives feel to that one sad blue square who has perhaps been unfairly passed over for a promotion, we are far better off as a community when we truly level the playing field.

Happiness from Diversity and Inclusion

UPDATE: amirmc on the YC News thread of this post points out this wonderful site The Parable of the Polygons which is a great interactive illustration of some similar points. I wish I had known about it so I could more properly steal from it.

[1] Footnotes are sometimes used like this to imply that there is a reputable source backing up an anecdotal claim. Most people don’t bother to read them. Don’t use footnotes like this. It’s intellectually lazy.

Discussion (28 comments)

  1. The Laughing Man says:

    Is that a bias against footnotes? I will say if it is used to adorn an argument it is lazy, but usually find they contain a bit more depth to whatever point is being made by the writer without making the reading so disjointed.

    1. Tristan says:

      Yeah, occasionally. But I do have a bias to just not get so off topic if that’s all footnotes are used for.

  2. Deryn Warren says:

    Excellent argument.

  3. Deryn Warren says:

    Excellent argument, and the circles and squares all together at the end illustrate the argument well.

  4. Deryn Warren says:

    Well argued, and the circles and squares all together at the end illustrate the argument well.

  5. YM says:

    If a blue square’s chance of being hired goes up from ten to eleven percent, isn’t that a ten percent bias?

    1. Tristan says:

      It’s an increase of 10% an absolute difference of 1%. Whatever terminology you prefer I think.

    1. Tristan says:

      Discrimination is a preconceived opinion not based on reason or evidence that is used to justify the treatment of groups or categories of things or people.

      It doesn’t matter if the facts are right if they’re used inappropriate. Blue squares are better at blending in against the sky but that is irrelevant if the job is being a wheel.

  6. The Laughing Man says:

    Did you read the “Google Manifesto”? Damore wasn’t discriminating against women, he opens with how he values diversity and inclusion,as cited, his views are based on reason that there are inherent biological differences between men and women and any approach to curb discrimination has to be tailored with this in mind, In no way did he use any of this to justify workplace discrimination. Also your definition and conclusion disagree, you say discrimination isn’t grounded in facts or evidence then proceed to say it doesn’t matter if the facts are right? I am loathe to engage in a terrible use of metaphors, since were not representing two groups for simplicity any more, please read that citied paper I think a MD, PHD, professor of neurobiology understands the underlying issues as a bit more complex then squares and triangles being a wheel?

    1. Tristan says:

      I have read both papers and am familiar with the topics. As I mentioned at the top of the article, I am not going to address the more obviously incorrect and inflammatory remarks.

      If you are genuinely interested in the logical aspects of racist argument, I will not draw any conclusions for you but I can suggest some avenues to explore.

      I would suggest looking at ad Hominem and arguments by authority. The manifesto is using a variant of the former and you are using a variant of the later.

      In regards to the “inappropriate facts” I probably used the wrong term here. It is not that the facts are using the wrong fork at a fancy dinner with the queen and are behaving inappropriately. Facts must be relevant to the conclusion at hand. The causal connection must be explained, or they are irrelevant regardless of whether or not they are true.

      To do this, always try and create a falsifiable hypothesis and then try and disprove it with historical data. There is lots of interesting and easily accessible historical data about gender diversity in the tech industry which I highly recommend exploring.

  7. Kate Hayward says:

    Thank you for sharing your Sunday walking through this argument. There is SO much to chew on.

    I found I wanted to watch the blue squares and the red triangle hiring process influence the management population over time like this does for game theory http://ncase.me/trust/. But I got it. Just not as in the face as I might have otherwise.

    Thank you for the case of Euclid, Inc. I’m trying to figure out how to use it to explore professional bias with a group of young emerging leaders.

    I have only recently become aware of my own blindness of privilege. I try to keep it very close to me at all times. It’s amazing what we don’t see. Thank you for seeing so clearly.

    1. Tristan says:

      Thanks for sharing the evolution of trust Kate!

      It’s been a long long time since I read a book on game theory so this is perfectly timed. It’s far too easy to miss our own biases and thinking about the rules of the game we’re playing in is a wonderful way to find them.

  8. Pingback: The best reasons to favor diversity in hiring: A response to Tristan Kromer’s “Why Meritocracy Doesn’t Work” | maketheworldworkbetter

  9. John Miller says:

    I’m looking at your “The Bias of a Growing Company” worked example. This is not a typical hiring / promotion pattern where there are multiple open positions and positions are filled from a pool of candidates who are either qualified or unqualified. How does this example work out when positions are filled one at a time (ie each hire starts with a fresh pool of candidates) and candidates are ranked in strict order based on a biased evaluation of merit? If, for example, 100 candidates are ranked in order based on an evaluation that has a 10% bias (as used in your example) how far down the list should we go in order to overcome that bias? Example if the Triangle is ranked 2 is it reasonable to pass over one square to account for the bias? If the first Triangle is eleventh is it reasonable to pass over the first 10 squares? Assuming that Euclid Co. and Pythagoras LLC higher from the same pool of polygons ranked with substantially similar biased ranking with the first company attempting to account for bias by passing over up to ‘n’ squares and the second always taking the top biasly ranked polygon which is statistically more likely to get the truly best polygon?

    1. Tristan says:

      That’s a fascinating approach. I frankly have no idea about the best way to overcome biases but your suggestion of stack ranking is a compelling way of exploring it.

      I am a little doubtful that there is a situation where a company would have 100 employees stack ranked (I have never had this situation when hiring!), but this could be the case in college admissions.

      Another reader recommended this hbr post on why diversity programs fail that offers some insight as to what type of programs do actually work and is worth a read: https://hbr.org/2016/07/why-diversity-programs-fail

  10. Mehmet Yalçın Aydın says:

    A simple but novel way to express arguments! Especially appealing to engineers such as me. Excellent article that shifted my perception of minority quotas.
    Thank you!

    1. Tristan says:

      Thank you for reading Mehmet!

      I should emphasize that I do not know the best way to adjust for bias. There are a lot of diversity programs that succeed and plenty that fail, but it’s worth experimenting with them until we figure something out that gets us ever closer to that level playing field.

      Again, thanks for reading and commenting. It makes me feel the effort was worth it if even one person can look at the issue from a different perspective….even if they don’t agree in the end!

  11. Jimmy Zoto says:

    I was with you until :

    “Therefore diversity initiatives, however imperfect, might just tilt the playing field back to being level.”

    You spent the whole article arguing how biases and the imperfect processes they create introduce problems, but no effort on how the same biased people and the processes they create might create a more level playing field. Or even how such a more level playing field would be defined. I mean what is actually the perfect level playing field and who decides what that is?

    1. Tristan says:

      That’s an excellent question to which I don’t know the answer to and won’t pretend an easy answer exists.

      I have however been encouraged by some of the additional research some readers of this post have sent me. If you’re interested in the topic of how to make diversity programs more effective, please check out Why Diversity Programs Fail on HBR: https://hbr.org/2016/07/why-diversity-programs-fail

      Regarding how to define “good” in this context, I’ll deal with some of the reason why diversity is a great innovation metric in a subsequent post. However I think there are several definitions of good depending on what is the organization / society you’re talking about. i.e. Some contexts like innovation may want *more* diversity that society as a whole while society may want something that encourages more societal stability and might just want proportional representation in companies within that society.

      Please email me if you find any other sources that are worth reading on either topic or post them here.

  12. Jabu Dayton says:

    Tristan, I read this and have shared it many times since, I just realized I hadn’t mentioned it to you directly and wanted to. It is so unbelievably articulate and precise in it’s explanation that I wish more people could see it and benefit from it.
    I thank you personally – and professionally, as an HR consultant in tech, for taking a stand and speaking for those who cannot always speak for themselves.

    1. Tristan says:

      Thank you very much Jabu! My team and a few colleagues help criticize, tighten, and illustrate it so I cannot take complete credit!

  13. Pingback: Diversity is an Innovation Metric – Thoughts from a Lean Startup Coach & Innovation Ecosystem Designer

  14. Alex says:

    Your math is wrong. There is 10%, not 1% bias in your example. And no, it’s not a terminology difference

    1. Tristan says:

      1% bias to the number of qualified candidates considered, 10% bias relative to the qualification criteria. Argument stands either way. I suggest playing with the numbers in the spreadsheet, then you don’t have to worry about my math.

  15. Drew DeVault says:

    Hey, thanks for writing up such a detailed article, I enjoyed reading it. I found a small problem with the simplified view of the issue, though. You put forth that if the population is 60% square and 40% triangular, and 10% of the population is qualified for role A, then the qualified portion of the population will also be 60% square and 40% triangular. I think an error lies in this – it discounts a lot of important cultural context. All things being culturally equal, it makes sense that the population qualified for role A would be a reasonable approximation of the greater population. But what if triangles are generally raised to have values which align them more closely with role B? This would make the subset of the population which is suited to role A not have a similar diversity to the greater population, and I think this is in fact something we see in practice with human poluations and roles.

    Even without looking at diversity, this is something I’ve experienced for myself. I’m a software engineer, but I have no desire to manage people, and I don’t think I’d be good at it. But the disparity in salary exists regardless – managers are perceived as providing more value and therefore are paid more, though anyone with two brain cells to knock together can see the nonsense in that. It’s more likely that managers are the ones making decisions on who gets paid what and are end up paying themselves more. There are other cultural signals which transcend race/gender/etc, like introverts generally being more suited to engineering roles and extroverts being more suited to sales.

    I think we’d be wiser to continue hiring people into roles on the basis of merit, and work specifically to (1) find problems which introduce non-meritous bottlenecks (like your example of “Why are 90 degree angles the most perfect type of angle?”) and (2) ensure people in different roles are rewarded simialrly, rather than trying to devalue non-minority candidates to meet diversity quotas.

    Thanks for your time and attention, I hope that this makes sense. Thanks again for the great article!

    1. Tristan says:

      Hi Drew,

      Thanks for the response. You’re suggesting two different possibilities to justify that the triangles are truly not qualified for the job. Nurture vs. nature. You’re also mixing them together.

      Introverts are not inherently worse sales people, nor are they worse managers, nor are they inherently better suited to being engineers. That may be your experience, but that is not a representative sample. There are a ton of great and also a ton of terrible managers, sales people, and others that are both introverts and extroverts. There is no evidence that there is any sort of genetic reason for someone to be less qualified for a white collar job. While there is a ton of evidence that there is systemic bias in the selection process. (Even note the term “white” collar.)

      In general, saying that triangles aren’t qualified just pushes the bias upstream. If triangles, in this case, were somehow inherently worse than squares at the manager role, then the bias might actually be in the school system. If you’re interested in this aspect, just ask if Bill Gates would have had the same opportunity to work with computers at a pivotal moment in history if he didn’t go to one of the few schools which actually had a computer to work on.

      If anything, the bias is worse in the public school system….not better.

      The question goal here is not to devalue the majority, it is to valuing the minority. A large pool of untapped talent. It’s looking at hiring at a zero sum game rather than growing the pie for everyone.

      That’s something not addressed in my discussion above either.

      1. Drew DeVault says:

        Hey, I appreciate your detailed and prompt answer. Thanks! There’s a lot here and we could grown an exponential number of threads by addressing all of it, so I’m just going to hone in on this central point:

        I don’t suppose that a genetic reason for making people suited to different jobs exists, but I do think there may be cultural ones, which is why I limited my earlier comment to supposing that may be the case. Sometimes these cultural factors are related to genetics, but not directly, simply because people from the same cultures share a gene pool. In any case genes of course have nothing to do with what jobs a person is good at.

        Setting that aside, are you saying that there’s no possibility that cultural factors or upbringing could cause the pool of qualified candidates for some role to be an inaccurate representation of the greater population? If the major employer in a city is a car factory, and the people living there were brought up around cars and by parents who knew how to build and maintain cars, doesn’t it make sense that the population of this city would have a greater number of people interested in cars and qualified to work on cars than other cities might?

        1. Tristan says:

          Hi Drew,

          If culture IS a reason for an inaccurate representation of candidates….then it’s still a bias in the educational system. i.e. It’s a (potentially minor) bias in the base of the funnel leading to ever increasing disparity at the top.

          Regardless, I find the probability that the extreme bias evident in boardrooms, C-suites, and even mid-level managerial positions is simply due to “cultural factors” ill equipping people for some roles to be virtually zero.

          The article’s argument is just to show that with even a TINY bias, the effects at scale are significant. Moving the argument to early education based on cultural biases and lack of opportunities increases the effect at scale.

          In order to believe the bias is justified, it’s necessary to push all the bias down to genetics that cannot be overcome. Culture is just another form of education.

          In your example, the car city folk have increased opportunity to work with cars and an educational system that might start in the home enabling them to succeed in that industry.

          That is not to say that it is their FAULT. We can’t blame Bill Gates for being born white or having been in the right place at the right time and being able to take advantage of that. We cannot blame your car city folk for success.

          However, we can acknowledge and understand bias and it’s impact on minority groups and society. I’m a white man and I can walk down the street, into stores, and often into apartment buildings without getting stopped and questioned. I’ve never once worried about being shot when encountering the police.

          The same is not true of an African-American man with my same background. That disparity is the definition of privilege.

          I didn’t earn it, but I have it. I may not be to blame, but I’m certainly taking advantage of a system that is rigged in my favor. Even if I don’t know it, that’s privilege. I don’t even have to think about it, but beyond the education that was provided to me or the benefits I’ve had from my European “culture”…I have a hell of a lot of benefits just from being a 6’4” white man.

          There is a phenomenal amount of research as to what happens when the same resume is sent to employers with a female, latino, or African-American name attached. There is a statistically significant bias towards rejecting those applicants on the basis of only the name change. (Plenty on google if you’re interested. Check for .edu sources.)

          Even if that bias was unconscious, the existence of bias is an unavoidable conclusion looking at the data. Even looking at airbnb guest rejections has shown similar biases.

          It’s a pretty interesting subject and these biases are difficult (if not impossible) to remove. If you’re interested, there is a self-administered test you can do to show how pretty much everyone…including minorities…have similar implicit biases https://implicit.harvard.edu/implicit/takeatest.html

          End of the day, there are biases, and sadly, the impact increases at scale.

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