Why Diversity Matters
startuplessonslearned.com
startuplessonslearned.com
I say this with the utmost respect: "diversity leads to innovation/meritocracy" is a shadow belief (just like "all good programmers are geeky white males from MIT or Stanford" is a shadow belief). It may be true. It may also be catastrophically untrue. I'm agnostic. I can afford to be agnostic about this since I'm one guy in a rice field and nobody's opinions on diversity will change the race/gender/etc composition of my business (kinda hard to do with a one-man team), but if you're making hiring decisions based on this, you might want to start thinking of process design decisions which place more emphasis on the data and less on shadow beliefs.
(Possible solution not accounted for above: I suppose if you value diversity as an end goal to itself, then you could run your business in such a way as to maximize it even if it was not optimal among other axes.)
I don't see anything revolutionary in what Eric espouses and in practice I've found them to be quite successful in helping my team keep moving in the right direction.
ps. I come from a technical not business background but have taken on more of that role as the years have progressed.
An awful lot of academic writing about diversity starts with the conclusion and then finds results to justify it. I mean, hypothetically supposing one were to do a double-blind study of pick-your-favorite-improvement and find, to your surprise, that it resulted in more skew, not less. ("We scrubbed all the resumes of any indication of race/gender and ended up inviting more white males to interviews?! Dude, WTF.")
Would you want that study to be in your CV when tenure decisions were being made, knowing what you do about the shadow beliefs of the people on your tenure committee? Bury that data and bury it deep if you value your career.
Incidentally, some sources of repeatable bias in academic studies are actually documented in the literature (whoa, meta!) For example, people have a tendency to not publish null results and just file them away in a desk drawer, which means the published literature in e.g. marketing systematically exaggerates the magnitude of effects. If your team is doing A/B testing and every test moves the needle something is almost certainly wrong, but in the literature something almost invariably moves the needle.
It's not just an indicator of intelligence. A team with broad "cultural literacy", in my experience, tends to create software with broader appeal. At Terespondo we kicked Google, Yahoo and Microsoft's ass for years in search advertising in South America because we understood and catered to our customers.
This probably does not hold for all kinds of software but for consumer stuff it's been a real advantage.
Do you sell/support BCC in Japanese? Not many people here could do that.
So don't talk about "we need the data" for situations where it is clear that there will never be any data. In such cases, just do what you feel is the right thing: if you feel your business will only do well with a particular type of person, stick with that type of person.
Personally, I found the argument as constructed convincing - intellectual diversity is neccessary. And a homogenous crowd is not likely to be intellectually diverse.
I don't think that's legal in the United States for a sufficiently large company.
By what kind of a priori logic does the author presume that any given meritocratic filter will happen to have an equal pass rate along arbitrarily chosen orthogonal characteristics such as race and gender?
For any given test for any kind of ability, I would be shocked if the population selected happened to perfectly fit the hiring-brochure rainbow. It's just not very likely. It is very possible that a more meritocratic selection procedure results in less diversity.
In fact, that's precisely what happened when California passed a referendum in the late 90s to forbid considering race in admissions to the University of California. The process became less biased and the results became less diverse. My apologies to the author's preconceived ideas.
>"Demographic diversity is an indicator. It’s a reasonable inference that a group that is homogeneous in appearance was probably chosen by a biased selector. Even if men have an innate advantage at software development, the gap would have to be massive in order to explain why startup after startup has an all-male team."
Innate biological gender differences are not the only cause of differential average programming ability between gender populations. Sociological factors matter too.
While some of these sociological forces may be unjust and we might want to address them, that does not change the fact that by the time they reach adulthood the population of qualified programmers has many more males than females. Thus, a lack of diversity can emerge from a just meritocratic process.
The thinking here is just sloppy.
I don't think that's a correct reading of the argument. The right question to ask is "What would have to be true about the underlying population to support the observed result that a particular startup is 100% male?"
Let's assume that there is significant "differential average programming ability between gender populations" and that this has a combination of biological and sociological causes. Even so, I think we can agree that the two populations are probably normally distributed around each average, right?
Now, is it safe to assume a roughly equal standard deviation for both curves? I'm not aware of any data to suggest otherwise.
So now we can be specific: how large would that differential have to be before we find ourselves in the part of the curve that has many men but basically no women? I haven't done the math, as it seems intuitive to me that this is going to yield an absurd result, namely, that men would have to have an overwhelming advantage.
At that point, it seems more reasonable to me to infer that we have a selection bias in our filter, than that the curves are so far apart in reality. My personal, albeit anecdotal, experience has given me no reason to doubt that conclusion, either.
So, I don't think the argument depends on an equal pass rate, and I think it stands up even if you believe in substantial gender differences, regardless of cause.
No, not really. Why? Which bit of statistical theory tells you this would be the case?
"I'm not aware of any data to suggest otherwise."
Well you don't have any data going the other way either do you?
My argument here is simply the best reasoning I can muster given the data that I'm aware of. You are welcome to poke holes in the reasoning or present alternate data. I'm eager to learn more in either case.
What underlying argument? You are presenting a political position unsubstantiated by any facts. Arguing about the distribution of standard deviations about two imaginary curves is like arguing about something in Alice in Wonderland. (Note that i didn't make an argument either way about the std dev in my comment).
Speaking against political correctness will probably get me downvoted, but here goes anyway,
From your blog post, I gather you think "diversity" of genders/races makes better dev teams. All I am saying is "prove it". With data, not hypotheses.
I have yet to see any real evidence for this, whether in terms of sustained commercial success or even in a statistical sense.
Show us these supposed benefits from race/gender diversity as applied to software development and startups. Is that too much to ask?
And a 10% higher standard deviation is much less minor than you think if you are looking at an ability cutoff. If you have two populations of equal size with equal medians, one of which has a 10% higher standard deviation, then about 99.5% of the top 1% across both groups belongs to the group with the higher standard deviation. It is that extreme because the standard bell curve has tails that drop off very rapidly, so one curve has pretty much ended while the other curve is still going on.
And also, being good at programming is not just about having the smart genes. It's also about liking programming. I'm a nerd, I like spending most of time dealing with abstract logical concepts rather than with people. Most women are not like this. We have several women on our team in QA and product management. They have undergraduate degrees in engineering from top schools. But they don't program. Why? Because they don't like it. Whenever I talk with women about career plans, and mention programming, the usual response is something like, "meh". On the flip side, I'm pretty sure I would hate working in PR.
Men and woman are different. And you know what? That's OK. I don't see what this obsession is with putting all of society in a blender until every job as a perfect distribution of every demographic component. Celebrate and embraces differences!
A much simpler and more informative approach would be to compare the gender ratios of startups in less techy/programming fields. If what you say is correct, the predominance of men should be more or less equal in these.
It's kind of embarrassing to me that whenever these issues come up on forums like this, people immediately start getting into amateur evolutionary psychology. This is basically code for "I like things the way they are; end of discussion."
And a lifetime of observations tell me that men in general are greater risk takers and more competitive. I cannot prove my thesis with 100% rigor, but it's the best explanation of the facts that I've got.
It's kind of embarrassing to me that whenever these issues come up on forums like this, people immediately start getting into amateur evolutionary psychology.
There's never going to be perfect data on either side that proves any of this. We'll never know for sure how much of the male/female divide is genetic or environmental. Neither sociology nor psychology is a Popperian science. So all we can do is combine knowledge of what science might apply, personal observations, stories from others, personal experimentation, readings from histories, etc, and make a judgement. In a word, we use Phronesis - http://en.wikipedia.org/wiki/Phronesis
As you say, there simply isn't any good evidence available for any position in this domain. For this reason, I don't see the point in making these highly speculative connections between reproduction strategies and startup gender balance. It only serves to cloud the issue by offering a pseuoscientific defense of the status quo (and by going off on a hell of a tangent).
The whole thing is a complete distraction from considering the issue in a practical way. If we actually got significant numbers of women in these positions, we could find out whether or not they were inherently unsuited to them.
No.
Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender Similarities Characterize Math Performance. Science, 321(5888), 494-495.
A WSJ article summarizing: http://online.wsj.com/article/SB121691806472381521.html
Choice quote: "...there were more than twice as many boys among the top [math] scorers than girls."
My claim is not that the SD's are the same, but that they are roughly the same. In other words, I don't think the differences in SD are large enough to affect the underlying argument. If the data proves otherwise, I'm eager to learn why.
If you break out your table of normal distributions, you'll see this difference is amplified at the top of the distribution (i.e., for very smart people).
And this addresses only base mathematical ability. It completely ignores risk aversion, persistence and other factors which go into startup formation. A factor of 2 here and a factor if 3 there add up quite quickly.
Why? Because founders get together in teams based mostly on knowing each other, having worked together, hearing about one another's reputation, and the like. The focus is on merit as displayed by real individuals that one knows or hears about and not merit as tied to some form of abstraction such as "diversity."
This does not mean that diverse teams can't come together. They can and do all the time. But it does mean that, if a non-diverse team of founders sees one another as the best people for the venture at hand, no one in a normal real-world situation is going to say, "This won't work because we are not diverse enough." In such cases, founders simply do not measure merit by this sort of criterion - it is hard enough finding good co-founders without superimposing arbitrary rules on top of an already difficult process!
My point is that "diversity" as an abstraction simply does not figure into most decisions about how to constitute a founding team when it comes to specific cases. Thus, while the argument of this piece may be commendable, I think it approaches the issue from an ineffective perspective with its focus on the constitution of founding teams. Men and women alike are free to form whatever founding teams they like and they will do so or not for specific reasons relating to the merits of the individuals involved, not based on "merit" that is defined by gender-based averages or assumptions.
For you, diversity might be important, but for your trash-talking colleagues it's probably irrelevant. (Even if you buy that a lack of meritocracy is generally demoralizing, which I think is a pretty weak claim.)
I also don't think "trash-talking" is really the right phrase, as it connotes a kind of equal back-and-forth among peers. That's not the behavior I observe; usually it's pretty one-sided.
Women are only the majority of the population (in the US) because they live longer.
Immigrants with tech backgrounds tend to be mostly males, too (and probably immigrants period).
(some data taken from http://www.nationalatlas.gov/articles/people/a_gender.html . More data I didn't look into here: http://www.census.gov/population/www/socdemo/women02.html )