Imagine a game. Let’s call it ‘Ping’. Ping is easy to play, at many different levels (so both genius and not-so can play and enjoy Ping), and takes no physical effort or dexterity. There many many different games of ‘Ping’. It is widely popular and very cheap.
Writing a game of Ping is very much harder, slower and more demanding. Many try, but perhaps one in a hundred thousand players who try writes a good popular game, popular at least with some part of the very wide audience who share their level and interests. Getting into the Ping-production and selling industry is not nearly as difficult. But your work is Ping, and if you don’t love Ping, the salaries are mediocre to bad, and there is other work available.
Now: this is a statistical exercise… how should the ‘diversity’ of Ping writers and indeed the production and selling business stack up? Given that there are no barriers to entry for either, and the game is popular with very nearly everyone. It’s quite a broad and varied game. No-one is trying to socially engineer the diversity or anything like that.
The answer is: writers at 1 per hundred thousand of the players will reflect (within a narrow range) the diversity of the players (unless there is some HUGE reason) So if there are 200 000 bald Catholics playing Ping, at least two of the Ping writers will be bald Catholics. And if 1 in twenty thousand players want to be in the industry, odds are good there’d be ten bald Catholic folk there. No bias, no social engineering needed…
On the other hand if there are 50 bald Catholics writing the games, and 500 selling them… Or zero… or any other number that isn’t fairly close to proportional what the demographics of the players are, something is… odd, and needs to be looked at.
Now imagine that Ping is a rather private vice. You can’t know the number of players. BUT you can count the writers, and see what demographic they come from and the same with the industry-people. They should, coarsely speaking, reflect diversity in the representative proportion of the demographics of your players. If you want as many players as possible, you want that to reflect the demographics of your country. Not for the sake of social engineering, just that it means that everyone who can play, is. There is also, obviously a feedback: if people of a particular segment aren’t there, odds are, they will disappear from your customer base.
I’m simplifying this, obviously. Sometimes there can be reasons why a section of the demographic isn’t there or is over-represented. But it’s a great start to understanding this thing our social justice warriors don’t. In this way, if diversity is representative it really means the industry concerned is strong and popular with everyone. You can translate this into any industry, from publishing to the restaurant trade. People may dine at any restaurant, but if they’re going to open one (and it succeeds) you’ll find it is often rooted in food they know best.
Books are a very private vice. And the industry desperately plows through its figures to work out what is hot and what is not – to no effect, because they’re not statisticians, and don’t employ the same to correct for differences, so they compare apples with apples, not – as they do, very small rocks with brontosauruses.
The answer lies in the demographics of their possible audience, as represented by the demographics of their writers and, indeed, their staff, as those staff choose their writers, without the benefit of any real meaningful numerical guidance.
So: how are they doing, you may wonder? Well, here you go: Numbers for publishers are collected and available on this site. I used the CIA factbook for demographics where possible.The optimal number for percentage for the publishing industry over the National demographic is of course =1. So for example (imaginary figures) the percentage of male employees was 50.1% and the US demographic for males was 50.1% = 1. If Males in the industry were 75% the number would be 1.5 (or 50% more than naturally probable). over =1 is over-represented, under =1 under-represented.
Race (they appear to have separated ‘Latino’ as a separate race)
White (not Latino – their definition 73-11%) = 1.2
Asian (read the list they include) =1.4
Black = 0.4
Latino (a bit iffy as they may be listed as black or mixed or white too) = 0.36
Sex (I’m a statistician and biologist not a social scientist. I can’t be arsed to write about the 125 ‘genders’. Besides I can’t find any reliable demographic figures. You can work it out if you like. I’m betting at WAY over 1)
Male = 0.46
Female = 1.48
Sexual orientation. (taking the total non-vanilla hetero proportion in the overall population at 5%. The figures vary wildly. 5 seems on side of generous caution, to a biologist anyway.)
Heterosexual ordinary = 0.85
Other = 3.6
If you subtract gay men from male tally you end up with around 19% out of 45% or = 0.42. (So Heterosexual men are about as under-represented as black people) If you assume that in line with the rest of the figures (not a given) roughly ¾ of those are white – 14.25% = 0.51
I’m not even going to venture on ‘disabled’ or the bizarre intern figures where the sexual orientation figures hit a staggering = 9.8. That does bode well for the future being very representative, doesn’t it?
So: in short: some of the larger groups (whose representation is crucial to a healthy industry) just aren’t reading what they produce, because they’re not getting jobs there. It’s that… or there is massive discrimination, which will feed back into reduced sales.
Data on social factors such as education, social background and geographical origin and politics are sadly not there. But the usual scapegoats are in fact substantially under-represented.
Houston, they have problem. And far worse coming down the track.