More tech companies release diversity figures: They look a lot like Google’s and Yahoo’s

August 12, 2014 at 8:51 am 3 comments

Below is the article on Facebook’s diversity figure release. (Google really did lead the pack here.)  Here’s Twitter’s, LinkedIn’s, and EBay’s.  For those of us doing this work, these are not surprising results.  But they are super important for showing us where we are now.  We have very little diversity in the computing industry.  This gives us a sense of what we need to work on, and how to measure progress.

Sadly, Facebook’s numbers look a lot like the other four. I’ll let the figures speak for themselves:Globally the company is 69 percent male, 31 percent female. In terms of ethnicity the company is 57 percent white, 34 percent Asian, 4 percent Hispanic, 3 percent two or more races, 3 percent black and 0 percent other.Scrutinized further, in the tech force of Facebook, 85 percent are male and 15 percent are female. In terms of ethnicity in the tech division 53 percent are white, 41 percent are Asian, 3 percent are Hispanic, 2 percent are two or more races, 1 percent is black and 0 percent is other.

via Facebook releases diversity figures: They look a lot like Google’s and Yahoo’s – Salon.com.

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3 Comments Add your own

  • 1. alanone1  |  August 12, 2014 at 9:28 am

    This is “data”. But, the sane future “is not Big Data. it’s Big Meaning”. (Things are not very sane these days!)

    What does this actually mean? (I don’t know what it means, but there’s nothing in this account (or most accounts) that exhibits a substantiated model that can be used to assess the data.)

    Given rough census figures:
    Males: 51+%
    Females: 49-%

    “White” 63%
    Afro-Am 12.4%
    Asian 4.4%
    Hispanic 16%

    We can see that all ethnicities except for Asians are underrepresented. (However, if you look at CEOs of Silicon Valley companies, Asians are very underrepresented compared to their presence in the technical ranks given in the article.)

    We can see that the disparities for females are about *3 and for Asians about *10 the other direction.

    I think if I were trying to be scientific about this, I would try to put meaning to the Asian demographic first and then see if whatever best explained that could have some bearing on the other disparities.

    Cheers,

    Alan

    Reply
    • 2. Mark Guzdial  |  August 13, 2014 at 5:10 am

      I’m not convinced that investigating the meaning of the Asian demographic will inform us much about the women or other groups, e.g., Hispanics or disabled. Betsy DiSalvo’s work is suggesting that different demographic groups have dramatically different reasons for pursing (or not) computing, e.g., African-American men make decisions away from undergraduate CS degrees for reasons that are unlike even African-American women. There certainly are explorations of the meaning of the other groups (see my blog last year on how the disaggregating different Asian groups leads to new insights in STEM demographics), but it’s not clear that it gives us general insights.

      Reply
      • 3. alanone1  |  August 13, 2014 at 9:14 am

        Hi Mark

        In trying to understand “ethnographic” phenomena, in many cases, we want to find out the underlying reasons *why* people say what they say, rather than just *what* they say. I.e. quite a bit of human discourse involves rationalizations (with or without awareness) for actions and attitudes whose causes are elsewhere.

        This is why I’m advocating more depth and effort in trying to understand what is going on. There have always been problems in social sciences of the biases of observers, but things got a lot worse in the 70s when a number of them were heavily politicized (even “colonized”) in the service of belief.

        An idea from “classic anthropology” is that what most humans think is real and what they think is important fits universal categories that are set up by genetics, but that the categories are filled for most in a society by what the society believes (and children are as driven to internalize these as “reality” as they are driven to learn the local language). Quite a few of these impinge deeply on “identity”

        A simple hypothesis drawn from this would be that the underlying causes — regardless of the rationalizations — for most choices has to do with acculturation (with a few wildcards from genetics and variation). This would certainly be a good place to start to try to assess males across different ethnic and cultural groups.

        To add females to the assessment, we have to be careful about interactions between culture and possible differences of behavior that might appear from the real genetic differences. This is really difficult to do. Quite a bit has been learned about this by examining the changes in behavior brought when there are large changes — sometimes temporary — in cultural needs and sanctions (for example, in times of war, when women are encouraged to “do what men did” in factories, science, engineering, and even in battle). They not only do it very well, but report enjoyment. The evidence (from WWII, Israel, etc.) indicates that the cultural differences are generally stronger than genetics for a substantial population of females.

        This would allow us to provisionally include women in our “culture causes a lot” hypothesis above. If we could support the hypothesis for most humans, then we could better normalize many of the rationalizations that are given both by individuals and their critics.

        Then our most important task should be making a more enlightened next generation of adults (and especially parents and teachers).

        I should mention here that my motivations for improving education have always been along these lines rather than creating more STEM workers.

        Reply

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