Thoughts on being an old lady actor

I met an interesting guy at a filming today.  He does tech and stand-up.  Our conversation reminded me of some (wry humor) observations I've made over the years of doing acting.  I thought maybe it's time I resuscitated this blog.  I might even finish up the draft posts that have been languishing for 8 years and more.  Meanwhile here are my observations:

1. Actors are a dime a dozen.  Old lady actors are a dozen a dime.  

I first said that back when I wanted to do theater.  Then I started to do film, or what passes for it in San Francisco -  web ads, student films, spec shorts, occasionally a TV episode or an actual feature film - and discovered that it was just as true for film.  There aren't many film roles for old lady actors, which has made a lot of them (us) give up, but even with low numbers in supply the demand is a small number close to zero.  Result: We're still a dozen a dime.   

2. Theater auditions:  If there's a part for a woman over in her 60s or more, 73 women audition of whom at least 46 would be fine.   If there's a part for a 20-something tall good-looking male, nobody auditions and the director starts making phone calls.

I've been saying that when I started doing theater again in 2008.  It's still true.  Theater companies post an audition notice and then another and another and another.  All of them are looking for a young male.  Only the first one is looking for anyone else.  And the show has more than two characters.

3. Web ads: Does the advertised company hire people who look like the people in their ads?  Nah.

Ads these days for banks and large corporations try to hire from all ethnicities but limited to young Hollywood-looking people, maybe with an aging white-haired beauty and an overweight peppy young woman just to show they aren't unredeemable.  But look at the people who make half the total payroll at the company (which is far less than half the people on the payroll, as you'll realize if you think about it).  What do they look like?  Not the ad. 

Nowadays in the Bay Area, there seem to be many film opportunities for tech company web ads.  If the filming is done at the company, the actors get to see that the workforce is pretty much entirely under 35 and the vast majority is male.  But interestingly, young Asian males are hired more for tech jobs than  acting jobs.  Curious, eh?

 Anyway, it's nice that the companies want to look like they are EEO employers, even if it's not true.  Lip service may be the first step... These films may even include an old lady and a few young ladies as well as the usual tech bro types.  But guess what:  just about every time the person who is central to the ad is a young white male.  Not older.  Not female.  Occasionally it's someone non-white but still male and young.  And tall.  Heroes can't be short.  Heroines can't be short, either.  Short people may be in the last demographic to be silent about discrimination.  

4.  On film sets the actors are called "the Talent."  That's to make us feel good so we forget that we're paid less than the kid who makes the coffee.  

Well, at least they're not using avatars based on the people they hired a few years ago.  Yet.  See, it's cleaner to pay coders to make the avatars walk and talk than to pay actors.  Less paperwork.  Sure it may cost a lot more - those coders don't come cheap - but, well, nobody cares about how diverse the coders are.

My Contribution - a short comedy about a tech job interview

A few years ago I wrote "My Contribution," a short play about a tech job interview. It was given a staged reading at the 2017 Playwrights' Center of San Francisco's Playoff. I believe that it is not yet dated, although I hoped it would be by now.

The play reflects some ideas I had back when I wrote a series of posts on what I called, for lack of a better name, the Morris Number.  They begin here.

I also entered My Contribution in a play contest.  They were seeking slightly longer plays so I added some monologue rants for the main character to present after the play ended.  One rant is about landlines and one is about TWGLCMs (pronounced twiggle-kim), an acronym I had explained here. In the rant, however, I added a Y: TWGLYCM (twiggly-kim).  Why?  Read the play.

Andy Borowitz and Ms. Tracy Klugian

Back in 2011, I wrote about satirist Andy Borowitz and his habit of recycling the names of the imaginary people he quotes. The names that had caught my eye were Tracy Klugian, Harland Dorrinson (or Harlan Dorinson) and Davis Logsdon. I also observed that the pronoun "she" was a rarity among Borowitz' fictional characters. He had no choice but to use "she" when he wrote about an actual female, but even his oft-quoted Tracy Klugian, despite having a first name that is common for girls as well as boys, was always "he."

Today I noticed that Borowitz used "she" for Tracy Klugian. How refreshing! I then did a quick search on The New Yorker's website and found that so far in 2017 Tracy was "she" once - on July 31, 2017, "he" once - on Sept. 21, 2017, and without pronoun 4 times - on Jan. 22, Apr. 24, May 2 and May 4. By the way, on May 2, Tracy was a schoolkid as was Zach Dorrinson, no doubt the son of the fictional Harland. Zach was also quoted. Wonder why young Dorrinson wasn't Zelda or, to continue with unisex names and gender ambiguity, Pat, Leslie or Kim?

It was time to investigate the M/F data for Tracy. A 2007 yahoo best answer to the question whether Tracy is a boy's name or a girl's identified 7 famous people named Tracy, 4 male and 3 female. The famousbirthdays.com site's list of celebrity and celebrated Tracy Somethings included pictures for at least the first 12. The score there was 4 to 8, men to women.

The US Census publishes good data on names, too. On this page , you can scroll down to 'Popularity by Name' and enter a name, select male or female, and then choose how far back you want to go. The search automatically goes through 2016. I chose to start in the 1900s and got a bar chart showing all the years when the chosen name was among the top 1000. I looked at Tracy, both female and male. For most of the years between 1942 and 2004, Tracy was in the top 1000 names for girls, but it did not make the top 1000 before 1942 nor after 2004 when it was ranked 951 . In 1970, its high point, the rank was 10. Among boys, Tracy had a longer run but was overall much less popular. It was in the top 1000 for many of the years between 1900 and 1999 and in that last year it ranked 808th. Its high point was 1967 with a rank of 98. The question is close but on balance Tracy Klugian's preferred pronoun should really be "she."

In 2014 I added Carol Foyler to my personal list of favorite recycled names. I had discovered Carol when I revisited Borowitz' naming habits in response to a comment posted to my 2011 piece. (That writer had wondered about Borowitz' preference for the University of Minnesota.) Of course, Carol can also be a man's name but I haven't noticed Borowitz ever call Fowler "he." Carol is not in today's piece but was, sans pronoun, there with Tracy (gender M) on Sept. 21. A few days ago on Oct. 28, Carol had a husband but chose to leave him to have surgery alone in order to come to Washington and join the hopeful crowd outside Mueller's office after Friday's news. How big was that crowd? Andy writes that a policeman (a man not an officer whose pronoun was "he") thought it could reach a million, and added "We definitely didn’t see anything like this at the Inauguration.” Borowitz does seem to live in a man's (or mostly men, anyway) world, whether real or imaginary, but he does have a way with words.

If you haven't yet seen today's Borowitz piece, rest assured that it includes your old guy friends Dorrinson and Logsdon, this time a clinical psychologist at, yes, the University of Minnesota.

Thinking Outside the Box

Today I got another chance to quote myself on the subject of the tech world's lack of diversity.

The rest of us think outside the box because we've never been allowed inside the box.

Here's why the quote sprang to mind. Once again, a Windows 10 update messed up Microsoft's Edge. I looked to Microsoft Answers Forums and someone recommended uninstalling and reinstalling IBM's Trusteer software. Yes, I thought, that has solved previous Windows 10 problems. Perhaps Microsoft includes this annoyance to get people to abandon Trusteer and move to a Microsoft product. But simple incompetence could be the cause, such as Microsoft only hiring 20-something males to write and test software.

As far as I know, I'm the inventor of the quote and variants but other people may have had similar ideas. Here's one of my variants:

Outsiders routinely think outside the box. Duh.

This past spring the concept was the basis of my 10-minute play, My Contribution. It was produced in a staged reading by Playwrights' Center of San Francisco as part of PlayOffs - Round 2. The characters are a young ~20-something man and an old woman, ~50-99. The setting is the young man's office at a company called Goober.* The play ends soon after the woman beats the man at the Hamlet's Soliloquy game, another product of my invention. The play is under revision but one thing that will stay until Google, Microsoft, etc. open their boxes, is that the Old Woman will identify "True thinking outside the box" as what she'd contribute to the company, and she'll explain why.

* Old Woman: I haven't been keeping up with the news. When did you merge with Uber? Young Man: We didn't merge with Uber. The B is for Face*B*ook and the ER is for the end of Twitter. Old Woman: Should be BS for FaceBook, and I'd love to see the end of Twitter.

The March for Science: my goals

I just filled out a survey about the March for Science. I said that my goals for the march were:
     1. Fostering awareness among politicians and policy people that science has strong public support.
     2. Lifelong/long-term goal: Making it as socially unacceptable in the US to say 'I hate science' as it is to say 'I hate sports.'
     3. Showing that everyone who loves truth and respects the achievements of the human mind, should be proud to march for science.
     4. Reminding ourselves that SCIENCE HAS NO PARTY.
     5. Getting people to realize that if they like their [air, water, cell phones, cars, bikes, running shoes, garden, roads, your-noun-here], they like science.

Asked how effective I thought the March would be in the long-term for reaching those goals, I said 'slightly effective' for 1, 2 and 3, and 'moderately effective' for 4 and 5. I suppose my optimism grew as I focused more on 'long-term.' And maybe I thought, with cock-eyed optimism, that item 2 might get more attention by reason of my saying it in this survey.

I had mentioned item 5 to friendsincluding one who was among the early organizers of the march. Alas, it did not get on a mass-produced t-shirt. But at least during the march I saw other people with signs with the same basic idea. Maybe in the coming months it will get more traction.

Hey! 'Getting traction' is a science-based metaphor. Name three other science-based figures of speech in the Comments section below, ones that are not already named by other commenters, and win ... recognition on this blog, if not something even better.

Another survey question was about concerns about the march. Among the answers I checked was 'lack of diversity.' And it's probably not the lack you were thinking of. What struck me was that there were very few Asians. Yet the proportion of East Asians and South Asians who are in science or have family and friends in science is far greater than it is in the US population as a whole. I am aware that when I write "Asians," I am lumping South Asians and East Asians together, ignoring their vast differences in culture, and also ignoring West and North Asians because, based on appearances alone (not interviews or other information), they are part of the great mass of White.

The survey had other interesting questions, and I had other interesting answers, perhaps, but I'll stop now and wait to hear your favorite science-based similes and metaphors. And by the way 'greased lightning' and the like don't count. Please explain so I don't have to.

New Insect on San Francisco's Skyline - Salesforce Tower with Cranes (Cranes 01)

I don't post photographs as a rule because I'm not much of a photographer. But I'm posting this one, fuzzy though it is -- it was the best I got -- because I like the image of the insect-skyscraper. It's the new Salesforce Tower in San Francisco, a few weeks after it was topped off.

I took this picture out a car window. Don't worry, I was the passenger. I happen to love how cranes look against the sky and this sky was particularly beautiful. My lousy reflexes would have done better in a traffic jam but alas traffic was moving. I hope you can still make out the big bug's antennae. There's even a second set of feelers barely visible above the next-tallest building angled away from the viewer.

As locals may guess, I was on the ramp from 101 into the city. Or rather what folks in the Bay Area call "the city". We former New Yorkers know that in fact "the city" is 3000 miles away.

As to my love of cranes: I am now motivated to post two other crane photos, one taken during a walk on the High Line in New York City in 2015, one taken a couple of weeks ago from the roof of the Kennedy Center.

I loved cranes even before I read David Leavitt's The Lost Language of Cranes (1986). Probably that's why the book popped off the shelf at me back in the late 1980s at the Berkeley Heights, NJ library. Or was it the old Carnegie Branch on Amsterdam at about 69th? Anyway, the jacket cover told me that Leavitt meant construction cranes, not the whooping kind, so I read it. It's good so I kept reading Leavitt's books over the years, including the one I think is my all-time favorite, The Indian Clerk (2007).

I like the whooping variety of cranes too, ever since a high school classmate, an ardent supporter of saving those cranes from extinction, told me about their endangerment. I wonder if she'll be at our upcoming 50th next month. Hope so.

I also like apartment building water towers. I could see a few from my family's second apartment in Lincoln Towers. The first faced New Jersey but we didn't have a river view because the West Side Highway was in the way. Nowadays that view has been replaced by other Towers.

I suppose I just have a fascination with rooftops. Which brings to mind the wonderful and beautifully illustrated book Tar Beach by Faith Ringgold, published in 1991, a year after I moved away from the [real] city.

Free association, thy name is MINE. If you mind, or don't mind, please let me know.

May 9, 2017 rev 0

The Senate Rule Invoked to Silence Elizabeth Warren

On Tuesday, Senate Majority Leader Mitch McConnell (R-KY) employed a rarely-used Senate Rule to silence Senator Elizabeth Warren (D-MA) on Tuesday and force her from the chamber.

He was trying to prevent her from reading into the Congressional Record two letters, one by Senator Edward M. Kennedy and one by Coretta Scott King, concerning the nomination of Jeff Sessions (R-AL on Tuesday) to a federal judgeship in 1986. Warren referenced the letters in connection with Trump's nomination of Sessions to be Attorney General. Sessions was confirmed by the Republican members of the Senate the next day, after four Democratic male senators were able to read from those letters. (I refrain from discussing who has balls, what takes balls, and what shows ball-lessness.)

On Wednesday the Washington Post explained the origin of Senate Rule 19, the provision in question, explaining that it goes back to 1902 fistfight on the Senate floor. The full text of the Rule is here.

Rule 19, paragraph 2, was invoked to tell Warren (in the lingo of McConnell's boss, tweetybird the absurd) to "Shut Up." Paragraph 4 provided the authority for then telling her to leave the room.

I found Paragraph 2 the more noteworthy. It says

2. No Senator in debate shall, directly or indirectly, by any form of words impute to another Senator or to other Senators any conduct or motive unworthy or unbecoming a Senator.

Really? Never? I thought about that and looked up the right of Senators to do something about fellow members who acted unethically or even criminally. And then wrote a comment to the Washington Post article:

The text of Rule 19 (2) by its terms would make it impossible for the Senate to excercise its Constitutional right to expel a member (Art. I, Sec. 5) or its traditional right to censure. That's because the rule says that "No Senator in debate shall, directly or indirectly ... impute to another Senator ... conduct or motive unworthy or unbecoming a Senator." But the Senate has held explusion and censure proceedings in the 20th century. If nobody can impute unworthy conduct to a Senator, then how can the reasons for expulsion or censure be debated? Time to repeal this Rule. It is unconstitutional.

"Impute," which means "ascribe," can sometimes have the connotation of "falsely or unfairly." If deterring unfair imputation is the purpose of the rule, then we must ask: Was Warren being unfair? Whether or not Sessions was as bad as Kennedy and King wrote in their letters, the letters were written. And became part of the public record although they were kept out of the Congressional Record back in 1986. The letters tell us what "people were saying," and not just saying but writing, quite formally, to the Senate, and not just any people but a member of the Senate and the widow of the most famous civil rights leader of our time, a man who died from an assassin's bullet. The result of what these people were writing was that Sessions was not confirmed by the Senate. He was rejected. It was neither false nor unfair to discuss that rejection nor to cite information that was before the Senate in 1986.
Perhaps Rule 19 can be saved by adding "with reckless disregard for the truth," the standard for defamation of a public figure. That would make it impossible to stop things like Warren's reading of those two letters. She did not show any disregard for the truth. On the contrary. McConnell's action, however, showed a disrespect for the truth. That's a disease that seems to be going among politicians of the less popular party. We need a cure.

Post-Election Haiku

Some people think in 140 characters. These days I find I think in 17 syllables.

1. Pessimism
Two days after the election I was talking to a friend. She said "Oh, you're always such a pessimist." That inspired this haiku:

      We pessimists have
      the advantage that when we're
      wrong we can rejoice.

Over the next few days, as I shared my haiku with friends, I found myself writing 17 more syllables:

      Lately I haven't
      been wrong, so no rejoicing.
      But I can still hope.

And I still do hope, but it's getting harder.

2. Indiagate
Today I woke up thinking about Indiagate (time to start calling it that, isn't it?), see, e.g., the Chicago Tribune report), and wrote 3 more haiku. (To be added.)

12/16/2016: Indiagate has disappeared from public view. Hello? The lessons of The Big Lie and contemporary incarnations like Birtherism - repeat it repeat it repeat it - also apply to truth. REPEAT IT.

Indiagate was among the first instances of the loser-elect using the Presidency for the Brand. It was tame compared to what has happened since then, but it deserves to stay on the list.

Here are the haiku inspired by Indiagate, TaxReturnGate, etc.

      Some folks try to be
      Above reproach, others choose
      To be below it.

      Using high office
      To feather one's nest? That's not
      The Patriot's Way.

      No blind trust _for_ you?
      Then no blind trust _in_ you. You're
      all for you, not us.

Or shall we say, the motto is "All for one, one for one."

Nov. 23, 2016, added comment 20161216

4NT: The majority voted for NO TRUMP

The majority of the country, including in many so-called RED states, voted for NO TRUMP. The popular vote was 53 to 47. If the electoral votes are assigned to Trump or NO TRUMP, Trump loses, 198 to 340. That's using numbers available on 11/11. It may be even higher for NO TRUMP when all the votes are counted.

That may not change the outcome, but it should be kept in mind when discussing the vote and what it means. Here's the map, with the NO TRUMP majority states shown in tan. (Click anywhere on the map to open a less fuzzy image.)

Acknowledgments: map is from 270towin.com, using the option to recolor states with tan instead of red or blue. Popular vote numbers are from uselectionresults.org, last viewed 11/11/16.

UPDATE: Today, 11/23/16, uselectionresults.org has the popular vote 1 point higher for No Trump
      4NT:   54%
      Trump: 46%
(rounding by the usual rules, or 53.58 and 46.42 to 2 decimals).
Hillary Clinton has the plurality of the popular vote by 2 million votes (1,963,091 to be exact).

UPDATE #2: Today, 12/16/16, CNN gives the popular vote numbers and percentages for Trump and Clinton "updated 11:20 pm ET, Dec. 14." Clinton's lead stands at 2.1%, more than 2.8 million votes. CNN does not provide the total vote nor the numbers for other candidates so I have estimated how many votes equal the 5.5% that didn't go to plurality winner Hillary nor to the loser-elect, and how many total votes were cast.

The map legend should now say:

Popular Vote: 4NT won 54 to 46

                   __%__      __Votes___
Trump       46.2 %      62,955,363
Clinton      48.3 %      65,788,583
Other            5.5 %        7,493,034(est.)
TOTAL     100.0 %   136,236,980(est.)

Conclusion: Less than 63 million voters voted yes Trump and more than 73 million voters voted for NO TRUMP.

-RJM

Ne-ner-nis - Rejected (Part 4) - LANGUAGE 04 [04a?]

Author's Note: Per Blogspot I last edited this post in February 2018 but left it as a draft. When I went to edit it today though, Blogspot says it was published on 1/31/2016.  However, when I click on the Permalink, the page is not found. I can't find it in my published posts either.  Nevertheless, the stats show 96 views. Well, I'll just publish it now, without further edits. FWIW. - RJM 12/14/23.

In three previous posts I have proposed NE-NER-NIS as neuter pronouns. Yesterday, the New York Times reported that several hundred lexicographers met to decide on the best neuter subject pronoun and they chose THEY. Needless to say, I think they are wrong, and THEY is wrong. Before ne-ner-nis disappears from the lexicon, I would like to eulogize it. Or rather as Marc Antony said, "I come to bury [them], not to praise [them]" but we know he meant the opposite.

Singular or plural?

They - the lexicographers, not the pronoun - decided that it was OK for THEY (the pronoun) to take a plural verb because YOU is also both singular and plural and takes a plural verb either way. Hmmm. There IS a difference. When you use YOU, the person(s) to whom you (the person not the pronoun) speak knows whether he, she or they (the YOU to whom you speak) is one or more than one. When you use THEY, the person(s) to whom you speak is different from the person(s) about whom you speak. He, she or they (the hearers) may not know whether he, she or they (the subject/object of your utterance) are one or more than one.

2. Because "he" was rejected for unknown or unspecified genders, and "she" was too new to some ears, many writers and speakers had schooled themselves to replace singular nouns with plural ones in order to use "they" thereafter. But that often led to ambiguity if there was another plural noun in the sentence. I discussed this in Ne-ner-nis (Part 2) (scroll down to "Natural Superiority"). Using "they" as a singular, makes the ambiguity a permanent feature of the language.

The Extinction Problem

As with natural species, these days we are losing words faster than we are gaining them. That is because when we use a word that has a specific meaning in place of another perfectly fine word with a different meaning, we lose the unique meaning of the first word and have to resort to multiple words to achieve what we had before done with a single one. (See The Reticent/Reluctant Hesitation).

The Veterinarian Problem

We need a neuter pronoun not only for transgender humans but also for animals. We also need one when (1) we speak of a human whose gender is unspecified, unknown or irrelevant and (2) it is logical to speak of that human in the singular. As to animals, the other inventor of ne-ner-nis, whose invention is independent of mine and preceded it by several years, was in fact a veterinarian named Dr. Al Lippart. If it's embarrassing and offensive to call Spot "he" when Spot is a "she" or vice versa, will it be better to call Spot "they"? I invite Dr. Al to weigh in on this question.

January 31, 2016, rev (minor) 7/13/16

The Obesity Epidemic

The obesity epidemic was the topic today on KQED's Forum. I emailed the show but my message didn't get chosen for on-air reading. I included a poem several of whose couplets I wrote almost 50 years ago. It has only become more true. For millennia the fat people were rich and the skinny people were poor. No more, not in this country.

SKINNY - AND WELL-NOURISHED

It's upper class
to have no ass

You'll fit in at the Ritz
If you have small tits

Be thin as a rail
And you just can't fail

No gut, much glory
Is a well-known story

Among the fast-paced
You'll find no waist*
     * pun intended

A FAT chance is tiny,
a big empty blank,
A SLIM chance is one you
can take to the bank

So if you'd like a mansion
or to look like you can buy it
then, my friend,
you'd best go on a diet.
                rev (add br's) 12/8/2015 rjm

Google's Diversity Numbers and the Women CS majors of the Class of 1994 (Morris Number 09)

Google has decided to publish its diversity numbers -- the very numbers it successfully prevented CNN Money from obtaining not so long ago. I am glad the company had a change of heart.

I doubt, though, that anyone at Google has thought about its Morris Number - the number of men above the fifth highest ranking woman - or about the diversity breakdowns of its compensation deciles. But somebody should.

Google's now-revealed EEO-1 report shows that the Morris Number cannot be less than 33. That's because the top management category has 36 people and only 3 of them are female. How many men besides those 33 are above the fifth highest woman? In part 10 of this series I will address Google's Morris Number range and how it compares to the ranges for the five companies for which CNN Money did have data. Right now, however, I want to discuss something else published by Google about gender and computer science.

A web search for "google diversity" led me not only to Google's EEO numbers but also to a Google Diversity page entitled "Inspiring the next generation of tech innovators." I clicked on the tab "For women" and saw the heading "CS: Education, Research & Advocacy: Some of our longer-term investments." What jumped off the screen at me there was this quote:

But today, women make up just 18% of CS degrees, down from 37% 20 years ago.

The next sentences explain that the company had commissioned a study so that it can "craft strategies that will change awareness and perception of CS education ..." Excellent. But did anyone at Google familiar with that study consider that CS would be more attractive to future female students if the glamorous and prosperous employers of Silicon Valley would show some interest in the female students studying CS right now? More young women might be convinced to pursue a bachelors in computer science if more jobs were offered NOW to the females who already have that degree. Their numbers may be low but they are not zero. Which brings me back to those percentages from 2014 and 1994.

According to Google's quote and my arithmetic, twenty years ago 3 out of 8 (37%) computer science majors were women. Where are they now? Sure, those ladies are over 40, but so are Google's founders, Larry Page and Sergey Brin, and they are still able to lead productive lives in the tech world despite their advanced age.

Google's history page indicates that its first employee was hired in 1998: a male CS major from the Harvard College class of 1994. Like that lucky young man, the women CS majors graduating in 1994 had been out of college for four years. Were any of them hired by Google in its first year of operation? Or in the next five years? How many female CS majors from the college class of 1994 have ever been hired by Google? (Marissa Mayer is a little younger; Sheryl Sandberg is a little older and her major was economics.) Are there any female CS majors from the class of 1994 at Google now? What about women CS majors in any graduation year up to 2000? Google has hired thousands of CS majors in the last 16 years. Compared to their male counterparts, what were the chances for women to land those jobs?

Google is now spending a good deal of money to improve its image, and I trust also its reality, with regard to gender discrimination. Why not try to find some of the women who were in that 37% and offer them jobs? Might that not benefit Google in all the ways companies say that a diverse workforce is good for business? Thinking outside the box is considered a necessity at places like Google. Those women would have the advantage of having lived outside the Google box in terms of their job experience and probably outside the Silicon Valley box, too, because Google's hiring practices -- characterized by homosocial reproduction, as the sociologists would say -- are typical for the region. A critical mass of new hires who are female and over 40 would undoubtedly be disruptive of the culture, and "disruptive" is considered a good thing in business these days. Those women would also
     - enrich Google's pipeline of internal female candidates for management positions, and
     - serve as mentors, role models and colleagues for younger women.
Win-win-win-win-win.

(Additional thoughts on how the tech industry could improve its EEO numbers sooner rather than later will be in part 11 of this series.)

***

A couple of months ago Michelle Quinn of the San Jose Mercury News wrote an excellent article entitled "Silicon Valley's Other Women Problem". She reported that:

Recently, 24 firms, including Google, Yahoo and eBay, submitted their internal data to the Anita Borg Institute for an assessment of how well they were doing recruiting, retaining and advancing female technologists.

I wonder if the Anita Borg Institute will recommend that Google change its answer to the question "Where are they now?" from "Who cares? Not us!" to "Right here with us and we are lucky to have them!"

Meanwhile, Google could continue being a leader in gender diversity transparency by publishing its Morris Numbers and the diversity breakdown of its compensation deciles. If Google does it, so might other tech companies who have largely avoided hiring women CS majors from the class of 1994 or any class before or since. It is easy and simple to calculate the numbers if you can do the math, and surely Google, Yahoo and eBay have a few people around who can do the math. Imagine if companies would compete over their Morris Numbers. Imagine if college career offices would not let companies participate in on-campus recruiting unless they published their numbers. Maybe Google and those who followed its lead would find after a few years that

- when the Morris Number in every department is less than 10, and
- when all the compensation deciles have similar diversity statistics, instead of white men being over-represented at the top and everyone else being over-represented at the bottom,

the companies enjoy higher profits and better customer and employee satisfaction and loyalty. Imagine.

Postscript:

1994: I wrote this post believing that Google's "37% 20 years ago" was accurate. I wanted a corroborating link for myself, though, so I did a search.

What I found is that "20" should be "30." For example, a blog post by Robert L Mitchell from April 2013 says that the "high water mark" for women in CS was 1986 not 1994. Mitchell associates the number "37%" with the academic year 1984/5. He gives the source of his data: U.S. Department of Education, National Center for Education Statistics, Higher Education General Information Survey (HEGIS), "Degrees and Other Formal Awards Conferred" but the linked page does not in fact have a breakdown by sex. A compilation by the Association of Women in Science of many statistics about women in science includes another table from the National Center for Education Statistics (NCES), one that has the M/F breakdown for computer science and information technology degrees, but only through 2004-5. If the highest female percentages in Computer Science were in the mid 1980s, how low had they fallen by "20 years ago"? By my calculation, the percentages for the years 1992-3, 1993-4 and 1994-5 were around 28%. That is still a good deal more than the 18% that Google quotes for today.

I decided to stick with 1994 in my discussion here. Convincing Google to hire a few dozen 40-something women with CS degrees will be difficult; 50-somethings would, I fear, be impossible in the TECMY culture.

June 1, 2014; updated 20140603 and 0605

Women in Silicon Valley: A Prediction from 2000 (Morris Number 08)

The Future for Women in Silicon Valley
in the 21st Century
as Predicted in October 2000

When I was doing some research about female CEOs for the Morris Number Series, I happened upon an article by Particia Sellers in the October 16, 2000 issue of Fortune entitled "The 50 Most Powerful Women In Business: Secrets of the Fastest-Rising Stars.". Sellers wrote:

Cisco CEO John Chambers has an opinion:

"When I first came out to the Valley in 1991," he recalls, "an Asian-American group talked to me about their glass ceiling. My view then was that while nothing is perfect, these people are talented--and they'll move up."

He continues,

"Today more than 29% of Silicon Valley CEOs are Asian-born--from a rounding error a decade ago. It's primarily because the talent is there, waiting to be tapped. You'll see the same thing happen with women."

In 2014, we have yet to see "the same thing happen with women." I do not know what fraction of the Silicon Valley population was "Asian-born" (Chambers' phrase) or ethnically Asian in 2000, but I am fairly sure that the females were and are about 50%. Woman CEOs? Not 50%. Not even 29%. And even including the near-CEOs -- the COOs (Sandberg at Facebook) and Presidents (James at Intel) -- we may just be seeing the Indira Phenomenon rather than that female talent "there, waiting to be tapped" is being tapped instead of left waiting. Which is why we need to publicize - and companies need to start addressing - Morris Numbers and Morris Deciles.
For woman today, the problem is less that the ceiling is glass (whether or not that was the problem for Asian-Americans in 1991) and more that the doors are padlocked and the key is a Y chromosome.

Interestingly, Chambers had not been asked "Will there be more women employees in Silicon Valley in the future?" He was in fact responding to this question:

Do the guys who rule corporate America (yes, it is still guys in 88% of the senior jobs) know how to handle powerful women?

The way to handle powerful women that is most preferred by the powerful guys who rule Silicon Valley is a variation on an old grade school joke: If we don't give them an inch, they'll never be able to think they are rulers.

Read Sellers' whole article. It is very good and, because it was written more than 13 years ago, very sad.

Morris Number - Further Research (Morris Number 07) (PhD Ideas 06)

SOME PhD IDEAS RELATED TO THE MORRIS NUMBER

Graduate students in labor economics, sociology, political science, history, statistics and womens' studies who are looking for a research topic might want to explore Morris Numbers. Obtaining the data could, of course, be daunting.

Government agencies (see questions 10-12 below), may make public the salaries of their employees. For other kinds of public employers, some compensation data may be public by statute. The civil service, and some companies, also have a system of grades: entry level jobs are in a low grade, management jobs are higher. Among private employers, those with a genuine desire to improve their diversity might be willing to provide researchers with information about their workforce, including an organizational chart and title, grade or salary, and gender. If data is impossible to come by, then these ideas are for thought experiments and discussion.

1. Compare Morris Numbers for different industries.

2. Compare Morris Numbers for different regions of the country.

3. Compare the OMNs for employers with 100 or fewer employees to those with 1000 or more.

4. Tech companies young and old: Collect data to evaluate the two true/false statements in Morris Number 05.

5. Find businesses willing to participate in the study. This would not, of course, be a random sample but it would be a place to start. Graph the Morris Numbers as a function of number of employees, median salary, assets, annual budget, stock price, population of city in which the main office is located, etc. Which factors, if any, have a relationship, direct or inverse, with the Morris Number?

Note: The Morris Numbers, ordinary and weighted, do not factor in the size of the company at all. The underlying assumption is that the ordinary Morris Number of any company with more than 10 employees ought to be in the single-digits; size is no excuse either way. But once there is a body of data on Morris Numbers, PhD candidates and other researchers could investigate whether size is now, or ever was, a good predictor of Morris Number.

6. Find a variety of organizations willing to participate: corporations, partnerships, family-owned and employee-owned businesses, universities, colleges, private schools, foundations, government agencies, non-profits concerned with health, religion, social welfare, etc., etc., etc. As with question 5, you will not have a random sample if you use willing participants, just a place to start. Does the Morris Number predict whether the employer is for-profit or not? If for-profit, are the Morris Numbers on average different for private corporations v. public? Among privately-owned entities, are there any striking differences among the Morris Numbers (the median? the spread?) for sole proprietorships, partnerships, closely-held corporations, or employee-owned businesses? If non-profit, does the purpose of the organization make a difference to the Morris Numbers?

7. Have Morris Numbers declined over the last decade? The last 30 years? What events or conditions have accompanied sharp declines, plateaus, or even increases in Morris Numbers?

8. In 2013, what was the median Morris Number for the Fortune 500, the Ivy League universities, the executive branches of state governments?

9. Track the Morris Number over a 10 year period for organizations with a female CEO, starting with two years before the woman took office. One company where historical data might be available, at least anecdotally from old timers, would be the Washington Post. In 1972 Katharine Graham was the first female CEO to make the Fortune 500 list. Graham had attained the highest position in the company -- Publisher -- in 1969. The Post, however, was not large enough to be on the Fortune 500 list until three years later when it reached number 478 out of 500. Graham, like Indira Gandhi, had a father in the business. Which brings us to the Indira Phenomenon.

THE INDIRA PHENOMENON

As I thought about the question of female CEOs and whether women help other women, it occurred to me that sometimes in a highly male-dominated organization, a highly accomplished, intelligent and lucky woman rises to the top. Below her, however, it's men all the way down until the very bottom levels. The woman at the top is not the reason: the organization was like that before she came and will be like that after she leaves.

Consider Indira Ghandhi. Her becoming her country's leader did not signal that India had abandoned sexism. At least, however, it meant that the country had progressed far enough that Mrs. Gandhi, the daughter of a previous powerful leader (Nehru), could become Prime Minister.

10. Over the years when Indira Gandhi was in power (1966-77 and 1980-84), what was the lowest Morris Number in her government?

It would appear that entities dominated by men, whether governments or corporations, are more willing to have a woman at the top than anywhere else on the ladder except the bottom. The same could be said with whites/black substituted for men/women.

When Thurgood Marshall became a Supreme Court Justice in 1967, African-Americans in the federal judiciary were very few in number. Once Marshall was on the Supreme Court, there were 1 out of 9 justices = 11% Blacks. For the judiciary as a whole, the percentage was much worse: there were 455 judgeships in the federal courts, according to page 8 of tables available from uscourts.gov, and 13, or about 3%, filled by Blacks. (Both the 455 and the 13 include Marshall.) More than half of those judges were appointed by Lyndon Johnson. References: "Integration of the Federal Judiciary" on jtbf.org, Picking Federal Judges by Sheldon Goldman (1999), and Black Firsts by Jessie Carney Smith (2nd ed. 2003).

PhD students in sociology and social psychology probably have examined the Indira Phenomenon under another name. If not, it is worth examining.

11. Compare Indira Gandhi's government's lowest Morris Number with those of other female world leaders, such as, in chronological order: Golda Meir (1969), Maggie Thatcher (1979), Corazon Aquino (1986), Benazir Butto (1988), and Angela Merkel (2005).

12. Trace the Morris Numbers over time in the governments of India, Israel, Britain, the Philippines, Pakistan and Germany, starting with the male leaders who preceded each female head of state and ending five years after she left office (or in the present, in the case of Germany). Compare the numbers with those of male-led countries during the same time periods.
13. Female CEOs: Identify pairs of peer organizations who chose new CEOs at around the same time, one that chose a male and one a female. Evaluate whether or not "women don't help other women" by looking at how the Morris Number changed with time in each company, starting from a few years before each new CEO was hired.

THE MORRIS DECILES

The question of why I had chosen to look for the 5th highest ranking woman, rather than, say, 2nd or 10th, made me think about how the gender mix at different ranks might reflect (or not) the company as a whole, the population as a whole, etc. But ranks are hard to define. They are also hard to compare from one organization to another. I thought about a more objective way to compare companies and came up with compensation deciles:

THE CONCEPT

Find out how many employees there are in the organization and divide that number by ten. That is the number of people in each decile. Then comes the hard part. Obtain compensation (salary + benefits) paid to all employees, and order it from highest paid to lowest paid. No identifying information other than gender (or other demographic under study) would be needed.

Given the "Inequality for All" that infects so many US entities, we can expect that the range of compensation within each decile will be different for different deciles. The decile with the lowest salaries will have a narrow range. Within the decile with the highest salaries, however, the highest paid employee (the CEO in a company, the athletic director in some universities) might receive a compensation package that is 10 times - or many more than 10 times - that of the lowest paid person (who is still making more than 90% of the company's workforce).

The medical supply company McKesson, which according to Forbes, has had the highest paid CEO in the US for the last 13 years, currently pays him $131.2 million. At what compensation level does McKesson's top decile begin? If it is as high as $565,000, then the salaries in the top decile will differ by a factor of 200 (565K x 200 = 130M.) If it is lower than 565K, then someone in the top 10% of the company makes less than 1/200 of what the CEO makes. And it might be lower: McKesson's average [or do they mean median?] product manager, the title with the highest average salary according to this site makes 198K. Even adding a handsome benefits package and employer-paid social security and all the rest, the CEO probably makes 600 times more than that product manager.

14. Is the compensation spread in the top decile a predictor of the Morris Number?

HALF THE EMPLOYEES ARE WOMEN, BUT ...

Half the employees of an organization may be women but that does not tell us much about equal opportunity if all the women are at the bottom of the compensation scale. In the 1950s or 1960s (think Mad Men and How to Succeed in Business Without Really Trying), women in business were secretaries or file clerks and women in food services or hotels were waitresses and room cleaners. How much has that changed?

This may have been the subject of countless studies already. Here are some follow-up ideas.

15. For any organization, what is the percentage of women in each compensation decile? Is there any decile with gender equality? What is the highest of the compensation deciles at which the percentage of women is the same as it is for the company as a whole?

16. In a group of 100 organizations (businesses, law firms, nonprofits, etc.,) how often is the lowest compensation decile the one with the highest percentage of women? If that is the case for most entities in the sample, what characteristics, if any, are common to the outliers?

17. For any organization, identify the decile, if any, in which the compensation of the median woman equal to or better than the compensation for the median man. If none, in which decile is the median woman's compensation the closest to the median man's?

March 19, 2014; updated 20140331,0403

The Weighted Morris Number (Morris Number 06)

THE MORRIS NUMBER, WITH WEIGHTS

When I calculated the range of ordinary Morris Numbers (OMNs) for the five tech companies discussed in Julianne Pepitone's CNN Money article, I thought about adding weights. Weights help to discriminate (npi) between companies with the same OMN but a different ordering of females and males. Weights are a way to take account of the gender pattern above the fifth highest ranking woman.

A SIMPLE EXAMPLE

Two companies have an ordinary Morris Number (OMN) of 5: there are 5 men above the 5th highest ranking women. In one company the top 10 positions alternate between M and F. In the other company the top five positions are held by men, the next five by women.

To distinguish between such situations, we can use weights. Counting down from the top, the more men above each successive woman, the higher (worse) the metric will be.

ADDING WEIGHTS

The Weighted Morris Number (WMN) is calculated by using different weighting factors for the number of men whose position at the company is higher than one or more of the top five woman. The weights go down as you go down the hierarchy. The number of men higher than the top woman is multiplied by the biggest factor. Let us set it at 15. If the CEO is a woman, the contribution to the Morris Number for Female #1 is 0. If the top woman is 6th in the company, the contribution is 75 (5 x 15).

The set of multipliers might be:
     15 for the number of men above Female #1
     10 for the number of men between Female #1 and Female #2
       6 for the number of men between Female #2 and Female #3
       3 for the number of men between Female #3 and Female #4
       1 for the number of men between Female #4 and Female #5
The size of the steps between weights are 5-4-3-2.

ALTERNATING MEN AND WOMEN AT THE TOP

If men and women alternate in the top 10 positions, the ordinary Morris Number is either 4, if a woman is at the top, or 5, if a man is at the top. The difference between the OMNs for those two situations is 1. The difference between the two WMNs is more dramatic. With a man in the number 1 position, the weighted Morris Number is
     1 x 15
     1 x 10
     1 x   6
     1 x   3
     1 x   1
for a total of 35. If a woman is in the top position, the weighted Morris Number is 20.

The bonus for a female CEO might make the weighted Morris Number a less desirable metric: it could encourage companies to rely on the Indira Phenomenon rather than ending discrimination.

ANOTHER EXAMPLE

Company 1: There are 50 men before you reach any women. The next 5 positions are held by women. The OMN is 50.

Company 2: The CEO is a woman. Then there are 5 men between her and F2, 35 men between F2 and the next two women, F3 and F4, then 10 more men between F4 and F5. The OMN is 50.

In Company 1, the top 5 females are below the executive tier. (I say that because it is rare for an organization to have more than 50 people with substantial executive clout.) Company 2 has a female CEO and another female also within the top 10. So far so good, but the Indira Phenomenon may dominate after F2.

Now consider the WMNs for these two companies: Company 1:
     750 = 50 men above F1 x 15
         0 = No men between F1 and F5
     ------------------------------------------------
     750 = WMN

Company 2:
          0 = 15 x  0 men above F1
       50 = 10 x  5 men between F1 and F2
     210 =  6 x 35 men between F2 and F3
         0 =  3 x  0 men between F3 and F4
        10 = 1 x 10 men between F4 and F5
     ------------------------------------------------
     270 = WMN

Company 2's WMN is about a third of Company 1's. Imagine if that fact were publicized at job fairs and university career offices and in a tweet that went viral. Company 2 would attract more and better women job seekers and more and better women customers. It would be able to achieve a level of excellence that company 1 could only envy until its top management finally realized that quality women should replace mediocre men.

March 19, 2014; rev 1 20140331,0403

Morris Number Ranges for Some Tech Companies (Morris Number 05)

CALCULATING MORRIS NUMBERS FOR TECH COMPANIES

Employment data is not easy to come by. But almost exactly a year ago, Julianne Pepitone published an article in CNN Money entitled "Black, female, and a Silicon Valley 'trade secret'" and subtitled "Silicon Valley Boys' Club" in which she sought to answer the question "How diverse is Silicon Valley?" Pepitone had managed to obtain employment data from five tech companies willing to permit such information to be public: Cisco, Dell, eBay, Ingram Micro and Intel. I discussed the article, and its excellent interactive tables, here.

Pepitone had sought information from twenty tech companies. Only three, Dell, Ingram Micro and Intel, cooperated; Intel alone has a policy of making such data public. After a FOIA request was filed seeking employment information that government contractors have to give the government, information about two more companies, Cisco and eBay, was obtained. Final tally: 20 companies, 5 with data, 15 not. Ten of those fifteen, including Amazon, Facebook and Twitter, had no government contracts so the FOIA request did not touch them. The other five -- Apple, Google, Hewlett-Packard, IBM and Microsoft -- successfully petitioned to prevent disclosure on the grounds that it would cause "competitve harm," hence the 'trade secret' joke in the title of the article. Would a company with an excellent record of diversity want to make sure nobody saw the numbers? Hmmm. If you know of further developments in the quest for this kind of information, please let me know.

Pepitone's tables present the numbers of employees in six categories. She explains that these are averages over 5 years. [See "Methodology" below the interactive tables and click "Read More."] Those categories are related to a set of ten codes from an EEO-1 form that all companies with more than 100 employees must file with the EEOC. Those ten codes are aggregates from the 300 occupations the US census uses. See http://www.eeoc.gov/employers/eeo1survey/jobclassguide.cfm.

The EEOC, however, is prohibited by statute (subsection e of 42 USC 2000e-8) from divulging EEO-1 information, which is why the CNN Money investigators first asked the companies directly, and then used a FOIA request addressed to the Department of Labor. Unlike the EEOC, the Department of Labor is not subject to a gag rule but the contractors do have the right to petition to keep secret any trade secrets.

Pepitone reorganizes the data to focus more on upper level employees. She breaks out the subcategories of the EEO-1's top code (1.1: executive/senior officers and 1.2: First/Midlevel Officials and Managers), identifying the first as "Officers and Managers" and the second as "Midlevel Officer or Manager." She also collapses the lower five EEO-1 codes into a single "Admin/Other" category.

Total employees for each company are not displayed, so I have calculated them here.

Company Category	Cisco	Dell	eBay	Ingram Micro	Intel
Officer/Manager	225	125	55	236	41
Mid-level	6,496	3,374	1,804	574	5,027
Professional	24,100	11,237	3,977	846	28,306
Technical	59	3,370	58	119	10,970
Sales	2,850	5,895	163	714	577
Administrative/Other	902	4,820	2,700	2,123	2,000
Total	34,632	28,821	8,757	4,612	46,921

Apparently the five companies give different interpretations to the EEO codes. For example, Intel, which I calculate has almost 47,000 employees, has only 41 "Officers and Managers," less than 0.1% of the workforce. By contrast, Ingram Micro has 236 "Officers and Managers", almost 6 times as many as Intel, out of a workforce of 4600, one-tenth the size of Intel's. That means Ingram Micro's Officers and Managers are 5% of its workforce. Is Ingram Micro that top-heavy and is Intel that lean? Or what?

The CNN Money interactive tables let you view the total number of people the company designated in each category, as well as how many are men or women, or your choice of any combination of gender -- all, men or women -- and ethnicity -- all, Asian, Black, Hispanic, White, or Other.

Because all five companies have at least five women in the top category, it is possible to calculate a range of Morris Numbers. We cannot determine the precise value without more information about people's actual positions. (See Part 03 of this series for a discussion of different ways to rank employees using objective data.) Sometimes the companies' webpages help, at least for the very top of the top. I analyze some of this information below.

A suspicious mind might suspect that companies use as broad a definition as they can for the top category in order to have as few zeroes in the non-white-male boxes as possible. The range for the Morris Number [LOW, HIGH] for each company does not prove or disprove that theory. The greater the overall number of employees in the top category, however, the higher (worse) the upper limit on the Morris Number range.

MORRIS NUMBER RANGES FOR THE FIVE TECH COMPANIES

If we do not know the precise rankings of the women in a company's top management, we can at least calculate the range. The best (lowest) Morris Number is obtained by assuming the first five woman are clustered at the top, above all the men, the highest (worst) Morris Number by assuming that the fifth and all lesser-ranked women in that category are clustered at the bottom. (The ordinary Morris Number is the same whether the first woman is in that bottom cluster, too, or is the CEO. I propose the weighted Morris Number to take into account the actual positions of the four women you pass on the way to finding the fifth highest ranking woman.

Let's take an example, first with numbers, then without, to understand how this works.
        Total people in the category Officer and Managers is 105.
        Number of women in that category is 5
        Number of men = 105-5 = 100

If we assume the least sexist (genderist?) case, the 5 women are all at the top, the 100 men come below them, and the Morris Number is 0. In the worst case, the 5th woman is ranked below the 100 men and the Morris Number is 100. We can write the range as [0,100].

We can derive a formula, too. If T = total and W = women, then the worst number is T-W. If there are 5 or more women in the category, the best number is 0. If there are less than 5, then the next category has to be included to get the Morris Number.

Now let's consider Pepitone's data.

MORRIS NUMBER RANGES FOR FIVE TECH COMPANIES

The Officers and Managers row of the interactive tables shows that
Cisco: Women are 44 of the 225. Morris Number Range: 0 to 181
Dell: Women are 27 out of 125. Morris Number Range: 0 to 98
eBay: Women are 10 out of 55. Morris Number Range: 0 to 45
Ingram Micro: Women are 47 of the 236. Morris Number Range: 0 to 189
Intel: Women are 6 out of 41. Morris Number Range: 0 to 35.

Conclusion: Intel looks the best. Ingram Micro looks the worst, with Cisco a close second.

WHAT CISCO, INGRAM MICRO AND INTEL
MAKE PUBLIC ABOUT THEIR EXECUTIVES

CISCO: The senior management (according to http://newsroom.cisco.com/exec-bios, last viewed 3/14/14), has only 71 people, compared to the 225 that CNN Money reported was on Cisco's EEO-1. The top level is called the Executive Leadership Team. The 15 members, 11 men and 4 women, are listed alphabetically by last name. The women's titles are SVP and Chief Marketing Officer; Chief Technology and Strategy Officer; CIO and SVP; and SVP and Chief Human Resources Officer. We need only look for one more woman to calculate the Morris Number. Of the 56 people in the next level, the Senior Leadership Team, 6 are women, 50 are men. (The fact that there are more women proportionally in the top level (4/15 = 27%) than in the second level (6/56 = 11% )reminds me of the Indira Phenomenon.) If one of the six women in Senior Leadership is above all the males in the category, the (lowest) Morris Number is 11. If all the men are above F#5, the (highest) Morris Number is 61. Range for Morris Number: [11,61].

INGRAM MICRO: The webpage listing Ingram Micro's senior management (corp.ingrammicro.com/About-Us/Executive-Leadership.aspx, last visited 3/17/14) has only 20 people, compared to 236 on the company's EEO-1 filing as shown on the CNN Money table. Of those 20, apparently listed in rank order, there are 2 females and 18 males. F1 is 11th, F2 is 14th. That means F3, F4 and F5 are below the 20 Executive Leaders. If they are directly below, the Morris Number is lowest: 18. If not, they must be somewhere among the remaining 216 (236-20, consisting of 189-18=171 men and 47-2=45 women). If F3, F4 and F5 are at the bottom with the rest of the women and below the 171 men, the worst Morris Number is 189. Range for Morris Number: [18, 189].

INTEL: The webpage identifying senior management (http://www.intel.com/newsroom/assets/bio/CorpOfficers.htm , last viewed 3/14/14) has the same number of employees, 41, as Intel had reported to the EEOC. The Chairman of the Board, a non-employee, brings the page's total to 42. Within each title -- Executive Vice President, Senior VP, Corporate VP -- people are listed alphabetically by last name so no ranking is revealed. The President, listed after the CEO so overall ranked #2, is female. There is also a female EVP (1 of 4), a female SVP (1 of 6) and six female Corporate VPs (6 of 29). F#5 is thus among the Corporate VPs, but we do not know her rank within that group. If she is top-ranked, Intel has the lowest Morris Number: 9. If she is below all the male Corporate VPs, Intel has the highest Morris Number: 32. Range for Morris Number: [9,32].

Conclusion: Intel still looks best, Cisco looks better, Ingram Micro looks about the same.

FACEBOOK AND YAHOO

I also looked on the web for information about the younger tech companies with high profile women, Facebook (Sheryl Sandberg) and Yahoo (Marissa Mayer). The result was: they like to keep secrets. The Indira Phenomenon may be present at both companies.

Facebook lists only 4 executives on its main company information page, Sandberg and 3 men. The Board of Directors has the same ratio: 2 women and 6 men. A 2012 article in the Business Insider has a photo of 18 people from Facebook who had visited Walmart, 13 men and 5 women. Author Owen Thomas points out that the picture includes some "junior [Facebook] staffers who work closely with Walmart" which seems to include at least one of the women, a customer marketing manager involved with the Facebook-Walmart relationship. The other four women are COO Sandberg, her executive assistant, a (the?) director of design, and the VP for Human Resources (so often the only high level job for someone outside the homosocial reproduction group). If these are the four highest ranking women at Facebook, who and how far down is F5? Is Facebook better than Intel or Cisco?

Yahoo's Proxy Statement filed in 2013 has a table showing the ten most highly compensated current or former executives. Two are women, one with the highest compensation on the list, CEO Marissa Mayer, and one with the lowest. Other than the Proxy Statement, I was unable to find anything from Yahoo with any executive leadership biographies or other information from which to calculate a Morris Number range.

True of false? 1. The younger the tech company, the more secretive about diversity data. 2. The younger the tech company, the worse the Morris Number. (Two more PhD ideas there.)

March 19, 2014; rev 1 20140330,0403

Calculating the Morris Number - Hypotheticals (Morris Number 04)

The ordinary Morris Number, described here, is the number of men you pass, starting at the top of an organizational chart, on your way to finding the fifth highest ranking woman. 

Here are some illustrative examples.  A note on notation:  Female #3 or F3 means the 3rd highest ranking female.

A.  WOMEN'S ORGANIZATIONS

In a nunnery, the Morris Number is probably 0. If the Mother Superior is not really the top person but reports to a male Priest, the Morris Number is 1.

Note:  I thought the Girl Scouts might have a Morris Number of 0 but then I found the senior leadership here and realized I was wrong.  There is one man on that page and six women: Is the Morris Number 0 or 1?  We can infer that it is 1. The man apparently ranks third overall. This is suggested by the fact that the various Chiefs below the CEO and Chief of Staff are not in alphabetical order by title nor last name.  Mr. Boockvar, Chief Customer Officer, is listed above Chief Officers for Development, Information, and Financial, and the General Counsel (named, respectively, Taft, Miller, Olden and Rochon). If we were to use weights in calculating Morris Numbers, we would need to know M1's rank vis-a-vis F1 through F5. See Part 06 of this series.

Is a female as highly placed in the Boy Scouts organization? No. Their leadership team consists of eight men: three holding positions called "National" and designated volunteer -- the President, Commissioner and President-Elect, then four Scout Executives of different ranks and last the Chief Financial Officer.

B.  SOME POWERFUL FEMALES

This hypothetical organization has
    - one CEO, a female (F1)
              Contribution to Morris Number =   0
    - a COO, CFO and CTO, all reporting to the CEO; the first two are male and the CTO is female (F2): 
              Contribution to Morris Number =   2
    - five Executive Vice Presidents (EVPs); one is female (F3):
              Contribution to Morris Number =   4
    - eleven Senior Vice Presidents (SVP); one is female (F4): 
               Contribution to Morris Number = 10
    - one Associate Senior Vice President, a female (F5, so we can stop looking)
               Contribution to Morris Number =   0
                               The Morris Number is 16

Note that in this example we do not have to know the standing of Female #3 or Female #4, the lone female EVP and SVP, respectively, relative to the men with the same title because all those males are above Female #5. 


C.  ALL-MALE TOP MANAGEMENT

In this hypothetical organization:
    - The highest ranked woman is an Account Representative (AR).  Above her, there are 100 positions, all filled by males. 
            Contribution to Morris Number = 100.
     - There are 420 ARs, 20 of whom are women.   Without reliable information to know about the rank of the 5th women among the 420 ARs, we can nevertheless make some educated guesses.

       1. We can estimate the Morris Number assuming that the women and men in this organization have the same range of abilities. 

This may not be the case, especially given the absence of women in the first 100 positions and the widely accepted belief that people who do not conform to homosocial reproduction, which in this hypothetical means women, have to be better than men to attain the same level. Here is one citation for that proposition from thirty years ago: "Neutralizing Sexism in Mixed-Sex Groups: Do Women Have to Be Better Than Men?" by M. D. Pugh and Ralph Wahrman, American Journal of Sociology, 88:746-782 (Jan. 1983), available at http://www.jstor.org/stable/2779483, which references a study by the same authors from 1974 ("Sex, Noncomformity and Influence," Sociometry 37:137-47) in which they found that "[M]ale groups refused to be influenced by an obviously competent female despite the fact that without her they clearly failed at their task and lost money." Forty years later, how much has changed?

The 5th woman out of 20 is above 3/4 of the women.  She should therefore also rank above 3/4 of the men and therefore below 1/4 of them.
            Contribution to Morris Number:  1/4 of 400 = 100
            Morris Number assuming equal gender abilities:  200.
OR
        2. We can caculate the range of Morris Numbers, and the average within that range, obtained by calculating the lowest and highest possible numbers:
    - the Lowest Morris Number is obtained if the first five women are above all the men: 
            Contribution to Morris Number: 0 
            and
            the Morris Number is at least 100
    and
    - the Highest Morris Number:  all 400 men are above the first five women: 
            Contribution to Morris Number: 400
            and
            the Morris Number is at most 500

Morris Number Range: [100,400]. Average: 250

March 19, 2014; rev 1 20140328,0403