Sunday, January 31, 2016

Ne-ner-nis - Rejected (Part 4) - LANGUAGE 04

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 that THEY (the pronoun) takes a plural verb on the grounds that 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

Monday, November 30, 2015

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.


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

Monday, June 2, 2014

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.

(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.

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

Thursday, May 8, 2014

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.

Wednesday, March 19, 2014

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


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.


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, 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, 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 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:


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 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)


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.


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.

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.


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.

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)


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
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.
Ingram Micro

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.


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.


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.

CISCO: The senior management (according to, 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 (, 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 ( , 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.

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