Wednesday, March 19, 2014

The Morris Number - Introduction (Morris Number 01)

I propose a new metric I'm calling the Morris Number.  It is a way to quantify how gender-neutral an organization really is. It makes it possible to compare employers -- businesses, non-profits, universities, governments -- with regard to their equal opportunity characteristics.  I thought of the concept when I wrote about Silicon Valley: Boys Club last year.  Whatever the organization, the Morris Number answers the following question:
How many men do you pass on the way to finding the fifth highest ranking woman?
Other discriminatory behavior can also be evaluated with Morris Numbers.  Instead of men and women, any two demographic categories can be compared, e.g., whites and non-whites (or, for tech companies, TECMYs and non-TECMYs).
In a perfect world, the Morris Number averaged over many organizations would be 4.5. Assumptions behind the phrase "in a perfect world" are discussed in Part 02. That post also includes questions raised by friends when I first mentioned the Morris Number.

In the real world, almost all employers have a Morris Number above 4.5, often far above. Perhaps if we start calculating and publicizing Morris Numbers we can start to see them change.

The Morris Number may remind some readers of the Bechdel Test, which evaluates movies on a scale from 0 (bad) to 3 (good) depending on the presence of female characters and what they do. Both the Morris Number and the Bechdel test are metrics concerning unequal opportunities for women. They differ, however, in that
  • the Morris Number is for all employers, not just movie producers,
  • unlike Bechdel, and instead like golf and cholesterol, a lower Morris Number is better than a high one, and
  • the Morris Number can be much higher than 3, and almost never, alas, is that low.
A Morris Number in the single digits means that the employer's hiring and promotion practices today are what I would have predicted back when I was in law school.  A Morris Number above 10 reminds me that change sometimes takes a long time.

In the Fortune 500, is the average Morris Number below 30? What about in Silicon Valley?  Among major nonprofits? At the top research universities?  Wouldn't you like to know? I would.


You may be wondering how to identify the fifth highest ranking woman in an organization.  For a start, we can use the employer's organizational chart. 

Starting from the top person in the hierarchy, usually the CEO, count all the men you have to pass before you get to the fifth woman.  That is the Morris Number in its ordinary form.

True, there is an obvious problem with org charts.  If several people have the same title, how do you determine who has a higher rank?  You might want to base your answer on how much power each person has. To evaluate that, you might try objective indicia of power such as salary, budget, or direct reports.  Ideas about going beyond the org chart are discussed in Part 03.


In Part 04 I calculate ordinary Morris Numbers for some hypothetical entities and discuss a way to deal with the same-title-different-power problem.  Morris Numbers calculated from the tech company data presented in Julianne Pepitone's 2013 CNN Money article can be found in Part 05.


Part 06 presents a comparison of the ordinary Morris Number, which we  might call the OMN, with a weighted Morris Number, WMN.  The reason to add weights is to distinguish between two companies with identical OMNs but different distributions of the sexes. For example, a company with a Morris Number (OMN) of 5 might have men and women alternating in the top 10 positions, or it might have men in the top 5 positions and women in the next 5.  The WMN quantifies the difference.


Comparing Morris Numbers among companies, times, or regions, would be interesting. So would looking at how a company's Morris Number changes when it has a female CEO. We might also use Morris Numbers to examine what I call the Indira Phenomenon. (It probably has a name already but I am not aware of it.)

Another metric for gauging equal opportunity would be to identify the compensation deciles with a 50/50 gender ratio or an ethnic ratio similar to the population in the area.

Those and other ideas for PhD dissertations in Economics, Political Science, Statistics, Sociology, Gender Studies or American Studies are in Part 07.
RJM 3/19/2014; rev 1 20140321;0401

       May 08, 2014
Part 08 has a brief excerpt from Patricia Sellers' October 2000 Fortune article about Women CEOs in which she quotes Cisco CEO John Chambers on the future for women in Silicon Valley companies. It is 2014. Rank Chambers' prediction on a scale from 1 to 5 where 1 is totally wrong and 5 is totally right. And weep.

       June 2, 2014
Part 09 discusses Google's diversity numbers and the quote on its Diversity page about how women do not major in CS like they used to. There's an obvious idea there that Google is somehow missing.

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