Humans are habituation machines.  Once something becomes true for us, our brain starts incorporating it into our reality through selective attention and a variety of other cognitive biases, such that it is hard to remember a time when it wasn’t true.

Take the internet. If you’re old enough, you might be able to dredge out some specific memories about a time before ubiquitous connectivity.  But even those memories are fairly selective and it is hard to really emotionally connect with them; the internet simply is in our present reality.

Diversity reports are another example.  20 years ago, it wasn’t ubiquitously true that every major company released a comprehensive report on the demographics of its workforce.  And yet now it would be surprising to find a major company that doesn’t.  Accountability allows autonomy and transparent data is the first step toward that accountability.

The link between accountability and autonomy isn’t just for big companies; it is a core building block of any service relationship.  Beginning several years ago, I offer my time as a public service in the form of open office hours, which I wrote a guide to when I started.  

But in order for me to offer that public service in an accountable away, I also need to be transparent.  This post is my attempt to do that, in what I hope to make a yearly practice, by releasing diversity statistics for my 2021 office hours.

First, a few quick notes on methodology.  To gather the data, we setup a Google Forms survey and then used Zapier to automatically email participants with a link after each meeting.  In addition to asking for qualitative feedback to help us improve, we asked basic demographic questions about age, gender identity, sexual orientation, ethnicity, etc.  No questions were required, all were multiple choice, with “Other” and “Prefer not to say” options included.

For 2021, I committed to two hours per day of office hours in 30-minute slots or ~1K potential meetings.  While obviously I couldn’t always manage that, we did have a utilization rate ~80%, so ~800 meetings in total.  Since we started collecting diversity data in November, we have only two months of participants to work with or ~130 people.  We received 40 survey responses, giving us a response rate of ~30%.

Generally speaking, that’s a lot.  Typical survey response rates are less than 5%, so we can make some reasonable assumptions that this data is representative of the larger population of participants.  That said, you could always make an argument that some segments are more likely to respond, so take it all with a grain of salt.

On to the 2021 data!  Each section has a two paragraph format: data, then interpretation.  There will be a separate section at the end for commentary and 2022 commitments.  I’m open to questions and feedback on the analysis, as well as suggestions on what commitments you’d like me to make; just shoot me an email.

Pie graph of Open Office Hours for 2021, expressing percent of participants by number of underrepresented identities, including gender, ethnicity, sexual preference, and other (e.g. first-gen college). 12.5% have no underrepresented identities, 17.5% have one underrepresented identity, 35% have two underrepresented identities, 25% have three underrepresented identities, and 10% have four underrepresented identities.

Age

The mean age in respondents was 33.6 and the median was 31.  The best comparison is probably the median age of the US working population, which is 42, so overall we’re skewed a little younger.  However, the standard deviation was around 9, with participants ranging from 20 to 59, so there was a good bit of variability.

It is hard to interpret this in terms of representativeness.  I was 39 in this period of 2021, so there are a variety of reasons why people older than me might not have felt I could be supportive to them.  And younger people are probably more comfortable with the idea of digital open office hours generally; both might be factors.

Gender

Among respondents, 55% identified as women, 42% identified as men, and 3% identified as non-binary.  For women and men, these numbers are essentially the same as the workforce participation rates.  For non-binary, this is likely a bit higher than the base rate of less than 1%, although for people is a very small sample size and the population-level data is unreliable.

Candidly, I was initially disappointed by these results.  In that my office hours are an attempt to democratize access and systemic sexism is an issue, I had hoped to reach a group that was more heavily skewed.  This shows the danger of univariate thinking, however; as we continue to look at the other forms of diversity, a different picture emerges and so I’d like to withhold judgment for a bit.

Sexual Orientation

75% of respondents identified as heterosexual, with 20% identifying as bisexual and 5% preferring not to answer.  This is significantly different than the base rate of 94% and 6%, respectively.

I honestly don’t have a ready explanation for this.  Because participants skew younger and the proportion of the population that identifies as non-heterosexual also skews younger, it may simply be due to a mediating variable.  It could also be a network effect driven by homophily and my political stances also tend to be relatively public, so it could be self-selection.  I simply don’t know.

Race and Ethnicity

40% of respondents identified as White (base rate 77%), 15% as Black or African American (13%), 30% as Asian (6%), and 15% as More Than One Ethnicity (2%).  In addition, 13% identified as Hispanic or Latino/a/x (18%), with Mexican, Mexican American, or Chicano/a/x as the largest group.

There is a lot to unpack here.  It is unclear why there is a massive overrepresentation of Asian people and people who viewed themselves as having a mixed ethnicity; all of the factors from sexual orientation could potentially be at play here.  There is certainly room for growth in other categories, although as with gender, it is hard to look at these results in isolation.

Other

23% of respondents are first-generation Americans (base rate 14%), while 18% are first-generation college graduates (base rate 35%).  40% view themselves as underrepresented in their field, while 38% did not add any additional tagging.

I was surprised by the base rate of first-generation college graduates, although I probably shouldn’t be: because almost all of the people I interact with in a professional context have degrees, it is easy to forget that higher education is far from ubiquitous.  I was also surprised by the overrepresentation of first-generation Americans; I can theorize as to why they might be more likely to be interested in office hours but have no proof.

Commentary and Commitments

As with any personal feedback, it is hard to know how to react to this data.  I have long believed that public, open office hours on a first-come, first-served basis could be a potential lever for reducing some forms of systemic bias.  If they remain only at the level of mentorship, office hours are unlikely to create real change: we have evidence that women are over-mentored and under-sponsored and there is reason to believe that is true of other underrepresented groups as well.  But to the extent that we are able to use them as a catalyst for sponsorship, where resources are expended to create new opportunities, they have power.

If the purpose of open office hours is to specifically focus on the underrepresented, then we’ve achieved some success: only 13% of participants were straight, cis white men who didn’t identify with any underrepresented categories.  But there are still clear places where there is much room for growth (like Black or African Americans, where we only achieved parity with the population).  The question becomes how to create that change.

For 2022, I’m going to concentrate on two key pressures: reducing suspicion (an inhibiting pressure) and increasing followup (a promoting pressure).

In a perfect world, everyone would know that office hours exist, decide for themselves if they are beneficial, and then take a slot that works in their schedule.  But we live in an imperfect world.  I’m frequently asked whether there is a fee and many people have expressed disbelief that someone would offer free support.  And these doubts were not evenly distributed; anecdotally, it was more often underrepresented participants who expressed the most suspicion.

To me, this is entirely logical.  We know underrepresented people are receiving the least help and are the most likely to be exploited.  So when faced with an opportunity for free support (from a cis white dude, no less), being cautious is a reasonable reaction.

Here is what I’m going to do about it:

  • Release recordings.  We use Vowel as a platform for office hours, so that participants can view the video, transcript, and notes after the call has been completed (plus, it has the handy live “percentage talked” counter that helps me to remember to shut up).  In 2022, we’re going to start releasing edited clips of office hours to help clarify what people can expect and they can see proof that it is a free service.  We’ll select clips likely to be useful to others, edit them to just my video and voice, and not use anything that mentions participant details.  In our pilots so far, underrepresented groups that were shown a clip of office hours were significantly more likely to subsequently sign up for a slot than those that didn’t see a clip.
  • Clarify cost (and the lack thereof).  Previously, I relied on the academic understanding of “office hours” as a term that indicated freely available support.  But we’ve now clarified the language on both the Get Support page and LinkedIn to be clear that these slots are available completely free.

We cannot simply reduce inhibiting pressures, however – we must also increase promoting pressures.  Our follow-up surveys are generally positive but I recognize that I don’t always follow through on commitments that I make in office hours, mostly out of inattention.  So here is what I’m going to do about it:

  • Add team review.  One of my team members will review each office hours recording and document any action items I’ve agreed to, following up with support and reminders as needed.  The hope is that we deliver on every commitment that I make; this will have the added benefit of making it more likely that we transcend mentorship to full sponsorship.
  • Create a followup budget.  Some followup items require money to accomplish.  In 2021, we did this on a one-off basis but that opens the door to inequitable distribution and also makes it hard for me to limit my commitment to a level I can sustain.  So this year, I’m setting aside an initial budget of $5K that the team can tap into directly, without approval from me, to take action on items that require financial support.

Finally, we’re adding a few more tweaks simply to improve our processes and make things generally more inclusive.

  • Taking a more holistic view.  For example, adding a “disabled” option to the self-identification question, as well as a question about country of residence to capture international participation.
  • Varying the times of office hours.  For most of the year, my office hours were during working hours for people in both PST and EST.  This might create barriers for some, so I’ve created a more flexible schedule designed to allow for a wider range of participation.

I fundamentally believe that transparency helps drive accountability and accountability allows for autonomy.  My hope is to be able to offer an updated diversity report yearly for as long as I am able to continue doing office hours at this pace and with this team.  As I mentioned earlier, I’m open to questions and feedback on the analysis, as well as suggestions on what commitments you’d like me to make; just shoot me an email.

Side Note: Sometimes, doing the right thing feels absolutely ridiculous.  Pulling this report together took a few weeks and there were moments where I almost abandoned it; posting it could easily be seen as communal narcissism (which I willingly admit to being at times), so it was tempting to simply analyze the data and make the changes entirely privately. Talking about social justice action often feels like a Catch-22: do it and look performative, don’t do it and be complicit in the racist, sexist, classist status quo. So I often think of the extremity test: is the universe where nobody does a behavior better or worse than the one where everyone does?  In the case of diversity statistics, I’d far rather a world where everyone releases them than nobody does, so I posted mine in an effort to tip the scales in that direction.  Social pressure works – talking about what we do makes it incrementally more likely, on the whole, that other people also do it.  And if that feels (and is) ridiculous and results in a cascade of clown emojis…well, at least I was entertaining.

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