For the last several years, I have been making myself available for free, first-come-first-served meetings in the style of academic office hours. They’re 30 minutes, 1:1, virtual, and guided by the participant on topics ranging from career advice to applied behavioral science. And they’re specifically designed to address the inequities inherent in gatekeeping culture.
I’m a big believer in the power of framing to shift how we think about our behaviors. When we talk about volunteering our time to help out people via warm intros, it sounds positive. And it is; we could be spending that time on ourselves. But that same time, there is another frame: that meeting via warm intro is a form of active discrimination against those who don’t have the same social access. Yes, we’re helping people get ahead, but we’re also often helping the people who are already ahead to get even further ahead. Warm intro meetings are more likely to be white, male, educated, etc. because those are the people already most likely to have access to the social elite and so rather than addressing inequity, we’re magnifying it.
To measure our ability to create equitable access through open office hours, in 2021 we released our first Diversity Report, a concentrated effort to make sure that this system is in fact serving a broad range of underrepresented people. In our 2022 edition, we started doing trend analysis, which we’ve continued this year.
I use the plural repeatedly throughout this report. That’s because making office hours happen is a team effort; even if I’m the one actually showing up, there is a tremendous amount of work from a number of people to make sure that we follow up on action items, share our learnings, and prepare this report every year. In particular, Melanie Perera and Alaanah Sallay from Oceans and Lorraine Minister are instrumental in connecting job seekers with opportunities, sending out materials, and preparing the Mentor Minutes for social media. I am grateful for all they do and hope you take a moment to celebrate them.
Before looking at the results, a few quick notes on methodology. To gather the data, we set up a Google Forms survey and then used Zapier to automatically email participants with a link after each meeting, along with an end-of-year followup reminding them of the survey. 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 options presented in random order, with “Other” and “Prefer not to say” options included.
For 2023, we went from ~550 meetings last year to more like ~750 meetings this year. We received 140 survey responses, giving us a response rate of ~19%.
That is lower than last year, so we’ve made some changes for 2024 to try to make sure we’re getting more accurate data. For example, we’re now using Zoom’s automatic followup feature to give people the survey immediately after the meeting, which should also improve data accuracy. Even with a lower response rate, however, we still have a significant sample, so we can make some reasonable assumptions that this data is representative of the larger population of participants. You could always make an argument that some segments are more likely to respond; caveat emptor.
I’m changing the format somewhat this year, as most data has remained relatively flat from last year (I’ve included the change from the 2022 numbers in parentheses), and so it is more appropriate to save the commentary for the end. As always, 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.
Age
The mean age in respondents was 36 (+2) and the median was 35 (+2). 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 (+0), with participants ranging from 20 to 60, so there was a good bit of variability.
Gender
Among respondents, 57% (+1) identified as women, 37% (-1) identified as men, and 6% (+1) identified as non-binary/genderqueer. This is a fairly large overrepresentation of women and potentially a large overrepresentation of non-binary/genderqueer people, although that number is harder to evaluate because of the correlation with age.
Sexual Orientation
78% (+0) of respondents identified as heterosexual, with 22% (+0) identifying as some form of LGBQ. This is significantly different than the base rate of 93% and 7%, respectively.
Race and Ethnicity
53% (+0) of respondents identified as White (base rate 77%), 9% (+2) as Black or African American (base rate 13%), 23% (+4) as Asian (base rate 6%), 1% (+0) Native American (base rate 2%), and 14% (-6) as More Than One Ethnicity (base rate 2%). In addition, 17% (+5) identified as Hispanic or Latino/a/x (base rate 18%), with Mexican, Mexican American, or Chicano/a/x as the largest group.
Other
27% (+7) of respondents are first-generation Americans (base rate 14%), while 27% (+4) are first-generation college graduates (base rate 35%). 37% (+10) view themselves as underrepresented in their field, while 10% (+1) are living in poverty and 9% (+4) identify as disabled. 27% (-14) did not add any additional tagging. 73% (-1) are currently living in the United States.
Commentary and Commitments
Demographically, there were surprisingly few changes this year to individual categories; most were within the margin of error for our sample size. The largest change actually came in the number of folks who identified with no underrepresented categories of any kind, 9.3% (-3.3). Neither gender identity, ethnicity, or sexual orientation meaningfully changed, which suggests that this year, more Straight White Males saw themselves as reflecting other underrepresented identities.
On the one hand, that could indeed be progress: we could be reaching a different audience than in previous years. Or perhaps Straight White Men are simply coming to recognize a broader array of potential ways in which someone can face challenges. The cynical view, however, is potentially quite worrisome: that the language of representation is being co-opted by those who struggling to find any possible way in which they can distance themselves from the trappings of privilege.
Regardless of how you choose to interpret it, it does present a compelling case for why intersectionality needs to become the default way of looking at representation. Saying that 90.7% of office hours were devoted to underrepresented folks obscures the fact the very real difference between facing one demographic underrepresentation and several.
We made two important commitments in our 2022 report, both of which we were able to honor. The first was to repurpose more of the content generated in office hours for a wider audience. It isn’t particularly efficient for me to say the same thing over and over again, when we could be using office hours for custom questions and content. So we did more editing this year to distribute clips of the advice I repeated most often.
We also promised to increase our available tooling. This year, for example, we refined our job tracking spreadsheets so we can more easily refer people for open positions and released self-paced courses on applied behavioral science, thanks in large part to Lorraine Minister’s efforts as our Head of Education.
For 2024, we’re going to continue to focus on scalability:
- AI-identified content. Using new tooling from a stealth partner, we’re now able to automatically identify the phrases and examples I use most often and clip them for sharing. The shift from manual to automatic identification should significantly increase our ability to release in a timely fashion.
- Structured education. Our approach to office hours was born out of my experiences in academia and I believe that 1:1 conversations benefit most when they’re an augment to structured learning content. So we’ll be introducing new guides and classes this year to help cover some of the basics and make our 1:1 time more efficient.
Side Note: It is eerie how many of these percentages were the same as last year, despite having a different sample size. I went back and checked the data repeatedly, just because it felt so unusual that, for example, the percentage of White participants remained exactly identical. It is a good reminder that things often don’t change as much as we think they do; even if they feel different day-to-day, the prevailing pressures that created the circumstance remain the same and so repeats are likely. It also reminds me that I need to do more to put my finger on the scale to make sure that next year, things do change.
Also, CapGemini still hasn’t released diversity numbers. So that makes it two years in a row that I’ve done what they are unwilling to.