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Calling All Data Nerds: Let's Fix Gender Data
18 Plays3 months ago
In this episode of Gender in Focus, Kai and El are tackling a crucial aspect of workplace inclusion: how we collect gender data. We discuss ways to update our approach to gender data collection, with a particular focus on being inclusive of trans, non-binary and Two-Spirit people. We explore why it’s time to rethink how this information is gathered, and how a more thoughtful approach can make all the difference for inclusion.
We also break down why clarity, consistency and intentionality matter when collecting gender data, and how organizations can avoid common pitfalls. With these principles in mind, Kai shares practical advice for organizations looking to update their approach, from setting up gender data collection systems to avoiding common mistakes.
And don’t miss out on our free downloadable decision-making tool to help guide your organization through this process. If you’re ready to rethink how your workplace handles gender data and improve inclusion for all, this episode is for you!
For more information about our Gender Data Program, visit our site here!
To download our free Trans Day of Visibility, check it out here
For TransFocus' TDOV Live Sessions - check them out here!
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Transcript
Introduction and Focus on Gender Data Challenges
00:00:02
Speaker
This is Gender in Focus. I'm El and each week I sit down with the president of Trans Focus Consulting and all-round gender diversity genius, Kai Scott. I get to ask all the questions you've ever wanted to ask about how to make the workplace and the world a better place for trans and non-binary people.
00:00:20
Speaker
Let's get into it.
00:00:27
Speaker
In this episode, we are talking about the wonderful world of gender data, my favorite topic. And I know you're not supposed to have favorites, but I do. We're here to give you some important tips and tools to help you sidestep challenges when it comes to data collection and uses.
Contexts and Controversies in Gender Data Collection
00:00:44
Speaker
And I'm really excited to get into all the nitty gritty and give you some tools at the end of the session in the side notes.
00:00:51
Speaker
I mean, as you've already alluded to, Hai, you are the biggest data nerd out there. Wow. I love it. right When it comes to collecting data at work, what context does that, what does that look like? And what context are people collecting data as a whole?
00:01:08
Speaker
Yeah, there's so many different places where gender data shows up. The most famous of them are the employee surveys that are conducted, whether workplace culture survey or a census, or there's many different ways that um companies or organizations collect that type of data.
00:01:28
Speaker
ah So it might show up there as a demographic data along with some other pieces, you know age, race and ethnicity, ability, neurodiversity, et cetera, or immigration status as well.
00:01:42
Speaker
So that's one avenue where gender data could be collected. And then there's also forms where employers can and do collect you know that data and then input it into a system.
00:01:55
Speaker
And so you know yeah and on the new employee form, they might collect that type of data. Now, it gets a little tricky because there's also third-party providers, such as um extended healthcare benefits providers that organizations dovetail with that collect on behalf of that third-party provider.
00:02:15
Speaker
And their data collection is ah not so much in their control, but they are collecting it. ah from the employee and giving it to the third party providers.
00:02:26
Speaker
um And there it sometimes can be gender, but sometimes it's sex. So it's a little bit murky or messy there and whether we should be collecting sex at all.
Necessity and Application of Gender Data
00:02:37
Speaker
So won't be speaking to that because that's its own kettle of fish. And there's some ways that companies and organizations can navigate that, but that's another source of data collection.
00:02:49
Speaker
I think a lot of people kind of take for granted that um gender is one of those things that needs to be collected in the first place. um Do you always need to collect under data ah when you are doing things like forms or surveys?
00:03:04
Speaker
It's a really important question to pose. I know, like you said, it's kind of the way we've always done it. So we may not question or think to question the the collection of that, but it really is important to use the data that's collected. So if there's not a really an intentional or direct use of that data, then it really warrants a ah close look to see if it's really necessary.
00:03:31
Speaker
ah So there are instances in which it's not necessary, especially if it's not applied in any context. Or in some cases where we help organizations out with strategy around gender data, we find that it's underutilized. Maybe here and there it's kind of peppered in, but it's very light and doesn't really have any material positive impact on people's you know day-to-day lives at work.
00:03:55
Speaker
And so that there's kind of um that that should be factored in to make sure that it's properly and adequately utilized. ah So it's it's asking that question at the outset and having a real good intentional plan towards using that data so that it's not just collected and goes ah shelved somewhere, but that actually lives to powerfully impact people's workplaces and and cultures and you know, maybe systems or challenges that they face. And those can be addressed by looking at the the gender data, among other data.
00:04:31
Speaker
I mean, you you did actually just, I had a question in my head then, and you answered it kind of, but I just wanted to go in a little bit with that. And let's say you have collected um gender as a sort of data point.
00:04:44
Speaker
How can you use it? Like once you've collected it, what are some examples where people might use it?
Intersectionality and Expanding Gender Categories
00:04:50
Speaker
Yeah, that's super important. There are obviously very good uses of data, particularly around equity objectives or even assessing workplace culture.
00:05:01
Speaker
If you're able to disaggregate that by a variety of different demographics, you know, slices, if you will, of the data, that can be really important, gender being one of them.
00:05:13
Speaker
If you can go even further and do an intersectional analysis of, you know, combination of gender plus other factors or other experiences, that can provide really deep, rich insights into exactly where folks are falling behind or struggling or you know, don't have a sense of safety or belonging or that their voice is heard. And so that's where that that um analysis can be really useful.
00:05:41
Speaker
um It's good if you have if the organization has objectives for what type of workforce they want to have represented. Typically, that's and kind of measured against the demographics of the place in which they have headquarters or their offices are located.
00:05:58
Speaker
ah so if you're in Vancouver, for example, you'll want to replicate ah the demographics of that of that of that city. or the province or territory or state if you're down in the United States. So there's many different ways to kind of set up goals that then you can measure ah against the data that's collected, the gender data or other types of equity data.
00:06:22
Speaker
And it really helps to see the progress along the way and where things are falling short, especially within ah positions of leadership within an organization.
00:06:33
Speaker
So typically, um trans and non-binary folks, among other other equity groups, are kind of at the lower echelons of organizations. And so setting up and measuring along the way can help highlight where there are still gaps and where things need to be put in place to intentionally help increase those numbers. So that's where all these types of data can be really useful and and very powerful.
00:07:00
Speaker
When people are um setting up their own kind of ah surveys or ways to collect that data, um when we're looking at response options, there's often man or woman.
00:07:13
Speaker
ah Are there categories kind of beyond that and and how how would you set that up exactly? Or what are those options? Yeah, so that's the kind of million dollar question. People often recognize or ah have are coming to an understanding that categories of man and woman are insufficient. They don't cover everybody.
00:07:35
Speaker
ah There are folks who are non-binary, agender, gender fluid, gender queer, gender There are many other terms beyond the binary of men and women.
00:07:46
Speaker
And it is really important if data is are being collected, ah that it be an expanded form so that you know there's there's ways to measure ah folks who are non-binary or um otherwise.
00:08:01
Speaker
Now, when it comes to what are the options, there are a few different ways to approach this. And there's a really important discussion happening in trans and non-binary communities to, in particular, desires and interests in having something that feels not as boxed in, which is understandable given that, you know, people have been put in the wrong category and so feel very boxed in ah by societal expectations or,
00:08:32
Speaker
you know, what they were assigned at birth in terms of their sex, et cetera. And so there tends to be in the community, a strong desire for many categories and a lot of flexibility in terms of what to provide in the data ah collection context.
00:08:49
Speaker
Now, I will say as a social scientist um who has dealt with data so much, we've, you know, surveyed about, you know, 15 20,000 people now,
00:09:01
Speaker
maybe even more.
Ensuring Data Representation and Ethical Handling
00:09:03
Speaker
Have you really? Yeah, it's been a lot. so So we've tried so many different ways of doing it along with the ones that are strongly desired by community.
00:09:16
Speaker
But unfortunately, check all the apply and fill in the blank are notoriously difficult from a statistical standpoint. And I'm not there to say let's dismiss it entirely.
00:09:29
Speaker
But from an organizational standpoint, mostly cisgender people are analyzing the data at the backend. So we want to be very thoughtful about not giving people just a ah pile of data that they don't know how to handle and don't treat properly and respectfully.
00:09:46
Speaker
um And so that's where we've given it a lot more thought. And that's where you have to think about how many people are going to respond to your survey. or a form.
00:09:58
Speaker
And if it's a lot of people, like in the thousands, that's where it becomes easier to introduce some of this flexibility and openness. But unfortunately, if you have fewer than a thousand, that's where you're now risking having the data suppressed.
00:10:15
Speaker
And what do I mean by suppressed? Basically, if there's um a number below a certain ah value, ah It varies by organization. That, ah from a confidentiality and privacy perspective, can't be released because it could identify someone.
00:10:33
Speaker
And so there's this really fine balancing act we're trying to to do as somebody who is both part of the trans community and non-binary communities and um a social scientist, and I find that unfortunately we have to go when it's fewer so that we don't get to this risky suppression levels because we do want that data represented.
00:10:53
Speaker
It's very important to be visible. ah That's where it tends to be better to have fewer options um and even just select one other option beyond the binary to keep everybody together.
00:11:06
Speaker
um Appreciating that mostly there's a lot that experiences shared in common. Of course, there are some unique experiences as well. But for the purposes of moving things forward, it's important to keep people together if there are low numbers below 1,000.
00:11:23
Speaker
So it's a bit of a a threshold that we try to help people and different pathways. And we've got all of this outlined in this fantastic decision-making tree that kind of step-by-step helps organizations or even community members walk through and to get to the best option possible.
00:11:43
Speaker
So just going into data suppression a bit more, could you, could you kind of um talk more about that and like what the options are and ah what the kind of pros and cons are around ah piecing things together?
00:11:56
Speaker
Yeah, so data suppression this is this really interesting i world unto its own, almost. Many organizations, ah whether it's like Stats Canada or ah BC Stats, that I mean, they're operating at a much larger scale.
00:12:12
Speaker
And so they tend to suppress at a very high, in my mind, high level. So for example, in some instances, it's um this is population wide, they'll say any number below 40 respondents gets suppressed, right?
00:12:28
Speaker
And so yeah that would very definitely, ah you know, pretty much eliminate trans and non-binary responses in that instance. um And so as a result, when it comes to surveys, it's really important as much as possible to have a really ah lower suppression number.
00:12:47
Speaker
ah we've We've gone down as low as five levels. in in one question or responses to one question and you know for a particular category, say a trans and non-binary category of folks.
00:13:01
Speaker
And so as much as possible to to so you know to have that be a low number, many organizations are going to kind of be like around 10, 15, but unfortunately that and you know really jeopardizes the representation of trans and non-binary folks.
00:13:17
Speaker
And people may not even realize it and have not thought it through. And so that's why we're paying particular attention to this and and raising awareness around the the kind of the role of suppression while important and trying to protect confidentiality and privacy of folks.
00:13:33
Speaker
a There's still a bit of a balancing act and and try to get that as low as possible um for for that um representation of folks. So, yeah. So how do we count trans men and trans women when we come to collecting this data.
00:13:51
Speaker
Yes. Also another important thing. So when we talk it talk about expanding the gender categories, that includes know women and men already existing categories and adding either one or more categories for folks who are beyond the binary or between.
00:14:08
Speaker
a And of course that captures you know non-binary, agender, gender fluid, gender queer, et cetera, in a good way. But if there's not a follow-up question, the men and women categories have both cisgender and transgender people in them.
00:14:27
Speaker
um They do now, still. um And if there are a sufficient number of people above 1,000, then it there can be a second question that can be asked.
00:14:39
Speaker
ah That essentially allows folks to, know, do you, are you transgender, which you can define, is your gender different than your sex assigned at birth ah for clarity, and then have people say yes or no.
00:14:53
Speaker
And in doing so, you can then separate men, the categories of men and women into cisgender and transgender gender. and have more information there. And it's really critical because as you can appreciate, there are differences of experiences among trans women compared to transgender men. And so it is important to disaggregate even though you may not always be able to do it based on the numbers that an organization has.
00:15:22
Speaker
If it's more than a thousand, you can have that clarity. um Unfortunately, below that, you just won't have enough numbers to be able to separate in oh before hitting that suppression level.
00:15:35
Speaker
ah So that's just something to keep in mind. But if you can, it's really important to understand those distinctions. So then you have categories, kind of people beyond the binary, you have trans men and trans women.
00:15:48
Speaker
And those three separate categories are really important to see what's happening and whether it's a ah pay equity issue, right? How much are they being paid? We're starting to see early data where non-binary folks are paid the lowest,
00:16:02
Speaker
among ah ah all the trans and non-binary folks, but then trans women are paid less ah than trans men.
Building Trust for Marginalized Participation
00:16:12
Speaker
So it's kind of like, you know, in in order. And so to prioritize non-binary and trans women in terms of figuring out pay equity issues.
00:16:23
Speaker
If we know that we need to suppress data, when there are not very many respondents and obviously that when we're talking about trans and non-binary people, we don't have that it's we we don't have that big of a population to to go on really.
00:16:37
Speaker
um How can we encourage trans and non-binary people to take part in in the data collection so that we can get the information that we really need? Yeah, that's really important and such a critical one because there is a lot of distrust among trans and non-binary folks. And so they may not realize or understand about suppression.
00:17:00
Speaker
And so maybe kind of at arm's length or want to avoid or be frustrated that there's not more response options ah or you know avoid it in some way, which I totally understand. like i i I don't fault anybody who is a bit wary, especially these days. right Now, that said, organizations can build trust by carefully explaining one you know, in the question, provide explanation for why the data are being collected, the gender data are being collected, what they will be used for.
00:17:36
Speaker
Also talk about the suppression level to say, hey, we're going to suppress at, you know, a sample size of five and lower for a question. And that can then prompt trans and non-binary folks to make an informed decision to be like, oh, I want that number to be higher than five so that we can be on the graph, literally, right?
00:17:57
Speaker
um and to understand what's at play ah to to be able to show up and be counted and be represented ah to help move things forward.
00:18:08
Speaker
I also say very much encourage organizations to have a bit of a written document where people can you know read more about this because it's a bit complicated.
00:18:20
Speaker
Like, ah you know, it's not always, it's sometimes a bit technical, but in some sort of lay person's terms, very simple, accessible language to break things down ah so that people can understand, okay, this is why this has been set up this way.
00:18:36
Speaker
um And this is how I want to participate in this and hopefully encouraging them to participate, knowing that there could be a positive outcome. If they're counted, there's enough people to be counted to put on the graph and and for the organization to take next steps to improve things, especially if what we find time and time time again in TransFocus's research is there's a huge gap ah between cisgender and transgender respondents in surveys, whether it's on belonging, safety, ah facing discrimination, structural issues, you know misgendering.
00:19:11
Speaker
Although surprisingly, there's also cisgender people who are experiencing misgendering. So it's just, you know, it's good to to discover and unearth these issues. And so any way that an organization can explain that to survey takers, particularly trans and non-binary folks, so much the better.
Survey Methods and Indigenous Identity Considerations
00:19:29
Speaker
And even saying we really want to hear from trans and non-binary folks in the survey introduction can go a long way. i' Obviously, you want to hear from everybody. But if you call out specific groups that are typically underrepresented or cautious, ah that can really have a powerful impact to be like, okay, they they want to hear from me and I want to make the effort, um even if I'm a bit hesitant or nervous, but I've got to override that because I see they've been thoughtful and really want to hear from me.
00:20:01
Speaker
So you mentioned earlier about check all the apply and then single select options. And then what about... um the kind of fill in the blank fields and then sort of, could you go into all of that and and kind of the pros and cons of all of it and and the differences in in collecting data?
00:20:18
Speaker
Yeah, those are highly sought after functionality for trans and non-binary communities. We've done extensive research, talked to community, what ah what are the options that are desired?
00:20:30
Speaker
And I get it, right? Like check all that apply, especially for people who are bi or polygender is like really important to represent the fullness, ah the many layers of their identity.
00:20:43
Speaker
And I will talk briefly about how difficult it is. It's not impossible. ah We have done, we have tried it all these different ways at TransFocus because we really believe in making sure we can do what is necessary.
00:21:00
Speaker
But I will say it does take kind of very advanced level knowledge around how to put those together. Because what typically organizations do is kind of a really quick and done approach where they just do it for each of the categories. of like this percentage for agender, this percentage for genderqueer.
00:21:20
Speaker
And for me, that's not good enough. Maybe it is for some, but for me, it's actually the composite of somebody's identity. It's not the individual responses that they provided, right? So you could have like a ah non-binary man and if they're separately,
00:21:39
Speaker
um kind of represented, it doesn't show the fullness of it together. And it's that's the challenge of when they're operating together. Some things are great and other things aren't. And so it's about creating what's called separate variables. So you actually put everybody in the same bucket that selected non-binary and man, rather than just the individual selections.
00:22:03
Speaker
Now that's getting a bit into the weeds. And usually I have visuals that show that more easily than me just verbally talking about it. um But what we found was really interesting, but it just takes so much time to do this.
00:22:17
Speaker
And I also worry if there's not to any slight against cisgender data analysts, but if they don't know the sensitivities around how to do it,
00:22:29
Speaker
then they might miss the mark um and mishandle the data, not through any ill will, just because they don't quite understand all of the nuances. So it would almost take additional training to allow for somebody to do that adequately.
00:22:45
Speaker
It could be done, but that's extra time, extra money. And oftentimes organizations are wanting to kind of fast track publishing data um understandable.
00:22:56
Speaker
And so that's where we have to kind of balance these different needs and consider the real the reality of it is likely that people organizations are going to want to do the the fast thing.
00:23:10
Speaker
um Now, we could you know have training to provide people with that data ah analytical approach to combine the different identity like different aspects of people's identity to allow for check all that apply to work going forward.
00:23:28
Speaker
I think that's probably like a next phase thing once people get comfortable with one added category where we keep people together and then keep building from there.
00:23:39
Speaker
I will say for fill in the blank, I totally get it. This is a beautiful thing in theory of like, I can write the thing that I describe myself as, and it's so empowering and affirming, and I get why people like it.
00:23:58
Speaker
Now, I will say, sadly, the reality on the back end is that it's essentially a statistical dead end, right? So it's this group of people words that can't be reincorporated into the data ah the quantitative data. It's a qualitative data data set and you can't bring it back, at least ethically.
00:24:24
Speaker
as a u k So you have a response of demigirl. Where does that go in the ah the categories up above? And I don't think, at at least I don't find it ethical to figure out, and I couldn't figure out for that person where they would be comfortably put in.
00:24:41
Speaker
Would I put them in the women category? You know? So hang it just, it's so that basically just people like, I don't know what to do with this. And that's it. So people have provided something that's comfortable, but unfortunately doesn't get added to the graphs.
00:24:58
Speaker
Right. And people probably don't realize that um because they feel like, oh, it's been I've been kind of represented in some way. Now, I will say when we do get that data, I do look at it carefully to see if we need to update the categories of the quantitative data.
00:25:18
Speaker
So there could be value like long term, but not for that specific graph, unfortunately.
00:25:26
Speaker
How do we account for um two-spirit experiences when it comes to collecting this? Yes, also super important topic and something that a lot of Two-Spirit folks have spoken on. So I'm only echoing and conveying what I've been taught and heard.
00:25:44
Speaker
And so this is as a non-Indigenous person, I don't want to be saying that I hold the truth on this matter. ah There's also a lot of community dialogue going on about it. But to date, what is really important is that it ah Two-Spirit is Indigenous-specific term that can represent sexual diversity and or gender diversity.
00:26:08
Speaker
And because of that layer and that it's Indigenous-specific, it's really important to collect it separate from any gender data or perhaps sexual data sexual sexuality data that an organization collects.
00:26:22
Speaker
There might be a strong impulse or um intuitive sense to include two-spirit as a response option to gender, ah but from my understanding, it's best as something collected after the question of are you Indigenous or not?
Standardization and Concluding Resources
00:26:39
Speaker
And if somebody says yes, then only they get to see the follow-up question of are you two-spirit or some other um nation-specific term. And in that way, non-Indigenous people do not see this question because unfortunately it's a very lovely and very beautiful term. And some non-Indigenous people feel drawn to that term, even though that's it's essentially a cultural appropriation, um even if they realize it or not.
00:27:09
Speaker
And so we don't even want them to be able to have that um as an option to incorrectly select it if they're not Indigenous. So it's kind of ah rerouting it in a different pathway ah to allow only those who are Indigenous to respond to that specific question.
00:27:27
Speaker
And because it can span both gender and sexual diversity, it needs to be its own question and not listed in gender data collection or sexuality collection.
00:27:39
Speaker
You mentioned earlier about the decision making tree that um that you've put together. We can include that in the show notes so that people can download it. um So I'll add that.
00:27:50
Speaker
Do you have any kind of final thoughts or additions or anything else you wanted to say about it? Yeah, absolutely. i i think it's really important for organizations to think this through in a careful way.
00:28:03
Speaker
i know gender data collection, we started the podcast with, you know, this is, we're we're so used to collecting data that we might not think through it very much and to be very thoughtful about how we expand the categories.
00:28:16
Speaker
And then how do we use the data at the back end for hopefully the most use, get the most use out of it? And that takes time. I know sometimes organizations are um under pressure or there's crunch time and they've they've got to get this done.
00:28:32
Speaker
And so they kind of rush through the process, but I think there is value in taking that time ah to think it through. Obviously not just for gender data, ah there's all other equity categories that need to be considered as thoughtfully as well.
00:28:47
Speaker
ah And then doing that in a way that you're you're writing it down. So it's very clear what the expectations are and then making sure that staff are aware of what those expectations are.
00:28:58
Speaker
What is our suppression level? ah What are we using the data for? How are we collecting it and documenting it properly so that everybody's on the same page? Because what we find is, you know, human resources will do one thing and then you've got maybe an EDI team that's doing it maybe a slightly different or a very different thing.
00:29:18
Speaker
um You know, so there'll be some other random department that shows up and is doing something else. Maybe they're like, engagement, I don't know. And so it's just making sure that it's an organization-wide approach, because otherwise, ah whether there's a trans or a non-binary employee or a member of the public or a client, they're very confused by these different things. And I will say trans and non-binary people are particularly attuned to gender questions. We've seen the gamut, right?
00:29:52
Speaker
And we get kind of frustrated or annoyed if there's a lot of different things going on. ah My favorite is when people say, ah ah ah what is your gender slash sex?
00:30:07
Speaker
And then I'm like, oh okay oh boy, oh no. Yeah. I guess a trans person just going in circles. oh So it's just, it's really helpful to have thought it through so that, you know, and there's a consistency and a standardization ah so that yeah we don't have trans people and non-binary folks scratching their heads wondering what's going on.
00:30:29
Speaker
So that's why we've got a course just purely dedicated on what are the issues and what are the best practices, ah solutions for gender data collection with different realities at play, different organizations. so Besides the free tool that will give you the decision-making tree, we'll also ah provide you with access with a few free ah few pieces that will be free content for you to look at to see if that could be a good fit for your organization as you're trying to work through it.
00:31:00
Speaker
Always the door is open at Transfocus for strategy sessions where we can real-time work on these things together with you. ah But I just encourage you, whether it's with us or not, just to be very thoughtful in your approach to gender data.
00:31:16
Speaker
Great. And where can everyone find you on social media? Yes. so we're on all the socials, whether LinkedIn, Facebook, Instagram, TikTok, threads, et cetera.
00:31:28
Speaker
I think there might be one other, but I can't remember it right now. It's also very important too. but And we're you know constantly um posting but all kinds of topics, gender data being one of them.
00:31:41
Speaker
So you may see other points that we weren't able to cover because there's much more to it, but these are the kind of highlights. ah So yeah, check us check us out. Join our newsletter where you'll you'll get free content or free resources like the one we're linking in the show notes.
00:31:59
Speaker
Cool. Thank you so much. Thank you. was fantastic to talk about gender data.