Gender Census 2023: Worldwide Report


Contents

A sleepy welcome from unseasonably warm and sunny Wales, where my existence is currently occupied by questionably applied spreadsheets! This year’s survey was the tenth, which feels like a significant milestone.

The survey took place for a little over a month, between 9th April and 10th May 2023, with 40,375 usable responses. It’s a community-based project that is not affiliated with any organisations, companies or academic institutions, so it was promoted entirely on social media and by word of mouth.

Before starting the survey, participants were required to check the following two boxes:

  • Yes, I confirm that I don’t really fit into just one of the two boxes of “always, solely and completely a woman/girl” or “always, solely and completely a man/boy”.
  • Yes, I understand that I can back out of the survey at any time before the end and my answers won’t be counted, and I understand that if I complete and submit the survey my anonymised response will be made publicly viewable.

You can read a much shorter summary of the main three questions here.


Q1. Identity words

This question is asked annually, and the wording hasn’t changed much over the past several years. Participants were asked: Which of the following best describe(s) in English how you think of yourself? They had a choice of 18 identity words/phrases, plus none / I do not describe myself and questioning or unknown. Underneath the checkboxes there were 20 textboxes that invited participants to type extra words not otherwise listed. Checkboxes were randomised to reduce primacy and recency bias. This question was optional.

Checkboxes

Content note: Some of the words in the checkbox list are often used as slurs against the LGBTQ+ community.

This year there were significantly fewer checkbox options, as there have been some changes in how the checkboxes are chosen for the survey.

Previously, anything entered into the textboxes by over 1% of participants automatically became a checkbox. This year, I used a new and more complicated method:

  1. Take a guess at how much more popular a pre-written checkbox makes an identity term. (Based on previously added textbox-to-checkboxes, the guess this year is 8.9.)
  2. Multiply the count for each textbox word by this multiplier, to get an estimate of how popular the textbox word would have been if it had been a checkbox instead.
  3. Combine all checkbox terms and all textbox terms into one list, with their as-is and multiplied popularity respectively.
  4. Sort by popularity descending, and choose the 20 most popular to be next year’s checkbox list.

Another smaller change I made was based on feedback. I split none / I do not describe myself / “I’m just me” into none / I do not describe myself (a lack of description) and a person / human / [my name] / “I’m just me” (an affirmative description, based on textboxes).

Here’s all the identity checkboxes in a bar graph:

For a screen-reader-accessible list of identities and their popularity, please go to the Google Sheets spreadsheet of results for the identity words question.

Here’s our top 5:

  1. nonbinary: 63.1% (down 0.8% from last year)
  2. queer: 54.8% (up 0.2%)
  3. trans: 46.7% (up 8.5%)
  4. a person / human / [my name] / “I’m just me”: 42.5% (new this year)
  5. transgender: 40.3% (up 6.4%)

(This list doesn’t contain any terms that are affected by the “multiplier”.)

Last year’s none / I do not describe myself / “I’m just me” got 16.9%, but when split the difference is very striking:

  • a person / human / [my name] / “I’m just me”: 42.5% (much higher than before the split)
  • none / I do not describe myself: 4.8% (much lower than before the split)

Also new to the list this year were dyke and fag, the first checkboxes to be chosen using the multiplier method. After a smattering of complaints about slurs in the first few hundred entries I added a content warning to the identity question, which did seem to help somewhat. The main thing I noticed about these words in the checkbox list are that they matched lesbian and gay, in that the feminine-coded words were entered more often in textboxes, but when the pairs were added to the checkbox list the masculine word was chosen more often instead. This is an interesting and strange phenomenon, and I don’t whether I can consider twice to be a pattern, but I’ll keep my eye on it.

Here’s the top 10 words for the 30-and-unders compared to the 31-and-overs:

Ranked lists of identities for age groups 30 and under and 31 and oer.
See more on the age differences in checkbox identities on the Google Sheet here.

Here’s our top 10 since 2015:

People who use screenreaders may want to see this graph on Google Sheets here.

A person / human / [my name] / “I’m just me” is that little dot between the yellow and orange lines on the very far right, because it’s technically a new option this year.

This graph is fun but a bit of a plate of spaghetti, you can’t really see much of what’s going on. There’s nonbinary way up top, floating between 60% and 70% for the entire time. Queer was added as a checkbox option in 2019 and rocketed to second place, and it has risen almost every year since. Aside from that, it looks like most things have been gradually on the up for the past few years, except maybe the green enby line.

Here’s a bar graph that might help untangle things:

People with screen readers may appreciate being able to view this graph on Google Sheets here.

Queer is in second place and is up (but barely) this year, and nonbinary is in first place and down a bit. Last year I said that I was wondering if queer might become a contender for the most popular. I wondered about whether it’d be suitable as an umbrella term for all genders beyond the F/M binary, since it is also used to describe sexual orientations and people of a non-straight nature and is therefore quite ambiguous in meaning without context. In the line graph nonbinary and queer seem to be leveling out, but based on the bar graph I feel like it’s too soon to know for sure. In a few years this should become clearer.

In promotional posts I advertised this as the 10th annual survey, but it wasn’t exactly annual to begin with. The first survey was in 2013, the second was in 2015, and it’s been every year since then. I start the graphs in 2015 because I think of the first survey as more of a pilot survey that informed a better survey in 2015.

Write-ins

There was a big change this year compared to last year. In 2022 there were only 5 textboxes for identity write-ins, which I’m like 75% sure was a limitation of the survey software at the time. This year the maximum allowed by the software was 20, so that’s what I picked, because I like to live dangerously.

Last year had double the unique textbox entries compared to the year before, even though there were still only 5 textboxes and fewer participants, so I was expecting this year to see at least a quadrupling of unique textbox entries for identity – but no, there were 17,854, which is only an increase of 22%? 🤷 Anyway that’s one unique word for every 2.3 respondents, which is more unique words per person than last year (2.7). 3,447 were entered more than once.

The additional textboxes may have caused another change: textbox identities seem to have been entered more times each. The average percentage for the top 20 identity write-ins was 1.4% last year, and 1.7% this year. (And that’s before we consider that perhaps the top 3 from last year should be omitted from this calculation due to some accidentally very leading question design. There’s a tab on the public spreadsheet for this here.) The average number of selected checkboxes dropped this year from 6.0 to 5.3, which makes sense because the number of checkbox options was lower; it also makes sense that the average number of textbox entries per participant has gone up from 1.1 to 1.5. (Worth mentioning that I can’t speak to whether this is statistically significant, because I’ve not taken previous years into account and done the appropriate calculations.)

Let’s get to the top 10 textbox entries, which is kinda what we’re mainly interested in, let’s be honest.

  1. bigender: 2.3%
  2. butch: 2.3%
  3. guy: 2.1%
  4. demigirl: 2.0%
  5. boy: 1.9%
  6. trans(s)sexual: 1.9%
  7. entries containing “boygirl”: 1.8%
  8. entries containing both “xeno” and “gender”: 1.7%
  9. boygirl: 1.7%
  10. creature: 1.7%

There are 28 terms that were entered by over 1% of participants, which makes me very glad that we’ve found a better system than “if over 1% of write-ins, add to checkbox list” – this would more than double the checkbox list for next year.

Overall

The most common number of identity terms chosen or entered was 4-6. I can’t single one out, because there was only 0.2% in it! Here’s a graph that shows you the distribution split by age group:

See the table and graph here on Google Sheets.

It follows the usual trend, that younger people are choosing more words and older people are choosing fewer. But overall there’s not that big a difference between the age groups, and the graph is basically the same shape as last year.

Generating the 2024 checkbox list

Content note: This section refers to words that are sometimes used as slurs against LGBTQ+ people.

This year was the first using the new system for generating the checkbox list, which shortened the list to something a lot more manageable.

I don’t have a solid way to measure “manageable” for the checkbox lists, I just kind of eyeball it based on how often people type things into textboxes that are already on the checkbox list. So, for example, if nonbinary is already a checkbox, if a lot of people are typing nonbinary into the textboxes I might suspect that participants are struggling to find it in the checkbox list.

2022 was an off year for the textbox list because I messed up the help text and biased the responses. So, if we refer to 2021 instead:

  • The first identity word on the write-ins list that was already in the checkbox list is indeed queer at #12 on the list with 0.4%. This year the first was also queer, typed in by 1% of participants, but it doesn’t appear until #29.
  • In 2021, the next word on the write-ins list that was already in the checkbox list is enby at 0.3% and #20. This year, it’s person at 0.7% and #37.
  • In 2021, next up is nonbinary at #25, with 0.3%. This year it’s human, with 0.4% at #54.

And so on. So the main thing I’m noticing is that percentages are higher, but percentages for all textboxes were higher across the board anyway due to the extra textboxes in the form design, so that’s not surprising. But the positions on the list are much lower, and I think that’s a really good sign. I’m not totally sure the new system is better, but to me this suggests that it is.

Next up, a lot of terms were removed from the checkbox list this year because of the new system, and this gives us an opportunity to collect more data on how much less often a term is entered in the textboxes compared to when it was a checkbox. This data lives on the slightly chaotic 2013-2023 spreadsheet here.

The terms that were removed for this year were:

Term2022 % (checkbox)2023 % (write-ins)Multiplier
trans*14.6%0.1%146.0
neutral15.6%0.2%78.0
gay (in relation to gender)18.7%1.1%17.0
androgyne10.3%0.7%14.7
genderless15.8%1.1%14.4
femme11.4%0.9%12.7
gendervoid6.8%0.7%9.7
lesbian (in relation to gender)13.8%1.6%8.6
demigender5.9%0.7%8.4
boy14.7%1.9%7.7
girl11.1%1.6%6.9
genderflux7.6%1.1%6.9
demiboy7.5%1.7%4.4
demigirl7.7%2.0%3.9
butch7.9%2.3%3.4
bigender5.5%2.3%2.4

This is kind of a table of memorability? Because at the top you’ve got the words that people will totally opt into when they’re reminded of its existence, but fail to type in when it’s not there as a checkbox. And at the bottom are the words that people, when they identify with them, really, really identify with and will go to the trouble of typing it in. So trans* (no footnote) is entered 146x less often when it’s not a checkbox, which is a lot less often! But when bigender isn’t on the checkbox list, bigender people are damn well gonna type bigender into the box.

And on the other hand, dyke and fag were added to the checkbox list this year, and here’s how that went down:

Term2022 % (textbox)2023 % (checkbox)Multiplier
fag2.3%17.5%7.6
dyke3.2%11.9%3.7

So fag was picked 7.6x more often as a checkbox than it was entered as a write-in and dyke was picked only 3.7x as often, but the multiplier used to calculate the list was 8.9, meaning these terms were less popular than the multiplier predicted.

I put all of these new multipliers into ~The System~ and got a new multiplier (7.98), and ran it against this year’s stats. Here’s what I’ve got for next year’s checkbox list, in order of popularity after applying the multiplier, and with some terms on there because they have to be there even though they’re less popular. The words that will be new for 2024 are bolded.

  • nonbinary
  • queer (in relation to gender)
  • trans
  • a person / human / [my name] / “I’m just me”
  • transgender
  • gender non-conforming
  • genderqueer
  • enby
  • transmasculine
  • genderfluid/fluid gender
  • agender
  • bigender
  • butch
  • fag
  • demigirl
  • questioning or unknown
  • demiboy
  • transfeminine
  • none / I do not describe myself
  • cisgender
  • binary

We’re also losing dyke, man and woman from the list.

You can see the checkbox list calculation sheet on the Google Sheet of results here.

Here’s a little more detail, if you’re curious:

  • Most of these words were on the checkbox list this year and will remain on the list next year.
  • Bigender was typed in often enough in the 30-and-under group that it will become a checkbox.
  • Butch was typed in often enough in the 31-and-over group that it will become a checkbox next year. This has happened before, and at the time I added femme as its counterpart (for comparison), but I’ve since learned that there is no consensus on its binary-gender “mirror” term, so next year I will be adding butch and not adding a counterpart.
  • Demigirl was typed in often enough by 31-and-overs that it will be added to the checkbox list next year, and I will add demiboy as its mirror/counterpart.
  • Man and woman are dropping off the checkbox list, whaaaaaaat??????
  • Dyke was new this year and is dropping back off the list again for next year. Fag was new this year, and will be sticking around.

I know I shouldn’t be surprised because of the main demographic of the survey, but… man and woman!! Gone!! I don’t know, that feels big, they’ve been on the list since the first year! Gosh.

Frivolous side-survey on names

Partway through the big annual survey I also ran a separate, smaller survey for fun, to find out what the most nonbinary name is. Long story short, 1.6% of 5,179 participants sometimes or always go by Alex, which is an impressive 1 in 62 nonbinary people.

The survey design was not good, but I think I can improve on it, and I would really like to run it again (regularly?) as an Official Gender Census Side Survey.


Q2: Titles

The title question was shaken up a little bit this year. Last year it was just one [slightly ambiguously worded] question about titles on forms, but this year I split it into two questions, based on several years of “please, what is a gender-neutral version of sir/ma’am??” feedback. I also redesigned the “title not listed” question to get more specific information on abbreviations, longer forms and pronunciation.

Titles (with name)

Participants were asked: When someone writes “Dear [your name]” at the top of an addressed letter/email, what title would you want someone to use when writing to you, if any? There were several specific titles to choose from, plus some hypotheticals, no title at all, unknown, I choose on the day depending on how I’m feeling – and a new option, a title not listed here. All of these were presented as radio buttons (one answer only) to mimic the “filling in a form” context, and the order was randomised to prevent primacy and recency bias.

Here’s what the top 5 looks like:

  1. No title at all: 40.1% (up 1.5% from last year)
  2. Mx: 18.7% (down 1.4%)
  3. Mr: 11.5% (up 2.1%)
  4. Non-gendered professional/academic title: 9.4% (up 1.0%)
  5. Ms: 5.5% (up 1.1%)

Last year no specific titles aside from Mx got over 10%, and Mx was over twice as popular as the next title down. Neither of those are true this year, as Mx is less popular and Mr is more popular. No title is more popular than the top 3 specific titles combined.

Here it is as a pie chart:

People who use screen readers may wish to view this pie chart here on Google Sheets.

And here’s how that fits into the ongoing trends:

Those of you who use screen readers may want to view the original graph on Google Sheets here.

The top two lines are No title at all (blue) and Mx (red). Mx was holding its own for a while there, and then for a few years I wasn’t ready to declare its demise, but it’s down again (a little bit) this year and it’s now less than half as popular as no title. Having said that, among the identity words genderqueer and genderfluid are making comebacks after a few years of decline, and Mx is still in second place, so I think it’s too soon to start writing obits.

Here’s how that looks as several smaller bar graphs, which is hopefully a bit clearer for any title getting less than 15%:

People with screen readers may appreciate viewing this on Google Sheets here.

Of the people who chose a title not listed here (2.3% of all participants):

  • 17.5% (0.4% of all participants) entered M, and most intended it to be pronounced as the letter M, “em”. Most people entering that title (40.5%) said that it was a gender-inclusive title that anyone of any gender can use.
  • 13.7% (0.3% of all participants) entered something involving the word Mistrum, which was mostly abbreviated to Mm. 58% categorised it as a gender-exclusive nonbinary title, “typically used to express any nonbinary gender”.

Among the people who chose a standard title that is used only by people other than men and women (0.9% of all participants), 80 people entered titles into the ensuing textboxes, and the following titles were entered more than twice:

  • Mx: 34 (0.07%)
  • M: 5
  • Mistrum: 5
  • Mm: 4
  • Dear: 3

New this year was a hidden section for people who chose a title not listed here, asking for the abbreviation, full word, pronunciation notes and gender connotations of the participant’s unlisted title. Now that I’ve processed the results I feel satisfied with the design of it. This option also makes the option a standard title that is used only by people other than men and women redundant, so I’ll remove that one next year.

Titles (without name)

Participants were asked: When a stranger addresses you and they don’t know your name, what title(s) would you want someone to use, if any? This was the first year of us seeking an answer to the age-old USian Nonbinary Question: “What should we use instead of sir/ma’am?”

Participants were able to choose as many options as they wanted, and answer options were presented in a randomised order to prevent primacy and recency bias. The question was optional.

Here’s the graph of checkbox options:

Those of you with screen readers may find the original graph on Google Sheets more helpful.

And the top 5:

  1. No title at all: 69.3%
  2. Sir: 36.5%
  3. Friend: 32.2%
  4. Mx: 23.3%
  5. Miss: 14.5%

The question was followed by 5 textboxes, so that participants could enter any titles they like that weren’t provided as checkboxes. 2.2% entered comrade, which surprised me – this title, in my part of the world, has connotations of military service, or some kind of established relationship based on shared trauma, or communism. All of that is so niche that I wouldn’t consider using it to address a stranger.

2.2% is a lot for a textbox, so out of curiosity I compared with age. 1.8% of participants aged 30 and under typed in something containing comrade, compared with 4.4% of participants aged 31 and over. I have no idea what it means, but the percentages are different enough that it’s noteworthy, I think.

It’s tempting to conclude from these results that we should advocate for just omitting the “sir/ma’am” part altogether, or start making “gender-neutral sir” a more widespread thing. But the trouble is, in a lot of cultures sir/ma’am isn’t really a thing (hello to the Australians in the feedback box), and in some areas of the USA you can’t really opt out of it without offending people (hello to the Deep South in the feedback box). So really, the best option is to download the spreadsheet of results and delete all entries from not-your-country, to see what the best option is for your region.

Sometime between now and next year, I’ll have to decide how to decide what to put in the checkbox/radio button lists for the title questions. And if I choose the same method as I use for the identity words, that means I’ll have to pick a multiplier, and I don’t really have enough data to do that yet. I might have to add/remove some lesser-used titles to see what happens and calculate a multiplier based on that? So, I’ll keep you posted, and I’ll try not to do anything drastic…


Q3: Pronouns

The pronouns question was split into two sections, as usual.

The initial icebreaker was: Supposing all pronouns were accepted by everyone without question and were easy to learn, which pronouns are you happy for people to use for you in English? Participants could choose as many of the 13 randomised checkboxes as they wanted. Checkboxes included various common and uncommon pronoun sets, plus options like “any”, “avoid pronouns”, “my pronouns vary”, “change my pronouns frequently” and “questioning or unknown”.

One of the checkboxes is “a pronoun set not listed here”, and if you choose that it takes you to a second section where you can enter up to five new pronoun sets in detail.

All questions about pronouns were optional.

Checkboxes

They all fit on a graph, just about:

If you use a screenreader, you can see the table on Google Sheets here. (There is also a graph on there.)

The top 5 pronouns (or lack thereof) were the same five in the same order as last year:

  1. They – they/them/their/theirs/themself: 74.5% (down 1.2% from last year)
  2. He – he/him/his/his/himself: 42.5% (up 2.1%)
  3. She – she/her/her/hers/herself: 32.7% (no change)
  4. It – it/it/its/its/itself: 19.4% (up 3.2%)
  5. Avoid pronouns / use name as pronoun: 13.2% (up 2.1%)

They/them was selected more than both he/him and she/her combined, but only just. 11.5% of people weren’t happy to be called they, he or she, which is about the same as last year.

Here’s how the pronouns are looking since 2015:

If you use a screenreader, it might be easier to look at this table or graph on the Google Sheet.

That blue line at the top is they/them, and the red and yellow lines below it are he/him and she/her. They/them has been fairly consistent over time, fluctuating by only about 6% since 2015. He/him and she/her have been increasing in popularity, especially he/him in the last couple of years for some reason. This year is the smallest gap there’s been between they/them and anything else.

Here’s that same graph with they/them, he/him and she/her removed, so that you can see the remaining pronouns (under 20%) more clearly:

Again, if you’re using a screenreader, it may be easier to decipher using the table or graph directly on Google Sheets.

It/it continues to climb steeply, and most others haven’t changed much over the years.

Here’s the top 10 for each age group:

This is another one that might be easier for people with screenreaders to view on the Google Sheet as data.

Looking at the two age groups presented like this you can see that even though some pronouns are chosen more among one age group than another, for most of the top 10 the 31-and-overs chose their ranking pronouns less than the 30-and-unders. (#2 in the younger age group was chosen more than #2 in the older age group, etc.) That’s because people in the younger age group tend to choose more pronoun sets each.

The only exception to this is they/them, which was more popular among the older age group. This is noteworthy, because before this year they/them has been consistently more popular in the 30-and-under age group, like so:

People who use screen readers may want to see the table of data and the graph on this Google Sheet.

I only started collecting age info in the 2020 survey, so there aren’t many years to show here, but you can see that the gap has been closing before this year already. It may be a trend, and I look forward to collecting more information on this over the coming years.

As in previous years, people in the older age groups are choosing/entering fewer pronoun sets each:

If you use a screenreader, looking at this graph on Google Sheets may be easier.

The average number of pronoun sets chosen is 2.2, slightly higher than last year. It’s been rising for the last two years, but only very slightly, and in the grand scheme of things there has been very little change:

People using screen readers can see the table of stats and the graph on the original Google Sheet here.

According to the current system for the pronouns checkbox list, pronoun sets get removed when they get under 3% in both age categories. Nothing met that criterion this year, so if I didn’t change the checkbox-choosing system for pronouns everything would still be around next year. The lowest was Spivak (e/em), which got 3.9% overall.

Neopronouns

The most popular neopronoun overall was in the checkbox list: Xe – xe/xem/xyr/xyrs/xemself. That makes sense, because anything in the checkbox list is selected a lot more often than anything that is written into a textbox. It’s been climbing by about 1% per year for a few years now, and it’s currently at 11%.

After that the checkbox options include:

  1. Fae – fae/faer/faer/faers/faeself: 6.5%
  2. Ze – ze/hir/hir/hirs/hirself: 5.2%
  3. Elverson – ey/em/eir/eirs/emself: 5.0%
  4. Spivak – e/em/eir/eirs/emself: 3.9%

Even though I don’t go by neopronouns myself (I’m personally a they/them default Ready Salted kinda [gender-neutral] guy), I like seeing what’s popular lately. Plus, so many people whine about how they/them is grammatically incorrect that I’d enjoy if one neopronoun set took the lead, so that I could challenge those people to learn an entirely new pronoun set instead. Just to shake things up a bit, in the way where everyone wins, you know?

When a participant selects the checkbox “a pronoun set not listed here”, they’re guided through a separate section where they can enter all 5 forms of up to 5 neopronoun sets. There are example sentences with fill-in-the-blanks, to help ensure that we get accurate information that can be counted easily.

11.6% of participants chose a pronoun not listed here, which is about the same as last year. Age-wise, people aged 30 and under were well over twice as likely to select “a pronoun set not listed here”, which is fairly typical for this question based on past surveys.

Last year ae/aer took a significant lead, being over twice as popular as the next most popular write-in neopronoun, with 0.7% of all participants – and nothing else got over 0.3%. This year ae/aer has a less extreme lead but it’s still the most popular write-in, and star/star has jumped up to replace ze/zir for second place with 0.34%.

Here’s ae/aer‘s most popular set (ae/aer/aer/aers/aerself with singular verbs) in use:

I’m in a coffee shop with my friend Sam. Ae is buying aerself a coffee in aer reusable takeaway cup. “Is this your coffee?” the barista asks me, holding up Sam’s coffee. “No,” I reply, pointing to Sam, “it’s aers.”

More thoughts on the top 10 write-in pronouns:

  • Most of the top 10 are the same, but ne/nem is out and pup/pup is in.
  • Just over half of the people typing in they/them (as opposed to choosing the checkbox they/them) are typing in they/them/their/theirs/themselves (plural verbs). That’ll be some people who just prefer to say themselves when referring to one person, and some people who go by “plural they” and want to be referred to in the plural/as a group.

2,418 unique pronoun sets were entered this year (based on subject/object/reflexive only), which is one new pronoun set for every 17-ish participants. Of those 2,418 sets, 487 were entered more than once. All of these numbers are a little lower than last year’s, perhaps because participants this year were on average a little older than last year.

Pronoun checkbox selection method

So, first of all, I think I need to move the following into a separate question, because they’re not pronoun sets, they’re how you want your pronoun sets to be used:

  • I want people to frequently change the pronouns they use for me
  • My pronouns vary depending on specific conditions

Second, in the hope that the new multiplier method of choosing checkbox terms is more fair, I’m wondering if I should also apply it to the pronoun checkboxes.

However, the main issue is I have very little to go on with regard to where I should set the multiplier. I’ve started a sheet on the chaotic “over time” spreadsheet to keep track of pronoun multipliers, but there isn’t much to see. All I’ve got is, when I removed the pronoun co from the checkbox list it went from 0.8% to 0.01% (80x), and no other reliable info.

I am expecting the multiplier for pronouns to be higher, because a neopronoun set is much harder to write in than a noun/adjective for identity. But one pronoun set removed 5 years ago just isn’t enough to go on, especially when you consider that for identity the multiplier range when removing something from the checkbox list has been as high as 146x and as low as 2.4x.

When I remove the two non-pronouns (“my pronouns vary” and “change my pronouns frequently”), set the checkbox list length to 15, and use the multiplier calculated from the identity words (which is probably too low), none of the current checkbox options get removed, and two sets get added:

  • ae/aer/aer/aers/aerself (singular verbs) – from the 30-and-under group
  • ze/zir/zir/zirs/zirself (singular verbs) – from the 31-and-over group

So I think for 2024 I will:

  • split the “my pronouns vary” and “change my pronouns frequently” checkboxes into a separate question;
  • make the pronouns checkbox list 15 items long;
  • add those two new pronoun sets, to see how much more often they get selected as checkboxes compared to the write-ins this year.

As a first multiplier it might not be very accurate, but I’ve got to start somewhere, and I like the idea of doing it without removing any pronouns from the list.


Q4: Family/relationship terms

It’s not unusual for people to ask about this in the feedback box, along the lines of “I want to know what people use instead of [aunt/uncle], [boyfriend/girlfriend], etc.”

This year I decided to give it a try. I think in order to not get overwhelmed I will ask about one new family title (with textboxes) per year and the family title from last year (with checkboxes based on last year’s textbox responses). With this approach we will slowly but surely get through all of the family words in a reasonably fair way, without adding 10 new questions for people to get through.

So, this year I asked about parent terms/titles. I asked: What would you ideally like your children to call you when they are speaking English? I then provided five textboxes, with some help text to make sure that people entered just the term as simply as possible, to make them easier to count.

And next year I will ask the same question again and provide the top 20 or so terms as checkboxes (taking into account the age groups as usual), and also ask about another family title with five textboxes. I will continue in this way every year until I have asked about as many family titles as we can think of.

Here are the top 10 parent words from this year’s textboxes:

  1. dad: 15.7%
  2. mom: 11.6%
  3. parent: 10.1%
  4. my name: 4.6%
  5. mama: 4.2%
  6. papa: 3.8%
  7. father: 3.1%
  8. mum: 2.7%
  9. baba: 2.2%
  10. mother: 1.9%

And here they are as a bar graph:

Those of you using screen readers may find it easier to view the original graph on Google Sheets here.

At first glance the popularity of dad makes it seem like participants were more likely to lean in a masculine direction identity-wise, but I think at least part of that is “vote-splitting” based on cultural differences, because most participants are from the USA, the UK and Canada – and this splits the feminine equivalent of dad. In the USA mom is more common, whereas in the UK we tend to say mum. Canada splits it even further, as a brief online search suggests that they use both mom and mum, and also sometimes maman. In our survey that comes out like this:

  • mom: 11.6%
  • mum: 2.7%
  • maman: 0.1%

So if you’re investigating gender-neutral terms for mother/father and you want to prioritise regionally relevant terms, I recommend downloading the spreadsheet of results and cherry-picking the data for your country (or whichever specific country you have in mind).

Titles in lots of different tones/contexts were entered, including more formal terms (parent, guardian, mother, father), words that babies and small children might be able to say more easily (baba, mama, papa), and words that older children might use in a more casual way (pops, ren, old man). In the feedback box, some people expressed confusion because they weren’t sure whether to give words that they’d expect their kids to use when addressing them directly or when talking about them in the third person; either would be welcome, and I will try to include that clearly in the help text for next year’s questions.

My name was entered exactly like that (my name only in the textbox) by 4.6% of participants, but 9.5% of parent titles/terms included the word “name” somehow, including entries like first name, just my name, [my name], nickname of their choice, and simply just name.

You can see the full list with count and percentage here.


Meta

The last page of the survey asks participants for various bits of information, some of which help me to design a better survey and promote effectively (age, feedback, referrer), and some of which make the data more useful for others (age, country).

  • Age (grouped in 5-year increments)
  • Country
  • How you found out about the survey today
  • Whether you’ve taken the survey before

Most of the graphs and summaries that I got out of them can be found on this year’s public participation sheet.

The top 5 countries represented by number of participants were:

  1. United States: 22,666 (56.1%)
  2. United Kingdom: 4,497
  3. Canada: 2,825
  4. Australia: 1,654
  5. Germany: 1,617

That’s the same countries in the top 5 as last year, but Germany and Aus have swapped places. 60 countries got 10 or more responses, and any country with under 10 responses has had the country redacted to ensure participants’ privacy and safety.

Here it is as a bar graph:

Users of screen readers may wish to view the table of statistics and the original bar graph on Google Sheets here.

A special shout out to New Zealand, who get the award for nonbinariest country – they had the highest participation as a percentage of their population at 0.0075%.

For referral methods, there’s not a lot to mention that’s different from previous results reports. You can see it all here on the public participation sheet, and here’s a few bullet points for the stuff that piqued my interest:

  • Last year the mailing list was in 6th place, but this year it was up to third place. Very cool! It’s a reliable way to get participants of all age groups involved every year, and for people who don’t really use social media it’s essential.
  • Twitter is down from third place (13.2%) last year to seventh place (3.9%) this year. Admittedly I’ve done a lot less PR on Twitter for this year’s survey, but also, since Elon Musk bought the platform things have been going downhill over there very quickly, and data suggests that Twitter use is in decline. Considering how [increasingly] awful it is over there, I’ll be surprised if it’s still worth me posting on there for the 2024 survey.
  • Relatedly, Fediverse (Mastodon, etc) has doubled. It’s still below Twitter… but only just.

Regulars will know that it’s always a struggle to reach and represent older people with the survey, and this is largely due to the way the survey is shared. Internet users (and especially regular internet users) tend to be younger, and of those, social media users are younger still. Since I started collecting age data I’ve been keeping tabs on this by tracking what percentage of survey responses come from people aged 31 and over, and this year was the highest it’s ever been, at 16.5%. It doesn’t sound like a lot, but this is progress!

Those of you using screen readers may want to check out the table and graph on the Google Sheet here.

Last year I started to collect information about who had taken the survey before, and it was 17%. This year it was 26.6%, and I’m not sure why it’s so much higher. Possibly the success of the mailing list? But whatever the reason, that still leaves a significant majority as newcomers (61%), plus a sprinkling of people who aren’t sure if they’ve taken the survey in previous years (12%).


Some of the questions this survey seeks to answer
  • What should the third gender option on forms be called? – Nonbinary is holding steady at around two thirds of participants – or just under, this year. That leaves one in three who don’t identify as nonbinary, but there are no other comparably popular words in the top 10 that are unambiguous in meaning. I would recommend the third gender option be called nonbinary, and future surveys will monitor this issue closely.
  • Is there a standard neutral title yet? – Mx is far and away the most popular gender-neutral title, but at just under one fifth of participants. Internationally, participants are well over twice as likely to prefer to not have any title at all. Those designing forms collecting personal information must ensure that Mx is an option alongside Mr and Ms, but it is far more important (and increasingly important) that title fields in those forms are optional.
  • Is there a standard gender-neutral way to respectfully address superiors and strangers (sir/ma’am) yet? Too soon to say, we only have one year of data, and a lot of that data includes comments that suggest there is a lot of cultural variation.
  • Is there a pronoun that every nonbinary person is happy with? – No. They/them is a safe bet if you’re not sure what to use, being chosen by around 75% of participants since the first survey over a decade ago. But that leaves one quarter of us who don’t want to be called they, and 12% of us consistently don’t like to be called he, she or they, so it’s always good to check.
  • Is there any consensus on a gender-exclusive nonbinary pronoun? – No. Over 1 in 10 participants were happy with xe/xem/xyr/xyrs/xemself (singular verbs), and although it has been climbing in popularity among younger participants in recent years it is not significantly more popular overall compared to other neopronouns or compared to when it was added to the survey.

This year in review

New questions. There was a new question seeking a gender-neutral or nonbinary alternative to sir/ma’am, and the first of a series of questions investigating preferred family/relationship terms. Plus, some “not listed” textboxes newly asked for your answers in a specific format, to make it easier to extract the useful info.

New checkbox selection method. The “multiplier” method was used to select the checkbox options, instead of the previous “over 1%” method, and I tentatively consider it perhaps a success, maybe.

Still on Google Sheets, somehow? I’m not sure how to feel about that, but this laptop is still going strong and handling Sheets well, and I do like showing the results to you all in a format that is hopefully somewhat human-readable without you having to download anything. Avery (my statistics friend) looks at my spreadsheets with a critical eye and says, “you know, this is what programming is supposed to be for.” I worry that it’s a bit like using superglue to build a rocket. Maybe I’ll have gotten my head around some more advanced statistics programs by year 20?

Crowdfunding. My costs were covered by Patreon pledges, which is always a relief. Thank you everyone for your support! If you would like to follow on Patreon (or add a pledge, every £1-per-year helps), you can visit the Gender Census project page here. And I haven’t quite done the maths yet, but anything left over will go to:

  • Andréa, who kindly handles the custom coding side of things with the survey software. (If you would like to thank Andréa for supporting me, her Amazon.fr wishlist can be found here.)
  • Ryan Castellucci, who is raising funds to cover the cost of challenging the UK government to issue correct nonbinary documentation in accordance with their own laws. (This case is actually a pretty big deal, at the moment you can’t get nonbinary documents issued by the UK government at all, Ryan has a good case and if they win it may force the UK government to do the same for all other nonbinary people.)
What I’ll do differently next year

The pronoun checkboxes. There are two changes for next year, which I think (hope) will be fairly inconsequential:

  • Which pronouns you like and how you want people to use them will be split into two separate questions.
  • The checkbox options will be limited to a list of 15, and will be selected using the new “multiplier” method.

Family/relationship words. The parental terms question will become checkboxes based on this year’s textboxes, and a second textbox question will be added for the next family/relationship term.


Closing thoughts

Ten (10) years! Blimey.

It is hard to describe how weird and excellent it is to have tens of thousands of people respond to my survey shout-outs with such celebration. Every year the survey takes place in a different month because I am a chaos person, and every year people say stuff like, “it’s that time of year again!” in the Tumblr tags and the Twitter QTs. I wonder if I will ever get used to it. I like to hope that the survey design gets a little bit better/clearer/easier every year as I try things out and get feedback and tweak it bit by bit, and I like how each year the resulting stats add to the cumulative longitudinal coolness as well as being interesting on their own as a cross-section of the year’s international nonbinary population.

I am so grateful to all of you for taking part and sharing your experiences with me, for helping to spread the survey to everyone who might need it, for valuing it enough that some of you will even lay down some cold hard cash, and for just generally enabling my Eccentric Citizen-Scientist vibes.

Thank you, everyone. I feel very lucky.


Support me!

Thank you for reading! If you find this report and this project to be valuable and would like to give something back, you could pledge to support the survey financially on Patreon, or increase your chances of taking part in future surveys by following on Tumblr, Facebook or the Fediverse, or subscribing to the mailing list. Alternatively, you could take a look at my Amazon wishlist! 🍫


Links to files

2023-06-06