Mona Chalabi: Which box do I check?

Recorded atNovember 16, 2021
EventAm I Normal? with Mona Chalabi
Duration (min:sec)03:17
Video TypeOriginal Content
Words per minute215.11 very fast
Readability (FK)39.49 very difficult
SpeakerMona Chalabi
CountryUnited Kingdom
DescriptionBritish data journalist

Official TED page for this talk


How we identify makes a difference in our lived experiences, but it's not always reflected in the checkboxes we see on forms. In this episode, Mona Chalabi explores why language matters in data collection, and why the categories we use should reflect who we really are. Want to hear more from Mona? Check out her podcast Am I Normal? with Mona Chalabi, from the TED Audio Collective.

Text Highlight (experimental)
100:00 If you've been watching this series,
200:01 you'll know I care about data.
300:03 But data has its limitations, especially when it comes to language.
400:07 Basically, if you get your categories wrong,
500:09 you can wind up with some pretty misleading statistics,
600:12 and the US Census is a prime example.
700:15 [Am I Normal? with Mona Chalabi]
800:17 Taken every 10 years,
900:19 this survey aims to collect demographic data
1000:21 from each and every resident of the US and its territories.
1100:24 Those responses help the government to determine everything,
1200:28 from the allocation of seats in Congress and the Electoral College,
1300:31 to the allocation of hundreds of billions of dollars in federal funds.
1400:34 And those funds pay for things like new hospitals,
1500:37 road improvements and school lunch programs.
1600:39 And crucially, the statisticians that work there are nonpartisan.
1700:43 They sit at the same desks, applying the same formulas,
1800:46 no matter who is in charge at the White House.
1900:48 So undoubtedly, the US Census Bureau does important work,
2000:51 but it does have some blind spots.
2100:53 For example, there has been a decades-long effort
2200:55 to add the category Middle Eastern or Northern African or MENA to the census.
2301:00 Currently, the census defines people from these regions,
2401:03 and that includes me, as white.
2501:06 Yeah, that's incorrect.
2601:07 In 2015, the census did test a version of this survey that included MENA.
2701:12 It found that when given the MENA option,
2801:14 the number of people from that region who identified as white
2901:17 dropped from 86 percent to 20 percent.
3001:20 See, when you reconsider language, the numbers can change dramatically.
3101:25 Unfortunately, though, the census still didn't make the change,
3201:28 saying that further tests were necessary to determine if MENA should appear
3301:31 under ethnicity instead of race.
3401:33 That means that those who have rallied for its inclusion
3501:36 will have to wait another decade to see if our community can be recognized.
3601:40 This isn't the first time that language has restricted
3701:42 how people are represented in the census.
3801:44 The very first one, way back in 1790,
3901:47 only had three broad categories,
4001:49 and I quote: "slaves, free white men and women, and all other free persons."
4101:54 It would be another 30 years
4201:56 before distinct categories for free Blacks
4301:58 and another 40 years before American Indians
4402:00 would appear on the census.
4502:02 Since then, more and more categories have been added,
4602:04 but progress has been slow.
4702:06 It wasn't until 2000 that people could choose more than one race
4802:09 to describe themselves,
4902:10 and for the very first time in 2020,
5002:13 people who selected Black or white could go a bit more granular
5102:16 and provide more detail about their origins,
5202:18 like naming France or Somalia or spotlighting their Indigenous identity.
5302:22 Right now, you might be thinking:
5402:24 Why does the wording on a survey even matter?
5502:26 Race and ethnicity are social constructs anyway.
5602:29 But that doesn't change the lived experience
5702:31 of those who aren't truly reflected in these forms.
5802:33 Questionnaires need to ask the right questions
5902:36 if they want to capture what's really happening in the world.
6002:39 A Northern African non-binary person might be misgendered
6102:42 or considered white by the census,
6202:44 but face disproportional discrimination,
6302:46 health disparities or language barriers that are unique to their community.
6402:49 It's no wonder, then,
6502:51 that it's often marginalized and vulnerable communities
6602:53 ones whose identities are missing from these forms
6702:56 that lack access to governmental resources and protections.
6802:58 Now, there are some understandable historical reasons
6903:01 why people might not want to engage in this kind of data gathering.
7003:04 But without the data, it’s just easier to deny the inequality is real.
7103:08 If we want a more equitable society,
7203:11 we have to measure our reality,
7303:12 and the best way to start
7403:14 is by using language that recognizes our differences.