The Most Gender-Switched Names in US History
We use some names mostly for boys and some mostly for girls, but then there is a small percentage of names that, over time, switched mostly from one gender to another. Which names made the biggest switch?
It seems like a straightforward question, but it depends on how you define “biggest switch.” More on that in a bit.
I turned to our old friend: the baby names dataset from the Social Security Administration. Most citizens and residents in the United States have a Social Security number (printed on a piece of paper that you are somehow expected to keep safe from birth!), and the SSA releases a dataset of baby names annually. It provides the number of babies with a given name, split by sex and by year.
The first numbers were given in 1935, but the names in the dataset were retroactively counted pre-1935. So the data goes back to 1880. The people who were alive in 1935 were tallied by birth year. You can see the sharp increase post-1935 in the chart below.
Social Security Card Holders
Counts for boys and girls stabilized around 1930.
2.5 million
2.0
1.5
Girls
1.0
Boys
0.5
0
1880
1900
1920
1940
1960
1980
2000
2018
I used this dataset to look for the names that made the biggest switch. A couple of caveats:
- Not everyone in the United States has a social security number, and so this doesn’t represent all baby names every year.
- Only names with five or more occurrences during a given year are included, so more unique names are not in this dataset.
I don’t think either of these are an issue in this case though, because I looked for popular names that made significant shifts rather than unique names with only a few people.
Also, I limited the search to 1930 and on. The counts between boys and girls became more even around this time.
Most Basic Approach
To start, I looked for any name that switched sexes between 1930 and 2018. I defined that as any name that was at least 50 percent boys or girls and then switched majority to the opposite sex at some point. Out of the 95,137 names in the data, 2,987 switched, or about 3 percent. I quickly plotted the difference in counts over time for the 100 names with the highest counts.
Switching Between Boy and Girl
There were 2,987 names that switched between 1930 and 2018. These are the 100 with the highest annual peaks. Not much to look at here. That tall 50k+ peak in the 1990s is “Ashley.”
More Boys
More Girls
2018
2000
1980
1960
1940
1930
10k
Even
10k
20k
30k
40k
50k
This view wasn’t very useful, but at the least, you can see there’s a lot of fluctuation over time. Here’s a name-by-name view, which makes it easier to see individual trends for each name.
Switches Sorted by Greatest Span
Among the names that switched, which ones grew or declined the most?
Ashley
Taylor
Alexis
Madison
Tracy
Kelly
Joan
Lauren
Robin
Jamie
2018
More
girls
1930
Shannon
Courtney
Chelsea
Kim
Shawn
Angel
Jean
Addison
Morgan
Kelsey
More
boys
Avery
Riley
Harper
Stacy
Shelby
Sydney
Whitney
Lynn
Jaime
Lindsey
By the way, these are called difference charts. They show the difference between girl and boy counts over time. It goes from 2018 to 1930, top to bottom. Turquoise means there were more girls that year, and orange means there were more boys.
Looking at the difference charts above, maybe you noticed the names — with the exception of Riley and Jaime — don’t really look like there was a switch from one sex to another. It looks more like a name wasn’t very common for one sex and then became more popular for the opposite.
Interesting. I didn’t know Ashley used to be a mostly boys name. But I wasn’t looking for names that were so-so for one sex and then popular for another.
Looking for Even Swings
I was looking for names that were equally common for both sexes, or close to it at least. So unlike my first approach, where I found the peak count for both sexes and subtracted, I calculated the ratio of the peaks. If the peak count for boys is about the same as the peak count for girls, the ratio would be close to 1.
Here are the top thirty names with a ratio closest to 1.
Looking For Actual Switches
Take the ratio of peak girls to peak boys and look for the names closer to even.
Ryley
Landry
Azariah
Devyn
Britt
Stevie
Kerry
Jael
Germaine
Kirby
2018
More
girls
More
boys
1930
Lorin
Kenyatta
Carrol
Austyn
Rene
Kodi
Brighton
Sidney
Charlie
Casey
Jules
Jaedyn
Shay
Gentry
Codi
Santana
Torey
Tai
Skyler
Rian
Now we’re getting somewhere. This subset of names shows some swings instead of just waning peaks.
However, these calculations don’t account for the changing totals over time. They just use raw counts for the ratios. For example, there might have been more girl Ryleys one year, but maybe there were more girls born (or received Social Security numbers) that year also.
So instead of using raw counts, it’s better to look at rates so that values are comparable over time.
Most Switched Names, Relatively
Look for names with nearly-even female-to-male maximums. But this time use rates instead of counts.
1. Kenyatta
2. Rene
3. Brighton
4. Devyn
5. Austyn
6. Ryley
7. Kodi
8. Casey
9. Azariah
10. Jael
2018
More
girls
More
boys
1930
11. Lorin
12. Landry
13. Arden
14. Raleigh
15. Jackie
16. Stevie
17. Monroe
18. Skyler
19. Shea
20. Sol
21. Ashtin
22. Remy
23. Aven
24. Tristyn
25. Isa
26. Jaedyn
27. Carrol
28. Codi
29. Gentry
30. Daylin
Ryley is still in the top ten, but Kenyatta takes the top spot with a couple of switches from mostly boys to mostly girls and then back to mostly boys.
It’s still missing something though. While the ratio for Kenyatta was the closest to 1, it doesn’t seem correct to call it the most switched name when there are at the most a few hundred Kenyattas in a year.
That is to say, the name switched from a few hundred boy names to a few hundred girl names during the 1930 to 2018 timespan. On the other hand, Kerry, which was also close to 1 (but not as close) showed a swing in the thousands.
So, instead of ranking by ratio alone, I considered the maximum as well. Below, the top 100 most switched names in US history. Casey, Jackie, Kerry, Jody, and Finley lead the way in the top five.
Most Switched Names, by Rate and Count
Account for more even switches as well as how big the swings were too.
1. Casey
2. Jackie
3. Kerry
4. Jody
5. Finley
6. Skyler
7. Justice
8. Rene
9. Darian
10. Frankie
Max span: 7,713
6,305
4,380
3,930
3,154
2,842
1,878
1,785
1,543
1,468
2018
More
girls
More
boys
1930
11. Oakley
12. Robbie
13. Remy
14. Milan
15. Jaylin
16. Devan
17. Armani
18. Charley
19. Stevie
20. Channing
1,428
1,370
1,300
1,231
1,214
1,095
1,089
928
890
841
21. Gerry
22. Monroe
23. Kirby
24. Azariah
25. Santana
26. Landry
27. Devyn
28. Shea
29. Austyn
30. Arden
774
761
724
720
709
703
668
628
534
446
31. Kenyatta
32. Jaedyn
33. Jael
34. Carrol
35. Ryley
36. Ricki
37. Codi
38. Shay
39. Rian
40. Aven
443
411
408
391
372
343
336
334
332
316
41. Brighton
42. Tru
43. Raleigh
44. Tai
45. Ever
46. Britt
47. Ocean
48. Gentry
49. Merritt
50. Storm
299
297
296
293
288
283
282
280
279
268
51. Rosario
52. Ashtin
53. Teegan
54. Tristyn
55. Sol
56. Isa
57. Kodi
58. Torey
59. Lorin
60. Maxie
259
258
257
251
231
228
227
226
218
215
61. Ridley
62. Shia
63. Yuri
64. Linden
65. Kaedyn
66. Daylin
67. Tylar
68. Dru
69. Indiana
70. Kalin
212
203
193
186
186
185
180
179
178
178
71. Barrie
72. True
73. Jodeci
74. Tramaine
75. Shai
76. Cree
77. Allyn
78. Arion
79. Jaydyn
80. Lexington
175
174
171
168
165
161
160
160
160
159
81. Aris
82. Jaelin
83. Kylin
84. Jaidan
85. Clarke
86. Talyn
87. Ashten
88. Claudie
89. Amen
90. Krishna
158
158
157
157
156
155
153
151
151
150
91. Lakota
92. Vernie
93. Kit
94. Tenzin
95. Arin
96. Riyan
97. Jaziah
98. Parris
99. Tobey
100. Catlin
148
147
147
147
146
144
144
140
138
136
Looking closer at Casey, I’m not entirely sure if it’s so much a switched name than it became a unisex name. Well, I guess it switched to unisex. It was mostly a boy name and while remaining a boy name, it also grew popular as a girl name. I guess I can get on board with that.
Notes
The data comes from the Social Security Administration, and it continues to be a fun time series dataset to poke at. I analyzed and visualized the data in R.
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