USA Baby Names Analysis

Name concentration

The percentage of births covered by the top-10 names have fallen sharply over time. In 1880, the top 10 female names covered 24.6% of all female births. Now, it's 7.1%. For males, the top 10 names covered 44.2% of all male births. Now, it's 8.0%. Coverage was relatively steady from 1880 to 1950, with a sharp decline starting from 1960.

Name entropy

If the top names are covering less births, is it because non-top names are getting used more or because there are more unique names overall, or both?

Per-capita name variety — unique names per million same-gender births — traces a U-shape for both genders, with a mid-century trough and a steep recovery after 1980. There are many more unique names today on an absolute basis, but per 1M births, it's roughly the same number as in 1880.

When we look at Shannon entropy, which measures how spread births are across names, we see a strong inflationary rise compared to the per-capita number of unique names.

Trigram entropy vs. name entropy

Name entropy has grown much faster than phonetic entropy. The gap between them is likely explained by "creative spelling" — Caitlin, Kaitlyn, Katelyn; Brian, Bryan, Bryon. Before 1960, the two tracked in parallel. After 1960, name entropy accelerated away. The modern boom in distinct names appears to be largely orthographic, not phonetic.

Name half-life

It's not just that the top 10 names are covering fewer births, but top 10 names have been cycling faster and faster. In the 1880s, the top 10 names took 45 years to drop to half their peak share. By the 1990s, the half life is less than 20 years.

Gender-neutral naming over time

The share of births going to substantively gender-neutral names has risen ~8× since 1880, from ~0.7% to ~5.5% today. An early-1900s male spike (~3.3% in 1910) was driven almost entirely by "Willie".

Based on our definition, 1,648 names have ever qualified. The number of names that are actively qualifying as gender-neutral is on the rise. There's about 1000 names that qualified as gender-neutral in the past but are no longer in use.

The all-time gender-neutral champions

Four ways to rank the 1,669 names that ever met the strict GN gate: Max Prevalence (highest share of US births in a 10-year window during a qualifying year), Balance (closest to 50:50 F/M during qualifying years, restricted to names with ≥10K total qualifying-year births), Longevity (most qualifying years), and Total Usage (most births during qualifying years).

F : M aggregates births across each name's qualifying years only — not its lifetime mix, and not a single year. Total and Max Prev are likewise restricted to qualifying years. Yrs-GN is the number of years that met the strict gate (≥20% minority share and ≥100 minority births in a 10-year rolling window).

All 1,669 names that ever qualified, with full per-name stats: download CSV.

Stability of gender-neutral names

Once a name clears the gender-neutral bar, does it stay there?

39% are "stable neutrals" (Charlie, Blake, Blake). 25% are "brief crossers" (Jude, Dean, Tristan). The remaining ~36% oscillate. M→F flips are much faster than F→M flips, which play out more slowly.

Where do GN names start, and where do they end up?

The historical name pool has a high mortality rate. 57% of non-tail F-origin names (9,243 / 16,347) are inactive today — old female-coded staples like Vicki, Sue, Myrtie, Carole. 42% of M-origin names are inactive — Pat, Carroll, Gregg, Dick, Ed. Of 1,557 GN-origin names: 28% still GN, 19% drifted M-dominant, 11% drifted F-dominant, 42% inactive. A handful of names have undergone full gender crossovers: M-origin → F today: 80 names (1%). F-origin → M today: 49 (0.3%).

Ending bigrams as gender signal

The last two letters of a name carry an outsized share of its gender identity. Mutual information between ending bigram and gender has dropped from 0.64 bits in 1880 to 0.47 bits today — a 27% decline. Endings like -la, -ia, -na stay near-100% female; -rt, -hn, -rd stay near-100% male. More recently, ending bigrams like -ey, -ce, -ah are the most blurred.

Starting-letter frequency

J is by far the most dominant starting letter for boy's names, though more recently it is gradually giving way to other starting letter. Both genders have seen a surge in A-names. M, R, S, and C have been the most consistent and popular starting letters for girls. Male naming is more concentrated — fewer letters command large shares at any time. Female naming, by contrast, is slightly more evenly distributed.

Starting-letter correlation

Some letters rise and fall together. Others don't. For females, the M and J have strong negative correlations with the A- and K-name waves that replaced it. For males, J, R, W move inversely to A, B, E. Q, U, X, Z show weak correlations with everything — they've never been popular enough to participate in large-scale shifts.

Average name length

Male names were briefly longer than Female names from 1880-1905, but female names have been longer ever since. Length of names peaked in 1990 for both females (Stephanie, Christina, Jennifer, Samantha) and males (Christopher/Alexander/Nicholas), and both have been shortening since. The Pearson correlation between male and female name length is surprisingly high r = 0.954 (year-over-year changes correlate at r = 0.706).

Trigram contagion

When a trigram appears in a hit name, it tends to spread to brand-new names, particularly for girls. Ashley dominated the 1980s–90s. Then came Paisley, Kinsley, Brinley, Hadley. Jayden spawned Kayden, Brayden, Hayden, Zayden.

Beginning vs. ending innovation

The trigram space has filled up fast: Novel trigrams dropped to near-zero by the 1920s–30s. Modern creativity appears to be based on recombination of existing trigrams rather than the invention of new ones.

Phonotactic complexity

Female names have always been more "liquid" — more vowels, smoother consonant-vowel alternation. Male names tolerate more consonant clusters (Chr-ist-opher, Str-ong). That's changing too. Male phonotactics are converging toward female: consonant clusters have dropped from 22% to 14%, vowel sequences (VV) have nearly doubled since 1980.

Soundex phonetic groups

104,819 names compress down to ~13,000 Soundex codes — the average name shares its phonetic code with 7 others. Some phoneme-identical groups are huge: John/Jon (5.4M births), Steven/Stephen (2.2M), Sarah/Sara/Cera (1.5M), Brian/Bryan/Bryon/Brion/Bryen (1.6M). The largest grouping (S050) contains Saaim, Saam, Saamia and ~800 siblings built on the S + long vowel pattern.

Phoneme trends

"AH" (schwa) and "N" dominate female names; "R" declined (Mary/Margaret/Barbara fading); "L" rose (Olivia, Emily, Ella). For males, "N" has been rising steadily, tracking Aiden/Jayden/Ethan.

Cultural event fingerprints

Pop-culture characters with strong, narrow appeal (Kylo after The Force Awakens, Khaleesi after Game of Thrones, Farrah in 1977, Miley in 2009, Elvis in 1956) produce strong adoption followed by quick decay. Names attached to broader cultural moments (Madison after Splash, Luke and Leia after Star Wars, Ariel after The Little Mermaid, Trinity after The Matrix) ramp more slowly, plateau for a decade or more, and decline more gently.

Alexa was healthy and rising right up to 2015, then collapsed when Amazon's voice assistant took the name — the cleanest brand-killing fingerprint in the data. Isis sat in the 85–95 band through 2014 before falling off a cliff. Katrina and Karen were already declining before their events; the hurricane and the meme accelerated curves that were heading down anyway.

What's the impact of AI model naming?

The three voice assistants with people-names all cratered after launch: Alexa fell from 1,128 to 73 babies per million (a ~15× drop), Siri from 30 to under 2, and Cortana vanished.

The LLM era looks different. Gemini slipped modestly (8.3 → 5.7) and "Chad" (a ChatGPT stand-in) slid from 47 to 29, but Claude actually rose (10.1 → 12.1) and Gemma — already a popular name well before Google's model — has continued to climb (499 → 545).

Are baby names a reflection of our culture?

The loudest signal across 146 years of data is the collapse of the common name. The top 10 names for both boys and girls now cover under a tenth of all births, down from a quarter to a third a century ago, and the names that do reach the top now fade in half the time they used to. Individuality is rising.

But a deeper look shows that the explosion of "new" names comes mostly from creative respellings and more even distributions over them, rather than very novel names (like "X Æ A-Xii"). Unique phonemes and unique trigrams stopped growing in the 1930s.

Put together it feels kinda paradoxical: parents are making their baby names more distinctive, but reaching for the same handful of sounds and alternatives to do it.

Gender-neutral names feel like a recent invention, but they really aren't. Names have drifted across gender lines for over a century (Willie, Marion, Jamie, Jordan). While the usage of gender-neutral names is on the rise, it's doing so at a very gentle climb. And the names that do qualify as gender-neutral (over a broad set of heuristical definitions) still lean pretty heavily toward one gender. Altogether, it seems like gender lines are softening, but very gradually. The last two letters of a name predict gender better than almost anything else, and that has barely moved in 146 years.

Why I did this

A few friends have asked. The honest answer is that a lot of them are starting to have kids, and naming one is among the first and most permanent decisions a parent makes. If each name serves as a small vote about gender, individuality, sound, identity, is what we choose to name our babies like a slow-motion portrait of a culture?

Final thoughts

Candidly, I wouldn't have gone this deep without AI. The project still took somewhere between 20 and 30 hours in total. The bulk of that time wasn't making plots, but in deciding what questions to ask, and then understanding, checking, and curating the results.

I started this project before the 2025 SSA data was released, and when it landed I was pleasantly surprised at how painless it was to update all of the charts (and analysis). It makes sense that broad trends probably won't shift much year-to-year, but I think I'm curious enough to rerun the analysis every year for as long as I'm alive (and assuming the SSA keeps publishing new data). Maybe one day, we'll be able to ask AI to maintain it.

One thing I came away grateful for is that the SSA publishes this data every year. Last I looked, it seems like most other countries don't publish any baby names data this complete or easy to use. Let me know if you know of a good public source -- I'd love to run the same analysis for other countries. For now, if you're about to name a kid, I hope this was at least interesting, and good luck!