What Your Name Says
About When You Were Born
Your first name carries a timestamp. The US Social Security Administration has published baby name counts every year since 1910 — over 362M birth records across 115 years. Certain names cluster around specific decades. Jennifer is almost certainly a Boomer or Gen X. Liam almost certainly isn't. The data tells the story.
1910–2024
Years of data
104,737
Unique names
362M
Total births
Name Predictor
Type any first name to see when people with that name were most likely born, how its popularity changed over time, and the decade you'd predict for someone with that name today. Try , , or your own name.
Predicted birth decade
1970s
median
80% of people named Jennifer were born
between 1967 and 1994
Peak popularity
1974
Jennifer (Female) — share of all US births
The Comeback Names
Some names peaked generations ago, nearly vanished, and then came roaring back. To qualify, a name had to peak before 1950, drop below 10% of its original peak for at least 20 consecutive years, then recover to more than 50% of its original peak after 2000. These are the vintage names that parents rediscovered.
Format: original peak → trough (near-extinction) → comeback
Names That Crossed Over
Many names that feel firmly gendered today were once used for both. The charts below show the male/female split over time for names that have seen meaningful usage by both genders — at least 10% each. Some crossed from predominantly male to predominantly female (or vice versa) within a single generation.
Names That Belong to a Place
Some names are disproportionately concentrated in specific states. The ratio below compares a name's share of births in one state against its national average — a ratio of 10× means the name is ten times more common there than anywhere else.
How It Works
The predictor uses the SSA's historical name counts as a probability distribution over birth years. For a given name, the share of births in each year — normalized against total US births that year to remove population growth bias — forms the prior. The p10, p50, and p90 percentiles of that distribution define the prediction range.
Common names that are concentrated in a single era (Jennifer, Brittany, Liam) produce tight, confident ranges. Names used continuously across decades (James, Mary, Elizabeth) produce wider ranges, which is honest — there genuinely is less information in those names.
Data limitations
- The SSA dataset only includes names with at least 5 occurrences per state per year — rare names are systematically underrepresented or missing entirely.
- Gender is recorded as binary (M/F) based on SSA records at the time of registration.
- Pre-1937 data is incomplete: Social Security card issuance wasn't universal until then, so early records skew toward certain demographics.
- This is US-only data and doesn't capture naming patterns among US residents born abroad or immigrant naming traditions.