Names in the USA (1880-2025) - Lior Sinai

An exploration of baby name trends in the USA from 1880 to 2025

Table of Contents

1 Introduction

Baby name rankings are very popular on the internet and on social media.<br>A typical ranking is the top baby names for each gender in the USA in 2025 (source):

Ranking<br>Male<br>Female

Liam<br>Olivia

Noah<br>Charlotte

Oliver<br>Emma

Theodore<br>Amelia

Henry<br>Sophia

James<br>Mia

Elijah<br>Isabella

Mateo<br>Evelyn

William<br>Sofia

10<br>Lucas<br>Eliana

What these rankings often don’t show is the quantitative values behind these rankings.<br>That is, how many babies were given each name in the year 2025, and how does that compare to previous years?

Luckily the USA Social Security Administration (SSA) releases the full dataset for baby names going back to 1880.<br>I took this data, grouped the names by ranking and summed up their counts each year, and plotted them against each other. See the above graph. (The rankings are inclusive. For example, Top 10 also include Top 1.)

From this graph we can see that the popular names are getting less popular, while the number of births is still in line with what it was 50 years ago.<br>This is something that plain rankings obscure.

If “Liam” or “Olivia” were ranked in previous years with the same frequency as 2025, they would be ranked noticeably lower than #1.<br>For example, in 2000 “Liam” would have been ranked 11th and “Olivia” would have been ranked 12th.<br>In 1950, “Liam” would have been ranked 19th and “Olivia” 27th.<br>But names are more spread out these days, so their frequencies are sufficient to be ranked #1 in 2025.

The other sections present other charts and insights derived from the data.

These are the top level findings:

There has been an almost 20× increase in the number of births each year from 146 years ago.

The 5 year average increased 18.6× from 215,000 births in 1885 to 4.02 million in 2025.

These days there are more than 15× names to chose from than 146 years ago.

From 1000 names each for boys and girls in 1880, there are now more than 17,000 unique names for girls and more than 14,000 unique names for boys each year.

However, given all this choice, the names are still heavily skewed towards a relatively small subset.

In 2025, the top 100 names were given to more than 35% of newborns.

Over the whole 146 years, the top 100 names account for 46% of all names given.

That said, the top ranked names are less popular both absolutely and relatively since the 1900s, and this trend is continuing.

The frequency of the top 100 males names has decreased absolutely by 2.5×, from covering 1.62 million of all births in 1956 to 650,000 births in 2025. Relatively they decreased by almost half, now accounting for 38% of all births from a peak of 81% in 1880.

The frequency of the top 100 females names has decreased absolutely by 2.5×, from covering 1.31 million of all births in 1957 to 514,000 births in 2025. Relatively they decreased by more than half, now accounting for 31% of all births from a peak of 77% in 1880.

Girl names are consistently slightly more diverse than boy names.

Over the whole period there were on average 40% more girl names than boy names each year. Over the last 5 years, there were 24% more girl names on average.

Over the whole period the top 100 girl names accounted for 10% less of girls than the top 100 boy names did for boys. Over the last 5 years the 100 girl names have accounted for 7% less on average.

2 Methodology

2.1 Data

The dateset used in this article is the “National data” released in 2026 by the USA Social Security Administration (SSA) at SSA: Beyond the Top 1000 Names.<br>It has the following limitations:

It does not account for every US citizen, because not every US citizen has a social security number. The social security number was only introduced in 1936, so the data before then (from 1880-1935) is incomplete. Non-citizens are not included.

Only names with more than 5 births per year are reported. This is to protect identities.

The dataset consists of 146 CSV files for each year from 1880 to 2025.<br>Each CSV file has three columns: name, gender (M or F) and frequency.<br>The CSV files are ordered by gender (F then M), then descending frequency and then alphabetically for tied frequencies.

2.2 Dense Ranking

I used a dense ranking.<br>This means that identical counts are given the same rank and there are no gaps in the rankings.<br>So the top 100 ranked names could account for more than 100 names in a given year.<br>However, ties in the top 100 names are rare, but they get more common as the counts get lower.<br>At the lowest values there can be up to 2000 names per ranking.

No single year exceeded a dense ranking of 1000 per gender despite some years having more than 20,000 unique names for a single gender.

2.3 Code

I used Julia to analyse the data. I will not present the full code here, but here are some snippets.

Loading a single file and transforming:

using CSV, DataFrames<br>filepath = joinpath(data_dir,...