Names & Numbers

According to data from the US Social Security Administration, such names peaked in and around 2008.

Regex pattern: "(a[iye]+|.+a[iye]?)d[eaioyu]n$"

The above graph includes some 382 names. By total number, the top 30 are:

Mostly, yes--and the trend remains even when we weight by the number of usages.

It's important to weight by the number of usages because we want to consider the number of babies actually given each name.

Letter Z shows the most consistent trend:

Letters X and Y have increased in frequency, too, but the trend is reversing in recent years.

The average Scrabble score is increasing, but the trend falls apart when we weight by the number of usages.

It's important to weight by the number of usages because we want to consider the number of babies actually given each name.

Scrabble points from here:

Unisex/gender-neutral names can be categorized as follows:

Narrowly unisex names: used for each SSA sex at least 30% of the time.

Broadly unisex names: used for each SSA sex at least 20% of the time.

Technically unisex names: used for each SSA sex at least 10% of the time.

The below two plots show the top 20 unisex/gender-neutral names of the 1990s, based on total usages from 1990-9. You can see the gender trend (F% share) post-1999, split into two plots for better visibility.

The below two plots show the top 20 unisex/gender-neutral names of the 1970s, based on total usages from 1970-9. You can see the gender trend (F% share) post-1979, split into two plots for better visibility.

The below two plots show the top 20 unisex/gender-neutral names of the 1980s, based on total usages from 1980-9. You can see the gender trend (F% share) post-1989, split into two plots for better visibility.

These once-unisex names have sometimes remained gender-neutral. They haven't consistently "gone to the girls" as often alleged; neither have they consistently trended in a masculine direction.

Using SSA sex, the male ranking is more saturated than the female ranking.

The pattern holds when looking at the top 100 and top 1000 names.

The SSA dataset contains increasingly many unique names under both F and M rankings. Each year, the F ranking contains more unique names than the M ranking.

For privacy reasons, the SSA name dataset excludes very uncommon names.

The graph below shows the percent of Social Security card applicants that have been excluded from the SSA name dataset each year.

Per the stated limitations of the SSA name dataset:

To safeguard privacy, we exclude from our tabulated lists of names those that would indicate, or would allow the ability to determine, names with fewer than 5 occurrences in any geographic area. If a name has less than 5 occurrences for a year of birth in any state, the sum of the state counts for that year will be less than the national count.

Datasets from the United States Social Security Administration are available on SSA.gov.

- Beyond the Top 1000 Names: national, state-level, and territory-level data ^ Some important background and limitations

- Number of Social Security card applicants by year

Cumulatively, the top 100 names account for about 25% of babies born in recent years, versus almost 60% in the mid-1900s.

Even the top 1000 names have desaturated considerably. In the mid-1900s, over 90% of babies were accounted for by the top 1000 names. In recent years, the top 1000 names account for less than 70% of babies.

The top names of recent years are more quickly falling in rank and potentially exiting the top 20/100.

Analysis of the US Social Security Administration name dataset shows that, on the whole, gender-neutral names haven't "gone to the girls". Instead, the data indicates they've drifted masculine as often as they've drifted feminine.