How neutral are gender-neutral names exactly?

This project began with a simple premise: not all “gender-neutral” baby names are created equal. While many names appear under both male and female categories in official records, this alone doesn’t guarantee true neutrality in usage.

Now, of course, the point of this project isn’t to reinforce naming binaries, but rather, to explore how they appear and evolve in public data. A name can be considered gender-neutral even if it is used disproportionately by one gender. After all, just because a male baby were to have a traditionally female name (and vice versa) doesn’t mean that it isn’t a valid and meaningful choice. On the other hand, just because a name is present on both sides of the gender binary doesn’t mean it doesn’t challenge traditional gender norms.

This analysis dives into NYC Open Data’s Popular Baby Names dataset, where I investigate the actual distribution of gender-neutral names across the gender binary and how those distributions vary across time and different ethnic groups. The dataset is reported under the Department of Health and Mental Hygiene (DOHMH) and compiled from civil birth registrations. Although the years listed were from 2011 to 2021, I queried the dataset to the year range of 2011-2020 to focus on that decadal period.

Some questions I used to guide this analysis include:

• Which names appear under both male and female categories? How much?
• How did the frequency of these names change over time?
• What were the name splits by gender and mother’s ethnicity? What stories do they tell?

These questions shaped my approach to creating a project that explores not just which names are “technically” gender-neutral, but how neutral they actually are in practice.

Key Findings

1. Gender Imbalance in Baby Counts

Across all years from 2011 to 2020, there were consistently more male baby counts reported than female counts. This may reflect either a higher number of male births registered or a reporting bias in favor of male name popularity. As a result, names that lean masculine, such as Dylan or Ryan, tend to dominate among the most common gender-neutral names.

This imbalance introduces a form of representation bias: because there are more male babies recorded overall, a name shared by both genders is more likely to show up as “popular” on the male side even if the relative balance between male and female use is closer than it seems.

2. Abrupt Change in Data Starting 2015

An interesting trend emerged around 2015, where the overall name counts dropped significantly. This decline carries through the rest of the dataset. Yet in 2019, we see the highest number of unique gender-neutral names reported.

A few possibilities include changes in data collection or policies starting in 2015, increased cultural acceptance of nonbinary naming conventions around 2019, and shifts in social influence (i.e. pop culture) causing spikes in creative or unconventional naming choices.

This suggests that while overall baby name reporting became more limited, diversity in gender-neutral naming may have been increasing, an important contradiction to highlight when evaluating naming trends purely by volume.

3. The Most Gender-Neutral Name Also Being the Rarest

The name Nana stood out as the most balanced gender-neutral name, with a perfect 50/50 split between male and female babies. However, it only appeared in 2011.

Perhaps the name may not have been common enough in other years to make the dataset’s popularity cutoff. Or maybe the name entries were suppressed as NYC’s data may override names with very low counts, potentially explaining its disappearance after 2011. It’s even possible that the name might’ve surged briefly due to cultural or family reasons, but not long enough to maintain popularity.

Whatever the reason may be, Nana’s single-year appearance reminds us that gender-neutrality in naming may be short-lived, situational, and not necessarily indicative of long-term trends.

4. Cultural Influences of Gender-Neutral Names

Out of the 37 gender-neutral names I identified, 12 were exclusive to a single mother’s ethnicity across the entire dataset. This made me realize that just like gender neutrality, there are also discrepancies when evaluating cultural neutrality in names.

For example:

• Nana appeared only on Black Non-Hispanic babies, with an exact 50/50 split between boys and girls. According to Mom Junction, Nana is a name of Ghanaian origin with meanings related to chief, king, or royalty as it is a title for kings and queens in Ghana (Chakraborty).
• Tenzin was exclusive to Asian and Pacific Islander babies. The name “derived from the Tibetan bstan-’dzin, which means ‘upholder of teachings'” (Singh).
• Emerson appeared only among White Non-Hispanic babies, a name that has “Old English and Germanic origins” (Alex). It can be translated to brave or powerful.

Interestingly, no gender-neutral names in my sample were exclusively found among Hispanic babies. This could indicate that gender-neutral naming conventions differ by cultural values or that the top-name cutoff excluded relevant names from this group. Either way, it points to the cultural specificity of naming choices and how names reflect both gender and cultural influences.

This suggests that while overall baby name reporting became more limited, diversity in gender-neutral naming may have been increasing.

About the Data

I used the NYC Popular Baby Names dataset available on NYC Open Data. The data are collected through civil birth registrations and include the most popular names given to babies born in New York City. For this project, I focused on the decade from 2011 to 2020.

The primary variables used in my analysis were Child’s First Name, Gender, Ethnicity (reported as the mother’s ethnicity), and Count. I excluded Rank because ranks are calculated within individual ethnic and gender groups per year. This made them inconsistent for comparing name popularity across the broader dataset, especially when analyzing patterns across years, genders, and ethnicities at once.

I created a calculated field in Tableau to convert all baby names to uppercase to resolve formatting inconsistencies (i.e. “Avery” to “AVERY”), which allowed me to accurately group name data. I also filtered the names so that it would only display names found on both male and female babies by creating a condition using the gender field. If the distinct gender count on a name was 2, then it was included in the analysis (i.e. 1 for MALE, 1 for FEMALE).

There were several biases and limitations to note. One of them was selection bias — because only the most popular names per year and demographic group were included, less common gender-neutral names were not represented. The data also came from official birth records, which reflect registered legal names and may not capture nicknames, middle names, or gender-neutral names that don’t appear in the top lists. Ethnic categorization, too, had its limitations, as it only represented the mother’s reported ethnicity. It assumes that naming traditions are tied solely to the mother’s identity, excluding other cultural or familial influences. Furthermore, it categorizes people into limited ethnic groups, which may not fully capture multiracial or multicultural identities in babies.

Conclusion

This project reveals that the concept of a “gender-neutral” name is far from simple. Some names that appear gender-neutral in datasets are used in favor of one gender in practice. Others appear only within specific ethnic groups. And although we were able to identify a name that reached true gender neutrality (Nana), we only get a glimpse of the name within the timeline being evaluated.

Key takeaways:

• Dataset biases like male count dominance can blur meaningful trends.
• Presence across genders does not mean equal usage.
• Cultural and ethnic factors play important roles in naming choices.
• Shifts in reporting policies can impact how we interpret changes over time.

If I were to take this project further, I would try to deepen my analysis by investigating changes in NYC’s data collection or reporting policies. For example, to get a better understanding of the drop in name counts after 2015, I would merge the NYC baby names data with other NYC birth records or official statistics to see if the drop in names was due to fewer babies being born, stricter rules for which names were included, or missing data. I would also be interested in exploring other niche naming influences, like media trends, celebrity births, or cultural events via Google Trends or IMDb data to see how these external factors align with shifts in gender-neutral name popularity.

All in all, I learned that neutrality in names reflects more than just gender. Other factors like cultural norms, data policies, and shifting societal views add to the complexity of baby naming patterns. This project challenges the meaning of neutrality and how it’s measured. Furthermore, it encourages us to think critically about how names, culture, and identity intersect in our data and our daily lives.