Why We Only Cover Western Countries (And Why That's a Feature, Not a Bug)

Let's address the elephant in the room: GenderMyName only covers 23 countries. Meanwhile, some competitors claim coverage for 150+ countries. So why would you choose us?

Because coverage is vanity, accuracy is sanity.

The Global Coverage Trap

Here's what typically happens when an API claims "global coverage":

They have solid data for maybe 10-15 countries (usually US, UK, and some of Europe)
For another 30-40 countries, they have scraped or crowdsourced data of questionable quality
For the remaining 100+ countries, they're essentially guessing based on linguistic patterns

That last point is where it gets ugly. An API might tell you "Priya" is female with 95% confidence. But where did that confidence come from? A machine learning model trained on English-language baby name websites? A Wikipedia scrape? User submissions from an app with no verification?

You don't know. And neither do they.

The Dirty Secret: Where "Global" Data Actually Comes From

Let's talk about where most gender APIs actually get their data.

A significant portion of the industry's name-gender databases trace back to leaked or scraped social media data. The 2018 Facebook data breach exposed information on 533 million users. That data didn't disappear—it got packaged, resold, and quietly became the foundation for various "AI-powered" services, including gender detection APIs.

Think about that for a second. The data powering your customer personalization might literally come from a data breach.

And even if we set aside the ethical issues, there's a practical problem: social media data is garbage for gender detection.

Elon Musk famously claimed that up to 20% of Twitter accounts could be bots or spam. Independent researchers have estimated fake accounts on various platforms range from 5-15% on the low end to much higher in certain regions. Facebook has admitted to removing billions of fake accounts—2.2 billion in Q1 2019 alone.

So when an API tells you it has "500 million names" in its database, ask yourself: how many of those are "John Smith" accounts created by bot farms? How many are fake profiles with randomly generated names? How many are joke accounts, duplicate accounts, or accounts with intentionally false information?

You're building your customer intelligence on a foundation of spam.

The Problem with Crowdsourced Data

Beyond the data source issues, crowdsourced name-gender databases have systemic problems:

Selection bias: Who submits names to these databases? Primarily English-speaking internet users. So you get great coverage of names that appear in American media and terrible coverage of names that don't.

No verification: Anyone can submit. There's no birth certificate, no census data, no official source. Just trust.

Frozen in time: Most crowdsourced databases were built once and rarely updated. Names evolve. "Ashley" was 90% male in 1960 and 90% female by 1990. Which one does your API reflect?

Western lens on non-Western names: When a crowdsourced database does include names from, say, India or China, it's often through a Western interpretation. Transliteration inconsistencies, regional variations ignored, cultural context lost.

What Official Government Data Gets You

Our data comes from one source: official government records. Birth registrations. Census data. National statistics offices.

Here's what that means in practice:

Verified accuracy: Every name-gender association is backed by actual birth records. Not opinions. Not guesses. Records.

Population-weighted: We don't just know that "Maria" is female. We know that in Spain, 847,293 living women are named Maria, representing 3.6% of the female population. That's precision you can build on.

Temporal awareness: Our data includes birth year distributions. We can tell you that "Madison" was rare before 1985 and peaked in 2000. This matters when you're analyzing customer databases spanning multiple generations.

Regular updates: Government statistics offices publish new data. We incorporate it. Our US data updates annually with fresh Social Security Administration records.

The 95% Reality

Here's a question: What percentage of your users or customers are from the Western world?

For most B2B SaaS companies, e-commerce platforms, and marketing tools, the answer is somewhere between 80-98%.

So let's do the math.

Scenario A: Global API with 85% accuracy

Western names (95% of your data): ~90% accuracy
Non-Western names (5% of your data): ~60% accuracy
Weighted accuracy: 88.5%

Scenario B: Western-focused API with 96% accuracy

Western names (95% of your data): 96% accuracy
Non-Western names (5% of your data): No coverage (falls back gracefully)
Weighted accuracy on covered names: 96%

Which would you rather have? High confidence on 95% of your data, or mediocre confidence on 100%?

The Real Cost of Wrong Predictions

"Dear Mr. Sarah Johnson" is not a good look.

Every wrong gender prediction is:

A personalization fail that makes you look careless
A potential customer irritation
A data point that questions your overall competence

When your marketing email addresses "Jessica" as male because your API was trained on a dataset where "Jessica" appeared in some other language context, you've lost that customer's trust. And probably their business.

Silence is better than wrong. When we don't have data, we say so. We return an "unknown" result rather than a confident guess. Your fallback logic kicks in, and you avoid the embarrassment.

"But I Have International Customers"

Fair point. Here's our take:

For Western names in non-Western countries: Our data handles this well. An American expat in Singapore named "Michael" is still correctly identified.

For non-Western names in Western countries: This is where it gets nuanced. Many immigrants adopt Western names or use Westernized versions of their names in business contexts. "Wei" might go by "William." Our API won't guess at "Wei," but it'll nail "William."

For truly global needs: If you genuinely have significant traffic from countries we don't cover, use a tiered approach. Route Western names through our API for maximum accuracy. Use another service as a fallback for the rest. Best of both worlds.

Our Coverage Is Strategic, Not Lazy

We cover 23 countries because those 23 countries have:

Publicly available official data - We can verify and audit our sources
Sufficient population - Statistically meaningful sample sizes
Data quality standards - Regular updates, consistent formatting
Market relevance - Where the majority of API users' customers live

We could add more countries tomorrow by scraping some websites. We choose not to. Quality over quantity isn't just a slogan here.

The Bottom Line

If you're choosing a gender API, ask yourself:

Where are my customers actually from?
Would I rather have 96% accuracy on relevant names or 85% accuracy on everything?
Do I trust "crowdsourced" confidence scores?
What's the cost of a wrong prediction in my use case?

For most businesses serving Western markets, a focused, official-data API isn't a limitation. It's exactly what you need.

See our data sources: Check our accuracy page for full transparency on where our data comes from and how we measure quality.