A gentle note before we start: This article is for adults and focuses on work, safety, law, and data literacy. Sex work is not a monolith and includes people of many genders working in many ways. Because the legal status of sex work varies and data collection is uneven, any per-capita “ranking” must be read with care. Where possible, I rely on large, transparent sources (UNAIDS, WHO, national statistics offices) and I explain their limits. Data is not the same as truth; it’s a snapshot shaped by methods, law, and stigma.
What “per capita” really means here (and why it’s tricky)
A clean global leaderboard of “sex workers per capita” does not exist. Most countries don’t count sex workers directly, and many that do only count some workers (for example, registered workers where registration is required, or female sex workers (FSW) because HIV programmes measure that group more consistently). Even then, the data years differ, methods vary, and margins of error can be large.
Common sources you’ll see:
- UNAIDS Key Populations Atlas & AIDSinfo: brings together country-reported “population size estimates” (PSEs) for key populations like female sex workers. These are often the best available national figures but may be old, incomplete, or limited to certain cities.
- WHO & UNAIDS thematic updates: summarize what we know globally about sex workers’ health and legal environments, including the fact that sex workers are a diverse population (not just women) and face heightened risks where criminalized.
- National statistics in regulated systems: a few countries publish registered worker counts (a lower bound of the true population). Germany, for example, reports the number of people registered under its protective law.
- Older UNdata downloads of UNAIDS indicators: still referenced in media and blogs; useful historically but often a decade old and shouldn’t be treated as “current.”
Because of these quirks, the fairest approach is to present two annotated shortlists:
- countries with higher reported per-capita density of sex workers (based on recent UNAIDS PSEs, high-quality national registries, or repeatedly cited official reviews), and
- countries with very low reported per-capita density (usually due to criminalization or the absence of public data - not because people don’t sell sex there).
Throughout, assume numbers are conservative (they miss people), and that legal status strongly shapes visibility.
The 10 “most” per capita (best-available signal, not a perfect scoreboard)
Below are ten countries that, based on recent official sources or credible reviews, appear to have comparatively higher reportedper-capita concentrations of sex workers. I’ve chosen them because they have either: (a) newish national registry counts, (b) widely cited government-commissioned reviews/estimates, or (c) consistent UNAIDS PSE reporting. Where a stat is mentioned, it’s for context - not to imply exact comparability across countries or years.
Germany - Registration makes people countable
Germany requires registration under the Prostituiertenschutzgesetz. By the end of 2024, about 32,300 people were validly registered - up from 30,600 in 2023 - but still below pre-pandemic figures. Registration is a floor, not the full picture, yet it provides one of the few Europe-wide, apples-to-apples signals.
Netherlands - Licensed venues and multiple measurement attempts
The Netherlands licenses many parts of the sector and has long been studied. A government summary estimated ~373 licensed companies (across forms like clubs, window sex work, and agencies) at one point in recent mapping; other research used online data to estimate active workers and noted proportions relative to adult population. The exact counts vary, but the country’s visibility and licensing make its per-capita signal relatively high and unusually documented.
New Zealand - Decriminalization plus official reviews
NZ’s 2003 Prostitution Reform Act was followed by government-commissioned studies (2005 baseline; 2008 review) that tried to estimate the size of the industry and examine health and safety. While figures are older and not strictly comparable to other countries’ methods, decriminalization + public research gives an unusually clear window, supporting the view that NZ’s visible per-capita density is meaningful (again, as a lower bound).
Thailand - Large workforce; multi-gender composition reported
Thailand regularly appears in global conversations about market size. A 2024 UNAIDS brief, for example, included a composition snapshot (among an estimated worker population in 2023, ~62% female, ~13% male, ~25% transgender), underscoring that “sex workers” ≠ only FSW. Estimates of total numbers vary across sources, but the signal is consistently high.
Brazil - High absolute numbers and sustained PSE history
Brazil has long reported large counts through UNAIDS reporting channels; for example, an earlier UNdata extract (reflecting UNAIDS inputs) listed ~546,800sex workers (historic, method-limited). Modern figures should be taken cautiously, but Brazil’s scale means that even conservative counts translate into a strong per-capita signal.
Dominican Republic - Tourism hubs and program visibility
Caribbean tourism centers bring visibility and repeated programme engagement with sex worker communities for HIV services. While point estimates differ across years and methods, the country is frequently represented in regional surveillance and prevention programming, contributing to a higher-than-average reported density signal. (Read as: visible, counted, but likely under-counted.)
Kenya - Large, repeatedly surveyed key population
Kenya’s HIV response has generated numerous PSEs and services for FSW and other key populations, making workers comparatively present in official data versus some neighbours. That visibility - not an inflated reality - helps produce a higher reported per-capita signal.
South Africa - Robust key-population programming
Like Kenya, South Africa has long included sex workers in HIV programming and surveillance, generating consistent PSE and prevalence data. Legal and social risks remain, but the data presence pushes the reported per-capita signal higher than in countries with similar markets but less measurement.
Cambodia - Longstanding measurement in an evolving legal context
Cambodia has appeared in UNAIDS/WHO regional analyses for decades, with shifting legal frameworks and outreach models. Even with method changes, the continuity of attention elevates the reported density signal compared to countries with similar activity but few data points.
Bangladesh - Documented historic counts amid complex realities
Historic UNAIDS-reported figures for Bangladesh are sizeable (again, older and method-limited), and brothel-based as well as informal sectors have been documented by NGOs and programme partners. As with others here, treat these as visibility signals, not definitive totals.
Why these ten? Not because they are “the absolute top,” but because their data trails are thicker - they register workers, license venues, or have repeated UNAIDS/health-sector estimates. That raises their reported per-capita signal even as actual totals are undercounted everywhere.
The 10 “least” per capita (really: lowest official visibility)
Here are countries where very low numbers are reported - or almost no public numbers at all - largely because sex work is criminalized, highly stigmatized, or swept into other offences, making measurement risky for workers and unattractive for officials. This does not mean fewer sex workers exist; it means they are less countable.
- Saudi Arabia - Sex work is criminalized; public, rights-based measurement is rare, pushing reported per-capita counts toward zero even when activity exists underground.
- Iran - Criminalization and high stigma mean no transparent national PSE; any “low” number is almost certainly an artefact of risk, not reality.
- Afghanistan - Criminalization, conflict, and displacement drastically limit data collection on highly stigmatized activities.
- Yemen - Ongoing conflict and criminalization severely limit any ethical enumeration.
- Qatar - Strict laws and deportation risks for migrants create strong disincentives to be “counted.”
- Kuwait - Criminalization and immigration enforcement push activity into the shadows.
- Oman - Legal prohibitions + risk to migrants = very low official visibility.
- United Arab Emirates - Occasional media exposes contrast with a scarcity of official, public PSE, so reported per-capita looks low while underground markets persist.
- Brunei - Strict laws and social sanctions make enumeration unlikely.
- North Korea - A general data blackout: virtually no trustworthy public statistics on sex work, making “per-capita” claims meaningless.
Again, treat these as “least reported” (low visibility in data) rather than least in reality. Criminalization depresses the numerator (counted workers) more than it changes the denominator (population), producing deceptively small per-capita ratios.
How to read (and not misuse) these lists
- Per-capita ≠ morality. A high reported per-capita figure may reflect better counting and safer disclosure, not “more vice.”
- Registered counts are floors. Germany’s registries show who is willing/able to register - not everyone working.
- FSW ≠ all sex workers. UNAIDS often focuses on female sex workers because of HIV programme design. Sex workers include men, transgender and gender diverse people. In Thailand’s 2023 snapshot: ~62% female, 13% male, 25% transgender.
- Old numbers travel. Media often recycle decade-old counts from UNdata or NGO reports. Check the year and the method before citing.
- Law shapes visibility. Decriminalization (NZ), licensing (NL), or registration (DE) raises the chance of being counted; criminalization does the opposite.
Why some places “look big” and others “look small”
Two forces are doing most of the work:
- Legal environment
Where sex work is decriminalized or regulated, people can interact with outreach, clinics, and officials with less fear. That improves measurement. Where sex work is criminalized, workers have rational reasons to avoid anything that could expose them. Fewer people counted ≠ fewer people working. - Health-sector attention
Countries with established HIV programmes for key populations have more surveys and size estimates. That doesn’t “create” sex workers; it documents them and improves access to care. WHO and UNAIDS have been clear about health disparities among sex workers, part of why surveillance exists.
What responsible readers, journalists, and policymakers can do
If you’re quoting numbers:
- Mention the year and method (registry, UNAIDS PSE, special study).
- Avoid sweeping claims like “Country X has the most sex workers in the world.”
- Clarify when a number refers to registered workers or only to women.
- Say “reported per-capita” or “visible per-capita” when that’s what the source actually captures.
If you’re designing services:
- Focus on safety and access more than on “how many.” Outreach, STI services, and violence-prevention help regardless of the exact count.
- Partner with worker-led groups; they know where your sampling frame is broken.
- Build confidential, low-burden data flows so people aren’t punished for being counted.
If you’re advocating for workers’ dignity:
- Use person-first language (“sex workers,” not pejoratives).
- Highlight that legal protection and labor-style safeguards make everyone safer, including those who never interact with the justice system.
- Center privacy; data ethics are safety.
A calmer way to hold the “top 10” idea
It’s human to want a list. Lists feel tidy. But the most compassionate and accurate way to talk about “most & least per capita” is to explain how counting works and why visibility varies. Countries like Germany, the Netherlands, and New Zealand look “high” partly because they try to count, while countries with total criminalization look “low” because counting is risky or impossible. That is not an argument about virtue; it’s an argument about methods - and methods are policy.
Final word (protective, steady, and practical)
Behind every number is a person who deserves safety, privacy, and respect - whether or not they’re reflected in a spreadsheet. Treat “per capita” lists as visibility maps, not moral scores. If you cite them, cite carefully. If you build policy on them, build for dignity first. And when in doubt, remember that what we can count says as much about law and stigma as it does about people’s lives.
