Analyzing The Market Value Of Fat Titties
Analyzing The Market Value Of Fat Titties
and a lot more!
Men with money are attractive to women. We can thus find out how much other traits are ‘worth’, based on how much money women would trade for them. E.g., one study finds that women would take a $175k hit to a man’s income for him to be 6’0” instead of 5’6”.
So if we want to know the market value of a woman’s traits, we can see how much more men would be willing to pay to access women with those traits.
And what better place to go looking than at the hoes? In escort listings, women generally price themselves as high as they can while still maintaining enough bookings, and you get a list of their physical attributes — hair color, eye color, body type, height, etc.
Tryst is a website featuring escort listings. It’s exploded in popularity in the last few years, and I think it’s currently the top escort directory in the US. Someone scraped Tryst and sent me the data of ~17k profiles, and here I present them to you.
Tryst feels like a mid-range listing site to me. Lower-end sites tend to feature creative misspellings and really direct stuff like “I enjoy sucking your balls”. For example:
My guess is a lot of these are actually agencies, where you talk to some centralized office and then they send you out a girl that’s probably not gonna be the girl in the photos. To the extent sex trafficking happens at all for online-listed escorts, I’d guess it happens here (but reminder the actual rate of sex trafficking among sex workers is probably much lower than you think).
By contrast, Tryst has fewer emoticons and words in all caps:
Some disclaimers:
Obviously this isn’t perfect. The price an escort chooses to list herself at doesn’t necessarily correspond to income. For example, I have the highest price of any publicly listed escort in the world, but I rarely take appointments, and my total income from escorting is significantly lower than many other escorts who have cheaper rates than me.
But I’m an anomaly - I get plenty of income from non-escorting things, I’m escorting for fun, and I’m not optimizing for income. But still, we might expect listed price to be slightly affected by things like is this her full time job and to what degree is she willing to take a hit to total income in order to preserve ego/signaling.
Also my analysis is on self reported data. This is how women choose to represent themselves, which might be more flatteringly skewed.
Where Are The Escorts?
These are only looking at profiles that list one city, which is ~16k profiles. There’s an additional 2k profiles that list more than one city.
Gender Impact: High
It’s interesting that trans women are charging nearly as much as cis women! I’m also surprised that cis men have an average rate of over $300. I checked many of the highest priced men and they seemed like legit profiles, typically porn associated, typically involving a lot of muscles.
94.4% of profiles listed were marked as ‘woman’; 2.4% were trans women, 1.8% were men, 1.1% were non-binary, and 0.02% were trans men.
(Some notes on my data cleaning: People have widely varying pricing strategies, some designed for very fast appointments and some long. For example, if someone listed 15 minute appointments only, I would multiply this by four—though in reality, that person probably wouldn’t charge 4x the cost for an hour appointment. This means that shorter appointments - which are usually associated with lower end rates - are likely bumped up in escort prices, and the reverse holds true for high-end rates. Also - people have different rates for incall vs outcall. On average, incall was ~500, whereas outcall was ~550. To make the graph, I took people’s incall rate first, and if they had no incalls listed, then I inserted the outcall rate. This might warp the numbers, but probably only a little, because the vast majority of people have incall rates.)
Location Impact: High
Eye Color Impact: Low
obligatory reminder that I’m using ‘impact’ loosely; correlation does not equal causation!
Green $528
Blue $526
Black $504
Hazel $503
Grey $497
Brown $497
The gap here ranges only $31 dollars, which means this is a pretty mild impact! It’s probably confounded by ethnicity.
Hair Color Impact: Medium
Blonde $533
Brunette $529
Strawberry blonde $496
Auburn $488
Red $484
Black $476
Neon $441
Silver $417 (only 27 escorts for this category)
I’m actually a little shocked that red and auburn hair isn’t the highest! I thought guys loved redheads.
Ethnicity Impact: Medium (??)
In my data, the ‘ethnicity’ category was a little sus. There seemed to be a mix of ‘choose from a dropdown list’ and ‘fill in the blank’, and there were weird income inconsistencies between this (e.g., ‘white’ had meaningfully different income from ‘caucasian.’
I just grouped all reported ethnicities into overarching categories, and am only including ones with a sample size over 100.
So take the following with a grain of salt!
Asian: $576
Unknown (no ethnicity data): $535
White/Caucasian: $513
Hispanic/Latina $512
Other: $505
Mixed: $476
Black/African: $441
If the data here is good - reminder there were suspicious things in the original with causes I couldn’t track down so ??? - then this data roughly matches the average wealth classes of ethnicities in the US in other areas.
Among all the profiles, 41% were white/caucasian, 16% were ‘unknown’, 12% were black/african, 11% were hispanic/latina, 8% were ‘other’, 8% were ‘mixed’, 3% were Asian. Middle eastern, native american, and pacific islander all were around 0.04%
Body Type Impact: High
Pretty self explanatory! I originally tried to separate people describing themselves as ‘thin’ from people saying ‘thin but with curves’, but those groups earned exactly the same amount, so I just shrugged and merged them.
Keep in mind I had to make a lot of classification judgment calls here - where does “thicc” fall, exactly?— and if someone included multiple descriptors (e.g. “athletic BBW”), I included them in whatever category was heavier.
Boob Size Impact: High
Boob size is impacted a lot by a person’s overall weight, so to avoid weight as a confounder I plotted slim vs curvy people separately (and combined curvy and bbw into one category).
For slim escorts, boob size predicts their hourly rate a lot. For curvy/bbw, less so. I suspect the uptick in price at a B cup size comes from misclassification - thinner escorts with smaller breasts who used thicker words to describe themselves, and thus my cleaning caught them into the larger category. Not sure though!
There was no information about whether or not an escort had breast implants.
Height Impact: Low
I’m surprised height had this strong an effect at all, and that it favored 5’9-5’10”! I would have predicted 5’6” or so would have won out.
Age Impact: High
It’s interesting to see that women in their 30’s don’t have that significant a dropoff - the real ‘wall’, if we want to call it that, happens upon entry into a woman’s 40’s - and even then it’s not a huge wall.
OnlyFans Impact: Low
Women with onlyfans earned $30 more than those who didn’t have onlyfans.
31% of profiles (US, women) had an Onlyfans listed.
Vocabulary
Pretty self explanatory.
def clean_text(text):
text = re.sub(r'[^\w\s]', '', str(text).lower())
tokens = text.split()
stop_words = set(stopwords.words('english'))
tokens = [token for token in tokens if token not in stop_words and len(token) > 2] return tokens
all_bios = ' '.join(df_woman['Full_Bio'].dropna())
clean_tokens = clean_text(all_bios)
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(clean_tokens))
Let me know if I fucked that up!
High vs Low End Escort Vocabulary
I checked to see if escorts with high rates used vocabulary in their bios that was different than low-priced escorts.
The top words associated with high prices were:
life, NYC, model, website, world, connection, passport, exploring, passion, moments, adventure, toned, experiences, yoga, allure, heart, connections, art, form, curious, eyes, elegant, creative, fit, companion, city, star, twitter, adventures, restaurants, healthy, email, date, share, fly, deep, learn, sharing
The top words associated with low prices were:
anal, baby, outcall, cash, independent, won’t, service, fun, services, curvy, sexy, enforcement, real, snapchat, rush, satisfaction, law, gfe, location, incalls, cum, fetish, mature, ask, discreet, friendly, bbw, don’t, pics, bare, outcalls, safe, available, come, hey, clean, drama, text
In conclusion: if we combine all of these, our highest priced escort is a 5’9”, thin, green-eyed blonde in her 20’s, who uses classy language, lives in New York, and has really fat titties.
I enjoy these data-driven posts, but I always end up a little disappointed that you only present the surface level statistics and don't do deeper analysis to understand what is really going on. Real-world sociological data is always rife with confounding factors and if you don't try to identify and control for them you might end up drawing the wrong conclusions.
For example, you note that women in their 40s only earn ~10% less than women in their 30s, which sounds encouraging, but I suspect there is a survivor effect here, where the number of escorts in their 40s is just significantly lower. If an escort turns 40 and the offers she gets aren't as good as when she was younger, she might well quit, rather than lower her price, but your bar chart doesn't reflect the quitters. A line plotting the number of samples per age group would be helpful here.
I think the real “wall” occurs at the point where participation drops significantly. In any case, it would be a mistake to infer that the typical 35-year-old can expect to continue working ten years later with only a 10% drop in income.
A similar effect might explain why men earn “only” 40% less than women: those men probably represent a higher percentile within their gender, as supported by the fact that there are about 50 times as many female escorts in the data set. It doesn't imply the average man can earn around 60% of the income of the average woman.
Speaking of men, the age graph should be split by gender. It seems unlikely that the effect of age on attractiveness is the same for men and women.
You mention that both ethnicity and body type are highly impactful, but the two are also correlated. Stereotypically, Asians are more likely to be slender and Black women more likely to be fat, which is corroborated by obesity statistics. It would be nice to control for these factors to see how large they are individually. (My guess: BMI is more important than ethnicity, but I could be wrong.)
There are other correlations that you could try to control for. For example, age correlates positively with BMI (and to a lesser extent ethnicity). It could be of practical significance for an escort who is planning her “career” to know if she can realistically maintain her income level if she stays in shape, or if age itself will do her in.
Finally, the height graph should really be split by gender.
Interesting article!
I think one thing that could account for the different preferences/prices for a SW compared to a romantic partner is that it's not so much about what men want *in general* but what they cannot get without paying for it.
In this regard, the preference for 'classy' women (restricted social circles) and taller women (women generally prefer dating taller men, so a shorter man would find it harder to date a taller women) might be reflective of the fact that these are categories that men don't have access to in the dating world.