[Clemmo]

The writer of this article created a new buzzword "lookisist" because women don't find him attractive on Tinder. Sounds like a Lena Dunham type feminist whining that hot men don't find someone like her attractive. I found the article interesting because it shows data on women's hypergamy on Tinder.

Here's what he wrote

Quote:

You know how I know I’m old? I’ve never actually used the internet to get laid really. The last time I was single was 12 years ago and back then internet dating was still sort of fringey. I tried it for a month at one point, went on a few awkward dates and that was it. But even though I have zero experience with this stuff, these results don’t surprise me even slightly (even though this is by no means enough data to be considered fact). People are looksist as shit, that science has been around forever. It’s obviously going to be increasingly exaggerated on a hook up site, but still. (from Worst Online Dater):

Abstract (TL;DR)
This study was conducted to quantify the Tinder socio-economic prospects for males based on the percentage of females that will “like” them. Female Tinder usage data was collected and statistically analyzed to determine the inequality in the Tinder economy. It was determined that the bottom 80% of men (in terms of attractiveness) are competing for the bottom 22% of women and the top 78% of women are competing for the top 20% of men. The Gini coefficient for the Tinder economy based on “like” percentages was calculated to be 0.58. This means that the Tinder economy has more inequality than 95.1% of all the world’s national economies. In addition, it was determined that a man of average attractiveness would be “liked” by approximately 0.87% (1 in 115) of women on Tinder. Also, a formula was derived to estimate a man’s attractiveness level based on the percentage of “likes” he receives on Tinder:

attractiveness%=16.8*ln(like%)+52.3

To calculate your attractiveness% click here.

Introduction
In my previous post we learned that in Tinder there is a big difference in the number of “likes” an attractive guy receives versus an unattractive guy (duh). I wanted to understand this trend in more quantitative terms (also, I like pretty graphs). To do this, I decided to treat Tinder as an economy and study it as an economist (socio-economist) would. Since I wasn’t getting any hot Tinder dates I had plenty of time to do the math (so you don’t have to).

The Tinder Economy
First, let’s define the Tinder economy. The wealth of an economy is quantified in terms its currency. In most of the world the currency is money (or goats). In Tinder the currency is “likes”. The more “likes” you get the more wealth you have in the Tinder ecosystem.

Wealth in Tinder is not distributed equally. Attractive guys have more wealth in the Tinder economy (get more “likes”) than unattractive guys do. This isn’t surprising since a large portion of the ecosystem is based on physical appearance. An unequal wealth distribution is to be expected, but there is a more interesting question: What is the degree of this unequal wealth distribution and how does this inequality compare to other economies? To answer that question we are first going to need some data (and a nerd to analyze it).

Tinder doesn’t supply any statistics or analytics about member usage so I had to collect this data myself. The most important data I needed was the percent of men that these females tended to “like”. I collected this data by interviewing females who had “liked” a fake Tinder profile I set up. I asked them each several questions about their Tinder usage while they thought they were talking to an attractive male who was interested in them. Lying in this way is ethically questionable at best (and highly entertaining), but, unfortunately I had no other way to get the required data.

Caveats (skip this section if you just want to see the results)
At this point I would be remiss to not mention a few caveats about these data. First, the sample size is small (only 27 females were interviewed). Second, all data is self reported. The females who responded to my questions could have lied about the percentage of guys they “like” in order to impress me (fake super hot Tinder me) or make themselves seem more selective. This self reporting bias will definitely introduce error into the analysis, but there is evidence to suggest the data I collected have some validity. For instance, a recent New York Times article stated that in an experiment females on average swiped a 14% “like” rate. This compares vary favorably with the data I collected that shows a 12% average “like” rate.

The writer looks just as you expect.