Tinder Experiments II: Dudes, unless you’re actually hot you are probably best off perhaps not wasting some time on Tinder — a quantitative socio-economic research

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Published on: July 22, 2021

Tinder Experiments II: Dudes, unless you’re actually hot you are probably best off perhaps not wasting some time on Tinder — a quantitative socio-economic research

Mar 25, 2015 · 8 min read

Abstract (TL;DR)

This research ended up being carried out to quantify the Tinder prospects that are socio-economic men on the basis of the pe r centage of females that may “like” them. Feminine Tinder usage data had been gathered and statistically analyzed to determine the inequality when you look at the Tinder economy. It had been determined that the base 80% of males (with regards to attractiveness) are contending for the underside 22% of females as well as the top 78percent of females are contending for the most notable 20percent of males. The Gini coefficient when it comes to Tinder economy predicated on “like” percentages had been calculated become 0.58. Which means that the Tinder economy has more inequality than 95.1% of the many world’s nationwide economies. In addition, it had been determined that a person of normal attractiveness could be “liked” by roughly 0.87% (1 in 115) of females on Tinder. Additionally, a formula ended up being derived to calculate a man’s attractiveness degree on the basis of the portion of “likes” he gets on Tinder:

To calculate your attractivenessper cent click the link.


In my own past post we discovered that in Tinder there is certainly a difference that is big the sheer number of “likes” an attractive guy gets versus an unattractive man (duh). I wanted to know this trend much more terms that are quantitativealso, I like pretty graphs). For this, I made the decision to take care of Tinder as an economy and learn it as an economist socio-economist that is( would. I had plenty of time to do the math (so you don’t have to) since I wasn’t getting any hot Tinder dates.

The Tinder Economy

First, let’s define the Tinder economy. The wide range of a economy is quantified in terms its money. The currency is money (or goats) in most of the world. In Tinder the currency is “likes”. The greater “likes” you get the more wide range you’ve got into the Tinder ecosystem.

Riches in Tinder is certainly not distributed similarly. Appealing dudes have significantly more wealth into the Tinder economy (get more “likes”) than ugly dudes do. It isn’t astonishing since a big part of the ecosystem is dependent on physical appearance. an unequal wide range circulation would be to be likely, but there is however a far more interesting concern: what’s the level of this unequal wide range circulation and exactly how performs this inequality compare with other economies? To resolve that relevant concern we’re first have to some information (and a nerd to evaluate it).

Tinder does not provide any statistics or analytics about user use and so I had to gather this information myself. Probably the most essential information we required ended up being the % of males why these females tended to “like”. We collected this information by interviewing females who’d “liked” A tinder that is fake profile create. I inquired them each a few questions regarding their Tinder use they were talking to an attractive male who was interested in them while they thought. Lying in this real means is ethically debateable at most useful (and extremely entertaining), but, unfortuitously I experienced simply no other way to obtain the needed data.

Caveats (skip this part in the event that you simply want to start to see the outcomes)

At this stage i might be remiss never to point out a few caveats about these information. First, the test dimensions are tiny (just 27 females had been interviewed). 2nd, all information is self reported. The females whom taken care of immediately my concerns may have lied in regards to the portion of guys they “like” so that you can wow me personally (fake super hot Tinder me) or make themselves seem more selective. This self bias that is reporting undoubtedly introduce mistake to the analysis, but there is however proof to recommend the info we built-up possess some validity. For example, a present ny instances article claimed that within an test females on average swiped a 14% “like” rate. This compares differ positively using the information we accumulated that displays a 12% typical “like” rate.

Furthermore, i will be just accounting for the portion of “likes” and never the men that are actual “like”. I must assume that as a whole females get the men that are same. I do believe this is basically the flaw that is biggest in this analysis, but presently there isn’t any other option to analyze the information. There are two reasons why you should think that helpful trends could be determined from all of these information despite having this flaw. First, within my past post we saw that appealing males did quite as well across all female age brackets, in addition to the chronilogical age of a man, therefore to some degree all ladies have actually comparable tastes when it comes to physical attractiveness. Second, the majority of women can concur if a man is truly appealing or actually ugly. Women can be prone to disagree in the attractiveness of males in the middle of the economy. Once we will discover, the “wealth” when you look at the middle and bottom part of the Tinder economy is leaner compared to the “wealth” of the “wealthiest” (with regards to of “likes”). Consequently, regardless if the error introduced by this flaw is significant it mustn’t significantly impact the general trend.

Okay, sufficient talk. (Stop — information time)

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