Tinder Experiments II: Dudes, you are probably better off not wasting your time on Tinder — a quantitative socio-economic study unless you are really hot

Tinder Experiments II: Dudes, you are probably better off not wasting your time on Tinder — a quantitative socio-economic study unless you are really hot

This research had been carried out to quantify the Tinder prospects that are socio-economic men in line with the percentage of females which will “like” them. Female Tinder usage data had been gathered and statistically analyzed to determine the inequality into the Tinder economy. It had been determined that the underside 80% of males (when it comes to attractiveness) are contending for the underside 22% of females and also the top 78percent of females are contending for the most truly effective 20percent of males. The Gini coefficient for the Tinder economy according to “like” percentages ended up being determined become 0.58. This means 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 typical attractiveness is “liked” by roughly 0.87% (1 in 115) of females on Tinder. Additionally, a formula ended up being derived to estimate an attractiveness that is man’s based on the portion of “likes” he receives on Tinder:

To determine your attractivenessper cent follow this link.


During my past post we discovered that in Tinder there was a big difference between the amount of “likes” an attractive guy gets versus an unattractive man (duh). I desired to know this trend much more quantitative terms (also, i prefer pretty graphs). To achieve this, I made the decision to take care of Tinder being 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. Generally in most around the globe the money is cash (or goats). In Tinder the currency is “likes”. The greater amount of “likes” you get the more wealth you have got within the Tinder ecosystem.

Riches in Tinder just isn’t distributed similarly. appealing dudes do have more wealth into the Tinder economy (get more “likes”) than ugly dudes do. It isn’t surprising since a big percentage of the ecosystem is founded on looks. an unequal wide range circulation is always to be anticipated, but there is however a far more interesting concern: what’s the amount of this unequal wide range circulation and exactly how performs this inequality compare with other economies? To respond to that concern we have been first have to some information (and a nerd to evaluate it).

Tinder does not provide any data or analytics about user use thus I had to gather this information myself. Probably the most data that are important required was the % of males why these females tended to “like”. We accumulated this information by interviewing females that has “liked” A tinder that is fake profile put up. We asked 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 method is ethically dubious at most readily useful (and extremely entertaining), but, regrettably I’d no alternative way to obtain the required information.

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

At this time I would personally be remiss not to point out a caveats that are few these information. First, the test dimensions are tiny (only 27 females were interviewed). 2nd, all information is self reported. The females whom taken care of immediately my concerns might have lied in regards to the portion of guys they “like” to be able to wow me personally (fake super hot Tinder me) or make themselves appear more selective. This self bias that is reporting surely introduce error to the analysis, but there is however proof to recommend the info we obtained possess some validity. For example, A new that is recent york article claimed that in an test females on average swiped a 14% “like” price. This compares differ positively using the data I obtained that displays a 12% typical “like” rate.

Furthermore, i will be just accounting for the portion of “likes” and never the real males they “like”. I need to assume that as a whole females discover the same guys appealing. I believe this is actually the flaw that is biggest in this analysis, but presently there isn’t any other option to analyze the information. Additionally there are two reasons why you should genuinely believe that helpful trends could be determined from all of these information despite having this flaw. First, within my past post we saw that appealing guys did quite as well across all feminine age ranges, in addition to the age of a man, therefore to some degree all ladies have actually comparable preferences with regards to real attractiveness. Second, nearly all women can concur if a man is actually appealing or actually ugly. Women can be very likely to disagree in the attractiveness of males in the exact middle of the economy. Even as we will discover, the “wealth” within the middle and bottom percentage of the Tinder economy is gloomier compared to the “wealth” of the” that is“wealthiest (with regards to of “likes”). Consequently, no matter if the mistake introduced by this flaw is significant it willn’t significantly impact the general trend.

Okay, sufficient talk. (Stop — information time)

When I claimed formerly the female that is average” 12% of males on Tinder. It doesn’t mean though that many males will get“liked right right back by 12% of all ladies they “like” on Tinder. This will simply be the instance if “likes” had been equally distributed. In reality , the base 80% of males are fighting throughout the base 22% of females as well as the top 78percent of females are fighting on the top 20percent of males. This trend can be seen by us in Figure 1. The location in blue represents the circumstances where women can be more prone to “like” the males. The location in red represents the circumstances where guys are almost certainly going to “like” ladies. The bend does not decrease linearly, but rather falls quickly following the top 20percent of men. Comparing the area that is blue the red area we could observe that for a random female/male Tinder conversation the male probably will “like” the feminine 6.2 times more regularly compared to the feminine “likes” the male.

We are able to additionally note that the wide range circulation for men within the Tinder economy is very big. Many asiandate females only “like” probably the most appealing dudes. Just how can the Tinder is compared by us economy with other economies? Economists utilize two primary metrics to compare the wide range circulation of economies: The Lorenz bend plus the Gini coefficient.