• Women on Tinder are more selective than men on Tinder⁠.
• Women on Tinder have a preference for highly educated men.
• Men on Tinder are not intimidated by highly educated women.
• On Tinder, preferences for educational assortative mating are absent.

In this study, we examine the impact of an individual’s education level on her/​​his mating success on the mobile dating app Tinder. To do so, we conducted a field experiment on Tinder in which we collected data on 3,600 profile evaluations. In line with previous research on mating preferences from multiple fields, our results indicate a heterogeneous effect of education level by gender: while women strongly prefer a highly educated potential partner, this hypothesis is rejected for men. In contrast with recent influential studies from the field of economics, we do not find any evidence that men would have an aversion to a highly educated potential partner. Additionally, in contrast with most previous research—again from multiple fields—we do not find any evidence for preferences for educational assortative mating, i.e. preferring a partner with a similar education level.

[Keywords: returns to education, mating success, assortative mating, dating apps, Tinder]

…To the extent of our knowledge, only one study to date has examined the impact of education level on actual, revealed mate preferences ex ante to interactions and with random assignment of education level. Ong 2016 found that men’s visits to women’s profiles were unaffected by the profiles’ education level, while women’s visits to men’s profiles were increasing with the profiles’ education level. We build on this study by examining the impact of education level on mate preferences by means of a randomised field experiment on Tinder. Our study importantly differs from the study by Ong 2016 in 3 ways. First, we used a more precise measure of mating success: while Ong 2016 used the number of profile visits as an indicator of mating success, we used an explicit indication of interest by potential partners (infra, Section 3.5). Second, we set up our field experiment on a mobile dating app instead of on a classic online dating website. Third, we examined Western singles instead of Chinese singles.

…For this study, we created 24 fictitious Tinder profiles in multiple cities in Flanders, the northern, Dutch-speaking region of Belgium. We only let these profiles differ on our characteristic of interest, i.e. education level, which was randomly assigned to the 24 fictitious profiles. Education level was signalled by filling in the line ‘education’ on the main screen…Our subjects were other, real, Tinder users who fit our 3 criteria, i.e. (1) sexual preference, (2) age range, and (3) distance range. First, in this study we only looked at heterosexual preferences. Therefore, we indicated that we only wanted to see male (female) subjects with our female (male) profiles. Second, for the age range, we chose ages 23 to 27, in order to exclude students from our sample. Third, our distance range we gradually increased per kilometre from the minimum of 2 kilometres on, in order to find the subjects who were closest to us. We did this to ensure that our profiles were in the distance range of our subjects, so that our profiles would show up in the stack of profiles that our subjects evaluated. Only once we had to increase the range above the minimum of 2 kilometres and all subjects were found in a range of 3 kilometres. With each of our 24 fictitious profiles, between January 2018 and March 2018 we randomly liked 150 of the first Tinder users who were presented to our fictitious profiles, resulting in a sample size of 3,600 observations. We did not simply like the very first 150 Tinder users presented to us, as Tinder may then have perceived our fictitious profiles as robots. Therefore, for each Tinder user presented to us, we randomly generated a number between 0 and 1 and liked the Tinder user if the number was above 0.5. For each of our 24 fictitious profiles, all subjects were recruited from the first 325 Tinder users presented to our fictitious profiles.

…Table 2 gives an overview of the frequencies of the different outcomes. When considering all subjects, about one-third (33.2%) of our profiles (hereafter: ‘the evaluated profiles’) received a (super)like. However, this conceals remarkable differences between the male subjects and female subjects. Indeed, male subjects (super)liked 61.9% of the female evaluated profiles, while female subjects (super)liked only 4.5% of the male evaluated profiles. These findings are in line with previous research on online dating in general (Fiore et al 2010⁠, Todd et al 2007) and on Tinder in particular (Tyson et al 2016)…Very few subjects used the superlike option, i.e. only 1.4% of all matches came about in this way. This finding is in line with the limited amount of superlikes available to Tinder users (see footnote 8). Finally, we note that male subjects started a conversation with the female evaluated profiles much more often (42.3%) than the other way around (6.2%). The explanation for this finding is similar to the explanation in the previous paragraph for the higher selectiveness of women (compared to men) with regard to (super)liking a certain profile.

• Measured gender differences in preferences for mate income in an online field experiment.
• Men of all income levels visited our female profiles of different income levels at equal rates.
• Women of all income levels visited our male profiles with higher incomes at higher rates.
• These higher rates increased with the women’s own incomes and jumped discontinuously.

We measured gender differences in preferences for mate income ex-ante to interaction (“income attraction”) in a field experiment on one of China’s largest online dating websites.

To rule out unobserved factors correlated with income as the basis of attraction, we randomly assigned income levels to 360 artificial profiles and recorded the incomes of nearly 4,000 “visits” to full versions of these profiles from search engine results, which displayed abbreviated versions.

We found that men of all income levels visited our female profiles of different income levels at roughly equal rates. In contrast, women of all income levels visited our male profiles with higher incomes at higher rates. Surprisingly, these higher rates increased with the women’s own incomes and even jumped discontinuously when the male profiles’ incomes went above that of the women’s own. Our male profiles with the highest level of income received 10× more visits than the lowest.

This gender difference in ex-ante preferences for mate income could help explain marriage and spousal income patterns found in prior empirical studies.

[Keywords: online dating, field experiment, gender differences, matching, marriage]

[Today’s dataset: 1.54m votes, 596k messages, 64k profiles.]

This post investigates female attractiveness, but without the usual photo analysis stuff. Instead, we look past a woman’s picture, into the reaction she creates in the reptile mind of the human male. Among the remarkable things we’ll show:

• that the more men as a group disagree about a woman’s looks, the more they end up liking her
• guys tend to ignore girls who are merely cute
• and, in fact, having some men think she’s ugly actually works in woman’s favor

…Now let’s look back at the two real users from before, this time with their own graphs. OkCupid uses a 1 to 5 star system for rating people, so the rest of our discussion will be in those terms. All the users pictured were generous and confident enough to allow us to dissect their experience on our site, and we appreciate it. Okay, so we have: […] As you can see, though the average attractiveness for the two women above is very close, their vote patterns differ. On the left you have consensus, and on the right you have split opinion.

To put a fine point on it:

• Ms. Left is, in an absolute sense, considered slightly more attractive
• Ms. Right was also given the lowest rating 142% more often
• yet Ms. Right gets 3× as many messages

When we began pairing other people of similar looks and profiles, but different message outcomes, this pattern presented itself again and again. The less-messaged woman was usually considered consistently attractive, while the more-messaged woman often created variation in male opinion…Our first result was to compare the standard deviation of a woman’s votes to the messages she gets. The more men disagree about a woman’s looks, the more they like her. I’ve plotted the deviation vs. messages curve below, again including some examples…