- 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.