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I decided to sign up anyways and just match away and see what happened. My college roommate and I both would stay up on Tinder, not looking for anything serious also not looking for hookups though, just entertainment. My now husband and I matched on Tinder. I didn't think anything would ever come about from it, but one day I saw this stunning beauty come across the app.

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We both fell hard and fast and knew we had found our person fairly quickly. In order to use Tinder on the web please enable javascript in your browser settings. Tinder Tinder Close. Swipe Right®. Download for iOS. All photos are of models and used for illustrative purposes only.

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Research using data on offline couple formation has confirmed predictions from evolutionary psychology that women not men attach value to the earnings potential of a potential partner.

In this study, we examine whether the partner preferences with respect to earnings potential survive in an online context with fewer search and social frictions. We did this by means of a field experiment on the popular mobile dating app Tinder. Thirty-two fictitious Tinder profiles that randomly differed in job status and job prestige were evaluated by other, real Tinder users. We find that both men and women do not use job status or job prestige as a determinant of whom to show initial interest in on Tinder.

However, we do find evidence that, after this initial phase, men less frequently start a conversation with women when those women are unemployed.

Still, also then men do not care about the particular job prestige of employed women. Indeed, multiple independent studies using data from the United States have shown that approximately one in five committed relationships and one in six marriages over the past decade have begun through online dating Cacioppo et al. The latest development in online dating is the increasing popularity of mobile dating apps, of which Tinder is the most used.

Footnote 1. Despite the ubiquity of mobile dating apps, little is known about what drives partner preferences on these apps.

Indeed, previous research on partner preferences has mainly examined partner preferences in an offline setting. A first contribution of this study to the existing literature is examining whether job status being employed or being unemployed and job prestige have an impact on success on the mobile dating app Tinder.

This way, we examine whether earnings potential still plays a substantial role in online dating preferences, as it has been shown in the field of evolutionary psychology to do in offline dating preferences see also Sect. That is, we conduct a field experiment on Tinder in which we randomly vary both job status and job prestige across fictitious heterosexual Tinder profiles and then monitor which fictitious profiles are the most successful in a sample of other, real Tinder users.

This way, we are able to estimate revealed rather than stated partner preferences with respect to earnings potential in a setting with fewer search and social frictions see also Sect. The remainder of this study is structured as follows.

In Sect. Then, in Sect. Given that females contribute to the reproductive process by bearing offspring, males have a preference for females whom they perceive to have high reproductive capacity i. females whom they perceive to be highly fertile. Youth and attractiveness are strong cues for this fertility so that males have, in line with evolutionary psychology, a preference for young and attractive females Buss, ; Geary et al.

In contrast, as the contribution of males to the reproduction of offspring is rather limited, females expect them to compensate for this lack of investment by providing resources for offspring during their childhood. Therefore not surprisingly, recent research in economics found that the returns to labour market status in the marriage market are positive for men, i. for men a higher job status or higher job prestige also increases their value as a romantic partner.

For women, however, returns to labour market status in the marriage market have been shown to be neutral or even negative, i. for women a higher job status or higher job prestige does not increase their value as a romantic partner and could even decrease it as some men have a dispreference for a higher earning partner Bertrand et al. Today, the question presents itself whether the partner preferences with respect to earnings potential established in the field of evolutionary psychology—which has historically focussed both theoretically and empirically on partner preferences in an offline setting—still hold today in a society where people increasingly find their significant other online see also Sect.

com, eHarmony, and PlentyOfFish found evidence that partner preferences on such platforms do not differ from those established earlier in the field of evolutionary psychology—see Abramova et al. Under the assumption that partner preferences on Tinder are equivalent to those established using data from offline dating and classic online dating websites, we formulate the following two hypotheses:.

However, there are three main reasons why partner preferences on Tinder as measured in the present study may differ from results found by studies based on data concerning offline dating and dating via classic online websites. First, most studies examining partner preferences in offline dating and on classic online dating websites have relied on survey data.

In these studies, individuals stated which characteristics they found most desirable in a partner. In our field experiment, however, we were able to examine revealed partner preferences through the interest Tinder users show in our fictitious profiles. Second, offline dating and dating on classic online dating websites may be accompanied by social frictions, such as the time cost of showing interest in another person and the psychological cost in the case of rejection.

If these costs are high, people may want to avoid them by not showing interest in a highly desirable person, although they would ideally like to match with them. However, on Tinder showing interest in another person only takes a few seconds and is done without the other person necessarily knowing you showed interest in them—this is only the case if this interest is mutual see also Subsect. As a consequence, both time costs and psychological costs are nearly non-existent in the Tinder setting; therefore, true preferences come to the fore more readily.

Third, dating in an offline context and on classical online dating websites may also be accompanied by search frictions. Search frictions influence partner choice as a consequence of increased contact opportunities between individuals who are similar on various characteristics such as job status and job prestige.

On classical online dating websites, search friction are due to the ability of users to filter potential partners based on their job status and prestige , which is not possible on Tinder see also Subsect. However, search frictions may lead to a suboptimal partner choice as only a fraction of potential partners are met.

However, the fact that social frictions and search frictions on Tinder are lower compared to offline dating and dating on classic online dating websites does not mean Tinder is strictly superior for finding a partner compared to these channels. Indeed, offline dating, for example, may be more informative about personal characteristics compared to dating in an online environment. Additionally, search filters on classic online dating websites, for example, may cause this channel to be more efficient compared to dating on Tinder, as there is no need to evaluate profiles that one would be completely uninterested in, such as females who are only interested in males who are strictly taller than them.

Finally, due to the abovementioned differences between online dating on Tinder on the one hand and offline dating and dating on classic online dating websites on the other hand, we do not wish to claim findings from this study can be extrapolated to offline dating or dating on classic online dating websites. However, given the ubiquitousness of Tinder in the current landscape, we believe findings from this study are nonetheless valuable in itself.

The impact of the online dating app Tinder on couple formation and time allocation in OECD countries, particularly in the 18—35 age range, can hardly be overestimated. Tinder is the most popular dating app for iOS and Android with its users evaluating more than 2 billion other users per day, facilitating over 55 billion matches since its launch in , and therefore being at the root of over 1. Additionally, in August , Tinder became the number one app people log into with their Facebook account, beating other apps such as YouTube and Spotify Neyt et al.

Already in , the average Tinder user logged into the app 11 times a day and spent around 1. Although for some people Tinder has the connotation of being used mainly to solicit casual or short relationships, multiple independent studies have shown that this view is unjustified. Indeed, survey research among Tinder users by Sumter et al. Moreover, Timmermans and Courtois report that more than a quarter of offline Tinder encounters led to a committed relationship. Next, although they reported that one-third of offline Tinder encounters led to casual sex, Timmermans and Courtois argue that today, casual sex increasingly leads to a committed relationship.

Consequently, even Tinder users who initially use the app in search of casual sex may eventually end up finding a committed relationship. Additionally, we conducted an ex-post survey among a representative sample of respondents male and female respondents in their twenties in Flanders, i. the region of Belgium where we conducted our experiment see also Subsect. This confirms the findings from previous literature that Tinder is used to find long-term relationships also in the context in which we conducted our experiment i.

among people in their twenties in Flanders. To use Tinder, users first need to create a Tinder profile. This profile is based on the Facebook account of the user, from which the name and age of that user are imported. Although it is also possible to create a Tinder profile through a mobile phone number, this option is rarely chosen. After a profile is created, users can complete their profile with at the time of the experiment up to six pictures, a short bio, their education level, and their job title.

Next, users fill in three criteria with which they narrow down the number of other users whom they will encounter on the application. First, they indicate whether they want to see only male, only female, or both male and female users. Second, they indicate the minimum and maximum age of the people they want to encounter. Third, because Tinder is a location-based application, they indicate the maximum distance other users can be removed from them.

Then, users get shown, one by one, every Tinder user that fits their three criteria. Through swiping, they indicate—without the other users knowing unless there is a match—whether they dislike swipe left or like swipe right the users that they encounter.

No new users can be reviewed before making a decision about the presented profile. Only if both users indicate that they like each other they match and have the possibility to start a conversation with each other Ward, Our experiment is inspired by the many so-called correspondence experiments to measure and explain hiring discrimination conducted in the fields of labour economics, sociology of work, and organisational psychology.

In this literature, recently reviewed by Baert and Neumark , fictitious job applications to which a treatment—such as a foreign sounding name—is assigned in a random way are sent to real vacancies. By monitoring the subsequent call-backs from employers, the effect of the treatment of interest on the probability of a job interview invitation can be identified.

Moreover, this effect can be given a causal interpretation because, by design of the experiment, the treatment is not correlated to any other observed or unobserved candidate characteristic. In the present study, we transpose this method from the labour field setting to the Tinder field setting. That is, we randomly assign job status and job prestige to fictitious Tinder profiles while keeping other factors such as attractiveness constant to investigate the revealed partner preferences with respect to these characteristics among other, real Tinder users.

Thus, our study is close to that of Neyt et al. More concretely, we created 32 fictitious Tinder profiles—16 male and 16 female. Each fictitious profile comprised a set of three pictures of the same person. In four cities in Flanders Belgium , the same four sets of male pictures and four sets of female pictures were used to construct these fictitious profiles.

City by city, four levels of job status and job prestige were randomised over these four sets of pictures. Table 1 features a schematic overview of the randomisation procedure discussed in the following paragraphs. Our fictitious profiles were all aged 23 because this was the actual age of all people in the pictures.

We chose this age so that our profiles embodied people at the start of their professional career. We decided to not differ the age between the male and the female fictitious profiles, to be able to compare the effect of job status and job prestige for male and female fictitious profiles at the same phase in their lives, i. the start of their professional careers. Further, for the names of the people in our profiles, we used four of the most popular Flemish names for 23 year olds per gender.

More specifically, we used the names Jeroen, Thomas, Dennis, and Tim for the male profiles and Lisa, Laura, Anne, and Michelle for the female profiles De populairste Vlaamse jongensnamen van , n. Finally, we did not fill in the education level for our profiles. This is not unusual on Tinder. For example, in our sample, The cities in which we set up our fictitious Tinder profiles were the four biggest cities—in terms of population—in Flanders.

In particular, the cities were Antwerp, Bruges, Ghent, and Leuven. For each of the aforementioned four male and female fictitious names, we employed one of four sets of three pictures per gender so that no set of pictures and related names was used twice in the same city, which could have led to the experiment being detected.

Additionally, we ensured that the people in the different sets of pictures were similar in attractiveness. We did this by first conducting a pre-experiment on Amazon Mechanical Turk in which 32 people—16 male and 16 female—were rated for attractiveness. This was done by Amazon Mechanical Turk users.

Newest Photos:. But if you have However, we argue that findings about partner preferences in the first stages of the dating process are interesting because each mobile dating app user needs to pass these first stages in order to progress to the next phases of dating. com is more than happy to assist you on the way to successful love life. We received no messages that indicated that the subjects see also Subsect.

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