From https://papers.ssrn.com/sol3/papers.cfm?abstract_id=895442 :

https://i.imgur.com/nOcK7gf.png

Authors' comments:

First, we look at the trade-off between looks and income. Consider a woman evaluating the profile of a man whose looks rating is in the nth decile (n < 10) of all looks scores among men. We would like to know the amount of additional income this man would need to be as “successful” with the woman as another man whose looks rating is in the top decile. To that end, we calculate the income variation such that the woman’s utility index for either man is equal. Remember that the utility index allows for preference heterogeneity through attribute distance terms, and hence we also need to specify the income of the woman and the “baseline man” in the top looks decile. We assume (here and below) that the woman has an annual income of $42,500 and that the man has an annual income of $62,500. These are the median income levels for men and women among the dating site users in our data. Table 5.4 shows the income tradeoffs for all looks deciles. A man in the bottom decile, for example, needs an additional income of $186,000 (a total annual income of $248,500) to compensate for his poor looks. The table also shows that women cannot make up for their looks at all. The reason is that our preference estimates indicate that men’s marginal utility from (a mate's) income is approximately flat between income levels of $100,000 and $200,000 and declining for income levels higher than $200,000. Hence, even for a woman in the 9th decile of looks there is no amount of additional income that could make her as attractive in a man’s eyes as a woman in the top decile. Of course, these results should not be taken fully literally—functional form assumptions, distributional assumptions, and sampling error will generally influence the precise income compensation numbers. Hence, for example, our model will not be able to accurately predict how a man evaluates a woman with an annual income of $2 million. However, the results strongly indicate two basic messages: preferences for looks are quantitatively important, and there are strong gender differences in the relative preference of looks versus income.

Caveats

This graph is from the unpublished 2006 draft version of the “What Makes You Click” paper by Hitsch, Hortaçsu and Ariely. By the time the paper was actually published, 4 years later, in a SJR ~2 & IF ~1 journal, it had jettisoned all of the blackpills contained within (including the figure pictured). One suspects this had more to do with the more “political” aspects of the editorial and peer-review process than the actual legitimacy of the data. Nevertheless, it should still be acknowledged that, in the published version, the authors have a statement distancing themselves from their earlier drafts:

Note that previous versions of this paper (“What Makes You Click? – Mate Preferences and Matching Outcomes in Online Dating”) were circulated between 2004 and 2006. Any previously reported results not contained in this paper or in the companion piece Hitsch et al. (2010) did not prove to be robust and were dropped from the final paper versions.

http://faculty.chicagobooth.edu/guenter.hitsch/papers/Mate-Preferences.pdf

The fixed, effects discrete choice logit model, which was used to produce the information in the table, was preserved in the 2010 paper. However, the tradeoff tables themselves, and their accompanying explanations, were discarded.

Methodology

Unnamed online dating service with the following features:

After registering, the users can browse, search, and interact with the other members of the dating service. Typically, users start their search by indicating in a database query form a preferred age range and geographic location for their partners. The query returns a list of “short profiles” indicating the user name, age, a brief description, and, if available, a thumbnail version of the photo of a potential mate. By clicking on one of the short profiles, the searcher can view the full user profile, which contains socioeconomic and demographic information, a larger version of the profile photo (and possibly additional photos), and answers to several essay questions. Upon reviewing this detailed profile, the searcher decides whether to send an e-mail to the user. Our data contain a detailed, second-by-second account of all these user activities. In particular, we know if and when a user browses another user, views his or her photo(s), and sends an e-mail to another user. In order to initiate a contact by e-mail, a user has to become a paying member of the dating service. Once the subscription fee is paid, there is no limit to the number of e-mails a user can send.

Sample description

  • Full Sample Size: 22,000
  • Location: Boston and San Diego
  • Dates: Online activity observations took place over a 3.5 month period in 2003
  • targeted long-term partner-seeking daters
  • average number of first-contact emails received by gender: 2.3 for men, 11.4 for women
  • % of users who did not receive any email: 56.4% of men, 21.1% of women

Mate preference model: Discrete Choice Estimation: Heterogenous Preferences

Binary discrete choice, fixed effects logit model that assumes the decision to send a first contact e-mail (the mate preference indicator here) depends on observed own and partner attributes, and an additive random, “idiosyncratic preference shock” utility independent and identically distributed across all pairs of men and women. The estimates from this model were also compared to those from a random effects probit model, and found to be similar. Full explanation of parameters/terms in the full-text.