Beauty and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment

Online platforms seek to build trust among strangers, but markets that lack trust-building alternatives such as reputation mechanisms face an even bigger challenge. Mandatory verification is widely adopted to increase the credibility of the platform but it may suppress the transmission of useful information of individual users. Therefore, I study a different role of verification – its ability to serve as a credible signal for a user, when such verification is made optional and visible to other users. In collaboration with a leading online dating platform with no reputation mechanisms and only self-disclosed information, I design and conduct a randomized field experiment to examine not only who chooses to verify, but also the effectiveness of such optional verification for different types of users. Interestingly, I find users on the two sides use the same signal very differently. Only males are consistent with the conventional prediction of signaling with H- type (i.e., more popular) males being more likely to opt-in to verification. As for females I find M-type females are the most likely to opt-in to verification as compared to H-type females. In examining the underlying mechanism I find that such differential opt-in decisions are related to the difference in the credibility of the existing key attribute of each side, viz. income for males and beauty for females, which I extract by applying state-of-the-art deep learning techniques. In examining the outcomes of verification, I find that verified users receive more contacts from higher-type users, with the H-type males and M-type females benefitting the most. More importantly, I find that verified users become more proactive and reach out to more and to better potential partners. Further, the introduction of this voluntary verification signal facilitates desirable matching outcomes and benefits the platform as a whole. This study is among the first to document these differential opt-in decisions and impacts of verification across two sides of a matching platform and provide novel insights on optional verification and signaling in two-sided markets.