Platforms facilitating many-to-many matches in two-sided markets have become ubiquitous across industries ranging from professional services to dating. Differently from standard (one-sided) markets where consumers choose goods or services, in two-sided markets, both sides have preferences. Since these preferences can often be hard to describe, centralized matching is difficult to implement. The alternative option is for the platform to operate in a decentralized fashion, leaving the agents from both sides "free to find each other". While easier to implement, the downside of decentralized systems is that inefficiencies driven by congestion are likely to arise. In the present paper, we are primarily interested in understanding the power of "detail-free" levers that decentralized platforms can leverage to improve market outcomes. In particular, we focus on the lever of information design through competition signaling, where the platform discloses how much competition currently exists for a given supply unit. Signaling that there is competition for a supply unit may reduce the value of that unit but may also redirect the demand's attention to alternative supply units, potentially increasing the value for the platform. To quantify the trade-off at play and tackle the question above, we focus on a specific labor platform and the submarket of cleaning services to answer this question empirically. We partnered with the largest service labor marketplace in Latin America, which operates as follows. Service providers (agents) join the platform to purchase nonexclusive leads for jobs posted by supply-side customers. When they purchase a lead, they are not guaranteed to get the job, but simply purchase the contact information of the customer in order to apply for the job. A key characteristic of this market is the possible congestion on the lead side. In the context of such a platform, to understand the impact of any lever on market outcomes, it is fundamental to first understand how agents make their lead purchasing decisions and, in particular, how they take competition into account when making such decisions. We propose a structural model in which agents use a prediction function to forecast how much competition they may face. We show that if agents are strategic, a natural concept of equilibrium arises. By leveraging the platforms' data and an quasi-experiment, we estimate the structural parameters in the model. We find that agents react strongly to observed competition and predictions of future competition. We then conduct counterfactual analysis to study the impact of signaling competition. Our findings show that it is a powerful lever to improve market outcomes in this market. Signaling competition improves (decreases) congestion, and it also improves (increases) the probability that a lead will receive at least one applicant. Furthermore, displaying competition leads to an increase in overall leads purchased.

Motivated by our collaboration with a major US online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period to maximize the expected number of matches in a time horizon, considering that a match is formed only after two users like each other, possibly in different periods. Increasing match rates is a prevalent objective among online platforms. We provide insights on how to leverage users? preferences and behavior towards this end. Our proposed algorithm was piloted by our collaborator in major cities in the US. We introduce a model of a dynamic matching market mediated by a platform. The platform hosts a set of users and must decide, in each period, what subset of profiles to show to each user to maximize the overall expected number of matches. Each period, users log in with some time-dependent probability and, conditional on logging in, observe a set of profiles-an assortment-that satisfies the constraints imposed by the platform. Then, users decide whether to like or not like each profile in their assortment based on their preferences. If two users mutually like each other, possibly in different periods, a match is generated. Our goal is to find an algorithm to maximize the total expected number of matches generated by the platform over an entire time horizon. We show that the platform's problem is computationally hard.