Peng Shi

A two-sided marketplace is a digital platform that helps customers connect with suitable providers of goods or services. Many two-sided marketplaces, including Amazon, Alibaba, Google and Yelp, both rank providers based on quality and also allow them to purchase sponsored ads. This paper studies how such a platform can optimally combine quality-estimates and sponsored advertising to maximize social welfare, which is the sum of customer surplus, provider profits and platform revenue. If the platform could perfectly estimate the expected customer surplus resulting from each impression, then it is optimal to rank listings using this notion of quality, without the need for sponsored ads. The resultant social welfare is equal to the first-best, which is the maximum possible even if the platform could also control provider prices. In the more realistic setting in which quality-estimates are imperfect and providers set their own prices, ranking by quality may result in low social welfare, whereas sponsored advertising is necessary to guarantee the highest fraction of the first-best welfare. The welfare-optimal pricing for sponsored ads is a simple formula that discounts the per-impression price by an amount that is proportional to the quality-estimate. These results give platforms prescriptive guidelines on how to best recommend providers to customers, and give regulators a benchmark of what a welfare-maximizing platform should do.

Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are set by the providers based on supply and demand and can depend on the platform’s match recommendation policy. Despite the complications of price endogeneity, an optimal match recommendation policy has a simple structure and can be computed efficiently. In particular, an optimal policy always recommends the providers who deliver the highest conversion rates. Moreover, an optimal policy can be encoded simply in terms of the frequency of recommending each provider to each customer segment, without the need to encode which subsets of providers are to be recommended together. On the other hand, if the platform were to optimize its match recommendations without accounting for price endogeneity, then the resultant policy would be more complex, and the market is likely to get stuck at a strictly suboptimal outcome, even if the platform were to continually reoptimize its match recommendations after prices re-equilibrate.

Digital platforms that help customers find suitable service providers often monetize by selling customer leads to interested service providers. Examples of such platforms include Bark, Google Local Services, HomeAdvisor, Modernize, Porch, and Thumbtack. I analyze this platform design using a game-theoretic model and obtain insights on how the pricing of leads affects customer and provider welfare. When the platform raises the fee per lead for a customer type, providers who buy these leads quote more competitive prices to these customers, which benefits the customers whose leads are bought. However, providers may also buy fewer such leads, switch to other customer types, or exit the platform. For maximizing social welfare, the simple policy of charging a market-clearing fee-per-lead for every customer type guarantees at least 1/(e-1) ≈ 58.19% of the first-best welfare, and at least 79.15% of the welfare under the optimal fees. The first-best welfare can be achieved by paying providers an additional subsidy upon each job well done, so that experienced providers with cheaper sources of leads do not exit the platform. Understanding the nuances of these welfare effects can help platforms better grow both sides of the marketplace while maintaining an adequate revenue stream.

There is a shortage in the supply of cadaveric organs in most countries, but many successfully procured and medically tenable organs are currently being discarded. This wastage of cadaveric organs exacerbates the shortage in organ supply and the financial strains on healthcare systems. Many reforms have been or are currently being implemented to address the wastage problem. However, we show that waste will still be a problem as long as the allocation mechanism continues to prioritize patients by their waiting times, which incentivizes patients to reject organs of reasonable quality now to wait for better offers in the future. Such waiting is risky, as the patients' health conditions may deteriorate while they wait, and they may no longer be fit to receive transplants when the ideal offers come. Through analyzing a theoretical model, we show that the necessary and sufficient conditions to eliminating waste are to disincentivize waiting by allocating over-demanded organ types only to the patients who recently signed up for transplantation, and to give the patients who are not allocated their ideal organs an opportunity to take another offer. However, such a policy may be contentious as it no longer prioritizes patients by waiting times. Moreover, it may reduce the welfare of the patients who are most willing to wait. The benefits of eliminating waste should be weighed against these costs when making policy decisions.

Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies; some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder), some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork), and others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of these matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of these matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms.