Search Frictions, Sorting and Matching in Two-Sided Markets
with Song Yao, Ravi Bapna, Jui Ramaprasad
This paper studies the impact of frictions and preferences on the formation of a match in two-sided markets. Since agents on both sides have private preferences regarding each others’ characteristics, forming a match based on mutual compatibility requires extensive costly search. To better understand the relative impact of frictions and preferences on match outcomes, we use data from a field experiment conducted on an online dating platform wherein randomly selected users are given the ability to know upfront a piece of information about the private preference of the opposite gender, information which otherwise should have been searched for. We find evidence suggesting that reducing frictions through the provision of information can lead to less sorting between matched couples in terms of various characteristics such as race and education level. To investigate the relative contribution of frictions and preferences on assortative matching, we develop and estimate a model that incorporates frictions and preference heterogeneity across users. Our estimation results reveal that frictions play a significant role in shaping matching outcomes. Using model estimates, we simulate matches under the frictionless Gale-Shapley protocol, and find that removing frictions leads to significantly less sorting between couples. We also find that frictions in our platform lead to a significant departure from efficiency. These results highlight the importance of platform designs that aim to reduce frictions. In addition, with one-third of the marriages in the U.S. beginning online, this paper shows how the design of an online platform can contribute to diversity.
Hospital Competition and Quality: Evidence from the Entry of High-Speed Train in South Korea
with Maria Ana Vitorino and Song Yao
We leverage the entry of a high-speed train (henceforth HST) system in South Korea as a natural experiment to study the causal effect of competition between hospitals on the quality of clinical care and patient welfare. An important aspect of South Korean healthcare industry is that patients have the full freedom to go to any hospital of their choice and prices are fixed. The introduction of the HST represents an exogenous shock to the healthcare market in that it greatly reduced patients’ travel time, and enabled patients to consider hospitals that were previously unreachable due to long travel distances. This increases substitutability between hospitals, which in turn leads to increased competition. Using a difference-in-differences estimator, we examine the effects of competition on hospitals depending on their proximity to train stations, notably how increased competition impacts health outcomes as measured by 30-day mortality rates following admissions for cardiovascular and neurological surgery. Our results suggest that increased competition leads to an improvement in the quality of clinical care. To evaluate the overall impact of the HST on patient welfare, we estimate a structural model of hospital choice, allowing for a flexible formation of patients’ consideration sets. We find that patients living near a HST station experience an improvement in welfare arising from the reduction in travel time as well as improvements in hospital quality. Patients living further away from HST stations also experience an improvement in welfare although they do not gain from the reduced travel time due to the improvement in the quality of treated hospitals. We also find that the HST can have a beneficial impact on patient helath by facilitating patients’ sorting to better hospitals, even while holding quality of clinical care constant.
Using Machine Learning to Address Customer Privacy Concerns: An Application with Click-stream Data
with Song Yao, Luping Sun, Xiaomeng Du
The ever-increasing volume of consumer data provide unprecedented opportunities for firms to predict consumer behavior, target customers, and provide customized service. Recent trends of more restrictive privacy regulations worldwide, however, present great challenges for firms whose business activities rely on consumer data. We address these challenges by applying the recently developed federated learning approach - a privacy-preserving machine learning approach that uses a parallelized learning algorithm to train a model locally on each individual user's device. We apply this approach to data from an online retailer and train a Gated Recurrent Unit recurrent neural network to predict each consumer's click-stream. We show the firm can predict each consumer's activities with a high level of accuracy without the need to store, access, or analyze consumer data in a centralized location, thereby protecting their sensitive information.