I am an Economist working in the fields of Matching and Digital Platforms. Currently, I am an Assistant Professor in the Strategy Group from the School of Management
at the Pontificia Universidad Católica de Chile.
Education
Ph.D. in Economics, University of Maryland at College Park, May 2019
M.S. in Economics, Getulio Vargas Foundation Graduate School of Economics, 2013
B.A. in Economics, Federal University of Ceará, 2010 (Magna cum Laude)
Positions
2019-Present: Assistant professor at the School of Management at Pontificia Universidad Católica de Chile
2013: Part time instructor at the department of Statistics and Applied Math at the Federal University of Ceará
Many two-sided platforms are known to steer customers towards certain products based on their willingness to pay. In this article, we study platforms' incentives to adopt this type of market segmentation and present conditions under which this can generate distortions that negatively impact the surplus from buyers and sellers. In our environment, a monopolistic platform matches sellers with buyers. Upon being matched, each pair of buyer and seller negotiates prices. If they choose to transact, the platform receives a commission fee proportional to the value of the transaction plus a flat fee per transaction. The platform is assumed to have full information about customers' and sellers' outside options. We show that, as long as the market is in excess supply or as long as there is a crossing between the demand and supply curves, the platform's optimal matching is suboptimal from the perspective of buyers and sellers.
The Dorfman pooled testing scheme is a process in which individual specimens (e.g., blood, urine, swabs, etc.) are pooled and tested together; if the merged sample tests positive for infection, each specimen from the pool is tested individually. Through this procedure, laboratories can reduce the expected number of tests required to screen a population. The literature has often advocated in favor of using ordered partitions to screen the population, i.e., of pooling subjects with similar probability of infection together, as doing so simultaneously minimizes the expected number of tests, the expected number of false negatives, and the expected number of false positive classifications, provided that certain technical conditions hold. One potential limitation of using ordered partitions, however, is that they may incentivize some subjects to misreport their types to the tester. Indeed, if subjects wish to avoid being detected as infected, ordered partitions would incentivize them to falsely claim that they have a low probability of infection (assuming that pooled testing is subject to dilution effects). These incentives would disappear if subjects were matched randomly, regardless of their probability of infection. In this article, we derive conditions under which ordered partitions outperform matching subjects randomly, despite these incentives.
The Dorfman pooled testing scheme is a process in which individual specimens (e.g., blood, urine, swabs, etc.) are pooled and tested together; if the merged sample tests positive for infection, then each specimen from the pool is tested individually. Through this procedure, laboratories can reduce the expected number of tests required to screen the population, as individual tests are only carried out when the pooled test detects infection. Several different partitions of the population can be used to form the pools. In this study we analyze the performance of ordered partitions, those in which subjects with similar probability of infection are pooled together. We derive sufficient conditions under which ordered partitions outperform other types of partitions in terms of minimizing the expected number of tests, the expected number of false negatives, and the expected number of false positive classifications. These sufficient conditions can be easily verified in practical applications, once the dilution effect has been estimated. We also propose a measure of equity and present conditions under which this measure is maximized by ordered partitions.
This paper builds on Kojima and Pathak (2009)'s result of vanishing manipulability in large stable mechanisms. We show that convergence toward truth-telling in stable mechanisms can be achieved much faster if colleges' preferences are independently drawn from an uniform distribution. Another novelty from our results is that they can be applied to competitive environments in which virtually all vacancies end up being filled. So this paper adds evidence to the fact that, though stable matching mechanisms are not entirely strategy-proof, in practice, when the number of participants in the market is sufficiently large, they can be treated as being effectively strategy-proof.
