I am a PhD candidate at the Department of Economics of UT Austin.

I use methods from Empirical Industrial Organization to study the design of online platforms, particularly in the transportation sector. I also apply reinforcement learning tools to the study of optimal firm policies and counterfactual analysis.

I am on the 2024-2025 academic job market.

Curriculum Vitae


UT Austin | LinkedIn | GitHub

Working papers

Pricing the Right to Renege in Search Markets: Evidence from Trucking

Job Market Paper

Abstract | Draft pending confidentiality review, available upon request

In many search markets, advance contracts allow one party to renege against a penalty, granting them the option to keep searching for alternatives. This paper studies the efficiency of such arrangements in the trucking industry, leveraging novel data from an online freight auction platform. Reneging is widespread on the platform and more likely at low prices, suggesting opportunistic behavior on the side of the carriers. A dynamic model of the carrier search process rationalizes these patterns and links the platform's cancellation penalty to the search and bidding behavior of the carrier's. Using estimates of the model, I simulate counterfactual cancellation schedules with both increasing and uniform penalties. The findings suggest that increasing the current reputational penalty reduces both firm profits and overall welfare, as the burden of reduced flexibility is shifted to the platform through the bids. Additionally, I explore a switch to pecuniary penalties–common in the travel industry–which give the platform an additional revenue stream. I show that these can increase firm profits at the cost of overall welfare.

Squeezing More Juice Out of Lime: A Novel High-dimensional Pricing Algorithm

Abstract | Draft | Google Colab Example

Sophisticated pricing algorithms used by digital transportation platforms have renewed interest in price control policies, but little evidence exists on their redistributive effects. This paper studies a uniform price mandate in the market for shared electric vehicle platforms in Washington, D.C., which prohibits origin- and destination-based pricing. To compute price equilibria encompassing hundreds of prices for specific origin-destination pairs, I develop a new simulation-based pricing algorithm, adapted from the reinforcement learning literature. I apply the algorithm to a demand system estimated using geolocation data from all firms in the market. In the counterfactual exercise, I find that the redistributive effects of the price controls are mild, and mainly serve riders in the periphery of the city. Furthermore, I find that relaxing the price controls increases rides taken by consumers by 41%, firm profits by 34%, and increases consumer welfare by more than double the profit increase (80% of firm profits).

Work in progress

From Favorites to Fresh Faces: Viewer Loyalty and New Creators in Livestreaming

(with Alexander Tang)


As the gig economy grows, an increasing number of individuals are relying on content creation for their livelihood. The design of platform recommendation algorithms plays a critical role in the discovery and success of new creators. This study presents novel evidence from a natural experiment on the Twitch livestreaming platform to quantify this entry barrier. We analyze eight weeks of high-frequency viewership panel data for all World of Warcraft streams, focusing on the period surrounding the launch of a new game expansion that substantially increased both viewership and the number of streamers. Our findings highlight the significant role of viewer favorites, with the median viewer dedicating 60% of their time to a single streamer pre-launch and 48% post-launch. We also observe considerable stickiness in these preferences; fresh viewers in the post-launch period are twice as likely to watch fresh streamers compared to those active pre-launch. These patterns have important implications for the entry of new streamers and inform the design of recommendation algorithms. Future work aims to develop a theoretical model to understand the impact of these dynamics on the equilibrium distribution of viewership across streamers.

Equilibria in Decentralized Freight Networks

(with Nick Buchholz and John Lazarev)


Rot-Jaune-Verde. Language and Favoritism: Evidence from Swiss Soccer

(with Alex Krumer and Michael Lechner)

Kyklos, 2023, 76( 3), 380–406.

Abstract | Publication link | Working Paper (with causal forests)

We utilize data from 5,010 soccer games in the top two Swiss divisions between the 2005/06 and 2018/19 seasons. In these games, a referee can share the same linguistic area with one of the teams. Using referee-per-season fixed effects, we find that referees issue significantly more penalties, in the form of yellow cards, to teams that are not from the referee's linguistic area. We also find some evidence, in the highest level league only, that referees issue more red cards to teams that are not from their linguistic area and that away teams achieve fewer points when home teams share the same linguistic area with the referee. Our analyses suggest that referees' bias is likely to be subconscious and reflexive rather than being a deliberate act of discrimination.