Welcome to my web page! I am an economist at the Competition Bureau Canada. From 2023 to 2025, I was an assistant professor of economics (teaching stream) at the University of Toronto.
My research focuses on industrial organization. In particular, I am interested in the digital economy and platform markets.
The views on this site are my own and do not represent the views or the position of the Commissioner of Competition of the Government of Canada.
| 2025 - | Economist, Competition Bureau Canada |
| 2023 - 2025 | Assistant Professor (teaching stream), University of Toronto |
| 2018 - 2019 | Consultant, TWS Partners |
| 2019 - 2023 | PhD Economics, University of St. Gallen (summa cum laude, awarded the GPEF Prize for the best thesis in economics) |
| 2015 - 2020 | MSc Economics, University of Munich |
| 2013 - 2016 | BA Political Science, University of Munich |
| 2011 - 2015 | BSc Economics, University of Munich |
Abstract In this paper, I analyze the welfare effect of a vertically integrated gatekeeper platform selling its own first-party product, i.e., first-party selling, as well as the platform's incentive to favor the first-party product in the product recommendations it makes, i.e., self-preferencing. I find that, irrespective of self-preferencing, both consumer welfare and platform revenue are higher under first-party selling because first-party selling mitigates double marginalization. Additionally, third-party product prices are lower in expected terms under first-party selling, either because the platform reduces the commission fee (with self-preferencing) or downstream competition is fiercer (without self-preferencing). Finally, I show that both consumers and the platform are better off if the platform commits not to engage in self-preferencing.
Abstract We examine self-preferencing in Amazon’s “Similar item to consider” box. We document a stark difference across domains: while Amazon frequently recommends alternatives to Amazon Basics (AB) products on the US website, it systematically suppresses these recommendations on the Canadian website. Leveraging this discontinuity, we estimate the causal effect of algorithmic exclusion due toself-preferencing. We find that non-AB products that merit a recommendation in the US but are denied one in Canada suffer a 11% decrease in the volume of sales. This finding quantifies the significant market distortion caused when gatekeepers favor their own inventory over rival goods.
The Cost Of The Cold-Start Problem On Airbnb (with Regina Seibel)Abstract With peer-to-peer reviews, new products face a “cold-start” problem because consumers who review an unreviewed product generate an informational externality. Our structural estimates for Manhattan Airbnbs, featuring Bayesian learning and an endogenous supply side, put it at 3% of the rental price, 60% coming from stronger market selection through entry and exit. Without supply-side responses, the externality would be 2.5 times larger. We evaluate three policies: a booking subsidy, a first-review bonus, and a seeded-review program. The first two policies yield similar welfare gains, but all groups benefit only if hosts foot the bill. Seeded reviews deliver the largest gain.
Non-Price Diversion and Bias in Upward Pricing Pressure in Airline MergersAbstract Antitrust agencies often use diversion inferred from actual or hypothetical consumer switching to calculate the upward pricing pressure (UPP) resulting from a merger. However, when consumer switching is driven by factors unrelated to price, such diversion may not adequately capture the substitution patterns of consumers who would respond to a price increase, biasing the resulting UPP measure. I estimate the empirical model of the US airline industry in Berry & Jia (2010) and find that, relative to price-based diversion ratios, non-price diversion ratios understate diversion to ultra-low-cost carriers (ULCCs) by 18% on average and overstate diversion to legacy carriers (LCs) by 19%. As a result, UPP based on non-price diversion is too permissive toward unilateral effects from mergers with ULCCs and too stringent toward those from mergers with LCs. I find that, when using non-price UPP to predict the price effects of simulated mergers, it understates price effects by 16% on average for mergers with ULCCs and overstates them by 11% on average for mergers with LCs.
ECO380H1F Markets, Competition, and Strategy (syllabus)
L5101 Thursdays 5:00 PM - 7:00 PM (lecture) and 7:00 PM - 8:00 PM (tutorial), room MS2172
ECO204Y1Y Microeconomic Theory and Applications (for Commerce) (syllabus)
L0301 Wednesdays 11:00 AM to 1:00 PM (lecture) and Thursdays 1:00 PM to 2:00 PM (tutorial), room SS2118
L0401 Wednesdays 3:00 PM to 5:00 PM (lecture) and Thursdays 1:00 PM to 2:00 PM (tutorial), room SS2117
ECO380H1S Markets, Competition, and Strategy (syllabus)
L5101 Thursdays 5:00 PM - 7:00 PM (lecture) and 7:00 PM - 8:00 PM (tutorial), room MC252
ECO204Y1Y Microeconomic Theory and Applications (for Commerce) (syllabus)
L0301 Wednesdays 11:00 AM to 1:00 PM (lecture) and Thursdays 1:00 PM to 3:00 PM (tutorial), room SS2118
L0401 Wednesdays 3:00 PM to 5:00 PM (lecture) and Thursdays 1:00 PM to 3:00 PM (tutorial), room SS2117
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