"Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market" (February 2023 - Accepted Journal of Political Economy)

with Stephanie Assad, Robert Clark and Lei Xu


(CESifo Working Paper 8521)

(Data Appendix)


We provide the first empirical analysis of the relationship between algorithmic pricing (AP) and competition by studying the impact of adoption in Germany's retail gasoline market, where software became widely available in 2017. Because adoption dates are unknown, we identify adopting stations by testing for structural breaks in AP markers, finding most breaks to be around the time of widespread AP introduction. Because station adoption is endogenous, we instrument using headquarters adoption. Adoption increases margins, but only for non-monopoly stations. In duopoly stations, margins increase only if both stations adopt, suggesting that AP has a significant effect on competition.

"The Effects of Advertising Disclosure Regulations on Social Media: Evidence From Instagram" 

(August 2023 - Accepted RAND Journal of Economics)

with Matthew Mitchell


Extended Abstract published in ACM-EC 2020 Conference Proceedings (ABSTRACT, 2020 ACM-EC FULL)


We study the effects of advertising disclosure regulations in social media markets. Using data from a large sample of Instagram influencers in Germany and Spain and a difference-in-differences approach, we empirically evaluate the effects of German strengthening of disclosure regulations on post content and follower engagement. We measure whether posts include suggested disclosure terms and use text-based approaches (keywords, machine learning) to assess whether a post is sponsored. We show substantial adoption of disclosure, but also a 12% increase in sponsored content, and an increase in the share of undisclosed-sponsored content consumers are exposed to. We also find reductions in engagement, suggesting that followers were likely negatively affected.

"How Much Influencer Marketing is Undisclosed? Evidence from Twitter" 

(April 2024)

with Yanting He and Stephan Seiler



We quantify the prevalence of undisclosed influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts. We develop a novel method to detect undisclosed influencer posts and find that 96% of influencer posts are not disclosed as such. Despite stronger enforcement of disclosure regulations, the share of undisclosed posts decreases only slightly over time. Compared to disclosed posts, undisclosed posts tend to be associated with younger brands with a large Twitter following and are posted from smaller accounts that generate higher engagement per follower.

"Estimating Complementarity with Large Choice Sets: An Application to Mergers" 

(September 2023 - Accepted RAND Journal of Economics)

with Mathieu Marcoux, Scott Orr and Jean-William P. Laliberté



Standard discrete choice models assume that products are substitutes. Merger analyses based on these models may overstate consumer harm when producers of complementary products merge. Allowing for demand complementarity greatly complicates demand estimation, particularly when the number of choices is large. We introduce a straightforward Generalized Method of Moments estimator that identifies preferences allowing for (1) potential consumption complementarity, (2) price endogeneity and (3) large choice sets. Our estimator parsimoniously leverages information on consumer level bundle specific purchases and aggregate sales data. We apply this estimator to the chips and soda market and find a high degree of complementarity between these product groups. We show that a merger between PepsiCo/Frito-Lay and Dr. Pepper would increase soda prices by 30% less than suggested by a model that does not account for complementarity. Post-merger chip prices decrease. Overall, accounting for complementarity leads to positive welfare gains in some markets with large numbers of Frito-Lay varieties. 

"Variety-Based Congestion in Online Markets: Evidence from Mobile Apps" 

(March 2023 - Forthcoming AEJ:Microeconomics)

A previous draft of this paper was circulated under the title "Consumer Product Discovery Costs, Entry, Quality and Congestion in Online Markets" (2021, PDF). 


In many online markets, consumers have to spend time and effort browsing through products. The addition of new products could make other products less visible, creating congestion externalities. Using Android app store data, I take advantage of a natural experiment – a re-design of part of the store – to show evidence of congestion externalities online: more apps in the market directly reduce per-app usage/ downloads. The natural experiment also increases long-run entry, but a structural demand model that accounts for congestion externalities suggests that forty percent of consumer variety welfare gains are lost from higher congestion.

"Sharing News Left and Right: Frictions and Misinformation on Twitter" 

(April 2024 - Forthcoming Economic Journal)

with Juan S. Morales



On October 20, 2020, prior to the US presidential election, Twitter modified its user interface for sharing social media posts. In an effort to reduce the spread of misinformation on the platform, the new interface nudged users to be thoughtful about the content they were sharing. Using data on over 160,000 tweets by US news media outlets we show that this policy significantly reduced news sharing, but that the reductions varied heterogeneously by political slant: sharing of content fell significantly more for left-wing outlets relative to right-wing outlets. Examining Twitter activity data for news-sharing users, we find that conservatives were less responsive to Twitter’s intervention. Lastly, using web traffic data, we document that the policy significantly reduced visits to news media outlets’ websites.

"Competing with Superstars in the Mobile App Market" (March 2022)


(NET Institute Working Paper 18-02)


Firms in many markets face substantial demand uncertainty in deciding where to enter new products. The appearance of a popular competitor (superstar) resolves part of this demand uncertainty or even expands demand. At the same time, a superstar intensifies competition. These two forces generate horizontal and vertical product differentiation tradeoffs for entrants: whether to enter near the superstar and whether to invest in quality. The effects of a superstar on market structure, quality and prices in the market are ambiguous in theory. I empirically examine these effects using Google Play Store mobile game data, taking advantage of the surprise emergence of superstar products for identification. I show that entry increases in niches where a superstar appears, unless these niches were previously popular. I also show that the appearance of superstars reduces the quality of new entrants and intensifies price competition. Incumbents respond to a superstar's appearance by investing in higher quality.

"Interaction of Spectrum Auctions and Mobile Market Competition: Review of Theory and Evidence from European 4G Auctions" (November 2023 - Revise & Resubmit IJIO)

with David Salant



This paper examines the effects of spectrum auctions on mobile market competition and market outcomes. We provide a brief review of the theory, and present a simple analytic framework that suggests that bigger incumbents will acquire more spectrum in auction to pre-empt after-market competitors. Using novel data on European 4G spectrum auctions, we provide suggestive evidence of this effect for low-band (<1GHz) auctions. Exploiting idiosyncratic differences in auction timing across countries, we show that HHI and prices increase after low-band auctions, especially for markets where incumbent operators had higher market shares ahead of the auction. These changes are not accompanied by better coverage or more investment. Operator-level regressions confirm these effects are driven by the incumbent operators. 

"Estimating the Effects of Deregulation in the Ontario Wine Retail Market" (2016)

with Victor Aguirregabiria and Junichi Suzuki



This paper studies the impact of competition in the Ontario wine market and evaluates the effects of alternative deregulation policies. The wine retail market of Ontario, Canada, is characterized by the coexistence of the government-owned Liquor Control Board of Ontario (LCBO) and two private companies. These private firms can sell only a limited subset of Ontario wines, and they are restricted on the number of stores they can operate. Our empirical results build on the estimation of a spatial demand model for differentiated products using a unique dataset from LCBO with information on store sales, prices, and product characteristics for every store and product in this retail chain over a two year period. Given the estimated demand model, we then simulate the effects of deregulation proposals (relative to a pure monopoly): (i) allowing for retail competition from the two privately-owned retail chains; (ii) removing the current restriction on selling non-Ontario wines by the two private wine retailers; and, (iii) allowing for price competition among the retailers. We show that compared to a pure monopoly market, the entry of additional competitors increases consumption and consumer welfare. By contrast, expanding the product range of existing competitors also increases consumer welfare, but keeps consumption stable


"Managing Algorithm Development among Third Party Contractors"

with Elizabeth Lyons

"Learned Complementarity"

with Max Pachali and Adam N. Smith

"Do Consumers Prefer Local? Evidence from Ontario's Wine Industry"

with Jessica Burley and Michael Gilraine


"Autonomous Algorithmic Collusion: Economic Research and Policy Implications"

with Stephanie Assad, Emilio Calvano, Giacomo Calzolari,

Robert Clark, Vincenzo Denicolò, Justin Johnson, Sergio Pastorello,

Andrew Rhodes, Lei Xu and Matthijs Wildenbeest

Oxford Review of Economic Policy, forthcoming

“Market Incentives for Business Innovation: Results from Canada”

with Charles Bérubé and Marc Duhamel

Journal of Industry, Competition and Trade, Vol. 12(1), pp.47-65, 2012