with Yanting He and Stephan Seiler
Abstract:
We study the disclosure of influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts and a novel classification method to detect undisclosed sponsorship. Using our preferred empirical specification we find that 96% of sponsored posts are not disclosed. This result is robust to a series of specification tests and even a lower bound classification still yield an undisclosed share of 82%. 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 young brands with a large Twitter following. Using an online survey we find that many consumers are not able to identify sponsored content without disclosure. Our findings highlight a potential need for further regulatory scrutiny and suggest that researchers studying influencers must account for non-disclosed sponsored content.
with Max Pachali and Adam N. Smith
Abstract:
Product substitution patterns are often treated as static concepts in the analysis of consumer purchase behavior. In this paper, we study the do-it-yourself (DIY) home improvement market where within-consumer dynamics in product complementarity can exist due to changes in the complexity of projects and consumer skill over time. Using data on lumber purchases from a Fortune 500 specialty goods retailer, we first document that the likelihood of co-purchase tends to increase with category experience. We then develop and estimate a model of "learned complementarity" whereby consumers sequentially realize super-additive utility from co-consumption over time. Our estimates reveal significant heterogeneity in substitution patterns both within and across households, and that the economic value of moving one step on the path of learned complementarity is roughly $1 (or 5% of the average purchase price in our data). Lastly, we show how our model can be used to segment customers at different stages of the learning process and how such segments can support targeted marketing efforts.
with Matthew Mitchell
Abstract:
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.
with Mathieu Marcoux, Scott Orr and Jean-William P. Laliberté
Abstract:
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.
Abstract:
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.
with David Salant
Abstract:
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.
with Victor Aguirregabiria and Junichi Suzuki
Abstract:
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
with Stephanie Assad, Robert Clark and Lei Xu
2024 Journal of Political Economy
Abstract:
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.
with Juan S. Morales
2024 Economic Journal
Abstract:
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.
2024 American Economic Journal: 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).
Abstract:
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.
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
with Charles Bérubé and Marc Duhamel
Journal of Industry, Competition and Trade, Vol. 12(1), pp.47-65, 2012