Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third party sellers' products to consumers. These rankings decrease consumer search costs and increases the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging since rankings are endogenous: highly ranked products are also the most relevant ones for consumers. In this paper, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to identify the causal effect of rankings. Using this data set, I make three contributions. First, I show that rankings affect what consumers search, but conditional on search, do not affect purchases. I also exploit a feature of the data set (opaque offers), to show that rankings lower search costs, instead of affecting consumer expectations or utility. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $2.64, lower than previous estimates in the literature obtained without experimental variation. Finally, I show that a utility-based ranking build on this model's estimates leads to an almost twofold increase in consumer welfare, while also increasing transactions and revenue for the search intermediary.

This paper develops and estimates a model of sequential search that accounts for the full set of decisions consumers make while searching (which products to search, search longevity, sequence of purchases, and whether to purchase).

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This paper documents that consumers frequently take breaks during their search (“search gaps”) and develops and estimates a model that rationalizes search gaps caused by consumer fatigue.

We argue that users in social networks are strategic in how they post and propagate information. We propose two models -greedy and courteous -and study information propagation both analytically and through simulations. For a suitable random graph model of a social network, we prove that news propagation follows a threshold phenomenon, hence, "high-quality" information provably spreads throughout the network assuming users are "greedy". Starting from a sample of the Twitter graph, we show through simulations that the threshold phenomenon is exhibited by both the greedy and courteous user models.