Abstract
Online platforms often do not directly control users’ pricing strategy, and instead offer analytics and other information to help steer user behavior. I study how information provision by an auction platform to sellers shapes platform fees and outcomes using data from eBay auctions of children’s toys. I present evidence that new sellers face uncertainty about how to set optimal reserve prices: they set lower reserve prices and earn higher revenues as they gain more experience. I develop a model where new sellers learn to set reserve prices on an auction platform with selective participation, and I show that sellers choose reserve prices to both extract surplus from bidders and attract additional bidders to their auction. I provide conditions under which new sellers’ beliefs about the effect of reserve prices on bidder arrival are semiparametrically identified. Estimates from the learning model indicate that new sellers underestimate the effect of high reserve prices on deterring bidder entry, which leads to higher reserve prices and more items listed than for fully-informed sellers. Counterfactual simulations show that platform information provision can help new sellers learn the true bidder arrival process, which increases bidder entry as well as seller and platform profits.

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