Research

Publications:

Costly Search with Adverse Selection: Solicitation Curse vs. Acceleration Blessing with Kyungmin (Teddy) Kim, RAND Journal of Economics, Vol. 48, No. 2 (May 2017), pp. 1756-2171

We consider a dynamic trading model in which a seller, with private information about the quality of her good, can increase the frequency of strategic price quotes through more intensive search (or advertising). A low-quality seller benefits more from trade, and therefore searches more intensively than a high-quality seller. We identify two opposing effects of endogenous search intensity on buyers' inferences. On the one hand, a low-quality seller is more likely to find a buyer than a high-quality seller, and thus a seller's contact carries negative information (solicitation curse). On the other hand, a low-quality seller leaves the market even faster than a high-quality seller because she is willing to accept lower offers, and thus a seller's availability becomes a stronger indicator of high quality (acceleration blessing). We study how these two effects manifest themselves and interact with each other in both stationary and non-stationary environments. In the stationary environment, the two effects exactly offset each other for any strategy profile, and reducing search costs is weakly beneficial to the seller. In the non-stationary environment, the relative strengths of the two effects vary over time, generating a unique form of trading dynamics, and reducing search costs can be detrimental to the seller.

Online Appendix


Optimal Information Design for Search Goods with Michael Choi and Kyungmin (Teddy) Kim, AEA Papers and Proceedings (forthcoming)

We consider a monopoly pricing problem in which a consumer with an uncertain valuation of a search good receives a signal of value before deciding whether to visit the seller. She discovers her true value upon visit and before purchase. We characterize the consumer-optimal and seller-worst signals in such an environment and deliver two main insights. First, both the consumer-optimal and seller-worst signals generate a unit-elastic demand. Second, the two signals coincide if and only if visitation costs are sufficiently small.


Working Paper:

Shopping for Information: Consumer Learning with Optimal Pricing and Product Design Download Paper

Consumers often acquire product information in order to make a purchase decision. I study a seller's pricing problem in which consumers perform product research before purchase. Consumers receive a signal of quality via a Brownian motion process with a type-dependent drift, which is akin to reading online reviews or quality reports to learn about the good's underlying quality or an idiosyncratic match value. I characterize the consumers' optimal strategy; they buy the product when sufficiently optimistic about the quality and cease to pay for the signal when sufficiently pessimistic. I examine the implications of this behavior for the seller's pricing decision. I find that the seller prefers to encourage product research when quality is likely to be high and prefers to discourage research when quality is likely to be low. I show that a decrease in search costs or an increase in the quality of information can either raise or lower equilibrium price; if the seller wants to discourage search, he lowers price when search costs fall, and if he wants to encourage search, he may raise price as search costs fall. Intuitively, if the seller discourages search, then lower search costs result in more information rent for consumers in the form of a lower price, but if the seller encourages search, he must keep incentives balanced. Therefore, when search costs fall, the seller raises the price to compensate. I also extend the model so that the seller chooses both price and the level quality dispersion and demonstrate that the optimal level of dispersion need not be extremal.