Working Papers

Sponsored Product Ads, Algorithmic Pricing, and More Harmless Collusion

Preliminary draft available upon request

Analyzing Healthcare Price Transparency: Will Patients Shop for Services More Effectively?

with Ron Berman. Under Review

Abstract

Recently, the US mandated healthcare price transparency to facilitate easier comparison of healthcare prices. However, the potential effectiveness of this policy is an open question. We use a large-scale health insurance claims dataset to estimate the potential maximum savings from price transparency. We focus on short-term, demand-side estimates, where patients can shop around and switch to cheaper providers. We analyze the set "shoppable" services whose price information must be reported online. Initially, our data points to a large potential for savings due to a large degree of price dispersion. However, when viewed from the consumer shopping perspective, even the most optimistic estimates of potential savings become limited. The reasons are that the location and insurance network of the patient, the structure of healthcare insurance payments, and the information made available by the transparency rule lower patients’ incentive to save. We find that the best-case scenario for patients’ out-of-pocket savings from price - shopping is 3% of the total cost on average. Our analysis suggests that the existing estimates in the literature might be overestimated, as they overlook the consumer shopping perspective. Hence, patients’ potential savings and the demand-side impact of the transparency rule might not be as impactful as initially hoped for.

Strategic Recommendation Algorithms: Overselling and Demarketing Information Designs

with Ron Berman and Yi Zhu. Major Revision at Marketing Science

Abstract

We analyze recommendation algorithms that firms can engineer to strategically provide information to consumers about products with uncertain matches. Monopolists who cannot alter prices can design recommendation algorithms to oversell the product instead of algorithmically recommending perfectly matching products. However, when prices are endogenous or when competition is rampant, firms opt to lower their persuasive claims and instead choose to fully reveal the product’s match (i.e., maximize recall and precision). As competition strengthens, the algorithms will shift to demarket their products in order to soften competition. When a platform designs a recommendation algorithm for products sold by third party sellers we find that overselling is not an equilibrium strategy of the platform, but demarketing might be. Overselling entails designing an algorithm that recommends badly fitting products to consumers, which would lower the consumers’ ex-ante willingness to pay, and thus increase competition among the sellers and lower the platform’s profit. Demarketing, in contrast, softens the competition among sellers from the information perspective, which can be lucrative for the platform.

Ridge Distributions and Information Design in Simultaneous All-Pay Auction Contests

with Zhonghong Kuang and Jie Zheng. Major Revision at Games and Economic Behavior

Abstract

Two contestants informed of their own type compete in a contest, and the organizer ex-ante designs a public anonymous disclosure policy to maximize contestants’ total effort. While a mildly-correlated posterior leads to an efficient equilibrium with maximized surplus, a sufficiently-positively-correlated posterior achieves exploitation with zero contestant equilibrium payoff. We define and fully characterize ridge distributions, under which the equilibrium is both efficient and exploitative. Whenever a partial disclosure policy is optimal, it generates at least one posterior from ridge distributions. With a prior of sufficiently positive correlation, the optimal policy is both efficient and exploitative; otherwise, it is neither efficient nor exploitative.