Abstract: This paper evaluates the efficiency of market location choices for electric vehicle (EV) charging stations. The charging industry is central to electrifying the transportation sector and receives billions of dollars in government support. However, little is known about how the charging industry is organized and how government funds can improve the efficiency of this market. We use novel data about EV charging station utilization in the U.S. to test for the presence of entry complementarities, a form of positive spillovers, across charging locations. We develop a measure of how much firms in the charging industry internalize their positive spillovers on other locations. Our paper provides a micro-foundation for EV charging stations subsidy policies with location targeting or spatial restrictions.

Abstract: We study how a ranking algorithm affects equilibrium new product prices. A canonical algorithm, which attempts to learn product quality, determines product rankings based on past conversion outcomes (i.e., purchase conditional on impression). Such an algorithm creates a dynamic incentive for sellers to lower prices, boost conversion outcomes, and grow future rankings. In collaboration with a consumer durable goods platform, we present experimental evidence that sellers' pricing policies reflect such equilibrium beliefs, in particular for high-conversion-rate sellers. Our findings imply that the ranking algorithm lowers equilibrium prices for "good" products and separates them from "bad" ones.