Essential resources, like electricity and water, can experience rapidly changing demand or supply while the other side of the market is unchanged. Short-run price variation could efficiently allocate resources at these critical times but only if consumers exhibit short-run demand elasticity. The question for firms in these markets has always been how to enable this response. Randomized control trials are increasingly used to test dynamic pricing and technologies that can assist in response by providing information and/or automated response. However, the trials typically do not randomize short-run prices. This paper illustrates how demand from a randomly assigned control group can be used to test the effectiveness of different technologies in increasing short-term price elasticity. To do so, we use a nonparametric control function approach that eliminates the bias inherent in estimating short-term price response using only household random assignment. We find that only automation technology leads to the short-term price elasticity needed to justify real-time pricing. This paper was accepted by Matthew Shum, marketing.

This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003) . We highlight two dimensions in favor of the IV approach. First, an econometric model of the data-generating process reveals that the border approach requires a set of identification assumptions that are not easily satisfied in practice: the ignorance of some payoff-relevant information and conflicting spatial correlation assumptions. The IV approach, in contrast, exhibits greater internal validity because it is derived from the model that generates the data. Second, the border approach suffers from representative issues when the true effect sizes are different between border and off-border regions. We use a common political advertising example to evaluate these estimators and suggest ways to evaluate or limit the above concerns, such as excluding localities that are a large share of the policy making region and evaluating spatial correlations of observables. We find the border approach’s representative issue to be substantial when the ignorance assumption is most plausible and observe that spatial correlations do not reflect those needed in the unobservables for consistency of the estimator. The IV, in contrast, does not exhibit concerns related to local average treatment effects. We also derive the specific conditions when the border approach can reduce bias relative to OLS. This paper was accepted by Raphael Thomadsen, marketing. Funding: This work was supported by National Natural Science Foundation of China (NSFC) [72072004, 72131001]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4977 .