Note: ROSEI is cohosting this seminar with the Department of Economics.

Title: Are consumers more responsive to prices in the long run? Evidence from electricity markets

Abstract: One fundamental question of economics is how consumers respond to price variation in the long run, with applications across a variety of fields. But there is a dearth of causally-identified long-run elasticity estimates, due to challenging empirical conditions. In this paper, I leverage a novel source of exogenous and persistent price variation to estimate the long-run price elasticity of demand in the setting of residential electricity. In this setting, I find that consumers are sixteen times as responsive to prices in the long run compared to the short-run, with elasticity estimates of -2.25 and -0.14 respectively. I explore mechanisms and find that in the long run, consumers respond differently to temperature across price regimes, with these differences accounting for 34\% of the observed consumption differences. These findings highlight the potential impacts of price-based policies on demand and emphasize the importance of setting prices to reflect social marginal costs.

Bio: Jesse Buchsbaum has research interests in energy and environmental economics, applied microeconomics, and industrial organization with particular focus on consumer behavior, electricity markets, and the impacts of regulation. His research seeks to advance our understanding of economics and the impacts of policy by applying the tools of economics to important, policy-relevant questions. One strand of his research studies consumer demand in utility customer settings, with current projects on the long-run responses of residential electricity consumers, electricity bill affordability, and bill understanding for residential water customers. A second strand of research studies the impacts, effectiveness, and spillovers of regulation, with current projects on the unintended effects of policy changes in wholesale electricity markets, leakage and evasion from local heavy duty vehicle regulation, and using machine learning to improve regulatory targeting.