Abstract
Food price elasticities (PEs) are essential for evaluating the impacts of food pricing interventions to improve dietary and health outcomes. This paper innovates the use of experimental purchasing data from a recent New Zealand virtual supermarket experiment to estimate PEs for a large set of disaggregated foods across major food groups relevant for food policies in a Bayesian multistage demand framework. We propose the use of available prior information to elicit prior demand parameter assumptions that are consistent with published PEs and economic assumptions and are weighted according to expert knowledge, increasing precision in PE inference and policy predictions, and yielding somewhat stronger price effects.