A meta-analysis of U.S. food demand elasticities to detect the impacts of scanner data

Abstract

This paper investigates how scanner data affect demand elasticity estimates and develops methods for scientists to adapt estimated elasticities to analyses of specific policies. We conduct a meta-analysis of U.S. demand elasticities and find evidence that scanner data generate statistically different elasticities, with more elastic demand than other data types. Own-price elasticity estimates from household scanner quantity data appear to be more elastic than other quantity types. Household-level estimates using retail scanner price data, as proxies for prices, tend to be more price-elastic than other price types. These results suggest caution or adjustment when selecting elasticities for policy analysis.