Changes in Household Diet: Determinants and Predictability
We use grocery purchase data to analyze dietary changes. We show that households – including those with more income or education - do not improve diet in response to disease diagnosis or changes in household circumstances. We then identify households who show large improvements in diet quality. We use machine learning to predict these households and find (1) concentration of baseline diet in a small number of foods is a predictor of improvement and (2) dietary changes are concentrated in a small number of foods. We argue these patterns may be well fit by a model which incorporates attention costs.
We are grateful to Geoffrey Kocks for exceptional research assistance, as well as to Sofia La Porta, Julian De Georgia and Cathy Yue Bai. The conclusions drawn from the Nielsen data are those of the researchers and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. Results are calculated based on data from The Nielsen Company (US), LLC and marketing databases provided by the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Stefan Hut & Emily Oster, 2022. "Changes in household diet: Determinants and predictability," Journal of Public Economics, vol 208.