2009•AgEcon Search (University of Minnesota, USA)Open access

A Quarterly Food-at-Home Price Database for the U.S.

Jessica Erin Todd, Lisa Mancino, Ephraim S. Leibtag, Christina Tripodo, Todd, Jessica E., Mancino, Lisa, Leibtag, Ephraim S., Tripodo, Christina

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Abstract

This report provides a detailed description of the methodology used to construct ERS’s Quarterly Food-at-Home Price Database (Q-FAHPD). As the name suggest, these data provide quarterly observations on the mean price of 52 food categories for specific U.S. markets. We provide a description of the Nielsen Homescan data that was used to create this database, the methodology used to classify foods into food groups, how we determined the appropriate the level of aggregation (sub-regional markets) and our calculation of average prices for each food group. This report also contains an overview and summary of the resulting data.

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What this paper is about

This report provides a detailed description of the methodology used to construct ERS’s Quarterly Food-at-Home Price Database (Q-FAHPD). As the name suggest, these data provide quarterly observations on the mean price of 52 food categories for specific U.S. markets. We provide a description of the Nielsen Homescan data that was used to create this database, the methodology used to classify foods into food groups, how we determined the appropriate the level of aggregation (sub-regional markets) and our calculation of average prices for each food group. This report also contains an overview and summary of the resulting data.

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Available abstract

This report provides a detailed description of the methodology used to construct ERS’s Quarterly Food-at-Home Price Database (Q-FAHPD). As the name suggest, these data provide quarterly observations on the mean price of 52 food categories for specific U.S. markets. We provide a description of the Nielsen Homescan data that was used to create this database, the methodology used to classify foods into food groups, how we determined the appropriate the level of aggregation (sub-regional markets) and our calculation of average prices for each food group. This report also contains an overview and summary of the resulting data.

Key concepts: Construct (python library), Database, Food prices, Econometrics, Reference price, Economics, Computer science, Geography

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