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Scanner Data-Based Panel Price Indexes.

Chen Zhen1, Eric A Finkelstein2, Shawn A Karns3

  • 1Associate Professor, University of Georgia.

American Journal of Agricultural Economics
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

New price indexes using retail scanner data enable better consumption cost comparisons. Adjusting Supplemental Nutrition Assistance Program (SNAP) benefits for these costs reallocates funds, especially for beverages, reflecting spatial price variations.

Keywords:
C43D12SNAP allotment adequacypanel price indexscanner data

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Area of Science:

  • Agricultural Economics
  • Consumer Economics
  • Econometrics

Background:

  • Accurate price indexes are crucial for comparing consumption costs across regions and time.
  • Existing methods for constructing price indexes can suffer from issues like downward drift and data limitations.
  • Supplemental Nutrition Assistance Program (SNAP) benefit allocation may not fully account for regional price disparities.

Purpose of the Study:

  • To construct and evaluate novel panel price indexes using retail scanner data for spatial and temporal cost comparisons.
  • To apply these new indexes to adjust Supplemental Nutrition Assistance Program (SNAP) benefits for beverage consumption.
  • To compare the performance and implications of scanner data-based indexes against traditional USDA food price databases.

Main Methods:

  • Development of rolling-window panel extensions of multilateral price indexes (Cave-Christensen-Diewert and Gini-Eltetö-Köves-Szulc).
  • Utilization of county-level retail beverage scanner data to construct bilateral and panel price indexes.
  • Experimental adjustment of SNAP benefits based on calculated spatial and temporal beverage price differences.

Main Results:

  • The constructed panel price indexes allow for non-revisable, updated price comparisons across space and time.
  • Adjusting SNAP beverage benefits for purchasing power parity resulted in a reallocation of over 2% of beverage spending, averaging a 5% change in county allotments.
  • Significant differences were observed between scanner data-derived indexes and USDA Quarterly Food-at-Home Price Database (QFAHPD) indexes, potentially due to unit value and quality treatments.

Conclusions:

  • Retail scanner data offers a valuable resource for creating robust panel price indexes.
  • Incorporating spatial cost adjustments into SNAP benefits can lead to more equitable resource allocation, particularly for food items like beverages.
  • Further research is needed to reconcile differences between scanner data and traditional food price databases for improved policy applications.