Building A Public-Use Small-Area Panel Price Index 

Database Using Scanner Data

Chen Zhen

University of Georgia, Athens

Abigail Okrent

Economic Research Service

Lan Mu

University of Georgia, Athens

Gauri Datta

University of Georgia, Athens

FCSM Research & Policy Conference 

October 25-27, 2022
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o This work was funded under NIFA grant #2019-67023-29672.

o Any views expressed are those of the authors and should not be attributed to ERS, 

USDA, or IRI.

Acknowledgment
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Outline of presentation

• Motivation for creating small-area panel price indexes

• Methods

• Data

• Preliminary results

• Further research
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Price information is limited in many surveys of 

food purchase or consumption.

• Prices are important determinants of consumer food 

choice and knowing this relationship is fundamental to 

examining many food policy analyses (e.g., taxes, 

subsidies)

• Examples of public data sets used in food policy analyses 

–BLS’s Consumer Expenditure Survey

–NCHS’s National Health and Nutrition Examination Survey

–CDC’s Behavioral Risk Factor Surveillance System

–ERS’s 2012 FoodAPS (field test for FoodAPS-2 happening now) 
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Current public-use price information not granular 

enough to conduct food policy analyses.

• BLS’s Consumer Price Indexes

• BEA’s Regional Price Parities

• C2ER average prices

• ERS’s Quarterly Food-at-Home Price Database 

(discontinued)

• ERS’s Monthly Food-at-Home Price Database 

(MFAHPD) (forthcoming)
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The MFAHPD is a step in the right direction but...

• Covers only 10 MSAs

• Limited to 82 ERS Food Product Groups, including 8 

nonalcoholic beverages

–Sweetened coffee and tea

–Unsweetened coffee and tea

–Flavored milk and other sweetened milk-based beverages

–Low-calorie beverages

–All other caloric beverages

–Fresh, 100% fruit/vegetable juice

–Frozen, 100% fruit/vegetable juice

–Canned/shelf-stable, 100% fruit/vegetable juice
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This research provides more granular price information 

across geographies and products than MFAHPD.

• Roughly 5,000 Census places

• For 24 What We Eat In America nonalcoholic beverage categories

 Citrus juice

 Apple juice

 Other fruit juice

 Vegetable juice

 Diet soft drinks

 Diet sport and energy drinks

 Soft drinks

 Fruit drinks

 Sport and energy drinks

 Nutritional beverages

 Smoothies and grain drinks

 Milk, whole

 Milk, reduced fat

 Milk, low fat

 Milk, no fat

 Flavored milk, whole

 Flavored milk, reduced fat

 Flavored milk, low fat

 Flavored milk, nonfat

 Milk shakes and other dairy 

drinks
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Similar to MFAHPD, panel (time and geography) price 

indexes constructed using rolling-window GEKS.

• Multilateral indexes like the Gini-Eltetö and Köves-Szulc (GEKS) price index formula allows 

for price indexes to be transitive:

PPGGGGGGGG

0jj = �

ll=0

MM

PPFF

0ll × PPFF

ll 1⁄ MM+1

• This index also avoids chain drift but to do this, requires revision of published index 

numbers when new data arrive. 

• Adding rolling windows to GEKs price index formula solves revision problem such that the 

rolling window GEKS for entity k in month T + 1 (T = end of base period) is:

PPRRRRGGGGGGGG

0kk = PPGGGGGGGG

0jj �

ll∈IITT+1:TT−11

PPFF

jj × PPFF

ll 1⁄MMTT+1:TT−11

Fisher-Ideal price index 

between base 0 and entity l

Fisher-Ideal price index 

between entity l and entity j

Link entity j in T

the set of all entities 

between TT−11 and TT+1 

(window = one year)

the number of entities 

in the set IITT+1:TT−11
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IRI Infoscan data is primary data set for constructing 

the rolling window-GEKS spatial price indexes.

• Store- and barcode-level sales for over 40,000 food stores in contiguous United 

States that agree to release information to ERS.

• Additional 18,000 food stores aggregate barcode-level sales into retail marketing 

areas, which can range from MSA-sized areas to Census division-sized areas.

• InfoScan data represented an estimated 41.0% of retail establishments and 55.3% of 

retail food sales compared with Census Bureau benchmarks in 2012 (Muth et al. 

2016).

• Also use ERS’s Purchase to Plate Crosswalk to assign UPCs to 1 of 24 What We Eat In 

America nonalcoholic beverage categories.
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Some areas of the United States are not covered in the IRI 

retail data that ERS purchases.
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Because of disclosure purposes, rolling window GEKS prices 

only calculated for Census places with 3 or more stores.
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Population within Census places with more than 2 stores is 168 million 

(55% of total U.S. population) whereas those with more than 3 stores 

is 159 million (52%).

55%

45%

Population within Census places 

with 2 or more stores

>=2 Remainder

52%

48%

Population within Census 

places with 3 or more stores

>=3 Remainder

Remainder stores are 

aggregated into 

counties 
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Preliminary results show prices for fruit-flavored soft drinks (caffeine 

free) in Census places on the West coast are 8 to 33% higher than the 

US average.

0.85 - 0.96

0.96 – 1.02

1.02 – 1.08

1.08 – 1.15

1.15 – 1.33

Mean GEKS index for Census places with 3 or more stores 

(excluding RMA stores), 12/31/2008-1/10/2010

Average
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The Census places with the largest price increases between 12/31/08 

to 1/10/10 are in the New England area and a handful of places in 

Midwest.

-0.3258 – -0.0008

-0.0008 – 0.0000

0.0000 – 0.1502

0.1502 – 0.4007

Change in GEKS index for Census places with 3 or more 

stores (excluding RMA stores), 12/31/2008-1/10/2010

Change (end – beginning)
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The mean standard deviation across all Census places is 0.05, which 

implies 95% of all observations are within 0.1 points around the mean 

index for a particular place.

0.0103 – 0.0440

0.0440 – 0.0563

0.0563 – 0.0707

0.0707 – 0.0958

Standard deviation of GEKS indexes for Census places with 3 

or more stores (excluding RMA stores), 12/31/2008-1/10/2010

Standard deviation

0.0958 – 0.1955
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The next steps will be:

• Include stores for chains that only provide information at their retail marketing area

– Assume barcode level prices are the same across stores of a particular chain in a particular 

retail marketing area (DellaVigna and Gentzkow 2019; Dong 2022)

– Impute store-level sales for these chains using TDLinx sales information

• TDLinx has information for close to a census of food retailers, including total sales, square footage, 

number of employees, number of checkout registers.

• One way to do this is proportionately assign food and beverage retail marketing sales volume to stores 

within them based on store-level total sales in TDLinx.

• Calculate GEKS for remaining 23 nonalcoholic beverage products

• Calculate GEKS for other food subgroups, e.g., processed foods, fruits, vegetables
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Limitations

• Stores are nonrandom subset of all stores in the contiguous United States.

• Unlike MFAHPD, no weighting of stores to be more representative at regional level.

• UPCs sold at different chains are treated as the same product. This could introduce 

bias if shoppers see it as different products depending on where it is sold.
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