Comparing Food-at-Home Monthly Area Prices 

(F-MAP) with the Consumer Price Index (CPI)

Abigail Okrent, Megan Sweitzer, and Anne Byrne, USDA-ERS

Chen Zhen, University of Georgia

Mary K. Muth, RTI International

2023 FCSM Research and Policy Conference

Applications of Retail Scanner Data to Calculating Monthly Area Price Measures of Food

October 24─26, 2023
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The findings and conclusions in this presentation are those of the author(s) and should 

not be construed to represent any official USDA or U.S. Government determination or 

policy.

This presentation was supported in part by the U.S. Department of Agriculture, 

Economic Research Service.

The analysis, findings, and conclusions expressed in this report should not be attributed 

to Circana (formerly IRI).
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ERS calculates new panel food price indexes called 

Food-at-Home Monthly Area Prices (F-MAP). 

• Monthly price measures covering 2012─2020

–Based on Circana InfoScan retail scanner data

–Unit prices standardized to dollars/100-gram basis (mean and 

SE)

–6 price indexes: Laspeyres; Paasche; Törnqvist; Fisher Ideal; 

Gini-Elteto and Koves-Szulc (GEKS); Caves, Christensen and 

Diewert (CCD)

• 90 ERS Food Purchase Groups (EFPGs): classifies foods based 

on ingredients, nutrition, and convenience level

• National and for 14 geographic areas: 4 Census Regions and 

10 major metropolitan areas
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CPI F-MAP

Comparisons over time Comparisons over time/across areas

100 food categories nationally,

6 sub-nationally

90 food categories (EFPGs) nationally 

and sub-nationally 

Outlets selected through stratified 

probability surveys

Opt-in census of retailers

Representative sample of products All products at participating retailers

Modified geometric mean and 

Laspeyres index

4 bilateral and 2 multilateral price 

indices

Average unit prices for a selection of 

specific products

Average unit prices for EFPGs, which 

include all products sold at retailers

The Consumer Price Index (CPI) and F-MAP differ in 

scope and methods.
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To compare the F-MAP to CPI, an intermediate price 

index is constructed.

• Data sources and index construction both contribute to differences 

between F-MAP Laspeyres and CPI 

• Construct indexes using scanner data similar to how BLS constructs 

CPI

–Geometric mean index formula used to aggregate UPC-level prices in 

scanner data into EFPGs

–Laspeyres index formula used to aggregate geometric mean price indexes 

for EFPGs into food groups that closely align with CPI food groups: 

all food at home (FAH) ● cereals and bakery products ● meats ● dairy 

products ● fruits and vegetables ● beverages Infoscan weights or 

projection factors are used in construction of price indexes

• Intermediate price index called Geometric Mean-Laspeyres (GML) 

scanner price indexes.

• Difference between CPI and GML price indexes are due to differences 

in data
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The CPI and F-MAP generally track each other but 

with some important differences.

90

92

94

96

98

100

102

104

106

108

2016 2017 2018 2019 2020

Index (Jan 2016 = 100)

All food at home (FAH), all areas

CPI GML scanner F-MAP
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These data- and formula-driven differences vary 

across regions...

92

97

102

107

2016 2017 2018 2019 2020

FAH, Northeast

BLS

GML

scanner

F-MAP

92

97

102

107

2016 2017 2018 2019 2020

FAH, Midwest

92

97

102

107

2016 2017 2018 2019 2020

FAH, South

92

97

102

107

2016 2017 2018 2019 2020

FAH, West
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...as well as products.

90

95

100

105

110

2016 2017 2018 2019 2020

Beverages, all areas

BLS GML scanner F-MAP

90

95

100

105

110

2016 2017 2018 2019 2020

Cereals & bakery products, all 

areas

90

95

100

105

110

2016 2017 2018 2019 2020

Dairy products, all areas

90

95

100

105

110

2016 2017 2018 2019 2020

Fruits & vegetables, all areas

90

95

100

105

110

2016 2017 2018 2019 2020

Meat, all areas
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We isolate the contributions of data and formula 

effects using a two-stage regression.

• Two-stage regression

–Stage 1: OLS regression of F-MAP Laspeyres (PFMAP) on CPI (PCPI)

PPFFFFFFFF = αα + ββPPCC CC + uu1

–Stage 2: Regress residuals (u1) on GML scanner index (PGML)

uu1 = αα + ββPPGG GG + uu2

• R2 from stage 2 is data effect

• 1-R2 is formula effect

• Estimate regressions separately for national FAH, 4 regional FAH, 6 

national major food/beverage indices

• Compare 2016 forward because of lagged weights used in 

construction in GML scanner price indexes
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Variation in the CPI explained by FMAP varies by 

region and food category.

0 0.2 0.4 0.6 0.8 1

National

Northeast

West

South

Midwest

Meats

Dairy products

Beverages

Cereals and bakery products

Fruits and vegetables

R-squared

Stage 1 regression results
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The different formulas explain most of the discrepancies, 

but its effect varies by region and category...

0% 20% 40% 60% 80% 100%

National

Midwest

South

West

Northeast

Fruits and vegetables

Cereals and bakery products

Beverages

Dairy products

Meats

Stage 2 regression results

Data (R-squared) Formula (1 minus R-squared)
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...and even over time!

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

National, 2016-2020 National, 2016-2018 National, 2019-2020

Stage 2 results, by time period

Data (R-squared) Formula (1 minus R-squared)
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Limitations and Next Steps

• Current analysis examines short-run relationships 

Cointegration analysis to examine long-run relationships 

among indexes

• Current analysis is only for 4 regions  extend analysis to 

10 MSAs x 6 FAH categories

• Do these differences matter when applied in analysis? 

Estimate reduced-form demand equations using CPI and 

F-MAP and examine differences in price elasticities
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Thanks!

Abigail Okrent

USDA Economic Research Service

Abigail.okrent@usda.gov