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Store-Level Weights for 

Analyses Using InfoScan Data

FCSM Research & Policy Conference

October 24, 2023

Saki Kinney
Acknowledgement

o This work was funded under Contract No. GS-00F354CA between RTI International and USDA’s 

Economic Research Service (ERS).

o The views expressed are those of the authors and 

should not be attributed to ERS, USDA, or IRI.

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Overview

o Motivation

o Nielsen TDLinx population data

o Weight construction

o Use of weights

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OmniMarket Core Outlets (InfoScan) sample characteristics

o Retail point-of-sale data: we aggregated all food and beverage 

products sales to store level

• Unlike Consumer Network household panel, Circana (IRI) does not 

provide weights for retail scanner data.

o Nonprobability sample of stores participating in InfoScan ‘census’ 

component

• Predominately comprised of large chain stores

• We estimate that the sample represents about 20% of stores in the 

population but 50-60% of retail food and beverage sales (some variation 

by year) 

• Limited coverage outside of urban areas; varies by state

- Zero stores in a few states

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Why weights?

o Store-level InfoScan data are useful for characterizing the food 

environment, but the lack of weights prevents calculation of national or 

subnational sales estimates. 

• E.g. per-capita purchases used to estimate demand, market share of WIC 

formula purchases

o Weighted estimates will better approximate population values since they 

account for differences between sample and population.

• Since smaller stores are underrepresented, they will tend to have higher 

weights than larger stores.

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Population information source: TDLinx

o Nielsen data product that approximates a census of U.S. retail 

food and beverage stores with $1 million or more in annual sales

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TDLinx Census of Retail Trade (CRT)

Annual Every 5 years

ERS already purchases this data Requires restricted-use access to FSRDC

Store classifications align well with InfoScan Store classifications align poorly with InfoScan

Over 97% of IRI stores linked by ERS Additional record linkage effort, low match rate

Good estimates of total store sales Self-reported total store sales

Does not report food and beverage sales 

separately

Sales by product category but sparse, quality 

issues
Weight construction

Thus far weights have been created for 2012-2020 InfoScan data. 

1. Prepare store-level data from InfoScan

2. Compute population estimates (control totals) of store counts and 

sales by geography and channel using TDLinx

3. Use generalized raking procedure to create weights such that 

weighted counts and sales totals match control totals from step 2

4. Generate replicate weights for variance estimation

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InfoScan data preparation

o Aggregate product-level sales to annual store-level food and 

beverage sales

o Allocate RMA sales to individual stores proportionally based on 

TDLinx total sales

o Imputation of missing random weight and private label sales

• Nominal missing data rates, except for drug store PL sales 2013-on

• TDLinx and Census ACS tract characteristics used as imputation 

model predictors.

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Control total computation

• Merge InfoScan, TDLinx, and American Community Survey tractlevel data (eg median income, urbanicity)

• Impute food and beverage sales for non-IRI stores using multiple 

imputation

• For each geographic region and channel, average total food and 

beverage sales across imputations

- Geographic regions = Top 10 metro areas plus remainder by Census 

Region 

- Top 10 metro areas: New York, Los Angeles, Chicago, Houston, Dallas, 

Miami, Atlanta, Philadelphia, Detroit, Boston

- Channels: Club & Mass merchandiser, Drug, Grocery, Convenience, 

Dollar (2012-16)

• Store count totals are computed directly from TDLinx

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Linked InfoScan and TDLinx data, 2012

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Channel IRI Count TDLinx Count % of TDLinx

Club store 597 1,236 48%

Mass merchandiser 6,944 7,956 87%

Dollar 7,436 25,361 29%

Drug 19,492 39,947 49%

Grocery 12,641 46,079 27%

Convenience 9,348 148,269 6%

Total 56,458 268,848 21%
Total Sales Benchmarks – 2012

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IRI Channel

Total Sales ($M) # Stores

TDLinx CRT TDLinx CRT

Grocery, 

Convenience

793,648 1,066,491 194,348 184,178

Drug, 

Dollar, 

Club/Mass 

merch.

579,082 804,530 74,500 73,758

Total 1,372,730 1,871,021 268,848 257,936
Food and Beverage Sales Benchmarks - 2012

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Census Division

Census of Retail Trade Imputed TDLinx

Food & Beverage 

Sales ($M)

% of Total 

Sales

Control 

Total ($M)

% of Total 

Sales

New England 26,662 6.8 24,312 6.9

Middle Atlantic 60,211 15.4 53,149 15.0

East North Central 50,332 12.9 47,260 13.4

West North Central 23,944 6.1 20,243 5.7

South Atlantic 77,456 19.8 73,270 20.7

East South Central 17,308 4.4 16,302 4.6

West South Central 38,494 9.9 31,561 8.9

Mountain 24,260 6.2 23,904 6.7

Pacific 71,963 18.4 63,393 17.9

Total 390,629 100.0 353,394 100.0
Food and Beverage Sales Benchmarks – 2012, 2017

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IRI Channel

2012 Food & Bev. Sales ($M) % of Total Sales

CRT Imputed CRT Imputed

Grocery, 

Convenience 453,144 430,691 71.1 70.2

Drug, 

Dollar, 

Club/Mass merch.

184,217 182,648 28.9 29.8

Total 637,361 613,339 100.0 100.0

IRI Channel

2017 Food & Bev. Sales ($M) % of Total Sales

CRT Imputed CRT Imputed

Grocery, 

Convenience 570,840 550,729 72.1 69.6

Drug, 

Dollar, 

Club/Mass merch.

221,122 240,506 27.9 30.4

Total 791,961 791,234 100.0 100.0
Raking procedure

o Use generalized raking procedure to create weights such that 

weighted counts and sales totals computed from InfoScan sample 

match control totals

• Involves iterative estimation of adjustments until they converge to values 

satisfying the constraints imposed by the control totals. Adjustments are 

bounded to limit variance inflation.

o Commonly used to adjust sampling weights when sample 

characteristics are known to be different from the population, correct 

for nonresponse bias, and to adjust nonprobability samples

o Process is repeated on the replicate weights

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Variance inflation

o Replicate weights were created using bootstrap to allow 

variance estimation that takes into account weighting procedure

o Variance estimates using weights can and should be higher 

than what would be obtained under simple random sampling. 

• Overall unequal weighting effects (UWE) for weights range from 3.8 

to 4.8 over years

• Club/Mass Merchandiser and Convenience Stores have UWEs 

close to 1 while Drug and Grocery by geography can be much 

higher. 

• Bounds on adjustments/weights are applied during raking to limit 

variance inflation, but involves tradeoffs with bias, and overconstraining the raking model may prevent convergence.

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Limitations

o For both weighted and unweighted analyses, be realistic about the 

extent to which results generalize to the population.

• InfoScan is dominated by large chain stores with geographic coverage 

limitations. Weights reduce but do not eliminate bias. 

• Weights can be used to generate representative estimates at the 

metro/region geographic area and industry channel. InfoScan coverage 

limitations prevents calculation of weights for smaller areas. 

- Some weights are less than one.

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Thank you

Contact: Saki Kinney | email: skinney@rti.org

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For more information: https://www.rti.org/publication/user-documentation (more 

current information is forthcoming)