www.bidenschool.udel.edu

Joseph R. Biden, Jr. School of 

Public Policy & Administration

Predicting “Real-Time” Food Hardship with 

Retail Food Spending: A New Approach 

Linking Scanner Data and the Census 

Household Pulse Survey

Katie Fitzpatrick & Kejda Llana

Acknowledgements:

The findings and conclusions in this manuscript are those of 

the authors and should not be construed to represent any 

official USDA or U.S. Government determination or policy. This 

research was supported by the U.S. Department of Agriculture, 

Economic Research Service through a Cooperative Agreement 

(58-4000-1-0044). The findings from this 

presentation/report/research should not be attributed to IRI.
Food Hardship Rose Dramatically 

During Pandemic

Source: ERS, USDA 

https://ers.usda.gov/covid-19/food-and-consumers 

13.4% of households reported sometimes or 

often not having enough to eat

Spike in SNAP applications

Historic demand for food bank resources
Food Insecurity: The Official Measure 

of Food Hardship

Food insecurity provides valuable insight for 

policy

Challenges for responding to food hardship 

quickly

• Time

• Collected in December (CPS-FSS) and unavailable until nearly a year after 

collected

• National focus

• State and local level less reliable

New tools needed to complement the CPS-FSS
Research Question: Can “Big Data” on Food 

Expenditures be Used to Predict Food Hardship?

USDA’s Economic Research Service (ERS) has made 

investments in food retail scanner data collected by IRI

• National and State Weekly Time Series data on ERS website

• Household Panel available through restricted agreements

Can we use this data to create models of the relationship between 

food spending and food insufficiency that can be utilized by 

national, state, and local governments to address food hardship 

quickly?
Motivation: Food 

Hardship

During the 

Pandemic

The COVID-19 pandemic 

exacerbated food hardship

Precipitous Decline in 

Employment, Particularly Among 

Workers With Characteristics that 

Put them more at risk for Food 

insecurity

• Concentrated in Service Industry

• Concentrated among historically 

disadvantaged populations

• Concentrated among those with 

low SES 
Why might Food Expenditures be useful to 

Predict Food Hardship?

Food insecure households spend less on 

food than food secure households

• Evidence from CPS-FSS and FoodAPS

Survey (Tiehen et al. 2017)

• During early pandemic, households with 

individuals that lost their job spent 15% 

less on food and were 36% more likely to 

receive free food (Restrepo, Rabbitt, and 

Gregory 2021)

Households at-risk of food insecurity make tradeoffs about the composition of their 

food budget and where they shop (Griffith et 

al. 2015; Lewis et al. 2021)

Source: Coleman-Jensen, Alisha, Matthew P. Rabbitt, Christian A. Gregory, Anita Singh, September 2022. 

Household Food Security in the United States in 2021, ERR-309, U.S. Department of Agriculture, 

Economic Research Service
Data Used: Food Hardship

Household Pulse Survey collects data on food insufficiency

Aggregate responses at the national and state level to get 

estimates of food hardship

In the last 7 days, which of these statements best describes the food eaten in 

your household?

A. Enough of the kinds of food (I/we) wanted to eat

B. Enough, but not always the kinds of food (I/we) wanted to eat

C. Sometimes not enough to eat

D. Often not enough to eat
Data Used: Food Expenditures

Two sources:

1. ERS’s Weekly Retail Food Series

• National Data on Food Category and Food Subcategory 

(Dollars, Unit Sales, Volume Sales)

• State-level Data on Food Category (Dollars, Unit Sales, Volume 

Sales)

2. Household Food Expenditures collected by Information Resources 

Inc. (IRI)

• Product-level data and Household Characteristics

All collected based on a calendar week (Monday – Sunday)
Link Food Expenditures to Food Insufficiency 

Challenge in linking HPS “weeks” to 

retail sales data

• HPS weeks tend to be 13 days 

(versus 7 days)

• HPS weeks tend to be Wed – Mon 

(versus Mon – Sun)

• HPS not continuous throughout the 

year

Solution:

• Use retail food data that overlaps 

most with HPS week

• Average retail food data over relevant 

HPS week

HPS 

Week

HPS

Dates Retail Food Series

18 Wed. Oct 28 –

Mon. Nov. 9, 2020

Mon. Oct. 26 – Sun. Nov. 1, 2020

Mon. Nov. 2 – Sun., Nov. 8, 2020

19 Wed. Nov. 11 –

Mon. Nov. 23, 2020

Mon. Nov. 9 – Sun. Nov. 15, 2020

Mon. Nov. 16 – Sun. Nov. 22, 2020
Methods

Two Different Models:

• National Level Data

• State Level Data

Fixed Effects Regressions

• Time Fixed Effects

• Quarter (National Data)

• Week (State Data)

• State Fixed Effects (State Data)

Level-Log Models

• Dependent Variable: Food Insufficiency

• Key Independent Variable: 

llnn(WWWWWWWW WW RR RRRRRR FFFFFFFF SS SS)

Controls:

• Macroeconomic Conditions (Initial UI Claims)

• COVID Severity (Cases and Deaths)
Summary Statistics

National Data State Data

Mean

Standard 

Deviation Mean

Standard 

Deviation

(1) (2) (3) (4)

Household Pulse Survey

Food Insufficiency 0.100 0.011 0.095 0.028

Weekly Retail Food Sales Data

Total Weekly Retail Food Sales (in 

Hundreds of Million Dollars) 149 5.0 0.225 0.217

Unit Sales (in Hundreds of Million) 46.60 1.52 0.069 0.064

Volume Sales (in Hundreds of Million) 215.00 16.10 0.448 0.427

Observations 45 2,295
Relationship Between Food Insufficiency and 

Total Weekly Retail Food Spending
Results: National Weekly Retail Food Sales, Measured in 

Volume, Inversely Related to Food Insufficiency

-0.544

0.508

-0.605**

-0.108

0.387

-0.612***

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Dollars Unit Sales Volume Sales

Point Estimate

Limited Controls Full Set of Controls

Estimates Using National Data

Total Dollars Spent:

• Negative but insignificant

Unit Sales:

• Positive but insignificant

Volume Sales:

• 1% increase in Volume Sales 

associated with 0.6 percentage 

point decline in food 

insufficiency

All estimates include various sets of controls. Weekly retail food sales included in 

natural logarithmic form.

Suggests households may reduce 

spending and shifting to smaller food 

sizes if experiencing food hardship
Results: State Weekly Retail Food Sales Significantly Related to 

Food Insufficiency; Wrong-Signed Estimates

Estimates Using State Data

Total Dollars Spent, Unit Sales, and 

Volume Sales:

• 1% increase relates to ~ 0.1 point 

increase in food insufficiency

All estimates include various sets of controls. Weekly retail food sales included in 

natural logarithmic form.

Need better controls?

• Week Fixed Effects or Weekly 

Initial UI Claims & COVID 

Severity

• State Fixed Effects

0.165***

0.130***

0.123***

0.144*** 0.143***

0.0745***

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Dollars Volume Unit

Point Estimates

Limited Controls Full Set of Controls
Results: Lags of State Weekly Food Sales Associated 

with Increases in Food Insufficiency

0.350***

-0.102

0.00800***

-0.0711** -0.0903***

-0.00660*

0.0365 0.0362

0.00308

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Dollars Volume Unit

Point Estimates

Current Period Previous Period Two Periods Ago

Estimates Using State Data and Lags of 

Weekly Food Sales

Current Period Total Dollars Spent and 

Unit Sales:

• Positively related to food insufficiency

Prior Period Total Dollars Spent, Unit 

Sales, and Volume Sales:

• Negatively related to food insufficiency

All estimates include various sets of controls. Weekly retail food sales included in 

natural logarithmic form.

Reflects managing by using food 

from pantry, etc?
Results: Weekly Retail Food Sales Categories 

Related to Food Insufficiency

Categories Positively Related to 

Food Insufficiency:

• Meats, Eggs, and Nuts

• Sugar and Other 

Sweeteners

Categories Negatively Related 

to Food Insufficiency

• Beverages

• Fats and Oils
Household-Level Data on Food Spending

Can Examine:

• Are households more intensely related to “deals”? 

• Total Food Expenditures with and without discount

• Do relationships vary by presence of children or focusing on households below median 

income?

• How are households adjusting the type of food they purchase?

• Perishable versus Non-Perishable

• Inferior (Cheap Proteins; Frozen/Canned Fruits & Vegetables) versus Normal 

Foods (Fresh Produce; Fresh Meat; Snacks)
Next Steps

1. Include Lags in More Models

2. Add indicators for food from food banks and SNAP 

receipt

3. More exploration of Categories and Subcategories of 

Food Spending

4. Explore Marginal Food Insufficiency a better 

outcome

5. Other suggestions?
Conclusions

Food Hardship Has Lasting Detrimental Effects on 

Households

• Making Use of Federal Data Investments Might be a 

Promising Tool to Addressing Food Hardship Early

Early Results Suggest that Food Insufficiency Associated 

with:

• Food Spending Levels (and Lags of Food Spending)

• Spending in Food Categories Related to “Luxuries” 

versus “Necessities”
Biden School of Public Policy & Administration | www.bidenschool.udel.edu

Katie Fitzpatrick

Associate Professor

Kejda Llana

Ph.D. Student, Public Policy & Public Administration

Biden School of Public Policy & Administration

kfitzpat@udel.edu

kejda@udel.edu

www.bidenschool.udel.edu