A New Measure for Food Insecurity

A Curated Data Enterprise Demonstration Use Case

Social and Decision Analytics Division, Biocomplexity Institute

Cesar Montalvo

Vicki Lancaster

Stephanie Shipp

Joseph Salvo

October 2023
Curated Data Enterprise Framework

Develop Use Cases to identify and define capabilities 

for building the Curated Data Enterprise

Provides foundation for creating Statistical Products First Approach
Food insecurity: limited or uncertain availability of nutritionally adequate 

and safe food or limited or uncertain ability to acquire acceptable foods in 

socially acceptable ways.

10.2 % (13.5 million households) 

were food insecure

3.8 % (approx. 5 million households) 

were very low food insecure

Source: Coleman-Jensen et. al, 2022

Definition

Facts 2021

Purposes And Uses

Source: Coleman-Jensen et. al, 2022
Source: Capital Area Food Bank Hunger Report 2023.

Survey, May 2022 - Apr 2023, 5261 adults, DC Metro Area. 

Questions: eighteen-item screener for food insecurity. 

Food Insecurity in the DMV Area – 2023

-report released 2023-
Why a new measure of Food insecurity is important?

• Understand smaller and specific geographies (census tract level). 

• Account for household composition.

• Acknowledge income and cost of living differences.

• Predictive tool of food insecurity (e.g., housing cost or inflation).

• Identify “at-risk” populations and severity of food insecurity.

• Improve food assistance programs.

“Indicators inform action”
Household Living Budget (Data Discovery)

Amount of income necessary to meet a household’s needs to function at a modest yet 

adequate standard of living and to pay federal and state income taxes. 

Novel extension: Small geographies + Household composition

Household Combination: Adult/Teenager/Schooler/Preschooler/Toddler/Infant (6-digits)

Example: 210010  4-person household with 2 Adults/ 1 Teenager/1 Toddler

Household 

Living 

Budget

HLB

National 

Academy 

SEM
Households based on Income and Size (Stat Development)

To obtain the income-household size table per census tract, we use Iterative Proportional Fitting

Marginal information Seed distribution using

IPUMS microdata 

Number of households using 

Iterative Proportional Fitting by 

census tract

2

District of Columbia

5 PUMAs 

-Public Use Microdata Areasat least 100,000 people

Source: US Census Bureau Source: American Community Survey

1 3

+

Package: “mipfp” 

Estimate(seed, target.data, method = "ipfp")

Household 

size

Number 

Households

Margin of 

Error

1 146 ±55

2 398 ±82

3 197 ±63

4 229 ±81

5 82 ±44

6 10 ±19

7 34 ±45

Income bracket

1 2 3 4 5 6 7

Number 

Households

Less than $15,000 18

$15,000 to $29,999 66

$30,000 to $39,999 17

$40,000 to $49,999 10

$50,000 to $74,999 116

$75,000 to $99,999 93

$100,000 to $149,999 244

$150,000 to $199,999 87

$200,000 and more 445

Total: 146 398 197 229 82 10 34 1,096

Household size

Income bracket Number 

Households

Margin of 

Error

Less than $15,000 18 ±14

$15,000 to $29,999 66 ±14

$30,000 to $39,999 17 ±22

$40,000 to $49,999 10 ±19

$50,000 to $74,999 116 ±48

$75,000 to $99,999 93 ±19

$100,000 to $149,999 244 ±68

$150,000 to $199,999 87 ±86

$200,000 and more 445 ±76

Total 1,096 ±76

4

Sampling Exercise

+

From IPUMS data

+

For all census tracts

with household 

composition

Synthetic 

population 

Approx. 310000 

households 

with 

263 unique 

household combinations
HLB =

HLB-Nonfood Cost

Residual Food Income = Household Income - HLB-Nonfood Cost

Food Insecurity Determination using HLB (Stat Development) 

1

2

3 Food Coverage Ratio =

Residual Food Income

HLB Food Cost

4 Categories

0 -2 -1 1

Food Insecure At-Risk Secure 

Food Insecurity Food Security

Food Coverage 

Ratio

Severely Food Insecure 
PRELIMINARY RESULTS

Distribution of the Food Coverage Ratio, 

Washington DC, 2022

Food Insecurity Assessment Based on the HLB

For Washington DC, 2022 (Fitness-for-Purpose)

Food Insecurity

Severe 

Food Insecurity

At-Risk

Severe 

Food 

Insecurity Food 

Insec

urity At

Risk
Food Insecurity For Households with Children in DC, 2022

Filled circles represent a household combination. 

Household Combination: Adult/Teenager/Schooler/Preschooler/Toddler/Infant. 
Food Insecurity in Washington DC, 2022

Food Insecure 

Households

Severely Food 

Insecure Households

Synthetic Population using IPUMS 

and ACS 2021-5 YR Tables B11016 and S1906.
Provides a timely and cost-effective alternative to current food insecurity 

quantification.

Allows local governments to target specific areas of high food insecurity 

with more precision than the county data alone.

Presents new insight into areas that are not currently food insecure but are 

at risk for becoming food insecure so local governments can intervene 

before people are in need.

Helps local governments to use benefits (SNAP, WIC) to address food 

insecurity more efficiently.

Policy Insights

2

3

4

1

The New Measure for Food Insecurity 
Curated Data Enterprise Capabilities

• Household Living Budget (HLB) at census tract level

• HLB component calculations by household composition & size

• Creation of synthetic data using Iterative Proportional Fitting

• Food insecurity determination using HLB

• Maps and visualization of results

17
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Capital Area Food Bank, Hunger Report 2023. Accessed at https://www.capitalareafoodbank.org/.

Coleman-Jensen, Alisha, Matthew P. Rabbitt, Christian A. Gregory, and Anita Singh. "Household food security in the United States.” 

USDA Economic Research Report 155, 2022.

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Bold New Approach to Create Official Statistics, Technical Report BI-2022-1115. Proceedings of the Biocomplexity Institute, 

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References