United States Department of Agriculture

Estimating Prices for Foods in 

the National Health and Nutrition 

Examination Survey: The Purchase 

to Plate Price Tool

Andrea Carlson, Kevin Kuczynski, TusaRebecca Pannucci, 

Kristin Koegel, Elina T. Page, Carina E. Tornow, and 

Thea Palmer Zimmerman

Economic

Research

Service

Technical

Bulletin

Number 1955 

September 2020
Economic Research Service 

www.ers.usda.gov

United States Department of Agriculture

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Recommended citation format for this publication

Carlson Andrea, Kevin Kuczynski, TusaRebecca Pannucci, Kristin Koegel, Elina T. Page, Carina E. Tornow, 

and Thea Palmer Zimmerman 2020. Estimating Prices for Foods in the National Health and Nutrition 

Examination Survey: The Purchase to Plate Price Tool, TB-1955, September 2020. U.S. Department of 

Agriculture, Economic Research Service.
Economic

Research

Service

Technical

Bulletin

Number 1955

September 2020

United States Department of Agriculture

Estimating Prices for Foods in the 

National Health and Nutrition Examination 

Survey: The Purchase to Plate Price Tool

Andrea Carlson, Kevin Kuczynski, TusaRebecca Pannucci, 

Kristin Koegel, Elina T. Page, Carina E. Tornow, and 

Thea Palmer Zimmerman

Abstract

The 2018 Farm Bill mandates that the market basket of the Thrifty Food Plan (TFP) be updated 

every 5 years using current food prices, food composition, food consumption patterns, and 

dietary guidance. The TFP forms the basis of the maximum allotment for the U.S. Department of 

Agriculture's Supplemental Nutrition Assistance Program (SNAP) benefits. Current food composition and food consumption data are available through What We Eat in America, the dietary 

component of the National Health and Nutrition Examination Survey (WWEIA/NHANES). 

The Dietary Guidelines for Americans provides current dietary guidance. However, food price 

information that aligns with these data are not available, which limits the economic analysis that 

can be conducted with the survey data. This report details the construction of the Purchase to 

Plate Price Tool (PPPT), which uses retail or household scanner data to estimate prices for foods 

reported consumed by participants in WWEIA/NHANES and compares estimated total food 

expenditure of WWEIA/NHANES participants to other estimates of food expenditure.

Keywords: food prices, scanner data, IRI InfoScan, IRI Consumer Network, What We Eat in 

America (WWEIA), National Health and Nutrition Examination Survey (NHANES), Flexible 

Consumer Behavior Survey (FCBS), Food and Nutrient Database for Dietary Studies (FNDDS), 

USDA National Nutrient Database for Standard Reference (SR), ERS Food Expenditure Series, 

Purchase to Plate Crosswalk (PPC), Purchase to Plate Price Tool (PPPT)

Acknowledgments

The authors would like to thank the following individuals for their contributions: members of the 

USDA Linkages Working Group, Mark Denbaly, USDA, Economic Research Service (ERS); Mark 

Lino, USDA, Food and Nutrition Consumer Services (FNCS); Carrie Martin, USDA, Agricultural 

Research Service (ARS), Alanna Moshfegh, ARS; and Ilya Rahkovsky (formerly of ERS). We also 

appreciate suggestions from Brandon Restrepo, ERS. We are also thankful for the design assistance 

of Andrea Pimm and the peer reviews of Gianna Short, Mary Muth, and Parke Wilde. 

About the Authors: Andrea Carlson and Elina Page are research agricultural economists with the 

USDA, Economic Research Service. Kevin Kuczynski and Kristin Koegel are nutritionists, and 

TusaRebecca Pannucci is the lead nutritionist with the USDA, Center for Nutrition Policy and 

Promotion. Carina Tornow is a senior systems analyst, and Thea Palmer Zimmermann is a senior 

study manager with Westat, Inc. The analysis, findings, and conclusions in this report should not 

be attributed to IRI.
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Food Prices and the USDA Food Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Food Prices for Economic Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Purchase to Plate Price Tool Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

The Purchase to Plate Price Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Optional User Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Coverage of WWEIA/NHANES 2011–12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Purchase to Plate Price Tool Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Appendix A: List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
A report summary from the Economic Research Service September 2020

ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America. www.ers.usda.gov

United States Department of Agriculture

Estimating Prices for Foods in 

the National Health and Nutrition 

Examination Survey: The Purchase 

to Plate Price Tool

Andrea Carlson, Kevin Kuczynski, TusaRebecca Pannucci, 

Kristin Koegel, Elina T. Page, Carina E. Tornow, and 

Thea Palmer Zimmerman

Economic

Research

Service

Economic

Technical

Bulletin

Number 1955

September 2020

United States Department of Agriculture

Estimating Prices for Foods in the 

National Health and Nutrition Examination 

Survey: The Purchase to Plate Price Tool

Summary

What Is the Issue?

The 2018 Farm Bill mandates that the market basket of the Thrifty Food Plan (TFP) be updated 

every 5 years using current food prices, food composition, food consumption patterns, and dietary 

guidance. The TFP forms the basis of the maximum allotment for the Supplemental Nutrition 

Assistance Program (SNAP) benefits and uses the same modeling framework as the other three 

USDA food plans (namely, the Low-Cost, Moderate-Cost, and Liberal food plans). The Department 

of Defense uses the Liberal Food Plan in setting the Basic Daily Food Allowance for servicemembers. Current food composition and food consumption data are available through What We 

Eat in America, the dietary component of the National Health and Nutrition Examination Survey 

(WWEIA/NHANES). The Dietary Guidelines for Americans provides current dietary guidance. 

However, food price information that aligns with these data are not available.

Researchers and policymakers have used retail and household scanner data for almost 2 decades to 

estimate food prices. However, many foods in the WWEIA/NHANES are represented as an edible 

rather than a retail food, making direct use of scanner data to estimate WWEIA food prices difficult. The weight of an edible food does not include inedible parts such as seeds, skins, cores, shells, 

and bones that are typically included in the retail weight. In addition, foods prepared at home are 

often combinations of several ingredients. For these reasons, edible food prices must be estimated 

using standard recipes and conversion factors to convert the retail foods represented in scanner 

data into the edible foods found in WWEIA/NHANES. This report presents an approach to using 

scanner data to estimate food prices for WWEIA/NHANES.

What Did the Study Find?

The Purchase to Plate Price Tool (PPPT) is a set of SAS programs that allow users to estimate 

prices for foods reported consumed by WWEIA participants. Using sales data for more than 

350,000 products in the 2013 IRI retail scanner data, InfoScan, we constructed the PPPT. The tool 

calculates average prices for 97 percent of the food and beverages (measured in grams) reported 

as eaten by participants in the WWEIA/NHANES 2011–12 wave of the survey. Each price is an 

average of several products available in stores, and in some cases, a compilation of different types 

of products. The foods that are not included are those that were reported eaten fewer than 10 times 

by survey participants. Estimated prices do not include other costs such as the time, energy, and 

skills needed to buy and prepare food.

We used the PPPT to estimate the individual expenditures for foods reported by participants as food 

at home (FAH) purchases. The mean and median FAH expenditure estimates were $4.54 and $3.84 

per day, respectively. Half of the estimates (25th to 75th percentile) were between $2.13 and $6.03, 

but 10 percent (90th percentile) had a 1-day estimated FAH expenditure over $10.59. 

Andrea Carlson, Kevin Kuczynski, TusaRebecca Pannucci, Kristin Koegel, 

Elina T. Page, Carina E. Tornow, and Thea Palmer Zimmerman
www.ers.usda.gov

We also compared the PPPT-based food expenditures to two other government estimates of food costs. 

USDA’s Cost of Food at Home for June 2013 estimated the weekly cost of food at four expenditure levels 

(Thrifty, Low-Cost, Moderate-Cost, and Liberal). We compared our weekly total expenditure estimates to the 

Low-Cost, Moderate-Cost, and Liberal estimates by quartile of weekly food expenditures, where the first quartile is the lowest 25 percent of expenditures by individuals and the fourth quartile is the highest 25 percent of 

expenditures by individuals. The Low-Cost plan is comparable to the second quartile, the Moderate-Cost plan is 

comparable to the third quartile, and the Liberal Plan is comparable to the top quartile. We did not compare our 

findings to the TFP because the basis is not based on quartiles of expenditure. Note that our estimates were based 

on what individuals report eating, whereas the Cost of Food at Home was the cost of a healthy diet. 

• Compared to the Low-Cost “Cost of Food at Home” estimates, we estimate that for WWEIA/NHANES 

individuals, food expenditures in the second quartile of food expenditures were 10 percent lower ($190.60 

vs. $170.41).

• Expenditures for the third quartile were 3.8 percent higher ($238.20) than for the Moderate-Cost estimates 

($247.16).

• The Liberal Plan estimate ($289.30) is lower than the top quartile of expenditure estimates ($390.83).

The ERS Food Expenditure Series provides annual per capita FAH expenditures estimates for annual household food expenditures in the United States. Data are compiled from the Economic Census, U.S. Census annual 

surveys, and from other U.S. statistical agencies and trade associations. For comparison, we estimated an 

annual per capita FAH expenditure using the PPPT and the individual foods reported by WWEIA/NHANES 

participants.

• The PPPT estimate was about 91 percent of the per capita estimate from the 2013 ERS Food Expenditure 

Series, or about $190 less.

Finally, we compared the PPPT-based food expenditures to FAH expenditures contained in the Flexible 

Consumer Behavior Survey (FCBS) module of NHANES, using self-reported FAH expenditure data from the 

2011–12 FCBS/NHANES. 

• Mean per capita daily FAH expenditure from the FCBS was estimated to be $5.29 (95 percent CI $5.00 to 

$5.58). The PPPT mean estimate of 1-day FAH expenditure, including adjustments for food and alcohol 

taxes and food loss, was $5.34 (95 percent CI $5.08 to $5.62).

How Was the Study Conducted?

To estimate prices for foods that consumers in the United States reported eating, we relied on three data sources: 

WWEIA/NHANES 2011–12, the USDA Food and Nutrient Database for Dietary Studies (FNDDS) 2011–12, 

and IRI InfoScan 2013 (the most recent data available). We used the two 24-hour dietary recalls in WWEIA/

NHANES data to develop a list of foods eaten at home in the United States. The FNDDS includes recipes used 

to list the ingredients required to prepare the foods. IRI InfoScan 2013 was used to estimate prices for the recipe 

ingredients and the foods purchased in ready-to-eat form.

We used the Purchase to Plate Crosswalk (PPC) to transition between the FNDDS and the IRI InfoScan data. The 

PPC includes a link between the FNDDS and the food items in the IRI data, as well as conversion factors to convert 

the purchased weight (i.e., retail weight) of each food item to the as-eaten weight. Once we had the quantities and 

calculated an average price for each ingredient, we summed the ingredient costs to create the average food price for 

each of the foods reported eaten by WWEIA participants. The Purchase to Plate Price Tool (PPPT) estimates the 

prices for purchasing foods at a store and putting them on the plates of consumers in the United States; however, it 

does not include other food-preparation costs such as time, energy to transport, store, and prepare food, and skill 

level that may impact time and energy use. An update using 2015 IRI data to estimate prices for FNDDS 2013–14 is 

forthcoming, and future updates are planned in compliance with the 2018 Farm Bill.
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Estimating Prices for Foods in the National Health 

and Nutrition Examination Survey: The Purchase to 

Plate Price Tool

Introduction

The Agriculture Improvement Act of 2018: Title IV Nutrition, section 4002 states, “By 2022 and 

at 5-year intervals thereafter, the Secretary shall re-evaluate and publish the market baskets of the 

Thrifty Food Plan based on current food prices, food composition data, consumption patterns, 

and dietary guidance.” The Thrifty Food Plan (TFP) (Carlson, Lino, Juan et al., 2007) serves 

as the basis for the maximum Supplemental Nutrition Assistance Program (SNAP) allotments 

for low-income households. While the dollar value of the TFP is updated every month using the 

Consumer Price Indexes (CPI) for food, the change dictated by the Agriculture Improvement Act 

is that the underlying market basket will be updated every 5 years. As stated in the Act, updating 

the TFP market basket requires an understanding of current dietary guidance, food composition 

(nutrition) data, knowledge of consumption patterns, and food prices. Dietary guidance comes 

from the Dietary Guidelines for Americans, which is updated every 5 years by the Departments 

of Agriculture and Health and Human Services. USDA’s What We Eat in America, the dietary 

component of the bi-annual National Health and Nutrition Examination Survey (WWEIA/

NHANES), collects information on food composition and consumption patterns. However, an 

important barrier to updating the TFP is a mechanism to regularly update food prices that align 

with WWEIA/NHANES.

Food prices for WWEIA/NHANES are also an asset for researchers investigating questions such 

as how and why some consumers choose healthy foods and others choose less healthy options. 

Consumers consider many factors when selecting food, including how it tastes, how hungry they 

feel, how much time and energy they have to prepare the food, and how much the food satisfies them. Consumers also account for the price of the food and the total amount they can spend. 

NHANES provides a wealth of health information, and the NHANES module, the Flexible 

Consumer Behavior Survey (FCBS) collects information on U.S. consumers’ knowledge, attitudes, 

and beliefs regarding nutrition and food choices (Restrepo, 2019). Adding average food prices to 

WWEIA/NHANES will allow researchers to extend their NHANES- and FCBS-based research 

questions to understand economic factors affecting food choices.

Researchers and policymakers have used household and retail scanner data for almost 2 decades 

to study consumer decision making, the food environment, and the affordability of a healthy diet. 

Originally developed for food marketing purposes, scanner data provided researchers with detailed 

data on individual food items, the date purchased or sold, and the price paid.

However, estimating food prices for WWEIA is not as simple as using grocery store prices. Foods 

in WWEIA are in the edible form rather than the retail form. For ready-to-eat or ready-to-heat 

foods such as bread, milk, frozen or refrigerated pizza, and pre-cut produce items, the retail price is 

the same as the edible price. However, many foods eaten by U.S. households are a mixture of ingredients that need to be prepared, and the retail price includes the weight of non-edible parts such as 
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bones, skins, shells, peels, and seeds. In these cases, the edible price has to be estimated using 

standard recipes and adjusted for the inedible parts of the purchased food.

The Purchase to Plate Price Tool (PPPT) aims to bridge the gap by leveraging the specialties of 

WWEIA and scanner data. Specifically, the tool is a set of three SAS programs that provide a 

mechanism for users to estimate food prices for the foods reported eaten by WWEIA participants, using retail scanner data. It is an extension of the Purchase to Plate Crosswalk (PPC) 

(Carlson et al., 2019), which links scanner data to the USDA nutrition databases. Following 

earlier price estimates for NHANES foods and time-use studies (Carlson, Lino, Juan et al., 2008), 

the tool assumes that all ingredients are purchased in stores and can be prepared in 30 minutes or 

less. It is the first price estimation method that allows users to estimate retail food prices for foods 

reported eaten by NHANES participants using all of the IRI retail or household data (to calculate 

a national average price) or any subset of either dataset. Since the prices calculated by the PPPT 

are based on retail data, they provide a baseline for the price of food in its edible form. To obtain 

food-away-from-home prices, users could adjust these prices to account for the additional labor 

and other resources used by restaurants. 

This report outlines the methods used to create the PPPT, provides guidance on using the PPPT 

with a subset of the IRI data, and compares food expenditures estimated for WWEIA participants 

to other government estimates of food expenditures. This report covers the 2013 PPPT, which 

estimates prices for the WWEIA 2011–12, using IRI 2013 data. Since the 2-year FNDDS is typically released 2 years after data collection, and IRI data are available after 1 year, we chose to 

use the most current version of each dataset available when the study began to generate prices. An 

update using IRI 2015 data to estimate prices for WWEIA 2013–14 is forthcoming, and future 

updates are planned in compliance with the 2018 Farm Bill.
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Food Prices and the USDA Food Plans

The USDA Food Plans represent nutritious diets that align with the Dietary Guidelines for 

Americans at four expenditure levels. The market baskets of each Food Plan specify the types and 

quantities of foods that people could purchase and prepare at home to obtain a nutritious diet. The 

weekly quantities suggested by the Food Plans are not for specific foods, but groups of foods such 

as dark green vegetables or red meat. For each of the 4 Food Plans, there are 15 market baskets—1 

for each of 15 age-sex groups. Revisions to the Food Plan market baskets reflect current dietary 

recommendations, food consumption patterns, food composition data, and national average food 

prices.

Each food plan plays an important role in Federal program support or in the judicial system. 

The Thrifty Food Plan (Carlson, Lino, Juan, et al., 2007) is the lowest of all cost levels and 

serves as the basis for the maximum Supplemental Nutrition Assistance Program (SNAP) allotments for low-income households. Some divorce cases use the other Food Plans (Carlson, Lino 

and Fungwe, 2007) including the Low-Cost, Moderate-Cost, and Liberal Plans to set alimony 

payments. The value of the Low-Cost Food Plan is used by some bankruptcy courts to determine the portion of a bankrupt person’s income to allocate to necessary food expenses. The 

Department of Defense uses the value of the Liberal Food Plan to determine the Basic Daily 

Food Allowance rates for all servicemembers. If individual or household food expenditure is 

ranked from lowest to highest, the Low-Cost estimate is comparable to the second quartile 

(25th to 50th percentile), the Moderate-Cost to the third quartile, and the Liberal to the highest 

quartile.

Each month, USDA publishes the weekly and monthly cost of food for 15 age-gender groups at 

each of the four expenditure levels1. USDA also publishes the cost for a family of 4—defined as 

one 6- to 8-year-old child, one 9- to 11-year-old child, an adult female age 19–50, and an adult 

male age 19–50—and two 2-person households: one containing a male and female age 19–50, 

and the other a male and female age 51–70. The cost of each basket is adjusted for inflation each 

month, but the underlying basket is not modified.

The market baskets are established through a mathematical optimization model that accounts for 

dietary guidance, food composition, and consumption patterns and food prices (Carlson, Lino 

and Fungwe, 2008, Wilde and Lobrera, 2009). While the Agriculture Improvement Act dictates 

only that USDA re-evaluate the market baskets for the TFP, the estimation model for all four 

food plans is the same (Carlson, Lino and Fungwe, 2007). However, each food plan model has a 

different set of inputs. For example, the TFP is based on the foods that low-income households 

report eating in WWEIA/NHANES, while the other three food plans are based on the foods 

that individuals in the expenditure group, rather than income group, report eating in WWEIA/

NHANES. In the most recent update to the TFP, researchers used food prices based on lowincome households’ purchases (Carlson, Lino, Juan, et al., 2007), while national average prices 

of all consumers were used in the other three plans (Carlson, Lino and Fungwe, 2007). Previous 

1See the USDA Food and Nutrition Service, Center for Nutrition Policy and Promotion webpage, “USDA Food Plans: 

Cost of Food" (monthly reports).
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updates used the same set of prices for all four plans (Carlson et al., 2003, U.S. Department of 

Agriculture, 1999).

CNPP Food Price Database. Food prices for the most recent update of the Food Plans used the 

CNPP Food Price Database 2001–02 (Carlson, Lino, Juan, et al., 2008). This database provided 

prices for every food item reported consumed in NHANES 2001–02 and 2003–04 (Center for 

Nutrition Policy and Promotion (CNPP), 2009), using Nielsen Homescan data from 2001–02 and 

2003–04. While researchers could use the Consumer Price Indexes (CPI) for food to update prices 

to current price levels, using the CPI assumes that relative prices within a CPI category stay the 

same—for instance, romaine lettuce and broccoli prices changed at the same rate. In addition, the 

data do not allow researchers to break down prices by season, market areas smaller than the four 

census regions, or the type of store.

The PPPT updates the price data and allows for greater flexibility in how prices are estimated. In 

addition, the Purchase to Plate Crosswalk (PPC) (Carlson et al., 2019) uses machine learning to 

augment the human effort required to link scanner data to the USDA nutrition databases. This will 

make updates to the PPPT more efficient than updates to the CNPP Food Prices Database because 

of the reduced level of effort required to link scanner data to USDA data. However, to the best of our 

knowledge, revising recipes for price calculation is not possible with machine learning applications.

Food Prices for Economic Research

For economists, the basis of understanding consumer choices is the principle that consumers seek to 

maximize their utility (or satisfaction) through a series of choices, subject to a budget constraint, that 

also includes their time; energy to transport, store and prepare food; and skill levels that impact food 

acquisition and preparation time. In terms of food, households select foods that they like to eat and 

that will provide their desired level of nutrition and level of preparation. Since the average household 

spends about 10 percent of its total income on food, the budget constraint means that food prices are 

an important component of why consumers choose the foods they do. For low-income households, 

the budget share for food is about 35 percent of total income (Kuhns, 2018), and food prices play a 

larger role in food choices.

Economists use a variety of data sources to study the relationship between food prices and expenditure and food choices, including the ERS Food Expenditure Series, scanner data, and associated ERS data products such as the Quarterly Food at Home Prices Database (QFAHPD), the 

Cost of Fruits and Vegetables, and the National Household Food Acquisition and Purchase Survey 

(FoodAPS). Some researchers also collect their own data for their unique studies. One major limitation of these methods is that most represent purchase prices and thus are not compatible with dietary 

recall data. However, researchers have and continue to use each of these to contribute to our understanding of the relationship between food prices and food choices.

ERS Food Expenditure Series. The ERS Food Expenditure Series (Okrent et al., 2018) is the most 

comprehensive measure of the total value of food acquisition in the United States. Data are compiled 

from the Economic Census, U.S. Census annual surveys, and data from other U.S. statistical agencies and trade associations. The expenditure series measures the total dollar value of all food expenditures by final purchaser, including consumers, governments, institutions, and corporations such as 

airlines, schools, and hospitals where the food is given (not sold) to the final consumer. The series 

also breaks down expenditures by type of retail store (e.g., grocery store, supercenter) and type of 

food-away-from-home (FAFH) establishment (e.g., restaurants, recreation places, school cafete-
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rias), and includes the value of home-produced food. While it does not break down expenditures by 

individual foods, researchers can use it to create an annual or monthly price for food in time-series 

analysis of consumer choices. In this study we use it to verify estimates of total food-at-home expenditures using the PPPT and WWEIA.

Prices in scanner data. Scanner data includes detailed purchase information from individual 

households and the weekly sales (dollars and quantity) of products from stores. Scanner data refers 

to purchase data collected by consumers using handheld scanners or cell phone applications, and 

store-level sales data collected from scanners used at the check-out. Researchers can estimate prices 

paid over a period of time, geographic region or type of store. Unlike other sources of price data 

discussed in this section, these data are not publicly available, and researchers typically obtain 

scanner data in two ways: They get the data directly from a single retail chain or obtain permission to access data from several stores collected by one of two major market research companies, 

Nielsen and IRI.

Over the past 15 years, ERS has used scanner data from both Nielsen and IRI to estimate average 

prices to create a variety of food price databases, including the Cost of Fruits and Vegetables and 

the QFAHPD. The Cost of Fruits and Vegetables (Stewart et al., 2016) uses retail scanner data to 

estimate both the national average retail and edible cost for about 150 commonly consumed fruits 

and vegetables. The database separates the cost of fresh produce items into no-preparation required 

(e.g., baby carrots) and preparation required (e.g., whole carrots). The QFAHPD (Todd et al., 2011, 

Todd et al., 2010) used Nielsen Homescan household scanner data to estimate retail prices for 35 

retail markets in the country. The database contains an average price of 54 groups of foods for each 

of the retail markets. The QFAHPD has been used as retail2 regional prices for groups of foods in 

NHANES (Gregory and Coleman-Jensen, 2013).

Prices collected for a specific study. For some research questions, only a small sample of food 

prices are required, and researchers can use either data from the Bureau of Labor Statistics (BLS)’s 

calculation of the CPI or collect their own food price data.

The CPI for food (BLS, 2018) is measured by observing the change in price of a basket of about 

75 foods. Each year the BLS reviews the list of foods to ensure it consists of items most commonly 

purchased by U.S. households. Prices for the foods are collected through a monthly survey of 

randomly selected stores. When the list is revised, the data collector consults with store managers to 

identify the most common package sizes and brands at each store and refers to those package sizes 

each month in the survey report. The BLS Mid-Atlantic Information Office uses the prices collected 

for the 75 foods in the CPI market basket to publish the “Average Retail Food and Energy Prices for 

the United States” and for each region on its website (BLS, 2018). Since these prices are collected 

from a limited number of stores and brands, they do not represent the range of prices available to 

consumers, even for the products selected. However, these data do provide a consistent measure of 

prices over time.

When the research question covers a small geographic area, researchers can collect their own price 

data. For example, Cassady, Jetter, and Culp (2007) collected prices for two set market baskets of 

foods in Sacramento and Los Angeles. One basket contained more healthful food than the other 

2Retail prices are per unit purchased weight, so the weight includes. inedible parts such as skins, seeds, shells and bones.
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basket, and the study compared the prices of both. The team chose the 25 stores to represent a 

variety of income levels so they could measure the affordability of healthy diets, particularly the 

cost of fruits and vegetables. At the time of this study, the researchers had to visit each store to 

collect prices, rather than use online store websites as done by Nansel et al. (2016). In the latter 

study, researchers obtained food prices from two online grocery store websites for the foods 

reported consumed by 136 participants in a prospective study of youth with Type I diabetes and their 

parents. Since all study participants lived in the Boston area, the research question of the cost of 

diet improvement was best served by collecting local prices. While the Nansel et al. study collected 

prices for foods that study participants chose, given all available options in the store, studies such as 

Cassady et al. start with a predetermined market basket of goods.

The major limitation of using prices for a subset of foods is that one does not have access to prices 

for all available options; the basket might not allow for substitution. For example, if the basket specifies romaine lettuce, but green leaf lettuce is less expensive, a price-conscious shopper might choose 

the less expensive option. Within each food group, there is a wide range of prices available for both 

healthy and less healthy foods (Carlson and Frazão, 2012), and in analysis, researchers can account 

for the fact that consumers can make substitutions without compromising the healthfulness of their 

purchases. Collecting a limited number of foods could bias conclusions about the cost of a healthy 

basket of foods.

Total food acquisition in the National Household Food Acquisition and Purchase Survey 

(FoodAPS). A shortcoming of scanner data is that it covers only retail foods. The ERS Food 

Expenditure series estimates that about half of all food expenditures are made at non-retail sources 

such as restaurants, farmer’s markets, cafeterias, entertainment venues, and vending machines. 

In addition, households acquire free food from friends, food pantries, and USDA food assistance 

programs. To gain a complete picture of food acquisition, USDA collected a week’s worth of household food acquisition data from 4,826 households between April 2012 and January 2013 (Page and 

Kantor, 2020). To our knowledge, FoodAPS remains the only data source that includes detailed 

food acquisition data for both food at home and food away from home, nutrition data for each food 

acquired, and the prices paid by consumers. The individual food items also are tied to WWEIA 

foods so researchers can examine the relationship between the purchase prices paid by households 

and the healthfulness of food acquisitions.

Economic research using WWEIA/NHANES. In addition to these sources, researchers use 

dietary recall data such as the What We Eat in America/National Health and Nutrition Examination 

Survey (WWEIA/NHANES) (Centers for Disease Control and Prevention (CDC) and National 

Center for Health Statistics (NCHS, 2011-2012a) to study issues such as what U.S. households eat, 

(Lin et al., 2016), how healthy the food is (Kirkpatrick et al., 2018, Krebs-Smith et al., 2018), and the 

relationship between dietary health behaviors and chronic diseases (Lin et al., 2016), (Kirkpatrick 

et al., 2018; Krebs-Smith et al., 2018). When WWEIA/NHANES is combined with the ERS 

Flexible Consumer Behavior Survey (FCBS) (Restrepo, 2019) – a module of NHANES designed to 

collect information on U.S. consumers’ knowledge, self-reported food expenditures, attitudes, and 

beliefs regarding nutrition and food choices – researchers can examine the relationship between 

what consumers know about food and nutrition and their food choices. For example, the FCBS was 

recently used to study menu labeling impacts (Gregory et al., 2014, Restrepo et al., 2018).

Other than the CNPP Food Prices Database, none of the price sources discussed above allow 

researchers to estimate the edible cost of individual foods that NHANES/WWEIA participants 

report eating. Since data reported by Buzby and co-authors (2014) estimate that about 21 percent 
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of the food available to consumers is not eaten, it is not valid to assume that all foods purchased are 

actually eaten.

The Purchase to Plate Price Tool. The PPPT uses scanner data, the PPC (Carlson et al., 2019), 

and USDA-derived recipes used in the development of the food composition data associated with 

WWEIA to estimate prices for foods – both the as-eaten price and the as-purchased price – that 

U.S. households report eating in the WWEIA/NHANES. This allows users to use the wealth of data 

in NHANES and associated modules such as the FCBS to examine the role of food choices and 

other research questions. When used with the complete InfoScan data available to ERS, the national 

average prices are estimated from purchases representing about half of all retail food sales (Levin et 

al., 2018). The tool can be applied to various subsets of the data, allowing users to calculate prices 

much closer to what an individual spends.

Since the PPPT uses retail or household scanner data, the estimated prices generally do not include 

purchases made by food service establishments, institutions, or governments, meaning that the 

estimated food prices are drawn from household purchases. Thus, researchers are advised to use 

FoodAPS to examine questions covering all foods acquired by households if the research question 

covers all food purchases. Alternatively, users could adjust the PPPT prices to account for higher 

prices charged by food-away-from-home sources. The PPPT estimates a price for foods that WWEIA 

participants report acquiring away from home, but it assumes that the foods or ingredients are 

purchased from a retail food source and, in the case of foods not ready-to-eat, prepared or heated 

after paying for the food3. Food prices estimated by the PPPT include only the price of food, and do 

not include other costs such as the time to purchase and prepare food nor energy used to transport, 

store, or prepare the food.

The PPPT allows users to combine the wealth of health information in NHANES with edible 

food prices. This report covers the PPPT using 2013 data, which estimate prices for the WWEIA/ 

NHANES 2011-12 cycle. ERS expects to release a 2015 version corresponding to WWEIA/

NHANES 2013-14 cycle, and USDA will need to update both the PPC and PPPT to comply with the 

Agricultural Improvement Act mandate to update the TFP every 5 years.

3The price is the same whether the consumer heats the food at the store after paying for it or prepares the food at home, at 

work, at someone else’s home, or while traveling.
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Data

To estimate food prices for foods reported consumed by participants in WWEIA/NHANES 2011–12 

(CDC and NCHS, 2012), we relied on two data sources: USDA Food and Nutrient Database for Dietary 

Studies (FNDDS) 2011– 2012b (Martin et al., 2014) and IRI InfoScan 2013 (Levin et al., 2018, Muth et 

al., 2016). We used WWEIA/NHANES data to develop a list of foods consumed by U.S. households, 

the FNDDS to develop the types and quantities of purchasable ingredients required to prepare the 

foods, and the IRI InfoScan data as the basis for prices. We also used the Purchase to Plate Crosswalk 

(PPC) (Carlson et al., 2019) to transition between the FNDDS and the IRI InfoScan data.

What We Eat in America/National Health and Examination Survey (WWEIA/NHANES).

NHANES has produced vital health statistics for the United States since the 1960s (Johnson et 

al., 2014) with dietary information collected since the 1970s (CDC NCHS, 2012b). The survey is 

designed to assess the health and nutritional status of adults and children in the United States and 

has evolved to a survey with continuous data collection. A nationally representative sample of about 

5,000 individuals a year is examined, including two 24-hour dietary recalls and extensive biomedical 

data. NHANES public data releases are issued in 2-year cycles, with this study focusing on 2011–12 

data (CDC NCHS, 2012b), the most recently available data at the start of the study. For this study, 

WWEIA/NHANES data are used to determine a list of foods consumed in the 2011–12 survey. 

This study aims to calculate average prices for foods reported eaten at least 10 times by WWEIA/

NHANES participants. The foods consumed more than 10 times represent 96 percent of all incidences of reported foods and 97 percent of all grams of food consumed.

Food and Nutrient Database for Dietary Studies (FNDDS). Energy and nutrient values for foods 

and beverages reported in WWEIA/NHANES 24-hour dietary recalls were provided in the FNDDS

(Martin et al., 2014). The 2011–12 FNDDS release contained information on more than 7,500 foods. 

The FNDDS 2011–12 allowed researchers to review the nutrient profiles for specific foods and beverages in an edible form using standard food weights to obtain moisture and fat changes that occur with 

cooking/preparation, as well as to determine associated portions and recipes.

Many foods in the FNDDS, particularly those used in the WWEIA/NHANES, were represented 

in edible rather than retail form. The weight of an edible food did not include inedible parts such as 

seeds, skins, cores, shells, and bones that were typically included in the retail weight. In addition, 

edible foods were often cooked, or multiple retail foods were combined, to make a single mixed dish. 

For this reason, we could not directly apply scanner data retail prices to edible foods.

The FNDDS recipe file provided the relationship between FNDDS codes and the ingredients that 

make up the recipe. Recipes were based on reviews of popular cookbooks and online sites as well as 

market analysis. While these recipes do not necessarily indicate how an individual would prepare the 

food, they represented an average of all individuals' food preparation practices (table 1). For example, 

the recipe for “mixed salad greens, raw” combines iceberg, endive, spinach, romaine, and green-leaf 

lettuce in equal parts because there was a wide variety of mixed salad greens on the market, and each 

one had a different ratio of each lettuce type. The recipe represented an average of all available products, not necessarily how an individual household would prepare the item.
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Table 1

Example recipes from the Food and Nutrient Database for Dietary Studies (FNDDS 2011-12)

Food Code Description Sequence 

number SR code SR description Amount (g)

75114000 Mixed salad 

greens, raw

1 11252 Lettuce, iceberg (includes crisp 

head types), raw 20

2 11213 Endive, raw 20

3 11457 Spinach, raw 20

4 11251 Lettuce, romaine, raw 20

5 11253 Lettuce, green leaf, raw 20

75200110

Vegetables, 

NS as to type, 

cooked, fat not 

added in 

cooking

1 11584 Vegetables, mixed, frozen, cooked, 

boiled, drained, without salt 100

2 2047 Salt, table 0.6

73102210

Carrots, cooked, 

NS as to form, 

fat not added in 

cooking

1 11125 Carrots, cooked, boiled, drained, 

without salt 155.2

2 2047 Salt, table 0.96

58131110

Ravioli, NS as 

to filling, with 

tomato sauce 

(Includes 

Ravioli, NFS)

1 1123 Egg, whole, raw, fresh 75

2 14429 Water, tap, municipal 74.063

3 20081 Wheat flour, white, all-purpose, 

enriched, bleached 125

4 2047 Salt, table 3.75

5 1036 Cheese, ricotta, whole milk 81.918

6 21500100

Ground beef or patty, cooked, NS 

as to percent lean (formerly NS as 

to regular, lean, or extra lean)

85.05

7 11282 Onions, raw 26.667

8 1032 Cheese, parmesan, grated 6.25

9 4610 Margarine, regular, 80 percent fat, 

composite, stick, with salt 14.1

10 11549 Tomato products, canned, sauce 226.8

11 2003 Spices, basil, dried 0.35

12 19335 Sugars, granulated 2.083

13 1125 Egg, yolk, raw, fresh 8.5

14 2029 Spices, parsley, dried 0.075

15 18069 Bread, white, commercially prepared (includes soft breadcrumbs) 1.5

Note: SR=National Nutrient Database for Standard Reference; NS=Not specified; NFS=Not further specified; g=gram. All 

weights represent the weight of the ingredient in the form described (raw, cooked, dried) and do not include refuse such as 

shells, stems, or seeds. 

Source: USDA, Food and Nutrient Database for Dietary Studies 2011–12.
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The recipes were created to calculate the nutrient content, but they presented two major challenges 

for calculating food prices: Not all recipes included retail ingredients, and a few recipes separated 

commonly used short-cut products such as ravioli into more basic ingredients such as flour, spinach, 

and cheese. Following Carlson, Lino, Juan, et al., (2008), we assumed that most households did not 

have time to prepare ravioli from scratch each time they ate it, and we modified the recipe to include 

purchased ravioli. In the FNDDS, non-purchasable ingredients were used when several recipes use a 

similar ingredient that is prepared at home; this maintained consistency across recipes. For example, 

in the recipe for cooked carrots, the main ingredient was “carrots, cooked, boiled, drained, without 

salt.” While some retailers sold carrots in this form in the refrigerated section, most consumers likely 

prepared it from fresh, frozen, or canned carrots. Cooked carrots are generally not a purchasable 

ingredient, but are used in several recipes such as soups, casseroles, and pasta dishes, and WWEIA 

participants reported eating them. To address this issue, we modified the recipe to account for the 

other three forms of carrots. The Recipe Development section discusses how we modified recipes to 

overcome these challenges.

The foundation of the FNDDS 2011–12 was the USDA National Nutrient Database for Standard 

Reference (SR) 26 (U.S. Department of Agriculture, Agricultural Research Service, Nutrient Data 

Laboratory, 2013). At the time the PPPT was developed, SR was the primary source of nutrient data 

in the United States. SR-26 contained cooking yields from changes in food weight caused by refuse 

(i.e., removing the peel of a banana), moisture loss, water absorption, or net fat gains/losses during 

food preparation and cooking. This study used the recipes, moisture and fat changes, and refuse 

factors from both the FNDDS and the SR.

IRI InfoScan. The IRI data purchased by USDA consists of both store-based scanner data (InfoScan) 

and household-level scanner data (the Consumer Network). The Consumer Network provides demographic and purchase data for a nationwide panel of about 60,000 households that report almost 60 

million purchases annually (Muth et al., 2016). The Consumer Network draws from The National 

Consumer Panel, which is a partnership of IRI and Nielsen. The CNPP Food Price Database used 

Homescan, Nielsen’s version of The National Consumer Panel (Carlson, Lino, Juan, et al., 2008).

In this report, we focus primarily on InfoScan, but the tool could be applied to the Consumer 

Network. The statistical properties of both datasets are discussed in a series of ERS reports (Levin 

et al., 2018; Muth et al., 2016; Sweitzer et al., 2017). InfoScan provides weekly transaction data from 

retail food outlets such as grocery stores, club stores, convenience stores, and supercenters. There are 

60,000 reporting units—either an individual store or data summarized at each of the chain’s Retail 

Market Areas (RMAs). Each observation, or transaction record, is the weekly quantity and revenue 

for an individual item sold in one reporting unit (either the store or RMA). The data available to 

USDA include a mix of both brand-name and private-label products; a limited number of stores do not 

provide transaction data for their private-label products. In the 2013 data, there are 6.6 billion transaction records.

Both IRI datasets record transactions at the product level and products are identified by a Universal 

Product Code (UPC) or barcode. Other than barcodes used only within the store – such as randomweight items sold at the deli counter, loose produce, or a few private-label items – most UPCs are also 

Global Trade Identification Numbers (GTIN), an international set of codes for every good and service 

sold. Private companies maintain GTIN databases that contain detailed information on the products 

needed to conduct business, and each unique number represents a specific product. For example, two 

packages of the same food might have two different GTINs if the packages are different sizes, have 
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different flavors, or are packaged with different materials, such as cans or pouches. IRI creates 

barcodes for random weight products without a GTIN, and items sold by weight are included in the 

price estimation. Users should consult the IRI statistical properties reports and IRI user’s guides 

for more information on how these items are recorded. There are more than half a million barcodes 

used in either the 2013 InfoScan or the Consumer Network.

Purchase to Plate Crosswalk (PPC). To use InfoScan to estimate prices for the FNDDS recipe 

ingredients, we use the newly created PPC linking database and conversion factor files (Carlson et 

al., 2019). The 2013 PPC linking database provides a mapping between 4,390 USDA food codes and 

650,592 UPCs, representing 5.9 billion transactions in InfoScan 2013. The PPC conversion factor 

table provides a multiplier for each UPC to convert the purchased weight in the scanner data to the 

same form as the USDA food the UPC is linked to. The multiplier removes the weight of inedible 

parts such as skin, bones, seeds, and shells that are included in the purchase weight, and adjusts 

the weight for cooking loss or gain if the USDA food code represents a cooked food. Not all UPCs 

linked to the same USDA food have the same conversion factor. For example, baby carrots have no 

refuse; whole carrots have refuse, but both are linked to the same USDA food, raw carrots. For this 

reason, conversion factors are developed manually, rather than the more automated methods used to 

develop matches4. Thus, the conversion factor table covers only the 359,746 UPCs that represent 95 

percent of sales in the four InfoScan transaction files listed in table 2 that are included in this study.

Table 2

IRI transaction files used in the study

Data IRI Transaction File Name Included in 2013 PPC

InfoScan POS_RMA 2013 Data

InfoScan POS_store 2013 Data

InfoScan RW_RMA 2013 Data

InfoScan RW_store 2013 Data

Consumer Network Trip

2013 Data except UPCs in the product_dictionary_rwpanel, and UPCs not 

included in the InfoScan POS and RW 

transaction files

InfoScan PrivateLabel_RMA Not included

InfoScan PrivateLabel_store Not included

Notes: POS-point of sale; RMA-retail market area; RW-random weight. PrivateLabel_RMA and PrivateLabel_store contain 

information for private-label products from stores that do not release their private-label data to USDA in InfoScan. The privatelabel data from other stores are included in the POS and RW transaction files. The Trip data files contain transactions for all 

products purchased by Consumer Network participants, including all private-label products. 

Source: Compiled by the authors.

4Conversion factors were drawn from the FNDDS or Agriculture Handbook 102, Matthews, R.H., and Y.J. Garrison. 

1975. Food Yields Summarized by Different Stages of Preparation Handbook 102, U.S. Department of Agriculture, Agricultural Research Service.
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Purchase to Plate Price Tool Development

The main goal of the price tool is to estimate national average prices for foods reported consumed 

by participants in the WWEIA/NHANES survey using InfoScan. The price tool also provides a 

mechanism for users to estimate prices for these foods using subsets of InfoScan and the Consumer 

Network. For example, users could create a subset of supercenter transactions, transactions over a 

limited time frame, or by geographic region.

To begin, we identified a list of 5,706 FNDDS foods that were reported consumed by participants 

in WWEIA/NHANES 2011–12. Because of resource limitations, we focused on the 3,518 foods 

reported eaten more than 10 times, and the 24 that were modifications of these frequently reported 

foods. The final list of 3,542 food codes represented 219,171 reports of individual foods and beverages reported consumed by WWEIA/NHANES participants, or 96 percent of all reported food and 

beverage eating and drinking occasions.

Recipe Development and Price Estimation

We reviewed the 3,542 codes and identified five different pricing methods, which are explained 

below (table 3). We used direct or alternative matches (methods 1 and 2) for foods typically 

consumed raw or ready-to-eat such as breads, cold cereals, beverages, some fruits and vegetables, 

and frozen entrees. The original or modified FNDDS recipes (methods 3-5) were used for foods 

prepared away from the store, such as cooked vegetables and grains and mixed dishes such as 

burritos, casseroles, and stir-fried dishes. Since many of these foods were prepared from more than 

one purchased ingredient, we used and modified recipes for these FNDDS foods. The modified 

recipe specified the amounts of each ingredient needed to make 100g of the food. FNDDS was the 

starting point for each recipe. The IRI data were the basis for the average ingredient price, and the 

price of the food was the sum of each ingredient price multiplied by the quantity specified in the 

recipe. Tap water was assumed to be free.

Table 3

Number of WWEIA/NHANES foods using each price calculation method

Method Number of WWEIA/NHANES foods

Tap water (free) 1

1 Direct map of FNDDS food to ingredient in IRI data 1,613

2 Map to alternative FNDDS Food(s) 738

3 Use original FNDDS recipe 36

4 Modify recipe by replacing some ingredients with 

codes to allow for different forms of an ingredient 1,082

5 Modify recipes to account for convenience foods. 72

Total foods 3,542

Source: Author calculations using data from the Food and Nutrient Database for Dietary Studies (FNDDS) 2011-12 and 

What We Eat in America/National Health and Nutrition Examination Survey (WWEIA/NHANES) 2011-12.
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Regardless of price calculation method, the PPPT begins by estimating the average price per edible 

kilogram for every USDA food code in the PPC Linking Database5, using the transaction data 

submitted by the user. The user can choose all InfoScan transactions for a national average price, 

the Consumer Network Trip file, or a subset of either dataset. When using a subset of the data, the 

user will need to verify coverage of final price estimates because the tool will not calculate a price 

if an ingredient price is missing.

The first step is to calculate the edible weight—i.e., the weight of the food that is edible excluding 

the skins, peels, seeds, bones, and shells. Because some USDA food codes are linked to different 

purchase forms (e.g., fresh, fresh-cut, frozen, canned, dried, boneless), the edible weight sold must 

first be calculated at the UPC level and then summed over all UPCs that link to the USDA food 

code. For each UPC, i, the total edible weight sold is:

(1) 

1

_ _ _ ( )( _ )

T

i it i i

t

Edible Weight Sold Quantity Sold IRIweight Conversion Factor =

  =     ∑ 

where T is the number of transactions in the data chosen by the user for the tool for UPC i, Quantity 

Sold is the number of packages of UPC i sold in transaction t, the IRI weight is the kilogram weight 

of the package (or unit) for UPC i, and the conversion factor is from the PPC conversion factor 

table for UPC i. Note that the conversion factor takes on values from 0 to <1 when the retail weight 

includes inedible parts and >1 when preparation includes weight gain from water such as cooking 

dried pasta or rice. When using the Consumer Network data, the tool also applies the household 

projection factors to equation 1. After calculating the edible weight for each UPC, we sum the 

edible weight sold for all M UPCs assigned to a USDA food code and divide the total sales of these 

same UPCs by the total edible weight to get the price per edible kilogram of ingredient j used in the 

recipe:

(2)

 

This is equivalent to a weighted average calculation where the UPCs with greater sales volume 

contribute more to the calculated average.

Using the average prices for each ingredient represented by a USDA food code, the price tool calculates prices for foods in WWEIA/NHANES using one of five methods.

Method 1: Direct map to FNDDS Food. This is the simplest of the five methods and is used when 

the PPC linking database linked IRI foods to FNDDS foods that are the same, or that are the same 

after using a conversion factor. For example, the price for most crackers, bread, frozen or canned 

5The PPC uses a combination of SR and FNDDS codes, but all are found in the FNDDS-SR links table.

1

1

( )

_

( _ _)

M

i

i

j M

i

i

sales

ingredient price

edible weight sold

=

=

=

∑

∑
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fruits and vegetables, and frozen entrees is the price of the USDA food code in the linking database. 

That is, the ingredient price is the price of the food.

Method 2: Map to alternative FNDDS Food(s). The PPC linking database links the UPCs in 

the scanner data to the best match among the USDA foods. As a result, some WWEIA/NHANES 

food codes do not have a direct link, but a similar code does. For example, low-fat vanilla yogurt 

with low-calorie sweetener (11422100) is similar in price to non-fat vanilla yogurt with low-calorie 

sweetener (11424000). The PPPT uses the non-fat yogurt to estimate an average price for the low-fat 

yogurt.

Additionally, during development of the PPC linking database, it was more efficient to work with 

just FNDDS codes. Some of these were later replaced by SR codes to establish a better match. 

Unfortunately, there are a few instances of the linking database containing both the SR and the 

FNDDS code for the same food. For example, some raw carrot UPCs were matched to the SR code 

(11124) and others linked to the FNDDS code (73101010). To make use of all available price data, 

the PPPT estimated the price by dividing the total dollars sold of the alternative FNDDS code 

(including UPCs matched to both the original FNDDS and SR codes) by the total edible kilograms 

sold for the codes.

Method 3: Use the original FNDDS recipe. Some of the WWEIA/NHANES codes do not have a 

direct link or an alternative map, but all the ingredients in the FNDDS recipe are in the PPC linking 

database and conversion factor table. In this case, the PPPT uses the FNDDS recipe directly. For 

example, table 1 shows that “Vegetables, NS as to type, cooked, fat not added in cooking” (FNDDS 

Code = 75200110) is a combination of frozen mixed vegetables, cooked, and salt. Since both frozen 

mixed vegetables and salt are included in the PPC linking database, the price is calculated directly 

from the recipe quantities. Before including the recipe in the Price Tool, we adjusted the quantities 

of each ingredient so that the FNDDS recipe made 100 grams of the food using the same methods as 

the developers of the FNDDS (Martin et al., 2014).

To estimate the price of food j, the Price Tool multiplies the average price per edible gram of 

ingredient code i (equation 2) by the number of grams in the FNDDS recipe for each ingredient, and 

sums over all N ingredients in the recipe:

(3) 

Method 4: Modify recipe by replacing some ingredients with codes to allow for different forms 

of an ingredient. Most of the FNDDS recipes for home-prepared foods required modifications 

because the recipe ingredient is more general than the codes used in the PPC linking database. For 

example, table 1 shows that the main ingredient in cooked carrots is boiled carrots. Since boiled 

carrots might be prepared from whole carrots, baby carrots, frozen, or canned carrots, we replaced 

the single ingredient “boiled carrots” with multiple retail ingredients representing the different forms 

of the carrot (table 4). Note that the number of edible KG of raw carrots is calculated using equation 

1 and includes both baby and whole carrots. An ingredient-based conversion factor shown in table 4 

accounts for the moisture gain when raw carrots are boiled. The recipe also includes cooked carrots, 

which is matched to a convenience product—carrots sold in the cooked form—but the original 

form (fresh, frozen, canned) used by the manufacturer of the product is not clear in the IRI data. 

Most of these carrots are purchased in standard weight packages with a UPC, generally found in 

1

 ( _ ) ( _ _ )

N

j i i

i

Price ingredient price adjusted recipe quantity =

=∑
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the refrigerator section, rather than at a deli counter. Since the IRI data does not include standard 

weights for most deli items, the PPPT cannot use in-store prepared foods sold at deli counters in 

estimating prices. While an individual likely will use only one form of carrots in a recipe, this is a 

statistical recipe that represents the average of all purchased forms.

Table 4 

Example recipe using boiled carrots

Sub ingredient Amount Sold 

(edible KG) 

Conversion 

factor

Amount boiled 

carrots 

(edible KG)

Amount 

in recipe 

(percent 

edible KG)

Price per 

edible KG, 

prepared 

(dollar)

Carrots, raw 305,961,446 1.23 377730180.2 98.332 1.43

Carrots, frozen, 

unprepared 4,426,149 1.37 6063217.808 0.683 3.94

Carrots, cooked, from 

canned, fat not added in 

cooking

6,319,952 1 6319952 0.976 3.87

Carrots, cooked, NS as to 

form, NS as to fat added In 

cooking

59,348 1 59348 0.009 12.38

Total 316,766,895 390,172,698

Note: The percent of edible KG and the price per KG are based on the market share using 2013 transactions in the pos_ 

rma and pos_store as of late 2015, when this part of the tool was developed. Updated IRI data for 2013 may produce different results. The PPPT calculates the percent based on transaction data selected by the user. “Carrots, cooked, NS as to 

form, NS as to fat added in cooking” represents pre-cooked carrots, whereas the IRI data does not indicate the form (fresh, 

frozen, canned), but does include a package size. KG=kilogram, NS=Not specified. 

Source: Compiled by authors using data from IRI InfoScan 2013 and the Food and Nutrient Database for Dietary Studies 

(FNDDS) 2011—12.

Figure 1 shows the steps to estimate the price of the food. First, note that the raw and frozen 

carrots are not cooked and require a conversion factor to adjust the weight to that of cooked carrots 

in the recipe. We also estimate the market share of each form of the ingredient using the total 

edible grams sold of each ingredient, adjusted by the conversion factor. The price of the ingredient 

is the mean of all forms, weighted by the market share. We calculated the price of the FNDDS food 

following the revised recipe using equation 3.
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Figure 1

Steps to estimate food price using modified recipe

Source: Compiled by the authors

Method 5. Modify recipes to account for convenience foods. Early editions of the FNDDS recipes 

broke foods down into the most basic ingredients such as flour, individual spices, and raw meat and 

vegetables to facilitate calculating the nutrient content. The FNDDS recipes were based on common 

cookbooks at the time, and these recipes required more time than most U.S. households now spend 

cooking. In 2014, households spent about 51 minutes in preparation and clean-up if they prepared 

meat, and 26 minutes if they did not prepare meat (Hamrick and McClelland, 2016), and most 

consumers used prepared and convenience foods to save time (Kuhns and Saksena, 2017). While 

each update of the FNDDS modifies recipes to use convenience foods, there were a few recipes with 

complex sets of ingredients in the FNDDS 2011-12.

When an FNDDS recipe contained more than 10 ingredients, or when the Westat, Inc. and USDA 

nutritionists reviewing the recipes determined the recipe would take more than 30 minutes to 

prepare, we modified the recipes to use convenience foods. For example, the ravioli recipe in table 

1 included 15 ingredients because it assumed the ravioli were prepared from scratch, and the sauce 

was prepared from stewed tomatoes and spices. We replaced these 15 ingredients with commercially 

prepared ravioli and spaghetti sauce (table 5). As with method 4, the linking database contained 

Method 4: 

Ingredients with 

different forms

Identify ingredients 

with multiple forms or 

sub-ingredients.

Use conversion 

factors to convert 

sub-ingredient to same 

form as ingredient.

Calculate market 

share of each 

sub-ingredient.

Calculate price of 

ingredient as mean 

price of sub-ingredients, weighted by 

market share.

Method 5: 

Commercially 

prepared foods

Identify ingredients 

that are a commercially 

prepared food.

Combine adjusted 

weights to get recipe 

weight of commercially 

prepared food.

Do ingredients 

have multiple 

forms?

Calculate food 

price using 

modified recipe.

Yes

No 
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different types of ravioli and tomato sauces. We used the method 4 steps to adjust quantities and prices 

and calculated the price of the FNDDS food following the revised recipe using method 3 (figure 1).

Table 5

Modified recipe for ravioli 

Original FNDDS recipe Modified recipe

Ingredient Amount 

(edible g) Ingredient Amount (percent edible g)

Ravioli, NS as to filling, no sauce

Egg, whole, raw, fresh 75

Ravioli, NS as to filling, 

no sauce

2.006 Water, tap, municipal 74.063

Wheat flour, white, all-purpose, 

enriched, bleached 125

Salt, table 3.75 Ravioli, meat-filled, no sauce 15.525

Cheese, ricotta, 

whole milk 81.918 Ravioli, cheese-filled, no sauce

75.548

Ground beef or patty, cooked, 

NS as to percent lean 85.05

Ravioli, cheese-and spinachfilled, no sauce 6.921

Onions, raw 26.667

Cheese, parmesan, grated 6.25

Margarine, regular, 80 percent 

fat, composite, stick, with salt 14.1

Egg, yolk, raw, fresh 226.8

Bread, white, commercially 

prepared (includes soft breadcrumbs)0.35

Spaghetti sauce, meatless

Tomato, canned, sauce 

(stewed tomatoes) 2.083

Spaghetti sauce, meatless 99.961

Spices, basil, dried 8.5

Sugars, granulated 0.075 Spaghetti sauce, meatless, 

fat-free 0.039

Spices, parsley, dried 1.5

Note: The percent of edible grams is the percent of edible grams sold of ravioli (top part of chart) and spaghetti sauce (bottom 

part of chart) The percent of edible grams is based on the market share using 2013 transactions in the pos_rma and pos_

store as of late 2015, when this part of the tool was developed. The PPPT calculates these percentages based on transaction 

data selected by the user. Updated IRI 2013 data may produce different results. g=grams; NS=not specified.

Source: Compiled by the authors using data from IRI InfoScan 2013 and the Food and Nutrient Database for Dietary Studies 

(FNDDS) 2011-12.
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The Purchase to Plate Price Tool

The Purchase to Plate Price Tool is a set of three SAS programs that calculates the price for each of 

the 3,542 WWEIA/NHANES foods, using one of the five methods. The first two programs calculate 

a price for each code in the PPC linking database and the alternative FNDDS code used in method 

2, while the third program uses the recipes to calculate a national average price for most WWEIA/

NHANES foods. Foods that WWEIA participants reported from FAFH sources are priced as if 

ingredients (or a prepared food) were purchased in a store and do not include the additional costs 

charged by restaurants to cover labor, energy, and capital costs such as the building and equipment. 

The tool does not calculate prices for the 2,180 foods reported consumed fewer than 10 times by 

WWEIA/NHANES participants. The prices draw from a broad base of grocery items whose sales 

data are available to USDA at the barcode level. Sales of private-label items from stores that do not 

release detailed information on their products are not included in price estimates because the data 

do not include package sizes, but private-label sales are included for stores that do release these data 

at the UPC level (table 2). The PPPT prices do not represent economic costs, which would include 

the time to obtain and prepare the food, energy to travel to stores and to cook, skills to prepare food, 

and other associated costs.

Figure 2

Distribution of estimated daily Food at Home (FAH) expenditures for NHANES/WWEIA 

respondents

Notes: Prices estimated are national average prices. The normal distribution (blue line) shows what the histogram would 

look like if estimated daily food prices have a normal distribution. The kernel distribution (orange line) shows that prices are 

concentrated at the lower end but have a long tail. 

Source: Author estimates using data from WWEIA/NHANES 2011-12 and food prices estimates using IRI InfoScan, 2013. 

Estimates control for the NHANES complex sample design.

30

25

20

15

10

5

0

0 5 10 15 20

Percent of sample

Daily Food at Home expenditures (in 2013 USD)

Normal Kernel
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We used national average prices to estimate the daily expenditure for foods reported consumed by 

NHANES/WWEIA participants when they stated the food was purchased at a store (Food at Home, 

FAH). The results discussed in this paragraph were for the entire 1-day sample, across all ages, 

including both male and female participants, and they accounted for the complex sample design. 

The mean total expenditure was $4.54 (95 percent CI $4.431 to $4.77), and the median was $3.84 

(95 percent CI $3.57 to $4.12). In figure 2, the kernel density (orange line) shows that estimated 

expenditures were concentrated to the left of the normal distribution (blue line), but there was a 

significant number of individuals whose estimated expenditure was significantly above the mean 

(top of the normal curve), or in statistical terms, the right tail of the distribution was long. The 90th 

percentile was $8.54 (95 percent CI $8.12 to $8.98), and the 99th percentile was $17.14 (95 percent 

CI $15.88 to $18.40). All estimates controlled for the complex sample design used in NHANES.

Optional User Tools

The price tool allows researchers either to modify the price calculation method or change the input 

transaction files to suit individual research needs, such as subsetting by geographic region, season, or 

store type. Changing the price calculation method may be best suited to research questions involving 

a limited number of foods, since it requires the researcher to examine each individual recipe and 

may require the user to create new links and conversion factors to supplement the ones in the PPC. 

FNDDS recipe modification requires careful attention to yield factors, existing matches, and an 

understanding of how FNDDS recipes are developed. The methods used in the PPPT are based 

on available links and conversion factors in the PPC, and only one method is validated for each 

WWEIA/NHANES code. Users who choose to change the price calculation method will need to 

carefully document how they modified the recipes and evaluate the individual prices.

On the other hand, changing the input transaction files is relatively straightforward. While the price tool is 

designed using all InfoScan UPC-level transaction data, researchers either can create a subset of InfoScan 

transactions, such as different store types, or use the Consumer Network trip file. We include an important 

safeguard in the price tool: The tool will not calculate a price for a WWEIA/NHANES food if the price 

of an ingredient is missing. For recipes using ingredients with different forms (method 4 and 5), the ingredient price is based on the share of sub-ingredients available in the subset of transaction data.

Coverage of WWEIA/NHANES 2011–12

Coverage—or how much of the food reported consumed by WWEIA/NHANES participants has a 

price—is measured in terms of the number of individual foods reported eaten by WWEIA/NHANES 

participants, the number of occasions eating each food or the incidences of reported food intake, and 

the share of all food by weight that study participants report eating. The PPPT estimates a price only 

for foods consumed more than 10 times, or about 60 percent of the 5,734 foods reported consumed 

by WWEIA/NHANES participants on both days of the survey (table 6). While this may seem low, 

these foods with prices represent 96 percent of all incidences of reported foods and beverages, and 97 

percent of all grams of foods and beverages reported eaten. Although the price tool was not originally 

designed to use with the Consumer Network, using these data will generate a price for 57 percent of all 

foods and beverages reported eaten, 94 percent of the eating occasions, and 96 percent of all grams of 

food reported consumed. Foods without a price tend to be variations of other foods that have a price. 

For example, there are 48 codes for the pizzas that NHANES/WWEIA participants reported eating. 

Of these, 42 pizzas have prices and 6 do not. The maximum number of times a food without a price 

generated by the Consumer Network data was reported was 359 times in NHANES. In comparison, 

tap water was reported 13,412 times, and all foods with prices represent 219,171 total reports.
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Coverage varies by the subset of data used. Users may choose any subset but should verify that the 

coverage is adequate for the research question. For example, we estimated prices using transaction 

data from different store types as subsets (table 6). Using transactions from grocery stores to estimate prices produces very similar levels of coverage as using all transactions. Transactions from 

defense commissaries and club and mass-merchandise stores are only slightly lower. While coverage 

calculated using transactions from convenience, drug, and dollar stores are lower than the other 

types of stores, these transactions still produce prices for 90 percent of eating and drinking occasions for the 2-day sample of WWEIA/NHANES. It is important to note that stores in the IRI data 

tend to be chain stores, rather than independent stores. Independent stores may have different types 

of foods available than chain stores, and these results should not be used to assess food availability 

at different types of stores.

Table 6

Coverage of the Purchase to Plate Price Tool

Number of foods Eating occasions Grams of food

(Percent of all 

WWEIA foods)

(Percent of all eating 

occasions in WWEIA)

(Percent of all grams 

reported consumed in 

WWEIA)

Transaction source

InfoScan 60 96 97

The consumer network 57 94 96

Type of store

Grocery 60 96 97

Club and mass merchandise 56 94 96

Defense commissary 55 93 96

Convenience, drug and dollar 50 91 93

Note: All foods are priced as if the ingredients or prepared foods were purchased in a store. This includes foods that 

WWEIA participants report obtaining from Food Away From Home sources. 

Source: Calculated by the authors using What We Eat in America/ National Health and Nutrition Examination Survey (WWEIA/NHANES) 2011–12 and the Purchase to Plate Price Tool (PPPT). All calculations control for the complex sample design of 

NHANES and use the dietary recall weights for the 2-day sample.

Purchase to Plate Price Tool Validation

We evaluated the price tool by comparing estimated expenditures derived from it to estimated 

expenditures from other government sources, including the USDA Food Plans, self-reported monthly 

expenditures from the Flexible Consumer Behavior Survey in NHANES, and the ERS Food 

Expenditure Series.

We used the PPPT’s national average prices and the reported Day 1 dietary intake by individuals in 

WWEIA/NHANES to estimate the weekly cost of food for the family of 4 (one child age 6–8, one 

age 9–11, and two adults age 19–50, one male and one female) in the USDA Cost of Food at Home 

as found on the USDA Food and Nutrition Service, Center for Nutrition Policy and Promotion 
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website (table 7)6. Since these were prices from 2013, we compared them to the USDA Cost of 

Food at Home for June 2013. The USDA Cost of Food at Home estimates were based on the USDA 

Food Plans, which assumed that not all food purchased is eaten, and the amount that goes uneaten 

varies by expenditure level (10 percent for the Low-Cost Plan, 20 percent for the Moderate-Cost 

Plan, and 30 percent for the Liberal Plan) (Carlson, Lino and Fungwe, 2007). The Low-Cost Food 

Plan ($190 per week) was the midpoint of the second quartile of expenditures, or the 37th percentile of expenditures. Our estimate for the 37th percentile of expenditures, including a multiplier of 

1.10 to account for the food that goes uneaten ($170.41) was lower than the Low-Cost Food Plan 

($190.60), but the 62nd and 87th percentiles were higher than the Moderate-Cost and Liberal Food 

Plans, respectively7. It was not surprising that there is some variation since USDA does not have 

access to data on all food sold, particularly the private-label or store-brand data for all retailers. This 

omission may have caused a slight upward bias in PPPT prices. In addition, the base of the USDA 

Food Plans was last updated using prices from the 2001–03 Nielsen Homescan data (Carlson, Lino 

and Fungwe, 2007). USDA used the CPIs for food to inflate the cost each month for its published 

costs. The USDA Food Plans represented the cost of a healthy diet, and our estimates were based on 

the foods U.S. households actually report eating. Previous research showed that the total amounts 

and types of foods consumers purchase in stores are not in alignment with the Dietary Guidelines 

for Americans (Carlson et al., 2019). However, the calculations used in this report were not sufficient to draw conclusions about the relative costs of a healthy versus a less healthy diet.

Table 7

Weekly cost of food for a family of four

USDA cost of food at 

home estimate

Purchase to Plate Price Tool 

estimate of expenditure

(Dollar) (Dollar) (95 percent CI)

Low-cost/37th percentile 190.6 170.41 (159.88–180.85)

Moderate-cost/62nd percentile 238.2 247.16 (233.18–261.14)

Liberal/87th percentile 289.3 390.83 (365.84–415.82)

Note: Family of 4 consists of 2 children (one age 6–8 and one age 9–11), 1 adult female (age 19–50) and 1 adult male (age 

19–50). Following the estimation of the USDA Food Plans, the Purchase to Plate Price Tool (PPPT) assumes that 10 (20, 30) 

percent of purchased food is not eaten for the Low-Cost (Moderate-Cost, Liberal) Food Plans, respectively. 

Sources: Center for Nutrition Policy and Promotion (CNPP), Cost of Food at Home (June 2013), and author estimates using 

data from IRI InfoScan 2013, the PPPT, and What We Eat in America/National Health and Nutrition Examination Survey 

(WWEIA/NHANES) 2011–12.

The Food Plans estimated the cost of a healthy diet, while the Flexible Consumer Behavior Survey 

(FCBS) (Restrepo, 2019) estimated FAH expenditures, and the ERS Food Expenditure Series 

(Okrent et al., 2018) estimated what households, private companies, institutions, and governments 

spent on food, including food purchased from stores (FAH) and Food Away from Home (FAFH)

6The Low-Cost, Moderate-Cost, and Liberal Food Plans are based solely on food expenditure levels, and cut across income, education, household size, and other factors that impact food choices. However, to make a comparison with the Thrifty 

Food Plan, we would need to isolate the low-income NHANES participants. This isolation means we would need to control 

for other factors that impact food expenditure, and that is beyond the scope of the paper. This could be an area for future 

research.

7The expenditures for the third and fourth quartiles were also adjusted by a multiplier to account for food that is not eaten. 

For the third quartile, we used 1.2 and for the fourth quartile we used 1.3.
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foods purchased from restaurants, cafeterias, entertainment venues, and provided by institutions 

such as hospitals and school cafeterias. Since the price tool calculated prices only for FAH, we estimated the daily and annual expenditures for foods that WWEIA/NHANES participants reported 

having obtained from stores (FAH) and compared those estimates to the FCBS and Food Expenditure 

Series estimates. Following the estimates for the USDA Food Plans, we assumed that 15 percent of 

all food purchased is not eaten. We chose 15 percent because the Low-Cost (assumes 10 percent food 

loss) and Moderate-Cost (20 percent food loss) Food Plans straddle the midpoint of food expenditures. 

However, estimates of consumer food loss range from 1 percent to 55 percent, depending on the type 

of food (Muth et al., 2011). The ERS Food Expenditure Series also assumed an average sales tax of 2.2 

percent for food and 7.1 percent for alcohol, which we added to our estimate.

The daily per capita expenditure was comparable to the FCBS self-reported estimate for FAH. To 

derive a per capita expenditure for the FCBS, we divided the monthly household food expenditure 

($ spent on food from grocery stores + $ spent on food from other stores – $ spent on nonfood 

purchases from grocery stores) by the number of persons in the family and 30 days. We used the 

number of persons in the family rather than the household because the FCBS question specifically 

stated “family.” The daily FCBS self-reported per capita estimate was $5.29 (95 percent CI $5.00 to 

$5.58), which was about the same as the daily per capita FAH expenditure using the PPPT with all 

InfoScan transactions, $5.34 (95 percent CI $5.07 to $5.62). It is remarkable that estimates were so 

similar because the FCBS estimate was based on self-reported expenditures on food throughout a 

month, and our estimates were based on national average retail scanner data prices and self-reported 

food intake.

Our annual per capita estimate for FAH was $1,949.10 (95 percent CI $1,850.55 to $2,051.30), 

slightly lower than the ERS Food Expenditure Series 2013 per capita Nominal FAH expenditure 

estimate of $2,139.45 (Elitzak and Okrent, 2018). Differences in the two estimates might be due 

to differences in the assumed consumer food loss from the PPPT versus actual consumer food loss 

reflected in the Food Expenditure Series, methodologies, and measurement error. Food Expenditure 

data were estimated using sales surveys collected by the U.S. Census and other industry sources, 

while the PPPT estimate relied on the IRI InfoScan data and WWEIA/NHANES.
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Limitations

There are several limitations with both the price tool and the underlying data. First, the price tool estimates average prices, which will not reflect the prices all consumers face in the grocery store. Average 

prices are based on the type of store, package size, and brands that are included in the chosen subset 

of data. Second, the chosen price calculation method will also impact the price. When using the tool 

to estimate average prices, the price for an individual food also will be based on these item variations 

and assumptions. While future research will develop statistical properties for the prices, researchers 

should review prices with their research question in mind to ensure they are valid. Minor variations in 

individual prices are not meaningful, and researchers are advised not to draw conclusions based on the 

prices of individual food items (e.g., different flavors of yogurt) but to examine groups of foods appropriate for the research question (e.g., yogurt or all foods purchased). Finally, the prices do not adjust 

for consumer food loss of edible food; researchers should consider whether edible food loss is important to the research question and adjust prices accordingly.

In addition, both InfoScan and the Consumer Network have limitations that impact price estimation. For 

example, when using the PPPT with InfoScan data, some private-label data are not included because not 

all stores release their private-label data at the UPC level to USDA. This may cause a slight upward bias 

in food prices. While users have access to expenditures for these private-label items, the quantity is not 

available, and a price cannot be calculated. Also, when using the tool with the Consumer Network, users 

should note that the 2013 PPC and PPPT are designed for InfoScan, rather than the Consumer Network. 

As a result, not all UPCs in the Consumer Network are included, and the random-weight purchases made 

by Consumer Network participants are not included because participants record only expenditures, not 

quantities. Users will need to use InfoScan to generate these prices.

Finally, since the 2013 PPPT’s creation, there have been some changes to the IRI data that may impact 

price calculations. Unless otherwise noted, statistics in this report reflect these changes as of April 

2019. Researchers should consult the three IRI reports (Levin et al., 2018; Muth et al., 2016; Sweitzer 

et al., 2017) and the previous PPC report (Carlson et al., 2019) as well as the IRI user guides to 

determine the suitability of the data for price estimates for the individual research question.
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Conclusion

The Purchase to Plate Price Tool (PPPT) is available to researchers using USDA’s proprietary IRI 

scanner data. The tool allows researchers to estimate prices for foods reported consumed by participants in the 2011–12 WWEIA/NHANES survey. Users have the option to use any subset of the IRI 

data as long as the subset includes at least one UPC for each ingredient used to prepare the food. 

Using the 2013 InfoScan data, the PPPT estimates prices for 3,542 FNDDS foods, which represent 

about 97 percent of all the grams of food reported consumed by WWEIA/NHANES participants. 

When the InfoScan data are divided into store type (grocery stores; club and mass merchandise 

stores; defense commissaries; convenience, drug and dollar stores), the percent of grams with prices 

are 97, 96, 96, and 93 percent, respectively. Although the 2011–12 PPPT was not designed to be used 

with the Consumer Network, the Consumer Network data can be used to calculate a price for about 96 

percent of the grams reported consumed by WWEIA/NHANES participants. Bear in mind that these 

are food prices, not food costs, which could include factors such as time, energy, and other resources.

Using national average prices generated by the PPPT to estimate the total annual FAH expenditure 

of WWEIA/NHANES participants, the expenditure was $1,947.41, about 91 percent of the 2013 per 

capita Nominal Food At Home expenditure from the ERS Food Expenditure Series ($2,139.45). The 

95 percent CI of the daily per capita expenditure ($5.08 to $5.61) overlapped an estimate confidence 

of the daily per capita estimate from the FCBS ($5.00 to $5.58). For these comparisons, we added 

the cost of food purchased but not eaten and average sales taxes to the WWEIA/NHANES estimated 

expenditure because the ERS Food Expenditure Series and the FCBS self-reported expenditures 

included these factors. Similarly, we compared the WWEIA/NHANES total food expenditures to the 

USDA Cost of Food at Home estimates of a healthy diet. For individuals in the 25th to 50th percentile of food expenditure, the median expenditure was lower than the Low-Cost estimates, similar to 

the third quartile and the Moderate-Cost estimate; but the WWEIA/NHANES expenditures for the 

fourth quartile of food expenditure were higher than the Liberal cost estimates. It is important to 

remember that the USDA Cost of Food at Home is the cost of a healthy diet, while the estimates using 

WWEIA/NHANES data are for foods that participants reported eating. However, these estimated 

price differences between the Food Plans and recorded consumption are not sufficient evidence to 

draw conclusions about the cost of a healthy diet versus a less healthy diet.

The PPPT allows researchers to examine the relationship between food choices, nutrition knowledge, 

and food prices because it ties the extensive data in WWEIA/NHANES and the Flexible Consumer 

Behavior Survey (FCBS) to individual food prices. For example, a researcher could examine the 

cross-price elasticities of sugary beverages and other beverages chosen by individuals with bloodfasting levels in the diabetic range, who report that they have not been told they are diabetic, 

compared with the beverage choices of other NHANES participants. In addition, the Purchase to 

Plate Price Tool allows researchers to estimate the cost of a healthy diet or compare prices of healthy 

and less healthy diets using updated food prices. Future updates to the tool will draw from additional 

years of the IRI data.
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Appendix A: List of Acronyms

FAH – Food at Home

FAFH – Food Away From Home

FCBS – Flexible Consumer Behavior Survey

FNDDS – USDA Food and Nutrient Database for Dietary Studies

FoodAPS - Food Acquisition and Purchase Survey

GTIN – Global Trade Identification Number

NHANES – National Health and Nutrition Examination Survey

PPC – USDA Purchase to Plate Crosswalk

PPPT – USDA Purchase to Plate Price Tool

RMA – Retail Market Area

QFAHPD – USDA Quarterly Food at Home Prices Database

SR – USDA National Nutrient Database for Standard Reference

UPC – Universal Product Code

WWEIA – USDA What We Eat in America