Technical

Bulletin

Number 1926

April 2010

United States 

Department 

of Agriculture

Economic 

Research 

Service

Jessica E. Todd, Lisa Mancino, 

Ephraim Leibtag, and Christina Tripodo

Methodology Behind the 

Quarterly Food-at-Home 

Price Database
www.ers.usda.gov

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A Report from the Economic Research Service

Abstract

The Quarterly Food-at-Home Price Database (QFAHPD) was developed to provide 

market-level food prices that can be used to study how prices affect food choices, 

intake, and health outcomes. This report presents a detailed description of the methodology used to construct the QFAHPD. The database, constructed from 1999-2006 

Nielsen Homescan data, includes quarterly observations on the mean price of 52 food 

categories for 35 market groups covering the contiguous United States. Data from 2006 

indicate that cross-market price variation can be as much as three to four times greater 

than annual food price infl ation. 

Keywords: Nielsen Homescan, food prices, diet quality, market prices 

Acknowledgments

The authors thank Roland Sturm (RAND), Pablo Monsivais (University of Washington), 

Bettina Aten (Bureau of Economic Analysis) and Andrea Carlson (ERS) for their reviews, 

and Jimmy Zhou for his assistance in early stages of the project. Dale Simms provided 

editorial assistance, and design assistance was provided by Wynnice Pointer-Napper.

Jessica E. Todd, jtodd@ers.usda.gov

Lisa Mancino, lmancino@ers.usda.gov

Ephraim Leibtag, eleibtag@ers.usda.gov

Christina Tripodo

Methodology Behind the 

Quarterly Food-at-Home 

Price Database

Technical

Bulletin

Number 1926

April 2010
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Contents

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

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

Alternative Price Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Nielsen Homescan Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Food Classifi cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 Classifi cation Strategies Used by Others . . . . . . . . . . . . . . . . . . . . . . . . . 5

 The QFAHPD Classifi cation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Defi ning Markets and Calculating Average Prices. . . . . . . . . . . . . . . . . 11

 Defi ning Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Calculating Average Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Accessing the QFAHPD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Cross-Market Variation as Evidenced by the 2006 Data. . . . . . . . . . . . 19

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

Appendix A: Examples of Foods Included in Select Food Groups . . . . 27

Appendix B: Jackknife Methodology Used To Calculate 

 Standard Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Appendix C: Comparison of Price Variation Across Markets 

Observed in Full Sample to Low-Income Sample. . . . . . . . . . . . . . . . . . 30

Appendix D: Outlier Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Recommended citation format for this publication:

Todd, Jessica E., Lisa Mancino, Ephraim Leibtag, and Christina 

Tripodo. Methodology Behind the Quarterly Food-at-Home Price 

Database. Technical Bulletin No. 1926, U.S. Dept. of Agriculture, 

Economic Research Service, April 2010.
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Summary

Food prices are crucial for economic modeling of consumer food choice 

and dietary patterns. Variations in living costs and other market conditions 

across the United States imply that analyses using national-level food prices 

may not accurately capture the effects of food prices on consumer behavior 

or well-being. Measuring local food prices may better indicate the effect of 

prices on food choices. Since prices are also likely to fl uctuate across seasons, particularly for perishable goods such as fruits and vegetables, quarterly 

prices are preferred to an annual estimate when modeling food choices.

What Is the Issue?

Despite the fact that food prices are likely to vary across the country, a dataset that provides a consistent measure of market-level food prices does not 

exist. This lack of price data makes it diffi cult to study the effects that food 

prices have on consumer choices and diet/health outcomes, such as the potential impact of policies that would alter the relative cost of foods—possibly 

through taxes or subsidies—to encourage healthier food choices. 

What Is the Contribution?

The Quarterly Food-at-Home Price Database (QFAHPD) was developed to 

fi ll the gap in available food price data and to support research on the economic determinants of diet quality and health outcomes. The QFAHPD contains regional and market-level quarterly prices for 52 separate food groups 

between 1999 and 2006 for 30 market groups (for 1999 to 2001) and 35 market groups (for 2002 to 2006) that cover all 48 contiguous States. The food 

categories were created to correspond with the U.S. Department of Health 

and Human Services and U.S. Department of Agriculture’s 2005 Dietary 

Guidelines, as well as to capture price premiums for convenience and processing. Prices are presented in dollars per 100 grams of food as purchased. The 

QFAHPD demonstrates that food prices vary widely across geographic areas. 

How Was the Study Conducted? 

We used data from the 1999-2006 Nielsen Homescan panels. Information on 

household-level purchases of both UPC (Universal Product Code)-coded and 

random-weight food items was aggregated to estimate household-level quarterly prices for 52 food groups. The household-level prices were then aggregated to estimate quarterly market-level prices.

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Introduction

Variation in living costs and other market conditions across the United States 

imply that analyses using national-level food prices may not provide an accurate measure of the effects of food prices on consumer behavior or well-being. 

Previous research has shown that food prices are likely to vary by proximity 

to point of production, the types of food outlets in the area, and socio-demographic characteristics of the community, such as income (Volpe and Lavoie, 

2008; Hausman and Leibtag, 2007). Seasonality can also affect food prices, 

particularly for perishable foods such as fruits and vegetables. Thus, measuring food prices quarterly and at the market level, instead of national annual 

measures, may better indicate the effect of prices on food choices. 

Linking market-level, time varying food price data to existing nationally 

representative surveys on dietary patterns or health outcomes can support 

research addressing economic barriers to healthy diets and related health 

outcomes. Previous efforts at linking food prices to individual data on health 

outcomes have relied on currently available price data sets, which typically 

price only a few specifi c foods that are then classifi ed as either “healthy” or 

“unhealthy” (see, for example, Chou et al., 2004; Gelbach et al., 2007). 

The Quarterly Food-at-Home Price Database (QFAHPD) was developed 

to fi ll the gap in available food price data and to support research on the 

economic determinants of diet quality and health outcomes. The database 

contains quarterly market-level prices for food-at-home (primarily, grocery 

store) items, covering the 48 contiguous States for the period 1999 to 2006. 

To balance the need for coverage against tractability, foods are classifi ed 

into 52 separate groups based on the 2005 Dietary Guidelines and other factors relevant for food shopping and preparation. The QFAHPD prices are for 

foods as purchased by consumers and do not account for losses associated 

with inedible portions or changes in weights due to cooking since our goal 

was to provide a price database for foods as purchased (not as consumed). 
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Alternative Price Databases

Some existing food price datasets can be disaggregated beyond the national 

level. These include the Bureau of Labor Statistics’ (BLS) average price 

data, the Council for Community and Economic Research (C2ER, formerly 

known as ACCRA) price data, commercial store-based scanner data such as 

Nielsen’s Scantrack or Information Resources Inc.’s (IRI’s) Infoscan, and 

the Food Prices Database developed by the Center for Nutrition Policy and 

Promotion (CNPP). Each source has its strengths, but none are ideal for studies on how food prices affect food choices, diet quality and health outcomes. 

For example, as part of its calculation of the Consumer Price Index, the BLS 

collects average price data using rigorous sampling procedures for a limited 

number of food products.1 However, these prices are only available at the 

national and broad regional level—Northeast, Midwest, South, and West. In 

addition to the limited number of food items and markets, researchers have 

shown that the BLS price data may be biased. Hausman and Leibtag (2007) 

show that the methodology BLS uses to calculate the food Consumer Price 

Index (CPI) may overestimate the price of food, especially in areas with 

Walmart supercenters, as lower prices observed at these and at other nontraditional retail outlets are not fully captured when consumers shift more of 

their food spending to these lower priced outlets.

The C2ER price database has broader geographic coverage, but includes 

fewer foods than the BLS data. C2ER data are used to make cross-market 

comparisons of the cost of living by pricing a number of consumer goods 

each quarter in over 300 metropolitan areas across the United States. The 

prices collected include 24 food-at-home (grocery store) items and 3 foodaway-from-home (restaurant) items. Within each market area, price collectors are instructed to select grocery stores that individuals from professional 

and managerial households (defi ned as households with incomes in the 

upper quintile of the local distribution) would normally shop and to collect 

prices from these stores each quarter.2 However, there is little detail on the 

sampling frame used to collect these data, leaving questions about the representation and coverage. There are also some challenges in using these data 

for time series analysis since they were designed for cross-sectional analysis 

only (Sturm and Datar, 2005).

Store-based scanner data, such as Nielsen Scantrack, provide records of 

weekly dollar sales and units sold of all UPC transactions at participating 

grocery stores. Average weekly prices can be calculated for selected UPCs, 

as well as broader food categories, by dividing the dollar sales volume by the 

number of units sold. Thus, the prices refl ect the transaction-weighted average of all purchases made. 

One shortcoming of standard store-based scanner data is that foods without 

UPC codes (random weight foods)—such as some fresh fruits and vegetables, baked goods, and deli items—are not included. Also, these data do not 

include price data from discount supercenter or warehouse club stores such 

as Walmart and Costco, which now comprise over 30 percent of consumer 

food-at-home expenditures. The omission of random-weight food items 

and supercenter purchases may put a signifi cant upward bias on price estimates. A comparison of U.S. egg prices over time among the four food price 

1For the full list of food prices collected by BLS, see the BLS website at 

http://data.bls.gov:8080/PDQ/outside.

jsp?survey=ap.

2For each metro area, 5 to 10 stores 

are selected each quarter. For more 

information on the C2ER methodology, 

see their cost-of-living index report at 

http://www.coli.org/surveyforms/colimanual.pdf..
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datasets—BLS average price data, C2ER, Nielsen Scantrack, and Nielsen 

Homescan—shows that while all four datasets show similar trends in the 

price of eggs over time, the price estimates from Nielsen Homescan data are 

always lowest (Leibtag, 2008). 

USDA’s Center for Nutrition Policy and Promotion (CNPP) created another 

food price dataset using Nielsen Homescan data (described in the next section) in order to evaluate whether the Thrifty Food Plan is adequate to consume a healthy diet from food prepared at home. The CNPP Food Prices 

Database provides the price of each food, as consumed (accounting for 

inedible portions, water loss and/or gain from cooking, etc.), reported in the 

2001-02 National Health and Nutrition Examination Survey (NHANES). 

Details about the database and its construction are documented by Carlson 

et al. (2008). Prices for foods reported in the 2003-04 NHANES have also 

been released and include regional prices in addition to national mean prices. 

These data provide detailed information about the average cost of foods as 

consumed, which as designed, is useful for determining the minimal cost of 

a healthy diet. However, the geographic variation is limited to four census 

regions, and the data are not designed to provide information about the cost 

of healthier alternatives. The dataset also does not include the price of substitutes and complements to the foods consumed, which are important determinants of individuals’ food consumption.
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Nielsen Homescan Data

Nielsen Homescan data contain detailed information about household foodat-home purchases. The 1999-2003 data include purchase and demographic 

information for about 8,000 households each year. These households reported 

all UPC and random-weight transactions from all outlet channels, including 

grocery, drug, mass-merchandise, club, supercenter, and convenience stores. 

This sample of households is also referred to as the Fresh Foods subpanel. 

Beginning in 2004, ERS purchased the full Homescan panel that includes 

about 40,000 households per year, but the additional 32,000 households only 

report their UPC-coded transactions, and not their random-weight items. 

Nielsen calculates household-level weights to project a demographically 

balanced panel to match the U.S. population as closely as possible at the 

metropolitan market (52 are identifi ed, see below), regional, and national 

level using census demographic information for each geographic area.3 In 

the 2004-06 data, two weights are computed for households in the Fresh 

Foods subpanel—one that makes the Fresh Foods sample representative of 

each market and the entire contiguous 48 States, and one for use when the 

Fresh Foods households are part of the entire sample. For each food item 

purchased, the data include date of purchase, item description, number of 

units purchased, price paid, and any promotional prices or coupon savings. 

For purchases made in stores tracked by Nielsen Scantrack, Nielsen assigns 

the store-level, weighted-mean weekly price for the item to the household’s 

purchase; for non-Scantrack stores, panelists directly report the price paid.4

3See Muth et al. (2007) for a more 

complete description of the Nielsen 

sampling design and weighting system.

4For more details of the impact of 

this two-tiered price reporting system, 

see Einav, Leibtag, and Nevo (2008).
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Food Classifi cation 

Classifi cation Strategies Used by Others

Previous research on the relationship between food prices and diet/weight 

outcomes had to rely on the limited price data described above, and food 

classifi cation has mainly been limited to food-at-home versus food-awayfrom-home or healthy versus unhealthy foods. For example, Chou et al. 

(2004) look at the effect of the price at full-service restaurants, the price at 

fast-food restaurants, and the price of food at home on adult obesity. They 

obtain the full-service restaurant price from the Census of Retail Trade, while 

prices for fast food and food at home come from the C2ER Cost of Living 

Index. The price index for food at home is constructed from the prices of 

only 13 specifi c food items. 

Gelbach et al. (2007) examine how the relative cost of healthful to unhealthful foods affects obesity among adults. They classify 44 individual foods, 

priced by BLS, as either healthful or unhealthful, but do not provide any 

justifi cation for how the foods are divided. This lack of detail leads to some 

questionable groupings. For example, whole milk, soda crackers, and jelly 

are classifi ed as healthful, while peanut butter is considered unhealthful. The 

majority of the foods classifi ed as healthful are fruits and vegetables, but 

many of these have relatively low nutritional value (e.g., iceberg lettuce) or 

make up a very small proportion of overall consumption or recommended 

daily intake (e.g., cucumbers, celery, mushrooms, radishes, lemons, and 

grapefruit). In addition, each food item is weighted equally to comprise the 

total price index within each category, despite the fact that actual purchase 

and consumption can vary widely across foods. 

Sturm and Datar (2005) use prices of 16 foods from C2ER to construct prices 

for 3 at-home food groups (meat, fruits and vegetables, and dairy), as well as 

1 price for food away from home. The price of each at-home food group is a 

weighted average of each individual food, in which the weights are the share 

of the consumer basket in the food group, while the food-away-from-home 

price is a simple mean of the price for three fast-food meals. The prices for 

each group are then normalized by the average for all areas, and then by the 

cost of living in each Metropolitan Statistical Area (MSA). It is unclear how 

the share of the consumer basket for each food was determined. 

Other approaches to food classifi cation are based on nutrient profi ling or 

energy density. The Food Standards Agency (FSA) in the United Kingdom 

uses nutrient profi ling to determine which foods are subject to limitations 

on advertising to children (FSA, 2009). The model adds points for higher 

content of calories, fat, sugar, and sodium, while it subtracts points for fruit, 

vegetable, nut, fi ber, and protein content. Higher scoring foods are classifi ed 

as less healthy (see Rayner et al. (2005) for more details). This type of classifi cation approach requires that the exact nutrient content of each food is 

known. To date, we know of no single dataset that includes both the nutrient 

content and the prices of foods that would allow for the estimation of geographic differences in the prices of nutrients in food as purchased.
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Drewnowski (2004) classifi es foods based on their nutrient density, arguing 

that low-income households, with limited budgets for food, maximize calories. He fi nds that more energy-dense foods are the least expensive per calorie, which he suggests is a main reason why low-income households struggle 

to have a nutritious diet. However, the assumption that households maximize 

caloric intake, or optimize by choosing specifi c nutrients rather than foods, 

contrasts with the way we observe how most households shop and make 

food purchase decisions. Furthermore, classifi cation based on caloric density 

requires information that is not available with purchase data.

The QFAHPD Classifi cation Strategy

To support research on the economic determinants of diet quality and adherence to dietary recommendations, we had two main considerations when 

determining how to group foods: USDA dietary guidelines (DGs) (USDA 

and DHHS, 2005) and convenience premiums (premiums paid for preparation (e.g. frozen, ready-to-cook meals) and other processing). Our fi rst step 

was to categorize foods in the Nielsen data by the seven main food groups 

identifi ed in the DGs: grains, vegetables, fruits, milk, meat and beans, oils, 

and discretionary calories. 

Each major grouping was further subdivided into the specifi c form recommended by the DGs. For example, individuals are encouraged to ensure that 

at least half of their grains are whole grains. Thus, whole grains are separated 

from refi ned grains. The guidelines also distinguish between whole fruits and 

fruit juices. Thus, fruit juices are separated from whole fruits. In addition, 

individuals are encouraged to choose low-fat dairy products over the regular versions. We classify dairy products as low fat if they contain 2 percent 

or less milk fat. The DGs distinguish dark green and orange vegetables and 

legumes from other vegetables. Moreover, the Dietary Guidelines encourage the consumption of a variety of vegetables, identifying fi ve main groups 

of vegetables: dark green, orange, dry beans and peas, starchy vegetables, 

and other vegetables. We further divided the other vegetables category into 

two groups (table 1)—those listed in the DGs as a source of eight select 

nutrients (see Appendix A, USDA and HHS, 2005) and all other remaining 

vegetables. The goal of this division is to distinguish vegetables that can help 

meet dietary needs of key nutrients from those with lower nutritional quality. 

Legumes are also identifi ed as foods appropriate for meeting recommendations for the meat and beans category, providing another reason for pricing 

them separately from the dark green and orange vegetables. 

As for protein sources (the meat and bean category), the DGs encourage the 

selection of lean cuts of meat as well as a higher consumption of fi sh and 

nuts/seeds over meat and poultry. Meat and other protein sources are classifi ed into the fi ve groups identifi ed in the recommendations for this category: 

meat, poultry, fi sh, nuts and seeds, and eggs. The sixth group identifi ed in 

this category is legumes, which were already identifi ed in a separate category for vegetables. Meat is further divided into low-fat and regular cuts. 

Following FDA requirements, low-fat meats are those that can be labeled as 

fat free, low fat, lean, or extra lean (table 2).

Discretionary calories are identifi ed as the additional calories in foods coming from the addition of fat, sugar, or alcohol. Because the DGs distinguish 
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between oils and solid fats, our classifi cation also follows this breakdown of 

fats. We include a single category for sugars and sweeteners and three separate categories for beverages—water; carbonated non-alcoholic drinks; and 

sugar-sweetened beverages such as fruit drinks and powerades. 

The Dietary Guidelines also recommend limiting intake of total fat, added 

sugars, and sodium. Often, these nutrients are added in food processing. 

Fruits canned in syrup contain added sugars, canned meats and vegetables are 

usually pre-cooked and often have added sodium, and processed nuts (such 

Table 1

Classifi cation of vegetables, with examples

Dark green Starchy vegetables

Bok choy Corn

Broccoli Green peas

Collard greens Lima beans (green)

Dark green leafy lettuce Potatoes

Kale

Mesclun

Mustard greens Other vegetables (source of

Romaine lettuce select nutrients)

Spinach Artichokes

Turnip greens Avocado

Watercress Brussels sprouts

Cabbage

Orange vegetables Caulifl ower

Acorn squash Green or red peppers

Butternut squash Okra

Carrots Parsnips

Hubbard squash Tomatoes

Pumpkin

Sweet potatoes

Other vegetables 

Dry beans and peas Asparagus

Black beans Bean sprouts

Black-eyed peas Celery

Garbanzo beans (chickpeas) Cucumbers

Kidney beans Eggplant

Lentils Green beans

Lima beans (mature) Iceberg (head) lettuce

Navy beans Onions

Pinto beans Mushrooms 

Soy beans Beets

Split peas Zucchini

Tofu (bean curd made from soybeans) Turnips

White beans Wax beans

Source: USDA My Pyramid, Inside the Pyramid (http://www.mypyramid.gov/pyramid/vegetables.

html) and authors’ separation of “other” vegetables.
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as peanut butter) usually include both added sugars and sodium. This motivates a further breakdown of fruits, vegetables, and meats into canned versus 

fresh or frozen and the distinction between raw and processed nut products. 

Finally, many foods purchased for consumption at home are in forms that 

reduce the preparation time required for consumption. Canned soups, frozen entrees, and many rice dishes are packaged ready to heat. Many other 

foods—such as cookies, chips, and deli items—are packaged ready to eat. 

These foods are often composed of several ingredients, making it diffi cult 

to separate the items into distinct categories. Thus, convenience items and 

commercially prepared composite foods are grouped separately from primary 

food items. The fi nal 52 food groups are summarized in table 3. Food groups 

that include random-weight items are identifi ed in the fi nal column.

Table 2

Defi nitions for various low-fat description labels

Label (synonyms) Defi nition

Fat free (without fat, no fat, zero fat) < 0.5 grams (g) of fat per serving

Low fat <=3g fat per 100g and no more than 30% 

of calories from fat

<=1g saturated fat per 100g and no more 

than 10% calories from saturated fat

<=20 milligrams (mg) cholesterol per 

100g and <=2g saturated fat per 100g

 

Lean <10g fat (and <4g saturated fat) per 

serving and per 100g

Extra lean <5g fat (and <2g saturated fat) per serving 

and per 100g

 

% fat free 90% fat free = lean, 95% fat free = extra 

lean

Source: “A Little ‘Lite’ Reading” (http://www.fda.gov/Fdac/special/foodlabel/lite.html).
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Table 3

Food groups and subgroups

Primary category Subcategory level 1 Subcategory level 2 Food group

Group includes 

random weight 

foods?

Fruits Whole Fresh/Frozen 1 yes

Canned 2 no

Fruit juice 3 no

Vegetables Dark green Fresh/Frozen 4 yes

Canned 5 no

Orange Fresh/Frozen 6 yes

Canned 7 no

Starchy Fresh/Frozen 8 yes

Canned 9 no

Other-nutrient dense Fresh/Frozen 10 yes

Canned 11 no

Other-mostly water Fresh/Frozen 12 yes

Canned 13 no

Legumes Fresh//Frozen/Dried 14 no

Canned/Processed 15 no

Grains Whole grain Packaged (bread, rolls, pita, rice, 

pasta, cereal) 16 no

Flour and mixes 17 no

Frozen/Ready to cook 18 no

Refi ned grain Packaged (bread, rolls, pita, rice, 

pasta, cereal) 19 yes

Flour and mixes 20 no

Frozen/Ready to cook 21 no

Dairy Low fat Milk 22 no

Cheese 23 yes

Yogurt & other 24 no

Regular fat Milk 25 no

Cheese 26 yes

Yogurt & other 27 no

Meats Low-fat meat Fresh/Frozen 28 yes

Regular meat Fresh/Frozen 29 yes

Canned 30 no

Poultry Fresh/Frozen 31 yes

Canned 32 no

Fish Fresh/Frozen 33 yes

Canned 34 no

Nuts and seeds Raw 35 yes

Processed/nut butters 36 no

Eggs 37 no

Fats and Oils Oils 38 no

Solids 39 no

Sugar and sweeteners Raw 40 no

—continued
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Table 3

Food groups and subgroups—Continued

Primary category Subcategory level 1 Subcategory level 2 Food group

Group includes 

random weight 

foods?

Beverages Carbonated nonalcoholic 41 no

Fruit drinks and other 

noncarbonated sugary 

beverages

42 no

Water 43 no

Commercially 

prepared items Sweet Frozen (ice cream, frozen desserts) 44 no

Mixes (pancake, muffi n 

and cake mixes) 45 no

Packaged (cookies, 

candy bars, bars) 46 yes

Ready-to-eat (bakery items) 47 yes

Not sweet

Frozen (pizzas, pizza rolls, french 

fries, breaded veggies, fi sh sticks, 

and entrees)

48 no

Canned (soups, sauces, etc,) 49 no

Packaged/Snacks 50 no

Packaged/Meals and sides 51 no

Ready-to-eat (hot and cold 

deli items) 52 yes
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Defi ning Markets and Calculating 

Average Prices

Defi ning Markets

Nielsen constructs the Homescan data by including households from both 

metropolitan (metro) and nonmetropolitan (nonmetro) areas in order to create a national sample of households. Nielsen metro households are defi ned as 

living in one of 52 large metropolitan areas5, while nonmetro households are 

those residing outside of one of those 52 areas (nonmetro). For 2002-2006 

data, all nonmetro households are identifi ed geographically based on the 

State and county in which they reside, allowing us to identify the census division in which the household lives, while the 1999-2001 data identify only the 

census region for nonmetro households. 

To construct prices for as many localized markets as possible, while maximizing use of all available purchase information, we had to make some decisions regarding aggregation. Our initial goal was to construct prices for as 

many of the 52 metropolitan areas as possible (along with nonmetro prices 

at the division/region level); however, the sample size in some metropolitan 

areas was too small to provide reliable estimates of the expenditure shares 

and prices for some of the food groups. This was especially a problem for 

food groups with a large share of random-weight food purchases since purchases of those items are recorded only by the Fresh Foods subsample. We 

therefore aggregated the metro area data into 26 market groups6 such that the 

Fresh Foods sample for each grouping contains at least 30 households and 

the differences between the Fresh Foods sample and remaining Homescan 

sample for average price and expenditures on UPC-coded products are minimized in the 2004-06 data.7 We use the same metro market group defi nitions 

for 1999-2003. For nonmetro households, we group households based on the 

nine nonmetro census divisions for 2002-06 (four nonmetro regions for 1999-

2001 due to a lack of detailed nonmetro location data for those years).8 Table 

4 lists the QFAHPD market groups and fi gure 1 maps them. 

Calculating Average Prices 

The Nielsen Homescan data provide detailed information about each food 

purchase, including number of units or packages, total weight, and total 

amount paid. Using this information, we calculate the price per 100 grams 

(unit value) for each purchase of each food item. For dry weights, we use a 

conversion of 28.35 grams per ounce, and a conversion factor of 29.57 grams 

per ounce for liquids.9 In some cases, however, only the number of items 

purchased (e.g., ears of corn) is reported. In these cases, we used the USDA 

National Nutrient Database for Standard Reference (Release 20) to convert 

the unit counts to weight, assuming the food was medium-sized (if there are 

multiple sizes in the database). Although it was possible to convert most 

unit counts to gram weights using this approach, not all purchases reported 

only as counts were convertible. Those food items that were not converted 

were excluded from the price calculations.10 We did not reduce the weight of 

foods purchased by the amount of the food that is inedible since our database 

is constructed for prices of foods as purchased, not as consumed. 

5See table 4 for the full list of 

Nielsen’s 52 metro areas.

6A market group sometimes includes 

just one metropolitan area. In these 

cases, our market defi nition is exactly 

the same as the Nielsen-defi ned market. 

For example, the Boston metro area is a 

Nielsen-defi ned market with suffi cient 

sample size to be a QFAHPD market 

group itself, while Cincinnati, Cleveland, and Columbus were combined 

into one market group due to sample 

size concerns.

7Prices for UPC-coded and randomweight food items differ within a food 

group. To ensure that the metro areas 

that we combine into a given market 

group are consistent, we check that 

the mean household price paid for 

UPC-coded foods is no different for the 

Fresh Foods panel households than for 

the full sample in each market group. 

This allows us to calculate prices for 

food groups containing random-weight 

foods as a weighted average of the 

price of UPC-coded and randomweight foods. 

8State of residence is not known prior 

to 2002 and nonmetro households for 

1999-2001 are identifi ed at the census 

region level only. 

9We recognize that the gram weight 

of liquids will vary according to the 

density of the liquid. However, the 

variation is quite small. For example, 

the difference between whole and skim 

milk is 10 grams per gallon or 0.08 

grams per ounce. We used a standard 

conversion for all liquids so that the 

prices could be easily converted back 

to ounces.

10For the most part, purchase exclusions were very limited, accounting for 

a negligible share of all purchases. Purchases of collard or mustards greens, 

swiss chard, broccoli rabe, or brocollini 

reported as counts (1.2% of all fresh/

frozen dark green vegetable purchases 

in 2006) were excluded from the fresh/

frozen dark green vegetable group because the USDA National Nutrient Database for Standard Reference (Release 

20) does not contain information for 

standard measures of these vegetables. 

Tortillas were excluded from both the 

whole and refi ned packaged grain food 

groups (1.2% and 4.5% of purchases in 

2006, respectively). Ice pops reported 

as counts (about 28% of all frozen 

sweet observations in 2006) were not 

convertible.
12

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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Table 4

Summary of regions, divisions, and market groups

Census 

region Census division QFAHPD market group Nielsen-identifi ed markets included in the QFAHPD market group

East New England Hartford Hartford

Boston Boston

Nonmetro New England n/a

Middle Atlantic Urban NY Urban NY

Western NY/PA Pittsburgh, Buffalo, Albany, Syracuse

Philadelphia Philadelphia

Other NY Suburban NY, Exurban NY

Nonmetro Middle Atlantic n/a

Central East North Central Metro Midwest 1 Indianapolis, Detroit, Milwaukee, Grand Rapids

Chicago Chicago

Metro Ohio Cincinnati, Cleveland, Columbus

Nonmetro East North Central n/a

West North Central Metro Midwest 2 Kansas City, Minneapolis, St. Louis, Des Moines, Omaha

Nonmetro West North Central n/a

South South Atlantic North Florida Jacksonville, Orlando

Metro South 1 Raleigh-Durham, Charlotte, Richmond

Baltimore Baltimore

South Florida Miami, Tampa

Atlanta Atlanta

Washington, DC Washington, DC

Nonmetro South Atlantic n/a

East South Central Metro South 2 Nashville, Birmingham, Memphis, Louisville

Nonmetro East South Central n/a

West South Central Metro South 3 Little Rock, Oklahoma City-Tulsa

San Antonio San Antonio

Metro South 4 Houston, Dallas, New Orleans

Nonmetro West South Central n/a

West Mountain Metro Mountain Denver, Phoenix

Salt Lake City Salt Lake City

Nonmetro Mountain n/a

Pacifi c Metro California San Diego, Sacramento

Los Angeles Los Angeles

Metro Northwest Seattle, Portland

San Francisco San Francisco

 Nonmetro Pacifi c n/a

n/a = Not applicable. QFAHPD = Quarterly Food-at-Home Price Database.

Note: See the U.S. Census Bureau map detailing the location of States within divisions, http://www.census.gov/geo/www/us_regdiv.pdf.
13

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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Figure 1

Quarterly Food-at-Home Price Database Market Groups, 2002-06 

Notes: For 1999-2001, markets 91 and 92 are combined as market 81; markets 93 and 94 are combined as market 82; 

markets 95, 96, and 97 are combined as market 83; and markets 98 and 99 are combined as market 84.

22

14

99

98

99

23

23

15

14

13

12 98

12

98

16

93

93 6

94

6

6

5

5 5

97

18

11

97

24

10

96

25

25

25 7

17

96

95

96 8

95

3

92

21 26 26

92

9

4

91

20

1

19

1 Hartford 19 Boston

2 Urban NY 20 Other NY

3 Western NY/PA 21 Metro Ohio

4 Philadelphia 22 North Pacific

5 Metro Midwest 1 23 San Francisco

6 Metro Midwest 2 24 Atlanta

7 North Florida 25 Metro South 4

8 Metro South 1 26 Washington, DC

9 Baltimore 91 Nonmetro New England

10 Metro South 2 92 Nonmetro Middle Atlantic

11 Metro South 3 93 Nonmetro East North Central

12 Metro Mountain 94 Nonmetro West North Central

13 Salt Lake City 95 Nonmetro South Atlantic

14 Metro California 96 Nonmetro East South Central

15 Los Angeles 97 Nonmetro West South Central

16 Chicago 98 Nonmetro Mountain

17 South Florida 99 Nonmetro Pacific

18 San Antonio
14

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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Since all households report both UPC-coded and random-weight purchases in 

1999 through 2003, we aggregated the purchase data by fi rst constructing for 

each household j the average price of food group k in quarter q (pj,k,q). The 

household average price weights each purchase occasion equally, so that an 

observed household price is not adjusted for the specifi c expenditure amount 

purchased during that shopping trip.11 For example, if a household purchases 

milk each week, the household mean quarterly price is the simple mean of 

the 13 prices paid per 100 grams each week for milk. The quarterly price for 

each food group in market group i is then the weighted mean of the household average prices in each market group:

 (1)

where HHweight is the household-specifi c weight assigned by Nielsen to 

each household j. We use this same approach for 2004-06 for food groups 

that do not include random-weight foods (see last column of table 3).

In order to be able to use the larger sample of households available for 2004-

06, we used a weighted-average method to calculate the market group price 

for 2004-06 for food groups that contain random-weight food items. For 

some food groups, random-weight purchases make up a large share of purchases (e.g., fresh fruit, vegetables, and meats). Since only about 20 percent 

of the households in 2004-06 reported purchases of these items, we do not 

observe purchase data for a large share of some food groups for many households in the data. One solution to unobserved random-weight food purchases 

is to ignore all such purchases and use only UPC-coded purchases to construct our market prices. However, as table 5 shows for fruit and vegetables, 

the mean price of random-weight foods is lower than that for UPC-coded 

foods. This is probably due to the fact that UPC-coded products include 

convenience and packaging premiums—such as pre-washed, pre-cut, or 

frozen forms of produce—while random-weight products are usually fresh, 

unprocessed items. Thus, ignoring the random-weight purchases could overestimate the mean price of each fruit and vegetable group and the magnitude 

11Alternatively, one could construct 

an expenditure-weighted household average price, but since our goal with the 

QFAHPD is to provide an estimate of 

average market group prices, we allow 

all price observations to count equally 

in the household-level price calculation.

1

1

=

=

=

∑

∑

n

j j ,k ,q

j

i,k ,q n

j

j

HHweight * p

p

HHweight

Table 5

National average prices ($ per 100 grams) for fresh/frozen fruit 

and vegetables, UPC-coded and random weight, 2006 

UPC-coded Random weight

Dark green 0.391 0.281

Orange 0.274 0.173

Starchy 0.244 0.208

Other - select nutrients 0.547 0.325

All other vegetables 0.376 0.235

Fruit 0.455 0.225

UPC = Universal Product Code. Average prices for UPC-coded and random-weight foods are 

statistically signifi cantly different at the 99% level of confi dence.

Source: Authors’ calculations using the 2006 Nielsen Homescan data.
15

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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of the bias depends on the share of purchases in each group that are not UPCcoded. The prices of meat, poultry, and fi sh would be similarly affected if the 

price of UPC-coded products also differs from random-weight products in 

those categories. 

In the interest of utilizing all available purchase data and reducing bias in our 

prices for 2004-06, we calculate a market group price as the weighted average of the price of UPC-coded foods and the price of random-weight foods, 

in which the weights for each type of food are the average share of expenditures on each type among the Fresh Foods panel households. More formally, 

for the food groups that contain random-weight items (see table 3), the price 

for food group k in each market group i in each quarter q is calculated as:

 (2)

where supc

k,i,FF is the food group- and market group-specifi c share of household expenditures on UPC-coded products by Fresh Foods (FF) panel 

households, srwk,i,FF is the food group- and market group-specifi c share of 

household expenditures on random weight products by Fresh Foods Panel 

households, HHweight is the household weight, pupc

k,j,q is the average price 

paid on UPC-coded foods in food group k by household j in quarter q, prwk,j,q

is the average household price paid on random-weight foods in food group 

k by household j in quarter q, m is the number of Fresh Foods panel households, and n is the total number of households in the panel (including Fresh 

Foods households). Within each market group, equation (2) assumes that the 

average price and expenditure share of random-weight items in each food 

group among the Fresh Foods panelists represents what would be observed 

among the full panel of households if the full panel also reported their 

random-weight purchases. This assumption is supported by our fi nding that 

there were no signifi cant differences between the Fresh Foods and full-sample households in the mean household price and mean household expenditure 

on UPC purchases of fruits and vegetables. 

Although the minimum sample size per market group used to aggregate the 

Nielsen-defi ned metro areas into market groups for the QFAHPD was 30 

Fresh Foods households, the average number of households in each market 

group is much larger. For 1999-2003, it is more than 225 (table 6), and for 

2004-06 it is over 1,000 households (table 7).12 Despite the large average 

sample size, some food groups are purchased infrequently (e.g., canned dark 

green vegetables, canned poultry) so that there are fewer than 30 households 

that report purchases in some market groups in some quarters. In these cases, 

the price is not included in the public-release version of the QFAHPD.13

12 There are two markets where the 

number of households is less than 30 

for at least one year from 1999 to 2003.

13 Prices for food groups with fewer 

than 30 purchasing households in a 

given quarter are available, for research 

purposes only, from the authors upon 

request.

1 1

1 1

= =

= =

= +

∑ ∑

∑ ∑

j

n m upc rw

k , j ,q j k , j ,q

upc rw j j

i,k ,q k ,i,FF k ,i,FF n m

j j

j j

HHweight p HHweight p

ps s

HHweight HHweight
16

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Table 6

Number of households by market and year, 1999-2003

QFAHPD market group 1999 2000 2001 2002 2003

1 Hartford 68 57 57 50 41

2 Urban NY 226 223 233 270 283

3 Western NY/PA 264 206 199 188 177

4 Philadelphia 106 536 646 75 69

5 Metro Midwest 1 242 230 220 224 196

6 Metro Midwest 2 210 212 189 188 161

7 North Florida 40 30 72 474 479

8 Metro South 1 60 68 130 45 39

9 Baltimore 265 253 246 250 256

10 Metro South 2 42 53 107 121 116

11 Metro South 3 15 19 46 36 50

12 Metro Mountain 157 151 132 126 117

13 Salt Lake City 97 90 76 619 738

14 Metro California 59 58 56 121 160

15 Los Angeles 690 657 654 714 710

16 Chicago 633 593 590 653 654

17 South Florida 80 81 132 146 152

18 San Antonio 461 434 425 620 638

19 Boston 79 54 58 49 49

20 Other NY 501 466 490 735 724

21 Metro Ohio 145 144 141 144 127

22 North Pacifi c 109 100 89 79 81

23 San Francisco 37 383 586 66 67

24 Atlanta 833 786 779 829 857

25 Metro South 4 62 66 127 152 190

26 Washington, DC 543 518 533 550 565

81 Nonmetro East 125 111 116 n/a n/a

91 Nonmetro New England n/a n/a n/a 46 51

92 Nonmetro Middle Atlantic n/a n/a n/a 52 53

82 Nonmetro Central 581 514 544 n/a n/a

93 Nonmetro East North Central n/a n/a n/a 275 263

94 Nonmetro West North Central n/a n/a n/a 211 188

83 Nonmetro South 105 328 123 n/a n/a

95 Nonmetro South Atlantic n/a n/a n/a 148 152

96 Nonmetro East South Central n/a n/a n/a 71 78

97 Nonmetro West South Central n/a n/a n/a 111 117

84 Nonmetro West 289 250 272 n/a n/a

98 Nonmetro Mountain n/a n/a n/a 116 109

99 Nonmetro Pacifi c n/a n/a n/a 117 103

QFAHPD = Quarterly Food-at-Home Price Database.

Source: ERS tabulations of Nielsen Homescan data, 1999-2003. 
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Table 7

Number of households by market and year, 2004-06

QFAHPD market group 2004 2005 2006

Full panel FF panel Full panel FF panel Full panel FF panel

1 Hartford 218 39 193 36 186 31

2 Urban NY 599 282 577 266 557 236

3 Western NY/PA 1,895 164 1,740 164 1,748 152

4 Philadelphia 260 63 248 54 237 49

5 Metro Midwest 1 1,481 177 1,614 164 1,617 151

6 Metro Midwest 2 2,985 152 2,900 142 2,825 131

7 North Florida 958 440 900 410 909 397

8 Metro South 1 1,269 43 1,191 48 1,133 49

9 Baltimore 333 229 356 215 334 192

10 Metro South 2 1,721 140 1,758 122 1,764 119

11 Metro South 3 819 47 879 45 811 41

12 Metro Mountain 2,433 122 2,339 109 2,247 110

13 Salt Lake City 832 657 921 656 882 569

14 Metro California 2,366 147 2,261 135 2,192 115

15 Los Angeles 1,238 668 1,151 625 1,150 581

16 Chicago 1,148 592 1,064 558 1,069 490

17 South Florida 1,629 156 1,548 137 1,509 121

18 San Antonio 1,018 717 995 711 963 662

19 Boston 1,280 40 1,275 42 1,197 34

20 Other NY 1,106 777 1,183 827 1,118 743

21 Metro Ohio 1,843 116 1,780 111 1,710 106

22 North Pacifi c 1,505 79 1,413 76 1,337 70

23 San Francisco 385 84 378 82 378 77

24 Atlanta 1,018 800 1,039 784 976 699

25 Metro South 4 2,464 172 2,378 138 2,482 135

26 Washington, DC 743 516 759 508 743 477

81 Nonmetro East n/a n/a n/a n/a n/a n/a

91 Nonmetro New England 185 45 167 41 160 40

92 Nonmetro Middle Atlantic 232 59 208 52 201 50

82 Nonmetro Central n/a n/a n/a n/a n/a n/a

93 Nonmetro East North Central 1,081 242 1,127 231 1,090 224

94 Nonmetro West North Central 761 173 760 163 687 150

83 Nonmetro South n/a n/a n/a n/a n/a n/a

95 Nonmetro South Atlantic 674 176 683 172 668 158

96 Nonmetro East South Central 792 86 813 71 793 59

97 Nonmetro West South Central 1,260 114 1,280 108 1,163 93

84 Nonmetro West n/a n/a n/a n/a n/a n/a

98 Nonmetro Mountain 542 114 512 121 520 120

99 Nonmetro Pacifi c 504 105 466 101 430 95

QFAHPD = Quarterly Food-at-Home Price Database. FF = Fresh Foods. 

Source: ERS tabulations of Nielsen Homescan data, 2004-06.
18

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Accessing the QFAHPD

The QFAHPD can be downloaded as a set of 4 Excel14 spreadsheets from 

http://www.ers.usda.gov/Briefi ng/CPIFoodAndExpenditures/QFAHPD.

htm: fruits and vegetables (food groups 1-15); grains and dairy (food groups 

16-27); meats, nuts, and eggs (food groups 28-37); and fats, beverages, and 

prepared foods (food groups 38-52). Each Excel fi le includes a codebook 

spreadsheet identifying the codes for food groups, market groups, regions, 

and divisions. Each food group is included in a separate worksheet. Quarterly 

prices are not reported when fewer than 30 households reported purchases of 

a particular food group. 

The variables included in the database are: market group, year, quarter, price 

($ per 100 grams), standard error (se, estimated via jackknife, see Appendix 

B), number of households (n) from which the price is estimated, Census division (division), Census region (region), the sum of household weights within 

the market (aggweight, can be used to aggregate market group prices to division- or region-level prices)15, and total (weighted) quarterly expenditure on 

the food group in the market group (totexp, can be used to construct prices 

for aggregated food groups). 

15Aggregation up to the census 

region is possible for all years (1999-

2006) and up to the census division for 

2002-06 only.

14Use of commercial and trade names 

does not imply approval or constitute 

endorsement by USDA.
19

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Cross-Market Variation as Evidenced 

by the 2006 Data

The paucity of market-level food price data has made it diffi cult to estimate 

the extent to which food prices vary across the country and to study the relationship between food prices and diet and health outcomes and overall wellbeing. In this section, we use the 2006 QFAHPD data to explore the extent of 

cross-market price variation. 

We use the coeffi cient of variation (CV, the standard deviation divided by 

the mean quarterly market group price) as a measure of cross-market dispersion because in addition to summarizing the cross-market variation, it allows 

us to compare cross-market variation across food groups.16 Table 8 reports 

the mean market group price and the coeffi cient of variation for each quarter 

of 2006 for a subset of the 52 food groups. There is quite a bit of variation 

across markets, with the coeffi cients of variation ranging from 5 percent 

(canned soups and sauces, 1st quarter) to 18 percent (eggs, 3rd quarter). 

To look more closely at the variation in market group prices, table 9 presents 

an extract of the QFAHPD that includes the 2006 fi rst-quarter price for fresh 

and frozen fruit (food group 1) for each of the 35 market groups. The lowest 

price (22.9 cents per 100 grams) is found in market group 94, which includes 

portions of Oklahoma, Nebraska, and North and South Dakota (see fi g. 1 on 

p. 13). The highest price (42.2 cents) is observed in Hartford (market group 

1). A t-test reveals that the difference between these two prices (19.3 cents, 

or 65 percent of the mean market group price that quarter) is statistically signifi cant at the 99-percent level of confi dence (p <.01). But not all differences 

in market group prices are statistically signifi cant. For example, the fi rstquarter price in market group 94 is not statistically different from the second 

lowest market group price (23.7 cents per 100 grams, a difference of 0.8 cent, 

or 2.7 percent of the mean) found in market group 99 (Nonmetro Pacifi c, 

which includes parts of California, Oregon, and Washington). 

Table 10 reports the minimum and maximum market group price for the 

fi rst quarter of 2006 and the maximum price as percent of the minimum 

price for the same subset of food groups presented in table 8. Among these 

food groups, the range in market group prices is lowest for canned soups 

and sauces (maximum market group price is 25 percent above the lowest 

market group price) and highest for low-fat cheese (117 percent). The difference between the minimum and maximum market group price is statistically 

signifi cant (p<0.01) for all food groups except packaged sweets and baked 

goods (food group 46). 

We also calculate the weighted mean prices and coeffi cients of variation for 

the four census regions for these same select food groups (table 11). The CVs 

reveal that cross-market dispersion within each census region depends on the 

specifi c food group and the region. In some food groups, the variation within 

regions (as measured by the coeffi cient of variation) is smaller than the variation observed across all market groups, but for other food groups the regional 

variation is just as large or larger than the overall cross-market variation. For 

example, the CVs for canned fruit are greater in the East (13.3 percent) and 

West (10.6 percent) than for all market groups (10.5 percent). On the other 

16Normally, the standard deviation 

(the square root of a variable’s variance) is used to examine dispersion 

of a variable. In a normal distribution, 

68 percent of the observations (in this 

case, market groups) will be within 

1 standard deviation of the mean and 

about 95 percent will be within 2 standard deviations. Since each food group 

has a different mean price, the standard 

deviations are not comparable. Because 

the coeffi cient of variation is normalized by the mean market group price, 

the measure can be compared across 

food groups.
20

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Table 8

Mean quarterly market group prices ($ per 100 grams) and coeffi cients of variation (percent), 

select food groups, 2006

Food group 1st quarter 2nd quarter 3rd quarter 4th quarter

(1) Fresh/frozen fruit 0.298 0.301 0.297 0.289

(10.7) (10.1) (9.0) (10.9)

(2) Canned fruit 0.288 0.279 0.290 0.282

(10.5) (9.5) (9.1) (8.1)

(3) Fruit juice 0.174 0.180 0.182 0.184

(9.6) (10.5) (12.0) (8.5)

(4) Fresh/frozen dark green vegetables 0.342 0.356 0.351 0.315

(10.1) (12.0) (10.8) (10.3)

(6) Fresh/frozen orange vegetables 0.247 0.253 0.258 0.233

(7.4) (8.2) (7.9) (6.7)

(8) Fresh/frozen starchy vegetables 0.219 0.221 0.225 0.222

(10.4) (11.2) (9.7) (11.1)

(15) Canned legumes 0.150 0.156 0.157 0.155

(11.5) (10.8) (10.3) (10.7)

(16) Whole-grain bread, rolls, rice, pasta, cereal 0.470 0.473 0.478 0.477

(5.8) (6.2) (6.3) (5.9)

(19) Refi ned-grain bread, rolls, rice, pasta, cereal 0.376 0.384 0.380 0.384

(7.8) (8.8) (7.7) (7.3)

(22) Low-fat milk 0.093 0.091 0.091 0.094

(13.1) (13.1) (13.1) (13.4)

(23) Low-fat cheese 0.639 0.634 0.628 0.646

(12.8) (12.8) (12.1) (12.9)

(29) Fresh/frozen regular fat meat 0.735 0.741 0.756 0.754

(10.4) (11.1) (10.3) (11.7)

(31) Fresh/frozen poultry 0.550 0.549 0.576 0.517

(11.6) (15.8) (12.5) (11.5)

(33) Fresh/frozen fi sh 1.190 1.259 1.260 1.310

(11.7) (11.8) (12.7) (10.2)

(37) Eggs 0.188 0.178 0.177 0.203

(15.5) (17.5) (18.5) (14.8)

(41) Non-alcoholic carbonated beverages 0.087 0.086 0.087 0.088

(6.8) (8.0) (7.7) (7.7)

(42) Non-carbonated caloric beverages 0.119 0.114 0.119 0.118

(12.0) (11.5) (26.1) (10.2)

(46) Packaged sweets/baked goods 0.972 0.935 0.893 0.910

(13.8) (14.3) (15.4) (8.3)

(48) Frozen entrees and sides 0.652 0.657 0.654 0.658

(6.8) (7.5) (6.8) (6.6)

(49) Canned soups, sauces, prepared foods 0.249 0.245 0.251 0.253

(5.0) (6.8) (6.7) (6.3)

(50) Packaged snacks 0.794 0.802 0.805 0.799

(7.7) (6.4) (6.9) (7.4)

Source: ERS calculations using 2006 Quarterly Food-at-Home Price Database; coeffi cient of variation in parentheses; weighted means reported.
21

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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Table 9

QFAHPD fi rst-quarter 2006 data, food group 1 (fresh and frozen fruit)

QFAHPD 

market 

group

year quarter price se n division region aggweight totexp

1 2006 1 0.4217 0.0591 132 1 1 1,220,140 $15,814,053 

2 2006 1 0.3164 0.0194 382 2 1 3,296,844 $43,328,030 

3 2006 1 0.3162 0.0128 1,296 2 1 4,350,804 $63,329,913 

4 2006 1 0.3137 0.0080 893 2 1 3,464,067 $67,899,846 

5 2006 1 0.2701 0.0092 1,261 3 2 5,889,043 $75,720,632 

6 2006 1 0.2914 0.0136 2,019 4 2 4,960,659 $56,332,924 

7 2006 1 0.2841 0.0110 299 5 3 2,523,582 $39,723,652 

8 2006 1 0.3255 0.0208 1055 5 3 3,980,952 $55,007,253 

9 2006 1 0.3430 0.0184 271 5 3 1,089,065 $19,000,109

10 2006 1 0.2761 0.0128 1,203 6 3 4,704,570 $44,905,832 

11 2006 1 0.2835 0.0298 511 7 3 2,339,893 $18,040,062 

12 2006 1 0.3088 0.0195 1,586 8 4 3,933,781 $48,339,432 

13 2006 1 0.2462 0.0232 162 8 4 1,289,480 $19,479,797 

14 2006 1 0.2616 0.0173 760 9 4 2,213,982 $26,200,949

15 2006 1 0.2974 0.0108 918 9 4 5,650,944 $98,105,794 

16 2006 1 0.3068 0.0115 868 3 2 3,343,922 $65,182,733 

17 2006 1 0.3161 0.0144 1,678 5 3 4,641,030 $63,330,287 

18 2006 1 0.3182 0.0162 736 7 3 1,423,906 $30,117,234 

19 2006 1 0.3296 0.0297 824 1 1 3,151,092 $35,943,906 

20 2006 1 0.3675 0.0133 698 2 1 4,173,092 $76,369,759

21 2006 1 0.3058 0.0146 1,320 3 2 4,380,276 $53,592,174 

22 2006 1 0.2793 0.0213 899 9 4 3,145,148 $40,051,625 

23 2006 1 0.3344 0.0098 798 9 4 2,607,957 $65,955,631 

24 2006 1 0.2914 0.0085 819 5 3 2,288,210 $43,336,196 

25 2006 1 0.2885 0.0166 1,676 7 3 5,896,993 $57,562,596 

26 2006 1 0.3334 0.0116 632 5 3 2,763,040 $64,142,661 

91 2006 1 0.3430 0.0275 113 1 1 889,672 $13,507,918 

92 2006 1 0.3035 0.0239 145 2 1 1,084,815 $14,145,782 

93 2006 1 0.2485 0.0102 836 3 2 5,136,716 $65,944,788 

94 2006 1 0.2290 0.0129 503 4 2 2,855,429 $24,419,772 

95 2006 1 0.2822 0.0150 485 5 3 4,973,485 $48,939,970 

96 2006 1 0.2911 0.0204 548 6 3 2,052,783 $18,738,228 

97 2006 1 0.3114 0.0243 767 7 3 3,260,843 $29,260,779

98 2006 1 0.2868 0.0178 379 8 4 3,073,814 $42,435,889

99 2006 1 0.2365 0.0147 312 9 4 2,372,146 $27,975,480 

Notes: se = standard error, n = sample size (number of households), aggweight = aggregate household weight, 

totexp = total quarterly expenditures. QFAHPD = Quarterly Food-at-Home Price Database.
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hand, the CVs for canned legumes are smaller in all census regions (9.1, 7.5, 

7.7 and 10.5 percent) as compared to the national CV (11.5 percent).

This brief overview of the 2006 data demonstrates that food prices vary 

widely across geographic areas. The more localized price estimates in the 

QFAHPD show that variation across market groups is much greater than 

food price variation over time, which averaged just 3 percent per year over 

the past 20 years (table 12). Fresh fruit and vegetable prices, which generally exhibit greater infl ation rates than do other food categories, averaged 

less than 5 percent infl ation per year, but the coeffi cient of variation (across 

market groups) was as high as 10 percent in 2006 alone. The greater price 

variation across market groups as compared to the price variation over time 

suggests that research investigating the determinants of cross-market price 

variation deserves greater attention, or at least as much focus as the determinants of price change over time. 

Table 10

Range in market group prices, fi rst-quarter 2006, select food groups

Food group Minimum 

price

Maximum 

price

Max as 

percent of 

min.

$ per 100 grams

1 Fresh/frozen fruit 0.229 0.422 184.1

2 Canned fruit 0.237 0.385 162.4

3 Fruit juice 0.146 0.223 152.3

4 Fresh/frozen dark green vegetables 0.259 0.399 154.0

6 Fresh/frozen orange vegetables 0.202 0.302 149.7

8 Fresh/frozen starchy vegetables 0.176 0.277 157.3

15 Canned legumes 0.116 0.208 178.7

16 Whole grain bread, rolls, rice, pasta, cereal 0.429 0.540 125.8

19 Refi ned-grain bread, rolls, rice, pasta, cereal 0.335 0.467 139.7

22 Low-fat milk 0.067 0.124 185.9

23 Low-fat cheese 0.432 0.938 217.2

29 Fresh/frozen regular fat meat 0.598 0.906 151.4

31 Fresh/frozen poultry 0.468 0.856 182.9

33 Fresh/frozen fi sh 0.891 1.506 169.1

37 Eggs 0.143 0.250 174.6

41 Non-alcoholic carbonated beverages 0.075 0.104 138.5

42 Noncarbonated caloric beverages 0.095 0.160 168.4

46 Packaged sweets/baked goods 0.803 1.463 182.3

47 Bakery items, ready to eat 0.399 0.652 163.3

48 Frozen entrees and sides 0.590 0.784 133.0

49 Canned soups, sauces, prepared foods 0.226 0.283 125.4

50 Packaged snacks 0.709 1.025 144.5

Source: ERS calculations using 2006 Quarterly Food-at-Home Price Database.
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Table 11

Mean market group prices ($ per 100 grams) and coeffi cients of variation (percent), fi rst-quarter 2006, 

by census region and overall

Food group East Central South West National

1 Fresh/frozen fruit 0.334 0.276 0.300 0.288 0.298

(8.9) (9.6) (6.6) (9.5) (10.7)

2 Canned fruit 0.306 0.267 0.281 0.306 0.288

(13.3) (5.9) (5.2) (10.6) (10.5)

3 Fruit juice 0.162 0.172 0.167 0.198 0.174

(5.0) (3.1) (7.0) (7.2) (9.6)

4 Fresh/frozen dark green vegetables 0.369 0.329 0.356 0.307 0.342

(4.5) (6.3) (8.5) (10.0) (10.1)

6 Fresh/frozen orange vegetables 0.242 0.248 0.256 0.236 0.247

(6.5) (1.7) (8.0) (7.7) (7.4)

8 Fresh/frozen starchy vegetables 0.246 0.206 0.217 0.214 0.219

(5.7) (4.9) (11.3) (7.8) (10.4)

15 Canned legumes 0.152 0.144 0.141 0.170 0.150

(9.1) (7.5) (7.7) (10.5) (11.5)

16 Whole grain bread, rolls, rice, pasta, cereal 0.497 0.451 0.463 0.477 0.470

(2.4) (2.6) (6.7) (4.4) (5.8)

19 Refi ned-grain bread, rolls, rice, pasta, cereal 0.379 0.361 0.365 0.410 0.376

(4.8) (5.1) (5.6) (7.7) (7.8)

22 Low-fat milk 0.100 0.079 0.099 0.090 0.093

(13.7) (5.6) (7.0) (11.2) (13.1)

23 Low-fat cheese 0.713 0.567 0.660 0.615 0.639

(11.7) (8.0) (7.7) (13.6) (12.8)

29 Fresh/frozen regular fat meat 0.820 0.688 0.705 0.764 0.735

(5.9) (5.9) (9.9) (8.4) (10.4)

31 Fresh/frozen poultry 0.613 0.528 0.532 0.550 0.550

(13.4) (9.1) (8.9) (8.7) (11.6)

33 Fresh/frozen fi sh 1.297 1.103 1.139 1.276 1.190 

(6.0) (7.9) (13.2) (6.9) (11.7)

37 Eggs 0.211 0.162 0.176 0.215 0.188

(11.9) (8.8) (5.9) (13.5) (15.5)

41 Non-alcoholic carbonated beverages 0.082 0.085 0.087 0.096 0.087

(6.2) (2.5) (2.6) (5.9) (6.8)

42 Non-carbonated caloric beverages 0.120 0.119 0.112 0.130 0.119

(10.8) (6.2) (12.2) (11.8) (12.0)

46 Packaged sweets/baked goods 0.992 0.885 0.937 1.110 0.972

(6.8) (5.0) (6.9) (18.4) (13.8)

48 Frozen entrees and sides 0.685 0.636 0.636 0.668 0.652

(5.9) (9.0) (4.7) (4.1) (6.8)

49 Canned soups, sauces, prepared foods 0.245 0.247 0.245 0.263 0.249

(5.8) (2.8) (4.0) (3.5) (5.0)

50 Packaged snacks 0.806 0.748 0.788 0.844 0.794

(5.9) (6.3) (5.3) (8.6) (7.7)

Source: ERS calculations using 2006 Quarterly Food-at-Home Price Database; coeffi cient of variation in parentheses; weighted means reported.
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Table 12

Average annual U.S. food price infl ation, 1989-2008 

Food group Average annual 

infl ation (%) 

All Food 3.0

Food away from home 2.9

Food at home 3.1

Beef 3.5

Pork 2.6

Poultry 2.6

Fish 2.7

Eggs 5.0

Dairy 3.4

Fats and oils 2.9

Cereal and bakery products 3.6

Fresh fruits 4.6

Fresh vegetables 4.5

Processed fruits and vegetables 2.9

Non-alcoholic beverages 2.0

Source: ERS calculations using Bureau of Labor Statistics Consumer Price Index for All Urban 

Consumers (CPI-U) data.
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References

Broda, Christian, Ephraim Leibtag, and David E. Weinstein. (2009). “The 

Role of Prices in Measuring the Poor’s Living Standards,” Journal of 

Economic Perspectives, 23(2): 77–97.

Carlson, Andrea, Mark Lino, WenYen Juan, Kristin Marcoe, Lisa Bente, 

Hazel A. B. Hiza, Patricia M. Guenther, and Ephraim Leibtag (2008). 

“Development of the CNPP Prices Database,” CNPP-22, Center for 

Nutrition Policy and Promotion, U.S. Department of Agriculture, http://

ageconsearch.umn.edu/bitstream/45851/2/PricesDatabaseReport.pdf.

Chou, Shin-Yi, Michael Grossman and Henry Saffer. (2004). “An Economic 

Analysis of Adult Obesity: Results from the Behavioral Risk Factor 

Surveillance System.” Journal of Health Economics, 23(3): 565-587.

Council for Community and Economic Research (C2ER), 2009. ACCRA 

Cost of Living Index: Manual, Arlington, VA, Nov. http://www.coli.org/

surveyforms/colimanual.pdf.

Dong , Diansheng, and Brian W. Gould (2000). “Quality versus quantity in 

Mexican household poultry and pork purchases,” Agribusiness, 16(3): 

333-355.

Drewnowski, Adam (2004). “Obesity and the food environment: Dietary 

energy density and diet costs,” American Journal of Preventive Medicine, 

27(3): 154-162.

Einav, Liran, Ephraim Leibtag, and Aviv Nevo (2008). On The Accuracy 

of Nielsen Homescan Data, Economic Research Report 69, Economic 

Research Service, USDA, Nov.

Food Standards Agency (FSA) (2009). “Nutrient Profi ling Technical 

Guidance,” United Kindgom, April. http://www.food.gov.uk/multimedia/

pdfs/techguidenutprofi ling.pdf.

Gelbach, Jonah B., Jonathan Klick, and Thomas Stratmann (2007). 

“Cheap Donuts and Expensive Broccoli: The Effect of Relative Prices 

on Obesity,” working paper, available at SSRN: http://ssrn.com/

abstract=976484.

Golan, Elise, Hayden Stewart, Fred Kuchler and Diansheng Dong (2008) 

“Can Low-Income Americans Afford a Healthy Diet?” Amber Waves, 

6(5): 26-33.

Guenther, P.M., J. Reedy, and S.M. Krebs-Smith (2008). “Development 

of the Healthy Eating Index-2005,” Journal of the American Dietetic 

Association, 108(11): 1896-1901.

Hausman, Jerry, and Ephraim Leibtag (2007). “Consumer Benefi ts from 

Increased Competition in Shopping Outlets: Measuring the Effect of WalMart,” Journal of Applied Econometrics, pp. 1157-1177, Dec.
26

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

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Leibtag, Ephraim (2008). “Measuring Retail Food Price Variation: Does the 

Data Source Matter?” AAEA Selected Paper, Orlando, FL, July. 

http://ageconsearch.umn.edu/handle/5976.

National Research Council (2005). Improving Data To Analyze Food 

and Nutrition Policies. Panel on Enhancing the Data Infrastructure in 

Support of Food and Nutrition Programs, Research and Decisionmaking. 

Committee on National Statistics, Division of Behavioral and Social 

Sciences and Education, Washington, DC: The National Academies Press.

Muth, Mary K., Peter H. Siegel, and Chen Zhen (2007). “ERS Data Quality 

Study Design. Final Report.” RTI International, Health, Social, and 

Economics Research, Research Triangle Park, NC.

Rayner, Mike, Peter Scarborough, Anna Boxer, and Lynn Stockley (2005). 

“Nutrient Profi les: Development of Final Model. Final Report.” British 

Heart Foundation Health Promotion Research Group, Department of 

Public Health, University of Oxford, Dec.

Reed, Jane, Elizabeth Frazão, and Rachel Itskowitz (2004). How Much 

Do Americans Pay for Fruits and Vegetables? Agriculture Information 

Bulletin No. 790, Economic Research Service, USDA,.

Sturm, R., and A. Datar (2005). “Body mass index in elementary school 

children, metropolitan area food prices and food outlet density,” Public 

Health, Vol. 119: 1059-1068.

U.S. Department of Agriculture, Agricultural Research Service (2004). 

USDA National Nutrient Database for Standard Reference (Version 

Release 16.1). Beltsville, MD.

U.S. Department of Agriculture, Agricultural Research Service (2005). 

USDA National Nutrient Database for Standard Reference (Version 

Release 18). Beltsville, MD.

U.S. Department of Agriculture, Agricultural Research Service, Food 

Surveys Research Group (2004). FNDDS USDA Food and Nutrient 

Database for Dietary Studies, 1.0. Beltsville, MD.

U.S. Department of Agriculture and U.S. Department of Health and Human 

Services (2005). Dietary Guidelines for Americans, 6th ed. 

Volpe, Richard J. III, and Nathalie Lavoie (2008). “The Effect of Wal-Mart 

Supercenters on Grocery Prices in New England,” Review of Agricultural 

Economics, Vol. 30(1): 4-26.
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Appendix A: Examples of Foods Included in 

Select Food Groups

Packaged grains (whole [16] or refi ned [19])

Oriental noodles

rice, packaged and bulk

rice, instant

pasta

ready-to-eat cereal, including granola

rice cakes

fresh baked bread, buns, bagels, rolls, biscuits

wheat germ

hominy grits

barley

Grain mixes (whole [17] or refi ned [20])

fl our, all purpose or single purpose

biscuit, rolls or bread mixes

matzo meal

corn meal

hushpuppy mixes

Frozen grains (whole [18] or refi ned [21])

frozen dough products

frozen biscuits, rolls, muffi ns, bread

refrigerated pasta

Commercially prepared sweet frozen [44]

ice pops, frozen yogurt, ice milk, sherbet, ice cream, etc.

frozen doughnuts, cheesecake, pies, breakfast cakes, sweet rolls, brownies

Commercially prepared sweet mixes [45]

cake, brownie, muffi n, cookie, and other dessert mixes

pie crust mix

pudding and gelatin mixes

ice cream mixes

Commercially prepared sweet packaged [46]

candy, cookies, ice cream cones

chocolate, marshmallows, refrigerated pudding

Ready-to-eat sweet (bakery items) [47]

Random-weight sweet baked goods (cakes, pies, cookies, etc.)

single-serving canned desserts

refrigerated gelatin and fruit salads

fresh muffi ns, cakes, sweet rolls, donuts, pies, etc.

Commercially prepared frozen, not sweet [48]

frozen entrees, hors d’oeuvres, and snacks

frozen pizzas

frozen vegetable dishes (breaded, with sauces, fried)
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Commercially prepared frozen, not sweet [48]—continued

frozen breaded/fried seafood

frozen corndogs

Canned soups and sauces, etc. [49]

canned vegetables/beans with meat

canned potato salad

tomato, pizza, pasta sauces

corned beef and roast beef hash

beef and other stews

canned Mexican dinners, refried beans

chili, ravioli, spaghetti, lasagna

soup

chow mein and other oriental foods

Commercially packaged snacks [50]

pork rinds, puffed cheese, potato chips, corn chips, popcorn, pretzels, 

crackers, trail mix, granola bars, breakfast bars

Commercially prepared packaged meals and sides [51]

dehydrated vegetables

shelf-stable entrees/side dishes

ramen noodles, misc. oriental foods

soup mixes

rice mixes

instant meals

ethnic mixes

Ready-to-eat hot and cold deli items [52]

prepared dishes

ready-made salads

sandwiches

combination lunches
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2

1

1 N

n n

N ( ) ˆ N =− σ = μ−μ ∑

Appendix B: Jackknife Methodology Used 

To Calculate Standard Errors

Standard errors of the quarterly prices are estimated via the following jackknife approach. 

Let N represent the number of households that purchase a given food group 

in a given market group in a given quarter. We obtain N estimates of the 

quarterly market group price by dropping one household at a time and estimating the price using the remaining households. 

Let  be the estimated quarterly market level price for a given food group. Let 

represent the estimated quarterly market level price in the food group when

household n is dropped. The jackknife estimated standard error for  is then:

n ˆ

μ
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Appendix C: Comparison of Price Variation 

Across Markets Observed in Full Sample to 

Low-Income Sample

One concern with the QFAHPD is that the geographic variation in food 

prices observed when all households are used to estimate the prices may not 

accurately depict the variation in prices that low-income households face 

when food shopping. Specifi cally, the foods available to low-income households may be of a different quality than those available to other households. 

This appendix considers how differences in household income may affect the 

cross-market variation in food prices through differences in quality of foods.

Quality can refer to any attribute of a food item that infl uences unit cost. The 

attributes can refl ect categorical differences in the item (type of fruit or vegetable, cuts of beef, species of fi sh), the processing or convenience (pre-cut 

melons and vegetables, grated cheese, or ready-to-cook entrees), packaging 

(single serving vs. family size), brand (national vs. store) or production methods (organic vs. conventional). In addition, the choice of outlet may affect 

the unit cost of food as different outlet types may face different operating 

costs, with different markups over wholesale prices. 

Classifying foods into particular subgroups addresses the issue of quality in 

part by separating higher priced forms of foods from lower priced alternatives (for example, fresh/frozen vs. canned vegetables, or raw ingredients 

from packaged/prepared versions). However, within each food group, it is 

possible that low-income households purchase different products, face different prices, or have different levels of access than households with higher 

income. If the variation in prices (across locales) faced by low-income households is similar to the variation in prices faced by higher income households, 

patterns that hold for the whole sample would reasonably hold for the lowincome households in the sample. 

We tested whether the variation in prices of specifi c food groups across markets is affected by household income. We compared the variation in mean 

prices calculated using the full sample to prices calculated using only lowincome households (income less than 185 percent of the Federal poverty 

line). We chose the 185-percent cutoff (rather than a lower income level, 

such as 100 percent or 130 percent of the Federal poverty line) as it allowed 

us to have at least 30 low-income households in each of the 35 market groups 

included in the QFAHPD. Since the sample of low-income households is 

much smaller than the full sample, we limited our comparison to food groups 

that do not include random-weight food items.

Mean market prices are constructed as in equation 1 for both the full and the 

low-income samples in each market for canned fruit, packaged whole grains, 

low-fat milk, eggs, and carbonated beverages. Prices in each market are normalized by the weighted national mean price for each sample to construct 

a price index for the food group. When the markets are ranked by the price 

index in each food group, we consistently see the most expensive markets as 

determined by the full sample also appearing as the most expensive markets 

in the low-income sample, and the same pattern holds for the least expensive 

markets as well (tables C1-C5). Thus, the relative price differences across 
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markets hold regardless of the sample we select. Although we recognize 

that within each market the average price paid by low-income households is 

lower than that paid by households with higher income, we are confi dent that 

the QFAHPD provides relevant price information for research on how lowincome households or individuals react to food prices.
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Table C-1

Comparison of price variation across market groups, full sample vs. low-income sample, canned fruit 

(food group 2), 2006 Nielsen Homescan

Full sample Low-income sample

Market group

Price relative 

to national 

mean (%)

 Market group

Price relative 

to national 

mean (%)

Nonmetro Middle Atlantic 84.5 Nonmetro Middle Atlantic 84.7

Nonmetro East South Central 87.2 Nonmetro East South Central 84.9

Nonmetro West North Central 88.4 Salt Lake City 87.2

Nonmetro East North Central 88.8 San Antonio 92.3

Metro South 3 89.9 Metro South 3 93.1

Salt Lake City 91.4 Nonmetro New England 93.9

San Antonio 91.7 Metro South 2 94.3

Metro South 2 91.7 Metro South 1 94.3

Nonmetro West South Central 91.8 Nonmetro West North Central 94.9

Metro Midwest 2 92.4 Western NY/PA 95.0

Metro Ohio 94.4 Metro Midwest 1 95.1

Metro Midwest 1 96.3 Nonmetro Mountain 95.3

Western NY/PA 97.2 Nonmetro East North Central 95.8

Metro South 1 97.3 Baltimore 96.7

Metro South 4 97.5 Metro Ohio 97.1

Nonmetro South Atlantic 98.3 Metro South 4 97.8

Nonmetro New England 99.2 Nonmetro West South Central 98.3

Atlanta 99.9 Atlanta 99.0

North Pacifi c 100.0 Metro Midwest 2 99.1

Philadelphia 100.2 North Florida 99.5

Nonmetro Mountain 100.5 Hartford 99.9

North Florida 101.0 Nonmetro South Atlantic 100.1

Chicago 101.0 Washington, DC 102.0

South Florida 102.0 Chicago 102.9

Nonmetro Pacifi c 105.6 North Pacifi c 103.5

Baltimore 105.8 Metro Mountain 103.7

Other NY 105.8 Nonmetro Pacifi c 104.0

Metro Mountain 105.9 Philadelphia 104.1

Washington, DC 106.1 Metro California 107.0

Hartford 106.6 South Florida 107.9

Metro California 109.9 Los Angeles 108.8

Los Angeles 111.9 Other NY 112.3

Boston 115.9 Urban NY 114.3

Urban NY 122.1 Boston 116.5

San Francisco 126.2 San Francisco 121.0

Note: National mean prices are weighted and calculated separately for each sample; low-income households have income less 

than 185% of the Federal poverty line.

Source: ERS calculations using 2006 Nielsen Homescan data.
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Table C-2

Comparison of price variation across market groups, full sample vs. low-income sample, 

packaged whole grains (food group 16), 2006 Nielsen Homescan

Full sample Low-income sample

Market group

Price relative 

to national 

mean (%)

 Market group

Price relative 

to national 

mean (%)

San Antonio 89.6 Salt Lake City 88.8

Salt Lake City 92.1 San Antonio 91.1

Nonmetro West North Central 92.9 Nonmetro South Atlantic 91.9

Nonmetro East North Central 93.6 Chicago 93.7

Nonmetro West South Central 94.1 Nonmetro West South Central 93.8

Nonmetro South Atlantic 94.2 Metro South 4 95.2

Metro South 2 95.1 Metro Midwest 1 95.8

Metro Ohio 95.4 Nonmetro East North Central 95.9

Metro South 1 95.6 Nonmetro West North Central 96.0

Atlanta 96.2 Metro South 2 96.9

Metro South 4 96.3 Nonmetro Middle Atlantic 97.1

Metro Midwest 1 96.4 Los Angeles 97.4

Metro South 3 96.5 Nonmetro Mountain 97.8

Nonmetro East South Central 96.6 Metro Ohio 98.6

Metro Mountain 97.7 Metro Midwest 2 98.7

Nonmetro Pacifi c 98.0 Metro Mountain 99.4

Nonmetro Mountain 98.4 Nonmetro East South Central 100.1

North Pacifi c 99.3 Metro South 1 100.6

Metro Midwest 2 99.6 Other NY 100.9

Nonmetro Middle Atlantic 99.7 Washington, DC 100.9

Western NY/PA 99.8 Nonmetro Pacifi c 101.4

Chicago 100.9 Boston 101.9

Los Angeles 101.5 North Florida 102.1

Washington, DC 102.8 Western NY/PA 102.2

Nonmetro New England 103.3 Philadelphia 103.2

North Florida 105.5 Metro South 3 104.6

Other NY 106.4 San Francisco 104.7

Metro California 106.8 Atlanta 106.5

San Francisco 106.9 Hartford 106.7

Philadelphia 106.9 North Pacifi c 106.8

Urban NY 108.8 Metro California 106.9

Boston 108.9 Nonmetro New England 107.2

Hartford 109.1 Baltimore 107.7

Baltimore 113.3 Urban NY 107.7

South Florida 115.0 South Florida 122.0

Note: National mean prices are weighted and calculated separately for each sample; low-income households have income less 

than 185% of the Federal poverty line..

Source: ERS calculations using 2006 Nielsen Homescan data.
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Table C-3

Comparison of price variation across market groups, full sample vs. low-income sample, 

low-fat milk (food group 23), 2006 Nielsen Homescan

Full sample Low-income sample

Market group

Price relative 

to national 

mean (%)

 Market group

Price relative 

to national 

mean (%)

Salt Lake City 72.8 Salt Lake City 65.6

Nonmetro East North Central 80.3 Metro Ohio 81.8

Metro Ohio 81.5 Nonmetro East North Central 82.1

Metro Midwest 1 82.4 Metro Midwest 1 82.6

Western NY/PA 83.4 Metro Mountain 84.0

North Pacifi c 86.7 Nonmetro Pacifi c 84.2

Chicago 87.3 Western NY/PA 86.0

Nonmetro West North Central 88.2 Chicago 87.4

Metro Mountain 89.0 North Pacifi c 87.7

Metro Midwest 2 89.2 Nonmetro Mountain 88.0

Metro South 3 93.7 Metro Midwest 2 90.7

Nonmetro Pacifi c 94.4 Metro South 3 91.5

Metro South 2 94.7 Nonmetro West North Central 92.5

Nonmetro Middle Atlantic 97.9 Metro South 2 96.2

Nonmetro Mountain 98.0 Boston 101.9

Philadelphia 105.6 Nonmetro West South Central 104.5

Nonmetro West South Central 106.3 Metro South 4 105.4

Nonmetro South Atlantic 106.3 Nonmetro East South Central 105.7

Metro South 4 106.6 Nonmetro New England 105.9

Boston 107.1 Philadelphia 106.0

Nonmetro New England 107.6 Other NY 106.1

Nonmetro East South Central 108.5 Washington, DC 108.9

Washington, DC 110.6 San Francisco 110.6

Baltimore 111.4 Hartford 110.7

Other NY 111.4 Metro California 112.5

San Antonio 111.6 Atlanta 113.3

Atlanta 111.8 North Florida 113.9

Metro California 111.9 Baltimore 114.2

Los Angeles 112.9 South Florida 116.2

North Florida 113.6 Nonmetro South Atlantic 116.4

San Francisco 115.1 Metro South 1 117.2

Hartford 115.5 Los Angeles 117.5

South Florida 116.8 San Antonio 118.8

Metro South 1 117.7 Nonmetro Middle Atlantic 122.5

Urban NY 128.7 Urban NY 131.4

Note: National mean prices are weighted and calculated separately for each sample; low-income households have income less 

than 185% of the Federal poverty line.

Source: ERS calculations using 2006 Nielsen Homescan data.
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Table C-4

Comparison of price variation across market groups, full sample vs. low-income sample, eggs 

(food group 37), 2006 Nielsen Homescan

Full sample Low-income sample

Market group

Price relative 

to national 

mean (%)

 Market group

Price relative 

to national 

mean (%)

Nonmetro West North Central 77.1 Salt Lake City 78.4

Nonmetro East North Central 79.1 Nonmetro West North Central 80.9

Salt Lake City 80.0 Nonmetro East North Central 83.3

Metro Midwest 2 82.0 Metro Midwest 2 85.4

Metro Ohio 86.4 Metro South 3 88.8

Metro South 3 86.6 Nonmetro East South Central 88.9

Metro South 2 86.7 Metro South 2 88.9

Nonmetro West South Central 87.0 Metro Ohio 90.9

Metro Midwest 1 88.5 Nonmetro West South Central 90.9

Nonmetro East South Central 88.9 Metro Midwest 1 91.3

Atlanta 89.8 Metro South 4 91.8

Metro South 4 91.3 North Florida 92.4

Western NY/PA 91.4 Western NY/PA 92.8

San Antonio 92.3 Atlanta 93.1

Nonmetro South Atlantic 93.7 Metro South 1 93.5

Chicago 96.0 San Antonio 95.2

South Florida 96.3 Chicago 96.3

Nonmetro Middle Atlantic 96.4 Nonmetro Middle Atlantic 96.4

North Florida 97.3 Nonmetro South Atlantic 98.0

Nonmetro Mountain 98.2 Nonmetro Mountain 98.4

Metro South 1 100.4 South Florida 100.0

Metro Mountain 102.1 Metro Mountain 101.5

Washington, DC 103.4 Baltimore 104.5

Philadelphia 105.0 Washington, DC 105.6

Baltimore 107.0 Philadelphia 107.3

North Pacifi c 110.3 Nonmetro New England 113.1

Nonmetro New England 113.3 North Pacifi c 117.5

Nonmetro Pacifi c 118.7 Other NY 120.4

Other NY 124.0 Hartford 123.4

Urban NY 125.6 Los Angeles 125.5

Boston 127.2 Boston 127.1

Metro California 129.3 Nonmetro Pacifi c 127.8

Hartford 131.1 Urban NY 130.0

Los Angeles 132.7 Metro California 130.5

San Francisco 140.8 San Francisco 138.8

Note: National mean prices are weighted and calculated separately for each sample; low-income households have income less 

than 185% of the Federal poverty line.

Source: ERS calculations using 2006 Nielsen Homescan data.
36

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

Economic Research Service / USDA

Table C-5

Comparison of price variation across market groups, full sample vs. low-income sample, carbonated 

beverages (food group 37), 2006 Nielsen Homescan

Full sample Low-income sample

Market group

Price relative 

to national 

mean (%)

 Market group

Price relative 

to national 

mean (%)

Nonmetro Middle Atlantic 87.3 Nonmetro Middle Atlantic 84.8

Western NY/PA 89.1 Chicago 88.5

Nonmetro New England 91.8 Metro South 1 89.2

Boston 92.5 Western NY/PA 89.8

Metro Ohio 92.5 Baltimore 90.9

Chicago 92.5 North Florida 91.3

Other NY 94.8 Nonmetro New England 92.5

Nonmetro South Atlantic 95.4 Boston 93.3

Nonmetro East North Central 95.8 Other NY 93.5

Metro Midwest 1 96.4 Nonmetro South Atlantic 93.9

North Florida 96.4 San Antonio 95.0

Nonmetro West North Central 96.7 Metro Midwest 1 95.4

Metro Midwest 2 97.7 Metro Ohio 95.7

San Antonio 98.0 Washington, DC 96.1

South Florida 98.1 Metro Midwest 2 97.1

Nonmetro West South Central 98.6 Philadelphia 97.2

Metro South 2 98.8 Los Angeles 97.4

Metro South 1 99.2 Nonmetro East North Central 97.6

Nonmetro East South Central 99.2 Metro South 2 98.7

Metro Mountain 99.6 Metro South 3 99.2

Metro South 3 99.8 Urban NY 100.2

Metro South 4 101.1 South Florida 100.6

Philadelphia 101.7 Metro South 4 100.7

Baltimore 102.2 Nonmetro West North Central 101.0

Urban NY 103.7 Nonmetro West South Central 103.0

Washington, DC 103.9 Nonmetro East South Central 103.3

Los Angeles 104.3 Metro Mountain 107.7

Salt Lake City 104.8 Hartford 108.9

Atlanta 106.6 Atlanta 110.4

Hartford 107.7 North Pacifi c 113.3

Nonmetro Pacifi c 107.9 Salt Lake City 114.2

North Pacifi c 112.7 Nonmetro Mountain 115.8

Nonmetro Mountain 113.2 Metro California 122.1

Metro California 119.8 San Francisco 124.7

San Francisco 124.6 Nonmetro Pacifi c 131.9

Note: National mean prices are weighted and calculated separately for each sample; low-income households have income less 

than 185% of the Federal poverty line.

Source: ERS calculations using 2006 Nielsen Homescan data.
37

Methodology Behind the Quarterly Food-at-Home Price Database / TB-1926

Economic Research Service / USDA

Appendix D: Outlier Cleaning

To ensure that the average market prices are not infl uenced by errors in 

household reporting or data entry, we drop extreme outliers. Any purchases 

for which the price per 100 grams is greater than 4 standard deviations above 

the market group quarter mean is considered an outlier and dropped from the 

household average price calculations.