United States 

Department of 

Agriculture

Economic 

Research 

Service

Economic 

Information 

Bulletin 

Number 102

November 2012

Assessing the Healthfulness 

of Consumers’ Grocery 

Purchases

Richard Volpe and Abigail Okrent
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Recommended citation format for this publication: 

Volpe, Richard, and Abigail Okrent. Assessing the Healthfulness of 

Consumers’ Grocery Purchases, EIB-102, U.S. Department of Agriculture, 

Economic Research Service, November 2012.

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A Report from the Economic Research Service

Abstract

The nutritional quality of Americans’ diets has increasingly become a focus of economic 

research due to its effects on health outcomes. Health care and other costs associated 

with obesity, overweight, diabetes, and other diet-related ailments continue to swell. 

This study focuses on consumers’ grocery (food-at-home) expenditures to measure the 

extent to which Americans are conforming to Dietary Guidelines for Americans with 

the foods they purchase at the supermarket. Overall, consumers purchase too few fruits, 

vegetables, and whole grains and too many refined grains, fats, and sugars/sweets. The 

average Healthy Eating Index score for food-at-home purchases was only 56.4 for an 

average consumer, far below the maximum score of 100, which would indicate full adherence to the Dietary Guidelines. The healthfulness of purchases varies somewhat across 

geographic regions and markets, with consumers in the Northeast and West purchasing 

more healthful food than consumers in the Midwest or South. Differences across income 

levels and across race are small, with all subgroups falling well short of the recommendations in the Dietary Guidelines. 

Keywords: Dietary quality, food-at-home, market concentration, Healthy Eating Index, 

consumer choices, USDA food plans, Homescan database

Acknowledgments

The authors wish to thank Ephraim Leibtag, Abebayehu Tegene, Andi Carlson, Mark 

Lino, Timothy Park, Laurian Unnevehr, Elise Golan, Jill Reedy, Ted Jaenicke, and an 

anonymous reviewer for their help and advice with this report. The authors gratefully 

acknowledge Dr. Mark Lino for providing the weekly dollar cost estimates, which are 

not available in the CNPP publications. The authors also thank Dale Simms and Wynnice 

Pointer-Napper for editorial and design assistance.

Assessing the Healthfulness of 

Consumers’ Grocery Purchases

Economic 

Information 

Bulletin 

Number 102

November 2012

Richard Volpe, rvolpe@ers.usda.gov

Abigail Okrent, aokrent@ers.usda.gov
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Contents

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

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

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Measuring Food Basket Healthfulness . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Assessing Food Purchase Healthfulness Across the Nation . . . . . . . . . . 8

Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Appendix A: Methodology for Measuring Food 

Purchase Healthfulness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Acronyms

Acronym Definition

USDA United States Department of Agriculture

FAH Food at home

FAFH Food away from home

QFAHPD Quarterly Food-at-Home Price Database

BMI Body Mass Index

CNPP Center for Nutrition Policy and Promotion

HEI Healthy Eating Index

TFP Thrifty Food Plan

DGA Dietary Guidelines for Americans

NHANES National Health and Nutrition Examination Survey

Errata

On December 18, 2012, Appendix Table 5 was corrected to 

reflect a layout error that repeated rows 7-14 and omitted rows 

15-20. The corrected table now has the previously missing 

rows (15-20) and rows 7-14 are no longer repeated.
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Summary

What Is the Issue?

Nutritionists, healthcare providers, and policymakers seek solutions to 

the ongoing proliferation of obesity, overweight, and other health-related 

concerns that can be linked, in part, to diet quality. Some recent studies 

have illustrated the deleterious effects of food away from home (FAFH) on 

Americans’ diets, but food choices made by consumers when grocery shopping may also affect diet quality. This report measures the extent to which 

U.S. consumers are adhering to the Dietary Guidelines for Americans (DGA) 

with their food-at-home (FAH) purchases and the role of selected demographic and market factors on food purchase healthfulness. 

What Did the Study Find?

Overall, Americans have a long way to go in conforming to dietary guidelines with their FAH purchases. Very few households meet the guidelines 

when food shopping. 

•	Consumers purchase too few fruits, vegetables, and whole grains and too

many refined grains, fats, and added sugars.

•	The average 2005 Healthy Eating Index score for food-at-home purchases

was 56.4, far below the maximum score of 100, which would indicate 

perfect adherence to the 2005 Dietary Guidelines.

•	The healthfulness of the average consumer food shopping basket did not

improve noticeably between 1998 and 2006. Households shifted from 

refined grains toward whole grains, but allocate less of their food budgets 

to fruits and vegetables and more toward processed and packaged foods.

Although food purchase behavior varies across some demographic and 

market segments, all subgroups fall short of meeting the Guidelines:

•	When comparing consumers across regions of the country, those in

the Northeast and West typically purchase more healthful food than 

consumers in the Midwest or South.

•	Higher income shoppers, on average, purchase slightly more healthful

food than do lower income shoppers. 

•	In terms of differences across races, Whites and Asians typically purchase

more healthful foods than Blacks and other non-Asian minorities.

•	Market concentration, defined by the number and size of retailers within a

market, has a very small negative effect on the healthfulness of consumers’ 

food purchases.

How Was the Study Conducted?

This study uses self-reported purchase data from the Nielsen Homescan database for the years 1998 through 2006. To facilitate the analysis of a data set 

featuring millions of purchase records, we use aggregate categories from the 
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Quarterly Food-at-Home Price Database, which organizes the Homescan data

into major food groups. Our healthfulness measures rely upon the Liberal 

Food Plan recommendations from the USDA Center for Nutrition Policy and 

Promotion as well as the USDA 2005 Healthy Eating Index.
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Introduction

Many of the most prevalent and costly health issues in the United States 

today can be attributed, in varying degrees, to diet quality. These include 

obesity, overweight, cardiovascular diseases, and diabetes. Americans eat 

fewer fruits and vegetables than recommended and more fats, added sugars, 

and calories than recommended (Krebs-Smith et al., 2010). Accordingly, 

a great deal of economic research has examined consumers’ demand and 

preferences, as well as the general food environment, to understand how the 

average American diet can better conform to healthy eating guidelines. 

Dietary quality is defined generally as adherence to the Dietary Guidelines 

for Americans (DGA), a joint product of USDA and the U.S. Department of 

Health and Human Services (see CNPP (2010) for complete details). Recent 

research has suggested that food away from home (FAFH) is generally less 

healthful than food at home (FAH). As consumers spend more of their food 

dollars on FAFH or get more of their total calories from FAFH, their overall 

dietary quality tends to decrease. Mancino and Kinsey (2008) found that 

increases in FAFH consumption lead to the intake of more total calories and 

lower overall dietary quality. Also, food away from home has been linked to 

excess calorie consumption and decreased consumption of fruits, vegetables, 

and whole grains for adults (Todd, Mancino, and Lin, 2010) as well as children (Mancino et al., 2010). Gregory et al. (2011) showed that consumers who 

eat more FAFH are less likely to rate their own dietary quality favorably. 

The breadth of evidence suggesting that FAFH is less healthful, however, 

does not imply that the quality of Americans’ FAH purchasing habits is 

meeting recommendations. The Bureau of Labor Statistics reports that 

Americans spend 61 percent of their food dollars on FAH, compared to 39 

percent on FAFH.1 Hence, the extent to which consumers adhere to the DGA 

when grocery shopping will also influence dietary quality.

A common approach among researchers examining diet quality is to focus on 

single product categories or nutrients that have established implications for 

diet quality and health outcomes. Bertail and Caillavet (2008) and Richards 

and Patterson (2005) focused on fruit and vegetable consumption, Mancino et 

al. (2008) and Lin and Yen (2008) studied whole grains, and Barros (2008) 

looked at added sugars. Each of these studies operate on the implicit assumption that increasing the intake of more healthful foods (DGA recommended 

to increase) or decreasing the intake of less healthful foods (DGA recommended in moderation) improves dietary quality and overall health. 

However, policy analysis based on targeting consumption of one food 

or nutrient ignores the potential unintended consequences of changes in 

consumption of foods and nutrients excluded from the analysis. Policies 

designed to decrease obesity may not be fully informed due to the limited 

scope of these studies. For example, Okrent and Alston (2011) found 

that while a price decrease for fruits and vegetables would increase their 

consumption—with implied benefits from greater fiber and micronutrient 

intake—overall calorie consumption per U.S. adult would also increase, 

causing average bodyweight to swell. Similarly, Lakdawalla et al. (2005) 

found that an increase in the price of hamburger, which is a high-fat food, 

1According to the latest relative 

importance weights of the Consumer 

Price Index, released December 2011.
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has a negative effect on the intake of iron and that an increase in the price 

of orange juice, which is a high-calorie beverage, leads to deficiencies in 

folate and vitamin C. Hence, a measure of healthfulness that accounts for the 

overall change in nutrient and calorie intake would be a useful enhancement 

to the literature.

Our strategy, therefore, in studying and measuring the healthfulness of 

consumer food choices focuses on total food purchases (the methodology 

could also be applied to consumption baskets, given data availability). We 

develop and apply three approaches for empirically assessing the extent to 

which consumers’ food shopping conforms to USDA recommendations. 

Depending on the data available to researchers, one of these methods may 

be more viable than the others, or several methods may be used for more 

robust findings. We apply these methods to the Nielsen Homescan data 

to examine the healthfulness of American FAH purchases as it varies 

according to demographic factors, including race and income, geographic 

region, and market conditions.
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Data

We use the 1998-2006 Nielsen Homescan data to measure consumer expenditures by food category for our analysis.2 The Homescan dataset consists 

of the self-scanned food purchases of a sample of U.S. households who are 

asked to record the complete purchases of each shopping trip by scanning 

Universal Product Codes (UPCs). The sample includes detailed information 

on product names and characteristics as well as household demographics. 

We organize the Homescan data, which include thousands of different products, into larger food groups that are tractable for comparison. The Quarterly 

Food at Home Price Database (QFAHPD) provides an excellent starting point 

because it aggregates the 600+ broad Nielsen food categories into 52 food 

groups based on the 2005 DGA.3 These food groups form the basis of our 

analysis and include fruits and vegetables, whole and refined grains, prepared 

foods, meats, fats, sweeteners, and more (see table 1).

We constructed shopping baskets of total expenditures by Homescan household and quarter. We then calculated expenditure shares for the 52 QFAHPD 

food groups. Expenditure shares for household I and food group g (g = 1–52) 

in quarter q (q = 1998Q1-2006Q4) are given by

(1) igq

igq 52

g 1 igq

, exp share

exp =

=

∑

where exp is expenditure as reported in the QFAHPD. To illustrate, the row 

for food group 1 in table 1 is the average household expenditure share spent 

on whole fresh or frozen fruit for quarterly shopping baskets. Hence, households responding to the Homescan survey spent an average of 2.25 percent of 

their FAH dollars on fresh or frozen fruit.

2The time period was selected based 

on the level of detail in the Homescan 

data, which changed after 2006. For 

2007 and beyond, it is not possible to 

examine purchase quantities for individual food products without UPCs.

3See Todd, Mancino, Leibtag, and 

Tripodo (2010) for more information 

on QFAHPD methodology.
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Table 1

Average expenditure shares of QFAHPD food categories

Food 

group Category Mean expenditure share

(N = 521,697)

Fruits

1 Whole fresh/frozen 2.25

2 Whole canned 1.01

3 Fruit juice 2.85

Vegetables

4 Dark green fresh/frozen 0.44

5 Dark green canned 0.06

6 Orange fresh/frozen 0.55

7 Orange canned 0.12

8 Starchy fresh/frozen 1.80

9 Starchy canned 0.37

10 Other—nutrient-dense fresh/frozen 0.49

11 Other—nutrient-dense canned 0.44

12 Other—mostly water fresh/frozen 1.52

13 Other—mostly water canned 0.52

14 Legumes fresh/frozen/dried 0.07

15 Legumes canned/processed 0.28

Grains

16 Whole-grain packaged (bread, rolls, pita, 

tortilla, rice, pasta, cereal)

1.81

17 Whole-grain flour and mixes 0.02

18 Whole-grain frozen/ready to cook 0.01

19 Refined packaged (bread, rolls, pita, tortilla, 

rice, pasta, cereal)

8.45

20 Refined flour and mixes 0.42

21 Refined frozen/ready to cook 0.92

Dairy

22 Lowfat milk 2.48

23 Lowfat cheese 0.43

24 Lowfat yogurt and other 1.45

25 Regular-fat milk 3.34

26 Regular-fat cheese 4.50

27 Regular-fat yogurt and other 0.21

Meats, eggs, and nuts

28 Lowfat meat fresh/frozen 1.19

29 Regular meat fresh/frozen 5.30

30 Regular meat canned 0.22

31 Poultry fresh/frozen 0.66

32 Poultry canned 0.07

33 Fish fresh/frozen 0.70

34 Fish canned 0.83

35 Nuts and seeds raw 1.34

36 Nuts and seeds processed/nut butters 0.57

37 Eggs 1.33
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Table 1

Average expenditure shares of QFAHPD food categories—Continued

Food 

group Category Mean expenditure share

(N = 521,697)

Fats and oils

38 Oils 0.86

39 Solids 2.31

Sugar and sweeteners

40 Raw 0.85

Beverages

41 Carbonated non-alcoholic 3.88

42 Fruit drinks and other non-carbonated sugary 

beverages

2.45

43 Water 1.22

Commercially prepared items

44 Sweet frozen (ice cream, frozen desserts) 4.97

45 Sweet mixes (pancake, muffin, and cake 

mixes)

1.18

46 Sweet packaged (cookies, candy bars, bars) 6.66

47 Sweet ready-to-eat (bakery items) 2.75

48 Not sweet frozen (pizzas, french fries, fish 

sticks and entrees)

9.21

49 Not sweet canned (soups, sauces, etc) 3.56

50 Not sweet packaged/Snacks 7.78

51 Not sweet packaged/Meals and sides 2.31

52 Not sweet ready-to-eat (hot and cold deli 

items)

1.02

Source: Authors’ calculations using Nielsen Homescan data, 1998-2006.
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Measuring Food Basket Healthfulness

Our measures of food basket healthfulness are designed to overcome the 

potential limitations of economic studies of food choices that single out individual foods, ingredients, or nutrients. A number of researchers have devised 

indexes for measuring overall dietary quality but none, to our knowledge, 

have examined food-at-home purchases specifically and very few have examined food baskets in relation to the concurrent DGA.4 Without complete data 

on household demographics such as health indicators or body mass index 

(BMI), food-away-from-home consumption, and at-home preparation and 

consumption of food, no measurement of basket healthfulness will be without 

limitations or assumptions. Accordingly, we measure basket healthfulness 

three different ways and then apply our analysis to each.5

The technical details for each measure are available in Appendix A. We 

calculated scores for each quarterly shopping basket based on recommended 

expenditure shares, by food category, as determined by the Center for 

Nutrition Policy and Promotion (CNPP) (Carlson et al., 2007). These scores 

are intended to provide guidance for households seeking to purchase food 

for at-home consumption that meets the DGA even on a limited budget. In 

constructing these scores, we assumed that shoppers who apportion their food 

expenditures according to these recommendations are meeting the guidelines, and penalties accrue for deviations from recommendations. The CNPP 

provides expenditure recommendations by age and gender, and the extensive 

demographics in the Homescan database allow us to calculate recommendations specific to each household without making assumptions based on census 

information.

While these scores take the importance of variety into account, they are adversely 

affected by missing data. Most Homescan households do not record the entirety 

of their FAH purchases and none record their food-away-from-home purchases. 

As a result, we calculated these scores two different ways to deal with common 

cases of unreported food categories (USDAScores 1 and 2). Additionally, the 

food categories for which CNPP calculates expenditure shares do not correspond 

perfectly with the QFAHPD groups, requiring a matching process. Details are 

available in appendix A, but in most cases this involved aggregating the more 

numerous QFAHPD groups to better correspond with the CNPP categories.

Finally, we assign each quarterly shopping basket a score based on the 2005 

Healthy Eating Index (HEI), a measure of diet quality designed by USDA in 

collaboration with the National Cancer Institute. At the heart of this approach 

are direct measures of how the consumption of various food categories affects 

the overall healthfulness of consumers’ diets. The crux of the HEI is to score 

overall diets based on how the total number of servings consumed compares to 

the recommendations of USDA’s MyPyramid. Consumption is organized into

nine major components and points are awarded for each individual component 

based on whether or not consumers meet the requirements of MyPyramid. (See 

Guenther, Reedy, and Krebs-Smith (2008) for details on the development of the 

HEI.) We apply this procedure to food purchases in the Homescan database. 

The scores assigned through this methodology (HEIScore) run from 0 to 100, 

with 100 representing perfect compliance with MyPyramid serving allocations. However, this approach also requires a matching process; the QFAHPD 

4Kant (1996) reviewed studies 

that had devised measures of overall 

dietary quality. Hu (2002) and Waijers 

et al. (2007) have done so since.

5All purchases recorded in the 

Homescan database without a UPC 

are classified as random weight (RW). 

In 2007, the recording of RW products became much more aggregated 

in nature, ceasing to differentiate 

among foods sufficiently to facilitate 

the calculation of our healthfulness 

metrics. Comparing total RW expenditures for the periods 1998-2006 

and 2007-2009 suggests that prior to 

the RW aggregation consumers were 

underreporting their RW purchases. 

This could be due to the additional 

burden of reporting RW purchases 

in the more disaggregated form as 

required in the 1998-2006 data, but 

we have no reason to expect that the 

underreporting is nonrandom across 

households or markets. So, although 

levels from Homescan may be lower 

than actual purchase behavior, shares 

and within-sample differences should 

not be affected. For additional discussion of how Homescan expenditures 

compare to other data sources, see 

Zhen et al. (2009).
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food groups need to align with those of the National Health and Nutrition 

Examination Survey (NHANES), as documented in appendix A. Food categories unreported by households are also an issue with HEI scoring.

The healthfulness measures also differ in their ease of interpretation. The 

HEI scores lend themselves to straightforward interpretation while the USDA 

scores, on their own, do not do so. However, simple summary statistics (see 

table 2) for the HEIScore, which stands on its own, indicate that, on average, 

consumers have a long way to go toward purchasing foods that conform to 

the DGA. The average basket earns an HEIScore of 56, and the maximum 

of the distribution is 73, which indicates much room for improvement toward 

meeting the DGA.6 For this readily interpretable measure, we never find full 

compliance with the DGA for quarterly shopping baskets.

While all healthfulness measures are rooted in the DGA, the differences in 

their construction allow for the possibility that they may not always agree 

qualitatively. To investigate this, we calculate pairwise correlation coefficients 

for all three measures (table 3). Each measure is positively correlated with the 

other two, and the test of each individual correlation coefficient equaling zero

is rejected at the 1-percent level. The two USDA scores are very closely correlated with each other. HEIScore has the weakest correlations of the measures, 

ranging from 0.31 to 0.38.

Given the correlation matrix, it seems improbable that the different healthfulness measures would yield conflicting results when subjected to economic 

analysis. However, depending on the question of interest, these measures may 

produce results of varying magnitudes. These measures allow for researchers 

to attain robustness in their work, but one measure may be more suitable for 

different data sets. For example, HEIScore may be most apt when using data 

on quantities purchased or consumed. 

6The average HEI score calculated 

by Guenther et al. (2008), using 

consumption data from the respondents 

of the National Health and Nutrition 

Examination Survey, is 58.2. This 

lends support for our purchase-based 

approach as well as the use of the 

Nielsen Homescan data to analyze

FAH quality.

Table 2

Summary statistics for the healthfulness measures

Measure Mean Median Standard 

deviation Min Max

USDAScore1 7.77 7.31 2.56 3.06 36.56

USDAScore2 9.31 8.67 3.21 3.62 65.80

HEIScore (0-100) 56.41 53.35 22.26 6.69 73.00

The complete details for the construction of each of these healthfulness measures are available 

in Appendix A.

Source: Authors’ calculations using Nielsen Homescan data and Carlson et al. (2007).

Table 3

Correlation coefficients for healthfulness measures

Measure USDAScore1 USDAScore2 HEIScore

USDAScore1 1.00 0.89 0.38

USDAScore2 1.00 0.31

HEIScore 1.00

Source: Authors’ calculations using Nielsen Homescan data and Carlson et al. (2007).
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Assessing Food Purchase Healthfulness 

Across the Nation

Examining trends and patterns in the FAH healthfulness measures described 

earlier helps to demonstrate which Americans are most closely abiding by 

the DGA and where. Such findings can indicate where policy implementation or education and promotion may be most effective or where such efforts 

are lacking. A solid understanding of the healthfulness of FAH—coupled 

with a similar analysis of food away from home and the potential interaction 

between the two—may help to link food purchase and consumption empirically with public health concerns related to obesity and diabetes.

Figure 1 compares the recommended expenditure shares for a comprehensive list of food categories with the average expenditure shares observed for 

these foods in the Homescan sample. It was necessary to aggregate QFAHPD 

food groups to best approximate the CNPP categories (table A.1). Deviations 

from CNPP recommendations occur consistently across the spectrum of 

food categories. Consumers allocate much more of their food budgets to 

refined grains, and less to whole grains, than is recommended. Average 

expenditures fall short for all vegetables except for potatoes, for which the 

CNPP-recommended household budget share is 2 percent. For whole fruits, 

the recommended budget share is 17 percent whereas consumers allot, on 

average, less than 5 percent. Assuming that meeting the recommended 

expenditure shares portrayed in figure 1 results in compliance with the DGA, 

consumers purchase much more red meat and cheese and much less fish than 

is recommended. Additionally, consumers allocate too many food dollars to 

beverages—including soda and other soft drinks—and sugars. 

Another lens through which to examine food purchase healthfulness is that of 

income. The interaction between food expenditures, income, and healthfulness has been of interest for at least 30 years (e.g., Davis, 1982) and is often 

a topic for researchers who study food choices among people with limited 

budgets (Dong and Lin, 2009; Eikenberry and Smith, 2004). Homescan shoppers demonstrate a clear pattern between income and healthfulness (fig. 2). The 

food purchases of households with very low annual incomes (< $12,000) have 

a USDAScore2 of about 8.5, on average. However, the food purchases of households with very high incomes (> $200,000) score an average of 10.1 per basket. 

The healthfulness difference between the two endpoints of the income distribution is significant at the 1-percent level, but the economic significance of the 

difference is relatively small. Both values indicate that consumers at all income 

levels have far to go to meet the DGA.

The Homescan demographics include data on household racial composition, 

and research has shown race to be an important covariate in understanding 

food consumption patterns (Block et al., 2004; Patrick and Nicklas, 2005; Xie 

et al., 2003). We examine average basket healthfulness, across our metrics, for 

four different racial groups (fig. 3). The USDAScores show minimal variation 

when comparing Whites, Blacks, Asians, and a fourth category that includes 

other races.7 However, according to the HEIScore, some small differences exist. 

Food purchases are slightly more healthful for Whites and Asians than they 

are for Blacks and other races. For example, the shopping baskets of Asians 

received an average HEIScore of 57, compared to 56 for Whites, 54 for Blacks, 

7The differences among races in terms 

of healthfulness are typically smaller 

in magnitude than would be expected 

based on previous findings in terms of 

overall diet quality or obesity and health 

outcomes. This may be attributable, 

in part, to the fact that our data do not 

account for FAFH expenditures. Food 

away from home may exhibit more variation across races due to differences in the 

food environment according to socioeconomic status (e.g., Rose and Richards, 

2004).
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Figure 1

Recommended household expenditure shares as compared to average 

expenditure shares, by CNPP food category,1 1998-2006

Percent of food expenditures

1The Center for Nutrition Policy and Promotion (CNPP) categories are aggregations of 

QFAHPD food groups. The QFAHPD groups included in each CNPP category are shown 

in table A.1. The list of foods that comprise the QFAHPD groups are, in turn, available from 

Todd et al. (2010). It is important to note that beverages consists of soft drinks, sodas, 

fruit drinks, other carbonated beverages, and ades but does not include fruit juice, which 

has its own category. Water is also not included in any CNPP categories. Frozen foods 

consists of frozen meals and entrees, including pizza and fish sticks, and does not include 

frozen juice or frozen fruits and vegetables. 

Source: Authors’ calculations using Nielsen Homescan data and Carlson et al. (2007).

Whole grains

Potatoes

0

 2

 4

 6

 8

 10

 12

 14

 16

 18

Fruit juice

Whole fruits

Other vegetables

Legumes

Orange vegetables

Dark green vegetables

Fish

Nuts

Eggs

Poultry

Low-fat dairy

Refined grains

Regular dairy

Cheese

Red meat

Other meats

Fats

Beverages

Sugars

Soup

Frozen foods

0

 2

 4

 6

 8

 10

 12

 14

 16

 18

Percent of food expenditures

Recommended

Average
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and 53 for other races. Average differences across races are significant at the 

5-percent level, but as with income the most salient finding in this respect is 

that all households fall considerably short of meeting the DGA.8

The differences in healthfulness of food purchases across income groups and 

races are small relative to general gaps between average shopping baskets 

and DGA-recommended baskets. The inconclusive findings with respect to 

income groups are perhaps not surprising given that the relationship between 

income and obesity/BMI is not entirely clear (Chang and Lauderdale, 2005; 

Wang and Zhang, 2006). The relationship between income and dietary 

quality is also complex. Lower income households have been shown to 

consume fewer fruits, vegetables, and whole grains, but there seems to be 

no overall difference in calories consumed or macronutrient intake among 

income levels (Darmon and Drewnowski, 2008). However, there are highly 

significant differences across races in obesity and BMI (Ogden et al., 2006), 

which suggests that factors such as FAFH purchases and exercise are also 

important drivers of these differences. 

Having data from 1998 to 2006 allows us to investigate how food purchasing 

habits may have changed over time. Evolving research on health and nutrition 

affects consumers’ understanding of healthful eating. Moreover, the supermarket industry is constantly evolving, updating its product offerings and 

promotional tactics. Hence, there is reason to anticipate changes in aggregate 

FAH purchasing patterns over time. 

8Given that most races vary in 

concentration across geographic 

markets, future research might investigate the extent to which tastes and 

preferences versus market conditions, 

such as prevailing store formats or food 

prices, are driving these small differences across races.

Figure 2

Average USDAScore2 for shopping baskets by household income, 

1998-20061 

USDA score

1USDAScore2 is a score assigned to quarterly household shopping baskets that is based on 

the expenditure recommendations of the CNPP Liberal Food Plan, as shown in Carlson et al. 

(2007). The food plan provides households with the shares of their food expenditures that 

should be allotted to various food categories in order to meet the Dietary Guidelines for 

Americans. By this methodology, shopping basket scores receive penalties for deviations 

from CNPP recommendations. In our data, USDAScore2 ranges from 3 to 65 depending on 

the specific household purchase basket. The complete details are available in Appendix A. 

Source: Authors’ calculations using Nielsen Homescan data, 1998-2006.

< 8 8-12 12-20 20-30 30-40 40-50 50-70 70-125 125-200 > 200

0

2

4

6

8

10

12

$1,000/year
11 

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To investigate this from a broad perspective, we aggregate consumer 

purchases in several large food categories that remain generally classifiable 

as healthful or not. The discernible changes in expenditure patterns over 

1998-2006 are mixed, in terms of healthfulness (fig. 4). Consumers spent 

more of their food dollars on whole grains and less on refined grains in 2006, 

which constitutes improvement. Meat as a share of FAH expenditures also 

decreased, for both regular and lowfat products. However, the expenditure 

share for processed and packaged foods increased considerably while the 

shares for fruits and vegetables decreased. Given that U.S. consumers have 

consistently eaten too few fruits and vegetables, any decrease in expenditures 

for these foods weakens overall purchase healthfulness. Examining food 

purchases from this broad perspective suggests that food purchase quality has 

not improved discernibly over the time period.

Figure 3

Food basket healthfulness by race, 1998-20061 

1USDAScore1 and USDAScore2 are scores assigned to quarterly household shopping baskets 

that are based on the expenditure recommendations of the CNPP Liberal Food Plan, as shown 

in Carlson et al. (2007). The food plan provides households with the shares of their food 

expenditures that should be allotted to various food categories in order to meet the Dietary 

Guidelines for Americans. By this methodology, shopping basket scores receive penalties for 

deviations from CNPP recommendations. The complete details are available in Appendix A. 

Source: Authors’ calculations using Nielsen Homescan data.

USDAScore2

USDAScore1

White Black Asian Other

0

2

4

6

8

10

12

White Black Asian Other

0

10

20

30

40

50

60

70

HEIScore
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Purchase healthfulness may also vary geographically. We investigate this 

possibility in light of evidence for significant geographic variation in food 

prices (Todd, Leibtag, and Penberthy, 2011; Leibtag and Kumcu, 2011). 

Established geographic differences in food preferences may help, for 

example, to explain the distribution and prevalence of obesity in the United 

States. We examine how measures of purchase healthfulness vary across 

selected U.S. markets, as defined by Nielsen (fig. 5).9

Average purchase healthfulness varies significantly across geographic 

markets. For example, the average HEIScore for New Orleans, LA, is 52 

while it is 62 for Syracuse, NY. On average, households in the Northeast and 

West purchase more healthful foods than those in the Midwest or the South. 

The most likely determinants of these geographic differences are variations 

9The cities used in figure 5 were 

selected to illustrate variation and 

do not represent a random sample of 

the Nielsen markets. For HEIscore, 

figure 5 portrays the minimum, 25th 

percentile, median, 75th percentile, and 

maximum (cities) among all markets.

Figure 4

Average annual food expenditure shares by category, 1998 and 2006

Source: Authors’ calculations using Nielsen Homescan data.

Fruits-9.78

Vegetables-9.26

Whole grains-0.72

Refined grains-6.21

Fats & oils-1.69

Sweets-1.50

Low-fat dairy-7.43

Regular dairy-9.37

Low-fat meats-9.54 Regular meats-2.66

Beverages

27.86

Processed and 

 packaged-16.55

Fruits-8.43

Vegetables-7.88

Whole grains-1.30

Refined grains-5.40

Fats & oils-1.86

Sweets-1.36

Low-fat dairy-8.38

Regular dairy-9.41

Regular meats-0.85 Low-fat meats-7.19

Beverages

28.85

Processed and 

 packaged-19.84

1998, percent

2006, percent
13 

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in relative prices across food categories, regional consumer preferences, and 

food choices/quality as determined by the food environment. Of particular 

interest would be research that strives to disentangle these factors. 

As a final exercise in assessing food purchase healthfulness among 

U.S. consumers, we examine the potential role of retailer concentration. 

Economists have long studied market concentration in food retailing, perhaps 

most notably to uncover the positive relationship between concentration and 

food prices (Cotterill, 1986; Lamm, 1981; Yu and Connor, 2002). That is, 

researchers have established that food prices rise, on average, as markets 

approach monopoly. The impacts of market structure on factors such as food 

preferences and purchase healthfulness remain largely unexplored. Standard 

economic theory might suggest a negative relationship between concentration and purchase healthfulness, as retailers have less incentive to compete 

in product quality or variety as the number of competitors decreases. But 

Ellickson (2006) as well as Bonanno and Lopez (2009) provide evidence that

many markets consist of a few stores offering high-price, high-quality foods. 

Thus, there may be reason to expect a positive relationship.

Our measure of market concentration comes from Market Scope, a Trade 

Dimensions (2006) publication. To identify a potential relationship, we 

rank all Homescan markets according to concentration and classify those 

markets in the bottom quartile as “unconcentrated” and those in the top 

quartile as “concentrated.”10 Then, we calculate average healthfulness scores 

for concentrated and unconcentrated markets (fig. 6). The USDAScores are 

slightly lower in concentrated markets. All differences across concentration 

levels are statistically significant at the 5-percent level, but as with socioeconomic differences they are small in magnitude. For example, the values for 

10We measure concentration using 

the Herfindahl-Hirschman Index 

(HHI), which is calculated as the 

sum of squared market shares for the 

retailers within a market. This unitless measure ranges from 0 in perfect 

competition to 1 in a monopoly setting. 

Among Nielsen markets, the 25th 

percentile is 0.18 and the 75th percentile is 0.25. Repeating the analysis 

using the 10th and 90th percentiles as 

cutoffs does not substantively change 

the findings.

Figure 5

Food purchase healthfulness by geographic markets, 1998-20061

1HEIScore is calculated via the application of the methodology of the 2005 Healthy Eating 

Index to the Nielsen Homescan quarterly household shopping baskets. It is a score intended 

to measure the extent to which the foods that households purchase, in aggregate, conform to 

the recommendations of USDA’s MyPyramid and the Dietary Guidelines for Americans. 

A perfect score is 100. Complete details are available in Appendix A. 

Source: Authors’ calculations using Nielsen Homescan data.

New Orleans, LA

Boston, MA

Buffalo, NY

Minneapolis, MN Syracuse, NY

46

48

50

52

54

56

58

60

62

HEIScore
14

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USDAScore1 are 8.06 and 7.67, respectively. It is not clear how important 

these differences may be when conducting economic analysis. 

Market structure—including store format, the spatial distribution of stores, as 

well as proximity, the presence of food-away-from-home outlets, and pricing 

strategies—may have significant impacts on consumers’ food purchases. 

However, our findings suggest that retailer concentration alone does not have 

a strong effect on the healthfulness of food purchases. 

Figure 6

Food purchase healthfulness by market concentration, 1998-20061 

USDA score

1USDAScore1 and USDAScore2 are scores assigned to quarterly household shopping baskets 

that are based on the expenditure recommendations of the CNPP Liberal Food Plan, as shown 

in Carlson et al. (2007). The food plan provides households with the shares of their food 

expenditures that should be allotted to various food categories in order to meet the Dietary 

Guidelines for Americans. By this methodology, shopping basket scores receive penalties for 

deviations from CNPP recommendations. The complete details are available in Appendix A.

Source: Authors’ calculations using Nielsen Homescan data and Trade Dimensions Market 

Scope (2006).

High concentration

Low concentration

USDAScore1 USDAScore2

0

2

4

6

8

10

12
15 

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Discussion and Conclusions

We apply three methods for measuring the healthfulness of consumer food 

purchases to a national data set of household food purchases in order to assess 

how well U.S. consumers abide by the Dietary Guidelines for Americans. 

Our results indicate that consumer purchasing behavior falls far short of 

what would be considered a healthy diet, rich in fruits and vegetables, whole 

grains, and lowfat meat and dairy products, while light on added sugars, fats/

oils, and most packaged products.

Virtually without exception among food categories, consumers allocate too 

much of their food budgets to less healthful options and too little to healthful 

options. We find that the average household falls far short in fruit and vegetable consumption and spends too much on sugars and refined grains. The 

correlation between income and purchase healthfulness warrants further 

research on both the food environment and consumer perceptions, particularly since healthy food options are typically not more expensive (Carlson 

and Frazao, 2012). Small, but significant differences in the healthfulness of

food-at-home purchases exist across races, as well as larger differences across 

geographic markets.

The healthfulness of FAH purchases did not improve from 1998 to 2006. 

Consumers shifted from refined grains toward whole grains, but have also 

shifted from fruits and vegetables toward processed and packaged foods. 

Since the Nielsen Homescan database contains extensive demographic information on participating households—many of whom have been participating 

in the sample for multiple years—a more rigorous, future research using a 

panel-based approach that examines the determinants of consumers’ decisions could be undertaken. 

However, the Homescan database is also subject to limitations. We are unable 

to observe consumers’ food-away-from-home purchases, which necessarily 

affect overall dietary quality. We also do not have measures of health or physical characteristics such as BMI. Such information would enable the measurement of a direct linkage from food purchases to health outcomes. Finally, we 

are only able to observe the products consumers purchase at retail outlets, not 

the entire product menu they choose from when shopping. Therefore, we cannot 

determine the extent to which variations in available product variety or quality 

may be driving food purchase healthfulness.

To get a fuller picture of the determinants and broader implications of 

food purchase healthfulness, future research could synthesize existing data

resources. For example, the National Health and Nutrition Examination Survey 

conducted by the Centers for Disease Control and Prevention, while not a 

panel, includes FAFH in addition to FAH, as well as key physical measurements such as BMI. Store-level scanner data can provide information on total 

product menus, which can be illuminating if linked to household purchase data. 

Given the economic and sociological impacts of health issues related to dietary 

quality, the design and implementation of future surveys or data sets on the part 

of statistical agencies could incorporate the necessary components for drawing 

better linkages between food purchases and health outcomes. 
16

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Appendix A: Methodology for Measuring 

Food Purchase Healthfulness

Method 1: Scoring Based on CNPP Expenditure Recommendations

The USDA’s Center for Nutrition Policy and Promotion (CNPP) calculates 

food plans to assist Americans in allocating their food budgets to meet the 

DGA. Assuming a close relationship between food purchases and food 

consumption, these food plans provide a framework for measuring basket 

healthfulness to compare expenditure shares directly. The CNPP report 

provides separate food plans for consumers with food expenditures in the 

second quartile of the United States (Low Cost), the third quartile (Moderate 

Cost), and upper quartile (Liberal). Since we are unable to accurately 

measure the total food expenditures for the households in our data, we rely on 

the Liberal Food Plan as our basis for comparison.1

The recommended expenditure shares pertain to food categories that do not 

match up perfectly with the QFAHPD food groups. We applied some degree 

of aggregation to both organizational schemes in order to facilitate compatibility (table A.1). In addition to combining food classifications within both 

systems, we used the USDA food plans to calculate recommended expenditure shares for households, rather than for individuals, in order to best facilitate measurements on Homescan household baskets. To do so, we began with 

the CNPP estimates of the weekly recommended dollar costs of feeding men, 

women, and children of all ages, by food category. From these values, it is 

possible to calculate total recommended food expenditure as well as optimal 

expenditure shares tailored for individual families, based on their demographic composition. As a basis for comparison, we calculated total weekly 

FAH costs for a representative American family, as defined by the USDA 

Thrifty Food Plan (TFP). Such a family consists of one male, age 19-50; 

one female, age 19-50; and two children, age 9-11 and 6-8. From total dollar 

costs, by food category, we calculated household-level recommended expenditure shares, as reported in the third column of table A.1.

Homescan households report the presence and age of male and female heads of 

household as well as the presence and age of children. With such information, 

we are able to calculate USDA-recommended total food expenditures as well as 

individual expenditure shares for every family in the data. To calculate household-specific recommended expenditure shares, we used the average weekly 

dollar costs, by food category and age-gender group (table A.2). Fortunately 

for our purposes, the prices used by CNPP to calculate weekly costs are drawn 

from the 2001 and 2002 Nielsen Homescan panels. Therefore, CNPP relied on 

the same relative prices among food groups to calculate expenditure shares as 

those faced by the consumers in our data. Table A.1 includes recommended 

expenditure shares for the TFP representative family. Recall that the TFP 

family consists of a male and female age 19-50 and two children, age 9-11 and 

6-8. Hence, the total weekly food expenditure for the TFP family is $273.38. 

Using this value as a denominator, we then calculate the USDA-recommended 

expenditure shares for each food category. 

Recognizing that the TFP family may not be representative of those households in the Homescan database, we use the demographic information 

1We selected the Liberal plan as 

the average household income in the 

Homescan data from 1998 to 2006 was 

$47,300, as compared to the median 

household income in the United States, 

according to the Census, of $45,018 

as of 2003. Thus, households in the 

data are likely to have higher incomes 

than the population at large. However, 

the relative expenditure shares for 

the various food categories compiled 

by CNPP are very similar across 

food plans. Using the Low-Cost or 

Moderate-Cost food plans does not 

change the findings substantively.
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Table A.1

Recommended expenditure shares by QFAHPD-compatible CNPP food category

CNPP food category QFAHPD 

food group

Household 

shopping basket 

expenditure share

Grains USDA1 Food Plan

All whole-grain products

Whole-grain breads, rice, pasta, pastries (including whole grain flours) 

Whole-grain cereals (including hot cereal mixes)

Popcorn, other whole grain snacks

16, 17, 18 10.09

Non-whole-grain breads, cereals, rice, pasta, pies, pastries, snacks, and flours 19, 20, 21, 50, 51 6.10

Vegetables

All potato products 8, 9 1.77

Dark-green vegetables 4, 5 5.59

Orange vegetables 6, 7 2.61

Canned and dry beans, lentils, and peas (legumes) 14, 15 8.32

Other vegetables 10, 11, 12, 13 8.66

Fruits

Whole fruits 1, 2 16.49

Fruit juices 3 1.86

Milk products

Whole-milk products

Whole-milk, yogurt, and cream 

Milk drinks and milk desserts

25, 27 0.86

Lower fat and skim milk and lowfat yogurt 22, 24 8.77

All cheese (including cheese soup and sauce) 23, 26 0.60

Meat and beans

Beef, pork, veal, lamb, and game 28, 29 5.31

Chicken, turkey, and game birds 31, 32 2.69

Fish and fish products 33, 34 11.92

Bacon, sausages, and luncheon meats (including spreads) 30, 52 0.91

Nuts, nut butters, and seeds 35, 36 3.16

Eggs and egg mixtures 37 0.12

Other foods

Fats and condiments

Table fats, oils, and salad dressings

Gravies, sauces, condiments, and spices 

38, 39 1.79

Coffee and tea N/A 0.02

Soft drinks, sodas, fruit drinks, and ades (including rice beverages) 41, 42 1.33

Sugars, sweets, and candies 40, 44, 45, 46, 47 0.41

Soups 

Ready-to-serve and condensed soups 

Dry soups (dry) 

49 0.51

Frozen or refrigerated entrees (including pizza, fish sticks, and frozen meals) 48 0.18

1The USDA Food Plan shares are based upon the recommended dollar costs of feeding a representative family consisting of one male and 

one female, age 19-50, one child age 9-11, and one child age 6-8, according to the Liberal Food Plan. QFAHPD = Quarterly food-at-home price 

database. 

Source: Carlson et al. (2007) and authors’ calculations.
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Table A.2

Weekly dollar costs for the liberal food plan by food category and demographic, 2001-2002

Age-gender Total cost Whole-grain 

breads

Whole-grain 

cereals

Popcorn & 

other snacks

Non-wholegrain breads Potatoes Dark green 

vegetable

Orange 

vegetable

Child 1 37.87 0.00 1.36 0.00 1.81 0.92 2.62 0.17

Child 2-3 41.15 0.02 0.46 2.44 3.05 0.95 1.12 0.75

Child 4-5 44.00 2.40 0.45 0.88 2.61 0.48 1.02 1.54

Child 6-8 57.81 1.49 0.27 2.22 3.86 0.49 1.20 0.87

Child 9-11 66.83 2.41 0.23 2.00 4.68 1.30 5.24 3.45

Male 12-13 74.02 0.74 0.71 7.55 7.02 1.70 2.26 1.09

Male 14-18 74.92 4.51 1.73 2.36 5.45 1.91 2.16 1.51

Male 19-50 78.40 2.82 6.38 1.69 4.58 1.60 2.95 1.65

Male 51-70 72.46 4.14 0.62 2.63 4.05 1.55 4.44 2.07

Male 71+ 73.12 3.48 0.66 0.67 2.69 1.76 9.35 1.22

Female 12-13 64.12 5.77 0.81 1.91 5.72 1.49 2.93 1.11

Female 14-18 65.01 8.09 1.34 0.15 3.40 1.32 3.45 1.59

Female 19-50 70.34 3.38 4.57 0.12 3.56 1.44 5.88 1.16

Female 51-70 64.70 4.03 0.36 3.59 1.06 1.83 2.27 1.33

Female 71+ 64.67 4.33 0.55 0.12 2.93 0.72 10.50 1.45

Family (TFP) 273.38 10.09 11.46 6.04 16.67 4.83 15.27 7.14

FC Shares 0.04 0.04 0.02 0.06 0.02 0.06 0.03

Age-gender Canned & 

dry beans

Other 

vegetable Whole fruits Fruit juices Whole milk, yogurt, etc.

Lowfat milk 

& yogurt Cheese Milk drinks & desserts

Child 1 1.63 7.38 7.98 1.59 5.18 0.00 0.05 0.16

Child 2-3 2.89 4.94 3.92 1.17 0.22 6.64 0.17 0.08

Child 4-5 2.90 2.32 10.88 1.33 0.12 4.48 0.17 0.20

Child 6-8 4.22 4.10 12.53 1.32 0.11 4.50 0.12 0.23

Child 9-11 2.60 6.11 12.16 1.41 0.15 5.66 0.21 0.19

Male 12-13 4.15 7.30 9.99 2.99 0.24 6.69 0.38 0.22

Male 14-18 3.63 8.65 12.63 1.13 0.36 5.69 0.33 0.27

Male 19-50 10.21 7.30 8.96 1.18 0.36 5.93 0.91 0.75

Male 51-70 3.72 6.76 10.96 0.93 0.32 6.51 0.38 1.74

Male 71+ 11.35 7.07 8.98 1.28 0.18 6.01 0.21 0.82

Female 12-13 3.67 4.36 9.82 1.28 0.19 5.71 0.36 0.22

Female 14-18 10.13 5.95 8.61 1.08 0.22 5.71 1.36 0.17

Female 19-50 5.71 6.17 11.43 1.17 0.24 7.88 0.42 0.33

Female 51-70 2.92 5.76 13.38 0.35 0.23 7.81 0.04 0.14

Female 71+ 3.46 5.22 10.73 0.36 0.17 8.61 0.02 0.11

Family (TFP) 22.74 23.67 45.08 5.08 0.86 23.97 1.65 1.50

FC Shares 0.08 0.09 0.16 0.02 0.00 0.09 0.01 0.01

Age-gender Red meat Poultry Fish Bacon, saus., 

& lunch meat

Nuts & nut 

butters Eggs Fats & oils Gravies & 

condiments

Child 1 1.34 0.31 2.74 0.04 0.99 0.01 0.34 0.55

Child 2-3 0.49 0.24 8.26 0.35 0.65 0.17 0.89 0.63

Child 4-5 1.32 0.66 7.88 0.12 0.86 0.03 0.32 0.51

Child 6-8 2.22 1.27 13.22 0.34 1.37 0.01 0.45 0.54

Child 9-11 2.79 0.97 10.48 0.93 1.93 0.04 0.44 0.29
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Table A.2

Weekly dollar costs for the liberal food plan by food category and demographic, 2001-2002—Continued

Age-gender Red meat Poultry Fish

Bacon, 

sausage, & 

lunchmeat

Nuts & nut 

butters Eggs Fats & oils Gravies & condiments

Male 12-13 4.62 0.63 9.49 0.22 2.37 0.32 0.94 0.90

Male 14-18 10.28 0.51 2.64 0.47 3.29 0.11 0.69 1.22

Male 19-50 5.91 3.61 4.32 0.83 1.73 0.18 0.94 0.83

Male 51-70 5.26 3.37 7.11 0.31 2.02 0.17 0.77 0.30

Male 71+ 3.83 5.75 3.45 0.22 0.86 0.08 0.89 0.46

Female 12-13 5.46 2.64 6.83 0.52 0.76 0.04 0.82 0.45

Female 14-18 3.90 0.63 2.39 0.57 1.93 0.07 0.69 0.72

Female 19-50 3.60 1.50 4.58 0.40 3.62 0.08 0.69 0.69

Female 51-70 4.03 1.67 9.55 0.02 2.83 0.12 0.36 0.27

Female 71+ 3.94 1.85 3.71 0.47 3.66 0.05 0.32 0.11

Family (TFP) 14.52 7.36 32.60 2.50 8.65 0.32 2.52 2.36

FC Shares 0.05 0.03 0.12 0.01 0.03 0.00 0.01 0.01

Age-gender Coffee & tea Soft drinks Sugars & 

sweets

Soups 

(nondry) 

Soups 

(dry)

Frozen/refrigerated entree

Child 1 0.00 0.16 0.45 0.02 0.06 0.01

Child 2-3 0.00 0.27 0.30 0.01 0.02 0.07

Child 4-5 0.00 0.21 0.09 0.08 0.07 0.08

Child 6-8 0.00 0.34 0.18 0.23 0.03 0.07

Child 9-11 0.00 0.66 0.28 0.10 0.04 0.07

Male 12-13 0.00 0.61 0.40 0.27 0.13 0.08

Male 14-18 0.01 1.19 1.43 0.67 0.07 0.02

Male 19-50 0.03 1.83 0.38 0.39 0.10 0.04

Male 51-70 0.10 1.39 0.36 0.35 0.06 0.07

Male 71+ 0.04 0.25 0.18 1.31 0.06 0.00

Female 12-13 0.01 0.66 0.28 0.18 0.10 0.03

Female 14-18 0.07 0.52 0.41 0.46 0.02 0.05

Female 19-50 0.03 0.80 0.27 0.46 0.04 0.14

Female 51-70 0.03 0.13 0.12 0.34 0.10 0.00

Female 71+ 0.01 0.06 0.01 1.12 0.07 0.00

Family (TFP) 0.06 3.63 1.12 1.18 0.21 0.32

FC Shares 0.00 0.01 0.00 0.00 0.00 0.00

Note: QFAHPD = Quarterly food-at-home price database; CNPP = Center for Nutrition Policy and Promotion. FC = food category. 

Source: Dollar cost estimates from analysis done as part of Carlson et al. (2007).
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provided by the Homescan households to construct recommended shopping 

baskets based on the CNPP weekly dollar cost estimates. The three categorical Homescan variables that enabled this construction are AgeM, the age of 

the male head of household; AgeF, the age of the female head of household; 

and AgeC, the presence and age of children. Every household-specific weekly 

shopping basket consists of a dollar cost based upon these three variables 

(table A.3). The CNPP age-gender groups do not correspond perfectly with 

the Homescan categorical variables, particularly because there are more of 

the former. Therefore it was necessary to use approximate matches or in 

certain cases average values to sync the two datasets.

Using the household-level recommended expenditure shares and the observed 

expenditure shares as calculated based on the QFAHPD food groups, we 

assign each household a score, by quarter and CNPP food category. This 

score, henceforth the USDAScore, is based on households’ adherence to 

USDA recommendations. The USDAScore is calculated two different ways, 

given by

( )

1 2

icq c icq ic (A.1) USDAScore1 expshare USDAexpshare ,

−   = −     ∑

( )

1 2

icq c icq ic icq (A.2) USDAScore2 expshare USDAexpshare expshare 0 ,

−   = −   >   ∑

where all subscripts are the same as used in equation (1) except for c, which 

denotes the CNPP food categories, as revised to enable compatibility with 

QFAHPD. Since the CNPP does not incorporate bottled water into its food 

categories, and the QFAHPD does not maintain prices for coffee and tea, the 

USDA scores do not incorporate any of these products.

The USDA scores are squared-error loss functions, designed to assign penalties for household expenditure shares that deviate from USDA recommendations.2 USDAScore1 is the simplest and operates on the assumption that the 

Homescan households report 100 percent of their FAH purchases to Nielsen. 

Therefore, we input an expenditure share of zero for those food groups for

which households report no purchases. 

USDAScore2 makes a different assumption for completely empty food categories, allowing for the possibility that households simply have not recorded 

these purchases. Non-recording of purchases is likely to be true for at least 

some cases in the data, because the sum total of recorded purchases for 

many household/quarter combinations is very low. These very low values 

suggest that certain households are not reporting some shopping trips or are 

purchasing many foods in these categories for consumption away from home. 

Therefore, USDAScore2 is calculated based only on those food categories 

with recorded purchases and expenditure shares greater than zero.

Method 2: Scoring Based on the Healthy Eating Index

The third method we use to score the healthiness of each household’s food 

basket is the 2005 Healthy Eating Index (HEI).3 The HEI was originally 

constructed in 1995 by CNPP and was revised substantially in 2006 in 

collaboration between USDA and the National Cancer Institute. It is designed 

as a way to quantify and track the changes in the diet quality of Americans. 

2An alternative score design would 

rely on the absolute value of deviations from USDA recommendations. 

Squared-error loss functions assign 

greater weights to deviations of high 

magnitudes, while absolute-value 

loss functions assume that each unit 

of deviation has the same effect on 

healthfulness.

3The latest iteration of the Healthy 

Eating Index is commonly abbreviated as HEI-2005. Given that we make 

no use of previous incarnations of the 

measurement, we denote this as HEI 

for notational clarity.
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The HEI is composed of 12 components where the maximum score for each 

component adds up to 100 (see table A.4).4 The standards for scoring each 

component are based on adherence to the 2005 DGA. An individual that 

completely follows the recommendations set forth in the DGA would achieve 

a maximum score of 100 (Guenther et al., 2008). Because the HEI is not tied 

to individual requirements, it can be used to assess the overall quality of any 

mix of foods. For example, Reedy et al. (2010) showed how the HEI can be 

used to measure the diet quality of fast-food restaurant menus and the U.S. 

food supply. 

The QFAHPD database does not contain the nutrient content of the foods 

purchased by households, and hence, HEI scores cannot be directly assigned 

to each household’s basket of goods. To overcome this obstacle, we used 

the nutrient characteristics of foods consumed by sample respondents in the 

2003-04 National Health and Nutrition Examination Survey (NHANES) 

4For example, if an individual 

consumed less than 20 percent of his 

or her energy from solid fats, alcohol 

or added sugars, then he or she would 

get the maximum 20 points for that 

component. However, an individual 

consuming 50 percent or more of his or 

her energy would get 0 points.

Table A.3

Homescan and CNPP demographic variables

Homescan 

variable name

Variable 

code Description CNPP Age-gender group(s)

AgeM 1 Under 25 years Male 19-50

2 25-29 years Male 19-50

3 30-34 years Male 19-50

4 35-39 years Male 19-50

5 40-44 years Male 19-50

6 45-49 years Male 19-50

7 50-54 years Male 51-70

8 55-64 years Male 51-70

9 65+ years Male 71+

0 No male head None

AgeF 1 Under 25 years Female 19-50

2 25-29 years Female 19-50

3 30-34 years Female 19-50

4 35-39 years Female 19-50

5 40-44 years Female 19-50

6 45-49 years Female 19-50

7 50-54 years Female 51-70

8 55-64 years Female 51-70

9 65+ years Female 71+

0 No female head None

AgeC 1 Under 6 only

Average (Child 1, Child 2-3, 

Child 4-5) (1)

2 6-12 only

Average (Child 6-8, 

Child 9-11) (2)

3 13-17 only

Average (Male 14-18, 

Female 14-18) (3)

4 Under 6 and 6-12 (1) + (2)

5 Under 6 and 13-17 (1) + (3)

6 6-12 and 13-17 (2) + (3)

7 Under 6, 6-12, and 13-17 (1) + (2) + (3)

9 No children under 18 None

Source: Carlson et al. (2007).
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to proxy for this missing nutrient information. The NHANES is a nationally representative sample of civilian non-institutionalized individuals that

collects 2 days of 24-hour dietary recall data from the sample participants 

and converts the dietary data into its nutrient composition (Centers for 

Disease Control, 2006). We used the nutrient content of foods in the 2003-04 

NHANES that closely match our QFAHPD food groups to indirectly assign 

an HEI score to household purchases in the QFAHPD.

First, we calculated the HEI for sample respondents in the 2003-04 

NHANES based on 1 day of dietary intake. Only individuals with reliable 

dietary recall were scored and included in our analysis (i.e., DR1DRSTZ=1). 

Foods purchased at retail outlets other than grocery stores (e.g., restaurants, 

school and residential cafeterias, vending machines and vendors, community food programs) were excluded from our HEI calculations (i.e., DR1FS 

= 1).5 We calculated the HEI scores using SAS program files and data sets 

published on the CNPP website (U.S. Department of Agriculture, 2010).6

Second, the foods consumed by sample respondents in the 2003-04 

NHANES, which are organized into 7,000-plus USDA food codes, were

assigned to 1 of 30 food groups (table A.5) that included one or more of the 

QFAHPD 52 food groups (see table A.2 for the concordance between the 

30 food groups and the 52 QFAHPD food groups). We then estimated the 

average effect of consumption of the 30 foods on the HEI for each sample 

respondent as 

30

0 1 (A.3) , NHANES HEI j n n jt Q j β β ε = = + ∑ +

where β0 is a constant, βn is the mean effect of consumption of food n on the 

HEI, NHANES Qin is the grams of food n consumed by individual j as reported 

in the NHANES, and εj

 is the error term. The sample estimates were inflated 

to represent the entire U.S. civilian non-institutionalized population by

using the inverse probability weights for the 1 day of dietary recall (i.e., 

5Altogether, approximately one-half of 

all grams recorded by NHANES were 

excluded.

6The SAS (Statistical Analysis 

System) program published on the 

CNPP website calculates the HEI 

score for each person in the 2003-04 

NHANES. First, the program matches 

the foods consumed by each sample 

respondent (by USDA food code) to 

cup equivalents from the MyPyramid 

Equivalents Database, 2.0. The cup 

equivalents for each food consumed 

by each individual are then aggregated 

into the components listed in table A.4. 

Third, the maximum score for each 

HEI component listed in table A.4 is 

standardized for every 1,000 kilocalories consumed by the individual 

in the day. If the individual consumes 

less than the maximum score, the 

score is linearly prorated. Hence, each 

individual has a score for each HEI 

component. The HEI score for the 

individual is then calculated as the 

sum of the HEI components. For more 

details, see SAS program on the CNPP 

website, http://www.cnpp.usda.gov/

HealthyEatingIndex-2005report.htm.

Table A.4

Components and standards of the Healthy Eating Index, 2005

Component Max. score Standard

Total fruit 5 ≥0.8 cup eq/1,000 kcal

Whole fruit 5 ≥0.4 cup eq/1,000 kcal

Total vegetables 5 ≥1.1 cup eq/1,000 kcal

Dark green and orange veg. and legumes 5 ≥0.4 cup eq/1,000 kcal

Total grains 5 ≥0.8 cup eq/1,000 kcal

Whole grains 5 ≥3.0 oz eq/1,000 kcal

Milk 10 ≥1.5 oz eq/1,000 kcal

Meat and beans 10 ≥1.3 cup eq/1,000 kcal

Oils 10 ≥12 g/1,000 kcal

Saturated fat 10 ≤7 percent of energy

Sodium 10 ≤ 0.7 g/1,000 kcal

Calories from fat, alcohol, and added 

sugar 20 ≤ 20 percent of energy

Source: Guenther et al. (2008).
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WRDRD1), and the standard errors were adjusted to account for the stratified 

multistage probability of the sample (i.e., strata = SDMVSTRA and cluster 

= SDMVPSU). Most of the regression coefficients are significant and have 

the expected signs (table A.5). The system R2 is 0.43, which implies that 43 

percent of the variation in HEI scores in the 2003-04 NHANES is explained 

by the exogenous regressors in equation (A.5).

Third, we used the estimated parameters of equation (A.5)—e.g., 0

ˆ

β and 

ˆ , 1, , βn n ∀ =  30—and quantities of foods purchased in the QFAHPD to 

predict HEI for each QFAHPD household. The quantities consumed each 

quarter by a household for a food group were calculated by dividing total 

Table A.5

Mean effects of consumption of different foods on HEI-2005

Regression 

coefficient

Standard 

error

QFAHPD 

codes

Intercept 45.8553 0.7321 -

Whole fruit 0.0343 0.0027 1,2

Fruit juice 0.0100 0.0011 3

Dark green vegetables 0.0302 0.0082 4,5

Orange vegetables 0.0399 0.0062 6,7

Starchy vegetables 0.0059 0.0039 8,9

Other-nutrient dense vegetables 0.0132 0.0035 10,11

Other-mostly water vegetables 0.0203 0.0035 12,13

Legumes 0.0153 0.0048 14,15

Whole grains 0.0268 0.0023 16,17,18

Refined grains 0.0109 0.0031 19,20,21

Low fat dairy 0.0036 0.0010 22,23,24

Regular fat dairy -0.0024 0.0006 25,26,27

Low fat red meat 0.0130 0.0049 28

Regular fat red meat -0.0150 0.0019 29,30

Poultry 0.0030 0.0026 31,32

Fish 0.0112 0.0041 33,34

Nuts and seeds 0.1070 0.0125 35,36

Eggs -0.0072 0.0040 37

Oils 0.0724 0.0823 38

Solid fat -0.0253 0.0076 39

Sugar and sweeteners -0.0092 0.0117 40

Carbonated beverages -0.0050 0.0004 41

Noncarbonated beverages -0.0052 0.0008 42

Water -0.0007 0.0003 43

Frozen commercially prepared sweet items -0.0150 0.0026 44

Other commercially prepared sweet items -0.0086 0.0021 45,46,47

Frozen commercially prepared non-sweet items -0.0033 0.0051 48

Canned commercially prepared non-sweet items -0.0017 0.0012 49

Packaged commercially prepared snacks 0.0428 0.0071 50

Other commercially prepared non-sweet items -0.0007 0.0007 51,52

Note: The estimation was performed using the svyreg procedure in SAS 9.2. The R2 for this 

model is 0.43. 

Source: Authors’ calculations using 2003-04 NHANES.
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expenditure on the food group by household average price as reported in 

the QFAHPD. The price for a food group that consisted of more than one 

QFAHPD price was calculated as a weighted average of each QFAHPD price 

weighted by its expenditure share of the food group. Because the data in the 

NHANES represent daily consumption by individuals, we divided household quantities by household size to derive per capita quantities and then 

converted the per capita quarterly quantities into per capita daily quantities. 

We predicted HEI for each household as

where 0

ˆ

β and ˆβn are the estimated parameters from table A.5 and QHANES Qin

is per capita daily consumption of food group n in household i.

30

0 1

ˆ ˆ (A.4) , QFAHPD ˆ

HEIi = β + ∑n n in = β Q