The Effect on Dietary Quality of Participation in the Food Stamp and WIC

Programs. By Parke E. Wilde, Paul E. McNamara, and Christine K. Ranney.

Food and Rural Economics Division, Economic Research Service, U.S.

Department of Agriculture. Food Assistance and Nutrition Research Report

Number 9.

Abstract

Participants in the Food Stamp Program consume more meats, added sugars,

and total fats than they would in the absence of the program, while their consumption of fruits, vegetables, grains, and dairy products stays about the same.

Participants in the Special Supplemental Nutrition Program for Women, Infants,

and Children (WIC) consume significantly less added sugars, which may reflect

the substitution of WIC-supplied juices and cereals in place of higher sugar soft

drinks and cereals. These findings come from a study of low-income Americans

using the Continuing Survey of Food Intake by Individuals.

Keywords: Nutrition assistance programs, food intake, dietary quality,

Continuing Survey of Food Intake by Individuals (CSFII)

Acknowledgments

This report benefited greatly from the suggestions and advice of Peter Basiotis,

Craig Gundersen, Mark Nord, Leslie Whitener, Steve Carlson, Jay Hirschman,

Anita Singh, and Patricia McKinney. We are grateful to Lindsay Mann for

excellent editing and Anne Pearl for creative production.

	

 
Contents

 

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Summary

Participation in the Food Stamp Program (FSP) tends to increase one’s intake of

meats, added sugars, and total fats. It does not significantly change one’s intake

of fruits, vegetables, grains, or dairy products. The effects of food stamp use

seem to be similar to the effects of having substantially more income. Food

stamps appear to help low-income Americans acquire more of the food energy

and other nutrients they need, but public policy is concerned with overall dietary

quality and not just with increasing the amount of food intake.

Participation in the Special Supplemental Nutrition Program for Women,

Infants, and Children (WIC) tends to reduce one’s consumption of food products

with added sugars, perhaps reflecting the substitution of WIC-supplied cereals

and fruit juices for high-sugar cereals and soft drinks. WIC participants also

seem to consume more fruits and dairy products than they might otherwise, but

this finding was not statistically significant.

The United States invests about $17 billion annually in the FSP and about $4

billion annually in WIC, to improve food security and dietary quality for lowincome Americans. This study uses data from the Continuing Survey of Food

Intake by Individuals (CSFII) for 1994-96. It measures seven categories of food

intake using the serving definitions employed in the Food Guide Pyramid developed by the U.S. Departments of Agriculture and Health and Human Services.


Introduction

How do the U.S. Food Stamp Program (FSP) and the

Special Supplemental Nutrition Program for Women,

Infants, and Children (WIC) affect dietary quality?

This report combines the most recent 3 years of

national survey data on food intake with a novel statistical approach to provide some answers to this question. The results have implications for nutrition monitoring and nutrition education efforts.

This question is significant in U.S. food and nutrition

policy. The United States invested about $17 billion in

the FSP in 1998 and about $4 billion in WIC to

improve food security and dietary quality for lowincome Americans. WIC had more than 7 million

clients on average each month in 1998. The FSP had

over 19 million clients on average each month that

year, representing about 7.3 percent of all Americans.

The effect of these public investments on dietary quality is a major topic in recent studies of public policy in

nutrition (Kennedy, 1999; Rossi, 1998; Levedahl and

Oliveira, 1999; Oliveira and Gundersen, 2000).

This issue is complicated by several factors:

First, like many questions in the social sciences, this

one involves trying to understand and interpret the 

choices made by millions of individuals, each of 

whom faces a distinct economic situation and each 

of whom has distinct knowledge and opinions. 

When it comes to dietary choices, in particular, each

of these individuals has quite literally his or her own

“tastes and preferences.” Each family differs from 

the next, and even within the same family, each 

member may be different. 

Second, dietary quality is not easy to evaluate. In 

the case of dietary quality for low-income 

Americans, public policy is concerned both with 

food insecurity, which may involve episodes of 

insufficient food intake, and also with the same problems of nutritional excess that are major concerns for

the American population at all income levels. 

Furthermore, dietary quality is difficult to assess by 

measuring the consumption of particular foods or 

nutrients on their own. Instead, dietary quality 

depends on the composition of a bundle of foods. 

Third, the evaluation of food and nutrition programs

stands at a junction between research disciplines.

The nature of spending choices subject to resource

constraints is, by tradition, in the research portfolio

of economics. Nutritional effects of food behaviors

have most often been the domain of nutrition science.

Assessing food and nutrition programs therefore

seems to call for an interdisciplinary approach. Such

methods for this area of research are still rapidly

developing.

This study investigates the dietary impact of the FSP

and WIC while addressing precisely these complications. These issues do not exhaust the list of important

factors that could have been considered in this analysis. In particular, this study does not simultaneously

address other important nutrition assistance programs,

such as school meals programs. This report explains

the main results and the reasoning behind the research

design for the study, and it refers the reader to a separate article for the more technical details of the analysis (Wilde, McNamara, and Ranney 1999).

In overview, the research uses data from the 1994-96

Continuing Survey of Food Intake by Individuals

The Effect on Dietary Quality of

Participation in the Food Stamp 

and WIC Programs 

Parke E. Wilde, Paul E. McNamara,

and Christine K. Ranney

	
	
		 	


✥ 1
(CSFII) to study intake of the five major “pyramid”

food groups (meats, fruits, vegetables, grains, dairy)

plus added sugars and total fats (fig. 1). The study

considers how intake of each major food category is

correlated with intake of the other categories. The

report offers some background on the FSP and WIC;

reviews recent research on nutrition programs and

dietary quality; describes the data and methods used in

this investigation; summarizes the main results; and

suggests some implications both for food and nutrition

policy and for future research.

Background on the FSP and WIC

The FSP is the largest Federal nutrition assistance program and one of the largest components of the Federal

social safety net. An early Food Stamp Program operated during the 1930’s, but it was discontinued during

the Second World War. In its current incarnation, the

program began in 1962. Initially, not all localities participated in the FSP, but it was extended nationwide

during the early 1970’s. To qualify for the program, a

household must have gross income less than or equal

to 130 percent of the official poverty threshold. The

household must also have “net” income less than the

poverty line, where net income equals gross income

minus certain deductions. Finally, the household must

meet restrictions on its ownership of certain assets

(USDA Food and Nutrition Service, 1999). 

The amount of benefits a family receives depends on

its net income. If it has no net income, after deductions, the family receives the maximum food stamp

benefit. This maximum benefit level equals the value

of the Federal Government’s “Thrifty Food Plan,”

which varies according to household size. If the family has some net income, its benefit level is reduced

at a rate of 30 cents for every dollar of net income.

The average monthly benefit in 1998 was about $71

per person.

Food stamp benefits may legally be used to purchase

only food and nonalcoholic beverages. The benefits

were traditionally distributed as coupons that could be

used at authorized retail stores to purchase food.

Currently, the FSP is switching to Electronic Benefit

Transfer (EBT) systems, which dispense benefits using

plastic cards similar to automatic teller machine cards. 

 ✥ 	


 	
	
		

Source: U.S. Department of Agriculture/U.S. Department of Health and Human Services

Figure 1

The Food Guide Pyramid

Bread, Cereal,

Rice, & Pasta

Group

6-11

SERVINGS

Fruit

Group

2-4 SERVINGS

Meat, Poultry, Fish,

Dry Beans, Eggs,

& Nuts Group

2-3 SERVINGS

Vegetable 

Group 

3-5 SERVINGS

Milk, Yogurt,

& Cheese

Group 

2-3 SERVINGS

Fats, Oils, & Sweets KEY

USE SPARINGLY Fat (naturally occurring Sugars

These symbols show fat and 

added sugars in foods.

and added) (added)

KEY
WIC provides food, nutrition counseling, and access to

health services to low-income women, infants, and

children. The program began as a pilot in 1972 and

was made permanent in 1974. Pregnant or postpartum

women are eligible, as are infants and children up to

age 5, if they meet income guidelines and are determined to be at “nutritional risk” by a health professional. The income cutoff is 185 percent of the U.S.

poverty threshold, somewhat higher than the cutoff for

the FSP. The “nutritional risk” determination takes

account of both medically based risks such as anemia

or underweight, and diet-based risks such as an inadequate dietary pattern.

WIC participants generally receive a voucher or credit, for use in purchasing specific authorized foods

selected for their nutritional content. WIC foods are

high in one or more of the following nutrients: protein, calcium, iron, vitamin A, or vitamin C. WIC

foods include infant formula, cereals, dairy products,

peanut butter, and other foods high in the target nutrients. The WIC program also offers a substantial

nutrition education program and serves as a gateway

to other forms of health services (USDA Food and

Nutrition Service, 1999).

Research on Nutrition Programs 

and Dietary Quality

In a recent article on the U.S. nutrition safety net,

Eileen Kennedy observes that the major nutrition problems in the United States have changed over the last

50 years:

Problems of over-consumption and excesses and 

imbalances are now, on average, more prevalent 

than problems of under-consumption and deficiency. For example, childhood obesity is now

more common than growth retardation. This is

true across all income strata, although the nutritionrelated disease burden is substantially greater in

low-income groups (Kennedy, 1999, p. 331).

These low-income groups are the target population for

the FSP and WIC. Levedahl and Oliveira (1999) note

how little is known about the effect of nutrition assistance programs specifically on dietary quality: “[T]heir

effect on the quality of the recipient’s diet has so far

been uncertain” (Levedahl and Oliveira, 1999, p. 322).

A substantial body of applied research has attempted

to measure this “uncertain” effect. The line of

research pursued most frequently has been to estimate

regression models, using survey data, to explain the

effects of economic and demographic variables —

including program participation and benefit levels —

on one or more food consumption variables. Devaney

and Moffitt (1991) found that food stamps have a significant and positive effect on the availability of food

energy, protein, and nine micronutrients. Rose,

Habicht, and Devaney (1997) found that food stamps

and WIC both have positive and significant effects on

iron and zinc intake for preschool children. By contrast, Butler and Raymond (1996) reported that food

stamps have no positive effect on intake of several

nutrients, after controlling for endogenous self-selection into the program.

In the 1990’s, nutrition scientists with expertise in survey research developed a new method for measuring

dietary quality using the same commonsense terms

that are employed by the Federal Government in its

dietary recommendations and the Food Guide Pyramid

(Cleveland and others, 1997a). Krebs-Smith and others (1995) used this type of pyramid servings data to

study fruit and vegetable intake. Another study,

Krebs-Smith and others (1996) used such data to study

food intake by children and adolescents. For adults,

Cleveland and others (1997b) found that intake of each

of the five main food groups increased as income

increased from below 131 percent of the poverty line

to 131-350 percent of the poverty line.

The one previous food assistance study that drew on

these methods for measuring intake in pyramid servings was by Basiotis and others (1998). That study

investigated how economic and demographic characteristics of families influence scores on the USDA’s

“Healthy Eating Index” (HEI) -- a measure of how

well diets adhere to the Federal Government’s dietary

guidelines. Using data from the 1989-91 CSFII,

Basiotis and others found that the HEI increased with

food stamp participation if household weekly benefits

exceeded $17.54. The HEI increased strongly with

WIC participation.

Data and Methods

The study reviewed here and in Wilde, McNamara, and

Ranney (1999) employed data from the 1994-96 CSFII.

That nationally representative survey collected basic

demographic information for all members of each

household and used a randomization strategy to select

certain members to participate in a complete food

intake survey. These “sample persons” were administered two 1-day survey modules about their food

	
	
		 	


✥ 3
intake, in each case asking them to recall all foods and

beverages consumed in the preceding 24 hours. The

data used in this study represent 3,642 sample persons

in 1,901 households with income less than or equal to

130 percent of the poverty line. See USDA (1998) for

more detail on the survey design and construction.

The detailed responses about food intake were used to

construct the pyramid servings variables used in this

study (USDA, 1998). The definition of a serving differs for each food group, but corresponds as closely as

possible to common usage. One slice of bread is one

serving in the grains group, and so forth for fruits, vegetables, and dairy. The units for the meats group are

ounces of lean meat or “ounce equivalents” of meat

substitutes, such as eggs or red beans. Added sugars

are measured in teaspoons, and total fats are measured

in grams.

The 1994-96 CSFII had several characteristics that

make it suitable for addressing the three types of complications discussed in the introduction. First, because

the survey asked detailed questions about actual food

intake, rather than just overall food spending, it permitted investigation of how economic and demographic factors affect the composition of a whole list of food

intake variables jointly. Second, because the survey

reported program participation and economic variables, and also measured food intake in the same intuitive terms as the Federal Government’s “Food Guide

Pyramid” and dietary guidelines, this data source lends

itself to interdisciplinary approaches drawing on both

applied economics and nutrition. Third, because the

data contain information on more than one member of

many families, they permit an exploration of how food

choices are similar or different for members of the

same family. 

The statistical model used here is a regression model

with seven equations, one for each of the seven main

food intake variables. It differs from the most familiar

ordinary least squares regression model in the way it

addresses the “random” aspects of food intake decisions — those characteristics of families and individuals that cannot be observed and explained by the analyst, and that are therefore treated as “random errors”

in the statistical model. The model measures how random factors that contribute to food intake outcomes

are correlated for individuals in the same families and

correlated across food groups. That means, for example, that if one member of a household is more likely

to consume high amounts of vegetables, other members of the same household may also consume high

amounts. Likewise, if a person is particularly fond of

vegetables, the same person might also tend to consume larger amounts of fruit than average. One advantage of taking account of such correlations is that it

permits more precise estimates of the effects of food

stamps and WIC. However, in this particular study,

this gain in precision proved modest. The most important advantage of this statistical model turned out simply to be that the correlations it measures are themselves interesting.

The main explanatory variables in the model are

income and two variables indicating whether anyone in

the family received FSP or WIC benefits. Other

explanatory variables include age, education, sex, race,

ethnicity, household structure, smoking habits, homeownership, body mass index, health status, rural residence, and region of the country. Because food intake

patterns change with age in a complex way, the effect

of age in the model is allowed to be highly nonlinear,

and the effect of income on food intake is allowed to

be different for people at different ages. Moreover,

food intake often does not increase in a linear way

with income, so a quadratic term is included to permit

the effect of income on food intake to be nonlinear.

The model produces two types of results. The first

type of result is the regression parameter estimates.

Because of the nonlinear specification for the age and

income variables, a table of parameter estimates is not

easy to interpret on its own. The most straightforward

way of explaining the implications of these parameter

estimates is through a simple type of simulation. In

this simulation, the model’s predictions are illustrated

in terms of food intake for a person with “typical”

characteristics — mean values of the economic and

demographic variables. Then, one can illustrate how

food intake would change if the person were older, for

example, or if the person had higher income. Most

important, one can illustrate how expected food intake

patterns would change, according to the model, if the

person shifted from nonparticipation in nutrition assistance programs to participation in the FSP or WIC, or

both. There are more sophisticated simulations one

could run, but this approach suffices to show the most

important results. For those who want more detail,

the complete table of the parameter estimates is available in Wilde, McNamara, and Ranney (1999).

The second type of result describes the correlations

discussed above. The statistical model assumes the

“random errors” that influence food intake have one

component that is shared by all members of the same

 ✥ 	


 	
	
		
household, and another component that is idiosyncratic

for each individual. For example, if the main food

buyer for a family has a special preference for pork

chops, that might show up in the household error component for the meat equation, because it affects the

meat intake of each family member. On the other

hand, one family member’s special preference for milk

at lunch might be part of the individual error component, because it is not necessarily correlated with the

dairy intake of other family members. Both error

components are permitted to be correlated across the

seven food equations — so, for example, the family

with the pork chops might also have higher intake of

total fats. For each error component, cross-equation

correlation coefficients show how the random factors

influencing intake of each food group are correlated

with those for the other food groups. A correlation

coefficient of zero means two variables are uncorrelated, while a correlation coefficient of one means the

two variables are perfectly correlated.

Effects of Age, Income, and Program

Participation on Dietary Quality

The analysis found that age, income, and program participation had significant dietary effects. Moreover, random factors that affected food intake were indeed correlated within families and across pyramid food groups.

Age

Table 1 and figure 2 illustrate how the baseline expected level of food intake for the seven food measures

varies with age and compare these levels to the recommendations.1 Baseline intake of meats and vegetables

is highest at age 30. Intake of grains, added sugars,

and total fats is highest at age 16, and intake of fruits

and dairy is highest at age 7. For fruits and dairy,

baseline intake for all ages falls short of even the lower

end of the recommended range. By contrast, for added

sugars and total fats, baseline intake is quite high relative even to the recommended maximums.

Additional Income

Because of the way income and age variables are specified in the model, the effects of higher income are

shown separately for several age groups (table 1, fig.

3). In this simulation, the “very low income” in the

baseline case is chosen such that only one-quarter of

the low-income sample is poorer (approximately $162

per person per month). “Higher income” is chosen

such that only one-quarter of the low-income sample

has more income (approximately $375 per person per

month). For meats, added sugars, and total fats, the

effect of higher income is uniformly positive and in

most cases statistically significant. The greatest

increases with income, relative to the baseline case, are

for intake of added sugar by young people (ages 7 and

16). For the remaining pyramid categories, the income

effect varies in sign and is less consistently significant,

but positive effects still predominate.

Program Effects

As with income, FSP participation has a significant

positive effect on meats, added sugars, and total fats

(table 1, fig. 4). The corresponding effect of FSP participation for the remaining food groups varies in sign

and is not statistically significant. WIC participation

appears to have a positive effect on intake of fruits and

dairy. However, these parameter estimates are not statistically significant. Thus, these positive results could

be due to random sampling variation. The one statistically significant effect for the WIC participation variable is a negative effect on intake of added sugars.

Correlations Within Families

Finally, consider some patterns in the “random error”

that the statistical model cannot explain. With regard

to correlations in food intake for members of the same

family, the key results may be seen in the variances of

the household error component and the individual

error component for each equation.2 If there were no

correlations within households — that is, if the random factors affecting food intake for two people in the

same household were no more related than the factors

for two people in different households — then the

variance of the household error component would be

near zero and the variance of the individual error component would constitute the total variance. Instead,

however, the variance of the household error component is at least a third as large as the variance of the

	
	
		 	


✥ 5

	





		





	
					

		


	


	


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individual error component in each equation (table 2).

This result implies that the household error component, which represents unobserved random factors that

are shared by members of the same household, contributes substantially to the unexplained random variation in food intake overall.

Correlations Across Food Groups

With regard to correlations across food groups, the

most striking contrast is between the cross-equation

correlations for total fats and for fruits (table 2).3 For

both the household and the individual error components, the three largest correlations are in the total fats

column. The error components for total fats appear to

be strongly correlated with those of every category

except fruits. Thus, a household with higher intake of

total fats (above the level that one would expect based

on its observed characteristics) tends also to have higher intake from these categories. On the other hand, for

both the household and the individual error components, the three smallest correlations are all found in

the fruits category. The random error for intake of

fruits is quite independent of the random error for

intake of the other food categories. This means that a

household with higher intake of fruits (above the level

that one would expect based on its observed characteristics) does not tend in general to have either higher or

lower intake from these categories.

& ✥ 	


 	
	
		

Table 1 Effects of income and program participation on food intake

Added Total

Meats Fruits Vegetables Grains Dairy sugars fats

Ounces Teaspoons Grams

Reference amounts 5 to 7 2 to 4 3 to 5 6 to 11 2 to 3 6 to 18 53 to 93

Baseline servings:

 Age 7 3.26 1.38 2.10 5.51 1.85 17.05 60.56

 Age 16 5.05 1.24 3.24 7.54 1.65 24.52 84.31

 Age 30 5.50 1.14 3.49 6.79 1.21 20.77 76.62

 Age 50 5.00 1.21 3.10 6.05 1.04 14.81 68.11

With higher income:

 Age 7 +.35 * +.08 -.06 +.16 -.02 +2.96 ** +2.84

 Age 16 +.36 ** -.36 * +.18 ** -.07 -.02 +6.32 ** +4.96 **

 Age 30 +.42 ** +.14 +.16 ** +.24 ** +.15 ** +1.41 * +6.96 **

 Age 50 +.26 * +.02 ** +.12 +.08 +.10 * +1.10 +4.52 **

With food stamps: +.25 * -.06 +.10 -.02 +.07 +1.99 ** +4.00 **

With WIC -.24 +.18 -.03 -.31 +.11 -2.36** -2.11 

Notes: Reference amounts for meats, fruits, vegetables, grains, and dairy are target intake levels for most consumers, while reference 

amounts for sugars and fats are recommended maximums. In the Food Guide Pyramid, the low end of the range of recommended servings is 

appropriate for somebody with a diet of 1,600 calories, and the high end of the range is appropriate for somebody with a diet of 2,800 calories. 

Baseline servings are expected values for a person with the given age, income equal to the first quartile of the low-income sample ($162 per 

adult male equivalent per month), no program participation, and mean values of all other variables. Higher income equals the third quartile of 

the low-income sample ($375 per adult male equivalent per month). Entries for higher income and program participation are reported in 

comparison with the baseline case. Asterisks denote significance: * = 10-percent level; ** = 5-percent level. The test statistic is a Wald chisquare statistic with one degree of freedom in the case of the food stamp and WIC parameters and two degrees of freedom in the case of the 

Data source: Continuing Survey of Food Intakes by Individuals, 1994-96.

 Servings 

income parameters (which include a quadratic term).


		 
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✥ 7

250

200

150

50

100

Meats Fruits Vegetables Grains Dairy Sugars Fats 

Data source: Derived from table 1. Continuing Survey of Food Intakes by Individuals, 1994-96.

0

Figure 2

Predicted food intake for individuals with very low (baseline) income

Age 7

Age 30

Age 16

Age 50

Percent

Note: Each column represents food intake as a proportion of the midpoint of the recommended range.

Figure 3

Predicted food intake for individuals with "higher" income

250

200

150

50

100

Meats Fruits Vegetables Grains Dairy Sugars Fats 

0

Age 7

Age 30

Age 16

Age 50

Percent

Data source: Derived from table 1. Continuing Survey of Food Intakes by Individuals, 1994-96.

Note: Each column represents food intake as a proportion of the midpoint of the recommended range.

300
Implications for Nutrition 

Assistance Programs

This study finds that FSP and WIC participation have

substantially different implications for dietary quality.

Like increased income, FSP participation permits lowincome households to purchase greater amounts of

food. For three of the seven categories — meats,

added sugars, and total fats — intake increases significantly with FSP participation. These results are

encouraging to the extent that some low-income

households may be short of food and need the additional calories and other nutrients from these categories. The results are less encouraging to the extent

that public policy is concerned about program effects

on the composition of the dietary bundle for lowincome families, beyond the problem of simply having

sufficient food. The two categories where intake falls

most short of the recommendation — fruits and dairy

— do not appear in this study to respond significantly

to FSP participation, although in the case of dairy

there is a small and statistically insignificant estimated

positive impact. Meanwhile, intake of the two categories that most directly represent excess consumption

and/or poor dietary quality — added sugar and total

fats — does appear to increase significantly with FSP

participation.

WIC participation is associated with significantly

lower intake of added sugars, which makes sense if

WIC-authorized fruit juices and cereals substitute for

colas and sweet cereals. For the other categories, the

effect of WIC participation is not significantly different

from zero, which may reflect the imprecision of measuring WIC effects with this model and this data source

rather than a true lack of effect. Though they could be

due to sampling variation, the positive signs for WIC’s

effects on fruit and dairy intake appear reasonable.

Income and program participation are not the only factors that affect food intake. Indeed, the whole list of

economic and demographic variables is capable of

explaining only a portion of the diversity in food

intake that one observes in real-world data. A distinctive feature of the present study is its investigation of

patterns in the distribution of these other factors — the

“random error” in statistical terminology.

The results suggest a substantial correlation in the random error for individuals in the same household. One

interpretation of this finding — though not the only

possible interpretation — would suggest that decisions

at the household level go a long way toward determining food intake by household members. If household

decisions are important, then nutrition education

efforts might be advised to focus especially on the

 ✥ 	


 	
	
		

Percent

250

200

150

50

100

Meats Fruits Vegetables Grains Dairy Sugars Fats

Figure 4

Predicted food intake for adult nonparticipants and participants in food and nutrition programs

Food Stamps

WIC

0

No Program

Data source: Derived from table 1. Continuing Survey of Food Intakes by Individuals, 1994-96.

Note: Each column represents food intake as a proportion of the midpoint of the recommended range.
principal food purchaser or food preparer in the household. On the other hand, if individual decisions dominate, then nutrition education efforts may have to reach

each household member to be effective, perhaps at

greater expense to the program.4

What are the implications of the large positive

observed correlations between total fats and the five

main pyramid food categories, except fruits? One

could argue that over-consumption from these categories is not a major concern. Intake from the main

pyramid categories is not higher than the recommended range, assuming that under-reporting is not too

severe. Moreover, consumers may in principle reduce

consumption of total fats without reducing the number

of servings in these main pyramid food categories, by

choosing smaller amounts of discretionary fats (for

example, by choosing leaner meats). This possibility

is reflected in the HEI, which measures over-consumption of fats through components for fats and saturated

fats, but which defines components for the five main

	
	
		 	


✥ 9

Table 2 Estimated variances and cross-equation correlations for error components

 Added Total

 Meats Fruits Vegetables Grains Dairy sugars fats

Variance for individual and

 household error components:

Individual component 5.20 ** 1.77 ** 2.97 ** 6.75 ** .93** 1.25** 8.10**

 (Standard error) .17 .06 .10 .22 .03 .04 .26

Household component 2.22 ** .77** 1.15 ** 2.78 ** .36** .51 ** 2.75**

 (Standard error) .19 .07 .11 .24 .03 .04 .28

Correlation matrix for

 individual error component:

Meats 1.00 .04 .32 .34 .15 .17 .65

Fruits .04 1.00 .06 .06 .05 .07 .07

Vegetables .32 .06 1.00 .24 .11 .14 .47

Grains .34 .06 .24 1.00 .27 .30 .61

Dairy .15 .05 .11 .27 1.00 .15 .46

Added sugars .17 .07 .14 .30 .15 1.00 .35

Total fats .65 .07 .47 .61 .46 .35 1.00

Correlation matrix for

 household error component:

Meats 1.00 .06 .15 .09 -.10 .12 .44

Fruits .06 1.00 .26 .05 .14 -.10 .01

Vegetables .15 .26 1.00 .18 .08 .17 .33

Grains .09 .05 .18 1.00 .23 .43 .56

Dairy -.10 .14 .08 .23 1.00 .15 .34

Added sugars .12 -.10 .17 .43 .15 1.00 .53

Total fats .44 .01 .33 .56 .34 .53 1.00

Data source: Continuing Survey of Food Intakes by Individuals, 1994-96.

Notes: Each entry in a correlation table takes a value between -1 and 1. An entry of zero means that the error component referenced in the 

column heading is uncorrelated with the component referenced in the row heading. An entry of 1 means that the two components are perfectly 

correlated (as is the case automatically for the correlation between any variable and itself). Asterisks denote whether the variances of the 

individual and household error components are significantly different from zero: * = 10-percent level; ** = 5-percent level. The test statistic is a 

Wald chi-square statistic with one degree of freedom. Standard errors for the cross-equation correlations are available in the appendix.

	
		


	



		

	




	
	
		



	
	
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"	
		
##$$
pyramid food groups to measure only under-consumption, not over-consumption. However, the cross-equation correlations between total fats and the main pyramid food groups, except fruits, do raise the reasonable

concern that nutrition education efforts may have a difficult time achieving lower intake of total fats without

focusing also on consumption from the main pyramid

food categories. In evaluating nutrition assistance programs, even for low-income Americans, it may be

important to monitor both under-consumption and

over-consumption of most of the major pyramid food

categories, even though the best current survey evidence indicates that intake from these categories is not

excessive on average.

Nutrition monitoring and nutrition education are both

major priorities for the U.S. Department of

Agriculture, and this emphasis has implications for

nutrition assistance programs. As Kennedy concluded

in her survey of public policy in nutrition, “Aggressive

nutrition promotion programs need to be built into the

food assistance and nutrition programs to increase the

likelihood of sustained effectiveness. Newer paradigms of education including the use of social marketing need to be woven into the development of nutrition

promotion programs” (Kennedy, 1999, p. 332). This

study provides some background information for

building this type of nutrition promotion emphasis into

food assistance and nutrition programs.

% ✥ 	


 	
	
		
References

Basiotis, P. P., C. S. Kramer-LeBlanc, and E. T.

Kennedy. “Maintaining Nutrition Security and Diet

Quality: The Role of the Food Stamp Program and

WIC,” Family Economics and Nutrition Review, Vol.

11, No. 1-2, pp. 4-16, 1998.

Bradbard, S., E. F. Michaels, K. Fleming, M.

Campbell. Understanding the Food Choices of Low

Income Families. Washington, DC: Lisboa Associates,

Inc., 1997.

Butler, J. S., and J. E. Raymond. “The Effect of the

Food Stamp Program on Nutrient Intake,” Economic

Inquiry, Vol. 34, pp. 781-98, Oct. 1996.

Cleveland, L. E., A. J. Cook, J. W. Wilson, J. E.

Friday, J. W. Ho, and P. S. Chahil. “Pyramid Servings

Data: Results from USDA’s 1994 Continuing Survey

of Food Intakes by Individuals.” U.S. Dept. Agr., Agr.

Res. Serv., March 1997a.

Cleveland, L. E., D. A. Cook, S. M. Krebs-Smith, and

J. Friday. “Method for Assessing Food Intakes in

Terms of Servings Based on Food Guidance,”

American Journal of Clinical Nutrition, Vol. 65, pp.

1254S-63S, April 1997b.

Devaney, B., and R. Moffitt. “Dietary Effects of the

Food Stamp Program,” American Journal of

Agricultural Economics, Vol. 73, pp. 202-11, Feb.

1991.

Kennedy, E. “Public Policy in Nutrition: The US

Nutrition Safety Net — Past, Present, and Future,”

Food Policy, Vol. 24, pp. 325-33, 1999.

Krebs-Smith, S. M., A. Cook, A. Subar, L. Cleveland,

and J. Friday. “U.S. Adults’ Fruit and Vegetable

Intakes, 1989-1991: A Revised Baseline for the

Healthy People 2000 Objective,” American Journal of

Public Health, Vol. 85, pp. 1623-29, Dec. 1995.

Krebs-Smith, S. M., A. Cook, A. F. Subar, L.

Cleveland, J. Friday, and L. L. Kahle. “Fruit and

Vegetable Intakes of Children and Adolescents in the

United States,” Archives of Pediatrics and Adolescent

Medicine, Vol. 150, pp. 81-86, Jan. 1996.

Levedahl, J. W., and V. Oliveira. “Dietary Impacts of

Food Assistance Programs,” in America’s Eating

Habits (E. Frazao, ed.), AIB-750, U.S. Dept. Agr.,

Econ. Res. Serv., 1999.

Oliveira, V., and C. Gundersen. WIC and the Nutrient

Intake of Children, FANRR-5, U. S. Dept. Agr., Econ.

Res. Serv., 2000.

Rose, D., J.-P. Habicht, and B. Devaney. “Household

Participation in the Food Stamp and WIC Programs

Increases the Nutrient Intakes of Preschool Children,”

Journal of Nutrition, Vol. 128, pp. 548-55, March

1997.

Rossi, P. Feeding the Poor: Assessing Federal Food

Aid. Washington, DC: American Enterprise Institute

for Public Policy Research, 1998.

U.S. Department of Agriculture, Agricultural Research

Service. 1994-96 Continuing Survey of Food Intakes

by Individuals and 1994-96 Diet and Health

Knowledge Survey, 1998. 

U.S. Department of Agriculture, Center for Nutrition

Policy and Promotion. The Food Guide Pyramid,

Home and Garden Bulletin No. 252, 1996.

U.S. Department of Agriculture, Food and Nutrition

Service. “Nutrition Assistance Programs.” Web

address http://www.fns.usda.gov/fsp/, 1999.

Wilde, P. E., P. E. McNamara, and C. K. Ranney.

“The Effect of Income and Food Programs on Dietary

Quality: A Seemingly Unrelated Regression Analysis

with Error Components,” American Journal of

Agricultural Economics, Vol. 81, pp. 959-71, Nov.

1999.

	
	
		 	


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Appendix table 1 Estimated variance-covariance matrices for error components

Added Total

Meats Fruits Vegetables Grains Dairy sugars fats

Individual error

 component:

Meats 5.196 * 0.130 1.263* 1.999 * 0.329 * 0.444 * 4.196 *

Fruits .130 1.768 * .130* .224* .069* .098* .254*

Vegetables 1.263 * .130* 2.973* 1.083 * .175* .262* 2.311 *

Grains 1.999 * .224* 1.083* 6.746 * .686* .875* 4.477 *

Dairy .329* .069* .175* .686* .928* .157* 1.268 *

Added sugars .444* .098* .262* .875* .157* 1.250 * 1.115 *

Total fats 4.196 * .254* 2.311* 4.477 * 1.268 * 1.115 * 8.098 *

Household error

 component:

Meats 2.218 * .079 .232* .216 -.087 .128 1.097 *

Fruits .079 .769* .246* .069 .075* -.062 .010

Vegetables .232* .246* 1.153* .321* .052 .127* .585*

Grains .216 .069 .321* 2.783 * .227* .515* 1.559 *

Dairy -.087 .075* .052 .227* .356* .062* .341*

Added sugars .128 -.062 .127* .515* .062* .507* .625*

Total fats 1.097 * .010 .585* 1.559 * .341* .625* 2.748 *

Notes: Asterisks denote significance: * = 5-percent level, two-tailed test. The test statistic is the absolute value of the variance-covariance in 

appendix table 1, divided by the corresponding estimated standard error in appendix table 2.

Data source: Continuing Survey of Food Intakes by Individuals, 1994-96.
	
	
		 	


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Appendix table 2 -- Standard errors of estimated variance-covariance matrices for error components

Added Total

Meats Fruits Vegetables Grains Dairy sugars fats

Individual error

 component:

Meats .169 0.071 0.095 0.142 0.051 0.059 0.176

Fruits .071 .059 .054 .080 .030 .034 .088

Vegetables .095 .054 .098 .105 .039 .045 .124

Grains .142 .080 .105 .217 .059 .069 .197

Dairy .051 .030 .039 .059 .030 .025 .069

Added sugars .059 .034 .045 .069 .025 .040 .077

Total fats .176 .088 .124 .197 .069 .077 .262

Household error

 component:

Meats .191 .081 .104 .156 .056 .066 .185

Fruits .081 .069 .062 .091 .034 .039 .097

Vegetables .104 .062 .109 .116 .043 .050 .131

Grains .156 .091 .116 .240 .065 .078 .211

Dairy .056 .034 .043 .065 .033 .027 .073

Added sugars .066 .039 .050 .078 .027 .044 .086

Total fats .185 .097 .131 .211 .073 .086 .275

Data source: Continuing Survey of Food Intakes by Individuals, 1994-96.

0