Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes.

By James R. Blaylock, Jayachandran N. Variyam, and Biing-Hwan Lin. Food and Rural

Economics Division, Economic Research Service, U.S. Department of Agriculture.

Food Assistance and Nutrition Research Report No. 1. 

Abstract

We found significant evidence that the more a mother knows about health and nutrition

the better is the overall quality of her children’s diet, for preschoolers more so than older

children. We also found that a mother’s years of schooling, smoking status, race, and

ethnicity influence her children’s diet. Our results imply that health and nutrition education may be more effective if targeted toward mothers with young children but directly

toward school-age children. We assessed overall diet quality using the Healthy Eating

Index, the U.S. Department of Agriculture’s instrument for measuring overall diet quality incorporating 10 recommended nutritional guidelines. 

Keywords: Child nutrition, child health, Healthy Eating Index, nutrition knowledge,

household production theory, latent variable model.

Washington, DC 20036-5831 October 1999
Contents

Summary iii

Introduction 1

Children’s Diet Quality and Nutrient Intakes in the Household Production Model 3

Data 5

Measuring Health and Nutrition Knowledge 9

Maternal Nutrition Knowledge and Children’s HEI 12

Maternal Nutrition Knowledge and Children’s Nutrient Intakes 20

Empirical Model 20

Estimates of the Nutrient Intakes and Knowledge Equations 20

Conclusions 26

References 27

Appendix: Measuring Diet Quality: The Healthy Eating Index 30

ii
Summary

We found significant evidence that greater maternal knowledge of health and nutrition

leads to better diet quality for preschoolers but not necessarily for school-age children

after accounting for the influence of maternal background characteristics, health habits,

and household characteristics. A mother’s years of schooling benefits her child’s diet

quality because more highly educated women tend to have more nutrition knowledge.

The results closely agree with previous findings on the maternal role in children’s health

and nutrition obtained from a variety of samples in developing countries. We also found

strong diet effects within families that are generated by the health habits of individual

family members. Significant evidence was found that maternal knowledge of health and

nutrition influenced children’s nutrient intakes and that such influence decreased as children grow older. Our results lead to the conclusion that health and nutrition education

may be more effective if targeted toward mothers with young children but directly

toward school-age children. 

Dietary patterns established in childhood and adolescence may significantly influence

the probability of acquiring certain chronic diseases in the future. The Dietary

Guidelines for Americans also notes that “Healthful diets help children grow, develop,

and do well in school” and recommends specific intake levels for all age groups above 2

years old. Healthy dietary habits established in childhood may also be carried into

adulthood. Many health authorities, therefore, recommend that parents, teachers, and

other influential adults guide children in developing healthful eating patterns and acquiring information on nutrition and diet-health relationships.

For these reasons, interest in understanding the determinants of the quality of children’s

diets is growing. Of particular interest to nutritionists and public health officials is the

role of the mother’s nutrition knowledge on the diet quality of her children. This relationship has special policy interest because provision of health and nutrition information

is a major tool of agencies promoting more healthful diets among Americans. Evidence

on the role of maternal nutrition information in children’s diets would indicate the likely

social return to the continued provision of nutrition information. The role of maternal

nutrition information is also of interest to economists studying children’s health issues.

The question of whether information plays a role after accounting for parental and family background variables is still unanswered. Most previous studies have lacked either

proper measures of information or controls for all relevant background characteristics to

reach a definitive conclusion.

This report studies the influence of maternal nutrition knowledge, and other maternal

and household characteristics, on the overall diet quality of U.S. children ages 2-17

years old and on the intakes of individual nutrients. Much of the existing research on

the maternal role in children’s health and nutrition is in the economic development literature, and only a few sources have provided direct evidence on the role of information.

Relatively little is known in the U.S. context about the relationship between a child’s

diet and the nutritional knowledge of the person responsible for meal planning or preparation, usually the mother. The existing U.S. evidence linking parental nutrition knowledge and children’s intake of nutrients is mixed. Lack of controls for measurement

errors and endogeneity of parental nutrition knowledge may have contributed to these

inconclusive results.

iii
We based our analysis on the household production model used by economists to study

health behaviors and outcomes among individuals. A mother’s influence on household

productivity through factors such as diet and the nutrition of children is also a crucial

aspect of economic models of the family. In these models, consumers make different

health-related decisions partly because access to and costs of acquiring health information and using it efficiently varies from one individual to another: the allocative efficiency effect. For example, higher incomes may make information more accessible, and

greater education may enhance the efficiency of information use. Our study examines

the sources, such as a mother’s education, of the relationship between a mother’s knowledge of nutrition and her children’s nutritional outcomes.

The first phase of the study estimated the influence of maternal nutrition knowledge,

and other maternal and household characteristics, on the diet quality of children ages 2

to 17. Diet quality is measured using the Healthy Eating Index (HEI), the U.S.

Department of Agriculture’s (USDA) instrument for measuring overall diet quality

incorporating 10 recommended nutritional guidelines. We include children from ages 2

to 17, allowing us to examine whether parental influence on children’s diets diminishes

as children grow older. Most dietary experts, as well as the Dietary Guidelines for

Americans advocate a diet that contains a variety of foods and nutrients. Consequently,

a measure of overall diet quality is desirable for estimating maternal influence on behaviors that are closely related to future health outcomes.

The second phase of the study uses a nationally representative sample of U.S. households to estimate the effect of maternal health and nutrition knowledge on children’s

intakes of fat, saturated fat, cholesterol, fiber, sodium, calcium, and iron. 

Both phases used a simultaneous equations framework to control for unobserved family

heterogeneity that may contaminate single-equation estimates of such relationships.

Another feature of this study concerns the measurement of information. USDA data

provide mothers’ responses to an extensive set of questions about nutrition and health

that can be used to develop direct measures of mothers’ knowledge of nutrition and

health. Our information measures capture the actual stock of maternal nutrition knowledge as opposed to indicators of access to information. Using latent variable methods,

we also account for measurement errors and endogeneity of maternal nutrition knowledge.

iv
Maternal Nutrition Knowledge and

Children’s Diet Quality and Nutrient Intakes

James R. Blaylock, Jayachandran N. Variyam, and Biing-Hwan Lin

Introduction

Dietary patterns established in childhood and adolescence may significantly influence the probability of acquiring certain chronic diseases as an adult (Kemm, 1987; McGill and others, 1997; McPherson, Montgomery, and

Michaman, 1995; Nicklas, Farris, and Smoak, 1988). USDA’s Dietary Guidelines for Americans notes that

“Healthful diets help children grow, develop, and do well in school” and recommends intake levels for everyone

over 2 years old (U.S. Dept. of Agr. and U.S. Dept. of Health and Human Services, 1995). Healthful dietary

habits established in childhood may be carried into adulthood. Many health authorities, therefore, recommend

that parents, teachers, and other influential adults guide children in developing healthful eating patterns and

acquiring information on nutrition and diet-health relationships.

For these reasons, interest in understanding the determinants of the quality of children’s diets and the pathways

of their influence is growing among health professionals. Of particular interest to nutritionists and public health

officials is the role of mothers’ nutrition knowledge on the diet quality of their children. This relationship has

special policy interest because provision of health and nutrition information is a major tool of agencies promoting more healthful diets among Americans (Thomas, 1991). Evidence on the role of maternal nutrition information in children’s diets might indicate the likely social return to nutrition information provided by the government. The role of maternal nutrition information is also of interest to economists studying children’s health

issues. Specifically, an issue still being debated is the role of nutrition information after accounting for parental

and family background (Behrman, 1995). Most previous studies have lacked either proper measures of information or controls for all relevant background characteristics to reach a definitive conclusion in this regard.

This report studies the influence of mothers’ knowledge of nutrition, and other maternal and household characteristics, on the diet quality and nutrient intakes of U.S. children ages 2-17 years old. We do not focus on both

mothers and fathers because of data limitations. Much of the existing research on mothers’ role in their children’s health and nutrition is in the economic development literature (Barrera, 1990; Behrman, 1995; Behrman

and Wolfe, 1987; Haughton and Haughton, 1997; Kassouf and Senauer, 1996; Senauer and Garcia, 1991;

Thomas and Strauss, 1992), and only a few have provided direct evidence on the role of information (Thomas,

Strauss, and Henriques, 1991). Relatively little is known in the U.S. context about the relationship between a

child’s diet and the nutritional literacy of the person responsible for meal planning or preparation, usually the

mother. The existing U.S. evidence linking parental nutrition knowledge and children’s intake of nutrients is

mixed (Colavito and others, 1996; Klesges and others, 1991). Lack of controls for measurement errors and

endogeneity of parental nutrition knowledge may have contributed to these inconclusive results.

Our analysis is based on the household production model used by economists to study health behaviors and outcomes among individuals (Behrman and Deolalikar, 1988; Rosensweig and Schultz, 1983). A mother’s influence on household productivity through factors such as diet and the nutrition of children is also a crucial aspect

of economic models of the family (Behrman and Wolfe, 1987; Leibowitz, 1974; Taubman, 1977). In these models, consumers make different health-related decisions partly because access to and costs of acquiring health

information as well as its efficient use varies from one individual to another: the allocative efficiency effect

(Grossman and Kaestner, 1995). For example, higher incomes may make information more accessible and

greater education may enhance the efficiency of information use. Our study examines the sources of the relationship between a mother’s nutrition knowledge and her children’s nutritional outcomes.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 1
This study was done in two phases: The first phase of the study estimated the influence of maternal nutrition

knowledge and other maternal and household characteristics on the diet quality of children ages 2 to 17. Diet

quality was measured using the Healthy Eating Index (HEI), the U.S. Department of Agriculture’s (USDA)

instrument for measuring overall diet quality incorporating 10 nutritional recommendations. By including children from ages 2 to 17, we can examine whether parental influence on children’s diets diminishes as children

grow older. This is the first analysis, to our knowledge, to focus on overall diet quality that accounts for foods,

nutrients, and variety, as opposed to the intake of individual nutrients only.

The second phase of the study focused on individual nutrients and dietary components. Specifically, the effect

of maternal knowledge of health and nutrition on children’s intakes of total fat, saturated fat, cholesterol, fiber,

sodium, calcium, and iron was estimated.

We used a simultaneous equations framework in both phases to control for unobserved family heterogeneity that

may contaminate single-equation estimates of such relationships. Another feature of this study concerns the

measurement of information. USDA data provide mothers’ responses to an extensive set of questions about

nutrition and health that can be used to develop direct measures of maternal knowledge of health and nutrition.

Our information measures capture the actual stock of maternal nutrition knowledge rather than indicators of

access to information (as in Thomas, Strauss, and Henriques, 1991). Using latent variable methods, we also

account for measurement errors and endogeneity of maternal nutrition knowledge.

2 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service
Children’s Diet Quality and Nutrient Intakes

in the Household Production Model

The household production model used by economists to study health behaviors and outcomes offers a rich

framework for estimating and interpreting the determinants of children’s nutrient intakes and diet quality

(Behrman and Deolalikar, 1988; Strauss and Thomas, 1996). The model integrates biological, sociodemographic, and economic factors, all of which influence household production efficiency and consumption

decisions. The basic idea behind this model is that households allocate time and goods to produce commodities,

including the health of children and other family members, so as to maximize a joint utility function. Subject to

technology, time, and income constraints, the household utility maximization generates individual and household

demand functions for health, health inputs, and other consumption goods.

The reduced-form food and nutrient demand equations resulting from household utility maximization can be

written as functions of a vector of prices p, income I, a vector of individual and household characteristics x, and

unobserved household and individual effects, u (for example, Haughton and Haughton, 1997; Senauer and

Garcia, 1991). The reduced-form nutrient demand functions resulting from the above maximization framework

have the general form 

where c is a vector of nutrients consumed.1 Because a child’s overall diet quality (measured here by the HEI) is

a composite measure developed from the foods and nutrients consumed, its reduced form has an identical form

given by

Most cross-sectional studies using the household production approach assume that prices are either fixed or captured by regional dummy variables and focus mainly on income and components of x. The usefulness of the

household production approach in estimating functions of the above form is in guiding what should be included

among x. With respect to child-health inputs and outcomes, the vector x has traditionally included four sets of

variables: child’s characteristics, parental characteristics, household characteristics, and community characteristics (Strauss and Thomas, 1996). Introducing mothers’ knowledge of health and nutrition as an additional variable into the above equation reflects its role as a factor mediating part of the influence of the x variables on children’s health and nutrition. The major question in this regard is whether the effects on health of some of the

variables in x reflect differences in parental knowledge of health and nutrition. Consider, for example, the effect

of education. Several studies from developing countries (Barrera, 1990; Kassouf and Senauer, 1996; Senauer

and Garcia, 1991; Thomas and Strauss, 1992) cite a link between mothers’ education and the nutritional status

and health of their children. Yet education is seen as working mainly by improving allocative efficiency: education increases the mother’s access to knowledge and information, enabling her to select a better input mix for

more efficient child health production (Grossman and Kaestner, 1995; Thomas, Strauss and Henriques, 1991). 

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 3

1In a fully specified household production model, the time and income constraints are expressed as a “full income” constraint that leads to reduced-form demand functions that contain wage rates (the value of time) and full or nonlabor income.

In such a model, money income is subject to endogeneity because it reflects time allocated to work in the labor force.

However, Senauer, Sahn, and Alderman (1986) found that empirical results are similar when either “full” or “money”

income is used.

c fp xu = ( ,I , ) |

HEI = f( ,I ,u). p x|

(1)

(2)
This view suggests that reduced-form health or health input equations estimated without explicit control for the

effect of health knowledge may overstate the effect of education. Other characteristics that influence child

health and nutrition, such as income, may also play a similar role partly through their effect on mothers’ knowledge. These issues can be addressed by introducing a measure of mothers’ nutrition knowledge into the

reduced-form equations 1 and 2.

A key empirical issue in sorting out the maternal information effect concerns the potential endogeneity of any

information measure. Any effect detected for mothers’ information on children’s diet quality may represent

unobserved maternal and household factors that are influencing both. To some extent, such effects can be controlled by including parental background characteristics such as height, weight, and household characteristics in

the HEI function or the nutrient intake equations. However, given that not all background characteristics are

observable, children’s diet quality and maternal information both need to be treated as jointly determined. In

this case, equations 1 and 2 can be expanded and written as

and

where K is a measure of mothers’ knowledge of health and nutrition, z is a vector of knowledge determinants,

and v is an unobserved individual effect that may be correlated with u.

4 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

HEI = f( ,I ,K,u),

K = g( ,v),

p x| 

z

(3)

c fp x u

z

= ( ,I ,K, ),

K = g( ,v)

| (4)
Data

Our studies used data from USDA’s 1989-91 Continuing Survey of Food Intakes by Individuals (CSFII) and the

companion Diet and Health Knowledge Survey (DHKS) (Cypel and others, 1996). For the HEI study, we used

households that completed the 1989-90 surveys because the 1991 survey does not have calculated values for the

Healthy Eating Index. For the nutrient phase, we used all 3 survey years.

The CSFII survey collects information on what, when, and where Americans eat and how much they eat. Each

CSFII participant was asked to provide 3 consecutive days of dietary data. Dietary intakes of children under age

12 were provided by an adult, usually the parent. The first day’s data were collected via an in-home interview

using a 1-day dietary recall. The second and third days’ data were collected using a self-administered 2-day

dietary record. Social, economic, and demographic characteristics of survey participants are also included in the

CSFII.

The CSFII uses a multistage, stratified selection procedure targeted at private households in the 48 contiguous

States. In the 1989-91 surveys, 23,142 housing units were selected, which after screening resulted in 8,443 eligible households, of which 6,718 (79.6 percent) participated.

The DHKS was conducted as a telephone or in-person follow-up to the CSFII. One respondent, usually the

household’s main meal planner, was contacted about 6 weeks after collection of the dietary data and asked to

answer questions about his or her knowledge of nutrition, diet, and health issues. Among the 6,178 households

participating in the 1989-91 CSFII, 5,730 (85.3 percent) completed the DHKS.

Because the Dietary Guidelines for Americans are aimed at individuals age 2 and over, children between the

ages of 2 and 17 were chosen for analysis. To explore the hypothesis that a mother’s influence on her children’s

diets weakens with children’s age, the analysis was conducted for two age groups: 2-5 and 6-17. Of the households with children in the two age groups, a large percentage had only a single child in one age group. To avoid

intrafamily effects, we randomly selected one child from households with more than one child in an age group

for all the analyses. After we eliminated observations with missing values, our final sample sizes were as follows: 308 preschool children and 538 school-age children for the HEI phase and 458 preschool children and 853

school-age children for the nutrients phase.

Table 1 contains the means and standard deviations for the variables used in the HEI study, separated into two

age groups: 2-5 years old and 6-17. Table 2 reports similar descriptive statistics for children’s intakes of seven

nutrients. In conformity with the Dietary Guidelines units, we expressed fat and saturated fat intake by their

percent contribution to a child’s total energy intake per day. The intakes of the remaining five nutrients were

expressed in a closely related unit, the nutrient-to-calorie density—grams of nutrient consumed per 1,000 kilocalories—employed widely in nutrition studies (Colavita and others, 1996; Lin, Guthrie, and Blaylock, 1996).

These relative measures give us an assessment of the nutritional quality of children’s diets. Details on the construction of the Healthy Eating Index are provided in the Appendix.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 5
6 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 1—Description of variables used in children’s HEI study

 Ages 2-5 Ages 6-17 

Variable (unit) Mean Standard deviation Mean Standard deviation

Child’s HEI (score) 65.44 12.44 62.64 11.74

Child’s characteristics:

Age (years) 3.48 1.09 11.04 3.52

Female (proportion) .43 — .52 —

Mother’s nutrition knowledge:

Nutrient Content Knowledge (score) 15.19 2.81 15.49 2.66

Diet-Health Awareness (score) 6.29 2.33 6.50 2.34

Mother’s characteristics:

Education (years) 12.30 2.43 12.24 2.57

Age (years) 29.79 5.57 36.53 6.71

Height (inches) 64.10 2.60 64.27 2.68

Weight (pounds) 147.11 34.51 153.31 36.85

Smoker (proportion) .30 — .28 —

Household characteristics:

Income ($000) 22.17 20.52 25.68 21.33

Household size (number) 4.44 1.69 4.30 1.57

Race (White omitted) —

Black (proportion) .16 — .16 —

Other1 (proportion) .02 — .02 —

Hispanic (proportion) .11 — .10 —

Any member on a vegetarian diet (proportion) .06 — .03 —

Receives Food Stamps (proportion) .30 — .20 —

Receives WIC2 benefits (proportion) .19 — .06 —

Region (Northeast omitted) —

Midwest (proportion) .29 — .26 —

South (proportion) .32 — .36 —

West (proportion) .18 — .20 —

Urbanization (city omitted) —

Suburban (proportion) .46 — .43 —

Nonmetro (proportion) .23 — .24 —

Year (1989 omitted) —

1990 (proportion) .51 — .47 —

1991 (proportion) .08 — .09 —

Season (winter omitted) —

Spring (proportion) .18 — .19 —

Summer (proportion) .29 — .27 —

Fall (proportion) .28 — .28 —

Intake recorded on weekend

(weekday omitted)—

Day 1 (proportion) .23 — .27 —

Day 2 (proportion) .24 — .30 —

Day 3 (proportion) .31 — .32 —

Number

Number of children 308 — 538 —

— = Not applicable.

1Asian/Pacific Islander, Aleut, Eskimo, or American Indian.

2Women, Infants, and Children.

Source: CSFII-DHKS, 1989-90, U.S. Department of Agriculture.
USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 7

Table 2—Description of variables used in children’s nutrient intakes study

 Ages 2-5 Ages 6-17 

Variable (unit) Mean Standard deviation Mean Standard deviation

Nutrient intake (per day):

Total fat (percent of total calories) 34.47 5.98 34.73 5.63

Saturated fat (percent of total calories) 13.65 3.18 13.12 2.76

Cholesterol (gm/1000 calories) .15 .07 .13 .06 

Fiber (gm/1000 calories) 6.45 2.21 6.69 2.15

Sodium (gm/1000 calories) 1.70 .36 1.70 .33

Calcium (gm/1000 calories) .59 .19 .50 .16

Iron (mg/1000 calories) 7.64 2.82 7.24 2.70

Child’s characteristics:

Age (years) 3.47 1.10 11.14 3.58

Female (proportion) .45 — .45 —

Mother’s nutrition knowledge:

Nutrient Content Knowledge (score) 15.61 2.70 15.82 2.62

Diet-Health Awareness (score) 6.21 2.44 6.46 2.38

Household characteristics:

Race (White omitted)—

Black (proportion) .16 — .16 —

Other1 (proportion) .03 — .02 —

Hispanic (proportion .11 — .10 —

Female head (proportion) .27 — .30 —

Any member on a vegetarian diet (proportion) .06 — .05 —

Low-fat/low-calorie diet (proportion) .05 — .08 —

Receives Food Stamps (proportion) .30 — .22 —

Receives WIC2 benefits (proportion) .19 — .07 —

Income3 (percent) 174.10 173.50 208.40 203.40

Region (Northeast omitted) —

Midwest (proportion) .30 — .29 —

South (proportion) .34 — .35 —

West (proportion) .19 — .20 —

Urbanization (city omitted)—

Suburban (proportion) .45 — .43 —

Nonmetro (proportion) .23 — .27 —

See notes at end of tables —Continued
8 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 2—Description of variables used in children’s nutrient intakes study—Continued

 Ages 2-5 Ages 6-17 

Variable (unit) Mean Standard deviation Mean Standard deviation

Mother’s characteristics:

Education (years) 12.23 2.45 12.22 2.56

Age (years) 29.66 5.76 36.66 6.86

Employment (full omitted) —

Part employed (proportion) .19 — .19 —

Not employed (proportion) .60 — .44 —

Smoker (proportion) .33 — .30 —

Disease (proportion) .04 — .10 —

Received diet advice (proportion) .05 — .06 —

Survey characteristics:

Year (1989 omitted) —

1990 (proportion) .35 — .32 —

1991 (proportion) .29 — .31 —

1992 (proportion) .07 — .09 —

Season (winter omitted) —

Spring (proportion) .17 — .18 —

Summer (proportion) .31 — .28 —

Fall (proportion) .28 — .27 —

Intake recorded on weekend —

(weekday omitted)

Day 1 (proportion) .24 — .26 —

Day 2 (proportion) .23 — .29 —

Day 3 (proportion) .28 — .31 —

Number 

Number of children 458 — 853 —

— = Not applicable.

1Asian/Pacific Islander, Aleut, Eskimo, or American Indian.

2Women, Infants, and Children.

3Expressed as a percentage of the poverty threshold.

Source: CSFII-DHKS, 1989-91, U.S. Department of Agriculture.
Measuring Health and Nutrition Knowledge

We used responses from two sets of questions in the DHKS to develop measures of mothers’ knowledge of

health and nutrition. The first set of questions aimed to capture the “nutrient content knowledge” (NCK) of the

respondents (table 3). Respondents were asked a series of questions about sources and occurrence of dietary

fiber, cholesterol, and fat in common food items. For example, respondents were asked to identify which of two

foods has the higher fiber content: fruit or meat, cornflakes or oatmeal, popcorn or pretzels. In general, there

was broad agreement between the HEI and nutrient samples. However, respondents were more successful at

correctly answering some questions than others. For example, over 90 percent of respondents from all samples

knew that wheat bread has a higher fiber content than white bread but only 33 percent knew that liver has more

cholesterol than T-bone steak. Other questions probed knowledge about different kinds of fat and the types of

foods that contain fat and cholesterol. The NCK measure represents the number of correct answers a respondent

gave to 21 such questions. Respondents in the HEI studies answered about 15 questions correctly while those

in the samples for the nutrient intake analysis answered about 16 correctly. The second set of questions to assess

mothers’ knowledge of health and nutrition was aimed at their “diet-health awareness” (DHA). These questions

take the general form: Have you heard about any health problems that might be related to how much of a particular nutrient (such as fat, fiber, salt, and so forth) a person eats? Nine such questions from the DHKS and their

percent “yes” are listed in table 4. The DHA variable is the sum of positive responses for the nine questions.

This measure is similar to Kenkel’s (1991) health knowledge measure in a study of adult smoking, alcohol use,

and exercise behavior. The average “awareness” level was about six for the mothers in all samples (tables 1 and

2).

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 9
10 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 3—Nutrient content knowledge questions and percent responses

 Correct responses by mother 

 HEI phase Nutrients phase 

Question/answer Ages 2-5 Ages 6-17 Ages 2-5 Ages 6-17

Percent

Which has more fiber?

Fruit or meat 81.8 82.7 82.8 83.9

Cornflakes or oatmeal 80.2 82.5 81.4 83.7

Whole-wheat bread or white bread 92.9 93.3 93.2 93.4

Orange juice or an apple 77.9 76.0 77.3 76.1

Kidney beans or lettuce 53.6 55.6 59.0 58.7

Popcorn or pretzels 68.5 73.8 69.0 72.6

Which has more cholesterol?

Liver or T-bone steak 33.1 38.5 34.7 41.0

Butter or margarine 83.8 86.1 84.9 87.4

Egg whites or yolks 79.2 81.6 82.3 82.2

Skim milk or whole milk 95.5 97.6 96.5 97.1

Which has more fat?

Regular hamburger or ground round 86.7 89.6 87.8 89.9

Loin pork chops or pork spare ribs 71.8 76.0 72.1 76.0

Hot dogs or ham 66.6 63.4 64.8 63.7

Peanuts or popcorn 89.6 92.2 90.4 93.3

Yogurt or sour cream 87.3 86.2 87.3 87.5

Porterhouse steak or round steak 52.3 54.5 53.5 55.7

Ice cream or sherbet 93.8 96.1 92.8 96.1

Roast chicken leg or fried chicken leg 98.1 97.8 98.0 97.8

Which kind of fat (saturated, polyunsaturated) is

more likely to be a liquid rather than a solid? Or

are they equally likely to be liquids? 26.9 29.0 24.9 29.1

Is cholesterol found in vegetables and vegetable oils,

animal products, or all foods containing fat or oil? 35.1 35.7 32.3 37.1

If a food is labeled cholesterol-free, is it also low in

saturated fat, high in saturated fat, or either? 64.9 60.8 65.7 61.7

Number

Number of children 308 538 458 853

Note: Correct answers are underlined.

Source: 1989-91 Diet and Health Knowledge Survey.
USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 11

Table 4—Mothers’ responses to questions about their diet-health knowledge 

“Yes” responses by mother 

 HEI phase Nutrients phase 

Question Ages 2-5 Ages 6-17 Ages 2-5 Ages 6-17

Have you heard about any health

problems that might be related

to how much: Percent

Fat a person eats? 70.8 74.0 72.9 75.7

Saturated fat a person eats? 57.5 61.9 55.5 60.9

Fiber a person eats? 47.1 51.3 46.3 51.8

Salt a person eats? 81.5 88.8 82.1 86.7

Calcium a person eats? 63.6 63.9 61.6 63.8

Cholesterol a person eats? 85.7 85.9 83.0 85.5

Sugar a person eats? 80.5 80.9 79.0 80.9

Iron a person eats? 54.9 52.0 53.3 50.4

A person is overweight? 87.0 90.9 87.6 90.5

Number

Number of children 308 538 458 853

Source: 1989-91 Diet and Health Knowledge Survey.
Maternal Nutrition Knowledge and Children’s HEI

Tables 5 and 6 give two alternative estimates for the preschoolers’ and school-age children’s HEI equations.

Column 1 of each table reports results of the empirical HEI equation

estimated by ordinary least squares (OLS). Both NCK and DHA were included as variables capturing maternal

nutrition knowledge, and Xp represents the other explanatory variables. The explanatory variables are listed in

table 1. These can be grouped into four broad categories: mother’s characteristics, child’s characteristics, household characteristics, and survey-related controls. Of the broad groups of variables suggested by the household

production model, we included all except prices and community characteristics. Both of these variables were

unavailable in the CSFII/DHKS data. Instead, we included in our analysis dummy variables representing household region, urbanization, survey year, season, and whether the reported intake was for a weekend, to capture

price and community effects.

The two children’s characteristics included are sex and age. Several household characteristics were included to

control for common family effects that may influence both the child’s intakes as well as the mother’s knowledge

of health and nutrition. These characteristics include household income, household size, race, ethnicity, participation of a household member in the Food Stamp Program or in the Special Supplemental Nutrition Program for

Women, Infants, and Children (WIC), and whether anyone in the household was on a vegetarian diet.

As the household production model suggests, a key variable that may affect a mother’s knowledge of health and

nutrition is her education level, represented in our analysis by the number of years of schooling. By including

both the mother’s education and information measures in the child’s HEI equation, we can examine what effect,

if any, a mother’s education has beyond its information-enhancing role. A mother’s age may capture the effects

of learning and experience that remain after controlling for her education level. There has been some concern

that maternal education and information effects may reflect maternal background characteristics (Behrman,

1995). We accounted for this possibility by including the mother’s height and weight in the child’s HEI equation.

One key variable suggested in the household production model, but overlooked in the children’s health and

nutrition literature, is maternal time preference. Several studies have emphasized that health habits as well as

other human capital choices may be affected by an individual’s time preference (Farrell and Fuchs, 1982;

Grossman and Kaestner, 1995). An individual who places a higher value on current enjoyment may place less

value on healthy habits, education, and information acquisition activities than those who place a higher value on

future enjoyment. In the mother-child empirical context, this means a positive effect of maternal education or

maternal health knowledge on child nutrition may be due to a mother’s lower time preference which places a

higher value on both her and her child’s future health. To estimate the “true” education or information acquisition effect, one must control for maternal time preference. In this study, we used the mother’s smoking status as

such an indicator (Evans and Montgomery, 1994).

The standard errors reported in parentheses have been corrected for heteroskedasticity by White’s procedure.

Statistical significance of coefficient estimates were judged by two-sided t-tests at the 10-percent probability

level.

12 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

HEI = + X + NCK + DHA +u, 0 pp 2

p=1

P

α αβ β ∑ 1 (5)
The DHA coefficient indicates that maternal awareness of diet-health relationships significantly increases

preschoolers’, but not school-age children’s, diet quality. On the other hand, the NCK coefficient is insignificant

for preschoolers and significant in the equation for school-age children.

These OLS results highlight an important empirical question concerning the difference in the impact of our DHA

and NCK variables. Given that both variables are measuring the same underlying information level, such differences could be due to measurement errors specific to each variable as well as the high correlation between NCK

and DHA. In this case, it may be possible to improve the accuracy of estimated information effects by using a

latent variable representation for maternal nutrition knowledge (Griliches, 1986). In this approach, the relationship between a set of error-ridden variables and the ‘true’ underlying variable they are attempting to measure is

specified explicitly using measurement equations. In our case, these may be written as

where K is the true nutrition knowledge level underlying NCK and DHA, the λ’s are unknown parameters, and

e’s are random measurement errors that are uncorrelated with each other and with K.

As noted earlier, a major focus of previous research has been to what extent information acts as a mechanism

underlying the link between maternal and household characteristics and children’s nutrition and health outcomes

(Strauss and Thomas, 1996; Thomas, Strauss, and Henriques, 1991). To address this question and also to

account for the possible effects of unobserved individual and family influences, we estimated a system of equations that treat children’s HEI and mother’s nutrition knowledge as jointly determined. A modified HEI equation

can be written as

Equation 6 was estimated along with equations 7 and 8 by maximum likelihood using the LISREL framework

(Joreskog and Sorbom, 1989) for our samples of preschoolers and school-age children (see Appendix in Variyam

and others, 1998, for the estimation procedure). Zq includes all Xp variables in the HEI equation except child

characteristics and the seasonal and weekend dummies. Zq also includes four additional variables for identification. These variables indicate whether the mother has received dietary advice from a physician or a dietitian, the

hours per day she spends watching television, the male head of household’s education level, and whether the

DHKS was conducted in person or by telephone. We expected these variables to capture sources of variations in

maternal nutrition knowledge that are unrelated to children’s diet quality. In tables 5 and 6, column 2, we report

the HEI equation estimates, and in table 7 we report estimates for the maternal nutrition knowledge equations.

A parameter restriction is required to identify the scale of the latent “nutrition knowledge” variable K (Bollen,

1989, p. 239). We normalize the coefficient of K in the NCK measurement (equation 6) to one (i.e., λ11 = 1) so

that NCK and K have the same scale; a unit change in K causes a unit change in NCK. The coefficient for

DHA, λ21, is free. All variables are expressed as deviations from their means, so the intercept coefficients in

equations 6-8 are zero.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 13

NCK = + K + e

DHA = + K + e

10 11 1

20 21 2

λ λ

λ λ

(6)

HEI = + X + K +u, 0 pp

p=1

P

(7) α αβ ∑

K= + Z +v 0 qq .

q=1

Q

γ γ ∑

where

(8)
For preschoolers, the maternal information effect is positive and much larger with the endogenous latent variable

model than in the OLS model (table 5). Given the estimates of λ’s and β, we can infer the relationship between

DHA or NCK, and the HEI. For example, the ratio β/λ21 gives the change in HEI score due to a change in

maternal nutrition knowledge (K) that corresponds to an additional correct answer recorded on DHA. This ratio

is estimated to be 3.757 (2.716/0.723). Therefore, a mother gaining information to answer an additional DHA

question correctly would increase her preschooler’s diet quality by about 3.75 points on the HEI scale according

to the latent variable model. The comparable effect estimated by OLS is 0.93. The HEI equation under the

latent variable model also has a higher R2 of 0.28 than the HEI equation estimated by OLS which has an R2 of

0.27.

A mother’s nutrition knowledge, however, has a negative but statistically insignificant effect in the school-age

child’s HEI equation. If maternal influence on children’s diet quality declines with age, especially after children

start school, the statistical insignificance of maternal knowledge is not surprising. However, the negative sign

and the relatively large size of the knowledge coefficient is troubling. We attempted many different specifications, including adding other identifying variables to the mother’s knowledge equation but were unsuccessful in

obtaining meaningful results. The central problem appears to be the lack of any variables that can sufficiently

capture the variation in the mother’s knowledge equation. The four variables we used successfully in the

preschooler’s equation: dietary advice, hours of television watching, male head years of education, and whether

the survey was conducted in-person or by telephone were all statistically insignificant in the school-age children’s equation. This statistical insignificance implies that we cannot adequately model and explain the variation in the nutrition knowledge equation. Thus, we feel that the coefficients in the equations for school-age children should be interpreted very carefully. Additional modeling research is clearly necessary, but perhaps this

14 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 5—HEI equation estimates for children ages 2-5

Explanatory variable Ordinary least squares Latent variable model

Mother’s nutrition knowledge:

Nutrient content knowledge .262 —

(.283) —

Diet-health awareness .925* —

(.365) —

Nutrition knowledge level — 2.716*

(1.369)

Mother’s characteristics:

Education -.462 -.726*

(.342) (.420)

Age .323* .221

(.151) (.172)

Height .645* .586*

(.269) (.282)

Weight -.147* -.074

(.085) (.117)

Weight squared (×10-3) .413* .218

(.232) (.321)

Smoker -4.874* -4.318*

(1.519) (1.655)

Child’s characteristics:

Child’s age -.892 -.904

(.619) (.617)

Female child .962 1.029

(1.241) (1.296)

See notes at end of table. —Continued
dilemma will not be solved until more data become available. For the remainder of this section, we will focus

on the preschooler model.

The latent variable model yields better results than the OLS model for preschoolers because the OLS model

ignores the effects of other maternal and household characteristics that are transmitted through knowledge while

the latent variable model accounts for such indirect effects. For example, both maternal education and household income have positive effects on maternal nutrition knowledge (table 7, column 1), confirming their role in

increasing access to and use of information (Ippolito and Mathios, 1990). Comparing column 1 and column 2

estimates in table 5, one can see that after the knowledge-enhancing role of maternal education is taken into

account, its remaining effect on preschoolers’ diet quality is negative. This negative educational effect is different from the Thomas, Strauss, and Henriques (1991) finding that maternal education level may not influence

Brazilian children’s height. A possible explanation is that, in the United States, education enhances nutrition

knowledge, but it may also increase the demand for convenience foods and food-away-from home, both of

which may be of lower dietary quality (Lin, Guthrie, and Blaylock, 1996).

Height is a key variable used in many previous studies to control for maternal background effects (Behrman,

1995). In conformity with previous results, we find that maternal height is significantly related to diet quality

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 15

Table 5—HEI equation estimates for children ages 2-5—Continued

Explanatory variable Ordinary least squares Latent variable model

Household characteristics:

Log income 1.232 0.073

(1.038) (1.349)

Household size .373 .072

(.482) (.538)

Black -4.083* -2.467

(2.179) (2.427)

Other race 2.068 5.722

(4.910) (5.578)

Hispanic -1.379 1.898

(2.349) (3.323)

Food stamp participant 1.778 1.667

(1.916) (2.030)

WIC participant .669 1.272

(1.896) (2.020)

Vegetarian diet 5.181* 4.231*

(1.623) (2.168)

Midwest -1.556 -2.506

(1.966) (2.111)

South -.593 -1.391

(1.921) (2.076)

West -.621 -3.060

(2.287) (2.808)

Suburb 1.248 1.532

(1.518) (1.629)

Nonmetro .537 -.193

(1.726) (1.966)

R2 .270 .282

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level on a two-sided t-test. All models also include 13 dummy variables representing

region and urbanization of the household, survey year, survey season, and whether any of the recorded 3-day intake was on

a weekend; see table 1.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-90 CSFII-DHKS.
(Barrera, 1990; Haughton and Haughton, 1997; Thomas and Strauss, 1992). However, maternal height has no

information-related effect, suggesting that it is capturing maternal background and endowment effects that do

not influence information acquisition.2

Excepting Haughton and Haughton (1997), few other studies have used maternal weight as an explanatory variable. While Haughton and Haughton (1997) find a positive relationship between child nutrition and maternal

weight, we find that maternal weight has an inverse relationship with children’s diet quality through its influence

on maternal nutrition knowledge. One explanation is that obesity may be discouraging mothers from investing

further in acquiring and processing information. This conclusion is supported by the fact that the adverse influence is greater at higher weight levels. These opposite findings are clearly related to the fact that in developing

countries, weight is a positive indicator of health because under-consumption of foods and nutrients related to

weight is the major problem, at least in the populations studied. In industrialized countries, overweight or obesity is a negative indicator of health because the major nutrition problem is over-consumption of fat- and calorierich foods. Therefore, both results confirm the underlying relationship that maternal health endowment is positively related to children’s nutritional outcomes. 

Maternal smoking has a substantial negative effect on children’s diet quality. The diets of children whose mothers smoke score approximately five HEI points lower than children whose mothers are nonsmokers. The effect

is estimated rather precisely with a relatively low standard error. The sizable effect of maternal smoking status

is also not related to information. This finding poses a challenge to nutrition educators because standard nutri16 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 6 —HEI equation estimates for children, ages 6-17

Explanatory variable Ordinary least squares Latent variable model

Mother’s nutrition knowledge:

Nutrient content knowledge 1.126* —

(.211)

Diet-health awareness .011 —

(.229)

Nutrition knowledge level — -3.587

(2.685)

Mother’s characteristics:

Education .087 1.339*

(.230) (.788)

Age .022 .121

(.083) (.123)

Height .295* .432

(.164) (.265)

Weight .071 -.023 

(.070) (.060)

Weight squared (×10-3) -.257* .047

(.151) (.314)

Smoker -6.016* -7.183*

(1.101) (1.670)

Child’s characteristics:

Child’s age -.035 -.042

(.152) (.155)

Female child -2.971* -3.000*

(.905) (.905)

See notes at end of table. — Continued 

2A quadratic term for maternal height was tried and found to be not significant.
USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 17

Table 6—HEI equation estimates for children, ages 6-17—Continued

Explanatory variable Ordinary least squares Latent variable model

Household characteristics:

Log income 0.827 1.852

(.773) (1.159)

Household size .493* 1.124

(.292) (.556)

Black -1.604 -9.326*

(1.528) (4.760)

Other race -8.170* -14.896*

(3.962) (6.881)

Hispanic 3.427* -4.116

(1.759) (4.924)

Food stamp participant .420 -.043

(1.552) (2.019)

WIC participant -3.224* -5.650*

(1.787) (3.117)

Vegetarian diet 2.768 4.403 

(2.183) (3.203)

Midwest -2.084 -.789

(1.418) (2.073)

South .016 -.212

(1.363) (1.962)

West -.417 2.793

(1.552) (2.817)

Suburb -4.237* -3.248*

(1.106) (1.624)

Nonmetro -2.892* .362

(1.265) (2.503)

R2 .238 .220

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level on a two-sided t-test. All models also include 13 dummy variables representing

region and urbanization of the household, survey year, survey season, and whether any of the recorded 3-day intake was on

a weekend; see table 1.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-90 CSFII-DHKS.

tion information programs are not likely to lower the dietary risk of children with smoking mothers. This result

also has important policy implications given that earlier studies have found that smokers’ diets are substantially

worse than nonsmokers’ (McPhillips, Eaton, and Gans, 1994). Another study of diet quality found that smokers

score about three to four HEI points lower than nonsmokers (Variyam, Blaylock, Smallwood, and Basiotis,

1998). The present results show that this negative effect is transmitted to children with a similar, if not a higher,

magnitude. Therefore, nutrition education programs need to target smokers, particularly those who are the main

meal planners of their household, and alert them to the possible negative influence of their health habits on their

children’s diets.

In previous studies, children’s age and sex have been included to capture possible gender and age discrimination

in the allocation of household resources. To the extent that children’s diets are under parental control, gender or

age differences were not expected in our sample and none was found. Among household characteristics, there

was a large, positive effect of about five points on the HEI scale for children from households where at least one

member was on a vegetarian diet. As the Dietary Guidelines for Americans (1995) notes, vegetarian diets are

consistent with its recommendations and can meet Recommended Dietary Allowances for nutrients. This result 
18 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 7—Estimates for mother’s nutrition knowledge equation in the children’s HEI study

Explanatory variable Ages 2-5 Ages 6-17

Mothers’ characteristics:

Education 0.129* 0.282*

(.065) (.042)

Age .041 .021

(.027) (.015)

Height .021 .022

(.049) (.037)

Weight -.038* -.021

(.018) (.014)

Weight squared (×10-3) .097* .070

(.052) (.039)

Smoker -.189 -.234 

(.280) (.220)

Received diet advice 1.684* -.306

(.603) (.284)

Hours watching TV -.190* -.050

(.082) (.049)

Household characteristics:

Log income .414* .149

(.208) (.161)

Household size .144* .125*

(.086) (.063)

Black -.489 -1.586

(.409) (.298)

Other race -2.106* -1.613*

(.895) (.811)

Hispanic -1.632* -1.695*

(.426) (.344)

Food stamp participant .081 -.080

(.359) (.295)

WIC participant -.435 -.500

(.332) (.407)

Vegetarian diet .483 .351

(.349) (.450)

Male head’s education .064* .020

(.029) (.016)

In-person survey .380 .468*

(.264) (.194)

R2 .601 .458

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level. Each equation also includes 7 dummy variables representing region and urbanization of the household and the survey year; see table 1.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-90 CSFII-DHKS.
and the result for smoking show how strong intra-family effects are generated by the health habits of individual

members of the household.3

Other household characteristics are generally insignificant, except race. Diets of preschoolers in Black households are found to score about four points lower on the HEI scale than diets of children from White households.

Given the potential effect of such higher dietary risk for preschoolers on their future health and schooling, this

finding clearly indicates that nutrition education programs should target Black households for special attention.

The effects of nutrition information sources are as expected. Time spent watching television is inversely related

to knowledge, possibly because it curtails more information-intensive activities like reading.4 The effects of

having received dieting advice from a physician or a dietitian, and the education level of the male head are both

positive. We calculated a χ2 statistic to test whether excluding the four variables used to identify the knowledge

equation from the children’s HEI equation is valid. The exclusion of each variable from the HEI equation could

not be rejected at the 10-percent level. When TV hours and in-person survey variables, which had the relatively

higher χ2 values, were included in the HEI equation, the knowledge coefficient remained fairly stable at 2.274

although its standard error increased to 1.727. The results on the whole, therefore, seem to support the endogenous latent variable specification.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 19

3When we included all preschool children from households with multiple preschoolers in the estimation sample

(instead of selecting one child randomly from households with multiple preschoolers), the effects of maternal height, maternal weight, and a household member being on a vegetarian diet became insignificant. This finding is likely due to the correlation of these variables with unobserved maternal and household characteristic and, therefore, justifies our procedure of

selecting one child randomly from households with multiple children. The knowledge coefficient estimate in this “full”

sample (N=439) was 2.394 with a standard error of 0.071. Other results were similar to those reported in table 5, column 2.

4To ensure that the TV variable is capturing only the information effect and not the effect of a mother’s level of physical activity, a dummy variable indicating activity level was included in equations 7 and 8 of the endogenous latent variable

model.
Maternal Nutrition Knowledge and Children’s Nutrient Intakes

We have shown that maternal knowledge has a significant influence on preschoolers’ HEI, and now we explore

which nutrients mother’s knowledge affects the most. Table 2 lists the explanatory variables used in this analysis. Because the nutrients study and the HEI study were conducted in two phases, the specifications used are

slightly different, with the HEI specification being more general. We grouped the explanatory variables into

four broad categories: child’s characteristics, household characteristics, mother’s characteristics, and surveyrelated controls. Among the notable differences from the HEI study, controls for mothers’ employment status

were included to account for possible time allocation effects on knowledge and food preparation (Horton and

Campbell, 1991) while maternal height and weight were not included.

Empirical Model

For the seven food components/nutrients (total fat, saturated fat, cholesterol, fiber, sodium, calcium, and iron),

we jointly estimated a system of latent variable equations similar to the HEI study of the form:

where K is a latent variable representing the mother’s health and nutrition knowledge, Cj is the amount of jth

nutrient, Xp and Zq represent explanatory variables, α, β, and γ are the parameters to be estimated, and u and v

are stochastic error terms distributed independently and identically across individuals but may be correlated

across the equations for a given child-mother pair due to unobserved family heterogeneity. The estimation was

carried out by the same maximum likelihood procedure used in the HEI study.

Estimates of the Nutrient Intakes and Knowledge Equations

As with the HEI equations system, the scale of the latent variable (K) is identified by normalizing the coefficient

of K in the NCK measurement equation in equation λ11 to one (i.e., λ11 = 1) so that NCK and K have the same

scale; a unit change in K causes a unit change in NCK. The coefficient for DHA, λ21, is free. All variables are

expressed as deviations from their means, so the intercept coefficients in equations 9-11 are zero.

Table 8 reports the estimates of the mother’s latent knowledge equations for the two age groups (that is, equation

10). The most striking result is the positive and highly significant effect of a mother’s education level on her

health and nutrition knowledge for both groups. Because the scale of the latent variable is set by normalizing

the coefficient for the NCK equation to one, any explanatory variable that affects K has an equivalent effect on

NCK. This suggests that an additional year of education increases the NCK score of the mother of a 2- to 5-

year-old child by 0.184 point. In elasticity terms, this implies that a 1-percent increase in education level leads

to a 0.14-percent higher NCK score. Thus, our results provide direct evidence supporting the knowledge- and

20 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

C X + K +u , j = 1,...,7, j = j0 + jp p j j α αβ

p 1

P

=

(9) ∑

K= + Z +v

q=1

Q

γ γ 0 ∑ q q

(10)

NCK = + K + e

DHA = + K + e

10 11 1

20 21 2

λ λ

λ λ

(11)
information-enhancing role of education, that is, the allocative efficiency effect stressed in previous studies

(Grossman and Kaestner, 1995).

Other determinants that have consistent effects on mothers’ nutrition knowledge include race, ethnicity, whether the

mother is a smoker, household income, and whether a member of the household participates in the Food Stamp

Program. The finding of lower knowledge levels for mothers who are Black or Hispanic compared with White and

non-Hispanic mothers is similar to earlier results for all household meal planners (Carlson and Gould, 1994; Gould

and Lin, 1994). Household income and smoking have the anticipated positive and negative effects, respectively.

Age and employment status affect nutrition knowledge positively, but only for mothers of preschoolers.

Tables 9 and 10 present the estimates of the nutrient density equations for the two age groups (that is, equation

9). The R2 is generally low because densities are computed from only 3 days of intake and, thus, have a sizable

random component. A mother’s health and nutrition knowledge has a beneficial effect on her child’s diet if the

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 21

Table 8—Estimates for mother’s nutrition knowledge equation in the children’s nutrient intakes study

Explanatory variable Ages 2-5 Ages 6-17

Mother’s characteristics:

Education 0.184* 0.239*

(.043) (.032)

Age .058* .003

(.015) (.010)

Part-time employed .763* .326

(.307) (.211)

Not employed .508* .170

(.261) (.179)

Smoker -.350* -.354*

(.180) (.142)

Disease -.084 .452*

(.396) (.217)

Diet advice .499 -.053

(.373) (.271)

Household characteristics:

Income (x 10-3) 1.680* .758*

(.687) (.432)

Black -.911* -1.290*

(.329) (.236)

Other race -.980 -.721

(.608) (.548)

Hispanic -1.330* -1.559*

(.346) (.267)

Female head -.012 -.180

(.280) (.192)

Food stamp participant -.553* -.385*

(.285) (.230)

WIC participant -.481* -.276

(.264) (.304)

Vegetarian diet .356 .234

(.256) (.260)

R2 .544 .418

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level. Each equation also includes 8 dummy variables representing region and urbanization of the household and the survey year; see table 2.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-91 CSFII-DHKS.
22 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service

Table 9—Intake equation estimates for children, ages 2-5

Total Saturated

Explanatory variable fat fat Cholesterol Fiber Sodium Calcium Iron

K -0.965* -0.620* -0.016* 0.275* -0.052* 0.010 -0.190

(.482) (.262) (.006) (.164) (.028) (.014) (.221)

Child’s age -.036 -.240* -.002 .197* .013 -.018* .013

(.253) (.134) (.003) (.087) (.015) (.008) (.119)

Female child -.023 .187 .006 .310 .027 .006 -.203

(.552) (.292) (.006) (.191) (.033) (.017) (.259)

Mother not employed -.478 -.132 .010 -.168 -.008 .017 -.048

(.809) (.439) (.009) (.276) (.048) (.024) (.371)

Mother part-time employed .268 .198 .010 .005 .025 .033 .396

(.992) (.538) (.011) (.339) (.058) (.029) (.455)

Income (x 10-3) .609 -.395 .026 -.057 -.023 -.078 1.014

(2.377) (1.290) (.027) (.812) (.140) (.070) (1.090) 

Black 1.153 .074 .006 -.002 -.010 -.075* -.780*

(1.027) (.558) (.012) (.351) (.060) (.030) (.471)

Other race -3.490* -1.644 -.007 .944 .150 .033 2.591*

(1.863) (1.011) (.022) (.636) (.110) (.055) (.854) 

Hispanic -1.343 -.636 .017 1.074* -.088 .028 -.320

(1.231) (.668) (.014) (.420) (.072) (.036) (.564)

Female head -.130 -.044 .005 -.283 .011 -.021 .224

(.836) (.453) (.010) (.285) (.049) (.025) (.383)

Food stamp participant -.956 -.865* -.006 .133 -.026 -.027 .452 

(.927) (.503) (.011) (.317) (.054) (.027) (.425)

WIC participant -.432 -.515 -.006 .389 .024 -.019 .358

(.846) (.459) (.010) (.289) (.050) (.025) (.388)

Vegetarian diet .278 -.226 -.006 1.292* -.011 -.059* -.332

(.785) (.426) (.009) (.268) (.046) (.023) (.360)

Low-fat/low-calorie diet -1.214 -0.569 -0.012 0.205 -0.134* -0.054 0.618

(1.276) (.679) (.014) (.440) (.075) (.039) (.596)

Midwest 1.259 .009 .007 .348 .110* .003 -.157

(.885) (.480) (.010) (.302) (.052) (.026) (.406)

South 1.070 -.353 .009 .321 .071 -.066* -.224

(.866) (.470) (.010) (.296) (.051) (.026) (.397)

West .589 -.249 .017 .880* .129* -.026 .435

(1.130) (.613) (.013) (.386) (.066) (.033) (.518)

Suburb -.066 .342 -.009 -.406* -.026 .022 -.141

(.706) (.383) (.008) (.241) (.041) (.021) (.324)

Nonmetro .217 .635 -.003 -.467 -.050 -.027 -.086

(.834) (.453) (.010) (.285) (.049) (.025) (.382)

R2 .085 .097 .174 .202 .088 .170 .097

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level. Each equation also includes nine dummy variables representing survey year, survey season, and whether any of the 3-day intake was on a weekend; see table 2. The R2s are the squared correlation

between observed and predicted intake values.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-91 CSFII-DHKS.
USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 23

Table 10—Intake equation estimates for children, ages 6-17

Total Saturated

Explanatory variable fat fat Cholesterol Fiber Sodium Calcium Iron

K -0.350 -0.194 -0.013* 0.187* -0.016 0.015 0.077

(.321) (.158) (.004) (.117) (.019) (.009) (.154)

Child’s age .070 -.032 0 -.025 .005 -.009* -.022

(.054) (.027) (.001) (.020) (.003) (.002) (.026)

Child female .792* .206 0 .053 .016 .006 -.039

(.381) (.187) (.004) (.140) (.023) (.011) (.185)

Mother not employed -.751 -.408* .002 .138 -.024 -.017 .294

(.470) (.231) (.005) (.171) (.028) (.013) (.226)

Mother part-time employed -1.018* -.910* -.006 .207 -.032 -.023 .335

(.560) (.275) (.006) (.204) (.033) (.016) (.269)

Income (x 10-3) -.507 -.239 .021 -.051 .044 -.043 .339

(1.223) (.600) (.013) (.446) (.072) (.034) (.587)

Black .231 -.112 .006 -.349 .109* -.042* -.246

(.748) (.367) (.008) (.273) (.044) (.021) (.359)

Other race -1.749 -1.498* .003 .310 .070 -.034 1.362*

(1.465) (.719) (.016) (.534) (.087) (.041) (.703)

Hispanic -1.634* -.633 .006 .518 -.017 .005 .324

(.908) (.446) (.010) (.331) (.054) (.026) (.436)

Female head -.549 -.234 -.009 -.054 -.018 .002 .441*

(.501) (.246) (.005) (.183) (.030) (.014) (.240)

Food stamp participant .221 .106 -.006 .422* .012 -.003 -.367

(.622) (.305) (.007) (.227) (.037) (.017) (.298)

WIC participant 1.790* .431 .015* .142 .052 -.003 .553

(.796) (.391) (.009) (.290) (.047) (.022) (.382)

Vegetarian diet -1.938* -.801* -.013* 1.507* -.022 .008 .386

(.688) (.338) (.008) (.251) (.041) (.019) (.330)

Low-fat/low-calorie diet -.849 -.085 -.002 .464* .068 .016 .280

(.709) (.348) (.007) (.261) (.042) (.020) (.344)

Midwest 1.225* .452 .005 -.087 -.018 .003 -.593*

(.603) (.296) (.007) (.220) (.036) (.017) (.290)

South .886 -.156 .002 -.127 -.072* -.041* -.457

(.603) (.296) (.007) (.220) (.036) (.017) (.290)

West .037 -.121 .022* .541* -.055 -.003 -.377 

(.658) (.323) (.007) (.240) (.039) (.019) (.316)

Suburb -.008 .423* .003 -.277 -.020 .024* -.142

(.490) (.240) (.005) (.178) (.029) (.014) (.235)

Nonmetro -.174 .193 .002 .055 .010 -.007 -.457*

(.543) (.267) (.006) (.198) (.032) (.015) (.261)

R2 .073 .061 .117 .120 .053 .153 .047

Note: Asymptotic standard errors are given in parentheses below the estimates. An asterisk indicates significance of the

estimate at the 10-percent or lower level. Each equation also includes nine dummy variables representing survey year, survey season, and whether any of the 3-day intake was on a weekend; see table 2. The R2s are the squared correlation

between observed and predicted intake values.

Source: Calculated by the Economic Research Service, USDA, using data from 1989-91 CSFII-DHKS.
coefficient for K is negative in the total fat, saturated fat, cholesterol, and sodium intake equations and positive

in the fiber, calcium, and iron equations. This is because K and NCK have the same scale, and a higher NCK

and, hence, a higher K, implies a higher knowledge level. A higher knowledge level, in turn, translates to a better diet by reducing (increasing) the intake of nutrients whose over-consumption (under-consumption) may cause

health problems. 

The coefficient estimates for K suggest that a mother’s knowledge of health and nutrition have substantial beneficial effects on the diets of her preschool children. A higher level of maternal knowledge translates into significantly lower intakes of total fat, saturated fat, cholesterol, and sodium, and a significantly higher intake of fiber

by 2- to 5-year-old children. In terms of magnitude, an increase in nutrition knowledge sufficient to answer one

more NCK question correctly would cause a decline of about 1 percentage point in energy intake from all fat, a

0.6-percentage-point decline in energy intake from saturated fat, a decline of 16 milligrams of cholesterol intake

per 1,000 calories, a decline of sodium intake by 52 milligrams per 1,000 calories, and an increase of fiber

intake by 0.3 gram per 1,000 calories. Given such sizable effects for these nutrients, the lack of significant

influence on calcium and iron intake of preschoolers is somewhat surprising. There may be several explanations

for this finding. First, mothers may not perceive the underconsumption of calcium or iron as a serious problem

and, secondly, these nutrients have not received the press attention of other dietary problems (for example,

dietary fat and cholesterol). Another reason may be that calcium and iron can come from mineral supplements

that are not reflected in the CSFII data.

The effect of mothers’ knowledge on intakes wanes for children ages 6 and above. For this age group, an

increase in mothers’ nutrition knowledge is accompanied by a statistically significant reduction in children’s

intake of cholesterol and an increase in children’s intake of dietary fiber. Fat and sodium intakes are not significantly affected by mother’s knowledge. The knowledge effect on fiber is less for the 6-17 age group than for 2-

5 year-olds while the effect on cholesterol is of similar magnitude.

Our empirical results strongly suggest that maternal nutrition knowledge has a positive effect on the diets of

children, particularly for preschoolers. The effect of a mother’s nutrition knowledge on the diets of her older

children is less for several reasons. First, older children likely make more dietary decisions independently of

their mother, and secondly, they tend to eat away from home more often and also receive a higher percentage of

their total caloric intake from away-from-home food sources (Lin, Guthrie, and Blaylock, 1996). These results

lead to the important conclusion that health and nutrition education may be more effective if targeted toward

mothers with preschool children but directly toward school-age children.

The allocative efficiency hypothesis implies that education promotes better nutrition outcomes through the

acquisition and use of information related to the health effects of nutrients (Grossman and Kaestner, 1995). In

the nutrients intake study, we imposed this hypothesis by excluding a mother’s education level from the children’s intake equations so that education influenced intake only indirectly through the knowledge equation. This

restriction can be tested by a likelihood ratio test. Such a test showed that the restriction could not be rejected at

the 10-percent level for preschoolers (χ2=4.92, df=7). While the restriction was rejected for the 6-17 age group,

mothers’ education did not have a statistically significant coefficient in any of the children’s intake equations.

These results, therefore, support the view that maternal education affects children’s diets wholly through its positive effect on maternal knowledge of health and nutrition. Previous evidence in support of the allocative efficiency hypothesis has been mainly from nutrition and health studies of children in developing countries (Strauss

and Thomas, 1996). Our results suggest that the relationship persists when the dietary problem is one of overconsumption rather than malnutrition. Further, our results are robust to fixed family effects since our models

include several variables, including the mother’s employment status, to control for such effects (Behrman, 1995;

Behrman and Wolfe, 1987).

Turning to other factors influencing children’s nutrient intakes, school-age girls had significantly higher energy

intakes from fat than boys. Gender had no influence on any preschooler’s nutrient intake densities. To the extent

24 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service
that children’s diets are under parental control, dietary differences between boys and girls are not expected.

Therefore, the result for the 2-5 age group is not surprising. The higher fat energy for school-age girls may be

related to their dietary patterns such as a greater tendency to skip relatively low-fat breakfasts and eating a higher

proportion of meals and snacks outside the home (Lin, Guthrie, and Blaylock, 1996).

The effect of a mother’s employment status on both her nutrition knowledge and on her children’s intakes differs

between the age groups and has some interesting implications. For preschoolers, the effect of mother’s employment status occurs mainly through nutrition knowledge while for school-age children, the effect occurs through

time available for food preparation at home. School-age children with mothers employed part-time have significantly lower intakes of fat and saturated fat than those whose mothers are employed full-time. Mothers who

work less may be able to exert more influence on the diets of their school-age children.

One variable with a systematic effect on nutrient intake densities for both age groups is whether any household

member is on a vegetarian diet. This variable has a positive and significant effect on the fiber intake of all children with a stronger effect for those 6-17 years old. If a household member is vegetarian, the fiber intake of 6-

to 17 year-olds increases by 1.5 grams per 1,000 calories. Concomitantly, the calories from fat and saturated fat

of school-age children fall by 2 percentage points and 0.8 percentage point, respectively, and their cholesterol

intake declines by 13 milligrams per 1,000 calories.

Older children in both age groups, but more so for preschoolers than school-age children, had lower calcium

intake densities than the younger children in the group, possibly because older children drink less milk. Older

preschoolers also tend to have lower saturated fat and higher fiber intake densities. 

Significant racial differences exist in calcium intake densities. Black preschoolers were found to have 75 mg

less calcium intake per 1,000 calories than White preschoolers, while Black school-age children had 42 mg less

calcium intake per 1,000 calories than White children. Black school-age children also have significantly higher

sodium intakes than White school-age children. Fat intake by Hispanic children was significantly lower than

that by non-Hispanic children in the 6-17 age group, while fiber intake by Hispanic children ages 2-5 was significantly higher than that by 2- to 5-year-old non-Hispanic children. Overall, after accounting for other factors,

including mothers’ nutrition knowledge, racial and ethnic effects on children’s diets were somewhat limited.

This result is contrary to the findings for adults where strong racial and ethnic effects have been detected

(Carlson and Gould, 1994; Gould and Lin, 1994).

Food Stamp Program participation by a household has beneficial effects on children’s diets. FSP participation is

related to a lower saturated fat intake by 2- to 5-year-olds and a higher fiber intake by 6- to 17-year olds. The

fat and cholesterol intakes of school-age children from households participating in the WIC program were significantly higher than those of children of the same age group from nonparticipating households, perhaps

because WIC subsidizes egg consumption.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 25
Conclusions

Using a nationally representative data set that allows us to develop unique instruments for measuring overall diet

quality and nutrition knowledge, we found that the mother’s knowledge of nutrition has a significant positive

influence on preschool children’s diet quality and children’s dietary intakes and that such influence decreases as

children grow older. These findings have growing significance as more nutrition researchers, economists, and

policymakers are focusing on the determinants of children’s diets. Among the numerous factors affecting diets

and nutrient intakes, health and nutrition knowledge is one factor that can be manipulated by health authorities

through the use of tools such as public information campaigns. Therefore, the potential impact of this factor on

diets is of considerable policy interest.

In this study, we used household production theory to formulate empirical models of children’s diet quality and

nutrient intakes. Household production theory attributes a positive effect for education on health and nutrition

outcomes through greater allocative efficiency, that is, the ability of the more educated to acquire and process a

greater amount of health information than the less educated (Grossman and Kaestner, 1995). Thus, if the allocative efficiency hypothesis is valid, a mother’s knowledge of health and nutrition should be positively related to

her education level, and children’s diet should be positively related to their mothers’ knowledge of health and

nutrition. Our results support this model for preschool children with respect to overall diet quality and nutrient

intakes. For children older than 5 years, the evidence is less strong, although significant effects were found for

cholesterol and fiber. 

Our results suggest that some information-related racial and ethnic effects on child diet quality exist and that targeting non-White and Hispanic mothers for nutrition education is likely to increase diet quality of preschoolers.

Our results also point to other powerful determinants of child diet quality that may need special attention from

nutrition educators. Preschool children of mothers who smoke have significantly lower diet quality, although

these mothers are as informed about nutrition and diet-health links as nonsmoking mothers. Therefore, special

strategies may be needed to alert smoking mothers to the dietary risk facing their preschoolers. At the same

time, having a member in the household who is on a vegetarian diet appears to significantly improve preschoolers’ diet quality. The recent addition of vegetarian diets as a means to attain dietary goals in the Dietary

Guidelines for Americans (1995) is therefore likely to help improve children’s diet quality. 

26 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service
References

Aigner, D. J., C. Hsiao, A. Kapteyn, and T. Wansbeek (1984). “Latent Variable Models in Econometrics,” in

Handbook of Econometrics, Vol. II, Z. Griliches and M. D. Intriligator, eds., pp. 1321-93. Amsterdam: NorthHolland.

Barrera, A. (1990). “The Role of Maternal Schooling and Its Interaction with Public Health Programs in Child

Health Production,” Journal of Development Economics, Vol. 32, pp. 69-91.

Behrman, J. R. (1995). “Household Behavior, Preschool Child Health and Nutrition, and the Role of

Information,” in Child Growth and Nutrition in Developing Countries: Priorities for Action, Per PinstrupAndersen, D. Pelletier, and Harold Alderman, eds. Ithaca, NY: Cornell University Press, pp. 32-52.

Behrman, J. R., and B. L. Wolfe (1987). “Women’s Schooling and Children’s Health,” Journal of Health

Economics, Vol. 6, pp. 239-54.

Behrman, J. R., and A. B. Deolalikar (1988). “Health and Nutrition,” in Handbook of Development Economics

Vol. I, H. Chenery and T. N. Srinivasan, eds., pp. 631-771. Amsterdam: North-Holland.

Behrman, J. R., A. B. Deolalikar, and B. L. Wolfe (1988). “Nutrients: Impacts and Determinant,” The World

Bank Economic Review, Vol. 2, pp. 299-320.

Behrman, J. R., and B. L. Wolfe (1987). “How Does Mother’s Schooling Affect Family Health, Nutrition,

Medical Care Usage, and Household Sanitation?” Journal of Econometrics, Vol. 36, pp.185-204.

Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.

Carlson, K. A., and Gould, B. W. (1994). “The Role of Health Knowledge in Determining Dietary Fat Intake,”

Review of Agricultural Economics, Vol. 16, pp. 373-86.

Colavito, E. A., J. E. Guthrie, A. A. Hertzler, and R. E. Webb (1996). “Relationship of Diet-Health Attitudes and

Nutrition Knowledge of Household Meal Planners to the Fat and Fiber Intakes of Meal Planners and

Preschoolers,” Journal of Nutrition Education, Vol. 28 (November-December), pp. 321-28.

Cypel, Y. S., J. A. Tamaki, C. W. Enns, A. B. Nowverl, L. E. Cleveland, and K. S. Tippett (1996). Nutrition

Attitudes and Dietary Status of Main Meal Planners/Preparers, 1989-91: Results from 1989-91 Diet and Health

Knowledge Survey and the 1989-91 Continuing Survey of Food Intakes by Individuals. U.S. Dept. Agr., Agr.

Res. Serv., NFS Report No. 91-1, January.

Evans, W. N., and E. Montgomery (1994). “Education and Health: Where There’s Smoke There’s an

Instrument,” NBER Working Paper No. 4949, Cambridge, MA.

Farrell, P., and V. R. Fuchs (1982). “Schooling and Health: The Cigarette Connection,” Journal of Health

Economics, Vol. 1, pp. 217-30.

Gould, B. W., and H. C. Lin (1994). “Nutrition Information and Household Dietary Fat Intake,” Journal of

Agricultural and Resource Economics, Vol. 19 (December), pp. 349-65.

Griliches, Z. (1986). “Economic Data Issues,” in Handbook of Econometrics, Vol. III. Z. Griliches and M. D.

Intriligator, eds., pp. 1466-514. Amsterdam: North-Holland.

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 27
Grossman, M., and R. Kaestner (1995). “Effects of Education on Health,” presented at the conference “Social

Benefits of Education: Can They be Measured?” Washington, DC, Meridian International Center.

Haughton, D., and J. Haughton (1997). “Explaining Child Nutrition in Vietnam,” Economic Development and

Cultural Change, Vol. 45, pp. 541-56.

Horton, S., and C. Campbell (1991). “Wife’s Employment, Food Expenditures, and Apparent Nutrient Intake:

Evidence from Canada,” American Journal of Agricultural Economics, Vol. 73 (August), pp. 784-94.

Ippolito, P. M., and A. D. Mathios (1990). “Information, Advertising, and Health Choices: A Study of the

Cereal Market,” Rand Journal of Economics, Vol. 21, pp. 459-80.

Jensen, H. H., T. Kesavan, and S. R. Johnson (1992). “Measuring the Impact of Health Awareness on Food

Demand,” Review of Agricultural Economics, Vol. 14 (July), pp. 299-312.

Joreskog, K. G., and D. Sorbom (1989). LISREL 7 User Reference Guide. Chicago: Scientific Software, Inc.

Kassouf, A. L., and B. Senauer (1996). “Direct and Indirect Effects of Parental Education on Malnutrition

among Children in Brazil: A Full Income Approach,” Economic Development and Cultural Change, pp. 817-38.

Kemm, J. R. (1987). “Eating Patterns in Childhood and Adult Health,” Nutrition and Health, Vol.4, pp. 205-15.

Kenkel, D. S. (1991). “Health Behavior, Health Knowledge, and Schooling,” Journal of Political Economy, Vol.

99 (April), pp. 287-305.

Kennedy, E. T., J. Ohls, S. Carlson, and K. Fleming (1995). “The Healthy Eating Index: Design and

Applications,” Journal of the American Dietetic Association, Vol. 95, pp. 1103-8.

Klesges, R. C., R. J. Stein, L. H. Eck, T. R. Isbell, L. M. Klesges (1991). “Parental Influence on Food Selection

in Young Children and its Relationships to Childhood Obesity,” American Journal of Clinical Nutrition, Vol. 53,

pp. 859-64.

Leibowitz, A. (1974). “Home Investments in Children,” in Economics of the Family, T. W. Schultz, ed., Chicago:

University of Chicago Press.

Lin, B-H., J. Guthrie, and J. Blaylock (1996). The Diets of America’s Children: Influences of Dining Out,

Household Characteristics, and Nutrition Knowledge, AER-746, U.S. Dept Agr., Econ. Res. Serv.

McGill, Jr., H. C., C. A. McMahan, G. T. Malcom, M. C. Oalman, J. P. Strong (1997). “Effects of Serum

Lipoproteins and Smoking on Atherosclerosis in Young Men and Women,” Arteriosclerosis, Thrombosis and

Vascular Biology, Vol. 7, pp. 95-106.

McPherson, R. S., D. H. Montgomery, and M. Z. Michaman (1995). “Nutritional Status of Children: What Do

We Know?” Journal of Nutrition Education, Vol. 27 (October), pp. 225-34.

McPhillips, J.B., C.B. Eaton, K.M. Gans (1994). “Dietary Differences in Smokers and Nonsmokers from Two

Southeastern New England Communities,” Journal of the American Dietetic Association, Vol. 94, pp. 287-92.

Nicklas, J. A., R. P. Farris, and C. G. Smoak (1988). “Dietary Factors Related to Cardiovascular Risk Factors in

Early Life - Bogalus Heart Study,” Arteriosclerosis, Vol. 8, pp. 193-99.

28 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service
Putler, D. S., and Frazao, E. (1994). “Consumer Awareness of Diet-Disease Relationships and Dietary Behavior:

The Case of Dietary Fat,” Journal of Agricultural Economics Research, Vol. 45 (January), pp. 3-17.

Rosenzweig, M. R., and T. P. Schultz (1983). “Estimating Household Production Functions: Heterogeneity, the

Demand for Health Inputs, and Their Effects on Birth Weight,” Journal of Political Economy, Vol. 91, pp. 723-

46.

Senauer, B., and M. Garcia (1991). “Determinants of Nutrition and Health Status of Preschool Children: An

Analysis with Longitudinal Data,” Economic Development and Cultural Change, Vol. 39 (January), pp. 371-89.

Senauer, B., D. Sahn, and H. Alderman (1986). “The Effect of the Value of Time on Food Consumption Patterns

in Developing Countries: Evidence from Sri Lanka,” American Journal of Agricultural Economics, Vol. 68

(November), pp. 920-27.

Strauss, J., and D. Thomas (1996). “Human Resources: Empirical Modeling of Household and Family

Decision,” Handbook of Development Economics, Vol. III, T. N. Srinivasan and J. R. Behrman, eds. Amsterdam:

North-Holland.

Taubman, P. (1977). Papers in Kinometrics: Determinants of Socioeconomic Success Within and Between

Families. Amsterdam: North Holland.

Thomas, D., and J. Strauss (1992). “Prices, Infrastructure, Household Characteristics and Child Height,”

Journal of Development Economics, Vol. 39, pp. 301-31. 

Thomas, D., J. Strauss, and M.-H. Henriques (1991). “How Does Mother’s Education Affect Child Height?”

Journal of Human Resources, Vol. 26, pp.183-211.

Thomas, P. R. (1991). Improving America’s Diet and Health, Washington, DC: National Academy Press.

U.S. Department of Agriculture (1995). The Healthy Eating Index, CNPP-1.

U.S. Department of Agriculture (1992). The Food Guide Pyramid. HGB-252, Agr. Res. Serv.

U.S. Department of Agriculture and U.S. Department of Health and Human Services (1995). Nutrition and Your

Health: Dietary Guidelines for Americans. Fourth ed. HGB-232.

U.S. Department of Health and Human Services, Public Health Service (1988). The Surgeon General’s Report

on Nutrition and Health. DHHS (PHS) Publication No. 88-50210.

Variyam, J. N., J. Blaylock, and D. Smallwood (1996). “A Probit Latent Variable Model of Nutrition

Information and Dietary Fiber Intake,” American Journal of Agricultural Economics, Vol. 78 (August), pp. 628-

39.

Variyam, J. N., J. Blaylock, D. Smallwood, and P. P. Basiotis (1998). USDA’s Healthy Eating Index and

Nutrition Information, TB-1866, U.S. Dept. Agr., Econ. Res. Serv.

Wolfe, B. L., and J. R. Behrman (1983). “Is Income Overrated in Determining Adequate Nutrition?” Economic

Development and Cultural Change, Vol. 31 (April), pp. 525-49. 

USDA/Economic Research Service Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes • 29
Appendix: Measuring Diet Quality: The Healthy Eating Index

The HEI measures overall diet quality by evaluating an individual’s diet against 10 dietary components reflecting recommendations in the Dietary Guidelines for Americans (U.S. Dept. of Agr., U.S. Dept. of Health and

Human Services, 1995) and the Food Guide Pyramid (U.S. Dept. of Agr., 1992):

• Components 1-5 measure the extent to which a person’s diet conforms to the Food Guide Pyramid serving

recommendations for the grains, vegetables, fruits, milk, and meat groups.

• Components 6-10 measure the extent to which a person’s diet conforms to the Dietary Guidelines recommendations for total fat, saturated fat, cholesterol, sodium, and variety. 

An individual’s diet was assigned a score of 0-10 for each of these 10 components in the following manner:

For each of the five food group components of the HEI, individuals who consumed the recommended number of

servings received a maximum score of 10. A score of zero was assigned for any food group where no items

from that food group were eaten. Scores between zero and 10 were calculated proportionate to the number of

servings consumed. For example, if the recommended number of servings was 8 and an individual consumed 4

servings, the component score for the individual is 5 points (one-half of 10). 

The scores for fat and saturated fat were related to their consumption in proportion to total food energy. Fat

intakes contributing to less than or equal to 30 percent of the total calories were given a score of 10. The score

declined to zero when the proportion of total calories from fat was 45 percent or more. Linear interpolation was

applied to fat intakes that contribute between 30 and 45 percent of total calories. Intakes of saturated fat were

similarly scored with a score of 10 given to less than 10 percent of total calories from saturated fat and a score

of zero for intakes that contribute to 15 percent or more of total calories.

Scores for both cholesterol and sodium were based on milligrams consumed in the diet. A score of 10 was given

for cholesterol intakes less than or equal to 300 milligrams per day. Zero points were given for intakes at or

over 450 milligrams. Intermediate scores were given for intakes between the two limits. For sodium, a maximum score was obtained for intakes less than or equal to 2,400 milligrams per day. A zero score was given for

sodium intakes at 4,800 milligrams or higher. Again, intermediate scores for intakes between the two cutoff

points were given proportionately. 

Dietary variety was assessed by totaling the number of “different” foods eaten by an individual in amounts sufficient to contribute at least one-half of a serving in a particular food group. Food mixtures were broken into their

component ingredients and assigned to relevant food groups. Similar types of foods were grouped together and

counted only once in measuring the score for variety. A maximum score of 10 was awarded if 16 or more different food items were consumed over a 3-day period. A score of zero was given if 6 or fewer food items were

consumed. Intermediate scores were awarded proportionate to consumption between the cutoffs.

An individual’s HEI is the sum of these 10 component scores. Therefore, the range of HEI is 0-100. The mean

HEI for our samples of preschoolers and school-age children was 65.4 and 62.6, respectively (table 1). For

complete details on the construction of HEI, see U.S. Department of Agriculture (1995) or Kennedy and others

(1995).

30 • Maternal Nutrition Knowledge and Children’s Diet Quality and Nutrient Intakes USDA/Economic Research Service