Abstract

The purpose of this study is to better characterize factors associated with the likelihood of

macronutrient excess or inadequacy among U.S adults by modeling parts of the conditional distribution of dietary intakes other than the conditional mean. The risk of dietary inadequacy or excess faced by an individual tends to increase as his or her intake moves from

the mean of a nutrient intake distribution toward its tails. Therefore, marginal effects of

explanatory variables estimated at the conditional mean using ordinary least squares may

be of limited value in characterizing these distributions. Quantile regression is effective in

this situation since it can estimate conditional functions at any part of the distribution.

Quantile regressions based on data from USDA’s 1994-96 Continuing Survey of Food

Intakes by Individuals indicate that differences in mean macronutrient intakes based on

sociodemographic characteristics can be quite different from intake differences at other

parts of the distributions. Therefore, judging dietary disparities between subpopulations by

comparing mean intakes only, and not by comparing intakes at other parts of the distributions, may lead to misleading or incomplete conclusions.

Keywords: Diet quality, health risk, heteroskedasticity, nutrition, quantile regression.

About the Author

Jayachandran N. Variyam is an economist, Food and Rural Economics Division, Economic

Research Service, USDA.

Acknowledgments

The author would like to gratefully thank Peter Basiotis of USDA's Center for Nutrition

Policy and Promotion, Betsy Frazao and Dean Jolliffe of the Economic Research Service,

and Rudy Nayga of Texas A&M University for reading earlier versions of the manuscript

and providing many useful comments and suggestions. Thanks also to Anne Pearl, Dana

Rayl West and Tom McDonald of the Economic Research Service for providing excellent

design and editorial assistance.

United States

Department

of Agriculture

Factors Affecting the

Macronutrient Intake 

of U.S. Adults

April 2003

Technical

Bulletin

Number 1901

www.ers.usda.gov

Electronic Report from the Economic Research Service

Jayachandran N. Variyam
ii  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Contents

Page

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

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

Economics of Nutrient Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Empirical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Quantile Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Estimation and Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Bivariate Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Quantile Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Test Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Marginal Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

Appendix Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  iii

Summary

Diet-related diseases and health conditions account for a major share of morbidity and mortality in the United States.

Just seven diet-related health conditions cost the United States $71 billion (in 1995 dollars) annually in medical costs

and productivity losses, according to the latest ERS estimates. Healthier diets can help reduce these costs. Various

segments of the U.S. population bear this burden differently. Differences related to gender, race, ethnicity, income,

and educational attainment are major sources of health disparities in the United States. Reducing such health disparities, and disparities in related risk factors, such as obesity and poor diets, is a goal of the Federal Government’s

Healthy People 2010 initiative.

With better knowledge of the dietary differences and potential excessive energy intakes among population subgroups,

public health professionals can devise more effective strategies for improving the diets and correcting the caloric

imbalance among vulnerable subgroups. A number of past studies have assessed dietary differences among various

subpopulations, but most have drawn conclusions based on a comparison of average nutrient intakes, either unadjusted or adjusted for sociodemographic differences. This approach overlooks an important feature of nutrient intake distributions. For many nutrients, the risk of dietary excess or inadequacy, and therefore, the risk of adverse health

effects, is greater at the upper or lower parts of the nutrient intake distributions rather than at the mean. Therefore,

judging intake difference between subpopulations by comparing their mean intakes only, and not differences at other

parts of the intake distribution, could lead to incomplete or potentially misleading conclusions.

The purpose of this study is to more accurately characterize differences in nutrient intake among U.S. adults by

focusing on the tails of the distribution of intakes instead of the mean. The study specifically examines the intakes of

five macronutrients—energy, total fat, saturated fat, cholesterol, and fiber. First, the study examines the differences

in nutrient intakes between selected sociodemographic groups by comparing their percentile curves. Second, the

study uses quantile regression to estimate intake differences at various percentiles attributable to specific characteristics, while controlling for other characteristics. By estimating a family of quantile regressions for each nutrient, the

study assesses intake differences between sociodemographic groups—not just at the mean, but along the entire distribution of nutrient intake.

The results of this study indicate that, for many sociodemographic subpopulations, differences in mean macronutrient

intakes can be quite different from intake differences at other parts of the distributions. For example, based on estimates of the mean and the quantiles adjusted for other characteristics, both Black men and Black women consume

significantly larger amounts of cholesterol than White men and White women, respectively. However, quantile estimates suggest a narrower disparity at the lower quantiles and a much wider disparity at the upper quantiles between

these groups, compared with estimates at the mean. Since the risk of excess intake is greater at the upper quantiles,

this suggests a more serious nutritional problem than if the conditional differences had been uniform across the distribution of cholesterol intakes.

In economic models of health, educational attainment plays a crucial role as a determinant of health outcomes by

influencing health behaviors and choices. Results of this study confirm previous findings that education is positively

correlated with better diets. However, compared with previous findings, the results here, especially for men, also

show something new. The beneficial effects of education are larger at parts of the conditional distribution that matter

most—at the upper quantiles of fat and cholesterol intakes where the risk of excess are higher. Together with similar

findings regarding the effects of income and age, this result suggests that, compared with younger, less educated, and

lower income men, older, more educated, and higher income men may have benefited more from health and nutrition

information initiatives such as the Nutrition Labeling and Education Act.

The results from this study have important implications for future studies evaluating the dietary impact of many nutrition-related policy interventions such as food assistance programs and food labeling regulations. For such studies to

fully uncover the extent and nature of the behavioral impact of interventions, it may be essential to look beyond the

conditional mean to parts of the dietary intake distributions where the risks of inadequacy or excess are higher.
Introduction

The flood of scientific evidence on the health effects

of foods, nutrients, and other dietary components has

heightened interest in the composition of U.S. food

demand and supply, as well as the quality of American

diets and their determinants (Adelaja, Nayga, and

Lauderbach, 1997; Bowman et al., 1998; Frazao, 1999;

Gould, 1996; Kantor, 1998; Lin, Guthrie, and Frazao,

1999; Nayga, 1994). It is well established that dietary

excesses and inadequacies are associated with several

chronic health conditions that can reduce productivity

and hasten mortality (National Research Council,

1989). Just seven diet-related health conditions cost

the United States $71 billion (in 1995 dollars) annually

in medical costs and productivity losses, according to

the latest ERS estimates. Healthier diets can help

reduce these costs. 

This study focuses on macronutrient intake in the

American diet. For significant proportions of the U.S.

adult population, excess intakes of total fat, saturated

fat, and cholesterol and inadequate intake of fiber constitute a persistent nutritional problem.1 Further, the

rapid rise in the prevalence of obesity among adults

suggests an imbalance between total energy intakes

and energy use through physical activity (Koplan and

Dietz, 1999). Excess dietary fats may contribute to the

energy imbalance that leads to obesity. High-fiber

diets may protect against obesity by lowering insulin

levels (Lichtenstein et al., 1998; Ludwig et al., 1999).

Coexisting with these nutritional excesses and inadequacies for the overall population is the evidence that

intake levels differ substantially among population

subgroups (U.S. Department of Agriculture, 2000). An

accurate understanding of these dietary intake differences is crucial, given the goal, set by national initiatives such as the Healthy People 2010, of reducing

health disparities. Besides the obvious use for monitoring relative nutritional status and guiding nutrition

policy, a clear assessment of dietary differences is

helpful for other uses such as targeting nutrition education efforts and forecasting dietary trends due to

changing demographics.

Understandably, explaining dietary intake differences

in the population by identifiable characteristics such

as gender, age, income, and racial and ethnic identity

has been an active research area. Dietary disparities

due to these characteristics have been examined in

many studies (e.g., Adelaja, Nayga, and Lauderbach,

1997; Chavas and Keplinger, 1983; Murphy et al.,

1992; Nayga, 1994). Such studies have typically

used the least squares multiple regression method to

estimate the regression-adjusted differences (or marginal effects) in the conditional mean of intakes due

to the characteristics. However, when studying distributions such as those of nutrient intakes, marginal

effects at the conditional mean provide only a very

limited characterization of distributional differences

among subpopulations. This is because, for many

nutrients, the risk of dietary excess or inadequacy,

and therefore, the risk of adverse health effects, is

greater at the upper or lower parts of the intake distributions than at the mean. Therefore, for one to infer

intake differences between subpopulations by looking

at the differences in their conditional mean intakes

only, and not differences at other parts of the intake

distribution, could lead to incomplete or potentially

misleading conclusions.

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  1

1Dietary fiber is not strictly a macronutrient but a dietary component. For ease of exposition, we include fiber under the term

macronutrient in this report.

0.5

1.0

0

Mean

Median requirement

Intake levels

Relative risk of excess

Figure 1

Risk of excessive intake

Frequency of intake

A man was searching under a streetlight. A

passerby stopped and asked, “What are you

looking for?” “My keys” the man replied.

“Where did you lose your keys?” the passerby

asked. “Over there” the man said, pointing

to the shadows. “Then why are you looking

over here?” the passerby inquired. “Because

the light is better here” the man replied.

Anonymous
Consider figure 1 where the bell-shaped curve on the

left shows the distribution of the usual intakes of a

hypothetical nutrient whose excess intake is of concern

(Institute of Medicine, 2000). The “S” shaped curve

on the right shows the cumulative distribution of the

required intakes for the same nutrient. This risk curve

gives the probability that any given intake exceeds the

requirement for the individual consuming that intake.

For example, for an individual with observed intake at

the median requirement level (denoted by the dotted

line), there is a 50-percent probability that his or her

intake is excessive. For this hypothetical nutrient, the

mean intake is well to the left of the risk curve so that

the risk of excess at the mean is close to zero.

However, a significant portion of the intake curve to

the right of the mean overlaps with the risk curve. For

individuals consuming the nutrient at these levels, the

probability of their exceeding the requirement rises

rapidly with intake.

Table 1 reports the mean and selected percentiles of the

intakes of five macronutrients by U.S. adults, excluding

pregnant or lactating women. These statistics were

estimated from USDA’s 1994-96 Continuing Survey of

the Food Intakes of Individuals (CSFII). For comparison, table 1 also reports the daily intake levels for these

macronutrients recommended by the health authorities

for the relevant gender and age groups.

Comparing the intake distributions with the recommended intake levels, it is clear that the mean and

median intakes of fats and cholesterol are at or close to

adequate levels. For example, the recommended total

fat intake for men between 21 and 50 years of age is

less than or equal to 96.7 grams of fat per day based

on a 2,900-calorie diet. The mean and median intakes

of total fat for this group of men during 1994-96 were

about 98 and 90 grams, respectively, which are close

to the recommended level. However, total fat intake at

the 90th percentile is 153 grams, considerably above

the healthful level. For women aged 21-50, the cholesterol intake even at the 75th percentile, 277 milligrams (mg), is below the recommended daily intake

of 300 mg, whereas at the 90th percentile, the intake is

402 mg, well above the healthful level.

Clearly, from a public health and nutrition policy perspective, regression-adjusted intake difference between

population subgroups at the 75th and 90th percentiles

for total fat, saturated fat, and cholesterol are of

greater interest than those at the mean. Similarly, for

fiber, although the mean intake is substantially below

the recommended level, one would be more interested

in the intake difference at the 10th or 25th percentiles.

Using marginal effects estimated at the conditional

mean to characterize intake differences over the whole

distribution would be meaningful if these effects were

equal to the marginal effects estimated at all other

parts of the conditional intake distribution. This equality will be realized only under the strict assumption

that the intake distributions are identically and symmetrically distributed with respect to the subpopulations of interest. If the intake distribution differs in

variance and shape among population subgroups, then

the marginal effect of the explanatory variable will not

be equal across different parts of the intake distribution and instead will differ from point to point along

the distribution. In this case, knowing the marginal

effect at the riskier parts of the distribution would be

more useful for policy purposes than knowing the marginal effects at the conditional mean.

The purpose of this study is to better characterize the

macronutrient excess or inadequacy among U.S. adults

by modeling parts of the conditional distribution of

dietary intakes other than the conditional mean. This

is achieved by employing the method of quantile

regression proposed by Koenker and Bassett (1978).

While the classical least squares regression estimates

the conditional mean of a dependent variable as a linear function of explanatory variables, the quantile

regression enables the estimation of any conditional

quantile of the dependent variable as a linear function

of explanatory variables. Therefore, the estimation of

quantile regressions allows us to obtain a more complete characterization of the dependence of macronutrient intakes on population characteristic of interest.

More specifically, quantile regression enables us to

look at the marginal differences in macronutrient

intakes among subpopulations at specific points of

interest along the conditional distribution, such as the

90th percentile for total fat, saturated fat, and cholesterol, and the 10th percentile for fiber.

The rest of this report is organized as follows:

The next section discusses a theoretical framework in

economics for examining nutrient intake behaviors.

The empirical approach for estimating and testing

marginal effects of key sociodemographic variables

on nutrient intakes are presented next.

2  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  3

Table 1—Recommended intakes of macronutrients and their observed distributions among U.S. adults

Observed daily intake

Percentile Mean

Nutrient Units Recommended

daily intake 10 25 50 75 90

Men, 21-50 years:

Energy Calories 2900 1463.0 1900.2 2396.4 3083.0 3758.1 2576.9

Total fat Grams ≤96.7 48.0 65.7 90.9 121.8 153.3 98.1

Saturated fat Grams <32.2 14.9 21.3 30.0 41.4 53.2 33.4

Cholesterol Milligrams ≤300 127.6 189.1 289.3 437.2 607.2 340.9

Fiber Grams 33.4 8.1 11.7 16.3 23.2 29.8 18.2

Men, >50 years:

Energy Calories 2300 1172.7 1535.8 1972.2 2450.8 2932.1 2035.1

Total fat Grams ≤76.7 35.6 51.4 72.3 96.4 122.8 76.7

Saturated fat Grams <25.6 11.1 15.9 22.7 32.0 41.6 25.2

Cholesterol Milligrams ≤300 110.0 163.2 258.7 391.6 542.5 300.3

Fiber Grams 26.5 7.9 11.7 16.4 22.5 29.8 18.0

Women, 21-50 years:

Energy Calories 2200 990.9 1292.7 1618.9 2036.9 2460.6 1693.8

Total fat Grams ≤66.7 29.0 42.1 58.6 78.3 100.8 62.5

Saturated fat Grams <22.2 9.0 13.1 19.4 26.4 35.0 20.9

Cholesterol Milligrams ≤300 72.9 116.3 179.9 277.2 402.2 215.1

Fiber Grams 23 5.8 8.4 12.3 16.5 22.0 13.3

Women, >50 years:

Energy Calories 1900 879.4 1115.2 1444.8 1754.5 2081.8 1464.7

Total fat Grams ≤63.3 24.9 36.5 50.5 67.2 83.6 53.2

Saturated fat Grams <21.1 7.6 10.9 16.1 22.5 28.6 17.3

Cholesterol Milligrams ≤300 72.4 112.7 171.7 264.4 371.6 201.8

Fiber Gram 21.9 6.5 9.2 12.9 17.6 22.7 14.0

Note: Estimates based on two nonconsecutive days of intakes from the 1994-96 CSFII, using sampling weights. The recommended intakes are from Lin, Guthrie, 

and Frazao (1999), table 4, p.6.
Details of the data and specific variables used in the

analysis are described.

The results are discussed next.

The final section presents the conclusions of the

study and the implications for future research.

Economics of Nutrient Intake 

Why do some consumers have healthful diets while

others do not? If healthful diets are a critical component of healthful living and avoidance of chronic disease, why do some people choose poor diets? To

economists, at the simplest level, the answer is that

individuals choose foods not only to meet nutritional

requirements but also for other reasons such as taste,

preferences, and pleasure. Further, these choices are

constrained by the income at their disposal, food

prices and prices of other consumption goods, time

available for cooking and food preparation, and the

ability to combine foods and other resources to produce a nutritious diet. The observed dietary differences among individuals are the outcomes of a complex interplay of tradeoffs among desires for health,

tasteful foods, other goods and services, and the constraints of limited resources. On a more formal level,

economists have brought together these preferences

and constraints, and the resulting choices, within a

framework called the theory of household production.

The theory of household production grew out of

Becker’s (1965) study of the allocation of time in

households and Lancaster’s (1966) development of the

characteristics model of consumer demand, which

views purchased goods in terms of their product attributes. The theory integrates a variety of biological,

sociodemographic, and economic factors, all of which

interact and influence household consumption decisions. Household production models developed from

the theory have been used to analyze many types of

consumer and household behaviors (Strauss and

Thomas, 1996). Such models are powerful tools for

analyzing choices involving intrafamily interaction,

such as maternal influence on children’s health.

Where intrafamily effects are not of interest, the models can be simplified to focus on individuals’ choices.

Households seek to maximize satisfaction (or utility)

through consumption of commodities. While some of

these commodities, such as cars, clothing, or food are

purchased in the market, others are “produced” by the

households for their members’ consumption. For

example, a household may purchase a variety of food

items and combine it with cooking skills, nutrition

knowledge, preparation time, and kitchen appliances

to produce healthful meals. The objects of desire,

including good nutrition, children’s health, as well as

the health of other family members, are not market

goods, but are produced with inputs of market goods

and time.

The household utility maximization is subject to an

array of income, time, and technology constraints.

Technology constraints are represented by production

functions which capture the notion that all households

are not able to produce the same amount of commodities, such as household members’ health, from a given

quantity of inputs. Households vary in their efficiency of producing commodities, depending on household members’ sociodemographic and other characteristics. For example, each family member’s health is

determined by a unique production function, which

depends on time, health inputs, the sociodemographic

characteristics of the household, community characteristics, and the genetic endowment of the individual.

These production functions could be interrelated

because some of the commodities are intermediate

goods produced by households as inputs into the production of a final commodity. For example, nutrients

produced by combining foods with cooking time and

nutrition knowledge appear as inputs in the health

production function.

Income constraint ensures that the household expenditure on purchased goods and services does not exceed

money income, which is equal to the sum of earnings

from wages and any non-labor income. The time constraint ensures that the sum of all time inputs into the

production of commodities plus leisure time and time

spent at work does not exceed the total time available.

Since labor earnings depend on time spent at work,

income and time constraints can be combined into one

“full income” constraint.

The solution to the household maximization problem,

subject to technology and full income constraints,

gives the demand functions for commodities produced

by the household (such as nutrients and health) and

commodities purchased in the markets (such as foods

and medical services). These functions depend on

prices of all purchased consumption goods and inputs,

wage rates, and household income, as well as the

sociodemographic characteristics of the households.

4  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA
Grossman (1972) pioneered the use of the household

production model to study the determinants of health,

health behaviors, and health inputs. Grossman’s

approach grew out of the recognition that many consumer choices, such as those relating to the amount of

exercise, the nutritional quality of diets, and the purchase of medical services, are not made because consumers gain utility from these choices directly, but

rather because these choices influence health. Health,

in turn, is demanded because it is a source of utility

and because it determines income and wealth. The

dependence of income as well as mortality (and thus

future time available for work and leisure) on health

implies that a fully specified Grossman-type model

would be a dynamic programming problem in which

households maximize the current value of its stream of

future utilities (Sickles and Taubman, 1997). Because

of the complexity and very specific data needs to solve

these types of problems, fully specified dynamic

health models have been used only in very specialized

cases (Sickles and Yazbeck, 1998).

In the absence of detailed longitudinal data on prices,

income, wages, and consumption, most of the empirical work using Grossman-type models have focused

on estimating static (or one-period) reduced-form input

demand and production functions (Strauss and

Thomas, 1996). Even here the estimation is often not

trivial since the production technologies and input

choices are simultaneously determined. Nevertheless,

the static approach can still answer important questions. For example, estimating health production functions along with health input demands can be useful

for examining pathways through which choice of

inputs factors affect the health outcomes of interest

(Rosenzweig and Schultz, 1983). 

In this study, we estimate a set of nutrient intake functions for U.S. adults that express the quantity of nutrients consumed as a function of income and sociodemographic and anthropometric characteristics. These

functions are not structural equations derived from a

fully specified household utility maximization problem. Rather, they may be viewed as linear approximations of reduced-form health input demand functions

derived from the household production model, or alternatively, as Engle functions derived from the traditional consumer theory (Adelaja, Nayga, and Lauderbach

1997; Chavas and Keplinger, 1983). Viewing the estimated functions as reduced-form health input demand

functions has the advantage that the effects of key

sociodemographic variables such as educational attainment and age can be interpreted in the context of predictions from the household production theory.

Under the household production theory, sociodemographic factors such as education and age enter input

demand functions because they influence production

efficiency. Educational attainment affects health production by raising technical and allocative efficiencies

of input use (Grossman and Kaestner, 1997).

Technical efficiency causes the more educated to produce a larger health output from a given level of health

inputs. Allocative efficiency causes the more educated

to acquire and use information about the true effects of

inputs on health. Similarly, if the demand for health is

inelastic, and if health stock depreciates at an increasing rate with age, then health investment will increase

with age (Grossman, 1972). In terms of nutrient

intake, this implies that older individuals will be more

likely to have better diets than younger individuals.

Income enters the nutrient intake functions because it

represents the budget constraint facing the consumer.

However, the household production theory does not

offer a clear prediction of the effect of income on the

intake of nutrients. This is because consumers choose

foods for multiple attributes that often have opposing

effects on health. For example, higher income may

increase consumption of fat-rich foods with tasteenhancing attributes. Higher income may also provide better access to health information that tends to

reduce consumption of these fat-rich foods. The net

effect of income will depend on which of these effects

is dominant. If the informational effect is dominant,

then income will have an effect similar to the effect

of education.

Other sociodemographic factors such as age, gender,

race, ethnicity, and a person’s height and weight may

also influence nutrient intakes, both by affecting the

relative amounts of food consumed and by influencing

the health production efficiency. Most of the effects of

age, gender, and anthropometric characteristics on

macronutrient intakes will be due to differences in the

amounts of foods consumed. Thus, lower intakes are

expected for older adults, women, and those with

lower weights and shorter stature. Both cultural differences in food choices and differences in health production efficiencies may be reflected in the influence of

race and ethnicity on macronutrient intakes. As with

income, the net effect of racial and ethnic status is

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  5
uncertain, and left to be resolved through empirical

estimation.

Empirical Approach

To estimate the impact of income, educational attainment, age, and other sociodemographic and anthropometric characteristics on macronutrient intakes of

U.S. adults, we specified nutrient intake functions of

the form:

(1) yi =β0 +β1 Incomei+β2 Εducationi +β3 Agei

+β4x4i +....+βKxKi +εi,

where yi denotes the intake of a nutrient by the ith

individual, i=1, ..., N, x4i, ..., xKi represent additional

explanatory variables influencing yi

, β1, ..., βK represent the coefficients of the explanatory variables, and

εi represents a random error term that accounts for

unobserved factors influencing yi

.

With intake data and the observed characteristics for N

individuals obtained from food consumption surveys,

equation (1) is typically estimated using Ordinary

Least Squares (OLS) regression. In this case, the β

coefficients represent the marginal change in nutrient

intake for a unit change in the explanatory variable at

the conditional mean of intake. Estimating the effect

of explanatory variables on intake at the conditional

mean is a convenient choice, often dictated by the ease

of applying and interpreting OLS regression.

However, this focus on change at the conditional mean

is not dictated by theory. Neither the theory of consumer demand nor the household production theory

gives any guidance as to the parts of distribution of

intakes where the effects of income, age, education, or

other explanatory variables are likely to occur.

Therefore, the question as to which part of the distribution to study will be answered by (a) the nature of the

observed distribution of the choice variable in the relevant population and (b) the potential implications of

the fact that underlying behavior is different at different points of the distribution. In fact, as noted earlier,

for the macronutrient intakes of the U.S. adult population, finding the effects of explanatory variables at the

tails of the conditional intake distributions is likely to

be more interesting and useful than finding the effects

of explanatory variables at the conditional means.

This goal can be accomplished by estimating equation

(1) for various quantiles of yi by quantile regression.

Quantile Regression

Koenker and Bassett (1978) introduced quantile

regression as a generalization of the sample quantile to

the conditional quantile, where the conditional quantile

is expressed as a linear function of explanatory variables. This is analogous to the OLS regression where

the conditional mean of a random variable is expressed

as a linear function of explanatory variables. By

enabling the estimation of any conditional quantile,

quantile regression allows one to describe the entire

conditional distribution of a dependent variable given

a set of regressors. The Least Absolute Deviation

(LAD) estimator is a special case of quantile regression that expresses the conditional median as linear

function of covariates.

The key factor that makes quantile regression’s ability

to characterize the entire conditional distribution so

useful is the presence of heteroskedasticity in the data

(Koenker and Bassett, 1982; Deaton, 1997). When the

data are homoskedastic, the set of slope parameters of

conditional quantile functions at each point of the

dependent variable’s distribution will be identical with

each other and with the slope parameters of the conditional mean function. In this case, the quantile regression at any point along the distribution of the dependent variable reproduces the OLS slope coefficients,

and only the intercepts will differ.

However, when the data are heteroskedastic, the set of

slope parameters of the conditional quantile functions

will differ from each other as well as from the OLS

slope parameters. Therefore, estimating conditional

quantiles at various points of the distribution of the

dependent variable will allow us to trace out different

marginal responses of the dependent variable to

changes in the explanatory variables at these points.

Figure 2 illustrates this point. It shows the scatter diagram of total fat intake against age for a 10-percent

random sample of men from the 1994-96 CSFII. A

visual examination of the scatter shows that the data

are clearly heteroskedastic with a declining variation

in intake by age. Regression lines estimated by OLS

and by quantile regression at the 0.1 and 0.9 quantiles

are superimposed on the scatter diagram. Clearly, the

line at the 0.9q has a greater negative slope than the

line at the conditional mean. If the data were

6  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA
homoskedastic, the three lines would have identical

slope and thus would be parallel to each other.

Two additional features of the quantile regression

model are relevant to our application (Buchinsky,

1998). First, the classical properties of efficiency and

minimum variance of the least squares estimator are

obtained under the restrictive assumption of independently, identically and normally distributed (i.i.d.)

errors. When the distribution of errors is non-normal,

the quantile regression estimator may be more efficient

than the least squares estimator. Second, the quantile

regression estimator is “robust” when the dependent

variable has outliers or the error distribution is “longtailed.” Since the objective function from which the

quantile regression estimator is derived is a weighted

sum of absolute deviations, the parameter estimates

are less sensitive to a few large or small observations

at the tails of the distribution.2 The distributional statistics reported in table 1 show that the mean intakes

of macronutrients are consistently higher than the

median (50th percentile) intakes. This suggests that

the intakes are asymmetrically distributed with some

influential observations at the upper tails (a feature

visible in fig. 2). Quantile regression ensures that the

parameter estimates are less sensitive to such observations compared with OLS.

Estimation and Testing

The quantile regression model for equation (1) can be

written as

(2) 

where xi denotes a (K+1)×1 vector of the explanatory

variables, βθ is the corresponding vector of coefficients, and Qθ(yi

|xi) denotes the θth conditional quantile of yi

, (0 < θ < 1). From equation (2), the quantile

regression estimator of βθ is obtained by solving

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  7

Age

18 36 54 72 90

0

100

200

300

0.9q 

OLS

0.1q

Total fat (grams)

Figure 2

Regression of total fat intake on age for a 10-percent random sample of men, CSFII 1994-96

2 This can be seen by comparing the objective function for the

conditional quantile (equation 3) with the objective function for

OLS, which is min . The OLS estimator is sensitive

to outliers because the objective function is squared; the farther

an outlier, the greater its influence on β. In the quantile objective

function, βθ is chosen so that is positive for (θ *100)%

of the observations and negative for the remainder. Therefore, an

increase in the value of any observation above or below a conditional quantile has no influence on it.

yi i − x ′βθ

Σ

i

N yi i = − ′ 1

2 ( ) x βθ

y Qy i N i i i ii i = x ′βε β θθ θ θ + = , , ,, ( ) x x ′ = 1 L
8  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

(3)

When K = 0 and xi is a (1×1) vector that includes only

the intercept for all i, this minimization problem

reduces to an estimator of the sample θ-quantile. The

minimization problem in equation (3) has a linear programming representation, which is guaranteed to have

a solution in a finite number of simplex iterations

(Buchinsky, 1998). Several estimators for the asymptotic covariance matrix for obtained from the above

minimization are available, but for obvious reasons,

those that rely on the assumption of i.i.d. errors are of

limited value (Deaton, 1997). Buchinsky (1995) has

shown that the design matrix bootstrap estimator provides a consistent estimator for the covariance matrix

under very general conditions. In the design matrix

bootstrap, quantile regression is estimated on a sample

of N observations drawn randomly

with replacement from the original sample. The

process is repeated B times to obtain bootstrap estimates, , b = 1, ..., B. The covariance matrix of 

is then obtained as the covariance of computed from

the B bootstrap estimates with as the pivotal value.

The minimum-distance method can be used to test 

for the equality of slope coefficients of a given 

dependent variable across all estimated quantiles

(Buchinsky, 1998). Let be a 

(KP+1) × 1 stacked vector of unrestricted parameter

estimates from quantile regressions at P quantiles.

Let be a (K+P) × 1 vector comprising P unrestricted intercepts and K restricted slope

parameters. The restricted parameter vector is

obtained by minimizing

(4)

where A is a positive definite matrix and R is the

appropriate restriction matrix. Under the null hypothesis of equality of slope coefficients, NQ(βR) is distributed χ2 with (PK-K) degrees of freedom. Since the

equality of slope parameters will hold if the i.i.d.

assumption is valid, this is a general test for heteroskedasticity. The optimal choice for A is the variance-covariance matrix of , denoted by 

(Buchinsky, 1998). Given , the usual

F-statistic for testing liner restrictions can be used to

test for the equality of the slope parameters for a specific explanatory variable at symmetrical quantiles

such as 0.1q and 0.9q. If the null hypothesis of

homoskedasticity or the equality of the slope coefficients is not rejected, the restricted slope estimates βR

give an optimal combination of the quantile slope estimates (Buchinsky, 1998). Also, given , the variance-covariance matrix of the restricted parameter vector can be obtained as 

The quantile regression parameter estimates are

obtained by estimating a separate equation for each

quantile of each macronutrient. The variance-covariance matrix of the estimates can be obtained by bootstrapping each of these equations separately.

However, to carry out tests of the equality of slope

coefficients for a given dependent variable across the

P estimated quantiles and to obtain the restricted parameter estimates and their standard errors, it is necessary to have , the variance-covariance matrix of

the stacked vector of parameter estimates at the P

quantiles. This can be obtained by simultaneously

estimating quantile regressions at the P quantiles for

each bootstrap sample. Thus, the following procedure

was used for the estimation and testing of the quantile

regressions for each macronutrient. First, the coefficient estimates for a macronutrient were obtained by

running quantile regressions separately at the P desired

quantiles. Second, a bootstrap sample was drawn for

that macronutrient and the bootstrap estimates for the

P quantiles were obtained by running quantile regressions separately at the P quantiles for that sample.

Finally, after repeating the bootstrap procedure B

times, was calculated to obtain the standard errors

of the coefficient estimates and to conduct the equality

tests. This estimation process was carried out in Stata

using the sqreg procedure (Gould, 1997). Additional

details regarding the estimation of the quantile regression model and the asymptotic covariance matrix of

the parameters are discussed in Buchinsky’s (1998)

methodological survey.

Λ$

P

Λ$

P

$ ( $ Λ Λ . R

P = − − R' R) 1 1

Λ$

P

Λ$

P

Λ$

P $

βP

βR

β θβ θ

ββ β R

P K = ( ) , ,, ,,

1

1 12 K K '

$ ( $ , , $ β β ) θ β P θ P = ′ ′ 1 K '

$

βθ

$

βθ

∗

$

βθ

$

βθb

∗

( , , ,, * *

i i y x ), i N = 1 K

$

βθ

βθ

θ

β

θ

β

θ

β

β

min 1

1

N

y

y

i yi i

i yi i

i i

i i

:

:

( )

. ≥ ′

− +

< ′

−

∑

∑

′

− ′





























x

x

x

x

θ

θ

θ

Q(β ββ ββ R

P

R

P

R ) ( ) ( ), $ $ =− − R ' A R - 1
Data

The macronutrient intake data for men and women

were obtained from USDA’s 1994-96 CSFII (Tippett

and Cypel, 1997). Each year of the 3-year CSFII comprised a nationally representative sample of noninstitutionalized persons residing in the United States.

Dietary data for selected members from a screened

sample of 9,664 households were collected on two

nonconsecutive days through in-person interviews

using 24-hour recalls. A sample of 15,303 people provided information on food intakes for both days giving

a 2-day response rate of 76.1 percent. From the sample persons providing complete 2-day dietary intake

records, we selected adults aged 18 or above and

excluded pregnant and lactating women.

By combining the food records with a nutrient database, CSFII provides information on the intakes of a

variety of macronutrients, vitamins, and minerals. In

this study, we focused on the five major macronutrients—energy, total fat, saturated fat, cholesterol, and

fiber. As noted earlier, these nutrients have drawn

interest because of their links to obesity, cardiovascular disease, and certain types of cancer (National

Research Council, 1989). The means of the 2-day

intakes were used to represent the daily intakes of

these nutrients.

Along with the quantities of foods and nutrients consumed by individuals, CSFII provides a detailed set of

personal and household characteristics of each sample

person. From this set, we selected explanatory variables for quantile regressions of the macronutrient

intakes based on our theoretical discussion in section 2

(Economics of Nutrient Intake) as well as evidence

from previous literature. The selected explanatory variables, along with their means, are listed separately for

men and women in appendix table 1. The explanatory

variables fall into three groups: household characteristics, personal characteristics, and survey-related variables. Income, household size, education level, age,

height, and weight are continuous variables. The

remaining variables are dummy indicator variables.

Income is the gross household income in the previous

calendar year from all sources before taxes. Rather

than restrict the effects of income and household size

by entering income on a per capita basis, we included

income and household size as separate variables. The

region and urbanization variables are included to capture possible variations in intakes due to geographic

differences in food prices and other location-related

factors. Education is represented by years of schooling completed at the time of survey. On average, the

adults in our sample had completed about a year more

than high school. The average age for men and

women was about 49 years. The height and weight of

the respondents were included to control for the influence of body mass on the amount of food intake.

While previous studies have often used the Body Mass

Index (BMI) for this purpose, estimating a single coefficient for BMI implies a restriction on the coefficients

for BMI components, height, and weight (Bhargava,

1994). Therefore, we left height and weight in the

unrestricted form.

Racial and ethnic indicator variables are included to

capture differential food intakes due to differences in

culture and traditions. While Asians and other race

groups (Asian/Pacific Islander, Aleut, Eskimo, or

American Indian) are included in the sample, we do

not focus on the estimates for these groups due to their

relatively low proportions in the sample (see appendix

table 1). Fairly significant proportions of men and

women (15 and 21 percent, respectively) were reported to be on special diets at the time of the survey. A

significant part of the variation in food intakes measured through consumption surveys can be attributed

to factors such as survey season, whether the intake

was recorded for a weekend, and whether, for a variety

of reasons, a person’s food intake on the day of recorded intake was less than or more than his or her usual

intake. We included a detailed set of variables to control for such sources of variation in nutrient intakes.

Numerous intermediary variables such as nutrition

knowledge, nutrition label use, and food program participation affect nutrient intakes. These variables may

act as pathways through which basic sociodemographic variables influence dietary behavior (Gould and Lin,

1994; Kim, Nayga, and Capps, 2000; Variyam,

Blaylock, and Smallwood, 1996). However, the objective of this study was not to estimate the effects of

consumers’ intermediate choices on dietary intakes.

Rather, the objective was to map the net effect of key

sociodemographic variables at different points along

the conditional intake distribution. Therefore, we

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  9
excluded such intermediary variables from our estimated functions. After dropping observations that

were incomplete with respect to the included explanatory variables, the final sample sizes were 4,725 men

and 4,362 women.

Results

Based on our discussion of the household production

theory, we expect to find specific relationships in the

behavior of macronutrient intakes with respect to

changes in the level of education and age. More educated and older people are likely to have lower intakes

of energy (calories), total fat, saturated fat, and cholesterol, the overconsumption of which is a problem. For

dietary fiber, which is underconsumed by most, more

educated and older people are likely to have higher

intakes. Evidence on these relationships as well as

evidence on the effects of income, race, ethnicity, and

other sociodemographic factors have been provided in

many previous studies (Murphy et al., 1992; Nayga,

1994). But, as noted earlier, this previous evidence

pertains to the relationships estimated at the conditional mean where the risk of inadequacy or excess for

macronutrient intakes is relatively small. 

A more interesting type of evidence is the effect of

sociodemographics at the parts of the distribution

beyond the mean, such as the tails where the risk of

dietary inadequacy or excess are higher. We begin

looking for such evidence by examining the bivarate

relationships between intakes and sociodemographics

at various points along the distribution of intakes.

Bivariate Associations

The mean and five selected percentiles of macronutrient intakes by categories of household income

(expressed as a percentage of poverty level), years of

education completed, age, race, and ethnicity are

reported in appendix tables 2-6. Certain bivariate relationships between intakes and the explanatory variables are evident by comparing the mean intakes

across categories of the explanatory variables.

However, the comparison of mean intakes tells only

part of the story of the relationships between the

explanatory variables and macronutrient intakes. A

more complete picture of the relationships emerges

when one looks at the differences in intakes across categories of the explanatory variables at the five percentiles reported in appendix tables 2-6. For easier

comparison of percentiles, intakes at the 10th to the

90th percentile for the outer categories of income, education, and age are shown in figures 3-8.

Men with income below 130 percent of the poverty

threshold consume about 230 more calories on average

than men with income above 350 percent of the poverty threshold. Low-income men have higher mean

intakes of total fat, saturated fat, and cholesterol, than

high-income men. The percentiles of intakes by

income levels in appendix tables 2-5 and fig. 3 give a

clearer picture of the intake difference associated with

income. The energy, fat, and cholesterol intakes of

men are not uniformly different between the income

categories across the percentiles. The higher intakes

of low-income men come almost entirely from the

upper end of the distributions, notably above the 70th

percentile for energy and fat. In fact, low-income men

have lower intakes of energy and fats at the bottom

part of the distributions. For cholesterol, although

lower income men have higher intakes at all parts of

the distribution, the differences are much larger at the

upper parts of the distribution than at the lower parts.

The differences in women’s cholesterol intake by

income level has a pattern similar to men’s (appendix

table 5; fig. 4). Lower income women have higher

mean cholesterol intake than higher income women

and the differences are larger at the upper end of the

cholesterol intake distribution. However, the relationship of women’s energy intake with income is the

reverse of men’s (appendix table 2; fig. 4). Women

under 130 percent of the poverty threshold consume

117 fewer calories of energy than women above 350

percent of the poverty threshold. Energy intake differences between the two groups of women are much

larger at the bottom part of the distribution.

Although men’s mean energy intake does not differ

much by level of education, men with more than high

school have higher intakes for much of the distribution

except at the upper end above the 85th percentile (fig.

5). A similar pattern is evident for fat intake as well.

The behavior of men’s cholesterol intake by education

level is very similar to that by income.

Unlike in the preceding examples, the difference at the

mean is representative of the difference at other parts

of the intake distribution for women’s energy intake by

education level. The energy intake of women with

more than a high school education is uniformly higher

at all parts of the distribution between the 10th and the

10  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA
90th percentiles (fig. 6). The situation is similar for

women’s total fat intake as well. For cholesterol, the

pattern noted for income reemerges with the difference

in intakes between less than high school and more than

high school groups widening at the upper end of the

distribution.

For both men and women, the strongest and most consistent association for energy and fat intakes is with

age. The mean intakes by age groups show that intakes

decrease with age, and the percentiles show that the

intake differences by age widen at higher percentiles.

Fiber is different from energy, fat, and cholesterol

because its dietary inadequacy is the issue rather than

dietary excess. This explains the different nature of

associations between explanatory variables and fiber

intake evident in appendix table 6 and figures 3-8.

Unlike energy, fat, and cholesterol, men’s mean fiber

intake either increases or remains unassociated with

increases in income, education, and age. However, the

percentiles show slightly wider differences at the bottom in men’s intakes by income and education level.

Among women, fiber intake differences by the level of

income, education, and age are fairly uniform at the

different percentiles.

These bivariate associations between macronutrient

intakes and key explanatory variables across various

percentiles have clearly shown how the mean differences can mask a richer pattern of differences at other

parts of the distribution. However, the bivariate associations may not capture the true association between

an explanatory variable and intakes since the relationship may be confounded by other variables. For

example, the association between education and

income may confound the bivariate association

between men’s intakes and their educational attainment. To estimate the marginal effects of explanatory

variables at different parts of the intake distribution

after controlling for such confounding effects, we

employed the method of quantile regression.

Quantile Regression Results

Test Statistics

The purpose of using quantile regressions is to estimate the marginal effects (or the slope coefficients) of

explanatory variables at various points along the conditional distribution of the dependent variable.

However, as noted earlier, if the distribution of the

dependent variable is homoskedastic (that is, the conditional variance of dependent variable’s distribution is

constant by the level of independent variables), the

estimated marginal effects will be identical between

quantiles and the conditional mean estimated by

Ordinary Least Squares (OLS). In this case, the quantile slope coefficient estimates do not provide any

additional information about the behavior of the

dependent variable with respect to the explanatory

variables beyond the information conveyed by the

OLS slope estimates. Therefore, the first step after

estimating quantile regressions is to test whether the

estimated slope coefficients are equal across the quantiles. As shown by Koenker and Bassett (1982), such

a test for the equality of slope coefficients across

quantiles is a robust test for heteroskedasticity.

We estimated conditional quantile functions for the

intake of the five macronutrients by men and women

at five selected quantiles (P=5). The equality of slope

coefficients across the five quantiles for each nutrient

was then tested by computing the minimum-distance

estimator in equation (4). The resulting chi-square test

statistics are reported in table 2. Since K=30 and P=5,

these χ2 statistics have 120 degrees of freedom. The

null hypothesis of homoskedasticity is rejected decisively (p<.01) in all cases. This implies that the slope

coefficients differ significantly across quantiles and are

likely to provide additional information about the

behavior of intakes beyond that conveyed by the OLS

estimates alone.

In tables 3 to 7, we report coefficient estimates for

energy, total fat, saturated fat, cholesterol, and fiber.

To keep the discussion manageable, the tables report

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  11

Table 2—Tests for equality of regression 

coefficients across the five quantiles

Macronutrient Men Women

Energy 417.3 336.0

Total fat 391.5 252.1

Saturated fat 370.3 261.4

Cholesterol 430.8 281.0

Fiber 271.5 216.1

Note: Under the null hypothesis of equality, the test statistic Q ~ χ2(120).
only the estimates for five key variables of policy

interest—income, education, age, race (Black compared with White) and ethnic origin (Hispanic compared with non-Hispanic). For comparison with the

quantile estimates, the second column in each table

presents the OLS estimates. The restricted coefficient estimates are reported in the last column of

the tables. The standard errors for the quantile

regression estimates were obtained using the design

matrix bootstrap with B = 500 replications. The standard errors for the OLS estimates were computed

using White’s method.

The R2s for the estimated regressions are generally

low, indicating high variation in the mean 2-day

intakes, but are in line with previous studies (Adelaja,

Nayga, and Lauderbach, 1997; Chavas and Keplinger,

1983).3 The F-tests for the OLS regressions showed

high significance levels (p<.001) in all cases.

Although the joint equality of all coefficients across

the five quantiles was rejected for all nutrients, it still

leaves open the possibility that the coefficients of individual explanatory variables may be equal across the

quantiles. Therefore, we also tested for the equality of

the slope coefficients of the selected explanatory variables across quantiles. While equality across any combination of quantiles could be tested, in table 8, we

present test statistics (F-values) for slope equality at

symmetrical quantiles (0.1q = 0.9q and 0.25q = 0.75q).

The p-values of the test statistics are reported in parenthesis. As noted earlier, if an F-test does not reject the

equality of slopes at symmetrical quantiles, then the

restricted coefficient estimate gives the optimal

combination of the five quantile slope coefficients.

Such estimates, in general, have lower variance than

least squares estimates (Buchinsky, 1998). Comparing

the restricted estimates with the corresponding OLS

estimates, we found that in almost all cases was

more precisely estimated with lower standard errors

than the corresponding OLS estimates (see also footnote 5).

Marginal Effects

The OLS income coefficients for both men and

women, for energy, total fat, and saturated fat intakes

are statistically insignificant, implying no relationship

between household income and the consumption of

these macronutrients, when other explanatory variables

are held constant. The income coefficients at the five

quantiles, and their weighted combination given by the

restricted estimate, however, portray a more subtle

influence for income. Income appears to have a

slightly positive effect on energy intake at the lower

quantiles with the effect declining toward the upper

quantiles (table 3). A better view of this trend can be

found in figures 9 and 10. These figures display plots

of income coefficients for each macronutrient against

the quantiles at which they were estimated.4 The

income coefficients for energy intake tend to decline

from 0.1q to 0.9q, especially for women. However,

except at the 0.25q of men’s energy intake, none of the

income quantile coefficients are statistically significant

in table 3. Therefore, this apparent trend has to be

interpreted with caution. The income coefficients for

total and saturated fat are mostly insignificant at the

five quantiles, and although the restricted estimates for

men are significant, the magnitudes are small.

The additional information revealed by the quantile

estimates compared with the OLS estimate comes into

sharper focus for cholesterol (table 6). The OLS estimates show that income has a negative (and healthwise, beneficial) effect on cholesterol intake of both

men and women. The quantile estimates reveal that

this beneficial effect is almost entirely located at the

upper quantiles. As the observed distribution showed,

at these upper quantiles, cholesterol intakes tend to

exceed the recommended level (table 1). For men, the

effect of income on cholesterol intake at 0.9q is 115

percent larger than the OLS estimate. This implies

that, as income increases, the upper conditional quantiles of cholesterol intake are decreasing more rapidly

than is the conditional mean. This trend is clearly visible in figures 9 and 10 where the income coefficients

on cholesterol intake reveal greater negative impact at

higher quantiles. For men and women, the equality

$

βR

$

βR

$

βR

12  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

3 The R2 for the quantile regressions are analogous to the conventional OLS R2. Let be the solution to equation (2) and let 

be the solution to equation (2) when xi is restricted to include only

the intercept. Then R2 for the θth quantile is defined as 

~

Vθ V$

θ

4 The coefficient estimates for these plots were estimated from

regressions at 17 quantiles from 0.1q to 0.9q at intervals of 0.05.

R VV θ θθ

2 = − 1 $ / ~ .
restrictions on income coefficients across symmetrical

quantiles (0.1q = 0.9q and 0.25q = 0.75q) are rejected

at the 10-percent level (table 8). 

Income has a significant positive effect on fiber intake

at all quantiles. However, for men and women, the

largest effect is at 0.9q. Since dietary risk for fiber

(inadequacy) is relatively greater at the lower end of

the intake distribution, one might expect to find the

largest effect for income at the bottom quantiles. This

was not the case, likely because, as table 1 shows,

most adults have inadequate intake even at the 90th

percentile.

The estimated effects of educational attainment on

men’s macronutrient intake clearly illustrate the importance of examining the whole conditional distribution

rather than focusing on just the conditional mean. The

OLS coefficient for education on men’s energy intake

is insignificant, implying that energy intake does not

change in response to a change in the education level.

However, the quantile estimates reveal a more interesting pattern that is masked by the OLS estimate. The

education coefficients for energy at the 0.1q and 0.25q

are both positive and statistically significant (p<0.1).

Thus, as education increases, energy intake actually

increases at the lower quantiles. Meanwhile, education coefficients at 0.5q and above are negative and the

estimate at 0.75q is significant (p<0.1). Thus, energy

intake declines in response to an increase in education

at upper quantiles. The plot of education coefficients

for energy intake in figure 11 displays this pattern

clearly. Therefore, when the effect of education over

the entire distribution is considered, the implication is

that greater educational attainment influences men

toward moderating their energy intake.

The differential effects of education across quantiles

are even more striking for men’s intake of fats and

cholesterol than they are for energy (tables 4-6). For

instance, an additional year of education reduces

men’s saturated fat intake by 0.18 gram at the conditional mean. However, at 0.9q, a similar increase in

education reduces saturated fat intake by 0.52 gram, a

nearly 200-percent larger estimated effect. Both for

saturated fat and total fat, quantile estimates below the

median are insignificant. In fact, for total fat, the marginal effect of education at 0.1q is positive and numerically large (0.32 gram). This is not surprising since,

at 0.1q, the observed intakes are substantially below

the recommended levels.

Educational attainment influences men’s cholesterol

intake in a similar fashion. The reduction in intake

attributable to education is larger at the upper conditional quantiles compared with the conditional mean or

the lower quantiles. The trend of larger negative

effects for education on the intakes of fats and cholesterol as one moves from the lower to the upper quantiles is strikingly visible in figure 11. Tests of equality

of education coefficients at symmetrical quantiles for

men’s energy, total fat, saturated fat, and cholesterol

are rejected at the 5-percent level (table 8).

For men’s fiber intake, education has a more uniform

effect across the quantiles (fig. 11). The equality of

education coefficients at symmetrical fiber intake

quantiles could not be rejected (table 8).

Consequently, this is one instance where it is meaningful to compare the restricted estimate, which is a

weighted combination of quantile estimates, with the

OLS estimate. From table 7, it can be seen that the

restricted estimate outperforms the OLS estimate in

precision with a larger t-value.5

Taken together, the results for men confirm previous

findings that education is positively correlated with

better diets, just as it has been shown to be positively

correlated with other desirable health behaviors

(Grossman and Kaestner, 1997). However, our results

also show something new. The beneficial effects of

education are much larger at parts of the conditional

distribution that matter most—at the lower quantiles

for energy where the risk of inadequacy is greater and

at the upper quantiles for energy, fats, and cholesterol

where the risk of excess intakes is the largest. For

fiber, the size of the education coefficients were statistically similar across the quantiles. Similar to our reasoning for income, one explanation for not finding

larger effects for education at the bottom quantiles

may be that for the most part, the entire observed distribution of fiber intake is below the recommended

fiber intake level.

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  13

5 Since the standard errors for the OLS estimates were computed

using White’s method and the standard errors for the quantile

estimates were computed using design matrix bootstrap, the t-values of the OLS estimates and the restricted estimates in tables 3-7

are not strictly comparable. Therefore, we computed OLS standard errors using design matrix bootstrap with 500 replications.

The OLS education coefficient on men’s fiber intake had a bootstrap standard error of 0.0512. The standard error of the restricted estimate was 0.0321—37 percent smaller than the OLS bootstrap standard error.
There is less evidence of an increasing marginal effect

of education at the higher quantiles for women.

Women’s energy intake increases significantly with

education at all quantiles except at 0.90q. Although a

trend of smaller influence at upper quantiles is visible

in figure 12, equality of coefficients at symmetrical

quantiles cannot be rejected (table 8). The restricted

quantile estimates suggest that for each additional year

of education, energy intake among women increases

by about 13 calories. However, this increase has to be

viewed in the context that the major part of women’s

energy intake distribution lies below the recommended

level (table 1). Surprisingly, education tends to have a

positive effect on women’s total fat intake. However,

the effects are significant only at 0.25q and 0.50q.

Education has no significant impact on women’s saturated fat intake, while for cholesterol the expected

negative effect is found. Similar to the effect for men,

the effect of women’s educational attainment on their

fiber intake is significant at all quantiles.

Consistent with the prediction from Grossman’s

model, men’s energy, total fat, saturated fat, and cholesterol intakes decline with age, but more interestingly, the rates of decline rise steadily from the lower to

the upper quantiles (fig. 13). The differences in the

estimated effects between 0.1q and 0.9q are over 200

percent. The strong intake response to age is not surprising given that the health risk of poor diets is likely

to be cumulative and increasing with age. Therefore,

older individuals will display a greater propensity to

improve their diets than younger individuals. In addition, our results show that the age effect is stronger at

parts of the intake distributions where the risk of inadequacy or excess are greater.

Women’s age has a similar pattern of impact across

quantiles of energy, total fat, and saturated fat intakes.

For cholesterol, the women’s age coefficient is

insignificant under OLS and at all quantiles. However,

the optimally combined restricted estimate is significant, showing that cholesterol intake among women

does decline with age. The age effect on women’s

fiber intake is uniformly positive across the OLS and

quantile estimates. The age effect on fiber intake of

men shows conflicting results under the different estimators. While the OLS and lower quantiles estimates

are insignificant, the 0.9q estimate is negative and significant. However, the restricted estimate is positive

and significant in accordance with our expectation,

although the numerical effect is small (0.013). It is

not clear why age, which had sizable effects on other

macronutrient intakes, would have such limited impact

on men’s fiber intake.

The difference in energy intake between Black and

White men is surprisingly large, with Black men consuming about 209 fewer calories than White men at

the conditional median. Although low t-values make

it hard to discern a trend by quantiles, the restricted

estimate shows that Black men have significantly

lower total fat and saturated fat intakes than White

men. For both intakes, interquantile equality cannot

be rejected and the restricted estimate is more precise

than the OLS estimate. While Black men’s intakes

look better than White men’s in terms of energy, total

fat, and saturated fat, the picture is starkly different

for cholesterol and fiber. After controlling for other

effects, Black men have higher cholesterol intake and

lower fiber intake than White men. The quantile estimate shows that Black men consume 116 milligrams

more cholesterol than White men at the 90th percentile. This is twice the difference at the conditional

mean. Black men also have lower fiber intakes than

White men across all quantiles, with the restricted

quantile estimate giving a Black-White difference of

about 1.9 grams.

Although the OLS estimate shows a lack of BlackWhite difference in women’s energy intake, the 0.25q

and the restricted estimates suggest that Black

women’s energy intake is significantly lower than

White women’s. Similar to the Black-White difference for men, Black women have higher cholesterol

intake and lower fiber intake than White women. The

Black-White difference in cholesterol is large, particularly at the upper quantiles and the interquantile

equality tests show significant difference between

intakes at the lower and upper quantiles. The restricted estimate suggests that Black women consume 1

gram less fiber than White women. The restricted

estimate has a much higher t-value than the OLS estimate.6 Unlike Black men though, Black women’s

intakes of total and saturated fats were not significantly lower than White women’s.

Hispanic men’s diets are significantly lower in total fat

and saturated fat intakes than the diets of non-Hispanic

14  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

6 The bootstrap standard error for the restricted quantile estimate

is only 0.191 compared with a bootstrap standard error of 0.343

for the OLS estimate.
men. For energy, the evidence is inconclusive since

only the Hispanic coefficient at the conditional median

is significant. Based on the restricted estimate,

Hispanic men consume 4.4 grams less total fat than

non-Hispanic men. For saturated fat, the quantile estimates indicate that the relative difference is located at

the upper ends of the distribution. This is confirmed

by interquantile equality tests, which are both rejected

at the 10-percent level. Due to the relatively low t-values, no significant differences between the Hispanic

and non-Hispanic groups are evident for cholesterol

and fiber intakes at various quantiles. However, the

restricted estimate does show a higher fiber intake of

0.86 gram by Hispanic men than non-Hispanic men.

Both the OLS estimate and the restricted quantile 

estimate show significantly lower energy intake by

Hispanic women than non-Hispanic women. Here

again, the restricted estimate is far more precise than

the OLS estimate. Hispanic women consume about 4

grams less total fat and 0.8 gram less saturated fat than

non-Hispanic women, based on the restricted estimates. While the quantile estimates for cholesterol

intake show an interesting trend with the

Hispanic/non-Hispanic difference reversing in sign

from lower to upper quantiles, none of the estimates

are significant. Hispanic women’s fiber intake is significantly higher at the bottom end of the distribution.

At 0.1q, the unconditional estimate of which is well

below the recommended level, Hispanic women tend

to have about 1 gram of higher fiber intake than nonHispanic women. Overall, the results for men and

women show that the Hispanics tend to have a healthier macronutrient intake profile than non-Hispanics.

The data tables compiled from the 1994-96 CSFII can

be used to speculate on the likely food sources of the

intake differences by race and ethnicity (U.S

Department of Agriculture, 2000). For example, Black

men over age 20 consume only 7 grams of cheese

compared with the 20 grams consumed by White men

over age 20. The most recent study on the sources of

nutrients based on the 1989-91 CSFII shows that

cheese is the largest source of saturated fat and the

fourth largest source of total fat among U.S. adults

(Subar et al., 1998).7 Black men also tend to consume

lower amounts of whole and low-fat milk, another

major source of fats, than White men. However, Black

men consume lower amounts of yeast bread and readyto-eat cereals, and higher amounts of eggs than White

men, which may account for their lower fiber intake

and higher cholesterol intake. Based on Subar et al.

estimates, yeast bread and milk are also the top and

third largest sources of energy among U.S. adults. The

1994-96 CSFII data tables show that Black men over

20 consume only 178 grams of total milk and milk

products per day compared with 262 grams consumed

by White men over 20. The lower caloric intake by

Black men may therefore be partly due to their lower

consumption of these energy sources.

The picture is not as clear regarding the sources of

Hispanic/non-Hispanic differences. For example,

Hispanic men over age 20 consume higher amounts of

beef and about the same amount of cheese as nonHispanic White men over age 20. Consequently, the

difference in their total and saturated fat intakes must

come from other sources. Similarly Hispanics consume less yeast bread and ready-to-eat cereal than

non-Hispanic Whites. Therefore, the higher fiber

intake of Hispanics must come from other fiber

sources such as legumes. Indeed, the 1994-96 CSFII

data tables show that Hispanics do consume larger

amounts of legumes than non-Hispanic Whites.

Conclusions

Understanding and quantifying the relative differences

in food and nutrient intakes among population subgroups are important for targeting nutrition promotion

programs and expenditures. The results can also contribute to improved understanding of health risk

behavior at a time of rapid change in the health information environment. However, the nature of intake

distributions is such that the risk of dietary excess or

inadequacy is greater at the tails of the distributions

than at the mean. Consequently, it is questionable to

assume that the marginal effects of population characteristics will be constant along all parts of the conditional distribution of intakes. In this case, any analysis

that focuses on only one part of the distribution, such

as the conditional mean, may give an incomplete picture of the sources of intake difference among populaEconomic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  15

7 The food intakes reported in the data tables are mean amounts

based on day-1 of the 1994-96 CSFII (U.S. Department of

Agriculture, 2000). The dietary sources of nutrients are based

on day-1 of the 1989-91 CSFII and are not separated by race or

sex (Subar et al.). These comparisons are meant to be illustrative, especially considering that the estimated differences reported in this paper are conditional (ceteris paribus), whereas the

figures from the data tables and the dietary sources study are

unconditional.
tion subgroups. In this study, we used quantile regression, a method suited for characterizing the entire distribution of intake, to examine macronutrient intakes

among U.S. adults.

The findings clearly suggest that for key sociodemographic variables such as age, education, and income,

the marginal effects at the tails of the intake distribution are often quite different from those at the mean.

A more complete picture of the location of differences

among population subgroups emerges from the quantile regression estimates than was available from the

OLS estimates alone. Of course, this requires considering a larger number of parameter estimates. But in

most cases, an optimal combination of the quantile

estimates—the restricted estimates—outperformed the

OLS estimates in precision.

These results have important implications for future

studies evaluating the dietary impact of many nutrition-related policy interventions such as food assistance programs and food labeling regulations. For

such studies to fully uncover the extent and nature of

the behavioral impact, it is essential to look beyond

the conditional mean to parts of the dietary intake distribution where the risk of inadequacy or excess is

greatest.

One plausible interpretation of these results is that

individuals, particularly men, at higher age, education,

and income levels may have benefited more from

health and nutrition information initiatives such as the

Nutrition Labeling and Education Act. Certainly, this

explanation is consistent with the effect of human capital on health behavior predicted by the household production model. By comparing the influence of these

variables on food and nutrient intakes over a sufficiently long timespan, it may be possible to verify the

validity of this potentially important linkage.

The results reported here have to be interpreted and

generalized cautiously given that the analysis is based

on self-reported food intakes recorded from 2 days of

24-hour dietary recall. Such data are well-known to

be prone to underreporting and may not fully represent

a person’s usual intake. In this study, we have

attempted to control for underreporting and day-to-day

intake variations to the extent possible by including

sources of these variations such as season, whether the

intake was recorded for a weekend day, and selfreported assessment whether the recorded intake was

less than or more than a person’s usual intake.

Nevertheless, as with most previous studies based on a

few days of recall data, the possibility cannot be ruled

out that some of the estimated effects may have been

influenced by underreporting or day-to-day variation.

Estimation of conditional quantile models based on

intake data from alternative data sources, which better

represent usual intakes and may have lower incidence

of underreporting, such as food-frequency questionnaires, may be able to resolve this issue.

16  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  17

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

 Gm/day Mg/day

Fiber

Gm/day

Percentile

Percentile Percentile

Percentile Percentile

Figure 3

Percentiles of macronutrient intake among men by income level

Income <131% of poverty level

Income >350% of poverty level

10 30 50 70 90

1000

2000

3000

4000

5000

10 30 50 70 90

0

40

80

120

160

200

10 30 50 70 90

0

20

40

60

10 30 50 70 90

0

200

400

600

800

10 30 50 70 90

0

10

20

30

40
18  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

 Gm/day Mg/day

Fiber

Gm/day

Percentile

Percentile Percentile

Percentile Percentile

10 30 50 70 90

20

40

60

80

100

10 30 50 70 90

5

10

15

20

25

30

35

10 30 50 70 90

50

150

250

350

450

Figure 4

Percentiles of macronutrient intake among women by income level

Income <131% of poverty level

Income >350% of poverty level

10 30 50 70 90

800

1200

1600

2000

2400

10 30 50 70 90

0

5

10

15

20

25

30
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  19

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

 Gm/day Mg/day

Fiber

Gm/day

Percentile

Percentile Percentile

Percentile Percentile

Figure 5

Percentiles of macronutrient intake among men by education level

Education <12 years

Education >12 years

1000

2000

3000

4000

10 30 50 70 90

0

40

80

120

160

10 30 50 70 90

0

20

40

60

10 30 50 70 90

0

200

400

600

800

10 30 50 70 90

0

10

20

30

40

10 30 50 70 90
20  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Percentile

Percentile Percentile

Percentile Percentile

Figure 6

Percentiles of macronutrient intake among women by education level

Education <12 years

Education >12 years

10 30 50 70 90

0

20

40

60

80

100

10 30 50 70 90

50

150

250

350

450

10 30 50 70 90

5

15

25

35

10 30 50 70 90

5

10

15

20

25

10 30 50 70 90

500

1000

1500

2000

2500
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  21

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Percentile

10 30 50 70 90

0

40

80

120

160

Figure 7

Percentiles of macronutrient intake among men by age level

Percentile Percentile

Percentile Percentile

10 30 50 70 90

0

200

400

600

800

10 30 50 70 90

0

20

40

60

10 30 50 70 90

0

10

20

30

40

Age 18-24 years

Age 65 years or older

10 30 50 70 90

1000

2000

3000

4000

5000
22  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Percentile

Figure 8

Percentiles of macronutrient intake among women by age level

Percentile Percentile

Percentile Percentile

10 30 50 70 90

20

50

80

110

10 30 50 70 90

5

12

19

26

33

40

10 30 50 70 90

0

100

200

300

400

500

10 30 50 70 90

5

10

15

20

25

Age 18-24 years

Age 65 years or older

10 30 50 70 90

800

1350

1900

2450

3000
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  23

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Fiber

Gm/day

Quantile

Gm/day Mg/day

Quantile Quantile

Quantile Quantile

Figure 9

Marginal effect of a $1,000 increase in income on the macronutrient intake of men across quantiles

0.1 0.3 0.5 0.7 0.9

-0.4

0

0.4

0.8

1.2

1.6

0.1 0.3 0.5 0.7 0.9

-0.02

0

0.02

0.04

0.1 0.3 0.5 0.7 0.9

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.01

0.02

0.03

0.04

-0.03

-0.02

-0.01

0

0.1 0.3 0.5 0.7 0.9

0.1 0.3 0.5 0.7 0.9
24  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

0.1 0.3 0.5 0.7 0.9

0.01

0.02

0.03

0.04

0.1 0.3 0.5 0.7 0.9

-0.03

-0.02

-0.01

0

0.01

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Quantile Quantile

Quantile

Quantile Quantile

Figure 10

Marginal effect of a $1,000 increase in income on the macronutrient intake of women across quantiles

0.1 0.3 0.5 0.7 0.9

-0.02

0

0.02

0.04

0.06

0.1 0.3 0.5 0.7 0.9

-0.6

-0.4

-0.2

0

0.2

0.1 0.3 0.5 0.7 0.9

-0.6

-0.3

0

0.3

0.6

0.9

1.2
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  25

0.1 0.3 0.5 0.7 0.9

-20

-10

0

10

20

Energy Total fat

Calories/day Gm/day 

Figure 11

Marginal effect of an additional year of education on the macronutrient intake of men across quantiles

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Quantile Quantile

Quantile

Quantile Quantile

0.1 0.3 0.5 0.7 0.9

-1.2

-0.8

-0.4

0

0.4

0.1 0 .3 0.5 0 .7 0.9

-0.6

-0.4

-0.2

0

0.2

0.1 0.3 0.5 0.7 0.9

-12

-9

-6

-3

0

3

0.1 0.3 0.5 0.7 0.9

0.1

0.2

0.3

0.4
26  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

0.1 0.3 0.5 0.7 0.9

0.2

0.3

0.4

0.1 0.3 0.5 0.7 0.9

-4

-3

-2

-1

0

1

0.1 0.3 0.5 0.7 0.9

0

0.03

0.06

0.09

0.12

0.1 0.3 0.5 0.7 0.9

0

0.1

0.2

0.3

0.4

0.5

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Quantile Quantile

Quantile

Quantile Quantile

Figure 12

Marginal effect of an additional year of education on the macronutrient intake of women across quantiles

0.5

0.1 0.3 0.5 0.7 0.9

0

5

10

15

20

25
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  27

0.1 0.3 0.5 0.7 0.9

-0.04

-0.02

0

0.02

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Quantile Quantile

Quantile

Quantile Quantile

Figure 13

Marginal effect of an additional year of age on the macronutrient intake of men across quantiles

0.1 0.3 0.5 0.7 0.9

-25

-20

-15

-10

-5

0.1 0.3 0.5 0.7 0.9

-1.1

-0.9

-0.7

-0.5

-0.3

-0.1

0.1 0.3 0.5 0.7 0.9

-0.4

-0.3

-0.2

-0.1

0.1 0.3 0.5 0.7 0.9

-2.5

-2.0

-1.5

-1.0

-0.5

0
28  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

0.1 0.3 0.5 0.7 0.9

0.02

0.04

0.1 0.3 0.5 0.7 0.9

-0.15

-0.05

0

Energy Total fat

Calories/day Gm/day 

Saturated fat Cholesterol

Gm/day Mg/day

Fiber

Gm/day

Quantile Quantile

Quantile

Quantile Quantile

Figure 14

Marginal effect of an additional year of age on the macronutrient intake of women across quantiles

0.06

-0.10

0.10

0.1 0.3 0.5 0.7 0.9

-12

-9

-6

-3

0

0.1 0.3 0.5 0.7 0.9

-0.4

-0.2

0

-0.10

0.1 0.3 0.5 0.7 0.9

-0.15

-0.05

0

0.05

0.15

0.2
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  29

Table 3—Quantile regression estimates: Energy intake, 1994-96

Variable OLS Quantile

0.10 0.25 0.50 0.75 0.90

Men:

Income (×10-3) .167 .888 1.125 .709 .532 -.327 .311

(.31) (1.47) (2.06) (1.15) (.68) (.30) (.88)

Education -1.360 13.65 9.205 -.365 -11.93 -6.875 9.772

(.32) (2.70) (1.77) (.07) (1.72) (.73) (3.26)

Age -17.38 -9.972 -11.83 -15.91 -20.97 -24.26 -13.86

(21.39) (11.41) (14.67) (16.46) (16.75) (14.93) (26.57)

Black -154.06 -126.07 -121.03 -209.08 -234.25 -159.95 -175.73

(3.40) (2.61) (2.43) (4.11) (4.19) (1.68) (5.87)

Hispanic -75.27 -28.67 -37.99 -133.90 -94.69 -87.85 -47.34

(1.45) (.49) (.71) (2.45) (.99) (.66) (1.36)

R2 .191 .093 .093 .106 .122 .147 —

Women:

Income (×10-3) .213 .866 .426 .467 -.419 -.252 .355

(.58) (1.63) (1.01) (1.08) (.77) (.32) (1.29)

Education 15.01 21.06 17.31 19.20 16.17 5.127 12.57

(4.90) (4.95) (3.95) (5.69) (3.27) (.64) (5.12)

Age -6.743 -2.572 -4.555 -5.896 -7.457 -10.74 -5.900

(12.19) (3.40) (8.06) (8.64) (8.24) (8.87) (14.99)

Black -8.483 -51.170 -75.33 -5.308 6.334 12.72 -47.94

(.29) (1.47) (2.31) (.18) (.18) (.20) (2.52)

Hispanic 60.06 53.01 -48.77 -58.31 -144.46 -117.03 -92.67

(1.74) (1.34) (1.18) (1.55) (1.68) (1.61) (3.80)

R2 .125 .072 .071 .068 .073 .090 —

— = Not applicable.

Note: Absolute t-values reported in parentheses. All regressions included an intercept and 25 additional explanatory variables; see appendix table 1 for definitions.

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βR
30  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Table 4—Quantile regression estimates: Total fat intake, 1994-96

Variable OLS Quantile

0.10 0.25 0.50 0.75 0.90

Men:

Income (×10-3) -.009 .009 .030 .001 .022 .022 0.043

(.35) (.33) (1.25) (.03) (.56) (.38) (2.67)

Education -.358 .320 .179 -.194 -.952 -1.158 -.354

(1.76) (1.50) (.77) (.73) (3.02) (2.38) (2.54)

Age -.661 -.316 -.452 -.594 -.797 -1.053 -.411

(17.69) (7.79) (12.53) (13.84) (14.47) (10.99) (16.44)

Black -3.823 -4.230 -2.926 -4.397 -5.934 -7.744 -3.325

(1.80) (1.95) (1.32) (1.88) (1.92) (1.31) (2.42)

Hispanic -5.095 -2.679 -2.250 -8.437 -9.026 -3.545 -4.407

(2.13) (1.11) (.83) (2.63) (2.14) (0.53) (2.66)

R2 .155 .082 .080 .085 .098 .107 —

Women:

Income (×10-3) .011 .031 .044 .014 .021 .018 .012

(.65) (1.56) (2.13) (.63) (.74) (.42) (.94)

Education .245 .234 .327 .319 .208 .086 .289

(1.58) (1.41) (1.80) (1.72) (.93) (.24) (2.79)

Age .245 .100 .138 .239 .298 .385 -.163

(9.30) (3.15) (4.46) (6.92) (6.77) (5.87) (8.24)

Black 1.746 .817 1.522 1.074 1.826 .721 1.216

(1.35) (.51) (.91) (.63) (.95) (.19) (1.22)

Hispanic 3.573 .671 1.760 2.833 5.596 4.734 -4.012

(2.03) (.32) (1.06) (1.24) (1.75) (1.07) (3.38)

R2 .108 .049 .054 .056 .065 .084 —

— = Not applicable.

Note: Absolute t-values reported in parentheses. All regressions included an intercept and 25 additional explanatory variables; see appendix table 1 for definitions.

$

βR
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  31

Table 5—Quantile regression estimates: Saturated fat intake, 1994-96

Variable OLS Quantile

0.10 0.25 0.50 0.75 0.90

Men:

Income (×10-3) -.016 -.009 -.005 -.011 -.016 -.019 -.012

(1.73) (.94) (.53) (1.11) (1.09) (.84) (1.97)

Education -.177 .053 .004 -.154 -.264 -.521 -.024

(2.36) (.65) (.05) (1.74) (2.13) (2.56) (.50)

Age -.243 -.126 -.169 -.222 -.277 -.363 -.183

(17.01) (9.15) (13.98) (15.49) (13.11) (10.60) (21.50)

Black -2.154 -1.182 -1.501 -1.967 -2.687 -2.046 -1.425

(2.85) (1.54) (2.28) (2.59) (2.54) (1.13) (3.06)

Hispanic -2.464 -1.507 -.747 -2.735 -3.756 -5.635 -1.71

(2.88) (1.72) (.98) (2.57) (2.35) (2.51) (3.21)

R2 .153 .078 .084 .087 .098 .105 —

Women:

Income (×10-3) -.010 .010 .001 -.012 -.009 .018 .007

(1.47) (1.64) (0.07) (1.68) (.84) (1.11) (1.62)

Education .035 .039 .042 .047 .015 .035 .031

(.64) (.74) (.65) (0.70) (.17) (.26) (.83)

Age .101 .036 .056 .096 .125 .150 -.058

(10.47) (3.65) (5.01) (9.23) (7.72) (6.58) (9.43)

Black -0.057 -.333 .099 .577 -.632 .328 -.470

(0.11) (.64) (.18) (1.16) (.85) (.27) (1.56)

Hispanic 1.459 .704 1.118 1.503 1.291 1.561 -.820

(2.29) (.99) (2.00) (1.98) (1.25) (1.21) (2.13)

R2 .104 .049 .050 .061 .065 .079 —

— = Not applicable.

Note: Absolute t-values reported in parentheses. All regressions included an intercept and 25 additional explanatory variables; see appendix table 1 for definitions.

$

βR
32  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Table 6—Quantile regression estimates: Cholesterol intake, 1994-96

Variable OLS Quantile

0.10 0.25 0.50 0.75 0.90

Men:

Income (×10-3) -.388 -.060 .052 -.315 -.615 -.835 -.197

(3.13) (.62) (.47) (2.60) (3.46) (2.70) (2.96)

Education -5.440 .411 -1.830 -5.195 -9.945 -7.131 -1.700

(5.19) (.48) (2.05) (4.65) (5.49) (2.80) (2.97)

Age -1.306 -.415 -.807 -1.174 -1.215 -2.357 -.721

(7.09) (2.95) (4.68) (5.59) (4.36) (5.21) (7.12)

Black 56.514 16.259 36.393 46.731 89.803 115.965 24.773

(4.86) (1.85) (3.43) (3.36) (3.50) (3.72) (3.92)

Hispanic -6.454 -1.759 -2.825 -.361 10.891 25.56 -11.118

(.47) (.18) (.25) (.02) (.48) (.77) (1.49)

R2 .084 .035 .035 .047 .062 .065 —

Women:

Income (×10-3) -.217 .022 .067 -.108 -.445 .363 .003

(2.44) (.31) (.84) (1.24) (2.75) (1.55) (.07)

Education -1.458 -.261 -1.215 -1.366 -1.271 -3.508 -2.439

(1.84) (.33) (1.80) (1.72) (.97) (1.58) (5.54)

Age .179 .032 .138 .114 .045 .073 -.180

(1.29) (.29) (1.28) (.80) (.19) (.18) (2.49)

Black 36.176 13.846 25.535 35.149 58.40 63.415 14.068

(4.88) (2.70) (4.14) (4.16) (4.96) (3.18) (3.81)

Hispanic 3.366 5.829 6.473 2.241 2.314 14.023 1.871

(.39) (.79) (.86) (.20) (.18) (.61) (.39)

R2 .060 .030 .030 .034 .046 .051 —

— = Not applicable.

Note: Absolute t-values reported in parentheses. All regressions included an intercept and 25 additional explanatory variables; see appendix table 1 for definitions.

$

βR
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  33

Table 7—Quantile regression estimates: Fiber intake, 1994-96

Variable OLS Quantile

0.10 0.25 0.50 0.75 0.90

Men:

Income (×10-3) .024 .017 .020 .021 .035 .040 .019

(4.08) (2.82) (3.19) (3.51) (4.12) (2.75) (5.03)

Education .239 .196 .291 .292 .284 .303 .248

(4.47) (3.77) (5.34) (5.61) (3.81) (2.22) (7.70)

Age -.004 .007 .010 .007 -.013 -.037 .012

(.50) (.96) (1.16) (.78) (1.00) (1.96) (2.52)

Black -2.188 -1.465 -1.444 -2.300 -2.780 -2.073 -1.866

(4.97) (3.16) (3.58) (5.65) (3.47) (1.80) (7.14)

Hispanic .789 .586 .173 .767 1.511 .984 .855

(1.39) (1.01) (.30) (1.14) (1.54) (.64) (2.25)

R2 .078 .049 .045 .050 .054 .057 —

Women:

Income (×10-3) .023 .016 .020 .026 .020 .039 .017

(5.18) (4.45) (5.00) (4.84) (3.05) (3.65) (6.93)

Education .351 .253 .270 .387 .470 .354 .277

(8.54) (6.15) (6.76) (8.15) (6.94) (4.79) (9.71)

Age .035 .029 .030 .042 .048 .035 .035

(5.18) (4.48) (4.80) (5.09) (4.80) (1.96) (8.30)

Black -.847 -.682 -1.195 -1.069 -1.206 -.660 -1.049

(2.61) (2.37) (3.84) (2.92) (2.18) (.82) (5.50)

Hispanic .836 .995 .918 1.047 1.036 .763 .927

(1.98) (2.29) (2.48) (2.23) (1.49) (.76) (3.57)

R2 .104 .063 .067 .066 .069 .067 —

— = Not applicable.

Note: Absolute t-values reported in parentheses. All regressions included an intercept and 25 additional explanatory variables; see appendix table 1 for definitions.

$

βR
34  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA Table 8—Tests for equality of slope parameters across symmetrical quantiles Energy Total fat Saturated fat Cholesterol Fiber Variable 0.1q=0.9q 0.25q=0.75q 0.1q=0.9q 0.25q=0.75q 0.1q=0.9q 0.25q=0.75q 0.1q=0.9q 0.25q=0.75q 0.1q=0.9q 0.25q=0.75q Men: Income .98 .57 .04 .04 .19 .57 6.02 12.45 2.34 2.99 (.32) (.45) (.84) (.84) (.66) (.45) (.01) (.00) (.13) (.08) Education 4.17 8.70 8.42 10.95 7.44 4.86 8.76 20.41 0.59 0.01 (.04) (.00) (.00) (.00) (.01) (.03) (.00) (.00) (.44) (.93) Age 63.93 49.20 53.94 38.76 45.26 27.14 20.15 2.16 4.63 2.82 (.00) (.00) (.00) (.00) (.00) (.00) (.00) (.14) (.03) (.09) Black .11 3.42 .33 .87 .21 1.29 10.25 4.81 0.27 2.99 (.73) (.06) (.56) (.35) (.65) (.26) (.00) (.03) (.60) (.08) Hispanic .17 .36 .02 2.45 3.08 3.97 .67 .41 0.07 2.01 (.68) (.55) (.90) (.12) (.08) (.05) (.41) (.52) (.80) (.16) Women: Income 1.65 2.25 1.16 .64 2.83 .74 2.62 10.26 4.49 .00 (.20) (.13) (.28) (.43) (.09) (.39) (.11) (.00) (.03) (.94) Education 3.40 .04 .15 .23 .00 .09 2.15 .00 1.66 10.27 (.07) (.84) (.70) (.63) (.98) (.76) (.14) (.97) (.20) (.00) Age 37.59 10.66 18.62 13.66 23.44 17.68 .01 .17 .12 3.43 (.00) (.00) (.00) (.00) (.00) (.00) (.92) (.68) (.73) (.06) Black .84 3.66 .00 .02 .27 .88 6.06 8.07 .00 .00 (.36) (.06) (.98) (.89) (.60) (.35) (.01) (.00) (.98) (.98) Hispanic 4.38 1.00 .77 1.54 .36 .03 .72 .46 .05 .03 (.04) (.32) (.38) (.22) (.55) (.86) (.40) (.50) (.82) (.86) Note: The numbers against the variable names are F-statistics with (1, N-K) degrees of freedom. The associated p-values are reported in parentheses.
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Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  37

Appendix table 1—Explanatory variables, means, and sample size

Variable Men Women

Household characteristics

Gross annual income ($000) 39.5 36.1

Household size 2.9 2.8

Region (Northeast omitted)

Midwest .24 .25

South .36 .37

West .21 .20

Urbanization (city omitted)

Suburb .46 .44

Nonmetro .26 .25

Personal characteristics 

Level of education (years) 12.7 12.6

Age (years) 49.0 49.1

Height (inches) 69.8 64.0

Weight (pounds) 183.4 151.7

Race (White omitted):

Black .10 .13

Asian .02 .02

Other1 .05 .04

Ethnic origin-Hispanic .08 .08

On any kind of diet .15 .21

Survey-related characteristics

Year (1994 omitted)

1995 .34 .34

1996 .34 .32

Day-1 season (Winter omitted)

Spring .18 .18

Summer .29 .29

Fall .28 .28

Day-2 season (Winter omitted)

Spring .16 .16

Summer .29 .29

Fall .29 .29

Day-1 on weekend .30 .29

Day-2 on weekend .25 .25

Day-1 intake (Usual omitted)

Less than usual .19 .20

More than usual .10 .12

Day-2 intake (Usual omitted)

Less than usual .20 .21

More than usual .09 .10

Sample size (N) 4725 4362

1Asian/Pacific Islander, Aleut, Eskimo, or American Indian.
38  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA Appendix table 2—Distribution of total energy intake (calories) by categories of selected socioeconomic variables Men Women Mean Percentile Mean Percentile Variable 10 25 50 75 90 10 25 50 75 90 Calories Calories Income1

≤130% 2626 1192 1647 2267 3027 4226 1547 801 1102 1469 1871 2375131-350% 2392 1351 1764 2245 2904 3544 1602 916 1213 1554 1898 2328>350% 2396 1418 1796 2280 2863 3401 1664 993 1288 1601 1957 2385

Education (years)

<12 2410 1142 1539 2103 2897 3738 1441 780 1049 1382 1740 222112 2390 1372 1785 2247 2884 3513 1612 937 1226 1554 1924 2341>12 2449 1441 1830 2312 2916 3531 1686 1028 1302 1619 1992 2428

Age (years)

18-24 2770 1535 1959 2484 3305 4235 1787 920 1332 1705 2173 266925-34 2624 1547 1998 2477 3170 3839 1711 1005 1316 1650 2063 245335-44 2585 1391 1856 2382 3014 3680 1692 1013 1298 1587 2008 247045-54 2311 1350 1767 2238 2796 3259 1594 1005 1233 1551 1903 225455-64 2143 1227 1624 2101 2597 3015 1492 906 1149 1487 1772 2112≥65 1882 1124 1461 1828 2241 2729 1403 839 1067 1373 1689 1999

Race/Ethnicity

Non-Hispanic

White 2422 1386 1794 2278 2902 3528 1623 958 1245 1564 1923 2346Black 2497 1149 1590 2160 2860 3644 1614 844 1137 1538 1896 2387Other2 2356 1240 1706 2228 2775 3724 1649 852 1173 1528 2111 2618

Hispanic 2395 1331 1783 2332 2993 3614 1551 924 1148 1482 1886 2306

1Annual household income expressed as a percentage of the poverty threshold.2Asian/Pacific Islander, Aleut, Eskimo, or American Indian.
Appendix table 3—Distribution of total fat intake (grams) by categories of selected socioeconomic variables 

Men Women

Mean Percentile Mean Percentile

Variable 10 25 50 75 90 10 25 50 75 90

Grams Grams

Income1

≤130% 101.1 39.2 52.1 82.7 114.5 — 57.7 24.6 37.0 53.0 72.7 96.4131-350% 90.7 43.2 58.9 84.8 114.0 145.7 59.1 26.8 39.5 55.9 74.1 94.2>350 90.2 44.5 61.1 83.2 114.6 140.8 60.0 29.3 41.2 56.2 73.8 94.6

Education (years)

<12 94.2 37.0 52.6 79.0 115.2 155.4 54.2 23.5 34.8 50.9 68.1 87.812 91.8 43.9 62.1 86.1 115.5 147.3 60.3 27.7 41.1 56.2 74.4 96.9>12 91.0 44.5 61.2 84.5 113.9 142.4 60.3 28.4 40.4 56.8 74.8 95.8

Age (years)

18-24 100.6 48.5 66.0 93.1 122.3 159.3 63.8 24.9 40.1 61.1 80.2 102.425-34 99.4 52.8 68.8 92.6 123.8 156.4 62.1 30.3 43.2 58.3 77.3 97.335-44 100.4 44.5 61.3 90.9 123.3 155.1 63.2 28.8 41.5 58.5 78.4 103.945-54 88.5 42.6 61.3 81.7 113.8 137.3 59.9 29.4 40.6 56.4 74.7 92.755-64 82.4 36.9 55.3 77.3 107.7 130.3 55.2 25.8 37.4 52.6 70.7 85.8≥65 69.3 34.3 47.8 65.4 85.7 109.5 50.1 24.0 34.7 47.9 62.5 76.0

Race/Ethnicity

Non-Hispanic

White 91.8 45.3 62.0 85.1 115.5 145.1 59.3 28.0 40.0 55.8 73.8 94.2Black 100.0 36.7 55.5 81.1 116.2 — 62.4 24.7 41.0 57.8 76.1 103.0Other2 77.0 29.5 46.2 73.2 107.6 127.6 55.1 22.9 35.2 51.1 71.9 90.0

Hispanic 89.8 40.8 60.4 84.7 111.5 146.1 55.7 25.8 36.3 52.2 72.4 90.3

1 Annual household income expressed as a percentage of the poverty threshold.2 Asian/Pacific Islander, Aleut, Eskimo, or American Indian.

=Not reported because standard error could not be computed.

Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  39
40

 Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA

Appendix table 4—Distribution of saturated fat intake (grams) by categories of selected socioeconomic variables

Men Women

Mean Percentile Mean Percentile

Variable 10 25 50 75 90 10 25 50 75 90

Grams Grams

Income1

≤130% 35.2 12.4 18.7 27.7 40.0 58.5 19.4 7.7 11.8 17.5 24.4 33.1131-350% 30.6 13.2 19.6 28.3 38.8 50.6 19.8 8.3 12.3 18.1 25.3 33.1>350% 30.2 13.5 19.5 27.7 38.5 49.6 19.6 8.7 12.5 18.4 25.1 31.9

Education (years)

<12 32.6 11.3 17.6 26.2 39.2 54.4 18.0 7.3 10.9 16.3 23.3 30.912 31.9 13.7 20.3 28.6 39.3 51.0 20.3 8.9 12.8 18.7 25.5 33.7>12 30.4 13.5 19.4 27.0 36.9 47.8 19.8 8.5 12.4 18.6 25.3 32.7

Age (years)

18-24 34.7 16.1 22.6 31.8 43.0 55.3 21.9 7.9 13.9 20.4 27.6 36.425-34 34.1 16.6 22.5 31.0 42.3 55.9 20.9 9.2 13.8 19.3 26.6 34.435-44 34.3 13.3 20.5 29.8 42.1 53.2 21.1 9.0 12.7 19.2 26.6 36.445-54 29.1 13.7 18.9 27.2 37.5 46.7 19.5 8.9 12.5 18.3 25.2 31.155-64 26.8 11.5 16.9 25.3 33.8 44.9 17.9 7.9 11.2 16.5 23.7 29.9≥65 23.0 10.2 14.9 20.8 28.6 36.7 16.3 7.3 10.4 15.4 20.7 25.9

Race/Ethnicity

Non-Hispanic

White 31.1 14.0 20.0 28.5 39.1 50.9 19.8 8.6 12.5 18.3 25.2 32.6Black 34.4 10.5 18.1 27.0 38.4 52.1 20.5 7.4 12.3 18.3 26.0 35.1Other2 24.8 8.4 14.2 20.6 34.7 47.4 17.1 6.7 10.8 14.3 22.1 30.3

Hispanic 29.4 13.1 19.3 27.9 38.2 50.4 18.5 7.9 11.8 16.9 23.7 30.9

1 Annual household income expressed as a percentage of the poverty threshold.2 Asian/Pacific Islander, Aleut, Eskimo, or American Indian.
Economic Research Service/USDA Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901  41 Appendix table 5—Distribution of cholesterol intake (milligrams) by categories of selected socioeconomic variables Men Women Mean Percentile Mean Percentile Variable 10 25 50 75 90 10 25 50 75 90 Milligrams Milligrams Income1

≤130% 398 128 189 334 509 722 226 73 116 188 308 428131-350% 329 122 179 283 423 595 211 72 112 173 274 393>350% 310 118 182 272 394 558 201 71 111 176 259 361

Education (years)

<12 379 112 182 318 488 714 221 72 120 182 290 40312 343 125 192 292 453 622 216 77 115 181 279 399>12 305 119 176 264 382 547 201 64 110 171 261 369

Age (years)

18-24 350 125 186 292 454 610 206 62 99 172 276 40225-34 337 129 189 289 432 612 218 77 117 185 273 39735-44 351 126 193 293 445 621 212 68 115 176 273 40045-54 328 117 185 279 428 593 214 71 116 181 291 40855-64 323 127 187 279 417 607 205 70 114 177 266 379≥65 272 101 149 241 354 492 199 76 113 166 264 352

Race/Ethnicity

Non-Hispanic

White 315 122 180 272 399 570 202 69 110 172 261 370Black 411 121 223 327 522 807 242 78 132 206 324 451Other2 322 111 157 287 418 584 246 65 126 222 320 445

Hispanic 359 127 177 310 485 647 216 78 121 178 297 417

1Annual household income expressed as a percentage of the poverty threshold.2Asian/Pacific Islander, Aleut, Eskimo, or American Indian
42  Factors Affecting the Macronutrient Intake of U.S. Adults/TB-1901 Economic Research Service/USDA Appendix table 6—Distribution of fiber intake (grams) by categories of selected socioeconomic variables Men Women Mean Percentile Mean Percentile Variable 10 25 50 75 90 10 25 50 75 90 Grams Grams Income1

≤130% 18.1 7.2 10.4 15.4 23.4 30.4 11.9 4.9 7.1 10.7 15.4 20.4131-350% 17.7 7.8 11.4 16.1 22.3 28.8 13.1 5.9 8.5 12.1 16.2 21.1> 350% 18.5 8.6 12.1 16.6 23.3 30.7 14.7 6.6 9.8 13.5 18.3 23.4

Education (years)

< 12 17.6 7.2 10.3 15.1 22.1 30.1 12.1 5.0 7.4 10.8 15.4 20.812 16.6 7.4 10.9 15.0 21.2 27.2 12.5 5.6 8.0 11.5 15.6 20.1>12 19.2 9.0 12.6 17.5 23.9 31.5 14.8 7.0 9.9 13.9 18.5 23.2

Age (years)

18-24 16.7 7.2 10.1 15.0 20.6 27.8 12.6 5.2 7.5 11.0 16.0 20.825-34 18.4 8.4 11.9 16.1 23.1 31.5 13.3 5.9 8.4 12.2 16.3 21.435-44 18.4 8.2 12.0 16.7 23.8 29.7 13.5 6.0 8.7 12.5 16.5 22.545-54 18.7 8.2 12.0 17.3 23.7 30.9 13.9 5.9 9.0 13.0 18.0 22.655-64 17.7 7.7 11.5 16.5 22.4 29.3 13.6 6.6 9.0 12.7 16.9 21.8≥65 17.9 8.0 11.8 16.0 22.2 30.2 14.1 6.4 9.1 12.8 17.8 22.9

Race/Ethnicity

Non-Hispanic

White 18.2 8.2 11.8 16.5 23.1 30.1 13.8 6.2 9.0 12.7 17.2 22.5Black 15.4 6.0 9.6 13.6 19.8 29.7 11.4 4.5 7.0 9.7 14.7 19.2Other2 17.6 7.5 12.7 16.1 21.1 27.3 14.5 6.5 8.9 12.9 17.5 23.5

Hispanic 20.0 8.5 12.2 17.7 24.3 32.8 13.7 6.1 8.8 12.5 18.1 23.2

1Annual household income expressed as a percentage of the poverty threshold.2Asian/Pacific Islander, Aleut, Eskimo, or American Indian.