1

Drive for Junk: An Investigation of the Correlation Between Income and Education to

Junk Food Consumption Patterns Amongst US Adults from 2003 – 2006

Amy Zhu

MMSS Thesis

June 7, 2009

2

I. Introduction

In today’s increasingly health conscious society, dietary patterns and their relation to

demographic variables are helpful in identifying risk factors that lead to the pursuit of an

unhealthy diet. The detrimental consequences of maintaining an unhealthy diet affects many

areas of a person’s life. The extensive list of physical diseases that arise as a result of a bad diet

is added to each day. Aside from the physical defects, a person could face body image issues as

the consequences of a bad diet leads to obesity. Junk food consumption is used in this study as a

measure of the relative healthfulness of the diets of the participants. Such foods that are high in

total fat, sugar, and general calorie without making any other significant contribution to the daily

intake of other micro nutrients are harmful to individuals when consumed in excess.

There are many factors that contribute to choices made about diet most obviously including

taste preferences, nutritional knowledge about foods and the fiscal capabilities to purchase the

preferred foods. Taste preferences vary widely across the population and are hard to change in

order to induce healthier diet behavior. However both nutritional knowledge and fiscal

capabilities can be altered in order to affect changes in men and women. Specifically policies

targeting either to increase nutritional knowledge or increase people’s ability to purchase foods

can be used in response to yield better diet choices.

What this current paper attempts to do is to investigate current patterns of junk food

consumption as it varies over groups that have different education and income levels in the

United States from 2003 – 2006 to see which of the two options have the potential to affect a

larger decrease in junk food consumption and a more healthy diet. General education and

household incomes are used to proxy for a person’s nutritional knowledge levels and fiscal

capabilities to purchase foods. Given a person’s income level what are the correlations between

3

different education levels and their average junk food intake, and vice versa, given a person’s

education level, what are the correlation between different income levels and their average junk

food intake.

The hypothesis is that at lower education levels, higher incomes will be correlated with

higher increase the intake of junk food whereas at higher education levels, higher incomes will

be correlated with lower intake of junk foods. Higher income for the lower educated individuals

leads to greater fiscal capability to purchase more foods and since these individuals are not as

aware of the nutritional benefits of a healthy diet, they will tend to purchase more junk foods.

Higher income for the higher educated individuals with lead to purchases of higher priced health

foods and thus decreased the consumption of junk foods since these individuals are more

conscious of the health benefits of a healthy diet. With regards for the comparison of individuals

within the same income level across different education levels, higher educated will lead to

lower junk food consumption.

The paper will first begin with a review of the current literature that is available relating

socioeconomic status to diet patterns and proceed on to an explanation of the model used and

finally to the results and a conclusion.

II. Literature Review

In the currently limited research on the topic, rising socioeconomic status has generally been

established to be correlated with improvements in diet. A perusal of the current literature shows

that authors employ different variables as proxies for socioeconomic status. Many use either

income or education but no available research that attempts to analyze the dietary patterns as

both income and education are considered simultaneously which is what the current paper

attempt to do since both factors contribute to diet choices simultaneously.

4

In the two studies that follow in this review, socioeconomic status is characterized by

education and a civil servant grade level. Both studies find that increasing socioeconomic status

is correlated with increasingly healthy patterns in diets.

In a study of 849 women living in three European cities, Maastricht, The Netherlands, Liège,

Belgium and Aachen, Germany, the authors use education levels as a proxy for socioeconomic

status after noting that education levels seem to be the best predictor of variation in the women’s

food consumption variables (Hupkens, et.al 2000). Women with an elementary or lower

vocational training as their highest level of education were considered to be the lower class;

women who had higher vocational training were considered to be middle class; lastly, those who

had a university education were considered to be the higher class. Aside from investigating class

differences in food consumption, the authors also analyze class differences in food

considerations, and the extent to which food considerations contributed to explaining differences

in food consumption both of which lies outside of the scope of the current paper.

To study food consumption, authors used a food frequency survey which included 120 food

items, to study the average number of grams consumed of select foods. The foods were grouped

into 30 food groups and only those which contributed significantly to the intake of fat and fiber

were included for analysis: meat products, milk products, cheese, dietary oil and fats, brown

bread, grain, fruit and vegetables and potatoes, chips, savory snacks and sweets. The authors

hypothesized that women from the middle class would consume fewer foods that contribute to

their intake of fat and more foods that contribute to their intake of fiber than lower class women.

The results showed that the differences in food consumption patterns were more pronounced

between lower class and middle class women than between middle class and higher class women.

Looking at the foods that contributed to the intake of fat, higher class, or the highly-educated

5

women ate less meat products, less milk products, and less oils and fats, but more cheese. There

was little difference in the consumption of chips, savories and sweets between middle and

highly-educated women, but both groups consumed less than lower-educated women. Thus

overall result regarding fatty foods corroborated with the authors’ initial hypothesis regarding the

group – middle-class women appeared to consume less snacks and foods that contributed to the

fat intake than lower class women; however they did consume more cheese than the latter group.

In a different studying using data from the Whitehall II study of London civil servants,

Martikainen, et.al studies the socioeconomic differences in dietary patterns identified by the

authors and goes further also to analyze the effects of these differences on certain health risks

facing the participants. Socioeconomic status in this paper is approximated by the grade level of

the civil servant. These grade of employment was determined by the subject’s grade title. The

three grades compiled were administrative – Grade I, Professional and Executive – Grade II, and

Clerical and Office Support – Grade III. The grades differed remarkably in the annual salary

obtained by individuals within those different levels. When the study was conducted in 1987 the

annual salary of the Grade III group ranged from £3000 to £6000 up to Grade I which saw

salaries ranging from £18,000 to £62,000. The authors also point out that other than annual

salaries, the grades also differed with respect to their education levels, housing tenure and car

ownership.

Analyzing changing patterns of diet as the servant grade levels varies will capture changes in

diet as both education and income varies simultaneously. It does not allow for a review of the

separated correlations of income or education holding the other variable constant. This current

study will serve to do exactly that through the analysis of varying education as income is held

constant and vice versa, varying income while holding education constant.

6

The authors of this British study collected data on food consumption patterns using once

again a food frequency survey including a select number of foods and proceeded to conduct a

cluster analysis to identify six different diets for the sample studied: Very Healthy, Moderately

Healthy, French, Sweet Unhealthy, Unhealthy, and Very Unhealthy. The diets varied on their

consumption of specific foods. For example, the Very Healthy diet included a low consumption

of meat, white bread, full cream milk, cream, butter, sugar, biscuits and pies, a moderate

consumption of wine and a low consumption of beer, a high consumption of fish, wholemeal

bread, fruit and vegetables. The French diet, also called the modern continental, consisted of

lower than average consumption of full fat milk; average levels of meat, white bread, biscuits,

tarts and jam; high consumptions of fish, wholemeal bread, cream, butter, wine and beer; and a

very high consumption of fruit and vegetables.

The authors find that women are over represented in the Very Healthy diet and

underrepresented in the Sweet Unhealthy diet compared to the men. For both men and women

the low grade subjects had a higher likelihood of consuming an Unhealthy or Very Unhealthy

diet while the higher grade subjects were more likely to consume a French diet. Looking at the

grade differences for men, higher grade men were more common in the Sweet Unhealthy and the

Moderately Healthy diet. For women this grade difference is exactly reversed with higher grade

women less commonly found in the sweet and moderately healthy diets.

The two above studies corroborate with other studies that have been done in which

improving socioeconomic classes have been shown to be correlated with an increasingly healthy

dietary pattern. However none of the studies attempts to take a closer look at the dietary patterns

within each socioeconomic class. This paper attempts to fill the gap by studying the patterns of

7

junk food consumption within specific income and education levels as the other variable, income

or education, varies.

Furthermore the use of the NHANES data rather than food frequency tables will allow a

more comprehensive analysis of individual’s diets. The finite number of foods included in food

frequency surveys limits the scope of their power to take into consideration the great variety of

foods actually consumed by people on a daily basis. The NHANES Dietary Survey including

two days of full 24 hour dietary recall data allows for a more complete array of foods consumed

by the participants to be considered for analysis. Given the heterogeneity in foods consumed

analysis based on a larger pool of foods is more meaningful than those that only list a certain

number of foods.

On a different note, following from the above studies we see that within the same

socioeconomic level men and women have different dietary behaviors. In a study of 330 people

living in Brisbane City, Australia, Gavin analyzes the differences in the men and women’s

relative compliance to dietary guideline recommendations. He finds that men and women’s

differences in healthy food behavior is due partly to a difference in preferences for the taste of

healthy foods with more women reporting that they enjoyed the taste of more healthy foods;

women were more likely to believe that following dietary guidelines will actually be beneficial

for their health; and lastly, women in general were more knowledgeable about food nutrition

than men. Given these very clear differences in the dietary behavior of men and women – in the

current study separate regressions are run for men and for women in analyzing the patterns of

junk food intake across various education – income clusters.

Lastly all the previously mentioned studies have been done in regions outside of the United

States. The current study employs a survey of participants across the United States thus any

8

unique dietary patterns particular to the US that might have been missed in these previous studies

will be identified here.

III. Data

The United States Department of Agriculture (USDA) and the United States Department of

Health and Human Services works in conjunction to produce the What We Eat in America

(WWEIA) survey which serves as the source of the dietary intake data for this survey. The

WWEIA (formerly the USDA’s Continuing Survey of Food Intakes by Individuals) is an

integrated portion of the DHHS’s National Health and Nutrition Examination Survey

(NHANES). The WWEIA survey can be found on the USDA website. The same information can

also be found in the Dietary Interview section of the NHANES. The data is released in two year

intervals with the most recent integrated survey of WWEIA released for 2005 – 2006.

The NHANES survey examines a representative sample of 5,000 individuals from 15

counties across the country each year conducting both an interview and a physical examination.

The survey includes demographics, socioeconomic, dietary, and health-related questions.

For the Dietary Interview portion, respondents are asked to recall two days of 24-hour dietary

intake, from midnight to midnight. During the Day 1 interview, respondents are interviewed at

Mobile Examination Centers using three-dimensional food models to indicate their intake

amount. A Day 2 interview is conducted over the telephone between 3 – 10 days after the first

day interview. Respondents are given an USDA Food Model Booklet and some three

dimensional models for estimating food amounts during the second interview.

The data from the Individual Food Files portion of the Dietary Interview from both the

2003 – 2004 and 2005 – 2006 NHANES is used for this current study. The database contains a

complete list of the individual foods reported by each participant as well as each food’s

9

nutritional value calculated from the USDA’s Food and Nutrient Database for Dietary Studies

2.0. Only the caloric contributions of select food items are taken into consideration within this

study. Using the USDA provided food codes associated with each item, all reported foods are

separated according to the leading digit on its USDA food code into nine separate categories

consisting of:

1. Dairy

2. Meats & Seafood

3. Eggs

4. Legumes and Nuts

5. Grains

6. Fruits

7. Vegetables

8. Edible Oils and Fats

9. Sweets & Alcohol

Junk food defined in this paper includes Group 8, Group 9, and certain items from Groups 1

& 5. Group 8 consists specifically of items such as butter, margarine, oils, etc. Group 9 items

falling under the Sweets category include sugar, sweetener, chocolate, jello, jelly; the group also

includes various soft drinks and alcoholic beverages. Select products from the Dairy group such

as Ice Cream and Custard and items from the Grains group, such as cake, pies, cookies, etc. are

included also in the Junk Food category. For a complete listing of all the food codes that were

gathered into the Junk Food category please see Table 1 below. Mainly items that contribute

mostly to the generalized intake of fat without contributing to other categories of micronutrients

are included in this group.

10

Table 1

Custard Danish Sugar

Cake Doughnut Jam

Cookies Coffee Cake Gelatin

Pie Salty Snack Ice Cream

Fritter Potato Chips Candy

Crisp Butter Chocolate

Cream Puff Margarine Coffee & Tea

Crepe Oil Carbonated Beverage

Strudel Shortening Fruit Drinks

Tamale Salad Dressing Alcoholic Drinks

Pudding Turnover

The NHANES also collects demographic information on all of its participants. In the 2003 –

2004 NHANES survey, a total of 9,950 participants were included in the Dietary Survey.

Similarly in the 2005 – 2006 NHANES survey, a total of 9,643 participants were included.

However not all individuals were included in the current analysis. Women who were pregnant

were eliminated from consideration as their diets are most likely skewed due to the fickle nature

of their taste buds during this particular time. A total of 647 individuals were eliminated from

consideration for this reason.

All children aged 17 and under were dropped from consideration. Diet decisions for minors

are most likely to be under the direct control of their guardians. It is very unlikely that they

exercise complete independence in the choice of foods consumed. Thus to include children in the

study will most likely also have the effect of double counting the preferences of their parents

from the same household. A total of 8861 observations were dropped.

The next step in further cleaning up the data for study was to look at the status of their

dietary recall data. The Individual Food Files contain a variable called Dietary Recall Status in

which the interviewer marked down whether the information collected from the participant was

11

reliable or not. Any observation with a status code other than 1, which indicated that the

observation was reliable and met the minimum requirements, was also dropped from

consideration. A total of 73 observations were dropped from consideration based on this

condition. It is not clear as to whether this small sample could have had different junk food

consumption patterns than those who remained in the sample given the same income and

education backgrounds. However comparing the sheer number of observations included in this

study aside from the observations dropped, it can be safely assumed that the elimination of these

73 individuals will not lead to any significant differences in final results.

In the final sample under study a total of 6778 subjects were included. In terms of

demographics ages 18 – 85 are represented in the study. The breakdown between males and

females is not completely even, with 39% of the sample being Female and 61% of the sample

being Male. In terms of ethnic diversity five distinct ethnic groups are taken into consideration

including Mexican Americans (20.29%) ; Other Hispanic (3.26%); Non-Hispanic White

(49.42%); Non-Hispanic Black (23.16%); Other Race (3.87%).

This paper uses the Household Income to approximate the income level of the participants

under consideration. According to the Census Bureau, household income is defined as:

“…the sum of money income received in the previous calendar year by all household

members 15 years old and over, including household members not related to the householder,

people living alone, and others in nonfamily households.”

The household income was used in this study rather than family income because family income

was reported only for households with two or more persons related through blood, marriage, or

adoption. Thus in order to account for the income levels of people who were living alone,

12

household income, the more encompassing measure was used as a measure of income rather than

family income.

Income information was gathered from the Demographics files of the NHANES from both

sets of years. The variable is an ordinal variable which included categories representing different

income ranges as shown in Table 2. For this study, income categories were recoded into three

different variables including Poor, Middle Class, and Wealthy. All those who had an income of

$25,000 per household and under were included in the Poor category. Those who had an annual

household income between $25,000 and $75,000 were included in the Middle Class category.

Only those with an annual household income of $75,000 or greater were included in the Wealthy

category.

Table 2

Household Income

Range

Income

Categorization

Females Males

$0 to $4,999 Poor 94 75

$5,000 to $9,999 Poor 116 168

$10,000 to $14,999 Poor 196 287

$15,000 to $19,999 Poor 186 311

$20,000 to $24,999 Poor 196 338

$25,000 to $34,999 Middle Class 334 594

$35,000 to $44,999 Middle Class 248 448

$45,000 to $54,999 Middle Class 238 399

$55,000 to $64,999 Middle Class 195 255

$65,000 to $74,999 Middle Class 163 241

$75,000 and over Wealthy 662 924

Over $20,000 Middle Class 31 53

Under $20,000 Poor 7 19

According to a release from the US Census Bureau the real median household income was

unchanged between 2003 and 2004 at $44,389. The real median household income in the United

States was $48,000 between 2005 - 2006. For a family of four, the average poverty threshold

level in the US was $18,810 in 2003; $19,307 in 2004; $19,971 in 2005 and $20,614 in 2006.

13

Thus the new income categories established within this paper are reasonably reflecting the actual

income distributions across the United States for the time period studied. In the sample there are

1993 people who fell into the Poor category, 3199 people who fell into the Middle Class

category, and 1586 people who fell into the Wealthy category.

The education levels were given as Less than High School, Some High School, High School,

Some College or Associates in Arts degree, and College Graduate or Above. In the sample 656

people had a Less than High School level of education, 1124 had Some High School, 1581 had a

High School education, 2162 had Some College or Associates in Arts degree, and 1255 had a

College or graduate degree. Table 3 shows the distribution of education levels by gender.

Table 3

Education Level Females Males

Less than High School 148 508

Some High School 450 674

High School 581 1000

Some College 976 1186

College or Above 511 744

The sample represented people from various ethnic backgrounds. Almost half of the sample

were Non-Hispanic White (49.42%), with Non-Hispanic Black as the second largest group

making up 23.16% of the sample. Mexican Americans (20.92%), Other Hispanics (3.26%) and

Other Races (3.87%) made up the remainder of the sample. According to the last Census

conducted in the US, 75.1% of the population were White, 12.3% were African Americans.

These two categories could have included people of Hispanic origin. The Census reported that

12.5% of the population was considered as Hispanic or Latino. Thus, looking at our sample, it

can be seen that Hispanics were heavily over represented and African Americans were only

slightly over represented. Whites and other Races were significantly underrepresented.

14

BMI distributions of the sample show that the sample is centered around a BMI level that is

considered overweight at 28.37. According to the Center for Disease Control, the average height

of American Men ages 20 and over currently stands at 69.4 inches (5’ 9.4”) and the average

weight is 194.7 pounds. The BMI for this average American Man is 28.8. For American Women

ages 20 and over, the average height is 63.8 inches (5’3.8”) and the average weight is 164.7

pounds. The BMI for this average American Woman is 28.4. Both the Average American Man

and Woman appear to also be in the overweight category. The average BMI level of the sample

seems in line with the ones reported for the US population at large. Thus the participants of this

sample are physically representative of the American population at large.

IV. Model

The current dataset is a conglomeration of two cross sections of the US population at two

different times. Given the relative proximity in time of these two cross sections it is assumed that

there are no significant differences in the populations considered. The dataset also provides

weights for each observation to account for the relative probabilities of selection within the

framework of the entire US population. A weighted linear regression is employed using the

weights provided in the dataset. The Two Day Dietary Survey weight was specifically used in

this analysis since we only consider participants who completed the Dietary interview.

The complexity of dietary patterns call for a careful choosing of the explanatory variables

aside from the income and education levels. There are many factors that could affect the junk

food caloric intake of different individuals. Age is included in the model to account for any

general trends in dietary patterns as people get older. The body’s caloric needs decrease as

15

people’s metabolisms slow down with age. The age variable allows us to account for this

particular trend in the dataset.

Differences in the following demographic variables are accounted for by dummy variables

given the categorical nature of these variables. Thus when included in the regression, one

dummy for one of the categories within each variable is left out in order to avoid the problem of

multicollinearity.

Gender differences between men and women in dietary patterns has been clearly established

by previous literature (Turrell 1997). In this analysis the variable female is used to code for

gender differences. A value of 1 indicates that the person is a woman and it is by this variable

that separate regressions are run for men and women.

Diet differences exist between the various ethnic groups. It has been shown that Whites

consume diets that are much higher in sugar than both African Americans and Mexican

Americans; African Americans consume diets that contain a significantly higher percentage of

fat than either the White or the Mexican American household (Schefske, et al 2009). Other

studies have also shown that ethnic minorities were more likely to consume diets on the extreme

ends of the healthy-scale (Martikainen et al 2003).

Since the junk food calories are a portion of the total diet there is a scale issue to be

considered in this analysis. When a person consumes a larger amount of calories in general they

will also consume more junk food calories. In order to account for this relationship of scale a

measure of the average total Non-Junk calories are included as a variable.

BMI and other physiological measures are not included in these regressions out of concerns

of multicollinearity. The Body Mass Index is a measure of the person’s weight divided by their

height squared. Due to patterns of slowing metabolism as age increases, people who are older

16

tend to put on more pounds. Thus there is a direct relationship between BMI and age.

Furthermore BMI being by construction a measure including a person’s weight, is linked to the

overall size of the diet, but since the average total non-junk calories already accounts for the

overall size of the diet, the inclusion of BMI would only serve to contribute to over-specification

of the model. Thus BMI is left out of the final model. The first set of regressions in Table 4

shows regressions using the above demographic variables and their relative significance without

looking at the effects of either income or Education.

The next stage of analysis looked to produce similar findings from previous studies which

showed that improving socioeconomic status lead to an improved diet when socioeconomic class

was represented by only income or education without really considering the interaction effects of

both variables. Table 5 and 6 shows the results from these groups of regressions.

Lastly all sets of regressions include fourteen of the fifteen following dummy variables

account for the fifteen different education income clusters possible within the sample. Table 7

lists all the different clusters. Within the regression one cluster is dropped to serve as the base

comparison group. Otherwise there would be an issue of multicollinearity.

17

Table 41

Female Male Female Male

R-Squared 6.76% 12.08% 6.76% 12.08%

Constant 336.58 545.87 336.58 722.51

Standard Error 78.82 74.57 78.82 61.49

p-value 4.27 7.32 4.27 11.75

Age -2.55 -4.68 -2.55 -4.68

0.89 0.62 0.89 0.62

-2.88 -7.58 -2.88 -7.58

Average Total Non-Junk

Calories 0.17 0.21 0.17 0.21

0.03 0.02 0.03 0.02

6.34 9.32 6.34 9.32

Mexican American 9.92 -25.97 -146.13 -202.60

63.92 58.11 31.95 29.14

0.16 -0.45 -4.57 -6.95

Other Hispanic -156.05 -176.63

58.89 55.78

-2.65 -3.17

Non-Hispanic White 156.05 176.63

58.89 55.78

2.65 3.17

Non-Hispanic Black 211.64 130.09 55.60 -46.54

62.09 57.13 28.38 26.38

3.41 2.28 1.96 -1.76

Other Race -5.40 -13.34 -161.44 -189.97

70.80 94.81 44.45 80.93

-0.08 -0.14 -3.63 -2.35

1 Underlined coefficients are significant at the 10% level. Bolded and underlined coefficients are significant at the

5% level. Bolded, underlined and red coefficients are significant at the 1% level.

18

Table 5

Female Male Female Male

R-Squared 7.11% 12.28% 7.11% 12.28%

Constant 331.00 526.36 347.96 526.36

Standard Error 78.63 74.72 80.74 74.72

p-value 4.21 7.04 4.31 7.04

Age -2.45 -4.62 -2.45 -4.62

0.90 0.62 0.90 0.62

-2.73 -7.45 -2.73 -7.45

Average Total Non-Junk

Calories 0.18 0.21 0.18 0.21

0.03 0.02 0.03 0.02

6.43 9.28 6.43 9.28

Mexican American 10.23 -19.18 10.23 -19.18

63.64 58.31 63.64 58.31

0.16 -0.33 0.16 -0.33

Non-Hispanic White 163.87 172.72 163.87 172.72

59.12 55.75 59.12 55.75

2.77 3.10 2.77 3.10

Non-Hispanic Black 210.48 133.57 210.48 133.57

61.82 57.20 61.82 57.20

3.40 2.34 3.40 2.34

Other Race -1.31 -16.05 -1.31 -16.05

70.99 93.66 70.99 93.66

-0.02 -0.17 -0.02 -0.17

Poor -16.96 18.34

28.57 29.12

-0.59 0.63

Middle Class 16.96 -18.34

28.57 29.12

0.59 -0.63

Wealthy -42.57 37.53 -59.53 55.87

30.99 36.11 27.02 29.78

-1.37 1.04 -2.20 1.88

19

Table 6

Female Male Female Male Female Male Female Male

R-Squared 8.28% 12.62% 8.28% 12.62% 8.28% 12.62% 8.28% 12.62%

Constant 216.37 457.73 314.44 541.46 390.71 589.41 345.97 542.08

Standard Error 85.87 82.83 82.14 76.55 84.29 78.42 80.96 76.74

p-value 2.52 5.53 3.83 7.07 4.64 7.52 4.27 7.06

Age -2.32 -4.34 -2.32 -4.34 -2.32 -4.34 -2.32 -4.34

0.88 0.65 0.88 0.65 0.88 0.65 0.88 0.65

-2.63 -6.65 -2.63 -6.65 -2.63 -6.65 -2.63 -6.65

Average Total Non-Junk Calories 0.18 0.21 0.18 0.21 0.18 0.21 0.18 0.21

0.03 0.02 0.03 0.02 0.03 0.02 0.03 0.02

6.62 9.19 6.62 9.19 6.62 9.19 6.62 9.19

Mexican American 13.33 -21.77 13.33 -21.77 13.33 -21.77 13.33 -21.77

64.55 58.01 64.55 58.01 64.55 58.01 64.55 58.01

0.21 -0.38 0.21 -0.38 0.21 -0.38 0.21 -0.38

Non-Hispanic White 153.43 167.48 153.43 167.48 153.43 167.48 153.43 167.48

61.69 55.63 61.69 55.63 61.69 55.63 61.69 55.63

2.49 3.01 2.49 3.01 2.49 3.01 2.49 3.01

Non-Hispanic Black 201.62 114.64 201.62 114.64 201.62 114.64 201.62 114.64

64.09 56.82 64.09 56.82 64.09 56.82 64.09 56.82

3.15 2.02 3.15 2.02 3.15 2.02 3.15 2.02

Other Race -4.27 -6.25 -4.27 -6.25 -4.27 -6.25 -4.27 -6.25

72.44 94.46 72.44 94.46 72.44 94.46 72.44 94.46

-0.06 -0.07 -0.06 -0.07 -0.06 -0.07 -0.06 -0.07

Less than High School -98.07 -83.72 -174.34 -131.68 -129.61 -84.35

53.82 44.16 54.55 42.24 51.80 44.48

-1.82 -1.90 -3.20 -3.12 -2.50 -1.90

Some High School 98.07 83.72 -76.26 -47.96 -31.53 -0.63

53.82 44.16

37.70 34.90 33.96 35.30

1.82 1.90

-2.02 -1.37 -0.93 -0.02

High School 174.34 131.68 76.26 47.96 44.73 47.33

54.55 42.24 37.70 34.90

30.91 32.05

3.20 3.12 2.02 1.37

1.45 1.48

Some College 129.61 84.35 31.53 0.63 -44.73 -47.33

51.80 44.48 33.96 35.30 30.91 32.05

2.50 1.90 0.93 0.02 -1.45 -1.48

College or Above 37.48 30.10 -60.60 -53.62 -136.86 -101.57 -92.13 -54.24

55.23 45.17 37.65 38.08 33.82 34.61 30.58 35.37

0.68 0.67 -1.61 -1.41 -4.05 -2.93 -3.01 -1.53

20

Table 7

Poor* Less than High

School

Middle Class * Less than

High School

Wealthy * Less than High

School

Poor * Some High

School

Middle Class * Some High

School Wealthy * Some High School

Poor * High School Middle Class * High School Wealthy * High School

Poor * Some College Middle Class * Some College Wealthy * Some College

Poor * College or Above Middle Class * College or

Above Wealthy * College or Above

Table 8 shows the averages for the Men across these 15 different clusters. These averages

show three types of patterns for men as we look across the rows. In three of the rows (Less than

High School, High School, College or Above) we observe that men’s average junk food caloric

intake increased consistently over the three different income categories. In the row representing

Some High School, the junk food caloric intake peaked in the Middle Class. For Some College,

the level troughed at the Middle Class. Looking down the columns, for the Poor, the calories

increased consistently until College or Above. For both the Middle Class and the Wealthy the

calories peaked at the High School Level and decreased afterwards although the magnitude of

the change for the Middle Class from High School to Some College is approximately 5 calories

and thus seemingly negligent.

Table 8

Male Poor

Middle

Class Wealthy

Less than High School 519.24 606.60 675.18

Some High School 766.61 845.27 789.93

High School 802.54 846.41 1028.12

Some College 886.86 841.73 924.48

College and Above 621.18 747.01 835.87

21

Table 9 shows the same averages for Women. Looking across the rows yield two different

patterns. As income rose there is a clear decline in average junk food calories except in the case

of those in the highest education class. It appears that among those with a College or Above

education, the Middle Class women consumed the most junk food calories. Looking down the

columns, the poor showed consistently increasing junk food caloric intake with higher education

until the Some College level. The Middle Class and the Wealthy both saw consistent increases in

junk food calories until the High School level after which it drops.

Table 9

Female Poor

Middle

Class Wealthy

Less than High

School 465.38 384.15 349.56

Some High School 600.05 637.80 637.39

High School 638.72 642.15 662.55

Some College 656.70 641.80 629.82

College and

Above 549.86 562.18 558.24

There is an interesting contrast between men and women in their patterns of junk food

consumption within each education class over different income groups. It seems that men may

have a tendency to consume more junk food as their income increases whereas women very

clearly reversed the pattern. It is also interesting to note that at the highest education level, there

was not a consistently decreasing pattern of junk food consumed as income rose.

While these averages paint a general picture of the junk food consumption of the population

under study, we need regressions to see the correlation between income, education, and junk

food consumption. The first portion of the study compares average junk food consumption over

different income levels for a specific education class. The second portion of the study compares

average junk food consumption over different education classes for a specific income class.

22

Table 10 shows the regression coefficients for the demographic variables that will be repeated

for each of the regressions using the 15 different income / education clusters. Table 11 and 12,

found in the Appendix, show regression results of the average junk food consumption by women

and men respectively over these fifteen categories.

Table 10

Male Female

R-Squared 13.26% 8.96%

Age -4.17 -2.28

0.66 0.90

-6.31 -2.53

Average Total Non-Junk

Calories 0.21 0.18

0.02 0.03

9.32 6.65

Mexican American -18.41 11.84

58.07 63.59

-0.32 0.19

Non-Hispanic White 160.58 147.58

55.71 60.83

2.88 2.43

Non-Hispanic Black 116.16 195.46

56.88 63.20

2.04 3.09

Other Race -5.65 -9.11

93.75 70.47

-0.06 -0.13

23

Table 11

Columns 1 2 3 4 5 6 7

P * <HS 35.14 -36.39 -67.19 140.96 205.88 -22.08

75.27 78.29 72.39 91.52 79.45 74.13

0.47 -0.46 -0.93 1.54 2.59 -0.30

P * ~ HS -35.14 -71.53 -102.32 105.83 170.75 -57.22

75.27

62.61 55.04 78.89 68.50 58.48

-0.47

-1.14 -1.86 1.34 2.49 -0.98

P*HS 36.39 71.53 -30.79 177.36 242.28 14.31

78.29 62.61

58.22 80.73 72.54 61.49

0.46 1.14

-0.53 2.20 3.34 0.23

P* ~College 67.19 102.32 30.79 208.15 273.07 45.10

72.39 55.04 58.22

75.28 66.45 54.06

0.93 1.86 0.53

2.77 4.11 0.83

P * College and > -140.96 -105.83 -177.36 -208.15 64.92 -163.05

91.52 78.89 80.73 75.28

86.78 77.99

-1.54 -1.34 -2.20 -2.77

0.75 -2.09

MC * <HS -205.88 -170.75 -242.28 -273.07 -64.92 -227.97

79.45 68.50 72.54 66.45 86.78

66.93

-2.59 -2.49 -3.34 -4.11 -0.75

-3.41

MC* ~HS 22.08 57.22 -14.31 -45.10 163.05 227.97

74.13 58.48 61.49 54.06 77.99 66.93 0.30 0.98 -0.23 -0.83 2.09 3.41

MC*HS 78.94 114.08 42.55 11.75 219.90 284.83 56.86

70.66 54.07 56.62 48.38 73.06 65.52 52.71

1.12 2.11 0.75 0.24 3.01 4.35 1.08

MC * ~ College 48.67 83.80 12.27 -18.52 189.63 254.55 26.58

68.52 51.83 54.82 46.35 71.64 62.72 50.61

0.71 1.62 0.22 -0.40 2.65 4.06 0.53

MC * College

and > -25.55 9.59 -61.94 -92.73 115.42 180.34 -47.63

79.32 63.67 66.16 59.08 81.00 75.18 62.75

-0.32 0.15 -0.94 -1.57 1.42 2.40 -0.76

W * <HS -97.77 -62.63 -134.16 -164.96 43.19 108.12 -119.85

173.08 167.77 169.38 167.11 176.56 169.32 166.91

-0.56 -0.37 -0.79 -0.99 0.24 0.64 -0.72

W * ~HS 43.71 78.85 7.32 -23.48 184.67 249.60 21.63

94.82 82.45 84.45 79.17 96.93 90.22 81.54

0.46 0.96 0.09 -0.30 1.91 2.77 0.27

W * HS 119.52 154.65 83.12 52.33 260.48 325.40 97.43

89.73 77.88 79.91 74.41 92.66 85.73 76.84

1.33 1.99 1.04 0.70 2.81 3.80 1.27

W * ~ College -22.54 12.60 -58.93 -89.73 118.43 183.35 -44.62

71.53 54.92 57.65 49.59 74.22 66.20 53.46

-0.32 0.23 -1.02 -1.81 1.60 2.77 -0.83 W * College

and > -71.13 -35.99 -107.52 -138.32 69.83 134.76 -93.21

69.31 52.21 54.67 46.33 71.54 63.96 50.56

-1.03 -0.69 -1.97 -2.99 0.98 2.11 -1.84

24

Table 11 cont.

Columns 8 9 10 11 12 13 14 15

P * <HS -78.94 -48.67 25.54 97.77 -43.71 -119.52 22.54 71.13

70.66 68.52 79.32 173.08 94.82 89.73 71.53 69.31

-1.12 -0.71 0.32 0.56 -0.46 -1.33 0.32 1.03

P * ~ HS -114.08 -83.80 -9.59 62.63 -78.85 -154.65 -12.60 35.99

54.07 51.83 63.67 167.77 82.45 77.88 54.92 52.21

-2.11 -1.62 -0.15 0.37 -0.96 -1.99 -0.23 0.69

P*HS -42.55 -12.27 61.94 134.16 -7.32 -83.12 58.93 107.52

56.62 54.82 66.16 169.32 84.45 79.91 57.66 54.67

-0.75 -0.22 0.94 0.79 -0.09 -1.04 1.02 1.97

P* ~College -11.75 18.52 92.73 164.96 23.48 -52.33 89.73 138.32

48.38 46.35 59.08 167.06 79.17 74.41 49.59 46.33

-0.24 0.40 1.57 0.99 0.30 -0.70 1.81 2.99

P * College and > -219.90 -189.63 -115.42 -43.19 -184.67 -260.48 -118.43 -69.83

73.06 71.64 81.00 176.56 96.93 92.66 74.22 71.54

-3.01 -2.65 -1.42 -0.24 -1.91 -2.81 -1.60 -0.98

MC * <HS -284.83 -254.55 -180.34 -108.12 -249.60 -325.40 -183.35 134.76

65.52 62.72 75.18 169.32 90.22 85.73 66.20 63.96

-4.35 -4.06 -2.40 -0.64 -2.77 -3.80 -2.77 2.11

MC* ~HS -56.86 -26.58 47.63 119.85 -21.63 -97.43 44.62 93.21

52.71 50.61 62.75 166.91 81.54 76.84 53.47 50.56

-1.08 -0.53 0.76 0.72 -0.27 -1.27 0.83 1.84

MC*HS 30.28 104.49 176.71 35.23 -40.58 101.48 150.07

42.56 56.31 167.06 77.96 71.76 46.40 41.82

0.71 1.86 1.06 0.45 -0.57 2.19 3.59

MC * ~ College -30.28 74.21 146.43 4.95 -70.85 71.20 119.79

42.56

55.23 166.12 76.45 70.61 44.77 40.17

-0.71

1.34 0.88 0.06 -1.00 1.59 2.98

MC * College

and > -104.49 -74.21 72.22 -69.26 -145.06 -3.01 45.58

56.31 55.23

170.44 84.62 79.92 57.97 54.50

-1.86 -1.34

0.42 -0.82 -1.82 -0.05 0.84

W * <HS -176.71 -146.43 -72.22 -141.48 -217.29 -75.23 -26.64

167.06 166.12 170.44

177.59 175.75 167.04 166.27

-1.06 -0.88 -0.42

-0.80 -1.24 -0.45 -0.16

W * ~HS -35.23 -4.95 69.26 141.48 -75.81 66.25 114.84

77.96 76.45 84.63 177.59

95.98 78.54 76.34

-0.45 -0.06 0.82 0.80

-0.79 0.84 1.50

W * HS 40.58 70.85 145.06 217.29 75.81 142.06 190.65

71.76 70.61 79.92 175.75 95.98

73.03 70.34

0.57 1.00 1.82 1.24 0.79

1.95 2.71

W * ~ College -101.48 -71.20 3.01 75.23 -66.25 -142.06 48.59

46.40 44.77 57.97 167.04 78.54 73.03

43.93

-2.19 -1.59 0.05 0.45 -0.84 -1.95

1.11 W * College

and > -150.07 -119.79 -45.58 26.64 -114.84 -190.65 -48.59

41.82 40.17 54.50 166.27 76.34 70.34 43.93

-3.59 -2.98 -0.84 0.16 -1.50 -2.71 -1.11

25

Table 12

Columns 1 2 3 4 5 6 7

P * <HS -141.80 -104.29 -145.66 -28.05 -45.45 -89.08

68.77 68.33 72.93 114.39 67.23 62.96

-2.06 -1.53 -2.00 -0.25 -0.68 -1.41

P * ~ HS 141.80 37.52 -3.86 113.75 96.36 52.73

68.77

65.57 68.07 112.58 66.53 58.08

2.06

0.57 -0.06 1.01 1.45 0.91

P*HS 104.29 -37.52 -41.38 76.23 58.84 15.21

68.33 65.57

69.37 112.57 66.62 59.34

1.53 -0.57

-0.60 0.68 0.88 0.26

P* ~College 145.66 3.86 41.38 117.61 100.22 56.59

72.93 68.07 69.37

113.59 69.95 61.37

2.00 0.06 0.60

1.04 1.43 0.92

P * College and > 28.05 -113.75 -76.23 -117.61 -17.39 -61.03

114.39 112.58 112.57 113.59

113.89 108.95

0.25 -1.01 -0.68 -1.04

-0.15 -0.56

MC * <HS 45.45 -96.36 -58.84 -100.22 17.39 -43.63

67.23 66.53 66.62 69.95 113.89

59.97

0.68 -1.45 -0.88 -1.43 0.15

-0.73

MC* ~HS 89.08 -52.73 -15.21 -56.59 61.03 43.63

62.96 58.08 59.33 61.37 108.95 59.97 1.41 -0.91 -0.26 -0.92 0.56 0.73 MC*HS 130.41 -11.39 26.12 -15.25 102.36 84.96 41.33

59.22 53.93 54.86 57.26 105.72 56.22 45.31

2.20 -0.21 0.48 -0.27 0.97 1.51 0.91

MC * ~ College 82.84 -58.97 -21.45 -62.82 54.79 37.39 -6.24

59.69 53.44 54.55 56.41 105.69 56.77 44.68

1.39 -1.10 -0.39 -1.11 0.52 0.66 -0.14 MC * College and > 23.23 -118.57 -81.05 -122.43 -4.82 -22.21 -65.84 66.91 63.25 63.93 66.45 110.37 64.89 56.29

0.35 -1.87 -1.27 -1.84 -0.04 -0.34 -1.17 W * <HS 40.19 -101.61 -64.09 -105.47 12.14 -5.25 -48.88

105.27 104.41 104.63 106.62 139.64 104.56 100.29 0.38 -0.97 -0.61 -0.99 0.09 -0.05 -0.49

W * ~HS 127.40 -14.41 23.11 -18.27 99.35 81.95 60.02 86.35 83.03 83.25 84.99 123.54 84.81 63.93

1.48 -0.17 0.28 -0.21 0.80 0.97 0.94 W * HS 295.30 153.50 191.02 149.64 267.25 249.86 206.22

77.00 72.18 72.84 74.06 116.31 74.46 65.88 3.84 2.13 2.62 2.02 2.30 3.36 3.13

W * ~ College 149.10 7.29 44.81 3.43 121.05 103.65 60.02 76.43 70.53 71.85 72.65 115.89 73.82 63.93

1.95 0.10 0.62 0.05 1.04 1.40 0.94 W * College

and > 83.41 -58.40 -20.88 -62.26 55.36 37.96 -5.67

63.33 58.23 59.12 61.10 107.57 60.88 50.31

1.32 -1.00 -0.35 -1.02 0.51 0.62 -0.11

26

Table 12 cont.

Columns 8 9 10 11 12 13 14 15

P * <HS -130.41 -82.84 -23.23 -40.19 -127.40 -295.30 -149.10 -83.41

59.22 59.69 66.91 105.27 86.35 77.01 76.43 63.33

-2.20 -1.39 -0.35 -0.38 -1.48 -3.83 -1.95 -1.32

P * ~ HS 11.39 58.96 118.57 101.61 14.41 -153.50 -7.29 58.40

53.93 53.44 63.25 104.56 83.03 72.18 70.53 58.23

0.21 1.10 1.87 0.97 0.17 -2.13 -0.10 1.00

P*HS -26.12 21.45 81.05 64.09 -23.11 -191.02 -44.81 20.88

54.86 54.55 63.93 104.63 83.25 72.84 71.85 59.12

-0.48 0.39 1.27 0.61 -0.28 -2.62 -0.62 0.35 P* ~College 15.25 62.83 122.43 105.47 18.27 -149.64 -3.43 62.26 57.26 56.41 66.45 106.62 84.99 74.06 72.65 61.10

0.27 1.11 1.84 0.99 0.21 -2.02 -0.05 1.02 P *

College and > -102.36 -54.79 4.82 -12.14 -99.35 -267.25 -121.05 -55.36 105.72 105.69 110.37 139.64 123.54 116.31 115.89 107.57

-0.97 -0.52 0.04 -0.09 -0.80 -2.30 -1.04 -0.51 MC * <HS -84.96 -37.39 22.21 5.25 -81.95 -249.86 -103.65 -37.96

56.22 56.77 64.89 104.41 84.81 74.46 73.82 60.88 -1.51 -0.66 0.34 0.05 -0.97 -3.36 -1.40 -0.62

MC* ~HS -41.33 6.24 65.84 48.88 -38.32 -206.22 -60.02 5.67 45.31 44.68 56.29 100.29 77.92 65.88 63.93 50.31

-0.91 0.14 1.17 0.49 -0.49 -3.13 -0.94 0.11 MC*HS 47.57 107.18 90.22 3.01 -164.89 -18.69 47.00

37.96 51.16 98.30 74.30 61.45 59.59 44.23

1.25 2.09 0.92 0.04 -2.68 -0.31 1.06

MC * ~ College -47.57 59.60 42.64 -44.56 -212.46 -66.26 -0.57

37.96

50.49 98.40 73.73 60.65 59.33 43.39

-1.25

1.18 0.43 -0.60 -3.50 -1.12 -0.01 MC *

College and > -107.18 -59.60 -16.96 -104.16 -272.07 -125.86 -60.17

51.16 50.49

103.52 81.16 69.97 69.01 54.98

-2.09 -1.18

-0.16 -1.28 -3.89 -1.82 -1.09

W * <HS -90.22 -42.64 16.96 -87.20 -255.11 -108.90 -43.21

98.30 98.40 103.52

116.92 109.85 108.54 100.83

-0.92 -0.43 0.16

-0.75 -2.32 -1.00 -0.43

W * ~HS -3.01 44.56 118.57 87.20 -167.90 -21.70 43.99

74.30 73.73 63.25 116.92

88.30 87.11 77.19

-0.04 0.60 1.87 0.75

-1.90 -0.25 0.57

W * HS 164.89 212.46 272.07 255.11 167.90 146.20 211.90

61.45 60.65 69.97 109.85 88.30

75.96 64.83

2.68 3.50 3.89 2.32 1.90

1.92 3.27 W * ~

College 18.69 66.26 125.86 108.90 21.70 -146.20 65.69

59.59 59.33 69.01 108.54 87.11 75.96

63.66

0.31 1.12 1.82 1.00 0.25 -1.92

1.03 W * College

and > -47.00 0.57 60.17 43.21 -43.99 -211.90 -65.69

44.23 43.39 54.98 100.83 77.19 64.83 63.66

-1.06 0.01 1.09 0.43 -0.57 -3.27 -1.03

27

V. Results

The regression shown in the first column of Table 4 only includes the selected

demographic variables. Men and women differ in the effects of aging on their average junk food

caloric intake. For men an increase of one year in age means on average a decrease of

approximately 4.68 calories per year of junk foods eaten. For women this change is much

smaller, a one year increase in age for women means a decrease of only 2.55 calories. The

difference in these two ratios may simply be a matter of the fact that men usually eat more than

women so the greater magnitude of change for men may not stray too far from the effects of the

rate of decrease for women on their total junk food caloric intake. What needs to be considered

however is the fact that in order for the given magnitudes of changes to have the same relative

effects on both the men and women’s diets – men have to consume 1.84 (4.68 / 2.55) times as

much food as women almost doubling the amount of calories that women consume. This leads is

a rather large differential. Comparing the average calories consumed by men and women in this

sample, (2423 calories for men versus 1854 calories for women), shows that men consumed on

average 1.3 times as much total calories as women. Thus a ratio of 1.84 in the diets of men

versus women required for the two rates of junk food caloric decrease to yield same relative

effects in men and women shows that in the current sample as men age their relative decline in

junk food calories is faster than that for the women.

The coefficient on Average Total Non-Junk Calories shows that once again men and

women differ. For men an increase of one calorie of non-junk food consumed means that junk

food caloric intake would increase approximately 0.21 calories for women, the increase is only

0.17. To test for the statistical significance of the two different returns to average total non-junk

consumed, a gender neutral regression is run including interaction terms between female *

28

average total non-junk calories and male * average total non-junk calories. An F test was used to

measure whether the coefficients were equal to each other and the resulting p-value of 0.000

shows that the two returns to average total non-junk calories is different for men and for women.

Thus for each calorie increase in diet that is not from junk food, men will consume more

complementary junk food calories than women.

Looking at the same regression, some of the coefficients representing race are also

statistically significant. For the regression shown the base group that is left out of the equation

for comparison is that of the Other Hispanic category; the coefficient on Mexican American does

not seem to be statistically significant however this may be due merely to the fact that similar

diet practices within the Hispanic culture yield the same junk food consumption patterns between

Mexican Americans and the Other Hispanic group. A similar regression was run using Non-

Hispanic White as the base group as shown in the second column of Table 4. This regression

shows that there are significant differences between Non-Hispanic White Americans and

Mexican Americans. Thus as a whole these dummy variables accounting for race are statistically

significant in accounting for cross racial differences in the junk food calories consumed. Looking

at the race dummy variables in the case when the comparison is made to Non-Hispanic Whites,

for both genders the race dummies are significant across the board. For men all the other

minority groups consumed less junk food calories than the Non-Hispanic Whites with Mexican

Americans showing the greatest difference of 203 calories, followed by men from Other Races,

Other Hispanics and lastly Non-Hispanic Blacks. For women, it was not the case that all the

other minority groups consumed less junk food than the Non-Hispanic White female. In fact,

African American women consumed 56 calories more of junk food than the Non-Hispanic White

29

female. The largest difference in junk food eaten for Non-Hispanice White females came from

women in the Other Races, followed by Other Hispanics, and Mexican Americans.

Moving on to consider the regressions containing only either the income or education

variables. First we consider the first column in Table 5 in which the dummy for the Poor is left

out of the regression allowing for comparisons of differences in junk caloric intake between the

Poor and the Middle Class as well as the Poor with the Wealthy. In the second set of regressions

within the same table, the Middle Class is omitted from the equation rather than the poor thus

allowing for a new comparison to be made between the Middle Class and the Wealthy that the

previous regression was not able to do.

The significance on these income dummy variables would indicate that after taking gender, age,

average size of the diet and race into consideration, the remaining differences in the average junk

calories consumed by these different groups is statistically significant.

For men, those in the Middle Class consumed 18 calories less than the Poor and those in

the Wealthy class ate 38 calories more. However the p-values on the coefficients of both of these

dummy variables indicate that there is no statistical difference in the average junk calories

consumed for men who are in the Poor class compared to men who are of the Middle Class or

Wealthy. For the Women those in the Middle Class consumed 17 calories more than the Poor

and those who are Wealthy consumed 43 calories less than the Poor. Both coefficients are not

statistically significant. Thus for women, as for the men, there is a similar conclusion that there is

no statistically significant difference in the junk calories consumed by the Poor versus the

Middle Class or the Wealthy.

The second regression leaves out the Middle Class to allow for comparisons between the Middle

Class to the Wealthy. For men the Wealthy consumed 56 calories more than the Middle Class

30

and the coefficient was significant at the 10% level. For women the Wealthy consumed 60

calories less than the Middle Class and the result was statistically significant at the 5% level.

Thus for both Men and Women there is no consistent pattern in junk food calories either

increasing or decreasing correlating to higher income. While the previous studies focusing on

health behaviors in which a more prominent difference was observed between the Poor and the

Middle Class, in this case when we focus on the relatively unhealthy behavior of junk food

consumption, differences in junk food consumption is more obviously correlated with

differences in income between the Middle Class and the Wealthy than with the Middle Class and

the Poor (Hupkens, et.al 2000).

Before considering dietary pattern over the 15 education / income clusters, we looked

also at the patterns simply across education alone. In this comparison, four different regressions

are necessary to completely analyze the differences between the five education classes. In Table

6 the results for the four different regressions are shown for each gender. In the first regression

the education class Less than High School was omitted as the base comparison group. For men

all the coefficients on the higher education classes were positive with High School men leading

the pack with the biggest difference compared to those who only had Less than High School

level of education. The differences in junk food consumption compared to the base group

increase until the High School level, after which the magnitudes of the differences decrease as

education levels increase. All variables were statistically significant except the coefficient on the

education level College or Above. For women a similar pattern is observed, all variables are

statistically significant except that on College or Above. The result is surprising in that it does

not indicate that as education levels increase there is an improvement in dietary behavior as

would be exemplified by negative coefficients on all education variables included in this

31

regression; furthermore one would expect to see increasing magnitudes of differences between

the base group and the higher education levels.

Within the second regression the education level Some High School is omitted from the

regression. The first regression has already accounted for the differences between Less than High

School and Some High School therefore, in this second regression, and similarly in all following

regressions, we would only need to consider coefficients on the dummy education levels that are

higher than the base group. For men, the coefficients on High School and Some College are

positive however the coefficient on Some College is not statistically significant. The coefficient

on College or Above is negative but not statistically significant. For women, similar to men, the

coefficients on High School and Some College are positive and the coefficient on College or

Above is negative. The coefficients on Some College and College or Above are not statistically

significant. Once again there is nothing to indicate that there is an improvement in dietary

behavior as education levels improve.

When high school is used as the base group, for Men and women all coefficients on the

other education variables become negative. This indicates that compared to the High School

group, all other education levels consumed less junk food calories. However for men, only the

Less than High School and the College or Above groups had statistically significant coefficients.

For Women, all groups except the Some College group showed statistically significant

coefficients.

The only remaining comparison is that between those who have Some College education

and those with College or Above levels of education. For men, the coefficient on College or

Above in the regression using Some College as the base group was not statistically significant.

For women however, the corresponding coefficient of is statistically significant showing that on

32

average women with a College or Above degree consumed less junk food than those with only

Some College education.

Over all the comparison of the education groups it seems that for men and for women,

there is no consistent pattern of junk food consumption decreasing as education rose. Those with

a High School level of education seemed to consume the most junk food calories for men as well

as for women. Having a College or Above level of education was correlated with lower junk

food consumption compared to most of the other education levels.

After considering the income and education variables separately, we now proceed to

investigate the joint correlation of income and education on the junk food consumptions within

the participants. Since there are 15 different clusters of income and education combinations, each

set of regression will omit one to be used as the base group. The results for holding education

constant and comparing over income groups will be presented first. The results for holding

income constant and comparing over education groups will be presented in the latter portion.

All results of the regressions are presented in Tables 10 - 12, the following discussion

will first cover the results for women followed by those for the men.

Women

Less than High School

In order to consider patterns of junk food consumption as income varies over the same

education level, a comparison of the coefficients on dummies with the same education levels is

necessary. For example, consider the case for those with Less than High School level of

education, three comparisons are necessary: Poor with Middle Class, Poor with Wealthy, and

Middle Class with Wealthy. Thus, looking at the regression results for Women, we look at

Columns 1 & 6 in Table 11; Column 1 uses Poor * Less than High School as the base

33

comparison group and Column 6 uses Middle Class * Less than High School as the base

comparison group.

Specifically from Column 1, the coefficients before Middle Class * Less than High

School and Wealthy * Less than High School indicate whether there is a significant difference in

junk food consumption in the diets of the Middle Class and Wealthy compared to the Poor. From

Column 6, we are interested in the coefficient before Wealthy * Less than High School. This

coefficient indicates whether there is a difference in the junk food consumption between the

Middle Class and the Wealthy correlating to the income differences between the two clusters.

The regression results from Column 1 show that significant at the 10% level, the Middle

Class consumed 206 calories less of junk food on average than the Poor. In the same regression,

it shows that the Wealthy consumed 98 calories less than the Poor, however the coefficient in

front of Wealthy was not statistically significant2. Column 6 shows that the Wealthy consumed

108 calories more than the Middle Class but the result is not statistically significant.

It seems that for women with a Less than High School level of education, differences in

junk food consumption is more prominent between the Poor and the Middle Class than between

the Middle Class and the Wealthy. The direction of the difference points to a negative correlation

between junk food consumption and income level.

Some High School

Regression shown in Column 2 and 7 use Poor * Some High School and Middle Class *

Some High School respectively as the base group. The coefficients of Middle Class * Some High

School and Wealthy * Some High School from Column 2 and that of Wealthy * Some High

School from Column 7 will indicate significant differences in junk food consumption correlating

2 All coefficients that are not statistically significant at the 10% level are considered to be statistically insignificant

from here forth.

34

to differences income between the income classes and the base group. Those in the Middle Class

consumed 57 calories more than the Poor; the Wealthy consumed 79 calories more than the Poor

and 22 calories more than the Middle Class. None of the three coefficients are statistically

significant. II Income alone is not sufficient to explain the existing differences in junk food

consumption amongst those with Some High School level of education.

High School

Column 3 and 8 show the regression results to be used for comparison of women who

have a High School level of education. In Column 3Coefficients before Middle Class *High

School is 43 and before Wealthy * High School is 83, but neither is statistically significant. The

coefficient before Wealthy * High School from Column 8 is 41 but is also insignificant. Similar

to the result forSome High School, income does not seem to be correlated with differences in

junk food consumption for women with a High School level of education.

Some College

Regressions in Column 4 and 9 are used for comparison of those with Some College level

of education. The base group in Column 4 is the Poor*Some College and thatin Column 9 is the

Middle Class*Some College. In Column 4 the coefficient in front of Middle Class * Some

College shows that the Middle Class consumed 12 calories more than the Poor but the result is

statistically insignificant. However in the same regression, the coefficient in front of Wealthy *

Some College is statistically significant at the 10% level; those who were Wealthy consumed 90

calories less of junk food than those who were Poor. In Column 9 the coefficient in front of

Wealthy * Some College showed that the Wealthy consumed 71 calories less than the Middle

Class but the result was insignificant. In this education cluster, the only notable difference

correlating to income lies between the Poor and the Wealthy.

35

College or Above

Column 5 and 10 are used for this comparison with the base group in Column 5 being the

Poor*College or Above and that in Column 10 being Middle Class*College or Above. The

coefficients in from of the Middle Class * College or Above and Wealthy * College or Above in

Column 5 are both positive, 115 calories and 70 calories respectively, but insignificant. The

coefficient in front of Wealthy * College or Above in Column 10 shows the Wealthy consumed

46 calories less than the Middle Class but the result is not statistically significant. Therefore for

those with a College or Above education, income is not significantly correlated with differences

in junk food consumption.

Men

For Men the exact same analysis was done. The Columns and coefficients used for

comparison are the exact same as that for the Women . All regressions and results for Men are

shown in Table 12.

Less than High School

The Middle Class consumed 45 calories more than the Poor and the Wealthy consumed

40 calories more than the Poor however neither result is statistically significant. The Wealthy

consumed 5 calories less than the Middle Class but the result is similarly statistically

insignificant. Income was not correlated with junk food consumption differences for Men with a

Less than High School level of education.

Some High School

The Middle Class consumed 53 calories less than the Poor and the Wealthy consumed 14

calories less than the Poor however neither result is statistically significant. The Wealthy

consumed 60 calories more than the Middle Class but the result is similarly statistically

36

insignificant. Income was not correlated with junk food consumption differences for those with

Some High School level of education.

High School

The Middle Class ate 26 calories more than the Poor and the Wealthy consumed 191

calories more than the Poor. While the difference for the Middle Class was not statistically

significant, that for the Wealthy was significant at the 1% level. This indicates a strong

correlation between the income and consumption of junk food for the Poor and the Wealthy. The

Wealthy also consumed 165 calories more than the Middle Class, result significant at the 1%

level as well. Thus for this particular education group, it seems that being Wealthy was strongly

correlated with a much higher level of junk food consumption when compared with the other two

income groups.

Some College

The Middle Class consumed 63 calories less than the Poor and the Wealthy consumed 3

calories more than the Poor. Both results are statistically insignificant. The Wealthy consumed

66 calories more than the Middle Class, and the result is also statistically insignificant. Income

seems to have little correlation with junk food consumption for men with Some College level of

education.

College or Above

For Men with a College or Above education, the Middle Class consumed 5 calories less

than the Poor and the Wealthy consumed 55 calories more than the Poor. The Wealthy also

consumed 60 calories more of junk food than the Middle Class. None of the coefficients were

statistically significant. Income does not seem to be correlated with junk food consumption for

men in the College or Above education stratum.

37

After looking at the differences correlating to Income within the same Education level,

we now look at the differences correlating to Education within the same Income level. As above,

we will proceed first with a description of the results for Women then for the Men.

Women

Poor

In this case we look at the results from Column 1 through 4. From each Column we

consider the coefficients before those dummy variables that are an interaction with Poor and an

education level higher than the omitted base group. For example, Column 1 uses Poor * Less

than High School as the base group, therefore, we consider the remaining four dummy variables

using the term Poor, i.e. Poor * Some High School, Poor * High School, Poor * Some College,

and Poor * College or Above. Those with Some High School level of education consumed 35

calories less, those with a High School level of education consumed 36 calories more, those with

Some College level of education consumed 67 calories more, and those with a College or Above

education consumed 141 calories less than the Less than High School educated base group.

However none of the coefficients are statistically significant.

Column 2 uses those who have Some High School level of education as the base group.

Thus the analysis focuses on the coefficients on the dummies interacting Poor with High School,

Some College, and College or Above, respectively. Those who with a High School education

consumed 72 calories more, those with Some College education consumed 102 calories more,

and those with a College and Above education consumed 105 calories less than those who have

Some High School level of education. Only the difference for Some College was statistically

significant at the 10% level.

38

Column 3 omits Poor * High School as the base group. Those with Some College level of

education consumed 31 calories more and those with a College or Above education consumed

177 calories less than those who have a High School level of education. The difference between

the High School educated woman and the College or Above woman is statistically significant at

the 5% level.

Lastly Column 4 uses Poor * Some College as the base group. In this the coefficient in

front of Poor * College or Above shows that those with a College or Above education consumed

208 calories less than the woman with Some College education, significant at the 1% level.

For women who are Poor, it seems that a College or Above education was correlated with

a lower junk food consumption compared to most of the other education groups. However there

is no indication that as education is increased, there is a consistent pattern of decreasing junk

food consumption.

Middle Class

Columns 6 through 9 show the regressions used for comparison of the Middle Class. As

in the case for the Poor, the base group in Column 6 is the education group Less than High

School, that of Column 7 is the group Some High School, for Column 8 is the group High School,

and for Column 9 is the group Some College.

Column 6 shows that those who had Some High School level of education consumed 228

calories more, those who had a High School education consumed 285 calories more, those who

had Some College education consumed 255 calories more, and those who had College or Above

education consumed 180 calories than the base group of Less than High School. All the

coefficients except that before the College or Above category were significant at the 1% level

and the latter was significant at the 5% level.

39

Column 7 shows that those with a High School education consumed 57 calories more,

Some College consumed 27 calories more, College or Above consumed 48 calories less than the

base group Some High School. None of the results in this case was statistically significant.

Column 8 shows that those with Some College level of education consumed 30 calories less and

those with College or Above education consumed 104 calories less than the base group High

School. The result for the College or Above group was statistically significant at the 5% level

while the Some College coefficient was statistically insignficant. Column 9 shows that those

with a College or Above education consumed 74 calories less than the base group Some College

however the result was not statistically significant.

For women who are in the Middle Class, it seems that the education level College or

Above is correlated with a lower junk food consumption when compared to at least half of the

other education levels. Having a High School education was correlated with higher junk food

consumption than two of the other education groups. Similar to the result for the Poor, there is

little indication that as education increases within the same income level, there is a consistent

pattern of decreased junk food consumption.

Wealthy

Regressions for the wealthy are found in Columns 12 through 15. The order of the base

group education levels are in the same order as it was for the Poor and the Middle Class. Column

12 shows that for those with Some High School level of education consumed 141 calories more,

those with High School level of education consumed 217 calories more, those with Some

College level of education consumed 75 calories more, and those with College or Above level of

education consumed 26 calories more than the base group Less than High School. None of the

results were statistically significant.

40

Column 13 shows that those who had a High School level of education consumed 76

calories more, those with Some College level of education consumed 66 calories less, and those

with College or Above level of education consumed 115 calories less than the base group Some

High School. None of the results were statistically significant.

Column 14 shows that those with Some College level of education consumed 142

calories less and those with College or Above level of education consumed 191 calories less than

the base group High School. The coefficient on Some College was statistically significant at the

10% level and the coefficient on College or Above was statistically significant at the 1% level.

Lastly Column 15 shows that those with a College or Above level of education consumed

49 calories less on average than those with Some College level of education. The result however

was not statistically significant.

For the Wealthy women, it seems that education was not very correlated with junk food

consumption. Unlike the case for the Middle Class and the Poor, having a College or Above

education did not point to a lower junk food consumption in comparison with at least half of the

other education levels.

Men

Poor

The analysis for Men follows from that for the Women. Table 12 shows the result for

Men and the columns used in this portion corresponds to that for the Women as mentioned above.

Column 1 shows that those with Some High School level of education consumed 142

calories more, those with High School level of education consumed 104 calories more, those

with Some College level of education ate 146 calories more, and those with College or Above

level of education ate 28 calories more than the base group Less than High School. Both of the

41

coefficients on Some High School and Some College are significant at the 5% level while the

other two coefficients are not statistically significant.

Column 2 shows that those with a High School level of education consumed 38 calories

less, those with Some College level of education consumed 4 calories more, and those with

College or Above level of education consumed 114 calories less than the base group of Some

High School. None of the coefficients are statistically significant.

Column 3 shows that those with Some College level of education ate on average 41

calories more and those with College or Above education consumed 76 calories less than the

base group High School; however, neither of the coefficients were statistically significant. Lastly

Column 4 shows that those with College or Above level of education consumed 118 calories less

than the base group Some College, but this coefficient was also not statistically significant.

Thus for the Poor Men, other than some significant differences correlating to the lowest

level of education, education differences do not seem to be correlated with differences in average

junk food consumption.

Middle Class

Column 6 shows that those with Some High School level of education consumed 44

calories more, those with a High School level of education consumed 85 calories more, those

with Some College level of education consumed 37 calories more, and those with College or

Above level of education consumed 22 calories less than the base group Less than High School.

Column 7 shows that those with High School level of education consumed 41 calories more,

those with Some College level of education consumed 6 calories less, and those with College or

Above level of education consumed 66 calories less than the base group Some High School.

None of the coefficients in Column 6 or Column 7 were statistically significant thus differences

42

in junk food when compared to those with Less than High School and Some High School levels

of education were not statistically correlated to the participant’s education level.

Column 8 shows that those with Some College level of education consumed 48 calories

less and those with College or Above level of education consumed 107 less than the base group

High School. The coefficient on College or Above was significant at the 5% level. Column 9

shows that those with a College or Above level of education consumed 60 calories less of junk

food than those with a Some College level of education, however the coefficient was not

statistically significant.

For the Middle Class the only statistically significant difference in junk food

consumption correlating to differences in education level was between those who had a College

or Above Education and those who had a High School Education. Thus within this income

stratum for men, there are no clear patterns of junk food consumption as it correlates to

education.

Wealthy

Column 11 shows that those with a Some High School level of education ate 87 calories

more, those with a High School level of education ate 255 calories more, those with Some

College level of education ate 109 calories more, and those with College or Above level of

education ate 43 calories more of junk food compared to the base group of Less than High

School level of education. Only the coefficient in front of High School was statistically

significant, at the 5% level.

Column 12 shows that those with a High School level of education consumed 168

calories more, those with Some College level of education consumed 22 calories more, and those

with College or Above level of education consumed 44 calories less than the base group Some

43

High School. The coefficient on High School is significant at the 10% level and the other two

were not statistically signIficant. Column 13 shows that compared to someone with a High

School level of education, those with Some College level of education consumed 146 calories

less and those with College or Above level of education consumed 212 calories less of junk food.

Both coefficients were statistically significant at the 10% and 1% level respectively. Lastly in

Column 14, those with a College or Above level of education consumed 66 calories less of junk

food compared to someone with Some College level of education, however the coefficient was

not statistically significant.

For the Wealthy Men, having a High School education was correlated with higher junk

food consumption than any of the other education groups significant at least the 10% level. Note

that in the previous section comparing different income levels within the same education class,

the Wealthy * High School Men also consumed more junk food calories than either the Poor or

the Middle Class High School Men. For Men being Wealthy and having a High School education

as the highest level of education seem to be correlated with the highest levels of average junk

food consumption.

Discussion

Considering the comparisons across income groups over the same education level, the

results for Women do not corroborate with previous studies which found greater differences in

diet between the Poor and the Middle Class than the Middle Class and the Wealthy. Only for

those with a Less than High School level of education, a significant difference between the Poor

and the Middle Class was found. Of the five different education stratums, only two stratums had

differences between income groups that were statistically significant, thus there is little evidence

to support our previous hypothesis that income would positively affect junk food consumption

44

amongst those with lower incomes and negative affect junk food consumption amongst those

with higher levels of education.

For men income seems to be less correlated with differences in junk food consumption

amongst those with the same education class. Four of the five education levels showed little

correlation between in junk food consumption patterns and income differences. A point of

interest for men was that that those who are in the Wealthy * High School cluster ate more junk

food than both their fellow High School graduates from both the Middle Class and the Poor

income bracket and the correlation was very strong. Perhaps something along the lines of having

more money to buy junk food leads to greater junk food consumption can be said in this very

specific case, but based on our results, this statement cannot be applied to any other education

stratum for men.

For both Men and Women there seems to be a parabolic pattern in the average junk food

caloric consumption that peak at the High School level. There is nothing to indicate that there is

a consistent decline of average junk food calories consumed as education levels increase.

Although it seems that beyond high school there is a relative decline in average junk food decline

as education increases. Having a College or Above education is correlated with lower junk food

consumption most consistently for the Wealthy in both Men and Women.

While the analysis does not yield any clear patterns as to junk food consumption over

either education or income holding the other variable constant, it does produce the surprising

finding that High School educated people tend to consume the highest amount of junk food.

VI. Conclusion

It is clear that there is no clear pattern in junk food consumption as the education level

increases for a given income class. Similarly it is equally unclear that there is any clear and

45

consistent pattern within the junk food caloric intake for people within the same education class

as income changes. Thus the results of this paper cannot be used to make a recommendation as to

whether increasing education or increasing income will affect healthier diets. However the

finding that High School educated people are the most likely to consume larger quantities of junk

food perhaps points to the fact that increasing nutritional education during the High School years

may serve to improve the diets of these at risk persons.

The complexity of the compositions of diets and the wide array of taste preferences all

could play into the lack of patterns observed in this paper. Most studies reviewed in this paper

have been conducted outside of the United States therefore perhaps there is something quite

particular about the American diet that makes the patterns less obvious to be observed. Further

research needs to be done in order to adequately account of the taste preferences of individuals.

With this factor taken aside, perhaps the patterns correlating to education and income might

become clearer.

46

References

Center for Disease Control. Available at http://www.cdc.gov/.

Fahlman, Mariane M., McCaughtry, Nate, Martin, Jeffrey, Shen, Bo. “Racial and Socioeconomic

Disparities in Nutrition Behaviors: Targeted Interventions Needed.” Journal of Nutrition

Education and Behavior 42.1 (2010): 10 – 16

Hupkens, Christianne L.H., Knibbe, Ronald, Drop, Maria J. “Social Class Differences in Food

Consumption.” European Journal of Public Health 10.2 (2000): 108 – 113.

Martikainen, Pekka, Brunner, Eric, & Marmot, Michael. “Socioeconomic Differences in Dietary

Patterns Among Middle-Aged Men and Women” Social Science & Medicine 56 (2003): 1397 –

1410.

National Health and Nutrition Examination Survey. Available at

http://www.cdc.gov/nchs/nhanes.htm.

Schefske, Scott D., Bellows, Anne C., Byrd-Bredbenner, Carol, Cuite, Cara L., Rapport, Holly,

Vivar, Teresa, Hallman, William K. “Nutrient Analysis of Varying Socioeconomic Status Home

Food Environments in New Jersey.” Appetite 54 (2010): 384 – 389.

Turrell, Gavin. “Determinants of Gender Differences in Dietary Behavior.” Nutrition Research

17.7 (1997): 1105 – 1120.

Turrell, Gavin. “Socioeconomic Differences in Food Preference and Their Influence on Healthy

Food Purchasing Choices.” Journal of Human Nutrition and Dietectics 11 (1998): 135 – 149.

US Census – Poverty Information. Available at

http://www.census.gov/hhes/www/poverty/threshld/thresh03.html

http://www.census.gov/hhes/www/poverty/threshld/thresh04.html

http://www.census.gov/hhes/www/poverty/threshld/thresh05.html

http://www.census.gov/hhes/www/poverty/threshld/thresh06.html.

47

APPENDIX

Distribution of the Races within the Particpants:

Distribution of Ages within the Participants

0

50

100

150

200

250

300

350

400

450

18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84

48

Food Items by Group

Group 1

Milk

Buttermilk

Evaporated Milk

Dried Milk

Soy Milk

Yogurt

Frozen Yogurt

Chocolate Milk

Hot Chocolate

Cocoa

Chocolate Syrup

Milk Shake

Meal Replacement Drinks

Infant Forumula

Protein Supplement, milk

based

Cream

Whipped Topping

Sour Cream

Spinach Dip

Ice Cream

Pudding

Custard

Tiramisu

Cheese

Group 2

Beef

Pork

Lamb

Goat

Veal

Rabbit

Venison

Moose

Bear

Caribou

Bison

Ground Hog

Opossum

Squirrel

Beaver

Raccoon

Armadillo

Wild Pig

Ostrich

Chicken

Turkey

Duck

Goose

Cornish Game Hen

Frankfurter

Cold cuts

Sausage

Fish

Clams

Crab

Lobster

Shrimp

Mussel

Oyster

Scallops

Snails

Stews

Chili

Cooked dishes

Sandwiches / Burgers

Group 3

Eggs

Different types of cooked

eggs

Group 4

Beans

Cooked Beans

Tofu

Tofu products

Nuts

Group 5

Flour

Bread

Pita

Rolls

Croissant

Bagel

Muffin

Biscuit

Scones

Cornbread

Hush Puppy

Tortilla

Cake

Cookies

Pie

Fritter, Crisp, Cream Puff,

Crepe, Strudel, Tamale,

Turnover

Danishes

Doughnut

Granola Bar

Coffee Cake

Crackers

Salty Snacks

Pancakes

Waffle

Macaroni

Noodles

Spaghetti

Cereal

Grits

Cornmeal

Millet

Oatmeal

Rice

Wheat

Enchilada

Burrito

Taco

Tamale

Nacho

Chalupa

Chimichanga

Quesadilla

49

Taquitos

Pizza

Croissant Sandwiches

Lasagna

Ravioli

Sushi

Soups

Group 6

Fruits

Fruit Juice

Applesauce

Group 7

Vegetables

Potato Chips

Group 8

Butter

Margarine

Shortening

Oils

Dressing

Mayo

Group 9

Sugar

Sweetener

Syrup

Honey

Topping

Icing

Jelly

Jello

Candy

Chocolate

Fudge

Coffee

Chewing Gum

Tea

Soft Drink

Fruit Flavored Drinks

Alchohol

Vitamin Water