Abstract (Centers for Disease Control, 2014). This problem



research will focus on whether socioeconomic factors, specifically education
and income, increase or decrease obesity rates. We will use a bar chart and
regression model using data from the Centers for Disease Control to show the
relationships between obesity, income, and education levels. The data was
collected from 2011-2016. We also used data from the U.S. Census Bureau to
determine the top ten states with the highest and lowest income and education
levels, in 2016, to compare obesity rates. Research results found that states
with the highest socioeconomic status had lower obesity rates than states with
lower socioeconomic status. This was documented using various income and
education levels.

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Income, Education, Rates, Socioeconomic


to the Centers for Disease Control (2016), obesity is defined as weight that is
higher than what is considered as a healthy weight for a given height. A body
mass index equal to or greater than 30 is considered to be obese. Body Mass
Index (BMI) is a tool used for screening overweight or obesity. The formula to
calculate BMI is kg/m2, your weight (in kilograms) divided by your
height (in meters) squared.

is a growing problem in the United States. “In 2007-2008 more than one-third of
United States adults were obese” (Centers for Disease Control, 2014). This
problem is only continuing to rise, with education and income playing a major

objective of this report is to prove that education and income levels affect
obesity rates in the United States.



data collected from the Centers of Disease Control and the Bureau of Labor
Statistics, the following is the final 
hypothesis: The states with higher socioeconomic status, as it relates
to education and income, have lower obesity rates than states with lower
socioeconomic status.



Obesity and Education


research of the correlation between education level and obesity had varying

in the Journal of Obesity, vol 2013,
“Black-White Disparities in Overweight and Obesity Trends by Educational
Attainment in the United States, 1997-2008” analyzed data nationally of 174,228
US-born adults. It found that obesity trends and racial disparities were more
prominent among individuals with higher educational levels, compared to their
counterparts with lower educational levels. The article stated “different
levels of education, suggested as the single most important social influence on
health, likely contribute to those obesity disparities and explanations for the
positive association between educational attainment and health are well
established” (Jackson, et al. 2013). More education is usually is associated
with healthier behaviors such as not smoking, physical activity and drinking in
moderation. Because lower education generally relates to lower income, access
to resources can also attribute to poor health and physical conditioning.


and educational attainment have increased dramatically since 1970. “Four
Decades of Obesity Trends Among Non-Hispanic Whites and Blacks in the United
States: Analyzing the Influences Educational Inequalities in Obesity and
Population Improvements in Education” discusses a study that analyzed the
influences of educational inequalities in obesity and population improvements
in education on national obesity trends between 1970-2010. This study looked at
adults aged 25-74. During this time period, the obesity rate nationally
increased from 15.7% to 38.8%. “There were increases in obesity probabilities
of non-college graduates compared to college graduates” (Yen 2016). According
to the study, obesity did not differ by education among black males and largest
among white females. Due to this difference, the study’s conclusion is obesity
reductions associated with educational improvements were somewhat limited.


studies have concluded the same as above, but are more definitive. “The
Changing Relationship Between Obesity and Educational Status” published in Gender Issues, Spring 2005, used annual
cross-sectional survey data of non-institutionalized adults. Their conclusion
was that “the increased educational level of the adult population has not
resulted in a decline in obesity” (Himes, et al. 2005). This study made the
point while the obesity rate has risen over the past 30 years, so has
educational attainment. In 2000, 84% of all adults age 25 and older had
completed high school compared to 50% in 1970. In the same time frame, the
obesity rate has risen 15%. Education has been linked to better health,
healthier lifestyles and lower incidence of chronic disease. “All else equal,
an increase in educational level of the population should have led to a decline
in obesity. However, the national patterns of obesity prevalence show
otherwise” (Himes, et al. 2005). According to the article, race may play a
factor in the discrepancy. The United States has become more diverse over the
decades. African-Americans and Hispanics generally have an higher BMI. The
growth of the African-American and especially Hispanic population in the United
States over 30 years is a factor when it comes to the obesity rate. Therefore,
when looking at the United States as a whole, the correlation of education
level and obesity is hard to measure.


stated earlier, There are different ways to observe and measure the
relationship between education and obesity. Our research shows different
studies had conclusions from varying viewpoints. Therefore, the literature
review is inconclusive when discussing education’s effect on obesity.


and Income

Obesity in the United States has been
increasingly cited as a major health issue in recent decades, resulting in
diseases such as heart disease, diabetes, depression etc. While many developed
countries have experienced similar increases, obesity rates in the United States are the highest in the
world. Obesity has continued to grow within the United States. Two of every
three American men are considered to be overweight or obese, but the rates for
women are far higher. The United States contains one of the highest percentage
of obese people in the world. (“Most obese countries”. Reuters.

Retrieved 24 September 2014)

People from all
economic backgrounds often eat for social, cultural, and emotional reasons, not
just for hunger according to the article, “Obesity in America: It’s Getting Worse,
2004, January.” While obesity levels have been rising for all, some groups are
more affected than others. Some researches show that socioeconomic status
related to obesity changed. According to the hypothesis, one can say that in
lower-income states, people with less socioeconomic status were more likely to
be obese. Conversely, in high-income states, those with higher socioeconomic
status were less likely to be obese.

Various studies have
shown that overweight people are seen as less conscientious, less agreeable,
less emotionally stable, less productive, lazy, lacking in self-discipline, and
even dishonest.  People are also seen as
sloppy, ugly, socially unattractive, and sexually unskilled. According to, The price of obesity: How your salary depends on your
weight,  there will be no surprise to say
that obese people are paid less than the other, who has thinner body and higher

According to the information above from the article, a
person can make a list about how the money affect poor people’s eating habits.

Poor families have limited food budgets and choices, and most of time stretch supplies toward the end of the month:

probably choose high-fat foods such as sugars, cereals, potatoes and processed
meat products because these foods are more affordable and last longer than
fresh vegetables and fruits and lean meats and fish.

families most likely live in disadvantaged neighborhoods where healthy foods
are hard to find.

insecurity, such as trouble paying bills or rent, leads to stress, and people
often cope by eating high-fat, sugary foods.

On the other hand, what makes higher socioeconomic status in
high-income states beneficial for staying thin? Conversely from these bullet
points, higher income will give you more freedom instead of thinking about at
the end of the month, rent or bills. People, who has higher income more likely
to do, activities such as reading, attending cultural events, and going to the
movies were associated just as much as exercise was with a lower BMI.

It may be that in lower-income states, less socioeconomic
status leads to consuming high-calorie food and avoiding physically tough
tasks. People who participated in activities such as watching TV, attending
sporting events, and shopping had higher BMI, but in higher-income states,
individuals with higher socioeconomic status may respond with healthy eating
and regular exercise.

In light of the information above, we can see that obesity
and income have a negative correlation. When income increases, obesity decreases;
vice versa, when income decreases, obesity increases.


data is taken from Center for disease control and prevention (BRFSS). This data
is from 2011 to 2016. Data was collected for 20 states. According to US Census
Bureau for 2016, top ten states with higher and lower income are taken. This is
to identify how overall higher or lower socioeconomic status will change
obesity level.


graph represent average obesity level for higher income states. Where X axis is
10 highest income states and Y axis range BMI values from 0 to 35. With BMI
greater than 30 is considered obesity. Here in the 6 years span for ten states,
the average BMI is seen below 30. However the maximum is 31.4 for Alaska in
2016 and minimum is 21.8 in Hawaii during 2011 and 2013. This shows that
obesity level was lower with higher income states.



graph represent ten states with lower income from 2011 to 2016. Thereby X axis
is states and Y axis BMI range from 0 to 40. As we see in this lower income
states BMI is mostly above 30 of average, which considers obesity level is
higher there. Where minimum was 26.3 in New mexico during 2011 and maximum was
in west virginia in 2016 with 37.7. 
Furthermore, higher income states obesity level is lower on average and
vice versa.

graph is taken from Center for Disease Control and Prevention. This graph
represents the nation wide data for 2016 based on income level. The income
level is divided into six parts and data not reported is also included. The
obesity is shown in y axis from 0 to 40. The income level less than $15,000 is
35.4. The income level between $15,000 to $24,999 is 33.4 and 31.9 at $25,000
to $34,999. From 35,000 to $49,999 is 32 and $50,000 to $74,999 is 31.1. Income
that is  greater than $75,000 is 25.4.

Further, it shows with increase in income the obesity level decreases from 35.4
to 31.1.


graph is taken from Centers for Disease Control and Prevention. This compares
the education level and it’s effect on obesity in 2016 for the U.S. (nationwide).

The X axis shows education level: less than high school, high school, some
college or technical school and college graduate. The Y axis contains BMI
levels from 0 to 40.


it shows the higher average BMI with lower education. The less than high school
BMI range from 34.5 to 36.5, high school graduate is 31.8 to 32.8, Some college
or technical school is 30.5 to 31.5 and college graduate is 21.9 to 22.6. So,
the median average was lower in college graduates at 22.3 whereas less than
high school was 35.5.


a regression analysis is done based on income and education with six states.

The higher income states are: Maryland, Alaska, and New Jersey; and lower
income states are: Oklahoma, Tennessee, and New Mexico.The data is taken from
the Centers for Disease Control and Prevention. Income is divided into 6
levels: 1-less than $15,000, 2-$15,000 to $24,999, 3-$25,000-$34,999, 4-$35,000
to $49,999, 5-$50,000 to $74,999 and 6-$75,000 and above. Education is divided
into 4 levels: 1-less than high schools, 2-high school grad, 3-some college and
technical school and 4-college graduate.


Maryland Regression Analysis of
Obesity based on Income and Education Level 2016


The multiple R is correlation coefficient, which says how
strong the linear relationship is. Where it has moderate relationship by income
level with 0.57. R squared is coefficient of determination, which is
statistical measure to know how close the data is to regression line. Which is
33% or 0.331 around the mean of the data by income level. The regression
analysis of Maryland state for 2016 based on education, where multiple R is
correlation coefficient is 0.88 says strong relationship and Coefficient of
determination is 78%.


Regression Analysis of Obesity by Income and Education Level 2016

regression analysis based on income level, where correlation coefficient has no
or negligible relationship and coefficient of determination is around 2%. By
education level, the correlation coefficient is 0.36 is moderate relation and
coefficient of determination is around 13%.


New Jersey Regression Analysis of
Obesity by Income and Education Level 2016

new jersey regression analysis by income level, there is strong relation of
correlation coefficient with 0.92.  The coefficient
of determination is 84%. The multiple r correlation coefficient is strong
relation with 0.95 and 90% is coefficient of determination by education level.


Oklahoma Regression Analysis of
Obesity by Income and Education Level 2016

regression analysis by income level for oklahoma, 0.74 is multiple r
correlation coefficient is strong relation and coefficient of determination is
54%. By education level correlation coefficient is 0.83 shows strong relation
and 69% of coefficient of determination.       


Tennessee regression analysis by income level, where correlation coefficient is
0.39 with moderate relationship. The R square is coefficient of determination
is 15%. By education level, the correlation coefficient has moderate
relationship and R square is coefficient of 
determination is 37%.  

Tennessee Regression Analysis of
Obesity by Income and Education Level 2016





New Mexico Regression Analysis of
Obesity by Income and Education Level 2016


obesity level in New Mexico by income level , the correlation coefficient has a
strong relationship. The R square is coefficient of determination which is
62%.The correlation coefficient by education level is 0.77, which means there
is a strong relationship and coefficient of determination is 60%.



statistical analysis to demonstrate the relationship between obesity, income,
and education was completed. Based on this analysis, a determination on if the
results accept or reject the hypothesis was able to be made. The hypothesis
states that states with higher socioeconomic status, as it relates to education
and income, have lower obesity rates than states with lower socioeconomic


majority of the evidence found proved the hypothesis to be true. As income and
education levels increased, obesity rates decreased. Education levels showed
significant differences in obesity rates. Based on the model, the more
education achieved, nationwide, the lower obesity rates were. Also, the more
income in the household, nationwide, the lower obesity rates were. However,
there was some evidence found that were exceptions to the hypothesis. In one of
the higher income states, Alaska, obesity rates were significantly higher than
the rest in the year 2016; just as high as those in the lower income states. In
New Mexico, a lower income state, there were significantly lower obesity rates
for all six years.


regression analysis of the top 3 higher income states: Maryland, Alaska, and
New Jersey; and top 3 lower income states: Oklahoma, Tennessee, and New Mexico
were conducted. The findings implicate for most of the above states, there is a
significant relationship between obesity, income, and education. There is an
exception to this; Tennessee showed no significant relationship and fit to the
model than the other states in the model.


It is
obvious that education and income levels have a significant impact on obesity.

We assume that those in higher income areas, have more money and resources to
maintain a healthier lifestyle than those in lower income areas.

are some cases where the hypothesis isn’t supported by the evidence but overall
there is enough evidence to support it. Therefore, we can accept the



It is
vital to continue research and develop more accurate models to represent
obesity and what causes it. In doing so, a way to decrease obesity can be
pinpointed.. Different ways to improve the regression analysis will be given,
since there were some instances where the hypothesis wasn’t supported by the
evidence. Also, limitations of the study and how to improve future research to
eliminate any sources of error will also be given.


Improve Future Regression Models

suggestion is to use one-on-one regression. This involves collecting the data
and then composing scatter plots for each independent variables plotted against
the dependent variable. A straight line( linear) will form if the variables are
in direct proportion of each other and when one variable changes the other will
be affected. Also, instead of regressing each of the independent variables
against the dependent variable; a multiple regression can be used where each of
the independent variables are regressed as a group against the dependent



are some constraints that have been found when it comes to using register-based
data. When dealing with income that is registered based, majority of the time
it is self reported. Self-reported income is subject to reporting bias and
can’t be used to determine if there is a direct correlation between obesity and
income. In future studies, the fact that many report themselves as having
higher or lower income should be taking into consideration. Also, when it comes
to income, people who were not gainfully employed should be excluded from the
study. This includes unemployed individuals who are receiving benefits,
individuals who receive pensions or sickness benefits and also maternity
allowances during a given year.


last constraint is using self-reported BMI. Self-reports underestimate the
prevalence of obesity and obese individuals tend to underreport their BMI.


Add Another SES Variable

future research, the SES category of occupational status can be added as
another independent variable. This category will include upper-white collar
employees, lower- white collar employees, manual workers, farmers, and
self-employed individuals including entrepreneurs. This study sought to
investigate only two variables when it came to socioeconomic status. Adding another
variable will provide a more clearer picture of which individuals are obese and



As the
prevalence of obesity is rising in the United States, obesity was studied to
determine if SES had any direct correlation or causation to the matter. In
doing so, the hypothesis of higher SES individuals being less obese than lower
SES individuals was analyzed. The findings suggest that as income and education
levels increased, obesity rates decreased. The more education achieved,
nationwide, the lower obesity rates were. As more information and data are
added to the regression models and from using the future directions provided, a
more in depth understanding on obesity as it relates to SES status should be
gained. Furthermore, it will provide insight on why obesity is so prevalent and
maybe determine different ways it can be decreased. Overall, obesity is a very
important factor nationwide.






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