Table of Contents

I. Abstract 2

II. Introduction 3

III. Literature Review 5

a. Influences to Obesity 5

b. Obesity in relation to healthcare cost: 9

c. Conclusion 11

IV. Research and Methods 12

V. Results 18

VI. Conclusion 25

VII. Works Cited 27

VIII. Appendix 29

1

Abstract

The obesity epidemic within the United States has reached new heights. The amount

of individuals who are overweight or obese has reached 69% in the year 2011 over two-

thirds of our population. Obesity can lead to various health problems including, but not

limited to heart disease, type 2 diabetes, cancer, depression, and physical impairments.

Approximately 300,000 deaths each year in the United States are associated with obesity or

being overweight (Miljkovic, Nganje, 2007). These effects are seen daily, whether through a

wage gap, physical job demand decrease and prominently through the cost of healthcare.

This study ran a two-part regression model, one specifically the correlation between

healthcare and obesity, and the other for the determinants of obesity. The data was

collected for a single year, 2015. Obesity in particular was contributable to a .64% increase

in the cost of healthcare per a 1% increase in obesity. These results imply the continual

growth of health spending is due to a controllable, and frightening factor.

2

Introduction:

The United States of America has been known as one of the most prosperous

countries in the world. It is known for its opportunities, wealth to be offered and freedom

to name a few. However, over the past decade the U.S. has taken a leading role on a new

scale as one of the fattest countries in the world. The adult obesity rate has increased

drastically over the past twenty years. In 1990 the obesity rate was estimated to be 12%

(Clinical Journal of Oncology Nursing, September/October 2002). Flash forward to a study

conducted by Cynthia L. Ogden, PhD, for the National Center for Health Statistics and

Centers for Disease Control and Prevention, observed the years between 2003 and 2012,

which found that the adult obesity rate (ages 20 and older) was a staggering 34.9%. This is a

third of our population, roughly 78.6 million people.

Obesity can lead to various health problems including, but not limited to heart

disease, type 2 diabetes, cancer, depression, and physical impairments. Approximately

300,000 deaths each year in the United States are associated with obesity (Miljkovic,

Nganje, 2007). With this, the associated health care cost has increased. Through 2006 the

increased prevalence of obesity is responsible for almost $40 billion of increased medical

spending, including $7 billion in Medicare prescription drug costs (Eric A. Finkelstein et al.,

2009). With the obesity rate drastically increasing, this can suggest the increase in the cost

of healthcare as well. These increased costs could potentially be pushed onto tax payers,

obese and non-obese individuals. With a higher premium for healthcare, the money and

resources for that purpose means that it is being taken away from others such as quality of

food, standard of living or other potential insurances and costs.

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The rise in obesity does not only effect healthcare cost. In a daily routine the effects

can be noticed. In a work place, subjects who are overweight will have a harder time

completing tasks. This leads to a decrease in productivity. This decrease in productivity will

ultimately have an effect on the wage given to employees. Though this may seem as a

discriminatory factor, the potential employee is seen as less valuable because of the lower

productivity that is associated with them. Also, in a study conducted by Jay Bhattacharya

and M. Kate Bundorf (2009) using data from the National Longitudinal Survey of Youth and

the Medical Expenditure Panel Survey, they found that in many cases, the cost of employer

sponsored health care was offset by the wage given to the employee. By this example, the

study conveys that employers potentially factor in the cost of sponsored healthcare when

deciding what wage to give an employee. Thus showing how obese individuals will lack a

certain percentage of wage.

Due to this effect on the economy this study will look at factors effecting the cost of

health care, specifically the obesity rate. Also, this study will control for factors including

income, region, education, age, race, a person’s access to physical activity, food insecurity,

and the uninsured adults in the United States.

4

Literature review:

Influences to obesity:

Obesity-related illnesses can lead to a staggering $209.7 billion industry (Cawley et

al., 2011). To grasp this, it is important to first have an understanding of what it means to be

obese or overweight. The World Health Organization (WHO) bases the definition of

underweight, overweight, obese, and healthy on BMI. BMI is the person’s weight in

kilograms divided by the square of the height in meters (kg/m2) (WHO, 1998). Table 1 is

used to more accurately describe how BMI is broken down.

CLASSIFICATION BMI

UNDERWEIGHT < 18.50

NORMAL RANGE 18.50-24.99

OVERWEIGHT ≥25.00

PREOBESE 25.00-29.99

OBESE CLASS 1 30.00-34.99

OBESE CLASS 2 35.00-39.99

OBESE CLASS 3 ≥40.00

The specific literature for this study will mainly focus on adults aged 18 and older as it is

difficult to accurately represent childhood obesity levels. This will be the main reference

point for classifying an individual. However, this is not a direct comparison to body fat

percentage. Therefore, there is the possibility of misclassification for athletes and muscular

persons (Rosin, 2008).

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The consequences of becoming overweight or obese are overwhelming. Large

portions of body fat stored can lead to illnesses such as coronary heart disease, stroke, high

blood pressure, Type 2 diabetes, cancers such as endometrial, breast, and colon, high total

cholesterol or high levels of triglycerides, liver and gallbladder disease, sleep apnea and

respiratory problems, degeneration of cartilage and underlying bone within a joint

(osteoarthritis), reproductive health complications such as infertility, and mental health

conditions (CDC). Obese individuals have a 50-100% increased risk of death when compared

to their normal weight counterparts (Mokdad et al.,2004; Flegal et al., 2005). These risks

greatly exceed the health problems associated with smoking and problem-drinking (Sturm,

2002).

With such great health risks the question is raised what would lead someone to

becoming overweight or obese. Rosin (2008) explains that much of the reason is due to

genetic, behavioral and environmental factors that affect a person’s energy intake and

energy expenditure. These main factors include schooling and education of obesity, price

levels when compared to other affordable options and income compared to the poverty

levels. These will be addressed.

In a study conducted by Kenkel (1991) he suggests that the amount of schooling one

possesses will contribute to that person’s weight, specifically the chance that they will

become overweight or obese. The argument is that the more someone has access to health

knowledge the less likely they will be to spend money or more cautious they will be on food

choices. However, a counter point to this is that the more education one possesses the

6

more money they will make, therefore they will be able to purchase higher quality food. In

this study, Kenkel (1991) controls for income to limit the bias for this.

The main focus of this paper is to look closely at the factors that affect the cost of

healthcare in relation to obesity. Because the years of education is valued for decisions in

this study, this may play a role when focusing on price level. The more years of education

the greater the possibility of a higher wage. This gives the consumer a choice to make on

food and lifestyle adoptions. Lakdawalla and Philipson (2002) and Cutler et al. (2003)

suggest that calories have become cheaper to produce and consume while the cost of

exercising has increased. In simple economics the individual wants to maximize their utility

in their personal budget constraint. The individual has a variety of choices such as less

eating and more exercise, or counterintuitively, the person may eat more and exercise less.

This may lead to the increase in their BMI and the obesity rate. This can then lead to the

previous diseases which in time could increase healthcare cost.

Prices have a tendency to fluctuate due to the region. This can come in many

fashions based off of the cost living for that area. The cost of living is much higher in dense

metropolitan areas as opposed to more rural areas. A strong example of this are cities such

as New York City or San Francisco. However, there are many determinants to this. This is

where state policies may effect purchases of a consumer. Certain states may impose taxes

on items that result in the cost becoming largely higher when compared to other purchases.

On a smaller scale, French et al. (1997, 2001) found evidence within a study using vending

machines that price differentials between high-fat and low-fat snack substitutes would

cause people to change their consumption behavior. This would have a direct relation to

7

obesity as the higher fat foods consumed might also lead to weight gain. Another study

examines more closely the price sensitivity in relation to the per capita number of

restaurants, specifically meals in fast food, full-service, food consumed at home, and

cigarettes and alcohol. This study by Chou et al. (2004) however, used a different measure

for obesity specifying that it depended on working hours, family income, relative prices,

schooling, and marital status, his results were still conclusive that the increase in weight

rose relative to prices of food at home declining. In other words, money spent on food at

home was less than that being spent on food consumed at convenience stores and fast food

restaurants (Chou et al., 2004). This can vary in magnitude depending on the urbanization of

the community. A more populated city will contain more restaurants, more convenience

stores, and more fast food options as opposed to rural areas.

More specifically, the built environment of a city can impact how healthy individuals

can be. A built environment consists of the neighborhoods, roads, buildings, food sources,

and recreational facilities in which people live, work, are educated, eat and play (Sallis,

Glanz, 2006). Communities that spend more money on areas for individuals to become

active will likely have a lower BMI rating and body fat. Sallis and Glanz (2006) state “People

who have access to safe places to be active, neighborhoods that are walkable, and local

markets that offer healthful food are likely to be more active and to eat more healthful

food.” This study being conducted will control for income. Therefore, the income and

poverty level in a area will contribute to consumption choices made. As with the built

environment, a lower income area will have less access to proper walkways and physical

activity stations. Also, there will be a reduction in the quality of food consumption.

8

Drewnowski and Specter (2004) discuss four points that explain how income impacts the

choices of food consumption made. First, the highest rates of obesity are located within

areas of the lowest income and least education. Second, the inverse relationship between

energy density and energy cost. Higher energy dense foods come at the lowest cost to the

consumer. Third, sweets and fats are associated with higher energy intakes. Lastly, poverty

and low incomes are associated with lower food expenditures, thus diets based on refined

grains, added sugars and fats are more affordable than diets containing lean meats, fish,

fresh fruits and vegetables. Other theories such as a phenomenon referred to as

carbohydrate addiction support the urge for consumption of these types of foods. Other

explanations are the myopic addiction theory and rational addictions theory. The myopic

addiction theory implies that only the current and past, but not future, price and

consumption changes of the addictive goods have an impact on the current consumption

(Miljkovic, Nganje, 2008). The rational addiction theory implies that future price and

consumption changes of the addictive good have a significant impact on the current

consumption (Miljkovic, Nganje, 2008). Miljkovic and Nganje (2008) use myopic addiction as

their explanation of obesity.

Obesity in relation to healthcare cost:

Overall, in 1998 the medical cost of obesity was responsible for as much as $78.5

billion (Finkelstein et al., 2009). In 2008 that number climbed to $147 billion (Finkelstein et

al., 2009). Thorpe et al. (2004) found that obesity was responsible for 27% of the rise of

inflation-adjusted health spending between 1987 and 2001. Finkelstein et al. (2003) found

that the average annual medical expenditures are $732 higher for those who are obese as

9

compared to normal weight individuals. This cost could be seen in a variety of aspects. One

view is the effect obesity has on employee-sponsored health care. Many companies will pay

a premium varying across individuals based on observable factors (Bhattacharya, Bundorf,

2009). The study conducted by Bhattacharya and Bundorf (2009) had two key findings. One,

it is employees who ultimately bear the cost of employee-sponsored healthcare. The wages

of obese workers varied across one another. The second finding was that obese workers

had lower wages than those of normal weight. They suggest that much of this may be due

to the higher cost to insure these workers. The study used a dependent variable as the

workers hourly wage. This would have helped to declare wage differences across various

types of workers, men, women, race, and obese or normal weight.

There has been a concern with technology advancements as a possible explanation

for the increasing cost of healthcare. The theory is that there needs to be funding for these

advancements and increasing healthcare would counteract this rapid increase. Thorpe et al.

(2004) notes that “the introduction of new medical technology is thought to account for

most of the growth in health care spending, while aging and population growth account for

smaller portions of the rise”. Thorpe argues this point in his study, stating that “studies have

not addressed the relationship between the increase in obesity prevalence and the growth

in costs over time”. In Thorpe et al. (2004) fourteen year study, the observations were that

the proportion of the population with normal weight had decreased by thirteen percent. At

this same time the proportion of adults categorized as obese increased by 10.3 percent.

These results have also been noted in the National Health and Nutrition Examination Survey

10

(NHANES). These findings have shown a stronger correlation and account for more of the

reasoning for the increase in healthcare cost.

Presumably, if you are overweight or obese you are spending more for health care

as the possibility for complications due to obesity related diseases are much greater than

that of a normal weight individual. There is statically significant difference in these groups.

The estimated per capita spending on health care in 1987 was $2,188 (in 2001 dollars)

(Thorpe et al., 2004). In that year there was a 15 percent difference between normal weight

and obese individuals. Transfer to 2001 and the spending among obese has risen to 37

percent higher than the normal weight group (Thorpe et al., 2004). Inflation-adjusted

spending per capita increased by $1,110 between the years 1987 and 2001. However, if the

obesity rate had stayed at 1987 levels, per capita spending would have increased only by

$809 (Thorpe et al., 2004). Spending for obesity related conditions is attributed to the extra

$301. Therefore, obesity is attributable to 27 percent of the $1,110 spending growth. When

obesity prevalence is isolated, it is found to contain a 12 percent increase for the real per

capita spending growth (Thorpe et al., 2004).

Conclusion:

Obesity is one of the major epidemics to take over the United States in recent years.

It accounts for nearly a $40 billion increase in medical spending including $7 billion in

Medicare prescription drug costs (Eric A. Finkelstein,et al., 2009). To understand this impact

of obesity, it is first important to understand how, where, and why it can occur. The

previous factors discussed play a role in determining obesity, but also how obesity will

11

ultimately determine the cost of health care. The discussion of obesity can relate widely to

the economy as it has a significant impact on work performance and employers decision.

Prices, region, access to physical activities, government policies, education and level of

income will ultimately play a role in obesity, but also how these obesity rates will impact the

economy and specially, health care cost.

Research and Methods:

My research will explore the relationship between the obesity rate and the

economic effects that follow this increasing statistic, specifically the cost of healthcare. The

data used for this research was taken from the County Health Rankings (CHR). The CHR

ranks each state and county from a sophisticated set of variables. The data the CHR uses is

from various sources from separate years. The years may be found on the CHR website. I

hypothesis that the increasing rate of obesity is largely positively correlated with the cost of

healthcare overall. I have developed a model to explain the variation in healthcare cost:

yhlthcarecoi=β0+β1obsi+β2MHHinc i+β3edu i+β 4male i+β5MA i+β6 southi+ β7midwest i+β8northeasti+β9blacki+β10whitei+β11hispanici+β12accessphysact i+β13 fdinsecurei+β14Uninsured i+e

My dependent variable is represented by yhlthcareco. The constant in the equation isβ0.

The independent variables are represented as β1through β14 with the regional variables as

dummies. The data collected for this study was on the county level for the entire United

States. This study was focused on one year, 2015 thus accounting for 3140 observations.

This amount of observations will give a significant insight upon the range for all variables.

Because the cost of healthcare is so large this study will take the natural log on healthcare,

meaning that a one unit increase in an independent variable with all others held constant

12

will lead to a β∗100 percent increase or decrease in the cost of healthcare. Specifically, this

is measured from the Dartmouth Atlas of Health Care which has documented glaring

variations in how medical resources are distributed and used in the United States

(Dartmouth, 2015). The project uses Medicare data to provide information and analysis

about national, regional, and local markets, as well as hospitals and their affiliated

physicians (Dartmouth, 2015).

Dependent Variable: Cost of healthcare (Yhlthcareco)

Healthcare has been a presidential debate over many decades. In recent events, the

cost of health care has risen drastically and many Americans are concerned about this

trend. It is important to look at the various reason as why healthcare cost has risen. Also,

these results may be significant for future expectations of increasing costs. I believe that

obesity and the health consequences derived from it will be a leading cause. I expect the

correlation between healthcare cost and obesity to be positively correlated.

Variable of Interest: % that report BMI > 30 (β1Obs)

The World Health Organization (WHO) bases the definition of obese on BMI. BMI is

the person’s weight in kilograms divided by the square of the height in meters (kg/m2)

(WHO, 1998). Specifically, to be obese ones BMI must be greater than 30. As the BMI

increases the more obese you become the greater of the chances one has of developing

health problems. The increase in health problems could lead to a greater need for health

coverage and the use of it more frequently. Thus, raising the cost of health coverage.

Independent Variables:

13

Median household income (β2MHHinc)

The ability for an individual to pay for healthcare can be dependent upon the

amount of income that person creates per year. Higher incomes would be more likely to

purchase higher, more costly health coverages. In contrast, the lower income an individual

holds will lead them to purchase lower healthcare, if any at all. Since many individuals

reside under their parents or guardians healthcare, this study has controlled for the median

household income. The hypothesis of this is that the more income within a household the

higher the health coverage will be making this positively correlated.

% attaining higher education (β3Edu)

Higher education levels allows one to be prosperous in many areas of life. For

example, the higher level of education a person has the better a job one should be able to

attain, therefore leading to higher wage. This higher wage would allow this person to buy

better qualities of food, improve living situations, and allocate more money into the

economy. However, this higher level of knowledge would in theory, allow someone to

obtain more knowledge of living a healthier lifestyle and avoiding the chance to become

obese. This study focuses on those adults aged 25-44 who have obtained some post-

secondary education, meaning there has been some college or more of experience. This

factor is hypothesized to be negatively correlated to healthcare.

Gender (β4Male)

14

The importance of gender can be found in a plethora of areas. The gender gap for

wage is one example. On average, men earn more per dollar than women. This may

influence the type of health coverage that a person may purchase. The propensity to

consume more food than the opposing female is also another potential explanation. In

general, men engage in work and home environments that are more dangerous. This

exposes men to more injuries and would in turn need better, more expensive, healthcare.

The percent of male population per county may have a significantly positive correlation to

the cost of healthcare.

Median age in the county (β5MA)

The median age of the county will provide insight on how they rank compared to

one another. BMI is a function from height and weight but, looking at the age will help

signify this even more. The younger population may not spend as much on healthcare as

their budget will be consumed by other factors. This younger population also tends to be

healthier when compared to older generations. The older the population the more likely the

cost of health care will increase as health problems ascend. A higher median age will likely

be significantly, positively correlated to healthcare.

Regions (Dummy variables) (β6South) (β7Midwest) (β8Northeast)

A regional dummy variable will help account for why different counties may

experience higher levels of health care cost. For example, those areas that experience

hotter weather will be able to exercise more therefore lower the cost of health care. In

15

contrast, the colder climates will have trouble getting outside and becoming active. This

dummy variable will also account for unobservable factors.

Race (β9Black) (β10White) (β11Hispanic)

The race of an individual has a significant impact on their ability to pay for health

coverage. Traditionally, minority groups such as those who identify as black/ African

American or Hispanic, obtain lower incomes making it harder to pay for premium health

coverage. Those with lower incomes are more likely to purchase lower quality health

coverages.

% with access to physical activity (β12Accessphysact)

The percent of the population that has access to physical activity accounts for the

opportunity that people within the county will take advantage of working out. The more

access someone has to physical activity the healthier they should become. The percent of

the population that has access to physical activity is likely to be significantly, negatively

correlated to the cost of healthcare.

% Food Insecure (β13fdinsecure)

Food insecurity is the most broadly used measure of food deprivation in the United

States. Food insecurity refers to USDA’s measure of lack of access, at times, to enough food

for an active, healthy life for all household members and limited or uncertain availability of

nutritionally adequate foods (Feeding America). This measure allows the quality of food

that a household is consuming to be examined. Assuming that the price of food impacts

buying decisions, the family may buy more amounts of lower quality food. The lower the

16

quality the higher the percentage of gaining weight. In contrast, the family may purchase

no food leading to malnutrition and other diseases. This does not mean, however, that

these families are food insecure all of the time. Food insecurity may reflect a household’s

need to make trade-offs between important basic needs, such as housing or medical bills,

and purchasing nutritionally adequate foods (Feeding America). These buying adaptations is

the reason for controlling this variable.

% Uninsured adults (β14Uninsured)

Some individuals choose to remain uninsured during a period of their life. This

involves taking a large risk. Tragedies may occur at any time. The data used for this variable

is collected for the Small Area Health Insurance Estimates (SAHIE) within the United States

Census Bureau. This accounts for individuals under the age of 65. The basis for these

estimates comes from the American Community Survey (ACS). The ACS specifically asks if

this person is currently covered as opposed to being covered only some time during the

year. The estimates for county levels come from a cross-classification defined by the same

age, sex, and income groups. We assume these survey estimates are unbiased and follow

known distributions. This will help explain the variation in average cost of healthcare. Due

to those that do not purchase health coverage the hypothesis is that this variable will be

negativity correlated, thus as the percent of uninsured adults increases it will lower the

cost.

Results:

17

The hypothesis of this study is to understand the correlation of obesity with the cost

of healthcare. Specifically, that the increase in obesity would cause a rise in healthcare. The

other controlled factors that could impact healthcare costs were the median household

income, education, being male, median age, region, race, access to physical activity, food

insecurity, and the percent that were uninsured within the county. Obesity itself

contributed a .64% positive correlation and statistically significant change in the cost of

healthcare. This measure is important because of the growing contributions to obesity.

More and more individuals are subjecting themselves to an increased financial hazard that

can be avoided. To continue the analysis, it is noted that the following values are in terms of

the robust, after performing a Breusch-Pagan test for heteroskedasticity. To further

understand the results from the study, the full results may be seen in table 2 of the

appendix.

The region dummy variables proved to have a significant impact upon the cost of

healthcare. The region of a consumer is large contributor due to many factors such as state

law, activities, weather and the cost of living. The regions tested were the south, midwest

and northeast all compared to the omitted, the west. Each was proven to be statistically

significant with being located in the south having the largest impact of a 22% increase to

healthcare. Second was the northeast with a 19.4% increase and lastly the midwest at

13.9%. This may make sense as those more towards the west have more sunny days which

result in more potential for active days outside thus producing a healthier body and lower

costs towards healthcare. However, this contrasts the results from the percent that have

access to physical activity. This variable had a very low relationship impact on healthcare

18

and have rejected the null hypothesis. There may be a possibility that even though

someone has the access to do the physical activity they may choose not to partake within

the activities present.

The demographic variables such as being African American, Caucasian or Hispanic

compared to the omitted group, Asian, performed poorly as well. The Caucasians and

Hispanics proved to be statistically significant and increased healthcare by .27% and .24%

respectively. In contrast, being African American did not affect healthcare nearly as much at

a .009% level increase and was also found to be statistically insignificant. Another

demographic was the difference between being male or female. The odds of being male led

to a .16% increase in healthcare. This may be due to that males tend to consume more and

are also traditionally in more physically demanding and dangerous jobs when compared to

women. However, this was also found to be insignificant at the 5% significance level thus

we reject the null hypothesis.

Certain variables add insight and explanation to the economy and eventually to the

study at hand. These variables were median household income, education, and the median

age. Median household income was found to be statistically significant, however, impact of

the coefficient was incredibly small, accounting for a 1.08e-04 percent affect upon

healthcare for every one percent increase in median income. This finding is shocking as the

amount of income being obtained has a significant power upon what type of coverage

someone can purchase. Education was found to be negatively correlated as predicted. For

every year of college experience obtained by someone, this would lower the cost of

healthcare by .059 percent. Lastly, median age proved to have one of the largest impacts.

19

For each additional year of aging the cost of health care would increase by .71 percent. This

was expected, as one ages the more possibility of health problems can occur.

Two unique variables used in this study were the food insecurity percent and the

percent that were uninsured. Food insecurity had the highest impact between these two

variables with a .92% increase to healthcare. The rationale behind this is could be that those

who are food insecure tend to choose foods that are cheaper, calorie dense foods like those

found in McDonalds, Burger King, and other fast food restaurants instead of higher quality

foods or the consumer may choose to skip meals at a time. In either case, this can lead to

weight gain and other contracted diseases from a weakened immune system. The second

unique variable is the percent that are uninsured. This statistic led to a .28% increase in

cost. Due to those not paying into healthcare, it could raise it for others since there is not

enough going into the funding, therefore, demanding more from others. Since the

description for this variable states that at the present moment if someone is currently

covered, this would ultimately raise the people’s rate in the future if they were not. Also,

due to inflation purposes this could also explain the increase for the future buying of

coverage.

To follow up my observations I performed another regression to assess the

relationship of my variables to obesity. This regression used the same previously stated

formula. The only differences were that the cost of health care was substituted out and

replaced with the percent that were obese within the counties. This equation was not

logged and contained the same sourcing of data as the previous regression, therefore, it

contained 3140 observations as well. The results for this regression found a significant

20

explanation for the variation in obesity with an R-squared value of .604, or 60.4% variation

in obesity. All explanations of values will be in terms after conducting a Breusch-Pagan test

for heteroskedasticity. The full results of this regression may be seen in table 3 of the

appendix.

The demographic variables, the south, Midwest and northeast accounted for a 3.9,

4.1 and 2.3 percent increases respectively towards obesity. These drastic numbers are in

line with the previous significant impact they played when regressing healthcare cost. This

makes sense as those that live in better weather may take full advantage of being more

active and healthier. The racial demographic variables showed that being white or Hispanic

would lower the obesity while African Americans increased the obesity percent.

The economic variables such as income, median age and education had strong

impacts upon obesity. Education and income were negatively correlated, reducing obesity

by -.07 percent for each additional experience of education and -.0001 percent for each

increase of income. Median age had one of largest accounts for increases in obesity. The

coefficient value of .36 would suggest a .36 percent increase in weight, or obesity, due to a

one year increase in age. This, again, makes sense as older people will hold more weight for

various reasons.

Two shocking statistics were the results for food insecurity and the percent that

were uninsured. Being food insecure led to a .013 percent increase in obesity. This can be

expected because of unhealthier food choices. However, the statistic was found to be

insignificant. Due to unforeseen factors this comes as a shock to the model. The percent of

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uninsured adults, those with no coverage, was found to be negatively correlated to obesity

with a -.07 percent change.

Lastly, the variable with the most shock came from the percent that have access to

physical activity. This was negatively correlated with obesity, as one would expect, however,

at a very small rate of -.013. As noted, this may be due to those that have this opportunity

but choose to forgo the actively to perform some other action.

A last check in analyzing the results for this study was to look at the correlation

between the variables. This is important to account for because a correlation between the

variables would indicate a bias within our study, thus leaving the results almost useless.

Based on this concern, a correlation matrix was constructed and it depicts that a high

degree of correlation between the independent variables was not found. The results of this

matrix can be found in table one of the appendix.

To further confirm the results from this study, they will be compared to previous

studies that have tried to accomplish the same goal. Comparing the results to other studies

will signify the importance of the results. The studies that will be compared are Finkelstein

(2009), Thorpe (2004), Bhattacharya and Bundorf (2009) and Cawley and Meyerhoefer

(2011). Each of these studies specifically looked at the effect the increased obesity rate has

had upon health and medical coverage, each using its own unique variable approach. It is

also important to note that these studies were conducted over multiple years, in which

time, were able to run multiple regressions and variable approaches to additionally explain

their hypotheses.

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Finkelstein (2009) discovered during a two year study between 1998 and 2006 that

overall, obesity had increased the cost of medical spending by 37%. When comparing

normal-weight individuals to obese individuals the cost that the obese exceeded $1429

more than normal-weight persons. The regression model used for this was a four part study

finding specific estimates for inpatient, non-inpatient or drug prescription services. While

Finkelstein used a deeper value as calculating the money spent and a four part regression

model, this study further exhibits the value of obesity increasing the cost of healthcare

spending. His findings of a positive correlation when controlling for obesity back up the

results that were found in the current study.

Thorpe (2004) like other studies used a two part model. Within these models he

discovered that in 1987 the difference of pay that normal and obese individuals pay is

15.2% less, and 37% by 2001. In a per capita income model, Thorpe explains that the

increase in spending growth was contributable to obese by up to 12%. He associates this

increase in spending to obesity by stating that with obesity comes greater risk for

contracting disease and the spending to cure or treat those diseases.

Bhattacharya and Bundorf (2009) collected data from the National Longitudinal

Survey of Youth (NLSY) and from the Medical Expenditure Panel Survey (MEPS). With this

data they were able to estimate the difference-in-difference of the effect of obesity on

hourly wage. With employee sponsored healthcare, obese workers earned approximately

$1.42 per hour less on average than their counterparts. In contrast, the difference-in-

difference estimate for employees that did not get healthcare from their employer was

23

$.25. This statically shows that obesity is effecting the cost of healthcare. In this form, it

offsets wages as employers fear that this individual is costing more than others.

Cawley and Meyerhoefer (2011) used a unique instrumental variable approach to

reduce the bias from reporting errors in weight. This study also used data from MEPS for

the years 2000-2005. The importance of this study is to show that many of the previous

studies have underestimated the contributable obesity factor to healthcare. Within their

study they concluded that the additional unit added of BMI will increase medical spending

by $49 and a $59 increase if you happen to be male. For total expenditures the estimated

marginal effect of obesity proved to bring an estimated $2741 of spending. However, two

key facts should be kept in mind when comparing his study. This study was comparing

obese to non-obese (this is included healthy weight and overweight). Secondly, this study

used adults with at least one biological child. This may bring about bias as this leans to

individuals that may be healthier.

Overall, the current study being performed was able to explained roughly 38% of the

variation in the cost of healthcare. Obesity specifically accounted for a higher portion of

change than most other variables with a 64.4 unit change. This is important to understand

because of the ability for individuals to change this. Most factors that apply to healthcare

cannot be easily changed. This study varies different from others as they did not correlate

their variables to explain obesity. Explaining obesity allows for a more in-depth look at how

obesity would increase, in turn increasing the cost of health care. Some changes that could

be altered for future studies would be a more specific allocation of money within a

household to gage what the household is paying for per month. Another factor would be to

24

take into consideration the type of laws that effect each state for healthcare. Many times

people are covered under very specific situations, which could also result in lower or higher

cost of coverage. Accounting for the population density would allow for more accurate

mapping of health cost. This would allow for notice of trends within the country of lower

costs. A final change to be done to this study be to find a more accurate measure of some

the statistics because most were self-reported. This may leave room for error on both sides,

those that report and those that choose not to report. Due to time factors, to better

understand these numbers, the optimal study would be to compare it to other years. This

would allow better understanding of exactly how big of an issue obesity has caused to

healthcare and our society.

Conclusion:

These results reveal continues economic burden upon health coverage, specifically,

the relevance of obesity. The connections between rising healthcare costs and obesity rates

is undeniable. More accurately, the results speak to say that obesity is a large contributor to

the consistent increase in costs. Unlike other studies, this one in particular accounted for

the ability to exercise and food insecurity. The ability to exercise was found to be

insignificant this can be due to foregoing the opportunity to partake in a physical activity to

perform a task deemed more important. However, it is very relative because of the chances

that the more exercise opportunities available to more likely it is that someone would lose

weight and prevent obesity. Food insecurity is extremely important because of the

situations where families must decide how much money to spend on what type of food or if

food is even purchased at all. In theory this impacts the amount of money put towards the

25

purchase of health coverage, but does not impact the cost that it would come to. The statics

show that food insecurity is positively correlated to the cost of health care and actually

raises the total amount to buy. These were the types of differences within this study

compared to others that should be noted.

Works Cited

Arrow, K.J., 1963. Uncertainty of the welfare economics of medical care. American Economic Review 53 (5), 941-973.

26

Bhattacharya, J., & Bundorf, M. (2009). The incidence of the healthcare costs of obesity. Journal of Health Economics, 28, 649-658.

Bing-Hwan, L., Elizabeth, F., & Joanna, G., Away-from-home- foods increasingly important to quality of American diet, Agriculture Information Bulletin no. 749 (Washington: U.S. Department of Agriculture, 1999).

Cawley, J., & Meyerhoefer, C. (2011). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31, 219-230.

Cutler, D.M., Glaeser, E.L., et al., 2003. Why have Americans become more obese? Journal of Economic Perspectives 17 (3), 93-118.

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Finkelstein, E.A., Fiebelkorn, I.C., Wang, G., 2003. National medical spending attributable to overweight and obesity: how much and who’s paying? Health Affairs, W3-W219.

Finkelstein, E.A., Trogdon, J.G., Cohen, J.W., Dietz, W., 2009. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs, Web Exclusive. July 27, 2009.

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How Healthy is your County?, County Health Rankings. (n.d.). Retrieved February 28, 2015, from http://www.countyhealthrankings.org/

Lakdawalla, D., Phillipson, T., 2002. The growth of obesity and technological change: a theoretical and empirical examination. National Bureau of Economic Research.

Miljkovic, D., & Nganje, W. (2007). Regional obesity determinants in the United States: A model of myopic addictive behavior in food consumption. Agricultural Economics, 38, 375-384.

Nayga Jr., R. M. (1997) Impact of sociodemopgraphic factors on perceived importance of nutrition in food shopping. Journal of Consumer Affairs, 31, 1-9.

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What is Food Insecurity? (n.d.). Retrieved May 8, 2015.

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Appendix

Table 1:

Correlation Table of Varibales | obese percap~e educat~n male median~e south midwest northe~t black white hispanic access~y foodin~e uninsu~s -------------+--------------------------------------------------------------------------------------------------------------------------------------- obese | 1.0000percapitai~e | -0.4704 1.0000 education | -0.4474 0.6134 1.0000 male | -0.0483 -0.0239 -0.2214 1.0000 medianage | 0.2002 0.1230 -0.0728 -0.2036 1.0000 south | 0.3379 -0.2840 -0.4182 -0.1066 0.0353 1.0000 midwest | 0.0769 0.1156 0.3016 0.0043 0.0203 -0.6476 1.0000 northeast | -0.1893 0.2036 0.1021 -0.0666 -0.1704 -0.2480 -0.1938 1.0000 black | 0.4103 -0.2618 -0.2344 -0.1208 0.0313 0.4937 -0.3214 -0.0747 1.0000 white | -0.1406 0.1421 0.2449 -0.0405 -0.3500 -0.3614 0.4144 0.0971 -0.6137 1.0000 hispanic | -0.2443 0.0213 -0.1613 0.1403 0.3138 0.0886 -0.2432 -0.0520 -0.1043 -0.5911 1.0000accesstoex~y | -0.3656 0.3927 0.3998 -0.1599 -0.0870 -0.2249 0.0622 0.1999 -0.1295 0.0158 0.0741 1.0000foodinsecure | 0.3938 -0.6206 -0.4742 -0.0529 0.1165 0.4414 -0.3937 -0.1872 0.6649 -0.6022 0.1126 -0.1861 1.0000uninsureda~s | 0.1778 -0.5102 -0.5557 0.1039 0.2583 0.5027 -0.4899 -0.3171 0.2048 -0.5331 0.4765 -0.2904 0.5552 1.0000

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Table 2: Log regression for healthcare

Linear regression Number of obs = 3137 F( 14, 3122) = 130.91 Prob > F = 0.0000 R-squared = 0.3810 Root MSE = .13054

------------------------------------------------------------------------------ | Robustlnhealthcost | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- obese | .0064882 .0009083 7.14 0.000 .0047073 .0082691percapitai~e | 1.08e-06 4.64e-07 2.32 0.020 1.68e-07 1.99e-06 education | -.0005903 .0003476 -1.70 0.090 -.0012719 .0000912 male | .0016106 .0015477 1.04 0.298 -.001424 .0046453 medianage | .0071428 .0011269 6.34 0.000 .0049332 .0093523 south | .2229643 .0096766 23.04 0.000 .2039912 .2419374 midwest | .1396424 .0096695 14.44 0.000 .1206831 .1586016 northeast | .1937065 .0126004 15.37 0.000 .1690005 .2184124 black | .0000998 .0004615 0.22 0.829 -.000805 .0010047 white | .00278 .000394 7.06 0.000 .0020076 .0035525 hispanic | .002453 .0004736 5.18 0.000 .0015244 .0033817accesstoex~y | .0000564 .0001298 0.43 0.664 -.0001981 .000311foodinsecure | .0091916 .0012639 7.27 0.000 .0067134 .0116698uninsureda~s | .0028193 .0008429 3.34 0.001 .0011666 .0044721 _cons | 8.081443 .1111022 72.74 0.000 7.863602 8.299284

Table 3: Regression for obesity

Linear regression Number of obs = 3140 F( 13, 3126) = 265.66 Prob > F = 0.0000 R-squared = 0.6045 Root MSE = 2.6769

------------------------------------------------------------------------------ | Robust obese | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+----------------------------------------------------------------percapitai~e | -.0001089 8.45e-06 -12.88 0.000 -.0001254 -.0000923 education | -.0783069 .0064695 -12.10 0.000 -.0909918 -.065622 male | .1109772 .028896 3.84 0.000 .0543201 .1676343 medianage | .3618362 .0213752 16.93 0.000 .3199254 .403747 south | 3.937393 .198734 19.81 0.000 3.547731 4.327056 midwest | 4.198521 .2078173 20.20 0.000 3.791049 4.605993 northeast | 2.288687 .2709909 8.45 0.000 1.757349 2.820025 black | .01483 .00933 1.59 0.112 -.0034636 .0331236 white | -.0459275 .0084471 -5.44 0.000 -.0624899 -.0293652 hispanic | -.112543 .0093201 -12.08 0.000 -.1308171 -.094269accesstoex~y | -.0134644 .0024084 -5.59 0.000 -.0181866 -.0087421foodinsecure | .0134432 .0236431 0.57 0.570 -.0329143 .0598007uninsureda~s | -.0761558 .0157445 -4.84 0.000 -.1070264 -.0452852 _cons | 29.19027 2.196229 13.29 0.000 24.88407 33.49647

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