AN EXAMINATION ON THE DETERMINANTS OF OBESITY-RELATED
MORTALITY RATES
Kay F. Saechao
B.A., California State University, Sacramento, 2006
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SUMMER
2011
AN EXAMINATION ON THE DETERMINANTS OF OBESITY-RELATED
MORTALITY RATES
A Thesis
by
Kay F. Saechao
Approved by:
__________________________________, Committee Chair
Craig A. Gallet, Ph.D.
__________________________________, Second Reader
Suzanne M. O’Keefe, Ph.D.
_____________________________
Date
ii
Student:
Kay F. Saechao
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
___________________________________, Graduate Coordinator
Jonathan Kaplan, Ph.D.
Department of Economics
iii
_______________
Date
Abstract
of
AN EXAMINATION ON THE DETERMINANTS OF OBESITY RELATED
MORTALITY RATES
By
Kay F. Saechao
This thesis examines the relationship between obesity and its related health risks. This
analysis accounts for the impacts of economic, socio-demographic, and lifestyle and
behavioral factors on five obesity related mortality outcomes. The data includes all 50
U.S. States from 1995 to 2004. While most of the previous literature focuses on broader
measures of mortality, such as infant mortality and years of life expectancy, this thesis
uses more specific measures to identify if the same methodology can be appropriately
applied. In the specification with two-way fixed effects, I find that obesity significantly
increases rates of deaths from colon cancer and diabetes. But surprisingly, obesity does
not significantly affect deaths from heart disease, blood pressure or stroke. Results also
show that both time and state-fixed effects are appropriate, but inclusion of the state-fixed
effects is more important. The age variables are also shown to be the most important
determinants among all that were examined. In whole, the variables used may be too
iv
general, and important factors may be missing for each specific mortality outcome, which
implies that the commonality of obesity and the same specification may not be
appropriate for each of these mortality outcomes.
__________________________________________, Committee Chair
Craig A. Gallet, Ph.D.
________________________
Date
v
ACKNOWLEDGEMENTS
I’d like to thank all of my friends and family for supporting me through this
journey, I definitely could not have done it without your help. I’d also like to thank my
thesis advisor Craig Gallet for always keeping me on top of everything and making this
experience as smooth as possible.
vi
TABLE OF CONTENTS
Page
Acknowledgements .………………………………………………………………......… vi
List of Tables ……....………...…………...…………………………………..…..…….. ix
List of Figures …….………………………...………………………………………….... x
Chapter
1. INTRODUCTION………...…………………………………………………………. 1
2. LITERATURE REVIEW..………...………………………………………………… 3
2.1. Economic Factors .………………………………………………………………. 4
2.2. Socio-demographic Factors …...……………………………………………...… 9
2.3. Lifestyle/Behavioral Factors …………………………………………………... 10
2.4. Obesity Issues …………………………………………………………………. 11
3. EMPIRICAL MODEL AND DATA ……………...……………………………….. 13
3.1. Empirical Model……………………………………………………………..… 13
3.2. Data ……………………………………………………………………………. 17
3.3. Descriptive Statistics and Time Trends…………....…………………………… 20
4. RESULTS ……………………...………………………...………………………… 24
4.1. Estimation Results Limited to Economic Variables ………...……….………… 24
4.2. Estimation Results Expanded to include Economic, Socio-Demographic, and
Lifestyle Variables…………………………………………………………...… 29
4.3. Estimation Results with 2SLS…………………………………………………. 38
vii
4.4. Elasticities ……………………………………………………………...……… 41
5. CONCLUSION……………………………………………………………………. 43
5.1. Summary of Findings………………………………………………….……….. 43
5.2. Recommendations for future research …...……….………………….…………45
References ……...………………………………………………………………………. 47
viii
LIST OF TABLES
Table
Page
Table 3.1. Variable Sources and Definitions..………………….……………………….. 19
Table 3.2. Descriptive Statistics ………………………………..………………………. 21
Table 4.1. No Fixed Effects, Economic Variables Only ……………………………….. 25
Table 4.2. Time-Fixed Effects Only, Economic Variables Only …….…………………. 26
Table 4.3. State-Fixed Effects Only, Economic Variables Only …………….…………. 27
Table 4.4. State and Time-Fixed Effects, Economic Variables Only …….………..…… 28
Table 4.5. No Fixed Effects ……………………………………………………………. 30
Table 4.6. Time-Fixed Effects …………………………………………………....…….. 33
Table 4.7. State-Fixed Effects …………………………………………..……………… 35
Table 4.8. State- and Time-Fixed Effects …………....……………………….………… 37
Table 4.9. Instrumental Variable Approach ………………………………….…….…… 40
Table 4.10. Elasticities ……………………………………………...………………….. 42
ix
LIST OF FIGURES
Figure
Page
Figure 3.1. Mortality Counts from 1995, 2000, and 2004 …………………………..…. 22
Figure 3.2. Average Obesity Rate and Real Per Capita Healthcare Expenditures …...… 22
x
1
Chapter 1
INTRODUCTION
The prevalence of obesity in the world, especially for the United States, has
contributed to the increased risk of mortality and morbidity of several health conditions,
including diabetes, cancer, high blood pressure, heart disease, and stroke; all of which are
known to be major health risk factors of obesity. While the prevalence of obesity has
increased by over 50 percent since the 1970s (Chou et al., 2002), it is also estimated that
300,000 US adults die of causes related to obesity (Allison et al., 2003). This thesis
examines the economic, socio-demographic, and lifestyle determinants of these health
conditions.
It is important to examine the relationship between obesity and related health
risks, particularly since there are high associated costs borne by public and private
entities. For instance, there was an estimated expenditure of $188 million in 2010 tied to
obesity issues, which includes in-patient, out-patient, and prescription drug spending,
with roughly half financed by Medicare and Medicaid (Finkelstein et al., 2009).
Previous literature has found that income and healthcare expenditures have a high
correlation with health conditions. Over the years, economic research examined the
determinants of health outcomes, generally focusing on economic and socio-demographic
variables, and has now more recently accepted the importance of including lifestyle-type
variables. However, previous research primarily uses life expectancy and infant mortality
rates as measures of health outcomes. Both of these measures are popularly used due to
2
the broad range of health conditions they capture and they are more easily measureable
across countries. Yet there still remain questions of whether the same determinants used
for infant mortality and life expectancy are directly aligned with more specific measures
of health.
This thesis is unique in that five specific measures of mortality are compared, all
of which have links to obesity. From an economic standpoint, this thesis contributes to
further research in health outcomes using specific mortality measures. In terms of health
and obesity research, this thesis examines obesity’s effects on the underlying health risks
that result in mortality and determines how similar factors impact different health
outcomes.
This thesis looks at these issues at the aggregate level. Panel data from 1995 to
2004 across the 50 United States is used, while most literature of similar topics utilizes
survey level data. The use of aggregate level data applied to a wider population is
beneficial because it helps policy-makers more effectively make choices and decisions
that help the population as a whole.
The approach of this thesis is aligned in the following manner. First, a
comprehensive review of the literature provides the theoretical foundation for this
research. Second, following the literature review, the empirical model is determined, and
a description of the data is provided. Third, the estimation results are presented with an
evaluation of the findings. Then finally, a conclusion is provided with a summary of the
results, closing remarks, and recommendations for future research.
3
Chapter 2
LITERATURE REVIEW
Research on the associated health outcomes of obesity is particularly important,
given the relative high morbidity and mortality levels tied to obesity. In particular, due to
poor diet, insufficient physical activity, and often-debated genetics, obesity is known to
cause several health-related problems, such as a higher risk of developing cardiovascular
disease, type II diabetes mellitus (DM), hypertension (high blood pressure), stroke,
dyslipidemia (high cholesterol), osteoarthritis, and some cancers (e.g., see Hirsch et al.,
1985). Indeed, deaths from these diseases make obesity second only to tobacco use as a
leading cause of preventable death (Chou et al., 2002).
The prevalence of obesity and its associated chronic health conditions, such as
cancer, diabetes, and heart disease, has contributed to increases in overall health care
costs by 35% and medication costs by 77% (Strum, 2002). Furthermore, a recent study
(see Eyre et al., 2004) estimated that in 2003 cardiovascular disease, stroke, cancer, and
diabetes accounted for approximately two thirds of all deaths in the United States and
created $700 billion dollars of direct and indirect economic costs per year.
Although much research has focused on the relationship between economic
growth and health, following the premise that economic growth leads to greater resources
that can be utilized to improve health outcomes, a number of studies have most recently
addressed the impact of a broad array of factors on health outcomes. In particular, these
studies have addressed the impacts of economic, socio-demographic, and
4
lifestyle/behavioral factors on various health outcome measures. Accordingly, this
chapter reviews these studies.
2.1. Economic Factors
The primary economic factors considered as determinants of health outcomes are
income, unemployment, and health care expenditure. Intuitively, income is considered
important to health because greater financial resources allow for greater accessibility to
healthcare, which in turn creates better health (Wolfson et al., 1993). Closely tied to
income, socioeconomic shocks, such as unemployment, have also been considered
detrimental to health because of the social stressors that disrupt many areas of life (Beal
and Nethercott 1987; Montgomery et al., 1999; Strully, 2009). Lastly, healthcare
expenditures are seen as a key input into health, such that greater health care expenditures
are a means of improving health outcomes (e.g., see Nixon and Ulmann, 2006).
In an early study, Hitiris and Posnett (1992) found indirectly that income is
connected to mortality. Observing patterns of income growth and mortality within a
panel of several OECD countries, they found a significantly large and positive
relationship between per capita GDP and per capita healthcare expenditures, with an
income elasticity of health care spending near unity. Accordingly, percentage changes in
per capita income yield similar changes in per capita health care spending. They also
found that healthcare spending is negatively related to mortality.1
1
However, health care spending has a modest impact on mortality, as the associated
elasticity was found to be approximately -0.07, meaning a 1 percent increase in health
care spending merely reduces mortality by 0.07 percent.
5
Ettner (1996) took the approach of using survey data from the United States and
initially treated income as an exogenous determinant of health, where she found higher
income has a strong positive correlation with self-assessed health status, yet a negative
association with depressive symptoms, work limitations, functional limitations, and bed
days. However, a number of other studies using individual-level data (e.g., Angell, 1993;
Hurowitz, 1993) argue that income may be endogenous in health outcome regressions
(i.e., poor health reduces income). To address this possibility, Ettner (1996) also tested
for endogeneity, and found that income and health are endogenous. To correct for
endogeneity, she applied a two-stage least squares procedure, using the unemployment
rate, parental background indicators, work experience, and spousal characteristics as
instruments for income. Controlling for income endogeneity, she found the impact of
income on health is higher than her initial estimations revealed.2
Cremieux et al. (1999) used province-level panel data from Canada to also
consider the impact of income on health, using life expectancy and infant mortality as
measures of health outcomes. All else equal, one would expect higher income to provide
greater access to health care, which would increase life expectancy and decrease infant
mortality. Cremieux et al. (1999) confirmed that income has a positive and significant
impact on life expectancy, but failed to find a significant impact of income on infant
mortality. Since they also controlled for the impact of health care spending on health
2
Interestingly, Cutler et al. (2006) find the link between health and income is tied to
public policy. Improvements in public health, such as having clean water, sanitation
systems, draining swamps, pasteurized milk, mass vaccination campaigns, and
antibiotics, reduced the mortality rate associated with water and food-borne diseases from
214 per 100,000 in 1848-54 to nearly zero by 1970.
6
outcomes, which studies (e.g., Hitiris and Posnett, 1992) find to be positively correlated,
it could be that multicollinearity is pronounced in their model.
Smith (1999) considers both directions of causality with respect to wealth and
health by examining the association between household income and wealth and the
presence of mild or chronic health conditions using survey data from the United States.
Although the author fails to identify the direction of causality in most cases, he does find
that in a linear specification both total household income and wealth have statistically
significant effects on self-reported health status.3 Yet the impact of income/wealth is
reduced by one-third when controlling for health risk behaviors such as smoking,
excessive drinking, exercise, and BMI (Body Mass Index).
While intuitively one would expect higher health care expenditure to reduce
mortality, it has often been difficult in the literature to show such a relationship, which
perhaps could be tied to differences in the quantity and quality of data across countries.
Indeed, Hitiris and Posnett (1992) were critical of previous cross-country studies that
used small sample sizes. And so, by observing 20 OECD countries for 28 years using
panel data, Hitiris and Posnett found a negative relationship between healthcare spending
and mortality, but as previously stated the relationship is modest. Cremieux et al. (1999)
were also critical of previous studies that used non-uniform data across different
countries, thus making it difficult to identify a relationship between health care spending
3
Smith also considers a non-linear specification with respect to the role of age and
wealth. He finds the association between wealth and health is strongest with the poor and
weakest with the affluent. Also, there appears to be structural break in the role of age at
retirement, with social security and pension income being negatively correlated with
health.
7
and health outcomes. Utilizing province-level data from Canada, Cremieux et al. (1999)
found that healthcare spending is negatively associated with infant mortality and
positively associated with life expectancy. Indeed, their results show that a 10% decrease
in healthcare spending is associated with an increase in infant mortality of about 0.5% in
males and 0.4% in females; whereas a 10% decrease in healthcare spending reduces male
life expectancy by 6 months and female life expectancy by 3 months.
Nixon and Ulmann (2006) also examined the relationship between healthcare
expenditure and health outcomes by beginning with an extensive review of the previous
research. They found that few previous studies have successfully identified a relationship
between healthcare expenditure and mortality, which they argued could be due to the
conflicting roles of a number of determinants of health outcomes, such as diet, lifestyle
choices, and the environment. In addition, model misspecification and data limitations
have proven difficult in identifying key relationships. In light of these issues, Nixon and
Ulmann used OECD data, in conjunction with modeling approaches most commonly
taken in previous studies, and found health care expenditure does improve life
expectancy modestly in both males and females.4
Turning to the role of unemployment, while at the macroeconomic level changes
in unemployment are seen as a key indicator of the business cycle, increases in
unemployment can be particularly detrimental to health. Indeed, the stressors attached to
4
It is possible, though, that health care expenditure is endogenous, since increases in
mortality and morbidity rates may lead to increases in health care spending to address
these increases. Accordingly, in the empirical model and results chapters of this thesis,
we will address this issue.
8
unemployment have been found to be associated with cardiovascular disease,
hypertension, and diabetes (e.g., see Gallo et al., 2004). Cardiovascular disease has been
typically found to be directly related to the unemployment rate, with a lagged effect up to
five years in length. Brenner and Mooney (1983) find the long-term impact of
unemployment on health does depend upon a number of factors, such as age, gender, and
other economic conditions.
Kessler et al. (1987) studied the unemployment effects on health in a small
community in Southern Michigan after the recession in 1980. They ran regressions
utilizing data on a variety of classifications of unemployed respondents, in which they
found unemployment has a significant effect on a number of health measures (i.e.,
physical illness, somatization, anxiety, depression, number of bed-ridden days, alcohol
usage, suicidal ideation, and use of tranquilizers). They also found that results are not
much different whether or not the individual was at fault for being unemployed. In order
to gauge the extent to which unemployment affected health, the authors utilized logistic
regression equations to measure relative risk and found that people who experienced
unemployment were between 54% and 68% more likely than stably employed people to
report levels of distress.
Strully (2009) also finds that unemployment negatively affects health. In
particular, relying on the Panel Studies of Income Dynamics (PSID) data from the 1999,
2001, and 2003 waves, Strully (2009) estimates a multinomial logistic model and finds
respondents who suffer a job loss have an 83% greater probability of developing a new
health condition. Furthermore, using a difference-in-difference approach, Bockerman
9
and Pekka (2007) find for Finland that unemployed individuals have poorer health
compared to individuals who are continually employed. Indeed, those who are
unemployed for long periods of time (i.e., more than six months) face the greatest
probability of encountering ill health.
2.2. Socio-demographic Factors
Socio-demographic factors, such as race, education, and age, have been shown in
a number of studies to be important determinants of health outcomes. For example, Kiula
and Mieszkowski (2007) considered a broad array of socioeconomic indicators as
determinants of mortality and self-reported health. Using U.S. data from the National
Health Interview Survey, similar to other studies they found, health improves with
income and education, yet decreases with age (particularly beyond age 65) and poor
lifestyle behaviors (e.g. cigarette consumption and obesity). With respect to education,
since greater education raises awareness of the benefits of healthy behavior, it is intuitive
that more education improves health. With respect to age, it is intuitive that as people get
older, more health problems would arise.
Racial disparities are also found to exist in health outcomes. For example,
Hummer et al. (1999) find American-born Blacks have the highest probability of death at
any age, whereas foreign-born Blacks and American-born Asians have lower probabilities
of death at any age.5 Indeed, Gornick et al. (1996) linked 1990 census data with 1993
5
With respect to obesity, Baum (2007) finds Black Americans are more likely to be
obese compared to White Americans. Rogers (1992) argues one reason for such a finding
is the lower socio-economic circumstances faced by many Black Americans. Yet other
10
Medicare administrative data of several diagnoses and procedures according to race and
income and found the mortality ratio for black- to-white is 1 to 19 for men and 1 to 16 for
women. Furthermore, they found these ratios were particularly sensitive to affluence,
with whites tending to have higher income than blacks.
Population density has also been found to influence health outcomes. For
instance, Cremieux et al. (1999) and Eyre et al. (2004) find population density is an
important determinant of health. Yet the direction of the relationship is unclear. In
particular, it may be that areas of higher population density improve health as this
provides individuals greater access to health care, or it may be that areas of higher
population density are associated with greater risks of contracting diseases or being
involved in traffic accidents.
2.3. Lifestyle/Behavioral Factors
A number of studies have found that lifestyle and behavioral factors also impact
health outcomes. Utilizing data from the Healthcare for Communities (HCC) survey, for
instance, Sturm (2002) found tobacco and alcohol consumption, as well as obesity, affect
the probability of developing heart disease, high blood pressure, stroke, and cancer.
Sturm further found that different behaviors have more profound effects on health
measures. In particular, smoking has a strong influence on the likelihood of developing
cancer and lung disease, whereas obesity has a most noticeable effect on chronic health
conditions, such as heart disease, diabetes, stroke, high blood pressure, and cancer. Other
studies (e.g., Elo and Preston, 1996) attribute this racial gap to other immeasurable
factors, such as culture and lifestyle.
11
studies (e.g., Cremieux et al., 1999) have also found that a greater percentage of the
population that smokes and heavily consumes alcohol reduces life expectancy and
increases infant mortality.
2.4. Obesity Issues
Research on the link between health outcomes, nutritional intake and BMI has
been more prevalent in the last decade, partly in response to the increase in the average
weight of people over the past twenty years, the realization that there is a link between
diet and health, and improvements in the quantity and quality of data on obesity-related
measures.6 Cremieux, et al. (1999) argued the omission of obesity-related measures
could lead to omitted variable bias in health outcomes regressions, and accordingly
included meat and fat intake as proxies for obesity in their regressions. They found that
higher levels of fat intake do lower health outcomes, although insignificantly so with
respect to female life expectancy. Chou et al. (2002) considered alternative obesityrelated measures and found that diet does indeed determine health outcomes.
Sturm (2002) compared the effects of obesity, weight, smoking, and problem
drinking on chronic conditions and healthcare utilization using survey data from
Healthcare for Communities for the 1997-98 period. The dependent variables considered
were self-reported health status and healthcare use. They found the effects of obesity on
6
According to the World Health Organization (2010), the most commonly used measure
of obesity is BMI, which is defined by calculating the weight of an individual (in
kilograms) and dividing by the squared height of the individual (in meters). Normal
weight is deemed as having a BMI in the 18.5– 24.99 range, overweight is deemed as
having a BMI in the 25-29.99 range, and obese is deemed as having a BMI of 30 or
higher.
12
chronic health conditions are larger than those associated with smoking and problem
drinking. Specifically, obesity is significantly associated with an odds ratio of 5.7 for
diabetes, 4.3 to hypertension, 2 for asthma, 1.8 for angina/heart disease, and 1.7 for lung
disease compared to the not obese. Kiula and Mieszkowski (2007) also found the hazard
ratios defined as the likelihood of mortality from obesity (measured as a BMI of 35 or
higher) to be detrimental for all age groups relative to a person with a normal BMI of
between 20 and 25, where the highest likelihood of mortality is 50% higher for a person
in the 45 – 64 age range, 35% higher for a person in the 25 – 44 age range, and 22% for
the 65 – 99 age range with a BMI greater than 35 as opposed to a person with a normal
BMI.
13
Chapter 3
EMPIRICAL MODEL AND DATA
3.1. Empirical Model
This thesis applies a production function methodology to study health outcomes.
Specifically, the empirical model uses economic, socio-demographic, and lifestyle
variables as the determinants (i.e., inputs) of obesity-related health outcomes (i.e.,
outputs). Obesity-related mortality differs from regular mortality, which is normally
represented as the infant mortality rate or life expectancy. The equations I estimate are of
the form:
𝐾
(1)
M
N
𝐻𝑖𝑡 = 𝛽0 + ∑ 𝛽𝑘 Ekit + ∑ γm Smit + ∑ ρn Lnit + θi + δt + εit
𝑘=1
m=1
n=1
In equation (1), we assume the obesity-related mortality outcome (denoted as 𝐻𝑖𝑡 )
in state i at time t depends on K economic variables (individually denoted as Ekit ), M
socio-demographic variables (individually denoted as Smit ), and N lifestyle variables
(individually denoted as Lnit ).
To estimate equation (1), I use annual panel data for all 50 states from 1995-2004,
thus yielding a total of 500 observations. Since panel data is being used, I also account
for a variety of fixed effects specifications by including in equation (1) state (θi ) and time
(δt ) fixed effects. The state-fixed effects controls for factors that vary across states but
not time (e.g., cultural attitudes), whereas the time-fixed effects control for factors that
vary across time but not states (e.g., Federal government policies). A classical error term
is given by εit .
14
There are five different obesity-related mortality outcomes that will be used as
dependent variables, and thus requires equation (1) to be estimated for each of these
dependent variables. Specifically, the mortality rates considered as obesity-related
account for deaths due to heart disease, stroke, type II diabetes, colon cancer, and high
blood pressure.7
The included economic determinants of these five mortality measures are real per
capita income, real per capita healthcare expenditure, and the unemployment rate. Based
on the results of previous research (e.g., Cremieux, et al., 1999; Ettner, 1996; Hitiris and
Posnett, 1992), since higher income is associated with greater financial resources that can
be devoted to improving health, higher income reduces mortality and morbidity. Hence,
we expect a similar finding in this thesis with respect to obesity-related mortality. We
also expect a negative relationship between healthcare expenditure and obesity-related
mortality, consistent with the Cremieux et al. (1999) finding of a negative relationship
between healthcare expenditure and infant mortality and life expectancy.8
Lastly, we also include the unemployment rate in our model, which is expected to
have a negative relationship with each of the five obesity-related mortality measures.
Such a result is consistent with Brenner and Mooney, (1983), Gallo et al. (2004), and
Kessler et al. (1987), who have found unemployment is harmful to health due to (but not
limited to) stress and depression which contribute to higher mortality and morbidity.
7
Across these five mortality measures, heart disease accounts for the most deaths in the
U.S., while high blood pressure accounts for the least deaths.
8
As we discuss later, we use total healthcare spending in our model. Ideally, we would
like to use healthcare spending on each of the five specific mortality measures, but
unfortunately such data does not exist across states and time.
15
Several socio-demographic variables are also included as determinants of obesityrelated mortality. In particular, age demographics are often considered determinants of
health outcomes, and so we control for two age groups: the percent of the population
aged 65 and older and the percent of the population aged 19 years and younger. All else
equal, we expect obesity-related mortality to be higher among the older age group and
lower among the younger age group.
Race is also deemed an important factor affecting mortality, with African
Americans having a much higher probability of death at any age (Baum, 2007), while
American-born Asians have a much lower probability of death at any age (Hummer et al.,
1999). Baum (2007) also finds that Blacks and Hispanics are significantly more likely to
be obese than other types of ethnicities. Accordingly, we include the percent of the
population that is African American, the percent of the population that is Hispanic, and
the percent of the population that is Asian. Education is also often considered a
determinant of mortality, with the argument that higher education allows individuals to
make better choices with regards to their health, and so we also include the percent of the
population that has a four-year college degree or higher. Lastly, population density is
also measured but the direction is unclear, because it may be that areas with higher
population density provide easier access to healthcare, thus reducing health risks; or it
may be that the quality of healthcare is adversely affected by population density.
Lifestyle factors, such as cigarette smoking, alcohol consumption, and weight
(i.e., obesity), are behavioral factors often linked to higher mortality and morbidity. For
instance, smoking has been widely understood to cause health problems. Indeed, the
16
Centers for Disease Control and Prevention (2011) reported that smoking most recently
caused approximately 438,000 premature deaths, and among adults 39.8% of deaths were
attributable to cancer, 34.7% to heart disease, and 25.5% to respiratory disease. Problem
drinking is another behavioral factor that also has ill effects on health and mortality, such
as higher rates of death due to liver disease. Accordingly, both cigarette and alcohol
consumption are included in this analysis to control for their impacts on obesity-related
mortality measures.
Regarding obesity, we expect higher obesity to correlate with higher obesityrelated mortality, since being obese or overweight results in higher body fat that has been
shown to be associated with a variety of chronic medical conditions. Indeed, Sturm
(2002) found that obesity has a much stronger association with morbidity than other
behavioral factors, such as smoking and drinking, and is essentially equivalent to an
additional twenty years of aging. Therefore, we include as our measure of obesity the
percent of the population in each state with a body mass index (BMI) score of 30 or
higher.
There is also the potential case of endogeneity between healthcare expenditure
and mortality, where healthcare expenditure and mortality are simultaneously predicted.9
To fix this, a possible solution for endogeneity is to apply a two-stage instrumental
variable approach, using infant mortality as an instrument to healthcare expenditure.
9
There are some studies (Ettner, 1996) that address the endogeneity of income on health,
which is generally a problem with individual-level data. This thesis is focused at the
aggregate level, which makes it less of an issue, and thus I only focus on the potential
endogeneity of healthcare spending.
17
Infant mortality is an appropriate instrument because infant mortality is likely correlated
with healthcare expenditure, but also has no influence on the five mortality rates in
question.
3.2. Data
As mentioned, the data used in this analysis covers all 50 states for the 1995-2004
period. The data sources and definitions are listed in Table 3.1. To begin, I use data on
five different obesity-related mortality rates (i.e., deaths per 100,000 persons), which
were obtained from the Centers for Disease Control and Prevention via CDC Wonder.
These particular mortality rates were chosen because they represent the causes of death
most highly correlated with obesity (e.g., see Must et al., 1999).
For the economic variables, real per capita income (in thousands of dollars) is
calculated by dividing gross state product by the state population, and then deflating this
figure by the consumer price index (CPI), which uses 1983 as its base year. Gross state
product data came from the Bureau of Economic Analysis, CPI data came from the
Bureau of Labor Statistics, and state population data came from CDC Wonder.
Healthcare expenditure is originally obtained in millions of dollars (from the
Centers for Medicare and Medicaid Services), but converted to real per capita health
expenditure (in thousands of dollars) in a similar fashion as per capita income (i.e., by
dividing personal healthcare expenditure by the state population and then deflating this
figure by the CPI). Data on the unemployment rate (i.e., the percent of the workforce that
is unemployed) came from the Bureau of Labor Statistics.
18
Turning to the socio-demographic variables, age and race data were obtained from
CDC Wonder. Data on population density and educational attainment was obtained from
the Statistical Abstract of the United States, with population density measured as the
number of persons per square mile and educational attainment measured as the percent of
the population that has a 4 year college degree or higher.
As for the lifestyle variables, data on per capita cigarette consumption came from
the 2008 Tax Burden on Tobacco, which is measured as the average number of packs of
cigarettes consumed per capita annually. Data on alcohol consumption came from the
National Institute on Alcohol Abuse and Alcoholism, with consumption measured as the
number of gallons of ethanol (i.e., pure alcohol) consumed per person (14 years and
older). Data on the obesity rate, which is measured by the percent of the population with
a BMI score of 30 or higher, came from the Centers for Disease Control and Prevention,
via the Behavioral Risk Factor Surveillance System. To recall, BMI is calculated
Table 3.1. Variable Sources and Definitions
Variable
Definition
Dependent
Colon Cancer Mortality
Deaths from Colon Cancer in the resident population.
Heart Disease Mortality
Deaths from Heart Disease in the resident population.
High Blood Pressure Mortality
Deaths from High Blood Pressure in the resident population.
Stroke Mortality
Deaths from Stroke in the resident population.
Type II Diabetes Mortality
Deaths from Type II Diabetes in the resident population.
Explanatory
% Population Asian
Percent of total state population that is classified as Asian.
% Population Black
Percent of total state population that is classified as Black/African American.
% Population Hispanic
Percent of total state population that is classified as Hispanic
% Population 19 years and younger
Percent of total state population that is under 19 years old.
% Population 65 years and older
Percent of total state population that is 65 years or older.
% Population with bachelor's degree or professional degree
Percent of total state population that has a bachelor's degree or professional degree.
% Population with a BMI >=30
Percent of total state population that has a calculated Body Mass Index of 30 or greater.
Per capita cigarette consumption
Calculated as the number of cigarettes consumed per person.
Per capita alcohol consumption per gallon of ethanol
Real Per Capita Disposable Income
Real Per Capita Health Care Expenditure
Calculated as the total gallons of ethanol sold or shipped, divided by the population of people 14 years or
older.
Real Per Capita Income calculated as the total Gross State Product divided by the total state population
and deflated with the CPI (Base Year = 1982 - 1984).
Personal health care expenditures by State of Provider estimates health spending by the location of health
care providers by state. Deflated with the CPI (Base Year = 1982 - 1984).
Non Seasonally adjusted unemployment rate
Percent of total state population that is classified as unemployed.
Population Density
Calculated as total state population divided by land area in square miles.
Deflator
Consumer Price Index
Consumer Price Index (Base Year = 1982 – 1984).
19
20
utilizing data on a person’s weight and height and gives a reasonable indicator of body
fatness and weight (Centers for Disease Control and Prevention, 2010). It is also
important to note that the obesity rate is obtained by survey data through telephone
interviews of persons age 18 or older, with an average of 3,000 respondents per state.
Although it would be best to obtain data for the total population, such data is not
available, and thus we settle with using survey data.
3.3. Descriptive Statistics and Time Trends
Table 3.2. shows the mean, standard deviation, minimum, and maximum values for
all the variables used in the analysis. As the table shows, heart disease has the highest
mortality rate with an average of 247.12 deaths per 100,000 annually by state, which is
more than quadruple the next highest mortality count from strokes. Heart disease may
have higher numbers due to a multitude of risk factors affecting it, such as amongst
others, obesity, smoking, and alcohol consumption; whereas the other measures of
mortality are more narrowly defined, and therefore are sensitive to a smaller set of risk
factors. The lowest mortality count is for high blood pressure, with an average of only
14.77 deaths per 100,000. Interestingly, this is also depicted in Figure 3.1., which
illustrates total U.S. mortality counts (i.e., number of deaths) for the five mortality
measures in 1995, 2000, and 2004. As the figure illustrates, the mortality counts for heart
disease and colon cancer have actually decreased from 1995 to 2004, whereas the counts
for type II diabetes and high blood pressure have increased over the same time period.
However, stroke mortality increased from 1995 to 2000 but then decreased from 2000 to
2004.
21
Table 3.2. Descriptive Statistics
Variable
Mean
Standard
Deviation
Minimum
Maximum
Dependent Variable
Colon Cancer Mortality
17.50
3.59
7.20
26.80
Heart Disease Mortality
247.12
55.83
84.70
385.40
High Blood Pressure Mortality
14.77
5.02
2.80
35.00
Stroke Mortality
58.98
11.62
21.50
94.60
Type II Diabetes Mortality
24.57
5.60
9.30
47.00
% Population Asian
0.04
0.09
0.005
0.67
% Population Black
0.10
0.09
0.003
0.37
% Population Hispanic
0.07
0.08
0.005
0.42
% Population 19 years or younger
0.29
0.02
0.24
0.38
% Population 65 years or older
0.13
0.02
0.05
0.18
Per Capita Cigarette Consumption
86.62
27.83
31.90
186.80
Per Capita Alcohol Consumption
2.26
0.49
1.20
5.10
% Population with College Degree
0.25
0.47
0.13
0.39
Obesity Rate
0.19
0.04
0.10
0.30
179.53
244.59
1.10
1,172.80
2.36
3.87
1.51
3.62
14.24
1.96
10.24
20.98
0.05
0.01
0.02
0.08
Explanatory Variables
Population Density
Real Per Capita Healthcare Expenditure
Real Per Capita Income
Unemployment Rate
The average obesity rate in each state between 1995 and 2004 is 19.42%, with the
highest being 29.5% in Mississippi in 2004 and the lowest being 10% in Utah in 1995.
Indeed, Figure 3.2 illustrates the trend in the obesity rate for the U.S. over the 1995-2004
periods, and shows an average increase in the obesity rate of 4.5% per year. Furthermore,
22
Figure 3.2 also illustrates U.S. real per capita healthcare expenditures over the same
period, which follows a similar upward trend as obesity.
Figure 3.1. Mortality Counts from 1995, 2000, and 2004.
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
Type 2 Diabetes
Heart Disease
1995
Stroke
2000
High Blood
Pressure
2004
Colon Cancer
Regarding the other explanatory variables, it is interesting to note that the
standard deviation for population density of 244.59 is higher than its average of 179.53,
suggesting a great deal of dispersion in population density across states and time. The
while the state with the highest population density is New Jersey (1,172.80 persons per
25.0%
$3,000
20.0%
$2,500
$2,000
15.0%
$1,500
10.0%
$1,000
5.0%
$500
0.0%
$1995
1996
1997
Obesity Rate
1998
1999
2000
2001
2002
2003
2004
Real Per Capita Healthcare Expenditure
1983 Dollars
Percent
Figure 3.2. Average Obesity Rate and Real per Capita Healthcare Expenditures
23
square mile in 2004). There is also substantial dispersion in the race demographic
variables. For example, Hispanics have an average percent of the population that is seven
percent but with a standard deviation of eight percent, while the average percent of the
population that is Asian is four percent, with a standard deviation of 9%. The state with
the lowest Asian population is West Virginia (0.5% in 1995), while the state with the
highest Asian/Pacific Islander population is Hawaii (67% in 2000). Yet Hawaii is an
outlier, as the next highest percent of the population that is Asian/Pacific Islander is
California, at roughly 13%. Lastly, there is a great deal of dispersion in real per capita
income across states, with Mississippi having the lowest real per capita income (10,240
dollars in 1995), and the highest real per capita income being in Connecticut (20,979
dollars in 2004).
24
Chapter 4
RESULTS
As mentioned previously, the aim of this thesis is to evaluate the impacts of
economic, socio-demographic, and lifestyle factors on obesity-related mortality. Since
early studies predominantly focused on economic determinants of mortality, initially I do
the same in this thesis. In subsequent regressions, I consider socio-demographic and
lifestyle determinants of mortality as well. Furthermore, given the possibility that
healthcare expenditure is endogenous, we also estimate several two-stage least squares
(2SLS) regressions, using infant mortality as an instrument for healthcare expenditure.
Lastly, we compare results across various fixed effects treatments.
4.1. Estimation Results Limited to Economic Variables
Before the data is analyzed, there were some calculations made to scale the
coefficients to a manageable size. In particular, income and healthcare expenditure
variables are scaled into thousands of dollars and all crude mortality rates are represented
in deaths per 100,000 people.
To begin, Table 4.1 provides results for the five obesity-related mortality rates,
using only economic variables as determinants, and without fixed effects. Overall,
explanatory power is quite low, as adjusted R-square falls in the range of 0.08 to 0.33.
Yet income and healthcare expenditure are significant at the 1% level for each mortality
rate. Moreover, as expected, the relationship between obesity-related mortality and
income is always negative. Unexpectedly, though, the relationship between healthcare
expenditure and obesity-related mortality is always positive. Nonetheless, since we are
25
working with panel data, it is likely there is substantial omitted variable bias in these
initial regressions, from factors beyond income and healthcare expenditure that likely
impact mortality.
Table 4.1 No Fixed Effects, Economic Variables Only
Type II
Colon Cancer
Intercept
18.974***
28.313***
331.304***
12.094***
89.501***
(1.715)
(2.219)
(20.521)
(1.232)
(2.422)
-0.751***
-1.920***
-16.684***
-0.638***
-3.718***
(0.060)
(0.054)
(1.294)
(0.050)
(0.185)
3.903***
9.987***
64.937***
4.982***
9.489***
(1.028)
(0.806)
(14.621)
(0.412)
(2.083)
All coeff.= 0
35.144
124.552
65.225
23.691
78.129
(p-value)
0.000
0.000
0.000
0.000
0.000
# Obs
500
500
500
500
500
Adj. R-Square
0.12
0.33
0.2
0.08
0.24
Income
Healthcare Exp
Diabetes
Heart Disease
High Blood
Variables
Pressure
Stroke
F-Statistics
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Accordingly, the second set of regressions (see Table 4.2) adds time-fixed effects
to the regressions estimated in Table 4.1. Such fixed effects account for factors that are
common across states, yet vary over time. For instance, these time-fixed effects capture
the impact of any Federal government policies. As Table 4.2 shows, the results changed
slightly, as adjusted R-square increased in four of the five regressions. Yet the signs and
significance of the income and healthcare expenditure coefficients remain the same as in
Table 4.1. Additionally, the fixed effect is not jointly significant for high blood pressure,
which may indicate that the specification still suffers from omitted variable bias. It is
26
also identified that the standard deviation for high blood pressure is unusually higher than
the other mortality rates.
Table 4.2 Time-Fixed Effects Only, Economic Variables Only
Type II
Colon Cancer
Intercept
9.765***
31.273***
206.374***
14.737***
80.488***
(1.204)
(1.673)
(14.234)
(1.895)
(0.898)
-0.726***
-2.012***
-16.345***
-0.644***
-3.833***
(0.050)
(0.039)
(1.217)
(0.054)
(0.187)
7.653***
9.292***
115.791***
3.897***
13.999***
(0.777)
(0.745)
(13.071)
(0.616)
(1.034)
All Coeff. = 0
24.225
26.260
27.302
4.853
18.343
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Time Fixed = 0
19.222
3.277
15.158
0.695
4.087
(p-value)
(0.000)
(0.001)
(0.000)
(0.7133)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.34
0.36
0.37
0.08
0.28
Income
Healthcare Exp.
Diabetes
Heart Disease
High Blood
Variables
Pressure
Stroke
F-Statistics
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
The third set of regressions, which are provided in Table 4.3, include state-fixed
effects. State-fixed effects account for factors that vary across states but not time. In the
results reported in Table 4.3, we now see explanatory power has greatly increased, as
adjusted R-square now ranges between 0.90 and 0.98. Also, several coefficients have
changed compared to Tables 4.1 and 4.2. For instance, now the coefficient of income is
insignificant in the high blood pressure and stroke regressions, and significantly positive
in the Type II diabetes regression. Interestingly, we also now find the healthcare
expenditure coefficients for colon cancer, heart disease, and stroke are significantly
27
negative, which is consistent with our expectations. Yet the healthcare coefficients for
type II diabetes and high blood pressure remain significantly positive. Bottom line,
although we continue to get some unexpected results, given several of the coefficients
change substantially in Table 4.3, this suggests omitted variable bias is likely an issue
with mortality regressions that only include economic factors as determinants.
Table 4.3. State-Fixed Effects Only, Economic Variables Only
Type II
Colon Cancer
Intercept
29.470***
8.322***
427.932***
4.976***
77.330***
(0.790)
(2.415)
(12.024)
(0.926)
(6.088)
-0.516***
0.809***
-6.909***
0.092
0.023
(0.058)
(0.192)
(1.533)
(0.137)
(0.696)
-1.958***
2.000**
-34.908***
3.597***
-7.909***
(0.495)
(1.086)
(6.479)
(0.73)
(2.956)
All Coeff. = 0
109.139
92.506
411.217
86.987
135.297
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
State Fixed=0
98.386
61.083
337.117
81.861
104.949
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.92
0.90
0.98
0.90
0.93
Income
Healthcare Exp.
Diabetes
Heart Disease
High Blood
Variables
Stroke
Pressure
F-Statistic
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
In the fourth set of regressions, the results of which are reported in Table 4.4, both
time- and state-fixed effects are included in these preliminary regressions. As expected,
the adjusted R-square values are the highest compared to the other three sets of
regressions. The income and healthcare expenditure coefficients are predominantly
28
negative, with the exception of the coefficient of income in the stroke regression and the
coefficient of healthcare expenditure in the type II diabetes regression.
While this last set of regressions appears to best fit our expectations, in terms of
negative coefficients, the impacts of income and healthcare expenditure are nonetheless
small. For instance, with respect to colon cancer, the elasticity of mortality with respect
to income (healthcare expenditure), evaluated at the means, is -0.49 (-0.24), meaning that
a one percent increase in income (healthcare expenditure) reduces colon cancer deaths
(per 100,000 population) by 0.49 (0.24) percent.
Table 4.4. State and Time-Fixed Effects, Economic Variables Only
Type II
Colon Cancer
Intercept
30.334***
24.917***
295.351***
19.232***
2714.93***
(2.721)
(3.761)
(27.773)
(2.285)
(612.310)
-0.602***
-0.327*
-0.343
-0.159
99.680***
(0.113)
(0.207)
(1.835)
(0.170)
(26.565)
-1.803**
1.826
-18.354***
-0.931
-393.679***
(0.743)
(1.052)
(4.805)
(0.889)
(147.873)
All Coeff.=0
96.194
91.202
411.787
80.854
3343.127
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
2.627
8.182
9.659
5.137
8.182
(p-value)
(0.006)
(0.000)
(0.000)
(0.000)
(0.000)
State Fixed=0
73.021
66.82
308.724
88.360
66.820
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Fixed=0
86.243
60.320
335.849
75.703
60.320
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.92
0.92
0.98
0.91
0.99
Income
Healthcare Exp.
Diabetes
Heart Disease
High Blood
Variables
Pressure
Stroke
F-Statistic
Time Fixed=0
State&Time
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
29
4.2. Estimation Results Expanded to include Economic, Socio-Demographic, and
Lifestyle Variables
The next sets of regressions add the socio-demographic and lifestyle variables
discussed in the last chapter. Similar to the presentation of results in the previous section,
we consider four sets of results, namely one without fixed effects, one with time-fixed
effects, one with state-fixed effects, and one with both time- and state-fixed effects.
In the first set of results (those without fixed effects), which are presented in Table
4.5 below, the adjusted R-square (ranging from 0.57 to 0.84) is higher compared to the
results presented in Table 4.1. Comparing these results to those in Table 4.1, the income
coefficient is now only negative in the type II diabetes and stroke regressions, but
insignificantly so in the stroke regression. As for healthcare expenditure, it is
significantly negative in the colon cancer and stroke regressions, and significantly
positive in the type II diabetes regression.
Accordingly, adding socio-demographic and lifestyle variables to the regressions
in Table 4.1 changes the results substantially. Regarding the other variables, the
unemployment rate is added to further capture economic determinants of mortality. At
the bottom of Table 4.5, we see the coefficient of the unemployment rate is insignificant
in the colon cancer and type II diabetes regressions, but of significant (and mixed) sign in
the heart disease, high blood pressure, and stroke regressions.
The socio-demographic variables appear to have the largest impact on mortality
rates. For instance, as the percent of the population that is young (i.e., age 19 and
younger) increases, as expected mortality rates decline (with the exception of heart
30
Table 4.5. No Fixed Effects
Type II
Colon Cancer
Intercept
4.114**
20.347***
-12.382
-10.238**
59.001***
(1.923)
(6.224)
(32.208)
(4.259)
(6.643)
0.207***
-1.418***
2.427***
0.255
-0.060
(0.075)
(0.110)
(0.811)
(0.186)
(0.200)
-0.875**
1.700*
-6.136
0.298
-10.148***
(0.450)
(0.927)
(4.386)
(0.305)
(0.768)
-0.206
-23.575*
175.051***
-6.900
-54.889***
(2.740)
(13.313)
(38.965)
(6.637)
(11.765)
130.349***
79.659***
2081.482***
88.324***
380.353***
(2.710)
(9.646)
(63.891)
(6.148)
(12.069)
-0.446***
-0.444*
-19.331***
1.725***
-2.588***
(0.141)
(0.240)
(1.684)
(0.260)
(0.533)
0.023***
0.010**
0.477***
-0.007
-0.016*
(0.003)
(0.004)
(0.042)
(0.005)
(0.009)
1.152
-4.543**
135.426***
36.964***
7.489***
(1.025)
(1.915)
(4.381)
(0.955)
(2.211)
-6.473***
-9.377***
-49.958***
0.756
-2.543**
(0.922)
(1.794)
(5.322)
(0.770)
(1.126)
-5.718***
0.285
-31.244***
4.867***
-39.645***
(0.923)
(1.245)
(7.544)
(1.629)
(3.106)
-13.765***
9.600*
-300.391***
-11.542***
-16.327***
(3.134)
(5.004)
(18.233)
(2.818)
(5.487)
-6.151***
73.841***
-127.13***
23.821***
58.072***
(2.510)
(2.697)
(37.643)
(3.581)
(13.044)
0.003***
0.006***
0.033***
-0.003***
-0.007***
(0.000)
(0.001)
(0.006)
(0.001)
(0.001)
-3.052
6.336
294.742***
60.504***
-79.762***
(6.209)
(7.921)
(84.216)
(5.856)
(35.684)
All Coeff.=0
142.901
51.544
208.674
73.584
98.499
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.79
0.57
0.84
0.65
0.72
Income
Healthcare Exp.
19 and Younger
65 and Older
Alcohol
Cigarette
Black
Asian
Hispanic
College
Obese
Pop Density
Unemp Rate
Diabetes
Heart Disease
High Blood
Variables
Pressure
Stroke
F-Statistic
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
31
disease mortality). Most noteworthy, across all five regressions mortality rates increase
with the percent of the population that is old (i.e., age 65 and older), which is logical,
since health problems intensify with age.
Concerning other socio-demographic variables, the percent of the population that
is Black is found to have a significantly positive impact on mortality, with the exception
of the colon cancer and type II diabetes regressions. Mortality rates are generally lower
as the percent of the population that is Asian and Hispanic increases, although high blood
pressure mortality increases significantly with the percent of the population that is
Hispanic. As expected, the percent of the population with a college degree generally
reduces mortality rates (with the exception of type II diabetes). Also, population density
has differing impacts on mortality, as the mortality rate associated with colon cancer, type
II diabetes, and heart disease (high blood pressure and stroke) increases (decreases) with
population density.
The lifestyle variables offer mixed results. Specifically, obesity is positively
related to mortality rates tied to diabetes, high blood pressure, and stroke, but negatively
related to mortality rates tied to colon cancer and heart disease. This goes against the
findings from Hirsch et al. (1985), Strum (2002), and Eyre et al. (2004) where they find
positive relationships with obesity and heart disease and cancer10. Regarding alcohol and
cigarette consumption, alcohol consumption tends to reduce mortality rates (with the
exception of high blood pressure mortality), whereas cigarette consumption increases
10
The type of cancers from the listed authors does not specifically indicate the kinds of
cancers are associated with mortality.
32
mortality tied to colon cancer, type II diabetes, and heart disease. Unexpectedly,
however, cigarette consumption has a negative impact on stroke mortality rates.
Overall, while some variables do not match expectations, most of the results in
Table 4.5 do match expectations and are generally consistent across the regressions.
Across all five mortality regressions, the one that best fits all expectations is that
associated with stroke. Since there is variation in the results, though, we consider further
panel data treatments to address the presence of potentially further omitted variables.
Similar to Table 4.2, the next set of regressions add time-fixed effects to those
reported in Table 4.5. As the results in Table 4.6 indicated, the adjusted R-square changes
modestly compared to the values in Table 4.5. This suggests the time-fixed effects matter
little. Indeed, many of the coefficients remain similar in sign and significance as those
reported in Table 4.5. Interestingly, healthcare expenditure is now positively related to
colon cancer (although insignificantly), type II diabetes, and heart disease; and obesity
now has a significantly positive impact on all five mortality rates, whereas initially
without the fixed effects showed a negative relationship with heart disease and colon
cancer. Some of the remaining variables do change slightly in terms of significance
across the five mortality regressions.
The next set of regressions adds state-fixed effects to the economic, sociodemographic, and lifestyle variables. As shown in Table 4.7, the adjusted R-square is
higher than reported in Table 4.6, indicating that most of the additional variation is tied to
state-specific factors. Yet the results concerning income and healthcare expenditure are
mixed. In particular, the coefficients of income and healthcare expenditure are
33
Table 4.6. Time-Fixed Effects
Variables
Colon Cancer
Type II
Diabetes
Heart Disease
High Blood
Pressure
Stroke
Intercept
-1.923
19.067***
-112.429***
-12.305***
48.156***
(1.968)
(6.457)
(26.994)
(3.386)
(6.013)
0.185***
-1.425***
2.317***
0.255
-0.057
(0.061)
(0.108)
(0.686)
(0.190)
(0.182)
Income
Healthcare Exp.
19 and Younger
65 and Older
Alcohol
Cigarette
Black
Asian
Hispanic
College
Obese
Pop Density
Unemp Rate
0.368
1.920**
10.934***
0.420
-7.760***
(0.268)
(0.790)
(4.020)
(0.310)
(0.737)
-9.424***
-18.004
82.169**
-9.121
-64.173***
(3.623)
(14.023)
(41.404)
(7.789)
(10.837)
122.187***
86.969***
2016.455***
87.196***
373.539***
(3.095)
(8.782)
(57.307)
(6.854)
(12.092)
-0.167
-0.586**
-16.012***
1.848***
-2.394***
(0.131)
(0.257)
(1.525)
(0.273)
(0.563)
0.013***
0.015***
0.363***
-0.01
-0.027***
(0.003)
(0.006)
(0.037)
(0.007)
(0.009)
-1.059
-3.309*
108.656***
36.157***
5.34*
(0.884)
(2.025)
(6.428)
(1.333)
(2.859)
-5.583***
-9.872***
-39.098***
1.045
-1.63
(0.95)
(1.639)
(6.938)
(0.831)
(1.48)
-3.061***
-0.809
-0.089
5.39***
-36.093***
(0.886)
(0.647)
(5.726)
(1.664)
(2.764)
-6.826***
9.330*
-201.931***
-9.115***
-7.118
(2.742)
(5.658)
(22.905)
(3.402)
(7.812)
26.490***
58.247***
272.642***
34.913***
93.809***
(3.198)
(7.487)
(50.481)
(8.490)
(19.374)
0.002***
0.006***
0.022***
-0.003***
-0.009***
(0.000)
(0.001)
(0.006)
(0.001)
(0.001)
-15.917***
35.054***
265.143***
57.121***
-75.392**
(4.565)
(10.070)
(50.068)
(8.856)
(38.601)
F-Statistic
All Coeff. = 0
113.538
31.195
159.988
43.869
63.212
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Time Fixed = 0
15.541
1.337
14.471
0.982
4.093
(p-value)
(0.000)
(0.215)
(0.000)
(0.454)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.83
0.57
0.88
0.65
0.73
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
34
expectedly negative and significant for colon cancer, heart disease, and stroke (although
not significant), but diabetes and high blood pressure show positive signs for both
coefficients, however are not significant. In terms of the unemployment rate, there is
significance with all of the mortality variables, but unexpectedly it shows a negative
relationship with colon cancer and stroke.
The lifestyle variables also show mixed results. Alcohol consumption has a
significantly negative impact on heart disease mortality, but fails to significantly affect
any other mortality rate. Smoking has a positive impact on mortality, but only
significantly so in the heart disease and stroke regressions. Interestingly, with the
exception of heart disease, higher rates of obesity significantly increase obesity-related
mortality.11 Finally, compared to Table 4.6, the coefficients of population density and the
unemployment rate drop off in terms of significance. Thus, the results are particularly
sensitive to the inclusion of state-fixed effects, which could be the result of many of these
additional factors being highly correlated with the state-fixed effects.
In the last set of regressions, both time- and state-fixed effects are added to the
model. As indicated in Table 4.8, the adjusted R-square increases slightly from the
results in Table 4.7. Unexpectedly though, income and healthcare expenditure continue
to have unexpected signs, where six of the ten coefficients are statistically insignificant,
but four of the coefficients are negative and significant. Additionally elasticities are
calculated for colon cancer in table 4.8 for comparison with the elasticities calculated
11
It is unexpected that increases in obesity reduce heart disease mortality, since obesity is
a leading cause of heart disease.
35
Table 4.7. State-Fixed Effects
High Blood
Variables
Colon Cancer
Type II Diabetes
Heart Disease
Intercept
14.588*
68.406***
178.472***
16.022**
41.986*
(8.464)
(11.425)
(62.352)
(7.533)
(22.213)
-0.478***
0.260
-2.534**
0.229
-0.776
(0.101)
(0.217)
(1.170)
(0.182)
(0.520)
-2.448***
1.192
-43.787***
1.393
-4.126**
(0.634)
(1.220)
(6.102)
(1.204)
(1.830)
Income
Healthcare Exp.
19 and Younger
65 and Older
Alcohol
Cigarette
Black
Asian
Hispanic
College
Obese
Pressure
Stroke
-4.506
-106.947***
-194.349*
-39.122***
21.306
(18.345)
(22.352)
(103.375)
(15.248)
(56.386)
129.519***
-120.161*
2003.281***
35.458
217.913**
(27.377)
(63.339)
(308.202)
(33.837)
(94.031)
-0.203
-0.097
-2.377***
0.268
0.688
(0.152)
(0.217)
(0.723)
(0.182)
(0.765)
0.015
0.008
0.113***
-0.003
0.059***
(0.012)
(0.013)
(0.044)
(0.009)
(0.017)
15.554
-31.439
56.99
-14.793
7.043
(15.375)
(29.445)
(170.173)
(36.586)
(34.679)
-33.038
5.762
-71.49
-38.949
195.906***
(34.049)
(30.942)
(106.017)
(25.65)
(30.066)
20.804***
-11.665
38.953
29.707***
-54.038**
(4.765)
(9.624)
(61.3)
(10.723)
(23.995)
-5.506**
-2.803
-61.629***
1.579
-8.25
(2.855)
(5.156)
(22.659)
(4.11)
(12.585)
8.647**
32.087***
-105.743***
10.478**
19.03*
(3.748)
(8.44)
(33.074)
(4.632)
(10.516)
-0.011
-0.034***
0.185***
-0.026**
-0.012
(0.007)
(0.012)
(0.018)
(0.013)
(0.019)
-7.14
-19.408
107.807
35.499***
-126.165***
(8.541)
(14.941)
(75.275)
(10.781)
(32.438)
All Coeff. = 0
104.856
93.103
496.825
78.617
135.225
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
State Fixed=0
20.443
44.355
87.948
27.599
40.610
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.93
0.92
0.98
0.91
0.94
Pop Density
Unemp Rate
F-Statistic
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
36
from Table 4.4. With respect to colon cancer, the elasticity of mortality with respect to
income (healthcare expenditure), evaluated at the means changed to -0.40 (-0.21),
meaning that a one percent increase in income (healthcare expenditure) reduces colon
cancer deaths (per 100,000 population) by 0.40 (0.21) percent. This is a decrease of 0.09
(0.03) percent from the initial calculations, and is intuitive because as more relevant
variables are added to the equation, the impact of income and healthcare expenditure
lessens on colon cancer mortality. Also, the unemployment rate continues to show mixed
results, as it is also significant in only two of the five regressions.
For the socio-demographic variables, the percent of the population that is age 19
and younger continues to show a negative relationship with mortality (except for high
blood pressure), but is significant in only the type II diabetes and heart disease
regressions. The percent of the population that is age 65 and older has a significantly
positive impact on mortality tied to colon cancer, heart disease, and stroke. Concerning
race, compared to Table 4.7, a few more of the race-related coefficients are significantly
different from zero, but because the coefficients are not the same sign across the
regressions, we cannot say there is a general pattern of racial influence on mortality.12
Similar to Table 4.7, college and population density drop off in significance.
Lastly, the lifestyle coefficients change somewhat when both state- and time-fixed
effects are included. In particular, compared to Table 4.7, a larger share of the alcohol
12
Interestingly, compared to Table 4.7, the coefficients of Black and Hispanic increased
substantially in the heart disease regression. Finding that Blacks and Hispanics have
much higher heart disease mortality rates is consistent with the findings of the Centers for
Disease Control and Prevention’s Heart Disease Facts and Statistics (2010).
37
Table 4.8. State and Time-Fixed Effects
141.029***
High Blood
Pressure
19.172***
44.859***
(56.532)
(7.616)
(16.424)
-0.508
0.164
-1.091***
(0.226)
(1.176)
(0.186)
(0.334)
1.109
-20.770***
-1.402
-0.032
(0.734)
(0.980)
(4.914)
(1.435)
(1.319)
Variables
Colon Cancer
Type II Diabetes
Heart Disease
Intercept
18.000**
74.170***
(8.223)
(11.604)
Income
-0.488**
-0.119
(-0.122)
Healthcare Exp.
-1.545**
19 and Younger
65 and Older
Alcohol
Cigarette
Stroke
-17.675
-85.238***
-730.216***
20.463
-34.065
(23.473)
(29.955)
(93.411)
(15.506)
(29.852)
111.903***
-102.338
2169.835***
9.457
271.76***
(25.503)
(64.958)
(280.406)
(25.278)
(49.547)
-0.151
-0.314*
-1.509***
0.28*
0.069
(0.131)
(0.163)
(0.612)
(0.151)
(0.655)
0.010
0.010
0.015
-0.002
0.058***
(0.012)
(0.012)
(0.042)
(0.010)
(0.011)
20.593
-62.281**
324.467**
-38.975
23.429
(13.849)
(29.366)
(135.297)
(39.449)
(21.671)
-27.391
-22.968
-39.947
-29.048*
139.4***
(31.036)
(32.678)
(162.951)
(17.355)
(27.581)
30.961***
-30.571**
415.43***
-7.403
-22.845
(5.086)
(12.987)
(130.834)
(12.704)
(26.156)
College
-3.953
-5.969
-38.752**
-1.796
-7.346
(3.029)
(4.946)
(16.078)
(5.365)
(9.635)
Obese
9.739***
16.518***
-25.45
8.023
-1.092
(3.49)
(4.463)
(26.445)
(5.333)
(12.968)
-0.014*
-0.031**
0.281***
-0.038***
0.002
(0.007)
(0.016)
(0.039)
(0.013)
(0.018)
-22.332***
-6.569
107.921
9.326
-85.708***
(7.061)
(17.597)
(69.632)
(7.579)
(23.321)
Black
Asian
Hispanic
Pop Density
Unemp Rate
F-Statistics
All Coeff. = 0
96.638
91.262
562.336
72.728
159.952
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
3.458
6.459
15.165
3.564
17.313
(0.0004)
(0.000)
(0.000)
(0.0003)
(0.000)
Time Fixed=0
(p-value)
State Fixed=0
15.118
49.070
89.554
29.011
52.689
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
S/T Fixed=0
18.687
42.688
98.330
25.100
48.521
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
500
500
500
500
500
Adj. R-Square
0.93
0.93
0.99
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
0.91
0.96
# Obs
38
consumption coefficients is significantly different from zero, whereas a smaller share of
the smoking and obesity coefficients is significantly different from zero. Indeed, while
Table 4.7 suggests obesity is significant determinants of mortality, in Table 4.8 I now
only find a significantly positive impact of obesity on colon cancer and type II diabetes
mortality. The magnitudes of the obesity coefficients have also lessened as more fixed
effects are added.
4.3. Estimation Results with 2SLS
As previously stated, in light of the possible endogeneity of healthcare
expenditure in the obesity-related mortality regressions, as a point of comparison I took
the specifications in Table 4.8 and re-estimated them using two stage least squares
(2SLS). It is important that the instrument chosen for this method is highly correlated It
is anticipated that infant mortality is a good instrument for this purpose, when regressing
real per capita healthcare expenditure on infant mortality, its coefficient was significant at
the 1 percent level. While infant mortality is proven to have a high correlation with
healthcare expenditure (Cremieux, et al, 1999), it is not substantially correlated with
colon cancer, diabetes, heart disease, high blood pressure, or stroke as these types of
mortality depend on the span of a person’s lifestyle choices. Thus, healthcare expenditure
was regressed on infant mortality, along with the exogenous variables. Predicted
healthcare expenditure from this first-stage regression was then used in place of
healthcare expenditure as a regresssor in the equation of interest (i.e., the second stage
regression).
39
Based on the results of this data, it does not seem that this approach is better than
the two-way fixed effects model. In particular, the Cragg-Donald F-Statistic value of
7.21, which tests the strength of the instrument is not significant at the ten percent level,
and thus infant mortality is a “weak” instrument. Additionally, in terms of the signs of
the coefficients the results remain similar to the fixed effects model. However, many of
the coefficients become far less significant in the 2SLS regressions. Also, adjusted Rsquare decreases slightly for diabetes and stroke mortality. The 2SLS procedure has a
large impact on the roles of income and healthcare expenditure, as none of the associated
coefficients is significantly different from zero. Also, the magnitudes of the coefficients
of predicted healthcare expenditure changed substantially, which suggests a deeper
analysis is warranted to fully address endogeneity concerns.13
For the socio-demographic variables, the percent of the population that is 19 years
and younger is positively associated with stroke and high blood pressure, but the
coefficient is insignificantly different from zero in each case. Compared to Table 4.8, the
coefficient of the percent of the population age 65 and older is similar in the 2SLS results.
Yet there were small changes that occurred for the race variables. In particular, the
percent of the population that is Black now has a significant positive impact on stroke
mortality. Also, the signs of the coefficient of the Asian variable changed with diabetes
and heart disease, such that it is now positively associated with diabetes and negatively
13
For example, the coefficient of healthcare expenditure in the type II diabetes regression
is 1.109 in Table 4.8, which becomes -19.668 in Table 4.9. Also, the coefficient of
healthcare expenditure in the stroke regression is 0.032 in Table 4.8, which becomes
-39.72 in Table 4.9.
40
Table 4.9. Instrumental Variable Approach
High Blood
Variables
Colon Cancer
Type II Diabetes
Heart Disease
Intercept
18.054***
85.018***
153.435***
23.874**
65.581***
(6.953)
(17.353)
(59.812)
(11.533)
(25.227)
-0.482
1.186
0.984
0.729
1.402
(0.48)
(1.075)
(2.355)
(0.976)
(1.448)
Healthcare Exp.
-1.649
-19.668
-44.53
-10.409
-39.72
(7.026)
(15.386)
(36.988)
(14.798)
(25.642)
19 and Younger
-17.419
-33.674
-671.247***
42.815
64.436
(34.206)
(35.984)
(132.494)
(33.541)
(69.027)
112.378**
-6.864
2279.02***
50.843
454.136***
(48.965)
(104.735)
(344.115)
(69.752)
(112.343)
-0.151
-0.208
-1.388
0.326
0.271
(0.116)
(0.484)
(0.898)
(0.236)
(1.153)
0.01
0.01
0.015
-0.002
0.058***
(0.012)
(0.014)
(0.04)
(0.011)
(0.017)
20.785
-23.655
368.642**
-22.231
97.216**
(21.411)
(35.032)
(178.865)
(45.408)
(45.067)
-27.172
20.976
10.308
-9.999
223.344**
(35.213)
(56.764)
(168.981)
(37.969)
(107.032)
30.371
-149.042*
279.945
-58.757
-249.154
(40.274)
(82.507)
(246.41)
(87.02)
(159.103)
Income
65 and Older
Alcohol
Cigarette
Black
Asian
Hispanic
College
Pressure
Stroke
-3.998
-15.045**
-49.132***
-5.731
-24.684
(4.472)
(7.395)
(17.812)
(8.263)
(15.356)
9.660
0.645
-43.603
1.143
-31.413
(6.812)
(14.454)
(42.214)
(11.234)
(24.916)
-0.014*
-0.04**
0.272***
-0.042***
-0.013
(0.008)
(0.019)
(0.048)
(0.013)
(0.024)
-22.100
39.988
161.165
29.508
3.229
(17.022)
(48.122)
(123.363)
(36.14)
(74.426)
All Variables=0
95.445
91.988
537.089
72.642
162.626
(p-value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
# Obs
500
500
500
500
500
Adj. R-Square
0.93
0.87
0.99
0.90
0.91
Obese
Pop Density
Unemp Rate
F-Statistic
Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
41
associated with heart disease (but still insignificant). The role of the Hispanic variable
did not change other than it is no longer significant with colon cancer and heart disease.
The roles of college and population density did not change.
The lifestyle variables have all dropped in significance. Except for smoking
having a significantly positive impact on stroke mortality, none of the lifestyle variables
matter in the 2SLS regressions. Of importance to this thesis, for instance, obesity no
longer impacts our five obesity-related mortality rates.
4.4. Elasticities
For further comparison, elasticities on income and healthcare expenditure are
calculated for all mortality rates. Calculating all the elasticities may not make sense, as
some of the results of the signs do not meet the expectations. However, it is useful that
the values are calculated to draw conclusions of the impact of these variables on
mortality. In Table 4.10, elasticities were calculated based on three of the equations: the
first is the state- and time-fixed effects model for the economic variables only, the second
is the state- and time-fixed effects model for all variables, and the third is the 2SLS
model. Excluding the elasticities from the 2SLS regressions, most of the results are
expected, with the elasticities being smaller in magnitude for the expanded regression
than when only economic variables are included (except for heart disease and high blood
pressure where it shows the elasticities are larger in absolute value).
Overall, it appears that the set of regressions from Table 4.8 (State- and TimeFixed Effects, All Variables) is the most favored. The approach of adding non-economic
variables into the equation proved significant as the coefficients of the age variables are
42
both large in magnitude and significantly affect mortality. Additionally, the R-square
indicates that both state- and time-fixed effects are important in the specification, where
the R-square values are higher when both are included versus a one-sided fixed effects
Table 4.10. Elasticities
Colon
Type II
Cancer
Diabetes
Heart Disease
High Blood
Pressure
Stroke
State & Time Fixed Effects, Economic Variables Only
Income
-0.49
-0.19
-0.02
-0.15
24.07
Healthcare Expen
-0.24
0.18
-0.18
-0.15
-15.75
State & Time Fixed Effects, All Variables
Income
-0.4
-0.07
-0.03
0.16
-0.26
Healthcare Expen
-0.21
0.11
-0.2
-0.22
-0.001
Income
-0.39
0.69
0.06
0.7
0.34
Healthcare Expen
-0.22
-1.89
-0.43
-1.66
-1.59
2SLS
treatment. Although we acknowledge not all of the coefficients show the expected signs,
it provides the best fit compared to all of the regressions. Obesity, also for the most part
has a positive significant effect on mortality. In terms of the 2SLS treatment, infant
mortality did not seem to be a good instrument. Although infant mortality is significantly
correlated with healthcare expenditure, its form as an instrument is weak. In comparison
to the two-way fixed effects regression, the significance of the variables lessened, some
of the signs became unexpected, and the r-square fell slightly.
43
Chapter 5
CONCLUSION
5.1. Summary of Findings
The focus of this thesis is to examine the determinants of obesity-related mortality
outcomes. Evaluated are the mortality rates of colon cancer, type 2 diabetes, heart
disease, high blood pressure, and stroke. While previous studies were successful in
finding significant links with obesity and these mortality rates (Hirsch et al., 1985, Chou
et al., 2002, Strum 2002), this thesis fails to find the same significance across the board.
Although initial estimates showed positive and significant links with obesity and health
outcomes, I found that after fixed effects are included, obesity does not significantly
affect deaths from high blood pressure, heart disease and stroke. Coefficients are
statistically significant for colon cancer and diabetes but the magnitude of the coefficients
is small.
This thesis uses similar approaches as studies of the determinants of overall
mortality. Included as determinants are such factors as economic, socio-demographic,
and lifestyle and behavioral variables, with the obesity rate also included as a common
lifestyle variable. One-way and two-way fixed effects and 2SLS techniques were also
assessed to address the potential presence of endogeneity.
The two-way fixed effects treatments were important in these regressions with the
state-fixed effects being more important than the time-fixed effects. Although a 2SLS
treatment was applied, the results of this approach did not prove as favorable to the fixed
effects model, which could be the result of using infant mortality as a weak instrumental
44
variable. This thesis followed similar methodology as previous literature on overall
mortality to test whether the results using more narrowly measured mortality rates are
similar to those when broader measures of mortality are used.
Overall, while there were some problems with the specifications, other notable
patterns emerged. The expansion of the regressions to include socio-demographic and
lifestyle and behavioral factors proved important, where age variables were primarily
statistically significant and of large magnitude compared with the other coefficients. The
race variables were significant for some mortality measures but not for others, which is
somewhat consistent with studies that find some mortality measures are tied to race and
ethnicity whereas others are not tied to race and ethnicity.
For the lifestyle and behavioral variables, the effects of alcohol and cigarette
consumption were found to be small in magnitude and not significant. For the purposes
of the focus of this thesis, based on previous literature I expected obesity to be
significantly and positively associated with mortality, however the results showed
otherwise. The effect of obesity was found to mostly have a positive effect on mortality
(except for heart disease and stroke), but significance and size of the variable dropped
when two-way fixed effects were added.14 While some of the results here support
previous literature of the positive effects of obesity on chronic health conditions, this
thesis questions whether the extent of the impacts are as large as what those authors have
14
When one-way time-fixed effects are used, obesity is positively associated with
mortality and its effect is significant at the 1% level. However, it could be that important
variables are omitted from this regression, causing this outcome.
45
found, because the results of this analysis shows the relationship to be fairly small or
insignificant.
Concerning the economic variables, income and healthcare expenditure remain
important but are only statistically significant with colon cancer, heart disease, and
stroke. While previous research on infant mortality and life expectancy found a strong
relationship between healthcare spending and health outcomes, the relationship is found
to be sensitive to model specification in this thesis. It could be, for instance, that the role
of income and healthcare expenditure is smaller when addressing the prevalence of
obesity-related mortality. The results of the effects of unemployment rate are also
different across the mortality rates. Previous literature linked a positive relationship with
unemployment and health status, and in effect links to higher probabilities of mortality.
However, the results of this thesis find the relationship to be questionable, since the
significance dropped when fixed effects were added and the signs are consistently
different amongst the mortality rates.
5.2. Recommendations for future research
There are various suggestions for future research that may improve on this thesis.
One suggestion is to consider each obesity-related mortality measure in greater detail,
and in so doing not constrain each mortality measure to the same specification. In
particular, there are likely factors that affect one mortality measure, but not others, and so
these unique characteristics should be explored more fully. Indeed, although heart
disease and colon cancer are medically related to obesity, deaths by both of these health
46
problems decreased from 1995 to 2004, whereas the numbers of people who are obese
increased, suggesting other factors may be at play in determining these mortality rates.
Additionally, as mentioned previously this thesis did not find a strong association
between economic variables and obesity-related mortality. For these particular measures,
it may be that health problems like diabetes and high blood pressure may be independent
of the effects of income and healthcare expenditure. This may be intuitive, as the
prevalence of obesity does not seem to be related to wealth, and healthcare expenditure
could be more reactive (i.e., endogenous) such that it is caused by increases in obesity
problems. With that said, though, there may be missing important determinants from the
model that are only important to some of these health outcomes.
Aside from changes to the empirical approach, there could be further
improvements made. For instance, other variables may be endogenously determined, and
so it would make sense to reevaluate the 2SLS approach with further instrumental
variables. It may also be beneficial to include other variables such as the poverty rate and
the Gini coefficient. The poverty rate represents the percentage of the population with
lower-income, which consequently with lower income limits the ability to have access to
the basic necessities of healthcare needs, that would negatively affect health. While the
income and race variables included in this study was meant to capture differences among
the states, it may also be useful to include measures of inequality, which can be
represented with the Gini coefficient. Perhaps including this variable can provide more
insight on how income or racial differences affect mortality.
47
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