AN INVESTIGATION OF THE LINK BETWEEN INCOME AND SPECIFIC
HEALTH INDICATORS
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Amanpreet K. Singh
SUMMER
2012
© 2012
Amanpreet K. Singh
ALL RIGHTS RESERVED
ii
AN INVESTIGATION OF THE LINK BETWEEN INCOME AND SPECIFIC
HEALTH INDICATORS
A Thesis
by
Amanpreet K. Singh
Approved by:
__________________________________, Committee Chair
Craig Gallet, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
iii
Student: Amanpreet K. Singh
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
Kristin Kiesel, Ph.D.
Department of Economics
iv
___________________
Date
Abstract
of
AN INVESTIGATION OF THE LINK BETWEEN INCOME AND SPECIFIC
HEALTH INDICATORS
by
Amanpreet K. Singh
This study examines the link between household income and specific health indicators,
namely body mass index, total cholesterol and systolic blood pressure. Data was taken
from the cross-sectional National Health and Nutrition Examination Survey conducted by
the Centers for Disease Control and Prevention over four periods between the years of
2001 and 2008. Both linear and logarithmic functional forms were applied to examine the
effects of income on health. A series of income brackets, as well as the income-topoverty ratio, were used as alternative measures of income.. The results indicate that
factors other than household income, most noticeably gender, race and educational
attainment, play a more important role in determining health outcomes.
_______________________, Committee Chair
Craig Gallet, Ph.D
_______________________
Date
v
ACKNOWLEDGEMENTS
I would like to thank Professor Craig A. Gallet for his clear support and guidance in
writing this thesis, and Professor Suzanne O’Keefe for assisting me with the empirical
analysis. I would also like to thank Professors Jonathan Kaplan and Kristin Kiesel for
serving as my graduate coordinators.
vi
TABLE OF CONTENTS
Page
Acknowledgements ........................................................................................................... vi
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
Chapter
1. INTRODUCTION ......................................................................................................... 1
2. LITERATURE REVIEW .............................................................................................. 3
2.1
Income and General Health Indicators .................................................................. 3
2.2
Income and Specific Health Indicators .................................................................. 6
2.3
Demographic Factors and Health Outcomes ......................................................... 8
3. DATA AND DESCRIPTIVE STATISTICS ............................................................... 13
3.1
Data and Variable Definitions ............................................................................. 13
3.2
Descriptive Statistics ........................................................................................... 16
3.3
Time Trends ........................................................................................................ 21
4. EMPIRICAL MODEL AND ESTIMATION RESULTS ........................................... 25
4.1
Empirical Model ................................................................................................. 25
4.2
Estimation Results .............................................................................................. 27
5. CONCLUSION ............................................................................................................ 46
References ......................................................................................................................... 48
vii
LIST OF TABLES
Table
Page
Table 1 Variables and Definitions .................................................................................... 14
Table 2 Descriptive Statistics............................................................................................ 17
Table 3 Pooled BMI .......................................................................................................... 31
Table 4 Pooled Systolic Blood Pressure ........................................................................... 35
Table 5 Pooled Total Cholesterol ..................................................................................... 38
Table 6 Linear Regression of BMI Over Time ................................................................. 42
Table 7 Logarithmic Regression of BMI Over Time........................................................ 44
viii
LIST OF FIGURES
Figure
Page
Figure 1 BMI Over Time .................................................................................................. 22
Figure 2 Systolic Blood Pressure Over Time ................................................................... 23
Figure 3 Total Cholesterol Over Time .............................................................................. 24
ix
1
Chapter 1
INTRODUCTION
Many studies have explored the relationship between household income and
health outcomes, and it has become generally accepted that greater income is associated
with better health. This study explores the relationship between income and three specific
health indicators: body mass index (BMI), total cholesterol and systolic blood pressure.
We examine this relationship using demographic variables, behavioral variables and two
measurements of household income, namely a series of income brackets and the incometo-poverty ratio. Data for the empirical analysis was taken from the National Health and
Nutrition Examination Survey (NHANES), which is conducted by the Centers for
Disease Control and Prevention (CDC) every two years. We use data from four survey
periods: 2001-02, 2003-04, 2005-06 and 2007-08. The NHANES offers respondents’
demographic data, laboratory data regarding current health status and self-reported health
habit data, all of which allow us to examine the effects of several factors on BMI, systolic
blood pressure and total cholesterol.
Some studies suggest that rather than income, it is socioeconomic status or social
class, as defined by the education level of the individual, which has the greatest impact
on health. Indeed, the socioeconomic status of an individual early in life can have a
lasting effect on health, as experiences early in life can determine one’s life-long health
trajectory. This study somewhat supports this assertion, as the findings suggest that
income itself is a less important determinant of health, while having a college degree is
2
almost always the most significant factor in determining BMI, systolic blood pressure or
total cholesterol. Granted, current higher household income can afford one the
opportunity to obtain higher education, for instance, but as other studies have stated, the
ability to attain higher education is often determined by one’s economic situation early in
life (Chandola et al., 2006).
This thesis is organized in the following manner. In Chapter 2, we begin with a
review of the existing literature on the relationship between health and various
characteristics to provide a foundation for the analysis. In Chapter 3, we summarize the
data used and present an analysis of trends over time for BMI, systolic blood pressure and
total cholesterol. In Chapter 4, we present the empirical model and discuss the results of
the empirical analyses. Finally, in Chapter 5 we summarize the results and make
suggestions for future research.
3
Chapter 2
LITERATURE REVIEW
Many studies have explored the impact of income on human health outcomes.
This chapter offers a review of the existing literature and a discussion of the methodology
used and the deductions made in previous studies. While many authors agree that an
association exists between income and health, a variety of hypotheses and instruments
have been employed to fully describe the phenomenon. Furthermore, some authors have
narrowed their research to examine specific health indicators (e.g., obesity), or specific
demographic characteristics (e.g., marital status and ethnicity) and their respective effects
upon health outcomes. This literature review will first focus on the dual relationship
between income and health as described in studies that use broadly-defined health
indicators. Then, the relationship between income and narrowly-defined health indicators
will be surveyed, as will be the relationship between health and demographic factors.
2.1
Income and General Health Indicators
Several studies examine the impact of income and socioeconomic status on health
outcomes by analyzing a variety of data. For example, in a multi-country, time-series
analysis estimating the impact of income on child mortality and life expectancy, Pritchett
and Summers (1996) attribute a nation’s current rate of mortality to the economic
4
performance of that nation’s economy in the previous decade.1 Although the authors use
instrumental variables to address the potential for reverse causation, their evidence
strongly favors the direction of causality running from income to mortality. Accordingly,
they conclude that changes in health status over time originate from trends in income, and
an upward income trend in a nation improves the health status of the residents of that
nation.
In another study of the dual relationship between income and health, Smith (1999)
seeks to pinpoint the direct cause of the association. Utilizing a life-cycle model, he
shows that poor health does have a negative impact on income. Moreover, not only is this
negative impact greatest among individuals who have less wealth, but the onset of illness
(and the subsequent financial strain tied to treatment costs and lost productivity) occurs
earlier for individuals with less wealth. He also notes that health is not merely affected by
wealth once an individual reaches adulthood, but that resource availability earlier in life
can set the path for one’s health trajectory throughout life.2 Thus, similar to Pritchett and
Summers (1996), Smith emphasizes the effect of income on health over an individual’s
life cycle. In his study, though, he finds that health and income affect each other, rather
than income simply being a predictor of health.3
1
Their study focuses primarily on infant and child mortality, as opposed to adult mortality, due to the
availability of nation-specific data on the former.
2
In particular, he finds that individuals who begin life with more resources are better able to delay the onset
of costly health concerns.
3
Pritchett and Summers (1996) also consider the importance of socioeconomic factors early in life as
determinants of health outcomes. For instance, their review of 14 household surveys on the part of the
5
In another paper, Ettner (1996) further explores the link between income and
health by deriving estimates of the impact of income on a variety of health-related
proxies using ordinary least squares (OLS) and two-stage least squares (2SLS), which are
applied to cross-sectional data based on two separate health surveys. With respect to her
regressions, Ettner initially treated income as exogenous by estimating her regressions
using OLS, and found a strong positive association between income and an individual’s
self-assessed health status (with higher values of health status indicating a more positive
opinion of one’s health). She also found a strong negative association between income
and symptoms of depression, functional limitations in the workplace, and the number of
sick days taken by an individual. Such findings were consistent across the two surveys.
Utilizing 2SLS, Ettner then finds that income and health are simultaneously
determined. Nonetheless, after using appropriate instruments, Ettner continues to find
that the effect of income on various health proxies remains significant. Indeed, the 2SLS
results reveal that the impact of income on health actually increases. Accordingly, based
on both the OLS and 2SLS results, Ettner argues that public policies that reduce
disposable income have adverse health outcomes. Moreover, since her study focused on
morbidity, whereas other studies have focused on mortality, she highlights the need to
consider the role that income plays as a determinant of various health indicators.
United Nations suggests that one additional year of education for an infant’s father reduces infant mortality
by 5 percent.
6
2.2
Income and Specific Health Indicators
Various studies discuss the impact of income on specific health indicators. In a
longitudinal study of adults in the Netherlands, Stronks et al. (1997) explore the
relationship between the three factors of income, health and employment status and find
that there is a strong association between income and health, with lower-income
individuals being less healthy. The health measures used in their study are chronic
conditions such as cardiovascular disease, cancer, back pain and diabetes. The percentage
of unemployed and disabled workers was consistently higher among lower income levels,
whereas this pattern was less pronounced among those with higher incomes. Even after
controlling for employment status, the findings indicated the strong association between
income and health. Stronks et al. note that their findings may be biased due to the use of a
proxy variable for income, and also note that while the results are pronounced for men in
the study, they are much less so for women in the study.
In another study, Winkleby et al. (1992) look at the relationships between income
and education, and the risk factors for cardiovascular disease, such as smoking, systolic
and diastolic blood pressure and high-density lipoprotein (HDL) cholesterol. In their
cross-sectional study of adults aged 25 to 64 years, they found that a higher risk for
cardiovascular disease (proxied by smoking habit and HDL cholesterol) is associated
with lower incomes. In terms of educational attainment, males with executive and
managerial careers had the lowest mean levels of diastolic and systolic blood pressure.
The correlation between income and education was low in their study, however, which
7
indicates that education is not the primary determinant of income. The individuals in the
study with the lowest educational attainment exhibited the highest rate of cardiovascular
disease risk factors, and Winkleby et al. suggest that those with higher education are
more likely to engage in preventative actions, such as avoiding cigarette smoking.
Although the study does indicate a strong relationship between cardiovascular risk factors
and income, Winkleby et al. conclude that income is a strong predictor of health, while
educational attainment is an even stronger and more consistent predictor.
In a longitudinal study of child obesity, which utilized NHANES data over the
1971 to 2001 period, Wang and Zhang (2006) estimated a series of logistic regressions to
examine the determinants of gender-specific body mass index (BMI) scores. Controlling
for household income with a series of dummy variables, Wang and Zhang (2006) found
the negative relationship between child obesity and household income is stronger among
children of ethnic minorities than it is for white children. They also showed the
relationship between household income and obesity in children has actually weakened
over time, yet the gap between overweight ethnic minorities and overweight white
children has widened. Indeed, over the 1971-1975 period, the prevalence of being
overweight was 7.1 percent, 6.4 percent and 3.8 percent for low-, middle- and highincome white adolescent girls, respectively, compared with 8.2 percent, 14.8 percent and
1.9 percent for low-, middle- and high-income black adolescent girls, respectively. Over
the 1999 to 2002 period, the respective values were 17.9 percent, 10.6 percent and 10.6
percent for white girls compared to 24.5 percent, 18.7 percent and 38 percent for black
8
girls. In Wang and Zhang (2006) they state that efforts to reduce obesity in children need
to target different ethnic groups in different ways.
In an English study examining the relationship between socioeconomic status
(SES) and BMI in children aged 5 to 10 years, Stamatakis et al. (2005) find that obesity
has risen sharply from 1974 to 2003, most markedly for individuals of low SES (which
was proxied by the occupation of the head of the household). The authors state that
family SES affects BMI over the long term, which is becoming more pronounced over
time, as rates of obesity in children of lower SES are increasing more rapidly than for
children of higher SES. However, similar to the study by Stronks et al. (1997), results
may be sensitive to the choice of proxy for income and/or SES.
2.3
Demographic Factors and Health Outcomes
In a study discussing marital differences in blood pressure among Polish men,
Lipowicz and Lopuszanska (2005) estimated logistic regression models, and amongst
other factors found that men who have never been married, on average, have higher rates
of both systolic and diastolic blood pressure than their married counterparts, as well as a
higher risk of hypertension. However, this study only focused on two categories of
marital status (i.e., married and never married), thus excluding previously married from
the study. According to the sample of participants, married men were on average more
educated and less likely to drink heavily than men who have never been married. While
the prevalence of alcohol consumption was comparable between the two groups, never-
9
married men were more likely to drink heavily. In comparison to married men, nevermarried men were more likely to exercise and accordingly had lower BMI scores. Despite
being slimmer and more likely to exercise, single men still had a significantly higher rate
of hypertension risk. Several explanations for this were offered. First, “marriage
selection” may eliminate unhealthy men from being selected for marriage. However, the
authors acknowledge that a limitation of this theory is that many participants in the study
were not necessarily unhealthy compared to their married counterparts. Second, married
men may benefit from the social and familial structures resulting from family life, for
such structures can eliminate stress that may adversely impact health. Additionally, the
authors state that married men may have better eating habits and may be less likely to
consume fast-food products than single men. Third, men living with a spouse may benefit
from great financial security and are able to live more economically in general. Such
benefits can foster a healthier lifestyle with reduced financial stresses.
Many studies have analyzed the interplay between demographic characteristics
and health outcomes. Williams and Umberson (2004) estimated ordered probit regression
models with longitudinal data to look at the impact of marital status on health. While
many authors have concluded that married individuals experience better health than their
unmarried counterparts, the dissolution of marriage can have a substantial negative
impact on health. Accordingly, Williams and Umberson (2004) examined the transitions
into and out of marriage to see if such transitions weaken the overall benefit of marriage
on health. Their findings suggest three central conclusions regarding marital status,
10
marital transitions and health. First, the marital status differences in health reflect the
stress of marital dissolution more than they reflect the health benefits resulting from
marriage. Secondly, the stress of marital dissolution has a greater impact on self-assessed
health for men than it does for women. Third, life course stages (or marital transitions)
matter as much as gender when describing health effects.
In a study exploring the relationship between gender, age and blood pressure,
Daida, et al. (1996), it was found that both diastolic and systolic blood pressures were
higher in male subjects than in female subjects, and that both diastolic and systolic blood
pressures were positively associated with age. Results were based on a treadmill exercise
test administered to healthy males and females from the ages of 20 to 79. Blood pressures
were consistently higher in males than in females across all ages (Daida, 1996).
There has been a great deal of analysis regarding the impact of educational
attainment on health. In an Italian study discussing the relationship between education
and the risk factors for coronary heart disease, Tenconi et al. (1992) took a look at risk
factors such as high cholesterol, high blood pressure and cigarette smoking, and found
that higher education is negatively associated with risky lifestyle choices, such as
cigarette smoking, heavy alcohol consumption and failure to engage in physical exercise.
Furthermore, Tenconi et al. (1992) found that those with higher education had lower
average values of total cholesterol and both systolic and diastolic blood pressure, which is
consistent with the belief that an increase in education results in the adoption of
11
protective and preventative health habits that ultimately lower the risk of coronary heart
disease.
Chandola et al. (2006) use structural equation models to explore the causal
association between education and health, outlining six different pathways linking these
two factors. First, cognitive ability or general intelligence could enhance a person’s
likelihood of engaging in protective lifestyle choices, as mentioned in Tenconi et al
(1992). Second, as also discussed in Smith’s (1999) analysis, childhood positive
socioeconomic circumstances may put individuals on a trajectory for better health, as
socioeconomic status is heavily influenced by parental education levels. Third, childhood
health can impact educational attainment, with poor health having a potentially adverse
effect on attainment. Fourth, adult socioeconomic status can also impact educational
attainment once an individual reaches adulthood. Fifth, as also discussed by Tenconi et
al. (1992), education can have a significant impact on health behaviors an individual
engages in. Sixth, higher education may lead to a greater sense of control over one’s
health, as manifested in long-term health planning. Chandola et al. (2006) emphasize the
fact that education significantly informs an individual’s lifestyle choices. However,
attainment of education is often contingent upon favorable economic circumstances early
in life.
In another paper about the relationship between education and health, Schnittker
(2004) examines the income “gradient” in health, which suggests that those of higher
socioeconomic status live longer and healthier lives. Schnittker (2004) finds that not only
12
does education improve health, but those with more education have better health at all
levels of income. The study stresses the role of socioeconomic position and health, rather
than the link between income and health, as the author was unable to explain the
interactions between the latter two factors. While the level of education attained is an
important determinant of future earnings, Schnittker (2004) posits that it is the attainment
of education in itself, rather than income, that has a greater impact on health outcomes. In
the next chapter we present the data that will be used to estimate a series of regressions.
We also present descriptive statistics of our data.
13
Chapter 3
DATA AND DESCRIPTIVE STATISTICS
3.1
Data and Variable Definitions
This chapter begins with a discussion of the data used in this analysis. All data
was taken from the National Health and Nutrition Examination Survey (NHANES),
which is conducted by the U.S. Centers for Disease Control and Prevention every two
years and consists of both survey and laboratory components. We used data from four
survey periods: 2001-02, 2003-04, 2005-06 and 2007-08. Respondents were asked to
complete surveys pertaining to their various health habits (e.g., how often they consume
alcohol, how often they exercise, etc.), and were also subject to laboratory examinations
which measured a variety of health characteristics, such as BMI. The NHANES research
program seeks to include respondents from across all demographics, with the purpose of
identifying prevalent diseases in the United States and corresponding risk factors, as well
as providing data to aid in subsequent health research and epidemiological studies
(NHANES, 2006).
Table 1 provides a list of the dependent and explanatory variables used in this
thesis, along with their respective definitions. In the next chapter, we will explain the
empirical model in detail.
14
Table 1
Variables and Definitions
Variable
Definition
Dependent
BMI
Body mass index.
Systolic Blood Pressure Systolic blood pressure, measured in mm/Hg.
Total Cholesterol
Total cholesterol, measured in mg/dL.
Explanatory
Age
Respondent’s age in years.
Cotinine
Amount of cotinine, a by-product of nicotine, in
respondent’s body, measured in ng/mL.
Exercise
Number of minutes of moderate exercise respondent
gets in one week.
Alcohol
Number days respondent consumes alcohol in one year.
Rooms in Home
Number of rooms in respondent’s home.
Male
Respondent is male.
Insured
Respondent has health insurance.
Married
Respondent is married.
Mexican
Respondent is of Mexican ethnicity.
Other Hispanic
Respondent is of non-White Hispanic ethnicity.
White
Respondent is of White ethnicity.
Black
Respondent is of Black or African American ethnicity.
Other Race
Respondent is of other ethnicity.
th
Less than 9 Grade
Respondent has completed an education of less than 9th
grade.
th
th
9 to 11 Grade
Respondent has completed some high school between
grades 9 and 11, but has not graduated.
HS or GED
Respondent has graduated from high school or obtained
a general equivalency degree.
Some College
Respondent has completed some college or received a
two-year or associate’s degree.
College Graduate
Respondent has completed a four-year college degree.
IPR
Ratio of monthly income to poverty.
$0-$4,999
Respondent’s yearly household income is between $0
and $4,999.
15
Table 1 continued
Variables and Definitions
$5,000-$9,999
$10,000-$14,999
$15,000-$19,999
$20,000-$24,999
$25,000-$34,999
$35,000-$44,999
$45,000-$54,999
$55,000-$64,999
$65,000-$74,999
$75,000-$99,999
$100,000+
Respondent’s yearly household income is between
$5,000 and $9,999.
Respondent’s yearly household income is between
$10,000 and $14,999.
Respondent’s yearly household income is between
$15,000 and $19,999.
Respondent’s yearly household income is between
$20,000
and $24,999.
Respondent’s yearly household income is between
$25,000 and $34,999.
Respondent’s yearly household income is between
$35,000
and $44,999.
Respondent’s yearly household income is between
$45,000 and $54,999.
Respondent’s yearly household income is between
$55,000 and $64,999.
Respondent’s yearly household income is between
$65,000 and $74,999.
Respondent’s yearly household income is between
$75,000
and $99,999.
Respondent’s yearly household income is $100,000 or
more.
Since the purpose of this thesis is to examine the various determinants of
individual health, the dependent variables used in this thesis include BMI, systolic blood
pressure and total cholesterol. Given the literature discussion in the previous chapter, the
explanatory variables used can be divided into two general categories: behavioral and
demographic. The behavioral variables include those health-related behaviors that are
under the control of the individual, in this case cotinine, exercise and alcohol. These
16
variables were chosen to explore the effect of respondents’ habits on their health
outcomes. Demographic variables include ethnicity, education level, age, sex, income,
etc. Two different approaches were taken to measure income. In the first approach,
individuals are assigned to different income brackets depending on their income levels.4
In the second approach, the income-to-poverty ratio was taken as the measure of income.
Finally, we also control for the role of wealth by using the number of rooms in a
respondent’s household as a proxy variable for household wealth.
3.2
Descriptive Statistics
In this section we provide and discuss various descriptive statistics of the data
used in this study. The total number of respondents across the four sample periods is
34,004. However, several of the variables have missing observations since not every
respondent was examined or surveyed for each section of the NHANES. Additionally,
respondents had the choice to decline to answer certain questions or opt out of certain
exams. For this reason, the descriptive statistics describe only 2,634 observations used in
the empirical analysis which is discussed in Chapter 4. For the sake of brevity, only
descriptive statistics for the pooled data will be discussed in this section, rather than data
for each of the four survey periods. Table 2 below shows the descriptive statistics, which
4
Exact household income amounts were not available from NHANES. Income brackets are thus used
instead.
17
include the mean, standard deviation, maximum value and minimum value for applicable
variables, as well as percentage amounts for certain demographic variables.
Table 2
Descriptive Statistics
Continuous Variables
BMI
Systolic Blood Pressure
Total Cholesterol
Age
Cotinine
Exercise
Alcohol
Rooms in Home
IPR
Mean
27.82
122.93 mm/Hg
196.32 mg/dL
44.78
63.46 ng/mL
57.66 minutes
4.65 days
5.77
2.78
Categorical and Dummy
Variables
Male
Insured
Married
Mexican
Other Hispanic
White
Black
Other Race
Less than 9th Grade
9th to 11th Grade
HS or GED
Some College
College Graduate
$0-$4,999
$5,000-$9,999
Percentage of
Sample
47.71%
76.29%
51.48%
20.83%
6.01%
48.18%
21.04%
3.93%
6.13%
13.30%
21.93%
32.04%
26.59%
2.21%
6.05%
Std. Dev.
5.94
18.06
41.10
17.37
129.21
61.16
19.04
2.09
1.60
Min
15.91
78
92
20
0.011
1
0
1
0.1
Max
63.87
210
431
85
1136
600
365
13
5
18
Table 2 continued
Descriptive Statistics
$10,000-$14,999
$15,000-$19,999
$20,000-$24,999
$25,000-$34,999
$35,000-$44,999
$45,000-$54,999
$55,000-$64,999
$65,000-$74,999
$75,000-$99,999
$100,000+
10.01%
9.87%
10.24%
16.09%
12.82%
11.03%
7.79%
6.42%
3.11%
4.36%
As mentioned, three different health indicators are explored in this study: BMI,
systolic blood pressure and total cholesterol. From Table 2, we see BMI has a mean of
27.82 and a standard deviation of 5.94. The BMI range for normal-weight individuals is
18.5 to 24.9, with an individual having a BMI of 25.0 to 29.9 being classified as
overweight, while having a BMI of 30.0 or above classifies an individual as obese
(CDC.gov, 2012). Accordingly, the average respondent in this study is overweight.
Concerning systolic blood pressure, it has a mean value of 122.93 mm/Hg and a
standard deviation of 18.06. According to the National Heart, Lung and Blood Institute
(2003), a healthy systolic blood pressure level should be below 120 mm/Hg, with levels
between 120 and 139 mm/Hg indicating prehypertension, and levels over 130 mm/Hg
indicating hypertension. Hence, on average NHANES respondents fall slightly into the
prehypertension range.
19
Finally, mean total cholesterol is 196.32 mg/dL, while its standard deviation is
43.08. According to CDC guidelines, a total cholesterol level under 200 mg/dL is
considered healthy (CDC.gov, 2012). Thus, the average NHANES respondent has a total
cholesterol level that is in a healthy range.
As for the other variables, several demographic, behavioral and income variables
are explored as independent variables. Ethnically, White Americans make up 48.18
percent of the NHANES respondents, Mexican Americans make up 20.83 percent of
respondents, Other Hispanics make up 6.01 percent of respondents, Black Americans
make up 21.04 percent of respondents, and Other Races make up 3.93 percent of
respondents.
Those who have completed a four-year college degree make up 26.59 percent of
NHANES respondents, whereas those who have completed less than high school make
up 19.43 percent of respondents. The average age in years of a respondent is 44.78, with
a standard deviation of 17.37, while the percentage who are male is 47.71, the percentage
who are married is 51.48, and the percentage who have health insurance is 76.29.
Three behavioral variables are used in this study: cotinine level, minutes of
moderate exercise per week and number of days alcohol is consumed in one year.
Cotinine is a by-product of nicotine exposure and is more stable in the human body than
is the level of nicotine. Serum cotinine levels, usually measured from human saliva
samples, offer a more accurate analysis of nicotine exposure, including that from
secondhand smoke (Adda and Cornaglia, 2006), and thus serve as our measure of an
20
individual’s choice regarding smoking. According to Table 2, the average NHANES
respondent has a serum cotinine level of 63.46 ng/mL, with a standard deviation of
129.21. A cotinine level close to zero indicates little to no nicotine exposure, whereas a
cotinine level of 300 ng/mL, for instance, would indicate heavy exposure (Adda and
Cornaglia, 2006). Thus, overall our respondents have modest exposure to nicotine.
When it comes to alcohol consumption, the average number of days in a year that
a respondent consumes alcohol is 4.65 with a standard deviation of 19.04. 88.03 percent
of the sample consumes alcohol 5 days or fewer per year, and 22.48 percent of the sample
consumes alcohol zero days per year. As with cotinine levels, while the average values
are low, the standard deviation indicates there is a great deal of variation in use across
individuals, with many choosing to consume those substances on a regular basis.
The average respondent engages in 57.66 minutes of moderate exercise each
week, with a standard deviation of 61.16. CDC guidelines state that adults aged 18 to 64
years should get at least 150 minutes of moderate exercise each week, and children aged
6 to 17 years should get at least 60 minutes (CDC.gov, 2012). Hence, according to the
data the average individual in this study does not get enough exercise.
Finally, as we mentioned earlier, two different measurements of household
income are used: income-to-poverty ratio (IPR) and various income brackets. The IPR is
a measurement of monthly household income relative to the poverty level of income, and
the average IPR in this study is 2.78, with a standard deviation of 1.60. The IPR ranges
from 0.10 to 5, with a higher value indicating higher income. For instance, with the
21
household poverty level baseline at $20,000, this means that a household income of
$100,000 would yield an IPR of 5.5Alternatively, several income bracket dummy
variables are also used, with Table 2 showing the most common being $25,000 to
$34,999 of yearly income, with 16.09 percent of the sample falling into that range. Only
4.36 percent of the sample has a yearly household income of $100,000 or more, and 8.26
percent of the sample has an income of less than $10,000. Lastly, the number of rooms in
a respondent’s home is used as a proxy variable for household wealth. While the number
of rooms in a home ranges between 1 and 13, the average number is 5.77 and the
standard deviation is 2.09.
3.3
Time Trends
As mentioned previously, the data used in this study is from four NHANES
periods: 2001-02, 2003-04, 2005-06 and 2007-08.6 In this section, we look at time trends
over those four periods for the dependent variables BMI, systolic blood pressure and total
cholesterol. The height of each box in Figure 1 corresponds to the average BMI for each
time period. As discussed in the previous section, having a BMI over 25 classifies an
individual as being overweight. According to the data, the average American is becoming
5
Data regarding household incomes of over $100,000 was unavailable, which is why the maximum
possible IPR is 5.
6
While the descriptive statistics as discussed in Section 3.2 describe the 2,634 observations used in the
empirical analysis, the time trends discussed in this section describe the total sample of 34,404
observations.
22
increasingly overweight over time, which is a trend also documented in other studies
(e.g., see Ogden, 2010). Indeed, beginning in the 2003-04 time period, the average
respondent in this study went from being just below overweight to overweight, with the
largest jump of roughly 0.40 occurring from the 2005-06 survey to the 2007-08 survey.
Figure 1 BMI Over Time
The height of each box in Figure 2 below shows the average systolic blood
pressure over time for NHANES respondents, as measured in mm/Hg, with the average
being 120.52, 120.59, 119.78 and 121.02 in each sample period, respectively. In the
2005-06 sample, systolic blood pressure dropped 0.81 points, but then rose again by 1.24
points in the 2007-08 sample. Except for 2005-06, there is a slight upward trend in
systolic blood pressure according to the data. As mentioned previously, CDC guidelines
state that systolic blood pressure should be under 120, and thus only in the 2005-06
sample was the average respondent in a healthy systolic blood pressure range.
23
Figure 2 Systolic Blood Pressure Over Time
Finally, the height of each box in Figure 3 indicates the average total cholesterol
over time, as measured in mg/dL, with the average being 186.52, 184.91, 183.98 and
186.73 in each sample period, respectively. During the 2003-04 and 2005-06 sample
periods, total cholesterol was lower, but then increased to 186.73 in 2007-08.
Collectively, Figures 1 – 3 indicate changes in the average respondent over the
four different sampling periods. As explained in the next chapter, although we pool the
data across these four periods, we also consider whether or not the estimation results are
sensitive to changes in survey participants over the four periods.
24
Figure 3 Total Cholesterol Over Time
25
Chapter 4
EMPIRICAL MODEL AND ESTIMATION RESULTS
4.1
Empirical Model
This study considers both linear and logarithmic regression models to measure the
impact of demographic, behavioral and income variables on the health indicators of BMI,
systolic blood pressure and total cholesterol. A key interest is how sensitive the results
are to various measures of income. Consider the following linear model:7
Yi = 0 + 1 Agei + 2 Cotininei + 3 Exercisei + 4 Alcoholi + 5 Roomsi +
6 Malei + 7 Insuredi + 8 Marriedi + 9 Ethnicityi + 10 Educationi +
11 Incomei + 12 Yearit + i
(1)
where i indexes the individual and Y designates the respective health indicator (i.e., BMI
systolic blood pressure, and total cholesterol). Several variables are as defined in Table 1
(i.e., Age, Cotinine, Exercise, Alcohol, Rooms (in home), Male, Insured, and Married),
while Ethnicity is a vector of ethnicity variables defined in Table 1 (i.e., Other Hispanic,
White, Black, and Other Race, with Mexican serving as the baseline), Education is a
vector of education variables defined in Table 1 (i.e., 9th to llth grade, HS or GED, some
College, and College Graduate, with less than 9th grade serving as the baseline), and
7
In the log version of equation (1), the continuous variables are transformed into natural logs, thus
allowing for nonlinearity in relationships.
26
Income is a vector consisting of alternative income measures.8 Specifically, in some
regressions we consider categorical measures of income as defined in Table 1 (i.e., eleven
different income categories, with $0-$4,999 serving as the baseline). In other regressions
we use the income-to-poverty ratio as our measure of income. Year represents year
dummy variables representing the cohort for that obsesrvation. Finally, i is an error
term.
Regarding the explanatory variables used in this study, they are similar to those
that have been explored in the existing literature. It is expected that higher education and
income will improve health. That is, as education and income go up, we expect total
cholesterol, for instance, to go down. It is also expected that the variables cotinine and
alcohol both reduce health outcomes (i.e., raise blood pressure and cholesterol). With
respect to BMI, though, we expect smokers to have lower BMI, and thus increases in
cotinine should reduce BMI scores (see Adda and Cornaglia, 2006).
As for the demographic variables, it is expected that older males will have lower
health outcomes, since previous research suggests that older males have a higher risk of
morbidity (e.g., see Lipowicz and Lopuszanska, 2005). It is also expected that BMI,
cholesterol, and systolic blood pressure are higher for Black Americans, since studies
find, for instance, that Black children have higher BMIs than their White counterparts
8
A Year variable is also included in the empirical analyses, which is a vector of year variables indicating
the NHANES survey period (i.e. 2001-02, 2003-04, 2005-06, and 2007-08, with 2001-02 serving as the
baseline).
27
and Black smokers also have higher systolic blood pressure (see Wang and Zhang, 2006;
Adda and Cornaglia, 2006). Since the number of rooms in a home is used as a proxy for
wealth, coupled with those being insured having better access to health care, we expect
both of these variables to have a positive influence on our health indicators. Finally, we
expect the marital status variable to positively impact health, and thus reduce our three
health indicators, as studies do find being married improves an individual’s health
outcome (e.g., see Lipowicz and Lopuszanska, 2005).
The remainder of this chapter presents the estimation results and draws
comparisons to the existing literature. Results of both linear and double-log versions of
equation (1) will be discussed.
4.2
Estimation Results
In this section the results from the estimation of the different versions of equation
(1) will be discussed. We will explore the impact of the explanatory variables on each of
the three health indicators using both linear and logarithmic functional forms, as well as
the two approaches to measuring household income. Initially, the data is pooled across
the four sample periods. As mentioned in the last chapter, however, there appear to be
differences in the means of key variables across the four sample periods, and so to see
whether or not the results vary over time we also estimated separate regressions for each
sample period for BMI, which among our three health indicators is the one most
commonly addressed in the literature.
28
As mentioned previously, while there are 34,404 total observations, many
variables have missing observations since every respondent did not participate in each
survey or laboratory exam; also, respondents had the option of declining to answer
certain questions, thus the number of actual observations used in each regression is much
lower due to the missing observations. As mentioned above, the descriptive statistics in
Table 3 describe the 2,634 observations used in the following empirical analyses, while
the time trends discussed in the previous chapter describe the total sample.
Table 3 shows the results of four different regressions used to examine the impact
of the independent variables on BMI using the pooled data. The four regressions
employed are as follows: a linear regression using the 11 income brackets to account for
different income levels, a logarithmic regression using the 11 income brackets to account
for different income levels, a linear regression using the income-to-poverty ratio (IPR) to
account for income in a continuous manner, and a logarithmic regression using IPR to
account for income in a continuous manner. By comparing the results using the income
bracket and IPR approaches, we can see how sensitive the results are to how income is
controlled for in the regressions. By comparing the linear and logarithmic results, we are
able to see if the results are robust across different functional forms.
Beginning with the behavioral variables, we can see in Table 3 that an increase in
serum cotinine is associated with a drop in BMI, which is consistent with the evidence
that regular smokers tend to weigh less than non-smokers (see Adda and Cornaglia,
2006). This result is statistically significant at the 0.01 percent level in all four
29
regressions. Despite the fact that the average adult respondent in this study engages in
only 57.66 minutes of moderate exercise per week, exercise is not statistically significant.
Furthermore, alcohol consumption does not have a statistically significant impact on
BMI.
Next, we explore the impact of demographic factors. In the log regressions, age
has a positive relationship with BMI, which is statistically significant at the 1 percent
level. In both the linear regressions, males tend to have lower BMIs. Since the coefficient
of male is insignificant in the log regressions, this suggests functional form does have
some influence on the results. Being married or insured, however, does not have a
significant impact on BMI across all four regressions. When it comes to race, White
Americans or Americans of other race have significantly lower BMI. Furthermore, in the
linear regressions, results show that Black Americans have a significantly higher BMI,
which parallels the results in other studies (e.g., see Wang and Zhang, 2006). In all four
regressions, college graduates have significantly lower BMI scores, compared to
individuals who have less than a 9th grade education. BMI scores for other education
categories, however, are not significantly different from the baseline case in any of the
regressions. Thus, education has the greatest impact on BMI for those individuals with a
higher education degree.
Finally, we explore the impact of income on BMI. Two criticisms of using the
income bracket approach are that (i) it fails to distinguish between the income levels of
individuals who happen to lie in the same income bracket and (ii) results are always
30
interpreted relative to the baseline (i.e., individuals earning under $5000 per year), which
makes it challenging to compare the role of income across individuals in other income
brackets. Thus, we also use IPR as an explanatory variable, since its continuous nature
allows us to compare individuals of different incomes, regardless of their level of income.
According to the results in Table 3, none of the coefficients of the eleven income brackets
are statistically significant in either the linear or logarithmic models. Also, although the
coefficient of IPR is positive, which implies individuals of higher income tend to have
worse health when it comes to their weight, the coefficient in both the linear and
logarithmic regressions is statistically insignificant. Thus, regarding the role of income,
it does not appear that the measure of income, be it dichotomous or continuous, affects
the results in a significant way.
These results indicate that the most significant variables impacting BMI are
cotinine level, being male, being White, being of other race, and having a college degree.
Based on the log specifications, whites have BMI 5.2% lower than Hispanics, college
educated have BMI 4.5% lower than people with less than a college education, each unit
increase in cotinine levels reduces BMI by 0.4%. Despite what other studies have
indicated, exercise, alcohol consumption and income, coupled with several other
demographic variables, do not have statistically significant impacts on BMI in this study.
31
Table 3
Pooled BMI
VARIABLES
Age
Cotinine
Exercise
Alcohol
Rooms in Home
Male
Insured
Married
Other Hispanic
White
Black
Other Race
9th to 11 Grade
HS or GED
(1)
Linear with
Income
Bracket
(2)
Log with
Income
Bracket
(3)
(4)
Linear with
IPR
Log with
IPR
0.008
(0.007)
-0.005***
(0.001)
-0.001
(0.002)
-0.002
(0.004)
-0.002
(0.064)
-0.464*
(0.241)
0.233
(0.341)
-0.040
(0.252)
-0.035
(0.521)
-0.716**
(0.354)
1.045**
(0.416)
-1.365**
(0.585)
0.430
(0.513)
0.425
(0.473)
0.038***
(0.012)
-0.004***
(0.001)
-0.003
(0.005)
-0.004
(0.005)
0.003
(0.013)
0.002
(0.009)
0.009
(0.012)
-0.005
(0.009)
-0.013
(0.019)
-0.052***
(0.012)
0.016
(0.015)
-0.073***
(0.022)
0.004
(0.020)
0.018
(0.019)
0.005
(0.007)
-0.006***
(0.001)
-0.001
(0.002)
-0.002
(0.004)
0.011
(0.062)
-0.473**
(0.240)
0.238
(0.340)
0.039
(0.248)
0.081
(0.519)
-0.755**
(0.356)
1.048**
(0.414)
-1.412**
(0.580)
0.386
(0.508)
0.330
(0.473)
0.035***
(0.012)
-0.004***
(0.001)
-0.003
(0.005)
-0.004
(0.005)
0.005
(0.013)
0.003
(0.009)
0.009
(0.012)
-0.002
(0.009)
-0.003
(0.019)
-0.053***
(0.013)
0.017
(0.015)
-0.073***
(0.022)
0.004
(0.020)
0.018
(0.019)
32
Table 3 continued
Pooled BMI
Some College
College Degree
IPR
$5,000-$9,999
$10,000-$14,999
$15,000-$19,999
$20,000-$24,999
$25,000-$34,999
$35,000-$44,999
$45,000-$54,999
$55,000-$64,999
$65,000-$74,999
$75,000-$99,999
$100,000+
2003-04
2005-06
2007-08
0.087
(0.474)
-1.390***
(0.492)
----0.598
(1.175)
-0.258
(1.103)
0.057
(1.086)
0.276
(1.040)
0.691
(1.032)
0.225
(1.031)
0.243
(1.027)
0.889
(1.083)
0.415
(1.050)
1.175
(1.089)
0.713
(1.060)
-0.213
(0.839)
0.267
(0.849)
0.511
(0.833)
-0.000
(0.019)
-0.045**
(0.020)
----0.020
(0.043)
-0.017
(0.040)
0.001
(0.040)
0.020
(0.038)
0.022
(0.037)
0.016
(0.037)
0.012
(0.037)
0.041
(0.039)
0.027
(0.038)
0.043
(0.039)
0.032
(0.038)
0.013
(0.028)
0.036
(0.028)
0.040
(0.028)
-0.013
(0.478)
-1.515***
(0.505)
0.129
(0.093)
---------------------------------------------0.396
(0.834)
0.146
(0.846)
0.458
(0.820)
-0.000
(0.019)
-0.043**
(0.020)
0.010
(0.006)
--------------------------------------------0.001
(0.029)
0.026
(0.029)
0.035
(0.028)
33
Table 3 continued
Pooled BMI
Constant
27.896***
(1.400)
3.153***
(0.064)
28.210***
(1.016)
Observations
R-squared
2,627
2,218
2,634
0.046
0.050
0.045
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
3.176***
(0.056)
2,209
0.048
Table 4 shows the results of the four different specifications similarly used for the
BMI regressions, but now using systolic blood pressure as the dependent variable.
Beginning with the behavioral variables, we see the coefficients of cotinine, alcohol, and
exercise are not statistically significant in any of the regressions. Thus, with the
exception of cotinine in the BMI regressions, behavioral factors have less impact on both
BMI and systolic blood pressure.
When it comes to demographic factors, there is a positive relationship between
age and systolic blood pressure, which is statistically significant at the 1 percent level in
all four regressions. Likewise, there is a positive relationship between being male and
systolic blood pressure, which is also statistically significant at the 1 percent level in all
four regressions. This result is in line with other studies that show that growing older and
being male increases one’s likelihood of having higher systolic blood pressure (e.g., see
Daida, 1996). Also, unlike BMI, being insured has a negative relationship with systolic
blood pressure, which suggests that preventative care is indeed helpful in reducing
cardiovascular concerns (e.g., see Winkleby et al., 1992). Being Black is also associated
34
with a higher systolic blood pressure, with the respective coefficient being statistically
significant at the 1 percent level in all four regressions. Many medical studies have noted
that Black individuals are much more likely to have hypertension than individuals of
other ethnicities (Kokkinos et al., 1995), which is also suggested by our results. Finally,
having a college degree is associated with significantly lower systolic blood pressure in
all four regressions.
Finally, although most of the coefficients of the other demographic variables are
not statistically significant, significance of the income-related variables is spotty. In
particular, while it does appear that individuals in households with income of at least
$65,000 per year have lower systolic blood pressure in the log regressions (with
significance dropping off in the linear form), the other income brackets, as well as IPR,
tend to have an insignificant impact on systolic blood pressure. Thus, the variables with
the greatest impact on systolic blood pressure are age, being male, being Black, being
insured, and having a college degree.
35
Table 4
Pooled Systolic Blood Pressure
VARIABLES
Age
Cotinine
Exercise
Alcohol
Rooms in Home
Male
Insured
Married
Other Hispanic
White
Black
Other Race
9th to 11th Grade
(1)
Linear with
Income
Brackets
(2)
Log with
Income
Brackets
(3)
(4)
Linear with
IPR
Log with
IPR
0.502***
(0.022)
0.000
(0.003)
-0.007
(0.005)
-0.006
(0.012)
-0.074
(0.184)
3.985***
(0.675)
-2.221***
(0.856)
-0.534
(0.726)
-0.769
(1.499)
0.292
(1.004)
3.514***
(1.201)
0.327
(1.714)
-2.325
(1.831)
0.162***
(0.008)
-0.000
(0.001)
-0.005
(0.004)
0.002
(0.003)
0.004
(0.008)
0.038***
(0.006)
-0.018**
(0.007)
-0.010
(0.006)
0.007
(0.013)
0.009
(0.008)
0.036***
(0.010)
0.018
(0.014)
-0.019
(0.014)
0.506***
(0.022)
0.000
(0.003)
-0.008
(0.006)
-0.006
(0.012)
-0.095
(0.178)
4.073***
(0.677)
-2.126**
(0.849)
-0.739
(0.724)
-0.398
(1.528)
0.237
(1.004)
3.573***
(1.189)
1.090
(1.785)
-2.555
(1.842)
0.165***
(0.008)
-0.000
(0.001)
-0.005
(0.004)
0.002
(0.003)
0.001
(0.007)
0.039***
(0.006)
-0.019***
(0.007)
-0.012**
(0.006)
0.011
(0.013)
0.009
(0.008)
0.036***
(0.010)
0.022
(0.014)
-0.022
(0.015)
36
Table 4 continued
Pooled Systolic Blood Pressure
HS or GED
Some College
College Graduate
IPR
$5,000-$9,999
$10,000-$14,999
$15,000-$19,999
$20,000-$24,999
$25,000-$34,999
$35,000-$44,999
$45,000-$54,999
$55,000-$64,999
$65,000-$74,999
$75,000-$94,999
$100,000+
2003-04
2005-06
-2.282
(1.768)
-2.380
(1.714)
-5.047***
(1.785)
----0.016
(2.849)
-3.018
(2.690)
-2.323
(2.618)
-3.134
(2.510)
-1.765
(2.428)
-3.649
(2.431)
-2.563
(2.440)
-2.692
(2.536)
-4.259*
(2.564)
-3.153
(2.528)
-3.170
(2.533)
4.318**
(2.078)
2.757
(2.054)
-0.022
(0.014)
-0.023*
(0.013)
-0.047***
(0.014)
-----0.024
(0.024)
-0.030
(0.023)
-0.038*
(0.022)
-0.029
(0.021)
-0.022
(0.021)
-0.036*
(0.021)
-0.028
(0.021)
-0.030
(0.022)
-0.042*
(0.022)
-0.045**
(0.022)
-0.041*
(0.022)
0.027
(0.019)
0.013
(0.019)
-2.632
(1.779)
-2.646
(1.747)
-5.327***
(1.830)
-0.197
(0.247)
--------------------------------------------3.883*
(2.046)
2.452
(2.026)
-0.026*
(0.014)
-0.027*
(0.014)
-0.053***
(0.015)
-0.001
(0.004)
--------------------------------------------0.028
(0.019)
0.016
(0.019)
37
Table 4 continued
Pooled Systolic Blood Pressure
2007-08
Constant
Observations
R-squared
1.744
(2.001)
103.518***
(3.470)
0.010
(0.019)
4.240***
(0.042)
1.303
(1.941)
101.876***
(2.700)
2,451
2,071
2,456
0.256
0.248
0.255
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
0.009
(0.018)
4.207***
(0.038)
2,062
0.248
Table 5 below shows the pooled results of the four different regressions using
total cholesterol as the dependent variable. Regarding the behavioral factors, the
coefficients of cotinine, exercise, and alcohol are insignificantly different from zero.
Thus, similar to BMI and blood pressure, behavioral factors also play a smaller role in
determining cholesterol.
In all four regressions, age has a positive and statistically significant relationship
(at the 1% level) with total cholesterol. Also, males and married individuals tend to have
higher cholesterol levels, as well as uninsured individuals (significantly so in the two
regressions with IPR as the measure of income). For the two linear specifications, being a
college graduate significantly lowers cholesterol level, compared to individuals with less
than a 9th grade education. For most other demographic variables, though, statistical
significance drops off appreciably.
Concerning the two different income measures, while the majority of the
coefficients of the income bracket variables are insignificantly different from zero (with
38
the exception of a few higher income brackets), the coefficient of IPR is positive and
statistically significant in the linear and log specifications. Thus, similar to BMI, the
method used to control for income appears to influence the estimation results. Also,
inconsistent with our expectations, higher income coincides with worse health, as
cholesterol increases.
These results present a few mixed findings. For instance, unlike the analyses for
BMI and systolic blood pressure, as well as the existing literature, being married appears
to increase total cholesterol. Also, having a college degree is associated with lower total
cholesterol, which parallels findings of other studies. Yet income brackets generally do
not play a statistically significant role in determining total cholesterol.
Table 5
Pooled Total Cholesterol
VARIABLES
Age
Cotinine
Exercise
Alcohol
Rooms in Home
(1)
Linear with
Income
Brackets
(2)
Log with
Income
Brackets
(3)
(4)
Linear with
IPR
Log with
IPR
0.370***
(0.052)
0.006
(0.007)
0.015
(0.013)
-0.015
(0.028)
-0.244
(0.440)
0.121***
(0.012)
0.000
(0.001)
0.007
(0.005)
0.001
(0.005)
-0.009
(0.012)
0.336***
(0.051)
0.006
(0.006)
0.018
(0.014)
-0.017
(0.028)
-0.174
(0.422)
0.111***
(0.012)
0.001
(0.001)
0.007
(0.005)
0.000
(0.005)
-0.005
(0.012)
39
Table 5 continued
Pooled Total Cholesterol
Male
Insured
Married
Other Hispanic
White
Black
Other Race
9th to 11 Grade
HS or GED
Some College
College Graduate
PIR
$5,000-$9,999
$10,000-$14,999
$15,000-$19,999
$20,000-$24,999
$25,000-$34,999
-9.408***
(1.657)
-3.358
(2.158)
4.583**
(1.796)
4.302
(3.769)
-0.730
(2.544)
-2.724
(2.934)
3.470
(4.526)
-1.797
(3.971)
-4.123
(3.798)
-0.898
(3.807)
-6.722*
(3.969)
-----0.002
(6.535)
-1.152
(6.381)
0.340
(6.358)
4.735
(6.202)
2.812
(5.929)
-0.041***
(0.009)
-0.018
(0.011)
0.017*
(0.010)
0.001
(0.020)
-0.015
(0.013)
-0.021
(0.016)
0.016
(0.024)
0.012
(0.022)
-0.002
(0.021)
0.011
(0.021)
-0.024
(0.022)
----0.004
(0.035)
0.004
(0.034)
0.026
(0.034)
0.036
(0.033)
0.041
(0.031)
-9.540***
(1.648)
-3.698*
(2.190)
5.113***
(1.768)
4.515
(3.783)
-1.348
(2.530)
-3.188
(2.907)
2.999
(4.440)
-2.441
(3.953)
-4.731
(3.787)
-2.075
(3.804)
-8.245**
(4.028)
2.090***
(0.617)
---------------------
-0.043***
(0.009)
-0.019*
(0.011)
0.021*
(0.010)
-0.003
(0.020)
-0.019
(0.013)
-0.021
(0.016)
0.013
(0.024)
0.013
(0.022)
-0.001
(0.021)
0.011
(0.021)
-0.023
(0.022)
0.019***
(0.006)
---------------------
40
Table 5 continued
Pooled Total Cholesterol
$35,000-$44,999
$45,000-$54,999
$55,000-$64,999
$65,000-$74,999
$75,000-$99,999
$100,000+
2003-04
2005-06
2007-08
Constant
Observations
R-squared
4.022
(6.059)
4.879
(6.026)
6.083
(6.396)
10.831*
(6.435)
6.591
(6.402)
8.974
(6.332)
-6.697
(5.416)
-8.674
(5.471)
-10.951**
(5.305)
193.977***
(8.787)
0.039
(0.032)
0.047
(0.031)
0.050
(0.033)
0.084**
(0.033)
0.054
(0.034)
0.063*
(0.034)
-0.026
(0.030)
-0.034
(0.030)
-0.041
(0.029)
4.831***
(0.066)
-------------------------6.050
(5.255)
-7.993
(5.322)
-10.087**
(5.115)
194.648***
(6.719)
2,642
2,229
2,649
0.044
0.069
0.045
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
-------------------------0.026
(0.030)
-0.031
(0.030)
-0.034
(0.029)
4.889***
(0.060)
2,220
0.066
As mentioned previously, there do appear to be slight differences in the means of
the dependent variables across the four sample periods. To see whether or not results are
sensitive to the sample period, we re-estimated some of the regressions. In particular,
among our four dependent variables, since BMI is most commonly studied in the
literature we estimated BMI regressions for each of the four periods separately. Also,
41
since using IPR had better results, in terms of significance, we focused on the
specifications given in the last two columns of Table 3. Thus, Table 6 provides the results
for the four linear regressions using data from the four separate NHANES survey periods
(i.e., 2001-02, 2003-04, 2005-06, and 2007-08), while Table 7 provides the results for the
four logarithmic regressions.
According to Table 6, higher cotinine levels are associated with lower BMI, with
the results being statistically significant at the 1 percent level in the 2003-04, 2005-06
and 2007-08 survey periods. Being a college graduate is also associated with lower BMI,
but the results are only statistically significant in the 2003-04 and 2005-06 time periods.
No other notable trends in statistical significance are exhibited. Thus, compared to the
pooled results, splitting the data up into separate periods does have a substantial impact
on the results. For instance, in the sample period-specific regressions, income no longer
has a statistically significant impact on BMI. Nonetheless, it should be noted that the
degrees of freedom, notably in the 2001-02 sample period, are substantially reduced in
Table 6.
42
Table 6
Linear Regression of BMI Over Time
VARIABLES
Age
Cotinine
Exercise
Alcohol
Rooms in Home
Male
Insured
Married
Other Hispanic
White
Black
Other Race
9th to 11 Grade
HS or GED
Some College
College Graduate
(1)
2001-02
(2)
2003-04
(3)
2005-06
(4)
2007-08
0.075
(0.063)
-0.008
(0.008)
0.019
(0.019)
-0.086
(0.169)
-0.399
(0.935)
-2.432
(1.891)
-0.986
(2.548)
-0.365
(1.709)
8.721*
(5.177)
-0.153
(2.208)
0.803
(2.790)
2.554
(4.825)
-1.032
(3.905)
0.322
(3.096)
-0.628
(3.249)
0.322
(3.255)
0.004
(0.013)
-0.005***
(0.002)
-0.004
(0.004)
0.005
(0.005)
0.040
(0.125)
-1.477***
(0.511)
0.120
(0.623)
0.120
(0.501)
-1.336
(1.072)
-1.058
(0.704)
0.981
(0.797)
-1.705*
(0.885)
-0.898
(0.848)
0.918
(0.878)
-0.927
(0.901)
-2.227**
(0.977)
0.011
(0.017)
-0.011***
(0.002)
-0.002
(0.006)
1.084***
(0.352)
-0.071
(0.159)
-0.149
(0.589)
-0.058
(0.676)
-0.835
(0.611)
-0.468
(1.139)
-0.065
(0.855)
0.739
(0.914)
0.437
(1.819)
0.701
(1.114)
0.207
(1.156)
0.332
(1.216)
-2.713**
(1.291)
-0.000
(0.010)
-0.004***
(0.001)
-0.001
(0.002)
-0.009
(0.007)
-0.018
(0.085)
0.107
(0.316)
0.339
(0.530)
0.215
(0.332)
0.191
(0.674)
-0.800
(0.498)
1.188**
(0.603)
-2.220***
(0.752)
1.183
(0.809)
0.186
(0.664)
0.477
(0.670)
-0.927
(0.691)
43
Table 6 continued
Linear Regression of BMI Over Time
IPR
Constant
Observations
R-squared
-0.577
(0.660)
30.154***
(9.323)
0.109
(0.207)
29.130***
(1.245)
0.201
(0.239)
26.899***
(1.573)
63
611
500
0.242
0.085
0.099
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
0.183
(0.121)
28.042***
(0.983)
1,460
0.040
Table 7 shows the results for the four logarithmic regressions using data from the
four separate NHANES survey periods. Briefly, the results indicate that alcohol
consumption has a statistically significant and positive impact on BMI in 2005-06, but a
statistically significant and negative impact on BMI in 2007-08. Also, as in the pooled
analysis, being White has a negative and statistically significant impact on BMI at the 1
percent level, but only for the 2003-04 and 2007-08 survey periods. Similarly, being of
other race has a negative and statistically significant impact on BMI, but again only in the
2003-04 and 2007-08 time periods. As in both the pooled and year-by-year analyses,
having a college degree is associated with a lower BMI, as the respective coefficient is
statistically significant at the 5 percent level in the 2003-04 and 2005-06 survey periods.
Of all the variables explored, though, only college graduate is statistically significant in
the pooled analysis, as well as several of the year-by-year linear and logarithmic
44
analyses. As mentioned previously, though, the degrees of freedom are noticeably lower
in Tables 6 and 7, which could be affecting the results.
Table 7
Logarithmic Regression of BMI Over Time
VARIABLES
Age
Cotinine
Exercise
Alcohol
Rooms in Home
Male
Insured
Married
Other Hispanic
White
Black
Other Race
9th to 11th Grade
HS or GED
(1)
2001-02
(2)
2003-04
(3)
2005-06
(4)
2007-08
0.127
(0.090)
-0.016
(0.010)
-0.007
(0.035)
0.016
(0.044)
-0.061
(0.145)
-0.042
(0.068)
-0.025
(0.102)
0.016
(0.054)
0.593***
(0.136)
-0.020
(0.083)
0.023
(0.092)
-0.050
(0.176)
0.056
(0.144)
0.038
(0.115)
0.029
(0.024)
-0.004
(0.003)
0.008
(0.010)
0.006
(0.010)
0.019
(0.023)
-0.033*
(0.020)
0.008
(0.023)
-0.012
(0.020)
0.002
(0.037)
-0.067***
(0.026)
0.011
(0.029)
-0.085***
(0.032)
-0.071**
(0.034)
0.029
(0.035)
0.025
(0.026)
-0.008***
(0.003)
0.003
(0.011)
0.065***
(0.021)
-0.012
(0.026)
0.000
(0.020)
0.006
(0.022)
-0.025
(0.021)
-0.034
(0.047)
-0.036
(0.028)
0.001
(0.031)
-0.033
(0.060)
0.023
(0.039)
0.011
(0.040)
0.038**
(0.016)
-0.002
(0.002)
-0.015**
(0.007)
-0.020***
(0.006)
0.003
(0.019)
0.032***
(0.012)
0.010
(0.018)
0.009
(0.012)
-0.006
(0.025)
-0.055***
(0.017)
0.019
(0.022)
-0.103***
(0.029)
0.038
(0.035)
0.024
(0.032)
45
Table 7 continued
Logarithmic Regression of BMI Over Time
Some College
College Graduate
IPR
Constant
Observations
R-squared
0.074
(0.116)
0.048
(0.118)
-0.040
(0.036)
2.924***
(0.431)
-0.060*
(0.035)
-0.091**
(0.038)
0.018
(0.012)
3.198***
(0.103)
0.018
(0.041)
-0.081*
(0.044)
0.017
(0.012)
3.211***
(0.108)
51
469
498
0.343
0.102
0.078
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
0.028
(0.033)
-0.009
(0.033)
0.008
(0.010)
3.225***
(0.077)
1,191
0.056
46
Chapter 5
CONCLUSION
The objective of this thesis was to present an analysis of the effects of
demographic characteristics, behavioral characteristics and income on the health
indicators of BMI, systolic blood pressure and total cholesterol. The data used was
collected from four different NHANES survey periods (i.e., 2001-02, 2003-04, 2005-06
and 2007-08). Various empirical models were estimated in this thesis. In particular,
adopting a specification similar to that of existing studies, both linear and logarithmic
functional forms were estimated, with much attention given to whether or not the
measure of income affects the results.
The first model was estimated with pooled data across the four sample periods to
address the impact of the independent variables on body mass index (BMI), whereas the
second and third models used the same pooled data to address the determinants of
systolic blood pressure and total cholesterol, respectively. We then examined whether or
not the results are similar across sample periods by estimating models of the determinants
of BMI for the four separate periods.
Estimation results from the pooled BMI analysis indicate that higher cotinine
levels, having a college degree and identifying as White or of other race have the greatest
impact on BMI, with all of these factors lowering BMI. Results from the pooled systolic
blood pressure analysis indicate that age, being male, and being Black have positive
47
impacts on blood pressure, while being insured, having a college degree, and having an
upper-bracket income have negative impacts on blood pressure. For the pooled total
cholesterol regression, it was found that age, being married, and the income-to-poverty
ratio have positive impacts on cholesterol, while being male and having a college degree
have negative impacts on cholesterol. We also find results vary somewhat depending on
whether a linear or logarithmic functional form is used. Thus, key determinants of health,
such as income, have varying impacts on health measures.
Estimating the BMI regressions for each of the four sample periods, results were
not as robust as the pooled regressions. For instance, in the linear model we find a higher
cotinine level, being of other race and having a college degree have a negative impact on
BMI, while in the logarithmic model we find being White, being of other race and having
a college degree have a negative impact on BMI. Thus, across the various regressions
while it appears that having a college degree is consistently a statistically significant
factor impacting health, the various income measures are statistically significant much
less often. This finding underscores that social status, rather than income, has a more
prevalent impact on health outcomes.
We also obtained some unexpected results. For instance, in addition to many
factors not having statistically significant impacts on our three health measures, we had
expected that being insured and being married would have had positive impacts on
health, but being insured was statistically significant only in the systolic blood pressure
regression, while being married actually increased total cholesterol.
48
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